Triage Ultimate Health Targets Assessment Tool

As a coach, I’ve watched smart, motivated people spin their wheels for years because they didn’t know what actually matters most for their health right now. That’s why I built the Triage Ultimate Health Targets Assessment Tool. It helps you to turn scattered data into clear, actionable priorities.

Here’s the rough idea of this tool: we measure the things that best predict long-term health (not just what’s trendy), score them on a simple 1-5 scale, and weight them by impact so your effort goes where it counts. In practice, that means core markers like blood pressure, lipids, glucose control, kidney function, VO₂ max, and body composition carry the most weight, while supportive factors (like stress, sleep quality, purpose, and community) still matter, but won’t overshadow the big rocks.

You’ll enter what you know (lab work, vitals, basic measurements, a few self-ratings). The calculator will auto-calculate helpful metrics (BMI, waist-to-height ratio, FFMI, and strength-to-bodyweight ratios), so you don’t need to do this yourself. It also makes age- and sex-aware adjustments where appropriate (VO₂ max, HRV, body fat %, menstrual status, morning erections, strength standards), so you’re compared to a realistic benchmark, not a one-size-fits-all ideal.

Your results come back in three parts:

1. A chart that shows your six health domains at a glance: Blood Work & Hormones, Cardiovascular Fitness, Strength, Recovery, Body Composition, and Psychosocial Health.
2. Colour-coded progress bars for each metric so you can see exactly what’s solid and what needs attention.
3. Priority recommendations ranked by potential risk reduction (the bigger the gap and the heavier the weight, the higher it climbs the list). That way, if your waist-to-height ratio and ApoB need work, they’ll surface ahead of less critical tweaks.

If you’re missing some inputs, no problem, the tool will estimate those at a neutral 3/5 and clearly mark them as “Estimated.” That keeps the big picture visible while highlighting which labs or measurements to get next. Over time, as you update your numbers, you’ll see progress reflected in your scores and recommendations, which makes this tool perfect for quarterly check-ins.

A quick but important note is that this is a screening and coaching tool, not a medical diagnosis. High or low scores are signals to guide your next steps, often actionable lifestyle changes, sometimes a conversation with your doctor for further testing or treatment.

How to get the most from this:

• Gather your latest labs and measurements (or start with what you have).
• Enter your data honestly and consistently.
• Focus your next 8-12 weeks on the top 1-3 priorities that the tool flags, as those will move the needle fastest.
• Retest and adjust. Improvement compounds.

This calculator turns guesswork into a game plan. It shows you where to invest your limited time and energy for the biggest health payoff; so training, nutrition, sleep, and stress work together, not against each other. So, get your data ready, and lets get started!

 

The Triage Ultimate Health Targets Assessment Tool 

Using The Triage Ultimate Health Targets Assessment Tool 

Once you enter your data, the calculator applies this logic step by step:

1. Individual Metric Scoring: Each input (like blood pressure or fasting glucose) is scored 1-5 based on evidence-based thresholds from research and clinical guidelines.
2. Age and Sex Adjustments: For metrics that vary by age or sex (like VO₂ max, HRV, morning erections, menstrual status, body fat %, or strength standards) the system automatically adjusts the thresholds so you’re compared against your peers, not a generic population.
3. Automatic Calculations: To save you time and eliminate maths errors, several values are auto-calculated:
• BMI from height and weight
• Waist-to-Height Ratio (WHtR) from waist and height
• FFMI from weight, height, and body fat %
• Strength ratios (squat, deadlift, bench) relative to body weight
4. Category Scoring: Each metric belongs to one of six major health domains. Within each category, the calculator:
• Multiplies each metric’s score by its weight
• Adds them up, then divides by the total possible weighted score
• Normalises the result back to a 1–5 scale
• This gives you a Category Score (0-5) for each domain.
5. Overall Score: Finally, your overall health score is simply the average of your six category scores. It’s a concise snapshot of your current healthspan profile.

If you’re missing a few data points (say you don’t have a recent lab for ApoB or a measured VO₂ max), the system will fill that gap with an estimated score of 3.0 (average) and clearly mark it as Estimated. That way, you still get a usable score without skewing the data, and you’ll know exactly which metrics to test next for a complete picture.

Once your scores are calculated, the system identifies where you’ll get the biggest return on effort. It looks at the gap between your current score and the optimal 5, then multiplies that by the metric’s weight.

• Gap = 5 – Current Score
• Risk Score = Gap × Weight

Metrics with the highest Risk Scores rise to the top, your top “focus points” are then displayed for each section, colour-coded by priority.

This is what makes the Triage Ultimate Health Assessment Tool so effective: it doesn’t just tell you what’s wrong, it tells you where to start.

 

Scoring Philosophy

Most people already know they should exercise more, eat better, or sleep longer, but without a clear framework, it’s hard to see where to start or how to measure progress. The goal of this Triage Ultimate Health Assessment Tool is to take dozens of important health metrics and translate them into one clear, evidence-based picture of where you stand, and what to tackle first.

 

The 1-5 Scale

Everything in the calculator is scored on a simple 1-to-5 scale. Think of it like a report card for your body. Straightforward, intuitive, and based on research, not guesswork:

• 5 = Optimal – Top-tier health. This is the “gold standard” zone associated with the lowest all-cause mortality risk.
• 4 = Good – Above average and protective. You’re solidly in the healthy range, with some room for fine-tuning.
• 3 = Average/Acceptable – Typical for the general population, but improvement would make a measurable difference.
• 2 = Below Average – Elevated risk; time to take action and intervene through lifestyle or medical attention.
• 1 = Poor – Significantly high risk; this deserves prompt, focused attention.

You can see exactly where you stand, and every point you gain has real, measurable impact on your long-term health trajectory.

 

Evidence-Based Weighting System

However, not all metrics are created equal. Some have a massive influence on healthspan and longevity, while others are supportive or indirect. That’s why each metric is weighted according to how strongly it predicts outcomes like cardiovascular disease, metabolic dysfunction, and all-cause mortality.

We use a three-tier weighting system based on the scientific literature:

Core Metrics (Weight: 2.0): These are the heavy hitters; the strongest, most direct predictors of healthspan and mortality:

• Blood pressure
• ApoB / LDL-C
• HbA1c / Fasting glucose
• VO₂ max
• Waist-to-height ratio
• Body fat percentage
• eGFR (kidney function)

Important Metrics (Weight: 1.5): Still highly meaningful predictors, but slightly less powerful or more context-dependent:

• Resting heart rate
• HRV (heart rate variability)
• CRP (inflammation)
• White blood cell count
• HDL cholesterol
• Triglycerides
• TG:HDL ratio
• ALT and AST (liver enzymes)
• AST:ALT ratio
• Sleep quantity, quality, and energy
• Strength metrics (squat, deadlift, bench, chin-ups)
• BMI
• FFMI (Fat-Free Mass Index)

Supportive Metrics (Weight: 1.0): These are protective lifestyle and hormonal factors that often act through the others above:

• Perceived stress
• Relationships/community
• Purpose/meaning
• Financial resilience
• Hormones (testosterone, thyroid, menstrual or erection health)

This weighting matters because it ensures your final score reflects real-world health impact. Improving a Core metric like blood pressure or VO₂ max moves the needle much more than, say, lowering your perceived stress alone (although both are worth addressing).

 

Visual Presentation

Now, I know that numbers are great, but visuals drive understanding and motivation. That’s why the calculator presents your results in a clean, intuitive dashboard:

 

Radar Chart

A six-spoke chart (one for each category) shows your strengths and weaknesses at a glance. Each spoke runs from 0 to 5, with your results forming a filled polygon in the middle. A balanced, wide shape means well-rounded health; a narrow or lopsided one shows where to focus.

 

Individual Metric Progress Bars

Every metric appears as a colour-coded progress bar, sorted from your lowest (worst) to highest (best) scores:

• 🔴 1 – Critical
• 🟠 2 – Poor
• 🟡 3 – Average
• 🟢 4 – Good
• 💚 5 – Optimal

Each bar shows the exact score, a quick percentage, and helpful badges. Metrics are grouped by category for context, so you can see, for example, that your recovery and sleep are solid, but your blood markers need work.

At the top of each section, you’ll see your top focus areas, ranked by overall health impact. It’s a simple but powerful way to turn raw health data into a clear action plan, showing you not just how healthy you are, but exactly what to do next.

 

The Six Categories

Now that you understand how the scoring and weighting work, let’s dig into what actually gets measured. 

If you’re someone who likes to know the why behind the numbers, this is where we get into the nitty-gritty and what each metric represents, why it matters, and how to interpret your scores.

 

🩸 Category 1: Blood Work and Hormonal Health

Most people don’t realise that your blood can often tell the story of your health years, sometimes decades, before you feel sick. By the time symptoms show up, you’re often already deep into disease progression. That’s why I’m such a stickler about getting regular labs with many of my health-focused clients. Blood biomarkers are like the check engine light for your body, except they come on early enough that you can actually prevent the breakdown.

Your bloodwork directly reflects how your metabolic systems are functioning and the state of your current cardiovascular risk factors right now, and gives you insight into where they are headed.

Let me walk you through each marker and why it matters for your long-term health.

 

ApoB / LDL-C (Weight: 2.0)

What it is: This measures the number of atherogenic (artery-damaging) particles in your blood. ApoB is the gold standard, so if you can get it tested, do. If not, LDL cholesterol works as a fallback.

Why I care about this: Here’s the thing most people get wrong about cholesterol. Two people can have the same LDL cholesterol number, let’s say 130 mg/dL (3.4 nmol/L), but wildly different actual risk. This is because what really matters is particle count, not just the cholesterol those particles carry. ApoB tells us exactly how many particles you have, which includes LDLs and other atherogenic ApoB-containing lipoproteins

More particles = more chances for atherosclerosis. It’s that simple.

What you’re aiming for:

• 5 (Optimal): ApoB <60 mg/dL (<0.6 g/L) or LDL <70 mg/dL (<1.8 mmol/L)
• 4 (Good): ApoB 60-79 mg/dL (0.6-0.79 g/L) or LDL 70-99 mg/dL (1.8-2.6 mmol/L)
• 3 (Average): ApoB 80-99 mg/dL (0.8-0.99 g/L) or LDL 100-129 mg/dL (2.6-3.3 mmol/L)
• 2 (Below average): ApoB 100-129 mg/dL (1.0-1.29 g/L) or LDL 130-159 mg/dL (3.4-4.1 mmol/L)
• 1 (Poor): ApoB ≥130 mg/dL (≥1.3 g/L) or LDL ≥160 mg/dL (≥4.1 mmol/L)

Real talk: Standard lab ranges will tell you LDL under 100 (~2.6) is “normal,” but optimal for longevity is much lower. We’re not aiming for “not diseased”, we’re aiming for thriving. The greater the number of other risk factors you have, including family history, and especially if you have known cardiovascular disease, the more you should be aiming toward (or below!) optimal here.

 

Fasting Glucose (Weight: 2.0)

What it is: Your blood sugar level after an overnight fast. It’s the most direct window into your insulin sensitivity.

Why I care about this: Elevated fasting glucose doesn’t just predict diabetes; it predicts cardiovascular disease, cognitive decline, and accelerated ageing. The scary part is that most people think they’re fine if they’re under 100 mg/dL (5.6 nmol/L) because that’s the “normal” cutoff. But research shows risk starts climbing well before that.

What you’re aiming for:

• 5 (Optimal): 70-85 mg/dL (3.9-4.7 mmol/L)
• 4 (Good): 86-99 mg/dL (4.8-5.5 mmol/L)
• 3 (Average): 100-109 mg/dL (5.6-6.0 mmol/L) – impaired fasting glucose
• 2 (Below average): 110-125 mg/dL (6.1-6.9 mmol/L) – pre-diabetes
• 1 (Poor): ≥126 mg/dL (≥7.0 mmol/L) – diabetes range

What to do about it: If you’re above 85, we need to talk about calorie intake, body fat level, meal timing, carb quality, strength training, and sleep. All of these directly impact insulin sensitivity.

 

HbA1c (Weight: 2.0)

What it is: Your average blood sugar over the past 3 months. Think of it as the “highlight reel” of your glucose control.

Why I care about this: Fasting glucose can fluctuate day to day, but HbA1c doesn’t lie. It shows me what’s been happening metabolically for the past quarter year. Even levels in the “pre-diabetic” range (5.7-6.4%; 39-46 mmol/mol) significantly increase cardiovascular risk.

What you’re aiming for:

• 5 (Optimal): <5.2% (<33 mmol/mol)
• 4 (Good): 5.2-5.6% (33-38 mmol/mol)
• 3 (Average): 5.7-5.9% (39-41 mmol/mol) – pre-diabetes territory
• 2 (Below average): 6.0-6.4% (42-46 mmol/mol) – higher pre-diabetes
• 1 (Poor): ≥6.5% (≥48 mmol/mol) – diabetes

The advantage: Because this is a 3-month average, it’s perfect for tracking whether your nutrition and training changes are working. We generally want to retest quarterly if this is something that has been flagged, and we want to watch the trend over time.

 

Blood Pressure (Weight: 2.0)

What it is: The force of blood against your artery walls, both when your heart contracts (systolic) and relaxes (diastolic).

Why I care about this: Blood pressure is called the “silent killer” for a reason. You can feel totally fine while your arteries are taking a beating. Every 20-point increase in systolic pressure doubles your cardiovascular mortality risk. Let that sink in.

What you’re aiming for:

• 5 (Optimal): 100-119 / 60-79 mmHg
• 4 (Good): 120-129 / <80 mmHg (elevated but manageable)
• 3 (Average): 130-139 / 80-89 mmHg (Stage 1 hypertension)
• 2 (Below average): 140-159 / 90-99 mmHg (Stage 2)
• 1 (Poor): ≥160 / ≥100 mmHg (very high risk)

Action steps: If you’re above 120/80, we’re looking at sodium intake, stress management, aerobic exercise, losing extra weight/fat, and possibly magnesium/potassium supplementation. This is highly actionable.

Note: The EU (ESC/ESH) guidelines consider up to 139 SBP to be “Elevated BP” and consider “Hypertension” to be >140, whereas my figures are based on the USA (AHA) guidelines. Depending on the exact guidelines you prefer to follow, you do potentially have some wiggle room here. 

 

hs-CRP (High-Sensitivity C-Reactive Protein) (Weight: 1.5)

What it is: A marker of systemic inflammation in your body.

Why I care about this: Chronic inflammation is the common thread linking heart disease, diabetes, cancer, and accelerated ageing. hsCRP above 3.0 mg/L doubles your heart attack risk compared to levels below 1.0, even if your cholesterol looks fine.

What you’re aiming for:

• 5 (Optimal): <1.0 mg/L (low inflammation)
• 4 (Good): 1.0-2.9 mg/L
• 3 (Average): 3.0-5.0 mg/L (high risk)
• 2 (Below average): 5.1-10.0 mg/L (very high)
• 1 (Poor): >10.0 mg/L (severe inflammation)

The fix: Elevated hsCRP usually responds to better sleep, omega-3s, reducing processed food, managing stress, and consistent training. It’s one of the most modifiable markers we have.

 

White Blood Cell Count (Weight: 1.5)

What it is: The number of immune cells circulating in your blood.

Why I care about this: This one’s tricky because both too high AND too low are problematic, it’s a U-shaped curve. High WBC suggests chronic inflammation or immune activation. Low WBC can indicate immune compromise or bone marrow issues. You should ultimately be discussing all your blood work with a doctor, and they will be able to help you interpret this data.

What you’re aiming for:

• 5 (Optimal): 4.5-7.0 ×10⁹/L
• 4 (Good): 3.5-4.4 or 7.1-8.0 ×10⁹/L
• 3 (Average): 3.0-3.4 or 8.1-9.0 ×10⁹/L
• 2 (Below average): 2.5-2.9 or 9.1-10.0 ×10⁹/L
• 1 (Poor): <2.5 or >10.0 ×10⁹/L

What this tells me: Consistently elevated WBC often points to chronic stress, poor sleep, overtraining, or underlying inflammation that we need to address.

 

eGFR (Estimated Glomerular Filtration Rate) (Weight: 2.0)

What it is: A measure of how well your kidneys are filtering waste from your blood.

Why I care about this: Your kidneys are critical for longevity, and kidney function tends to decline with age. But this decline isn’t inevitable. Every 10-point drop in eGFR increases cardiovascular risk significantly. Maintaining healthy kidney function is non-negotiable for healthspan.

What you’re aiming for:

• 5 (Optimal): ≥90 mL/min/1.73m² (normal function)
• 4 (Good): 75-89 mL/min/1.73m² (mild decline)
• 3 (Average): 60-74 mL/min/1.73m² (CKD Stage 2)
• 2 (Below average): 45-59 mL/min/1.73m² (CKD Stage 3a)
• 1 (Poor): <45 mL/min/1.73m² (CKD Stage 3b or worse)

Prevention is key: Blood pressure control, adequate hydration, avoiding excessive protein (if already compromised), and managing blood sugar all protect kidney function.

 

HDL Cholesterol (Weight: 1.5)

What it is: The “good” cholesterol that helps remove excess cholesterol from your arteries.

Why I care about this: Higher HDL is generally protective, as every 1 mg/dL increase drops heart disease risk by ~2-3%. But (and this is important) HDL functionality matters more than the absolute number. Very high HDL (>100) doesn’t necessarily mean extra protection.

What you’re aiming for:

• 5 (Optimal): ≥60 mg/dL (≥1.55 mmol/L)
• 4 (Good): 50-59 mg/dL (1.3-1.5 mmol/L)
• 3 (Average): 40-49 mg/dL (1.0-1.3 mmol/L)
• 2 (Below average): 35-39 mg/dL (0.9-1.0 mmol/L) (increased risk)
• 1 (Poor): <35 mg/dL (<0.9 mmol/L) (very high risk)

How to raise it: Regular exercise (especially aerobic training), healthy fats, moderate alcohol (if you drink), and avoiding trans fats all help.

 

Triglycerides (Weight: 1.5)

What it is: The amount of fat circulating in your bloodstream.

Why I care about this: Elevated triglycerides are a red flag for metabolic dysfunction, insulin resistance, and increased cardiovascular risk, especially in women. High triglycerides often mean you’re eating too many refined carbs, drinking too much alcohol, or are carrying too much body fat (especially central/abdominal/visceral fat)

What you’re aiming for:

• 5 (Optimal): <100 mg/dL (<1.1 mmol/L)
• 4 (Good): 100-149 mg/dL (1.1-1.7 mmol/L)
• 3 (Average): 150-199 mg/dL (1.7-2.2 mmol/L) (borderline high)
• 2 (Below average): 200-499 mg/dL (2.3-5.6 mmol/L) (high)
• 1 (Poor): ≥500 mg/dL (≥5.6 mmol/L) (very high)

The fix: Cut back on sugar and refined carbs, increase omega-3 intake, and add more aerobic exercise. Triglycerides respond quickly to lifestyle changes. Weight reduction is often required where excess body fat is driving insulin resistance and high triglycerides.

 

TG:HDL Ratio (Weight: 1.5)

What it is: Your triglycerides divided by your HDL (this is automatically calculated for you).

Why I care about this: This ratio is a sleeper metric. It’s one of the best predictors of insulin resistance you can get from a standard lipid panel. A ratio above 3.5 screams metabolic dysfunction, even if your other numbers look okay.

What you’re aiming for:

• 5 (Optimal): <1.0
• 4 (Good): 1.0-1.9
• 3 (Average): 2.0-2.9 (elevated)
• 2 (Below average): 3.0-4.9 (high risk)
• 1 (Poor): ≥5.0 (very high risk)

Why it matters: This ratio captures metabolic health better than either triglycerides or HDL alone. Low ratios = good insulin sensitivity. High ratios = trouble brewing.

 

ALT (Alanine Aminotransferase) (Weight: 1.5)

What it is: A liver enzyme that leaks into your blood when liver cells are inflamed or damaged.

Why I care about this: Your liver is your metabolic command centre. Elevated ALT is often the first sign of fatty liver disease (NAFLD), which affects nearly 25% of adults. The frustrating thing is that standard lab ranges say ALT up to 40-50 U/L is “normal” (depending on the lab), but optimal is much lower.

What you’re aiming for:

• 5 (Optimal): <20 U/L
• 4 (Good): 20-25 U/L
• 3 (Average): 26-35 U/L (elevated)
• 2 (Below average): 36-50 U/L (high)
• 1 (Poor): >50 U/L (very high)

Root causes: Usually a combination of excess body fat, insulin resistance, too much alcohol, or poor diet quality. All reversible with the right approach.

 

AST (Aspartate Aminotransferase) (Weight: 1.5)

What it is: Another liver enzyme, but it’s also found in heart and muscle tissue.

Why I care about this: AST elevation can indicate liver damage, but it can also spike temporarily after intense training (because it’s in muscle too). That’s why we look at AST alongside ALT and their ratio; it gives us context.

What you’re aiming for:

• 5 (Optimal): <25 U/L
• 4 (Good): 25-30 U/L
• 3 (Average): 31-40 U/L (elevated)
• 2 (Below average): 41-60 U/L (high)
• 1 (Poor): >60 U/L (very high)

Context matters: If both AST and ALT are elevated, we’re looking at liver health. If only AST is up and you just crushed a hard training session, that’s likely why. AST itself can also be increased due to high levels of muscle mass, so if someone is quite muscular, baseline AST could be higher, and this tool doesn’t necessarily fully account for this. 

 

AST:ALT Ratio (Weight: 1.5)

What it is: AST divided by ALT (this is automatically calculated for you).

Why I care about this: This ratio helps differentiate types of liver disease. A healthy liver should have a balanced ratio (0.8-1.2). When the ratio climbs above 2.0, it often signals alcoholic liver disease or advanced fibrosis, but this requires more precise interpretation of blood work than this tool can provide. Below 0.8 typically indicates fatty liver disease (NAFLD).

What you’re aiming for:

• 5 (Optimal): 0.8-1.2 (balanced, healthy liver)
• 4 (Good): 0.6-0.79 or 1.21-1.5
• 3 (Average): 0.5-0.59 or 1.51-2.0 (concerning)
• 2 (Below average): <0.5 or 2.01-2.5 (high risk)
• 1 (Poor): >2.5 (very high risk, possible advanced disease)

Clinical note: If your ratio is abnormal, we need to dig deeper, possibly with imaging or additional liver function tests.

 

TSH (Thyroid Stimulating Hormone) (Weight: 1.0)

What it is: A hormone that tells your thyroid gland to produce thyroid hormones, which regulate metabolism.

Why I care about this: Your thyroid is the master metabolic regulator. When it’s off, everything suffers: energy, body composition, mood, cognition, etc. Even “subclinical” thyroid dysfunction (TSH above 2.5 but below the clinical threshold) can potentially increase heart disease risk. Much like all of these, this does have to be interpreted with the fuller picture of the blood work though.

What you’re aiming for:

• 5 (Optimal): 0.5-2.5 mIU/L
• 4 (Good): 0.3-0.49 or 2.6-4.0 mIU/L
• 3 (Average): 0.1-0.29 or 4.1-6.0 mIU/L
• 2 (Below average): <0.1 or >6.0 mIU/L
• 1 (Poor): Severe dysfunction

When to dig deeper: If TSH is off, then we generally want to dig deeper by ordering a full thyroid panel (free T3, free T4, thyroid antibodies) to understand what’s really going on.

 

Testosterone (Men) (Weight: 1.0)

What it is: The primary male sex hormone, essential for muscle mass, bone density, mood, and metabolic health.

Why I care about this: Low testosterone isn’t just about libido or muscle; it predicts metabolic syndrome, diabetes, cardiovascular disease, and mortality. Men with testosterone below 250 ng/dL (~8.67 nmol/L) have a ~88% increased mortality risk. 

What you’re aiming for:

• 5 (Optimal): 600-1200 ng/dL (20.8-41.6 nmol/L)
• 4 (Good): 500-599 ng/dL (17.3-20.7 nmol/L)
• 3 (Average): 300-499 ng/dL (10.4-17.3 nmol/L)
• 2 (Below average): 200-299 ng/dL (6.9-10.4 nmol/L)
• 1 (Poor): <200 ng/dL (<6.9 nmol/L)

Lifestyle first: Before considering TRT, we optimise sleep, strength training, body composition, stress, and micronutrient status (especially zinc and vitamin D). These often move the needle significantly.

 

Menstrual / Reproductive Status (Women) (Weight: 1.0) 

What it is: Your menstrual cycle regularity or menopausal status, adjusted for age appropriateness.

Why I care about this: Menstrual health is a vital sign. Irregular cycles, amenorrhea (absent periods), or early menopause are red flags for hormonal dysfunction, stress, under-eating, over-exercising, or underlying health conditions. 

For regular cycles:

• 5 (Optimal): Regular cycle (24-35 days, consistent)
• 4 (Good): Slight irregularity (21-23 or 36-40 days)
• 3 (Average): Frequent irregularity (>7 day variation)
• 2 (Below average): Oligomenorrhoea (>40 days between periods)
• 1 (Poor): Amenorrhea (≥3 months, not pregnant)

 

For life-stage transitions (age-aware):

Perimenopause:

• Age <40: Score 2 (early, concerning)
• Age 40-44: Score 4 (slightly early but transitional)
• Age ≥45: Score 5 (expected, normal)

Post-menopause:

• Age <40: Score 1 (premature and requires investigation)
• Age 40-44: Score 2 (early menopause, concerning)
• Age ≥45: Score 5 (age-appropriate, healthy)

 

Why this matters: Premature menopause (before age 40) doubles cardiovascular risk and significantly increases osteoporosis risk. Natural menopause at 50-52 is totally normal and gets a perfect score. The calculator recognises this difference, and it doesn’t penalise women for healthy, age-appropriate transitions while still flagging genuinely concerning early menopause. Unfortunately, not all tools take this into account.

 

Morning Erections (Men) (Weight: 1.0) 

What it is: Frequency of spontaneous erections upon waking, adjusted for age.

Why I care about this: This might seem awkward to track, but it’s actually a non-invasive vascular health marker. Erectile function correlates strongly with cardiovascular health because the penile arteries (1-2mm diameter) are smaller than coronary arteries (3-4mm). ED often precedes heart disease by 3-5 years. It’s the canary in the coal mine.

 

Age-adjusted scoring:

Under 40:

• 5: Daily
• 4: 4-6 times/week
• 3: 2-3 times/week
• 2: <2 times/week
• 1: Rare

Age 40-59:

• 5: Daily or 4-6 times/week
• 4: 2-3 times/week
• 3: <2 times/week
• 2: Rare

Age 60+:

• 5: Daily or 4-6 times/week or 2-3 times/week
• 4: <2 times/week
• 3: Rare

Bottom line: Declining frequency, especially if sudden, warrants investigation. It’s not just about sexual health; it’s about cardiovascular health, testosterone levels, and overall vitality.

 

Ultimately, blood work isn’t just a bunch of random numbers; it’s a comprehensive snapshot of your metabolic and hormonal health. These markers predict where you’re headed years before symptoms appear, which means you have time to course-correct.

The beauty of the Triage Ultimate Health Assessment Tool is that it doesn’t just dump data on you. It weighs each metric by its impact on longevity, shows you exactly where you stand, and tells you what to prioritise. If your ApoB and blood pressure need work, those surface first, because they carry the most weight for your long-term health.

 

❤️ Category 2: Cardiovascular Fitness

If I could only test one thing to predict how long you’ll live, it would be your VO₂ max. 

Cardiorespiratory fitness is the single strongest predictor of all-cause mortality, and is stronger than smoking, diabetes, high blood pressure, or even high cholesterol (between the lowest and highest fitness). A Cleveland Clinic study of over 122,000 patients found that being unfit was worse for your longevity than any other risk factor they measured. Let me say that again: low fitness is more dangerous than being a smoker.

The good news is that, unlike your genetics or family history, your cardiovascular fitness is entirely modifiable. You can improve it at any age, and every improvement translates directly into years of life and quality of life. This isn’t about running marathons, it’s about actually maintaining the engine that keeps you alive.

 

VO₂ Max (Weight: 2.0) 

What it is: Your maximal oxygen uptake during exercise, essentially, how efficiently your body can deliver and use oxygen when you’re pushing hard. It’s measured in millilitres of oxygen per kilogram of body weight per minute (ml/kg/min).

Why I care about this: If there’s a single metric that deserves a weight of 2.0, it’s this one. I personally wanted to weight it much higher, but that is my personal bias, as an exercise professional and enthusiast. You see, for every 1-MET increase in VO₂ max (roughly 3.5 ml/kg/min) reduces your all-cause mortality risk by ~13% and cardiovascular mortality by ~15%. The difference between the lowest and highest fitness categories can translate to 4-8 extra years of life expectancy. That’s not hyperbole, and it doesn’t capture the fact that you will also have a much better quality of life by being fit.

Your VO₂ max reflects the integrated function of your heart, lungs, blood vessels, and muscles. When it’s high, everything works better. When it’s low, you’re fighting an uphill battle with every other health goal.

What you’re aiming for (age-adjusted):

The calculator automatically adjusts targets based on your age because VO₂ max naturally declines over time. But we can slow that decline dramatically with training, and in a lot of cases, reverse it.

Men under 50 (base standards):

• 5 (Excellent): ≥60 ml/kg/min
• 4 (Very good): 50-59 ml/kg/min
• 3 (Average): 40-49 ml/kg/min
• 2 (Poor): 35-39 ml/kg/min
• 1 (Very poor): <35 ml/kg/min

Women under 50 (base standards):

• 5 (Excellent): ≥55 ml/kg/min
• 4 (Very good): 45-54 ml/kg/min
• 3 (Average): 35-44 ml/kg/min
• 2 (Poor): 30-34 ml/kg/min
• 1 (Very poor): <30 ml/kg/min

Age adjustments:

• Ages 50-74: Subtract 5 points from the thresholds above
• Ages 75+: Subtract 10 points from the thresholds above

Example: A 55-year-old man with a VO₂ max of 50 ml/kg/min would score a 5 (excellent) because we’re comparing him to age-adjusted standards (≥55 becomes ≥50 for his age group). The same score would be a 4 for a 35-year-old.

How to improve it: Zone 2 training (conversational pace, 2-4x/week), VO₂ max intervals (3-10 minutes at 90-95% max HR, for 3+ sets, 1-2x/week), and consistency over time. Strength training helps too, by improving muscular efficiency. Losing weight also helps.

Testing options: Lab-based test (gold standard), fitness test on a treadmill or bike, or estimation from a Cooper test, Rockport walk test, or wearable device (least accurate but better than nothing).

 

Resting Heart Rate (Weight: 1.5) 

What it is: Your heart rate when you’re fully at rest, and ideally measured first thing in the morning before getting out of bed. 

Why I care about this: Resting heart rate is a window into your cardiovascular efficiency and autonomic nervous system health. Every 10-beat increase in RHR is associated with a 9% increase in cardiovascular mortality. People with a resting heart rate above 80 bpm have a 45% higher risk of cardiovascular death compared to those below 60 bpm.

But context matters. A resting heart rate of 38 bpm means something completely different for an endurance athlete versus a sedentary person. That’s why the calculator is smart enough to check your VO₂ max score before interpreting very low heart rates.

What you’re aiming for:

• <40 bpm: Score 5 (optimal) IF your VO₂ max score is ≥4 (indicating you’re trained), otherwise Score 1 (bradycardia concern, and you should probably see your doctor)
• 40-50 bpm: Score 5 (optimal, athletic)
• 51-65 bpm: Score 4 (very good)
• 66-70 bpm: Score 3 (acceptable)
• 71-80 bpm: Score 2 (elevated)
• >80 bpm: Score 1 (high risk)

Why the context-aware scoring matters: Elite endurance athletes often have resting heart rates in the 28-45 bpm range because their hearts are so efficient. They pump more blood per beat (high stroke volume) and have strong vagal (parasympathetic) tone. This is a healthy adaptation.

But if you’re sedentary with a VO₂ max of 35 ml/kg/min and your resting heart rate is 38 bpm, that could be sick sinus syndrome or another conduction disorder that needs medical evaluation. The calculator catches this by checking: “Is this person fit enough for this to be normal?” Now, this isn’t always the case, and some people do just have very low resting heart rates, but this does warrant further investigation if you aren’t fit, and have a low RHR.

How to improve it: Consistent aerobic training, especially Zone 2 work, will lower your resting heart rate over time. Better sleep, stress management, and cutting back on stimulants (caffeine, especially late in the day) also help. You can expect to see meaningful drops after 8-12 weeks of consistent training.

Tracking tip: Use a wearable or check manually first thing in the morning. Look for trends over weeks, not day-to-day fluctuations. A sudden spike (5-10 bpm above baseline) often signals overtraining, illness, or inadequate recovery.

 

Heart Rate Variability – rMSSD (Weight: 1.5)

What it is: The variation in time between consecutive heartbeats, measured in milliseconds. Specifically, we use rMSSD (root mean square of successive differences), which reflects short-term parasympathetic (recovery) activity (among other things).

Why I care about this: High HRV generally means your autonomic nervous system is balanced and responsive, with strong parasympathetic (rest-and-digest) tone. Low HRV indicates you’re stuck in sympathetic (fight-or-flight) dominance, which is associated with chronic stress, poor recovery, inflammation, and increased mortality risk.

Studies show that low HRV is associated with a 32-45% increased risk of death, independent of other risk factors. It’s one of the best real-time indicators we have of how well you’re recovering from training, managing stress, and handling life.

What you’re aiming for:

• 5 (Excellent): >75 ms
• 4 (Good): 50-75 ms
• 3 (Average): 30-49 ms
• 2 (Low): 20-29 ms
• 1 (Very low): <20 ms

Important context: HRV is typically said to decline with age, generally about 10-15 ms per decade after age 30. However, the calculator doesn’t auto-adjust for age on this metric, and that’s because I’m not convinced this decline is an inevitable physiological reality. I suspect it’s largely a reflection of the general population becoming progressively more sedentary and unfit as they age, not ageing itself.

I’ve worked with plenty of older clients who have excellent HRV scores, often better than sedentary 25-year-olds. And I’ve seen countless people drastically improve their HRV well into their 40s, 50s, and beyond with consistent training and lifestyle optimisation. So while population data shows an age-related decline, I believe much of that is modifiable, not hardwired.

That said, if you’re older and want to give yourself some credit relative to your age group, feel free to mentally adjust your interpretation. A 55-year-old with an HRV of 50 ms is doing exceptionally well compared to peers, even if the absolute score is a 4 rather than a 5.

What affects HRV (and what to do about it):

• Sleep quality: Poor sleep tanks HRV. Prioritise 7-9 hours and consistent sleep/wake times.
• Training load: Overtraining or under-recovery will drop HRV. If you see a consistent decline, dial back volume or intensity.
• Stress: Chronic psychological stress suppresses HRV. Meditation, breathwork, and stress management practices help.
• Alcohol: Even moderate drinking suppresses HRV for 1-3 days. If you drink, track the impact.
• Illness: HRV drops when you’re fighting an infection, often before symptoms appear. It’s an early warning system.

How to track it: Use a chest strap or wearable that measures HRV (wrist-based optical sensors are less accurate, but they at least give you something to work with). Measure first thing in the morning, ideally at the same time daily. Look at weekly averages and trends, not individual days.

Training with HRV: Some coaches use HRV to guide training intensity (high HRV days = go hard; low HRV days = go easy or rest), and it can be a useful tool if you’re serious about optimising recovery. However, you do actually have to learn to use it appropriately.

 

These three metrics (VO₂ max, resting heart rate, and HRV) tell a pretty complete story about your cardiovascular health and autonomic function (especially when combined with your blood lipids from the previous section):

• VO₂ max shows your ceiling: how much work your cardiovascular system can handle at maximum effort.
• Resting heart rate shows your efficiency: how little effort your heart needs to maintain baseline function.
• HRV shows your resilience: how well you’re recovering and adapting to stress.

If all three are strong, you’re in excellent cardiovascular health. If one or more are lagging, we know exactly where to focus.

The beautiful thing about cardiovascular fitness is how quickly it responds to training. You can see meaningful improvements in 6-8 weeks with consistent effort. And unlike some health markers that plateau, you can keep improving cardiovascular fitness well into your 60s and 70s with the right approach. There also does seem to be more and more benefits to be had from improving them, even beyond what this calculator considers “best”.

Now, you don’t need to become an elite athlete, but you do need to make aerobic fitness a non-negotiable part of your training. The data is crystal clear: your cardiovascular fitness is the best investment you can make in your longevity and quality of life.

 

💪 Category 3: Strength

Muscle strength isn’t just about looking good or lifting heavy things, it’s a powerful predictor of how long you’ll live and how well you’ll function as you age. The research is unequivocal: muscular strength reduces all-cause mortality risk by ~15-30%, independent of your cardio fitness. 

Here’s what strength protects you against:

• Sarcopenia: The age-related loss of muscle mass that accelerates functional decline
• Falls and fractures: Strong muscles and bones prevent the injuries that often mark the beginning of the end for older adults
• Metabolic disease: Muscle is metabolically active tissue that improves insulin sensitivity and glucose disposal
• Frailty: Strength is the best insurance policy against losing independence in your later years

The key thing to keep in mind here though, is that we’re measuring relative strength (your strength compared to your body weight) not absolute strength (the most amount of weight lifted regardless of your body weight). A 70 kg (154 lb) person squatting 140 kg (309 lbs) is demonstrating the same level of strength development as a 90 kg (198 lb) person squatting 180 kg (397 lbs). Both are lifting 2× their body weight, and both get the same health benefit (for health at least; there may be other benefits to be had from being stronger in an absolute sense).

I’ve chosen four movements that most people have access to or have done at some point: squat, deadlift, bench press, and chin-ups. These aren’t the only exercises that matter, and they’re not even necessarily the “best” exercises. They’re simply proxies for what we actually care about, which is the overall strength and function of your musculoskeletal system.

If you’ve never tested a 1-rep max (1RM) before, don’t worry, you can estimate it using our rep max calculator based on submaximal lifts you’ve done (e.g., if you can squat 100kg (220 lbs) for 5 reps, the calculator will estimate your 1RM).

 

A Note on Standards and Expectations

Now, depending on your training background, you might look at these standards and have wildly different reactions. Some of you will see a 2× bodyweight squat and think, “Never in a million years.” Others will look at the same number and say, “Oh, is that for the warm-up sets?”

Both reactions are understandable, but what I can tell you after reading the research extensively and training myself and others for more than half my life is that these standards are achievable for most people who train effectively and consistently for 5+ years.

Let me be clear about what that means:

“Most people” = barring significant injury, disability, or medical condition. Not everyone, but the vast majority of healthy individuals who commit to the process as a priority.

“Train effectively” = following a well-designed strength program with progressive overload, not just showing up to the gym and going through the motions. You need a plan, intelligent exercise selection, and a focus on getting stronger over time.

“Consistently” = training 3-4 times per week, year after year, without excessive breaks. Consistency compounds. Training hard for 3 months, taking 6 months off, then starting over repeatedly, doesn’t count as “5 years of training.”

“5 years” = this is an average. Some people with great genetics, prior athletic background, or optimal training conditions will hit these numbers in 2-3 years. Others might take 7-8 years. But five years of dedicated training is a reasonable timeframe for most people to achieve intermediate-to-advanced strength levels.

 

If These Standards Feel Impossible

If you’re looking at these numbers and feeling discouraged, I get it. Maybe you’re just starting out, recovering from injury, or dealing with significant body weight changes. Here’s the perspective shift I want you to make:

You don’t need to hit elite standards to be healthy. A score of 3 (intermediate) puts you ahead of 70-80% of the population in terms of functional strength. That’s still protective and meaningful. That will keep you mobile, independent, and resilient as you age.

Elite standards (5s across the board) are aspirational goals for those who really want to milk everything out of the benefits on offer with strength training. Most people are seriously under-exercised, and even doing the bare minimum with exercise, and improving your strength even a little bit, will result in major improvements in your health and function.

 

If These Standards Feel Too Easy

If you’re already hitting these numbers or beyond, congratulations, you’re genuinely strong, and you’ve put in the work. Keep getting stronger if you want to, but unlike with cardiovascular fitness, there does appear to be a bit of a levelling off with the benefits you can get from getting stronger. Getting to a 2x body weight squat is phenomenal, but getting to a 3x body weight squat doesn’t necessarily dramatically improve your health, and may actually take away from it with the increased injury risk and the opportunity cost.

 

Sex-Specific Standards: Why They’re Necessary

All strength metrics use sex-specific standards because men and women have fundamentally different physiological capacities for building muscle and strength. 

• Men have ~10-20× higher testosterone levels, which drives muscle protein synthesis.
• Men carry ~33% more skeletal muscle mass on average.
• Women possess ~40-60% of men’s upper body strength.
• Women possess ~70-80% of men’s lower body strength.

Female standards are set at approximately 60-75% of male standards, depending on the movement pattern. Upper body exercises (bench press, chin-ups) show larger differences (~60-70%), while lower body exercises (squat, deadlift) show smaller differences (~70-75%). This reflects the physiological differences in muscle distribution and leverage.

These standards ensure fair assessment without penalising women for biology. A 70 kg (154 lb) woman benching 70 kg (154 lbs) (1.0×BW, elite for women) and a 90 kg (198 lb) man benching 135 kg (298 lbs) (1.5×BW, elite for men) represent equivalent levels of strength development relative to their respective physiologies. Both have achieved elite relative strength; they’ve just done it within their biological framework.

 

Ultimately, these standards are designed to be:

• Realistic for dedicated individuals over time.
• Challenging enough to require real effort and progression.
• Evidence-based, reflecting what the research shows about strength and health outcomes.
• Fair, with sex-specific adjustments that respect biological differences.

Strength is never a weakness. Every bit of muscle you build, every kilo you add to the bar, is an investment in your long-term health, function, and quality of life.

Meet yourself where you are. Set realistic goals. Train consistently. And be consistent for 5-10 years.

 

Squat (1RM) – (Weight: 1.5)

What it is: Your one-rep max back squat (ideally to parallel or below), automatically calculated as a multiple of your body weight.

Why I care about this: The squat is generally considered to be the king of lower body movements. There may be better variations of this general movement pattern for your unique biomechanics and anatomy (hack squat, front squat, split squat, etc.), but there is no way for me to accurately compare all of these and make it easy to integrate into the calculator. This is the same for all the movements. The squat is a good option because it integrates strength from your glutes, quads, hamstrings, and core, and most people will have access to a squat rack if they train at a gym. Lower body strength is particularly protective against falls, maintains bone density in the hips and spine, and is essential for functional independence as you age. 

What you’re aiming for:

Men:

• 5 (Elite): ≥2.00×BW
• 4 (Advanced): 1.50-1.99×BW
• 3 (Intermediate): 1.25-1.49×BW
• 2 (Novice): 0.75-1.24×BW
• 1 (Untrained): <0.75×BW

Women:

• 5 (Elite): ≥1.50×BW
• 4 (Advanced): 1.25-1.49×BW
• 3 (Intermediate): 1.00-1.24×BW
• 2 (Novice): 0.65-0.99×BW
• 1 (Untrained): <0.65×BW

How to improve it: Progressive overload with compound lower body work 2-3×/week. Focus on squats, front squats, hack squats, Bulgarian split squats, and leg press. Adequate protein (1.6-2.2 g/kg body weight) and calorie intake are essential for strength gains.

 

Deadlift (1RM) – (Weight: 1.5)

What it is: Your one-rep max conventional or sumo deadlift, automatically calculated as a multiple of your body weight.

Why I care about this: The deadlift is considered to be the ultimate test of total body strength, as it involves your entire posterior chain (glutes, hamstrings, back, traps), quads, plus grip and core stability. It’s also the most “functional” of the major lifts, as you’re literally picking something heavy off the ground, which is something you’ll need to do for your entire life. We could argue about what the best hip hinge movement we should be doing, but again, the deadlift is a pretty good proxy, and most people will have access to it.

What you’re aiming for:

Men:

• 5 (Elite): ≥2.50×BW
• 4 (Advanced): 2.00-2.49×BW
• 3 (Intermediate): 1.75-1.99×BW
• 2 (Novice): 1.25-1.74×BW
• 1 (Untrained): <1.25×BW

Women:

• 5 (Elite): ≥2.00×BW
• 4 (Advanced): 1.75-1.99×BW
• 3 (Intermediate): 1.50-1.74×BW
• 2 (Novice): 1.00-1.49×BW
• 1 (Untrained): <1.00×BW

Training note: Most people can deadlift more than they can squat due to better leverages and muscle recruitment. If your deadlift isn’t significantly higher than your squat, there’s likely room for technical improvement or specific posterior chain work. However, some people are just much better built for this movement than others.

 

Bench Press (1RM) – (Weight: 1.5)

What it is: Your one-rep max barbell bench press, automatically calculated as a multiple of your body weight.

Why I care about this: The bench press is the standard measure of upper-body pushing strength. It reflects chest, shoulder, and tricep development. While upper body strength is less correlated with mortality than lower body strength, it’s still important for functional capacity, bone density in the upper body, and overall muscular balance. I personally think there are much better movements than the bench press for assessing and developing upper body pressing strength (I would think that with my extremely long arms), but again, this is an exercise that a lot of people will have access to and familiarity with.

What you’re aiming for:

Men:

• 5 (Elite): ≥1.50×BW
• 4 (Advanced): 1.20-1.49×BW
• 3 (Intermediate): 1.00-1.19×BW
• 2 (Novice): 0.65-0.99×BW
• 1 (Untrained): <0.65×BW

Women:

• 5 (Elite): ≥1.00×BW
• 4 (Advanced): 0.85-0.99×BW
• 3 (Intermediate): 0.75-0.84×BW
• 2 (Novice): 0.50-0.74×BW
• 1 (Untrained): <0.50×BW

Quick note: Upper body strength shows the largest sex differences. An elite female bench presser at 1.0×BW has achieved something physiologically equivalent to a man benching 1.5×BW. Both represent years of dedicated training and exceptional development relative to their biology.

 

Chin-ups (Strict, Full Range) – (Weight: 1.5)

What it is: Maximum reps of strict, full range-of-motion chin-ups (palms facing you or each other). Relatively dead hang at the bottom (you can still have somewhat active shoulders), chin over the bar at the top. No kipping, no momentum.

Why I care about this: Chin-ups are the ultimate bodyweight test of upper body pulling strength and relative strength. Unlike the other lifts where you add weight to a bar, chin-ups require you to move your own body through space. They integrate back, biceps, and core strength while also being super functional. I ideally would like these to be weighted, but this does become tricky to standardise, compared to just purely body weight reps.

What you’re aiming for:

Men:

• 5 (Elite): ≥15 reps
• 4 (Advanced): 10-14 reps
• 3 (Intermediate): 8-9 reps
• 2 (Novice): 3-7 reps
• 1 (Untrained): 0-2 reps

Women:

• 5 (Elite): ≥12 reps
• 4 (Advanced): 8-11 reps
• 3 (Intermediate): 5-7 reps
• 2 (Novice): 2-4 reps
• 1 (Untrained): 0-1 reps

Why the sex difference matters here: Women have proportionally less upper body muscle mass and longer relative limb lengths, making bodyweight pulling movements particularly challenging. A woman who can do 12 strict chin-ups is in the same strength category as a man doing 15, as both are exceptionally strong relative to population norms and biological capacity.

Progression path: If you can’t do a chin-up yet, start with:

1. Negative chin-ups (jump up, lower slowly)
2. Band-assisted chin-ups (or assisted chin-up machine)
3. Inverted rows and rows in general
4. Lat pulldowns (but transition to chin-ups as soon as you can, as you want to develop the skill aspect too)

Training tip: Chin-ups improve rapidly with consistent practice. Training them 2-3×/week with various rep schemes (max reps, weighted, negatives) will get you there faster than you think.

 

These four metrics give us a comprehensive picture of your strength across movement patterns:

• Squat: Lower body strength (knee-dominant)
• Deadlift: Posterior chain strength (hip-dominant)
• Bench Press: Upper body pushing strength
• Chin-ups: Upper body pulling strength

If you’re weak in one area, that’s your focus. If you’re strong across the board, congratulations, you’ve built serious protective muscle mass and functional capacity that will serve you for decades.

Remember that these standards are challenging but achievable. An “intermediate” score (3) represents solid, functional strength. An “advanced” score (4) means you’re stronger than 80-90% of the population. An “elite” score (5) means you’ve put in serious, consistent work and achieved something genuinely exceptional.

Strength doesn’t happen overnight, but it’s one of the most trainable qualities we have. Start where you are, train consistently with progressive overload, eat enough protein and calories, and watch these numbers climb. Your future self (the one who stays independent, mobile, and capable well into old age) will thank you for every bit of strength you build today. 

 

😴 Category 4: Recovery (Stress & Sleep)

If I had to pick the most undervalued category in this entire calculator, this would be it. Most people treat sleep and stress management as optional, nice-to-haves that they’ll get to “eventually” once everything else is dialled in. But the reality is that sleep and stress are the foundation everything else is built on.

Poor sleep doesn’t just make you tired. It:

• Undermines your fitness gains by impairing recovery and protein synthesis
• Increases systemic inflammation (elevated CRP, cytokines)
• Wrecks insulin sensitivity and glucose control
• Impairs cognitive function, memory consolidation, and decision-making
• Accelerates cellular ageing and shortens telomeres
• Increases all-cause mortality risk

You can’t out-train, out-diet, or out-supplement chronic sleep deprivation or unmanaged stress. They’re health wrecking balls. On the flip side, when sleep and stress are dialled in, everything else gets easier; training recovery improves, body composition changes happen faster, blood markers optimise more easily, and you simply feel better.

This category deserves the same attention you give to your training and nutrition. Maybe more.

 

Sleep Quantity (Weight: 1.5)

What it is: The average number of hours you sleep per night.

Why I care about this: Sleep follows a U-shaped mortality curve, where both too little and too much are problematic. The sweet spot for most adults is 7-9 hours. Getting less than 6 hours consistently increases mortality risk by about 12% and is associated with cardiovascular disease, obesity, diabetes, and cognitive decline. Getting more than 9-10 hours regularly can indicate underlying health issues (depression, inflammation, undiagnosed illness) or poor sleep quality, being made up for with quantity.

What you’re aiming for:

• 5 (Optimal): 7-9 hours per night
• 4 (Good): 6.5-7 hours or 9-9.5 hours (slightly short or long)
• 3 (Average): 6-6.5 hours or 9.5-10 hours (borderline)
• 2 (Poor): 5-6 hours or 10-11 hours (health risk rises)
• 1 (Very poor): <5 hours or >11 hours (linked to mortality)

A note on sleep debt: You can’t “catch up” on sleep over the weekend. Chronic sleep restriction accumulates as sleep debt that impairs performance, metabolism, and health, even if you don’t feel tired anymore. Your body adapts to feeling terrible, but the damage continues under the surface.

Individual variation exists: Some people are true “short sleepers” who genuinely thrive on 6-6.5 hours (rare, probably <5% of the population). Others need 9+ hours. But most people who claim they “feel fine” on 5-6 hours are either in denial or so adapted to impairment they’ve forgotten what “fine” actually feels like.

How to improve it:

• Consistent sleep and wake times (even on weekends)
• Cool, dark, quiet bedroom (18-20°C / 65-68°F is ideal)
• No caffeine after 2 PM
• Limit alcohol (it fragments sleep architecture)
• Manage light exposure (bright light in the morning, dim light at night, minimise screens 1-2 hours before bed)
• Address underlying issues (sleep apnea, restless legs, anxiety)

 

Sleep Quality (Weight: 1.5)

What it is: Self-rated on a scale of 1-10: “How well-rested do you feel when you wake up?”

Why I care about this: You can spend 8 hours in bed and still wake up exhausted if your sleep quality is poor. Sleep quality reflects the amount of time you spend in deep sleep (Stage 3, physically restorative) and REM sleep (cognitively restorative). Poor quality sleep is often fragmented, with frequent awakenings, or lacks sufficient deep/REM sleep.

What you’re aiming for:

• 5 (Optimal): 8-10 on self-rating (wake feeling refreshed)
• 4 (Good): 7-7.9 (generally good, occasional off nights)
• 3 (Average): 5-6.9 (inconsistent, hit or miss)
• 2 (Poor): 3-4.9 (frequently wake unrefreshed)
• 1 (Very poor): <3 (never feel rested, possible sleep disorder)

When quality is consistently poor despite adequate quantity: This is a red flag for sleep disorders like obstructive sleep apnea, restless leg syndrome, or periodic limb movement disorder. If you’re getting 7-8 hours but consistently wake feeling like crap, make sure you are looking after the basic sleep hygiene habits, and if you are, then you may need to talk to your doctor about a sleep study.

What disrupts sleep quality:

• Alcohol: Suppresses REM sleep and causes fragmentation in the second half of the night
• Late-night eating: Digestion interferes with deep sleep
• Stress and racing thoughts: Keeps you in lighter sleep stages
• Environmental factors: Light, noise, temperature, uncomfortable mattress
• Sleep apnea: Causes micro-awakenings throughout the night (often unnoticed)

How to improve it:

• All the sleep hygiene basics 
• Consider magnesium glycinate (300-400 mg before bed), as this helps with sleep quality and relaxation
• Address nighttime breathing issues (mouth breathing, snoring etc.)
• Manage evening stress with wind-down routines (reading, light stretching, meditation, journaling etc.)
• Track with a wearable if you want objective data on sleep stages

 

Daily Energy Level (Weight: 1.5)

What it is: Self-rated on a scale of 1-10: “How would you rate your overall energy through the day?”

Why I care about this: Energy level is an integration marker, as it reflects sleep quality, recovery from training, hormonal balance, nutrition adequacy, stress levels, and underlying health issues. Consistently low energy despite adequate sleep is a major red flag that something else is wrong.

What you’re aiming for:

• 5 (Optimal): 8-10 (high, sustained energy throughout the day)
• 4 (Good): 7-7.9 (good energy, occasional dips)
• 3 (Average): 5-6.9 (moderate energy, noticeable fatigue)
• 2 (Poor): 3-4.9 (low energy, struggling to get through the day)
• 1 (Very poor): <3 (severe, debilitating fatigue)

Common energy killers and what to investigate:

Low energy + good sleep = investigate:

• Thyroid function (order TSH, free T3, free T4)
• Anaemia (ferritin, haemoglobin, B12)
• Testosterone (if male, or DHEA-S if female)
• Blood sugar dysregulation (fasting glucose, HbA1c, continuous glucose monitor)
• Chronic inflammation (hs-CRP)
• Overtraining (check HRV trends, resting heart rate)
• Aerobic fitness/strength (you may not be tired, and you could just be unfit and weak, thus, everyday life is overly fatiguing)

Low energy + poor sleep = prioritise sleep interventions first. Fix sleep, then reassess. Most “energy” problems are sleep problems in disguise.

Low energy + good sleep + good nutrition + good training = possible overtraining or chronic stress. You may need a deload week, more recovery days, or stress management interventions.

Practical fixes:

• Optimise sleep 
• Ensure adequate calorie and carbohydrate intake (chronic low-carb can tank energy in active individuals)
• Check micronutrients: iron, B12, vitamin D, magnesium, etc.
• Manage training volume (more isn’t always better)
• Address chronic stress (meditation, therapy, time management)
• Consider caffeine timing (strategically, not as a crutch)

 

Perceived Stress (Weight: 1.0)

What it is: Self-rated on a scale of 1-10: “How stressed do you feel overall today?” (Reverse scored: lower stress = higher score)

Why I care about this: Chronic psychological stress is a slow poison. It dysregulates the HPA (hypothalamic-pituitary-adrenal) axis, leading to elevated cortisol, increased inflammation, impaired immune function, poor sex hormone health, poor sleep, and accelerated cellular ageing. High perceived stress increases all-cause mortality risk by about 20% and is strongly associated with cardiovascular disease.

What you’re aiming for:

• 5 (Optimal): 1-2 on stress scale (very low stress)
• 4 (Good): 3-4 (generally low stress)
• 3 (Average): 5-6 (moderate stress)
• 2 (Poor): 7-8 (frequent high stress)
• 1 (Very poor): 9-10 (extreme, unmanaged stress)

Acute vs. chronic stress: Acute stress (short-term challenges, hard workouts, deadlines) is normal and even adaptive. Chronic, unrelenting stress (financial insecurity, toxic relationships, constant work pressure, caregiving burden) is maladaptive and destructive.

The stress paradox: Some stress is unavoidable, and you can’t always change your circumstances. But you can change your response to stress through resilience-building practices. Two people can experience the same stressor and have vastly different health outcomes based on their stress management skills.

Practical stress management strategies:

High-impact, evidence-based interventions:

• Meditation/mindfulness: 10-20 minutes daily can significantly lower cortisol and improve stress resilience
• Breathwork: Box breathing (4-4-4-4) or extended exhales activate the parasympathetic nervous system
• Exercise: Both aerobic and strength training are potent stress relievers (but avoid overtraining, which adds stress)
• Social connection: Spending time with supportive people buffers stress (see Psychosocial category)
• Time in nature: Even 20 minutes in green spaces lowers cortisol
• Sleep: Poor sleep amplifies stress perception; good sleep builds resilience

Moderate-impact interventions:

• Journaling or expressive writing
• Therapy or counselling (especially CBT and ACT for stress management)
• Time management and boundary-setting (learning to say no)
• Hobbies and creative outlets
• Gratitude practices

When stress is extreme or unmanageable: If you’re consistently scoring 8-10 on perceived stress, this isn’t just a “wellness” issue, it’s a health crisis. You need professional support. Talk to a therapist, counsellor, or doctor. Chronic extreme stress is as dangerous as smoking or hypertension.

 

These four metrics form a tightly interconnected web:

• Poor sleep → low energy + high stress
• High stress → poor sleep quality + low energy
• Low energy → less capacity to manage stress + poor training recovery
• Poor training recovery → reduced sleep quality + increased stress

When one piece falls apart, the others follow. But the reverse is also true: fix sleep, and stress becomes more manageable. Manage stress, and sleep improves. Improve both, and energy skyrockets.

If your scores in this category are low, this is your top priority. Fix your sleep. Manage your stress. Everything else will fall into place faster than you think.

Ultimately, your body doesn’t improve during training, it improves during recovery, so if you want to get the most out of your training and your health more broadly, you have to respect it. In our exercise program design course, we teach the stimulus + recovery → adaptations → results model, and heavily emphasise the importance of recovery. Most people ignore the recovery aspect, and thus get much less from every unit of effort they put into the stimulus. 

Treat recovery with the respect it deserves.

 

⚖️ Category 5: Body Composition

Despite the fact that people excessively focus on it, your weight on the scale tells you almost nothing about your health. Two people can weigh exactly the same but have completely different body compositions, and therefore completely different health outcomes.

What actually matters is:

• How much of your weight is muscle vs. fat
• Where that fat is distributed (visceral vs. subcutaneous)
• Your muscle mass relative to your height

Body composition, not body weight, predicts metabolic health, cardiovascular disease risk, mobility, functional capacity, and longevity. A muscular 100 kg person with 12% body fat is in an entirely different health category than a sedentary 100 kg person with 30% body fat, even though the scale says the same thing.

This is why we track four different body composition metrics in this calculator. Each one tells us something slightly different, and together they give us a complete picture of your body composition and metabolic health.

 

Waist-to-Height Ratio (WHtR) (Weight: 2.0)

What it is: Your waist circumference divided by your height (this is automatically calculated for you). It’s expressed as a decimal (e.g., 0.48).

Why I care about this: This is one of the single best predictors of metabolic syndrome, cardiovascular disease, and all-cause mortality you can get with nothing but a tape measure. It’s superior to BMI, superior to waist circumference alone, and works across all ethnic groups.

The reason it’s so powerful is because it reflects visceral fat, which is the metabolically active fat that surrounds your organs and drives inflammation, insulin resistance, and cardiovascular disease. You can have a “normal” BMI and still have dangerous amounts of visceral fat if your waist-to-height ratio is elevated.

The rule of thumb is simple: “Keep your waist to less than half your height.” That’s a WHtR under 0.5.

What you’re aiming for:

• 5 (Optimal): <0.5 (low risk)
• 4 (Good): 0.5-0.54 (mild risk)
• 3 (Average): 0.55-0.59 (moderate risk)
• 2 (Poor): 0.6-0.64 (high risk)
• 1 (Very poor): ≥0.65 (very high risk)

Examples:

• Someone who is 70 inches (178 cm) tall should have a waist circumference under 35 inches (89 cm) for optimal health (35÷70 = 0.5)
• Someone who is 65 inches (165 cm) tall should have a waist under 32.5 inches (82.5 cm) (32.5÷65 = 0.5)

How to measure your waist correctly:

• Measure at the narrowest point of your torso (usually just above the belly button)
• Stand relaxed, don’t suck in, don’t push out
• Measure at the end of a normal exhale
• Use a flexible tape measure, snug but not compressing the skin

Why this gets a weight of 2.0: Visceral adiposity is one of the most direct, modifiable predictors of metabolic disease. If your WHtR is above 0.5, reducing it should be a top priority, as it will improve insulin sensitivity, reduce inflammation, and lower cardiovascular risk more effectively than almost any other intervention.

How to improve it: Fat loss through calorie deficit, resistance training (builds muscle, improves insulin sensitivity), aerobic exercise, improved diet quality (whole foods, adequate protein, fibre), and stress/sleep management. Visceral fat responds quickly to lifestyle changes, luckily.

 

Body Fat Percentage (Weight: 2.0) 

What it is: The proportion of your total body weight that is fat mass, as opposed to lean mass (muscle, bone, organs, water).

Why I care about this: Body fat percentage is a more complete picture of your body composition than weight or BMI. It tells us not just how much you weigh, but what you’re made of. Higher body fat is associated with increased mortality, even when BMI is “normal” (the “metabolically obese, normal weight” phenotype). Lower body fat (within healthy ranges) is associated with better insulin sensitivity, lower inflammation, and reduced disease risk.

But, and this is often missed in the health and fitness world, too low is also problematic. Essential fat is necessary for hormonal function, cell membrane integrity, vitamin absorption, and neurological health. Men need at least ~3-5% body fat; women need ~10-13% for basic physiological function.

What you’re aiming for:

Men:

• 5 (Optimal): 8-15%
• 4 (Good): 6-8% or 15-18%
• 3 (Average): 5-6% or 18-22%
• 2 (Poor): 4-5% or 22-28%
• 1 (Very poor): <4% or >28%

Women:

• 5 (Optimal): 12-20%
• 4 (Good): 10-12% or 20-24%
• 3 (Average): 9-10% or 24-28%
• 2 (Poor): 8-9% or 28-34%
• 1 (Very poor): <8% or >34%

The U-shaped curve: Both too low and too high body fat percentages receive lower scores. Going below essential fat levels risks hormonal dysfunction (low testosterone in men, amenorrhea in women), bone loss, immune suppression, and mood disorders. Going too high increases metabolic disease risk, inflammation, and mortality. The sweet spot is in the middle.

Why the sex difference? Women generally require higher essential body fat (10-13% vs. 3-5% in men) for reproductive health, hormone production, and normal physiological function. A woman at 12% body fat is at the lower end of healthy; a man at 12% is in the middle of the optimal range. However, there are some women who can be quite low body fat, and still be extremely healthy. So, this does need to be interpreted in comparison to other markers such as menstrual function.

Measurement methods (most to least accurate):

1. DEXA scan: Gold standard, ~1-2% margin of error
2. Hydrostatic (underwater) weighing: Very accurate, ~2-3% margin of error
3. BodPod (air displacement): Accurate, ~2-3% margin of error
4. Skinfold calipers: Decent if done by experienced practitioner, ~3-5% margin of error
5. Bioelectrical impedance (BIA scales): Highly variable, ~5-8% margin of error, very dependent on hydration
6. Online body fat calculators: These can actually be quite accurate, if done properly; however, most people simply don’t measure the required inputs correctly.
7. Visual estimation: Very rough but free; you just compare yourself to reference photos

Reality check: Most people overestimate how lean they are. Our body fat calculator can be quite helpful for getting your body fat, but if you’ve never had a DEXA scan, I’d recommend getting one as a baseline. The data can be eye-opening.

How to improve it: Fat loss requires a calorie deficit combined with adequate protein (1.6-2.2 g/kg body weight) and resistance training to preserve muscle mass. Crash diets and excessive deficits lead to muscle loss alongside fat loss, whereas the real goal is to lose fat while maintaining (or even building) muscle.

 

BMI (Body Mass Index) (Weight: 1.5)

What it is: Your weight in kilograms divided by your height in meters squared (this is automatically calculated for you).

Why I care about this (despite its limitations): BMI gets a lot of hate, and much of it is deserved. It doesn’t distinguish between fat and muscle, doesn’t account for body composition or fat distribution, and can misclassify muscular individuals as “overweight” or even “obese.” A lean, muscular athlete with 10% body fat can have the same BMI as a sedentary person with 30% body fat.

So why include it? Because at the population level, BMI still predicts mortality reasonably well, and it’s accessible, as you only need height and weight. For most people who aren’t heavily muscled, BMI and health outcomes do correlate relatively well. We just need to interpret it in context alongside body fat percentage, FFMI, and waist-to-height ratio.

What you’re aiming for:

• 5 (Optimal): 20-25 (healthy, lowest mortality risk)
• 4 (Good): 18.5-20 or 25-27.5
• 3 (Average): 17-18.5 or 27.5-30
• 2 (Poor): 15-17 or 30-35
• 1 (Very poor): <15 or >35

When BMI is misleading: If you have significant muscle mass, your BMI may be elevated (25-30) while your body fat percentage is optimal (10-15%). In this case, your body fat %, FFMI, and WHtR will tell the real story. The calculator accounts for this by weighing BMI at 1.5 (not 2.0) and including other, more informative body composition metrics.

When BMI is useful: For sedentary or lightly active individuals without significant muscle mass, BMI is a reasonable proxy for body fatness. If your BMI is 32 and you’re not an athlete with lots of muscle, you’re probably carrying excess body fat that’s impacting your health.

The bottom line: Don’t obsess over BMI, but don’t ignore it either. Look at the full picture. If your BMI is elevated but your body fat % is optimal and your FFMI is high, you’re fine, you’re just muscular. If your BMI is elevated and your body fat % is also high, that’s a different story.

 

Fat-Free Mass Index (FFMI) (Weight: 1.5) 

What it is: Your lean body mass (everything that isn’t fat, e.g. muscle, bone, organs, water) divided by your height squared (this is automatically calculated for you). It’s like BMI, but for muscle mass instead of total body weight.

Why I care about this: FFMI tells us how much muscle you’re carrying relative to your height. This is critical because muscle mass is protective, and higher muscle mass is associated with lower all-cause mortality, better insulin sensitivity, improved metabolic health, and greater functional capacity as you age.

Low FFMI indicates sarcopenia (insufficient muscle mass), which accelerates functional decline, increases fall risk, and predicts poor health outcomes. High FFMI (within natural limits) indicates you’ve built significant muscle mass, which is one of the best things you can do for long-term health.

What you’re aiming for:

Men:

• 5 (Optimal): 22-25 (healthy, athletic muscle mass)
• 4 (Good): 18-22 or 25-27
• 3 (Average): 16-18 or 27-28
• 2 (Poor): 12-16 or 28-30
• 1 (Very poor): <12 or >30

Women:

• 5 (Optimal): 15-22
• 4 (Good): 13-15 or 22-24
• 3 (Average): 11-13 or 24-25
• 2 (Poor): 9-11 or 25-26
• 1 (Very poor): <9 or >26

Natural limits: FFMI above 25 (men) or 22 (women) is rare without either exceptional genetics or anabolic steroid use. If you’re naturally achieving an FFMI of 26-27, congratulations, you’re in the top 1% of genetic responders to resistance training. FFMI above 30 is essentially impossible without pharmaceutical assistance.

Why the U-shaped curve? Low FFMI means insufficient muscle (sarcopenia risk). Very high FFMI (>28-30 men, >25-26 women) either indicates extremely dedicated training with great genetics or raises questions about artificial enhancement (performance-enhancing drugs) and likely comes with health risks and side effects.

A note on adjusted FFMI: Some calculators use “normalised” or “adjusted” FFMI to account for height differences (taller people naturally carry more muscle mass). We didn’t use adjusted FFMI in this calculator because I don’t think the adjustment is necessary for health assessment purposes, and the raw FFMI is sufficient. However, if you want to calculate your adjusted FFMI for comparison to strength sports standards (especially if you’re very tall or very short), you can use our FFMI calculator.

How to improve it: Build muscle through progressive resistance training (3-5x/week), eat adequate protein (1.6-2.2 g/kg body weight), maintain a calorie surplus or maintenance (can’t build significant muscle in a deep deficit), and be patient. Muscle builds slowly. 

 

These four metrics work together to give you a complete body composition picture:

• Waist-to-height ratio: Tells you about visceral fat and metabolic risk
• Body fat percentage: Tells you your overall body composition (fat vs. lean mass)
• BMI: Provides population-level context (interpreted alongside the other metrics)
• FFMI: Tells you how much muscle you’re carrying relative to your height

A few examples will show you why you have to interpret these together:

Example 1: The “Skinny Fat” Phenotype

• BMI: 22 (looks “normal”)
• Body fat %: 28% (high for a man)
• WHtR: 0.54 (elevated)
• FFMI: 16 (low muscle mass)
• Verdict: Metabolically unhealthy despite normal weight. Needs to build muscle and reduce body fat.

Example 2: The Muscular Athlete

• BMI: 28 (technically “overweight”)
• Body fat %: 12% (optimal)
• WHtR: 0.48 (excellent)
• FFMI: 24 (high muscle mass)
• Verdict: Excellent body composition. BMI is misleading due to muscle mass.

Example 3: The Sarcopenic Individual

• BMI: 20 (normal)
• Body fat %: 18% (reasonable)
• WHtR: 0.49 (good)
• FFMI: 14 (low muscle mass)
• Verdict: Needs to build muscle to protect against age-related decline, even though other metrics look okay.

The point here is that you need all four metrics to see the full picture. Don’t fixate on any single number. Look at the pattern.

Body composition is highly modifiable, more so than almost any other health category. You can’t change your genetics or your age, but you can absolutely change your body composition with consistent effort.

The goal isn’t to look like a fitness model or get jacked out of your mind (unless that’s your thing). The goal is to:

• Carry enough muscle mass to protect against sarcopenia and metabolic disease
• Keep body fat in the healthy range (not too high, not too low)
• Minimise visceral fat accumulation around your organs

Do that, and your body composition scores will take care of themselves, and your metabolic health, longevity, and quality of life will all likely improve.

🤝 Category 6: Psychosocial Health

Social connection and sense of purpose predict how long you’ll live just as powerfully as whether you smoke or have high blood pressure.

This isn’t soft science or motivational fluff. The data is overwhelming:

• Social isolation and loneliness increase mortality risk by 26-32%
• Having a strong sense of purpose reduces death risk by 15-20%
• Financial stress accelerates biological ageing at the cellular level (telomere shortening)

The “Blue Zones” (regions where people supposedly live to 100+ in good health) share common traits: they eat well, move daily, and manage stress. But the most consistent factor across all five zones is strong social ties, multigenerational living, and deep community engagement.

Your psychosocial health is a biological necessity. Humans are social creatures. When we’re isolated, purposeless, or chronically stressed about money, our bodies break down faster; inflammation rises, immune function declines, and disease risk skyrockets.

These metrics get a weight of 1.0 (not 2.0) because their effects are often mediated through other categories (e.g. stress affects sleep, social isolation affects health behaviours, financial insecurity drives chronic stress, etc.). But don’t mistake lower weighting for lower importance. This category matters enormously, especially as you age.

 

Relationships / Community (Weight: 1.0)

What it is: A combined measure of the quantity and quality of your social connections—how many close relationships you have and how engaged you are in community.

Why I care about this: Social connection is a fundamental human need, and its absence is deadly. Studies consistently show that socially isolated individuals have mortality rates comparable to smokers. The Tecumseh Community Health Study found that social isolation predicted death more strongly than smoking, blood pressure, or cholesterol.

But it’s not just about having people around, it’s about having meaningful relationships. The quality of your connections matters more than the quantity. One deep, supportive friendship is worth more than a dozen surface-level acquaintances.

What you’re aiming for:

• 5 (Optimal): Strong social network, ≥3 close confidants, active community involvement
• 4 (Good): Moderate network, 2 close confidants, some community involvement
• 3 (Average): Limited network, 1 close confidant, infrequent social support
• 2 (Poor): Very limited connections, no close confidants, but some social contact
• 1 (Very poor): Social isolation, minimal meaningful interaction

What counts as a “confidant”? Someone you can talk to about things like personal problems, fears, and vulnerabilities. Someone who knows the real you, not just the version you present to the world. This might be a spouse, a best friend, a sibling, a parent, or a close mentor. The key is trust and emotional intimacy.

What counts as “community involvement”? Regular participation in groups or activities where you see the same people repeatedly and form connections. This could be:

• Religious or spiritual communities
• Sports leagues or fitness classes
• Volunteer organisations
• Hobby groups (book clubs, maker spaces, gaming groups)
• Neighborhood associations
• Professional networks with genuine friendships

The critical element is repeated, face-to-face interaction with people you care about.

Why this matters biologically:

• Stress buffering: Social support reduces cortisol and physiological stress response. Having someone to talk to about problems literally lowers your stress hormones.
• Health behaviours: Socially connected people are more likely to exercise, eat well, avoid risky behaviours, and seek medical care when needed.
• Immune function: Loneliness is associated with increased inflammation (elevated CRP, IL-6) and reduced immune response.
• Psychological well-being: Social connection provides emotional regulation, meaning, and belonging, all of which affect physical health.

The loneliness epidemic: We’re in the midst of a loneliness crisis, especially post-COVID. More people than ever report having zero close friends. Remote work, digital communication replacing face-to-face interaction, and geographic mobility away from family all contribute. But the solution isn’t complicated; it just requires intentional effort.

How to improve this:

• Prioritise existing relationships: Schedule regular time with close friends/family. Don’t let months pass without seeing people you care about.
• Join something: Pick one community activity and commit to showing up consistently. Friendships form through repeated exposure over time.
• Be vulnerable: Deep relationships require opening up. Share what’s really going on, not just surface pleasantries.
• Reduce digital, increase face-to-face: Texting and social media don’t provide the same health benefits as in-person connection.
• Help others: Volunteering or mentoring creates connection and purpose simultaneously.

If you scored low here, this is worth taking seriously. Loneliness kills. 

 

Purpose / Meaning (Weight: 1.0)

What it is: Self-rated on a scale of 1-10: “Life feels meaningful and directed.” This metric is based on validated psychological scales like Ikigai (Japanese concept of life purpose) and the Life Engagement Test. 

Why I care about this: Purpose is the North Star that guides your health behaviours and gives you a reason to take care of yourself. People with a strong sense of purpose live longer, have lower rates of cardiovascular disease, experience slower cognitive decline, and maintain better physical function as they age. 

Individuals with greater purpose in life have a reduced risk of death. Purpose doesn’t just make life feel better; it literally extends it. As someone who reads a lot of existentialist philosophy, purpose and meaning take up a lot of my headspace. If I were to weight this the way I wanted to, it would get a 2.0, but I just can’t justify that based on the science. 

What you’re aiming for:

• 5 (Optimal): 9-10 on self-rating (very strong sense of purpose, life feels deeply meaningful)
• 4 (Good): 7-8 (generally purposeful, clear direction most of the time)
• 3 (Average): 5-6 (moderate sense of meaning, but inconsistent or unclear)
• 2 (Poor): 3-4 (weak sense of meaning, often feel directionless)
• 1 (Very poor): 1-2 (no sense of purpose, life feels empty or pointless)

What is “purpose”? Purpose is the feeling that your life has direction and meaning beyond just existing. It’s the answer to questions like:

• What gets you out of bed in the morning?
• What would you do even if you weren’t paid?
• What impact do you want to have on the world?
• What are you working toward that matters to you?

Purpose isn’t about having a grand, world-changing mission. It can be raising your kids well, mastering a craft, serving your community, creating art, helping others through your work, or building something meaningful. The specifics don’t matter; what matters is that you feel like what you’re doing matters.

The Ikigai framework (Japanese concept of purpose) is quite helpful here, and it suggests purpose lives at the intersection of:

1. What you love
2. What you’re good at
3. What the world needs
4. What you can be rewarded for (financially or otherwise)

If you’re struggling with purpose, exploring these four areas can help clarify what’s missing.

Why purpose matters biologically:

• Health behaviours: Purpose motivates self-care. People with purpose are more likely to exercise, eat well, avoid harmful behaviours, and take care of their health.
• Stress resilience: Meaning provides a buffer against adversity. When life gets hard, purpose gives you a reason to push through.
• Biological markers: Purpose is associated with lower inflammatory markers (CRP, IL-6), better sleep, healthier cortisol patterns, and slower cellular ageing.
• Cognitive protection: Purpose is protective against Alzheimer’s disease and cognitive decline.

Common purpose killers:

• Meaningless work with no autonomy or impact
• Lack of goals or future orientation
• Chronic depression or anxiety (consider professional help is this is you!)
• Life transitions (retirement, empty nest, career change) that remove previous sources of meaning
• Burnout from overwork without fulfilment

How to build purpose:

• Clarify your values: What actually matters to you? Write it down.
• Set meaningful goals: Not just “lose 20 lbs” but “be healthy enough to hike with my grandkids” or “build strength to feel confident in my body.”
• Contribute to something beyond yourself: Volunteer, mentor, create, teach, build.
• Engage in challenging, meaningful work: Flow states and mastery create purpose.
• Connect your daily actions to larger meaning: How does what you do today contribute to who you want to become or what you want to build?

If you scored 1-3 on purpose, this deserves serious attention. Consider reading some philosophy or religious texts that deal with this. If you don’t like these avenues, you should consider working with a therapist, coach, or counsellor to explore what’s missing and how to rebuild meaning in your life.

 

Financial Resilience (Weight: 1.0)

What it is: A measure of your financial buffer and security, focused on emergency savings and stress level rather than absolute income.

Why I care about this: Financial stress is a chronic, unrelenting stressor that impacts health in profound ways. Financial strain is associated with a large increased mortality risk in older adults, independent of actual income level. Economic hardship accelerates biological ageing by literally shortening telomeres and advancing cellular age.

Notice we’re not measuring income here, we’re measuring resilience. A person earning €150,000/year with no savings and high lifestyle expenses is less financially resilient (and more stressed) than someone earning €60,000/year with a 12-month emergency fund and low fixed expenses.

What you’re aiming for:

• 5 (Optimal): ≥12 months of living expenses in emergency savings, low financial stress
• 4 (Good): 6-12 months savings, manageable financial stress
• 3 (Average): 3-6 months savings, occasional financial stress
• 2 (Poor): <3 months savings, frequent financial stress
• 1 (Very poor): No financial buffer, high financial insecurity

Why financial stress is a health issue:

• Chronic stress activation: Money worries create persistent activation of the HPA (stress) axis, leading to elevated cortisol, inflammation, and immune suppression.
• Health behaviours: Financial stress reduces access to healthy food, gym memberships, healthcare, and preventive care. It also increases reliance on cheap, processed foods and discourages spending on “non-essentials” like fitness or therapy.
• Sleep disruption: Money worries are one of the most common causes of insomnia and poor sleep quality.
• Delayed medical care: Financial stress causes people to skip doctor visits, delay treatment, and avoid necessary testing, leading to worse outcomes when problems are finally addressed.
• Mental health: Financial insecurity is strongly associated with anxiety and depression, which in turn affect physical health.

The buffer vs. income distinction: We focus on savings rather than income because the absence of financial stress matters more than the presence of wealth. High earners living paycheck-to-paycheck experience chronic financial stress. Moderate earners with a solid emergency fund and low expenses experience financial security. Stress is what impacts health, not income per se. Although, all things considered, you would ideally like to have more money if you are dealing with a medical issue, so building your income and overall wealth are still important.

If you scored 1-2 (high financial stress): This is impacting your health right now by raising your cortisol, disrupting your sleep, increasing inflammation, and making it harder to prioritise health behaviours. It’s not always your fault, and it’s not easy to fix, but it’s worth addressing. Even small steps toward financial stability will reduce stress and improve health outcomes.

 

These three metrics form the psychosocial foundation of your health:

• Relationships provide emotional support, stress buffering, and motivation for healthy behaviours
• Purpose gives you a reason to take care of yourself and resilience against adversity
• Financial resilience removes a major source of chronic stress that undermines everything else

They’re interconnected: financial stress strains relationships and erodes purpose. Lack of purpose can lead to social withdrawal. Social isolation increases vulnerability to financial and existential stress.

But the reverse is also true: strong relationships buffer financial stress, purpose motivates financial discipline, and financial security creates space for deeper relationships and meaningful pursuits.

This category often gets overlooked because it’s less tangible than blood work or body composition. You can’t put “sense of purpose” on a lab report. But the research is clear that your psychosocial health is just as important as your physical health, and in many ways, they’re inseparable.

If you scored low in this category, don’t dismiss it. Loneliness, purposelessness, and financial stress are health emergencies just as much as high blood pressure or elevated blood sugar. They deserve attention, intervention, and support.

Your health isn’t just what happens inside your body, it’s also shaped by the quality of your relationships, the meaning in your life, and the security of your circumstances. Take care of all of it.

 

Why These Metrics?

You might be wondering: out of the thousands of health markers we could measure, why these specific ones? Why not bone density, cognitive testing, gut microbiome analysis, or hundreds of other potentially relevant metrics? Why not your favourite one?

The answer is that every metric in this calculator was selected based on four critical, non-negotiable criteria. A metric had to pass all four tests to make the cut:

 

1. Strength of Evidence in Epidemiological Studies

The standard: We ideally chose metrics with large-scale, prospective cohort studies demonstrating clear, consistent associations with mortality and morbidity (or improvements in healthspan).

We’re not chasing trendy biomarkers or preliminary findings from small studies. Every metric included here is backed by robust evidence, often from multiple studies involving tens or hundreds of thousands of participants, tracked over decades.

What we excluded: Metrics with promising but preliminary evidence, metrics with inconsistent findings across studies, or metrics where the association with health outcomes is unclear or indirect. For example, we don’t track gut microbiome composition (fascinating but still lacking clear, actionable clinical guidelines) or many esoteric blood markers (insufficient evidence of direct mortality impact).

The bar for inclusion was high. Does this metric predict (or act as a proxy for something that predicts) how long you’ll live and how well you’ll function? If the answer wasn’t a clear, evidence-based “yes,” it didn’t make the list. 

 

2. Modifiability Through Lifestyle Intervention

The standard: Metrics that can be improved through diet, exercise, sleep, stress management, or other behavioural changes.

This calculator is a tool for action, not just assessment. There’s no point in measuring something you can’t change. Every metric included here is modifiable, meaning you can improve it through deliberate lifestyle interventions.

Examples of what this means in practice:

• ApoB/LDL: Modifiable through diet quality, weight loss, exercise, and (if needed) medication.
• VO₂ max: Highly modifiable through aerobic training, despite what some people claim.
• Strength: Trainable at any age through progressive resistance training.
• Sleep quality: Improvable through sleep hygiene, stress management, and addressing underlying disorders.
• Waist-to-height ratio: Directly responsive to fat loss through calorie management and exercise.

What we excluded: Genetic markers, age, family history, and other non-modifiable factors. Yes, they predict health outcomes, but you can’t change them, so measuring them doesn’t help you take action. This calculator is about what you can control.

The empowerment principle: Every metric you see is an opportunity. If you score low, you have a clear target for improvement. If you score high, you’re protecting yourself through your choices. Health isn’t born of fate, decided before you draw your first breath, it’s the result of thousands of decisions compounded over time. You have agency in your own health trajectory.

 

3. Measurability Without Expensive or Invasive Testing

The standard: Accessible to most people through standard lab work, fitness assessments, or self-reporting.

A health assessment tool is only useful if you can actually get the data. We prioritised metrics that are:

• Available through routine blood work ordered by your regular doctor
• Testable at most commercial labs without special equipment
• Measurable at home or in a standard gym setting
• Self-reportable when subjective, but validated measures

Examples of accessibility:

• Blood work: Standard lipid panel, metabolic panel, CBC. This is available everywhere (and for those North Americans reading, this is often covered by insurance)
• Blood pressure: Measurable at home with a €20 monitor
• Body composition: Estimable in the gym with callipers, at home with a BIA scales, or DEXA scans (increasingly available)
• VO₂ max: Testable at many fitness centres, or estimable through field tests (Cooper test, Rockport walk) or with many fitness wearables
• Strength: Testable in any gym with basic equipment
• Sleep/stress/purpose: Self-reported, no equipment needed

What we excluded: Expensive speciality tests that require advanced imaging (CT calcium scoring, advanced cardiac testing), obscure biomarkers that require research-grade labs, or tests that aren’t widely available outside major medical centres.

The practical principle: This calculator is designed for real people, not just those with unlimited healthcare budgets or access to cutting-edge medical facilities. The metrics had to be practical and achievable for someone committed to getting a comprehensive health assessment.

That said, some metrics (like ApoB or VO₂ max) require effort to obtain. They’re worth it, but you can start with what you have and fill in gaps over time.

 

4. Independence from Other Metrics

The standard: Minimises redundancy to avoid double-counting similar risk factors.

We wanted a comprehensive assessment, not a bloated one. Each metric needed to contribute unique information about your health, not just repeat what another metric already tells us.

How we avoided redundancy:

• Cholesterol panel: We include ApoB (or LDL), HDL, triglycerides, and TG:HDL ratio, as each provides distinct information about lipid metabolism and cardiovascular risk
• Glucose control: We include both fasting glucose (snapshot) and HbA1c (3-month average), because together they give a complete picture
• Body composition: We include WHtR (visceral fat), body fat % (total adiposity), BMI (population context), and FFMI (muscle mass), as each tells a different part of the story
• Cardiovascular fitness: VO₂ max (capacity), resting heart rate (efficiency), and HRV (recovery/stress balance) are all distinct measures

What we excluded: Redundant metrics that would simply repeat information. For example:

• We don’t include both total cholesterol and LDL (LDL is more informative)
• We don’t include waist circumference alone, because WHtR (waist-to-height ratio) is superior
• We don’t include multiple different body weight measures (BMI provides context, but body fat % and FFMI are more informative)
• We don’t include multiple overlapping inflammation markers, as hs-CRP captures what we need

More metrics isn’t always better. We wanted the minimum effective dose of measurements that captures the maximum information about your health. Every metric included earns its place by telling us something the others don’t.

This independence also ensures that your overall score accurately reflects your health—not just the same risk factor counted multiple ways.

 

The Result: A Comprehensive but Non-Redundant Panel

By applying these four criteria rigorously, we arrived at a panel of metrics that:

• Predicts health outcomes with strong scientific evidence
• Can be improved through your choices and actions
• Is accessible without extraordinary expense or effort
• Provides unique, non-overlapping information

Together, these metrics capture the major modifiable determinants of healthspan (quality of life as you age) and lifespan (how long you live). They integrate:

• Metabolic health (blood work, glucose control, body composition)
• Cardiovascular function (blood pressure, lipids, VO₂ max, resting heart rate)
• Physical capacity (strength, muscle mass, fitness)
• Recovery systems (sleep, HRV, stress)
• Psychosocial resilience (relationships, purpose, financial security)

No single metric tells the whole story. But together, weighted by their impact on health outcomes, they provide a scientifically grounded, clinically useful snapshot of where you stand, and more importantly, where to focus your efforts for maximum impact.

This isn’t a random collection of health metrics. It’s a carefully curated, evidence-based assessment system designed to translate decades of research into actionable guidance for your health.

You can trust that if you improve these numbers, you’re improving the things that actually matter for living longer and living better.

 

Why Weight by Impact?

If you’ve been paying attention, you’ve noticed that not all metrics in this calculator are treated equally. Some carry a weight of 2.0, others 1.5, and still others 1.0. This wasn’t a coin flip, it’s the heart of what makes this calculator useful rather than just a data dump.

Let me explain why weighted scoring is essential for giving you an accurate health assessment and actionable guidance.

 

Problems with Equal Weighting

Imagine for a moment that we treated every metric the same, and have them all weighted equally. What would happen?

 

1. Undervalues Critical Predictors

VO₂ max is the single strongest predictor of all-cause mortality we can measure. Every 1-MET increase (about 3.5 ml/kg/min) reduces your death risk substantially. Meanwhile, perceived stress, while important, has a weaker direct association with mortality.

If we weight them equally, we’re telling you they matter the same amount for your longevity. That’s scientifically wrong. VO₂ max predicts how long you’ll live more powerfully than almost any other single metric. It deserves to carry more weight in your overall score.

Equal weighting would make the calculator less accurate and less useful, as it would hide which changes actually move the needle on your health outcomes.

 

2. Overvalues Supportive Factors

Purpose and meaning are important, and the research shows they reduce mortality risk and improve quality of life. But purpose doesn’t predict mortality as strongly or directly as kidney function (eGFR), which independently predicts cardiovascular disease and all-cause mortality with crystal-clear dose-response relationships.

If we weight them equally, someone with a strong sense of purpose but failing kidneys would score the same in those two categories. That’s absurd. Purpose improves your life and health, but it works largely through behavioural pathways (i.e. motivating you to exercise, eat well, and manage stress). Kidney function is a direct biomarker of metabolic health and vascular function.

Equal weighting would overvalue supportive factors at the expense of critical biological markers.

 

3. Creates Misleading Overall Scores

Someone with excellent stress management, strong relationships, deep purpose, and low financial stress but with terrible metabolic health (high ApoB, elevated blood pressure, poor glucose control, low VO₂ max) would score deceptively high with equal weighting.

Their psychosocial scores would pull up their overall average, masking the fact that they’re at high risk for cardiovascular disease and metabolic dysfunction. They’d look healthier on paper than they actually are.

Weighted scoring prevents this. The core metabolic and cardiovascular markers carry more weight, ensuring that the overall score reflects actual health risk, not just how well you’re managing stress.

 

4. Provides Poor Prioritisation Guidance

One of the most valuable features of this calculator is the priority recommendations. The system tells you which metrics to focus on first based on the gap between your current score and optimal, multiplied by the metric’s weight.

With equal weighting, this prioritisation breaks down. A small gap in a critical metric (like blood pressure) would rank the same as a large gap in a supportive metric (like financial stress). You’d get confusing, inappropriate guidance about what to tackle first.

Weighted scoring ensures the algorithm surfaces high-impact gaps first. So if your VO₂ max and ApoB are suboptimal, those rise to the top of your action list, not your stress levels (which, while important, have less direct impact on mortality).

 

What Weighting Ensures

The three-tier weighting system solves all these problems and creates a calculator that’s both scientifically accurate and practically useful.

 

1. Proportional Risk Contribution

Each metric is weighted according to its demonstrated association with mortality and morbidity in the research literature. Strong, direct predictors get higher weights. Supportive or indirect factors get lower weights.

This ensures your overall score reflects proportional risk contribution. The metrics that predict death and disease (and health!), most powerfully contribute most to your score.

 

2. Scientific Validity

Because the weighting reflects real-world impact, your overall score is a more accurate representation of your actual health risk. It’s not just an average of disparate metrics, it’s a scientifically valid integration of your health across multiple domains, weighted by what actually predicts outcomes.

A score of 3.5 means something concrete: you’re at moderate risk, with specific areas that need attention. A score of 4.5 means you’re in excellent health across the metrics that matter most for longevity.

 

3. Clear Focus

Weighted scoring helps you see where changes matter most for longevity. When you look at your results, the highest-weighted metrics with the biggest gaps jump out. These are your leverage points. Improve these, and you get the biggest return on investment for your effort.

It prevents paralysis by analysis. Instead of trying to optimise 40+ metrics simultaneously, you can focus on the 3-5 that will actually move your health trajectory.

 

Limitations & Disclaimers

Before you take your results as gospel, we need to discuss what this tool can and cannot do. I built this tool to be useful and empowering, but it has real limitations. Understanding them will help you use the Triage Ultimate Health Assessment Tool appropriately and avoid potentially dangerous misinterpretations.

 

What This Calculator Does 

Provides Evidence-Based Health Assessment

This calculator translates decades of research into a practical assessment of where you stand across the major modifiable determinants of healthspan and lifespan. Every metric, threshold, and weight is derived from scientific literature. Not trends, opinions, or marketing.

What this means: Your scores reflect what the research tells us about health risk. If you score low in a category, there’s strong evidence that this area deserves attention. If you score high, you’re doing well on metrics that predict longevity.

 

Identifies Priority Areas for Improvement

The weighted scoring system and risk calculation algorithm surface your highest-impact opportunities; the gaps that matter most for reducing your mortality risk and improving your healthspan.

What this means: You get a clear action plan. Instead of being overwhelmed by 40+ metrics, you see the top 3-5 priorities that will move the needle fastest. This helps you focus your limited time and energy where it counts.

 

Tracks Progress Over Time

By retesting quarterly or biannually or even annually, you can see whether your interventions are working. Scores going up? You’re on the right track. Scores stagnant or declining? Time to adjust your approach.

What this means: You have objective feedback on whether your training, nutrition, sleep, and stress management strategies are actually improving your health, not just making you feel busy.

 

Educates on Key Health Metrics

Most people have no idea what ApoB is, why VO₂ max matters, or how waist-to-height ratio predicts metabolic disease. This calculator (and the accompanying article you are currently reading) teaches you which metrics matter most and why.

What this means: You become a more informed health consumer. You can have better conversations with your coach/doctor, ask for the right tests, and understand what your results mean.

 

What This Calculator Doesn’t Do 

Now, there are clear boundaries to what this tool can responsibly claim.

 

Does Not Replace Medical Diagnosis

This calculator is a screening and coaching tool, not a diagnostic instrument. It identifies patterns and risks based on population data; it doesn’t diagnose disease.

Example: If your fasting glucose scores 2/5 (6.1 mmol/L (110 mg/dL), pre-diabetes range), the calculator flags this as a priority. But it doesn’t diagnose you with pre-diabetes, insulin resistance, or type 2 diabetes. Only a qualified healthcare provider can do that, ideally after additional testing (oral glucose tolerance test, continuous glucose monitoring, etc.).

What you should do: Use low scores as a prompt to have a conversation with your doctor, not as a definitive diagnosis.

 

Does Not Detect All Health Conditions

This calculator measures 40+ important health metrics, but there are dozens of other conditions, risk factors, and health issues it doesn’t capture.

Some things that are missing:

• Bone density: Osteoporosis risk (DEXA scan required)
• Cancer screening: No PSA, mammography, colonoscopy, skin checks
• Cognitive function: No memory tests, dementia screening, or neurological assessment
• Gut microbiome: No assessment of digestive health, dysbiosis, or microbiome composition
• Genetic risk: No family history or genetic predisposition assessment
• Autoimmune markers: No screening for rheumatoid arthritis, lupus, celiac, or other autoimmune conditions
• Mental health disorders: Self-reported stress and purpose don’t substitute for clinical assessment of depression, anxiety, PTSD, or other conditions
• Infectious disease: No screening for chronic infections (hepatitis, HIV, etc.)
• Respiratory function: No spirometry or lung function testing beyond VO₂ max

You could score well on this calculator and still have:

• Early-stage cancer
• Osteoporosis
• Undiagnosed autoimmune disease
• Cognitive decline
• Chronic infections
• Mental health conditions requiring treatment

So, you should continue getting regular preventive care, cancer screenings, and medical checkups appropriate for your age, sex, and risk factors. A good calculator score doesn’t mean you can skip mammograms, colonoscopies, or annual physicals.

 

Does Not Account for Genetics or Family History

Population-based thresholds don’t adjust for your unique genetic risk profile or family history.

Here are some example scenarios where individual risk differs from population averages:

Scenario 1: Familial hypercholesterolemia

• Your ApoB is 1.1 g/L (110 mg/dL) (scores 2/5 in the calculator)
• But you have a genetic mutation causing extremely high cholesterol
• Your actual cardiovascular risk is much higher than the calculator suggests
• You may need aggressive pharmaceutical intervention, not just lifestyle changes

Scenario 2: Strong family history of early heart disease

• Your father had a heart attack at 45, his father at 42, and his father 43, and so on…
• Even with optimal scores across cardiovascular metrics, your genetic risk is elevated
• You may need more aggressive targets and earlier intervention than the calculator suggests

Scenario 3: Protective genetics

• You have genetic variants associated with longevity (FOXO3, APOE2)
• You may tolerate slightly suboptimal metrics better than average
• But this doesn’t give you license to ignore the metrics; you’re still better off optimising them

Ultimately, you should be discuss family history with your doctor, as that will allow you to better interpret your numbers. If you have a strong family history of heart disease, diabetes, cancer, or other conditions, you may need more aggressive targets, earlier screening, or genetic testing.

 

Does Not Predict Individual Lifespan

The calculator estimates health risk based on population statistics, but it doesn’t tell you when you’ll die.

Mortality prediction is probabilistic, not deterministic. A low score increases your odds of living longer, but accidents, infections, rare diseases, and luck still matter. A high score increases risk, but plenty of people with suboptimal health live long lives (and plenty with optimal health die young from unpredictable causes).

Statistical vs. individual outcomes:

• A VO₂ max of 35 ml/kg/min (poor) is associated with higher mortality risk than 60 ml/kg/min (excellent)
• But some people with poor VO₂ max live to 95, and some elite athletes die at 50
• Statistics predict groups, not individuals

You should ultimately be using the calculator to help you assess and optimise your odds, not to predict your fate. Focus on what you can control, improving your scores and health, and accept that outcomes are probabilistic, not guaranteed.

 

Does Not Provide Medical Advice

This tool educates and guides, but it doesn’t prescribe treatments, recommend medications, or tell you whether to stop taking prescribed drugs.

What the calculator can say: “Your blood pressure scores 2/5 (elevated). Consider discussing with your doctor whether lifestyle interventions or medication are appropriate.”

What the calculator cannot say: “Start taking lisinopril 10mg daily” or “Stop taking your current blood pressure medication because your score improved.”

Why this matters: Medical decisions require comprehensive evaluation by a licensed healthcare provider who knows your full medical history, current medications, contraindications, and individual circumstances. An algorithm, no matter how well-designed, cannot replace clinical judgment.

What you should do: Use results to inform conversations with your healthcare team, not to replace them.

 

Key Limitations You Need to Understand

Beyond what the calculator doesn’t do, there are important limitations to how it works that you should be aware of.

 

Not Truly Comprehensive

Despite measuring 40+ metrics across six categories, this is still a subset of all possible health markers. We focused on the most important, modifiable, accessible metrics, but it’s not exhaustive.

Trade-off: Comprehensiveness vs. practicality. We could measure 200 biomarkers, but most would add marginal value, require expensive speciality testing, or overlap with metrics already included. The goal was to capture the major modifiable determinants of healthspan without making the assessment impossibly complex or expensive.

What this means: A comprehensive score of 4.5/5 means you’re doing well on the metrics we measure, not that you’re perfectly healthy in every conceivable way.

 

Population Averages, Not Personalised Targets

Optimal ranges are derived from population studies showing inflexion points in disease risk. They represent general health optimisation targets, not individualised prescriptions.

Individual variation exists for many reasons:

• Genetics: Some people naturally have higher or lower levels of certain biomarkers
• Athletic background: Elite athletes often have extreme values (very low RHR, very high VO₂ max) that aren’t achievable for most people
• Medical conditions: Chronic kidney disease, thyroid disorders, or other conditions shift optimal targets
• Medications: Some drugs (beta-blockers, thyroid replacement, testosterone therapy) alter biomarker ranges
• Age within bands: A 50-year-old and 74-year-old are both in the “50-74” age adjustment band, but optimal values may differ

So, use the thresholds as general targets, but recognise that your individual optimal values may differ based on your unique circumstances. Work with healthcare providers to determine personalised targets when appropriate.

 

Self-Reported Data Is Subjective

Metrics like sleep quality, perceived stress, daily energy, purpose, and relationships rely on self-assessment. Different people rate the same objective situation differently.

Example: Two people sleeping 7 hours might rate their sleep quality as 8/10 and 5/10 respectively, even with similar objective sleep architecture. One person’s “moderate stress” (5/10) is another’s “high stress” (7/10).

Why this matters: Self-reported metrics capture your subjective experience, which is valid and important—but they’re less precise than objective biomarkers. Your perception of stress or sleep quality can also change based on mood, recent events, or comparison to past experiences.

What this means: Be honest with yourself when self-reporting. Don’t inflate scores to look better. The calculator only works if you give accurate input. You also have to recognise that subjective metrics have inherent measurement noise.

 

Missing Critical Context

The calculator doesn’t know:

• Current medications: Beta-blockers lower resting heart rate; statins lower LDL; thyroid medication affects TSH. Your scores might reflect pharmaceutical management, not just lifestyle.
• Diagnosed medical conditions: Type 1 diabetes, hypothyroidism, chronic kidney disease, heart failure, etc., all shift optimal targets and interpretation.
• Recent injuries or surgeries: A torn ACL explains why your strength scores are low; post-surgical recovery affects multiple metrics.
• Contraindications: Some interventions suggested by low scores may be contraindicated for your specific situation (e.g., intense exercise with certain heart conditions).
• Training status: Someone in a heavy training block may have temporarily suppressed HRV or elevated resting heart rate; this is expected adaptation, not pathology.

The calculator interprets your numbers in a vacuum. It doesn’t know your story. You, your coach and your healthcare team need to add that context to interpret results appropriately.

 

Statistical Risk vs. Individual Outcomes

This is perhaps the most important limitation to understand:

High scores reduce risk but don’t guarantee good outcomes. You can do everything right, and have perfect 5s across the board, and still develop disease or die young from bad luck, accidents, rare conditions, or factors outside the calculator’s scope.

Low scores increase risk but aren’t deterministic. Plenty of people with suboptimal health live long lives. The relationship is probabilistic, not guaranteed.

What the research shows: If 1,000 people with poor VO₂ max and 1,000 people with excellent VO₂ max are followed for 20 years, the high-fitness group will have significantly fewer deaths. But many individuals in the low-fitness group will still outlive individuals in the high-fitness group.

Why this matters: Don’t become fatalistic if your scores are low (“I’m doomed anyway, why bother?”) or complacent if your scores are high (“I’m invincible, I can ignore symptoms”). Focus on improving your odds while accepting that outcomes are probabilistic.

 

Age Adjustment Limitations

Age adjustments use broad bands (under 50, 50-74, 75+) rather than continuous adjustment by exact age.

Why this creates limitations:

• A 50-year-old and 74-year-old are both in the “50-74” band, but their optimal VO₂ max differs
• Within-band variation isn’t captured
• Transition points (crossing from 49 to 50, or 74 to 75) create sudden threshold changes

Trade-off: Simplicity vs. precision. Continuous age adjustment would be more accurate but more complex to create (I am just one man!), and we don’t have enough data to make very granular recommendations here. The band approach is a reasonable compromise that captures the major age-related changes without overcomplicating the system.

What this means: If you’re at the edges of an age band (e.g., 49 or 74), your targets may be slightly too easy or too hard compared to someone in the middle of the band (e.g., 60). This is a minor limitation that doesn’t fundamentally compromise the calculator’s usefulness.

 

Epidemiological Studies Show Associations, Not Always Causation

Most of the evidence comes from observational studies that show associations between metrics and outcomes, not definitive proof of causation.

Example: We know that low VO₂ max is associated with higher mortality. But is low fitness causing early death, or is underlying illness causing both low fitness and early death (reverse causation)?

Fortunately, interventional studies (where people improve fitness through training) show improved health outcomes, supporting a causal relationship. But for some metrics, the causation question remains partially open.

What this means for you: The associations are strong and consistent enough to guide action. Even if we can’t prove causation with 100% certainty, improving these metrics is still your best bet for improving health outcomes.

 

Appropriate Use: The DOs and DON’Ts

To use this calculator responsibly and effectively, follow these guidelines:

 

DO: Use as a Screening Tool 

Use results to identify areas worth discussing with your coach/doctor.

If your kidney function scores 2/5, that’s a flag to get a comprehensive metabolic panel and discuss with your physician whether further workup is needed. If your VO₂ max scores 2/5, that’s a prompt to start (or intensify) your cardiovascular training program.

The calculator points you toward areas that deserve attention; then you and your healthcare team determine the appropriate next steps.

 

DO: Track Trends Over Time 

Retest quarterly or biannually and compare results.

Are your interventions working? Is your blood pressure coming down? Is your VO₂ max improving? Is your waist-to-height ratio declining? Objective tracking prevents self-deception and confirms that your efforts are paying off.

Trends matter more than single snapshots. One bad sleep quality score (maybe you had a stressful week) is less meaningful than consistent 2/5 scores over six months.

 

DO: Use to Prioritise Lifestyle Interventions 

Focus your training, nutrition, sleep, and stress management on the metrics that need the most work.

If your calculator highlights poor cardiovascular fitness, elevated ApoB, and suboptimal sleep as your top three priorities, you now have a clear action plan:

1. Add Zone 2 cardio and VO₂ max intervals
2. Adjust nutrition to lower ApoB (reduce saturated fat, increase fibre, consider plant sterols)
3. Improve sleep hygiene and address underlying sleep issues

This focused approach is far more effective than trying to optimise everything simultaneously.

 

DON’T: Self-Diagnose Based on Scores 

A low score is not a diagnosis.

If your HbA1c scores 2/5 (pre-diabetes range), you don’t have pre-diabetes, you have a biomarker in the pre-diabetic range that warrants medical evaluation. Only a healthcare provider can diagnose after considering your full clinical picture, potentially ordering additional testing (oral glucose tolerance test, continuous glucose monitoring), and ruling out other causes.

Self-diagnosis leads to unnecessary anxiety, inappropriate interventions, and missed alternative explanations for abnormal results.

 

DON’T: Ignore Symptoms Because Your Score Is Good 

A high overall score doesn’t mean you’re invincible.

If you score 4.5/5 overall but you’re experiencing chest pain, unexplained weight loss, persistent fatigue, or any other concerning symptoms, you should see a doctor immediately. The calculator doesn’t measure everything, and symptoms trump biomarkers.

Many serious conditions won’t be caught by this calculator:

• Early-stage cancers (no symptoms initially)
• Atrial fibrillation (may not affect resting heart rate or VO₂ max)
• Aneurysms (no warning until they rupture)
• Mental health crises (may be masked by high scores in other areas)

Trust your body. If something feels wrong, get it checked out, regardless of your calculator score.

 

DON’T: Skip Medical Care Because Your Score Is Poor 

A low score is a reason to seek medical care, not avoid it.

If you score 2.5/5 overall with multiple red flags, you might feel overwhelmed or fatalistic. Don’t let that stop you from seeing a doctor. Low scores mean you have modifiable risk factors, many of which can be addressed with lifestyle changes, and some of which may require medical intervention.

Avoiding medical care because you’re “too far gone” is the worst possible response. Every improvement matters. Even moving from 2.5/5 to 3.5/5 significantly reduces your mortality risk.

 

A Tool, Not an Oracle

This calculator is a tool (a very good tool if I say so myself), and it is backed by solid science and weighted appropriately, but it’s still just a tool.

It can:

• Show you where you stand on important health metrics
• Identify your highest-impact opportunities for improvement
• Track whether your interventions are working over time
• Educate you on what matters most for healthspan and lifespan

It cannot:

• Replace comprehensive medical care
• Diagnose diseases
• Account for your unique genetics and medical history
• Predict when you’ll die
• Make medical decisions for you

Use it as intended, as a assessment tool that guides your lifestyle decisions and prompts important conversations with your healthcare team, and it will serve you exceptionally well.

Use it inappropriately (self-diagnosing, ignoring symptoms, skipping medical care, or treating scores as absolute truth) and it becomes dangerous.

The choice is yours. I would suggest you use it wisely.

 

Coaching Integration: Using This Calculator in Your Coaching Practice

Now, I know we have a lot of coaches in our community, and I wanted to provide some practical guidance on how you can integrate this calculator into your coaching practice effectively.

This tool isn’t just for individuals tracking their own health. It’s designed to be a powerful coaching asset that increases client engagement, clarifies priorities, and provides objective tracking over time. Here’s how to leverage it.

 

How Coaches Can Use This

Client Engagement: Visual, Gamified Format Increases Motivation

Let’s be honest: most clients’ eyes glaze over when you show them a spreadsheet of lab values. Numbers without context don’t motivate, they overwhelm.

The calculator transforms data into something visual, understandable, and actionable:

• Radar chart: Shows all six health domains at a glance, clients immediately see their strengths (wide sections) and weaknesses (narrow sections)
• Colour-coded progress bars: Red, orange, yellow, green, and dark green instantly communicate “this needs work” vs. “you’re doing great”
• Gamification element: Clients naturally want to “level up” their scores. A 2/5 that becomes a 4/5 over three months feels like a big win.
• Clear overall score: A single number (e.g., 3.8/5) that can improve over time creates a tangible target

Why this matters: Engagement drives compliance. Clients who understand their results and see clear progress are far more likely to stick with your program than clients who are confused or don’t see the connection between their efforts and outcomes.

Coaching tip: During onboarding, walk through the radar chart together. Ask: “Which spoke would you most like to see wider six months from now?” This creates buy-in and helps clients take ownership of their priorities.

 

Priority Identification: Quickly See Client’s Biggest Gaps

As a coach, you probably work with multiple clients; each with different health profiles, goals, and limiting factors. The calculator does the triage for you.

Instead of:

• Manually reviewing 40+ metrics
• Deciding which issues to address first
• Wondering whether to focus on cardio, strength, sleep, or stress

You get:

• Automatic prioritisation based on weighted risk scores
• Clear visibility into which categories are dragging down the overall score
• Top 3-5 focus areas highlighted for immediate action

The algorithm does the maths: It calculates the gap between the current score and the optimal (5), multiplies by the metric’s weight, and ranks by potential risk reduction. The biggest opportunities rise to the top automatically.

Example client profile:

• Overall score: 3.2/5
• Top priorities flagged:
1. Waist-to-height ratio: 2/5 (Weight 2.0, high impact)
2. VO₂ max: 2/5 (Weight 2.0, high impact)
3. Sleep quality: 2/5 (Weight 1.5, moderate-high impact)
4. ApoB: 3/5 (Weight 2.0, moderate gap but very important metric)

Now you know exactly where to focus your coaching: Fat loss (WHtR), cardiovascular training (VO₂ max), sleep hygiene (sleep quality), and potentially nutrition counselling or medical referral (ApoB).

Coaching tip: Don’t try to fix everything at once. Pick 1-3 priorities per quarter based on the client’s readiness, capacity, and what will create the most momentum. Success breeds success, and early wins build confidence for tackling harder changes later.

 

Longitudinal Tracking: Compare Scores Over Time

One of the most powerful uses of this calculator is objective progress tracking. Clients often can’t see their own progress (or don’t believe it’s happening). The calculator provides irrefutable evidence.

How to use longitudinal tracking:

1. Baseline assessment (Week 0): Client completes calculator with all available data
2. Quarterly retest (Week 12): Client retests, ideally getting updated labs if timing aligns with routine bloodwork
3. Compare results: Pull up both assessments side-by-side and review changes
4. Celebrate wins: “Your VO₂ max went from 2/5 to 3/5, that’s about a 15% reduction in mortality risk based on the research.”
5. Adjust approach: If metrics aren’t improving, troubleshoot why and adjust the intervention

What to track over time:

• Overall score: Is the composite moving in the right direction?
• Category scores: Are the targeted domains improving?
• Individual metrics: Are the specific interventions working? (e.g., did Zone 2 training improve VO₂ max? Did sleep hygiene improve sleep quality?)
• Priority shifts: As some metrics improve, new priorities may surface

Coaching tip: Use visual comparisons. Show the client their before/after radar charts side-by-side. The visual impact of seeing the polygon expand is far more motivating than saying “your score went from 3.2 to 3.8.”

Realistic timelines for retesting:

• 3 months: Good for fitness metrics (VO₂ max, strength, body composition) and subjective metrics (sleep, stress, energy)
• 6 months: Better for blood biomarkers (lipids, glucose, inflammation), as these change more slowly and retesting labs too frequently is expensive and adds little value
• 12 months: Comprehensive annual reassessment to see long-term trends

 

Education Tool: Explains Which Metrics Matter Most and Why

Most clients don’t know what matters for longevity. They think abs = health. They chase trends (ice baths, supplements, biohacks) while ignoring fundamentals (blood pressure, glucose control, aerobic fitness).

The calculator is an educational intervention as much as an assessment tool.

What it teaches clients:

• Which metrics predict longevity most strongly: VO₂ max, blood pressure, ApoB, glucose control, kidney function, body composition (Weight 2.0 metrics).
• Why these metrics matter: The accompanying article explains mechanisms (how high blood pressure damages arteries, why visceral fat drives insulin resistance, how low VO₂ max predicts early death, etc.).
• What optimal actually looks like: Not just “normal” lab ranges, but truly health-optimising targets based on mortality data.
• How metrics interact: Poor sleep tanks HRV, drives up cortisol, worsens glucose control, and increases inflammation. Everything is connected.

Coaching application: Use the article as a client education library. When a client asks “Why do we care about my triglyceride-to-HDL ratio?” you can say, “Let me show you the research section, here’s why this predicts insulin resistance better than fasting glucose alone.”

Coaching tip: Don’t info-dump everything at once. Introduce concepts progressively as they become relevant to the client’s current focus. Education works best when it’s timely and actionable.

 

Compliance Support: Clear Targets for Client Goals

Vague goals lead to vague results. “Get healthier” doesn’t create behaviour change. “Improve your VO₂ max from 2/5 to 4/5 over the next six months” does.

The calculator provides:

• Specific targets: Not “exercise more” but “improve VO₂ max from 38 to 50 ml/kg/min” (a concrete, measurable target)
• Objective progress markers: Numbers don’t lie; either the metric improved or it didn’t
• Built-in accountability: Knowing there’s a retest in 12 weeks creates urgency and motivation

How to leverage this for compliance:

Step 1: Set outcome goals based on calculator priorities

• “We’re going to get your waist-to-height ratio from 0.58 to <0.5 over the next 16 weeks”
• “Our target is to improve your HbA1c from 5.8% (40 mmol/mol) to <5.2% (<33 mmol/mol)”
• “Let’s get your sleep quality score from 2/5 to 4/5”

Step 2: Break into process goals (behaviours)

• Waist-to-height ratio → fat loss → calorie deficit + strength training 3-4x/week
• HbA1c → glucose control → lower refined carbs, add post-meal walks, improve sleep
• Sleep quality → sleep hygiene protocol, address sleep apnea if needed

Step 3: Track both process and outcome

• Weekly: Check process compliance (Did you hit training sessions? Did you track food? Did you follow sleep protocol?)
• Quarterly: Retest outcomes (Did the metrics actually improve?)

Coaching tip: When compliance wanes, revisit the calculator results. Remind the client why these metrics matter and what’s at stake. Sometimes people need to be re-scared or re-inspired by the data.

 

Recommended Coaching Workflow

Here’s a step-by-step process for integrating the calculator into your coaching practice:

 

Step 1: Client Completes Calculator Before Initial Appointment

Send the calculator link as part of onboarding.

Ask the client to:

• Gather recent lab work (ideally within the last 6 months)
• Take basic measurements at home (waist circumference, weight, height)
• Complete self-assessment questions honestly (sleep, stress, energy, relationships, purpose, financial stress)
• Submit results before your first session

Why before the appointment? This gives you time to review their results and come prepared with an informed perspective. You can identify their biggest gaps and formulate initial recommendations before you even meet.

 

Step 2: Review Results and Validate Inputs

Before diving into recommendations, check for:

• Data entry errors: Did they enter height in inches when the calculator expected cm? Is body weight realistic? Are blood pressure readings plausible?
• Misunderstandings: Did they enter total cholesterol instead of LDL? Did they confuse fasting glucose with HbA1c?
• Missing critical data: Are they missing labs that would be valuable to have? (ApoB, HbA1c, VO₂ max)
• Estimated scores: Which metrics are estimated at 3/5 due to missing data? These are opportunities to get actual measurements.

Ask clarifying questions:

• “I see your ApoB is estimated; have you had a lipid panel done recently?”
• “Your VO₂ max is estimated; have you done any cardio fitness testing, or is this truly unknown?”
• “You rated your sleep quality as 3/10; tell me more about what’s happening with your sleep.”

Validation is critical. Garbage in = garbage out. Spend the time to ensure the data is accurate before building a program around it.

 

Step 3: Discuss Lowest-Scoring Metrics and Realistic Interventions

Walk through the results together:

1. Show the radar chart: “Here’s the big picture view of your health across six domains.”
2. Identify strengths: “Your psychosocial health is excellent. You clearly have strong relationships and clear purpose.”
3. Identify gaps: “Your cardiovascular fitness and blood work categories are scoring low. Let’s talk about why that matters.”
4. Explain the top priorities: “The calculator is flagging three areas with the biggest impact: your waist-to-height ratio, your VO₂ max, and your ApoB. These are all high-weight metrics, so improving them will have the biggest effect on your long-term health.”

Discuss realistic interventions:

• What’s achievable given the client’s current lifestyle, schedule, and resources?
• What’s the client actually willing to do? (Readiness assessment)
• What’s the logical starting point? (Don’t prescribe six training sessions per week to someone currently doing zero)

Coaching tip: Meet the client where they are, not where you think they should be. A perfect program the client won’t follow is worthless. A good-enough program they’ll actually do is gold.

 

Step 4: Set 1-3 Priority Goals

Focus beats scattered effort.

Based on the calculator’s priorities and the client’s readiness, select 1-3 goals for the next 8-12 weeks.

Example:

Client: 45-year-old male, sedentary, overweight, prediabetic. 

Top calculator priorities:

1. Waist-to-height ratio: 2/5
2. VO₂ max: 2/5
3. HbA1c: 2/5

Your 3 priorities:

1. Fat loss: Reduce waist-to-height ratio from 0.58 to <0.5 (lose ~15 lbs of fat)
• Process goal: 4 strength training sessions/week + daily 10k steps
2. Improve cardiovascular fitness: Increase VO₂ max from 35 to 42 ml/kg/min
• Process goal: 2-3× Zone 2 cardio sessions/week (30-45 min) + 1× VO₂ max intervals
3. Improve glucose control: Lower HbA1c from 5.9% (41 mmol/mol) to <5.5% (<37 mmol/mol)
• Process goal: Reduce refined carbs, add post-meal walks, improve sleep to 7+ hours

Why 1-3 goals? Because behaviour change is hard. Three focused priorities create momentum. Ten priorities create overwhelm and paralysis.

Coaching tip: Frame goals as both outcome-based (what the metric will become) and process-based (what behaviours will get you there). Clients control the process, not the outcome, so process goals drive daily action.

 

Step 5: Retest in 3-6 Months to Track Progress

Schedule the retest on the calendar immediately. Don’t leave it open-ended (“We’ll retest when you’re ready”). Create accountability through a specific date.

At the retest:

1. Recomplete the calculator with updated measurements and any new lab work
2. Compare before/after side-by-side: Use the radar chart comparison to show visual progress
3. Celebrate wins: Even small improvements (2/5 → 3/5) represent real health gains, and you should acknowledge them
4. Troubleshoot stalls: If metrics didn’t improve, why? Compliance issue? Need to adjust the approach? Underlying medical issue that needs investigation?
5. Set next priorities: As some metrics improve, others may surface as new priorities

Coaching tip: Progress isn’t always linear. Sometimes metrics plateau or even temporarily worsen (especially during high training stress). Look at trends over 6-12 months, not single data points.

 

When to Order Additional Tests

One of the calculator’s most useful features: it highlights missing data by marking estimated scores (3/5) for metrics where you didn’t have actual measurements.

If a client scored 3 (estimated) on critical metrics, consider discussing with their doctor (and the client) about ordering:

 

ApoB or Advanced Lipid Panel (if missing)

Why it matters: ApoB is the single best predictor of cardiovascular risk from lipids (better than LDL cholesterol). If a client has cardiovascular risk factors (family history, elevated blood pressure, diabetes, obesity), knowing their actual ApoB is invaluable.

What to order:

• ApoB (preferred)
• If ApoB is unavailable, advanced lipid panel (LDL-P, small dense LDL, Lp(a), etc.)
• Standard lipid panel (LDL, HDL, triglycerides) if nothing else is available

When to order: Especially important if the client has a family history of early heart disease, multiple metabolic risk factors, or discordant lipid results (normal LDL but high triglycerides, suggesting high particle count).

 

Continuous Glucose Monitor (if HbA1c borderline)

Why it matters: HbA1c gives a 3-month average, but it doesn’t show glucose variability or post-meal spikes. Someone with HbA1c of 5.6% (38 mmol/mol) might have stable glucose or might be riding a glucose rollercoaster with huge spikes and crashes.

What to order:

• 14-day continuous glucose monitor (CGM) like Freestyle Libre or Dexcom
• Provides real-time feedback on how food, exercise, sleep, and stress affect glucose
• Identifies hidden post-meal spikes that fasting glucose and HbA1c miss

When to order: If HbA1c is 5.5-6.4% (37-46 mmol/mol, pre-diabetic range) or if fasting glucose is repeatedly 95-125 mg/dL (5.3-6.9 mmol/L), a CGM provides actionable data for personalising nutrition and exercise timing.

 

VO₂ Max Test (if fitness unclear and client has CVD risk)

Why it matters: VO₂ max is the strongest predictor of all-cause mortality. If it’s truly unknown and the client has cardiovascular risk factors, getting an actual measurement (rather than estimating) is valuable for accurate risk assessment and targeted training.

What to order:

• Lab-based VO₂ max test (gold standard) on treadmill or bike with metabolic cart
• Submaximal fitness test (less accurate but still useful) at many gyms/fitness centres
• Field tests (Cooper 12-min run, Rockport walk test) as a budget alternative
• Wearables can also give a decent estimate too

When to order: Especially important if the client is sedentary, overweight, has a family history of heart disease, or has other cardiovascular risk factors. Knowing actual VO₂ max helps establish appropriate exercise intensity zones and track progress accurately.

 

Body Composition Scan (DEXA) for Accurate Body Fat %

Why it matters: Body fat % estimated from bioelectrical impedance (BIA scales), skinfold callipers, or visual assessment can be off by 5-8%. DEXA provides gold-standard accuracy (±1-2%) and also measures visceral fat and bone density.

What to order:

• DEXA scan (dual-energy X-ray absorptiometry)
• Provides: body fat %, lean mass, bone density, visceral fat area
• Allows accurate calculation of FFMI (fat-free mass index)

When to order: If body composition is a focus (fat loss, muscle gain), having a baseline DEXA scan ensures you’re tracking real changes, not measurement error. Retest every 6-12 months to track progress.

 

Comprehensive Metabolic Panel (if eGFR estimated and out of range)

Why it matters: eGFR is often calculated from serum creatinine on a basic metabolic panel, but a comprehensive panel provides additional context: electrolytes (sodium, potassium), liver function (ALT, AST), albumin, and more. For many individuals who have lots of muscle, exercise a lot, or take supplemental creatine, their creatinine levels can be quite high. This isn’t indicative of kidney issues, so eGFR may not be a good metric. 

What to order:

• Comprehensive metabolic panel (CMP)
• Includes: glucose, calcium, electrolytes, kidney function (BUN, creatinine, eGFR), liver enzymes (ALT, AST, ALP, bilirubin), albumin, total protein

When to order: If the client hasn’t had recent lab work (>12 months) or if kidney function is estimated rather than measured and is out of range, a CMP provides a comprehensive metabolic context for a relatively low cost.

 

Working Within Your Scope of Practice

Important note for coaches: Know your scope of practice and stay within it.

What you CAN do:

• Educate clients on what metrics mean
• Recommend lifestyle interventions (exercise, nutrition, sleep, stress management)
• Track progress over time using the calculator
• Suggest that clients discuss specific findings with their doctor
• Help clients understand which tests might be valuable to request

What you CANNOT do (unless you’re a licensed medical provider):

• Diagnose medical conditions based on calculator results
• Prescribe medications or supplements for medical treatment
• Order lab tests directly (unless you have ordering privileges)
• Tell clients to stop taking prescribed medications
• Provide medical advice or treatment

The line: You can say, “Your HbA1c is in the pre-diabetic range according to the calculator. I recommend discussing this with your doctor to determine if additional testing or treatment is needed. In the meantime, let’s work on improving your glucose control through nutrition, exercise, and sleep.”

You cannot say, “You have pre-diabetes. Start taking berberine and metformin.”

When to refer to medical providers:

• Abnormal biomarkers that warrant medical evaluation
• Symptoms that suggest underlying medical conditions
• Results outside your scope of practice or expertise
• Any situation where medical diagnosis or treatment is indicated

 

Ultimately, this calculator is a force multiplier for your coaching practice.

It increases client engagement through gamification and visual feedback. It clarifies priorities so you’re not guessing where to focus. It provides objective tracking so clients see their progress. It educates clients on what actually matters for longevity. And it creates accountability through clear targets and scheduled retesting.

Use it as part of a comprehensive coaching program, and not as a replacement for your expertise, but as a tool that makes your coaching more effective, more efficient, and more results-driven.

 

Triage Ultimate Health Assessment Tool Conclusion

This calculator represents a synthesis of the best available evidence on modifiable health predictors. It’s not a collection of trendy biomarkers or vanity metrics, it’s a carefully curated panel of the factors that actually predict how long you’ll live and how well you’ll function as you age. By weighting metrics according to their proven impact on mortality and morbidity, and providing age- and sex-appropriate targets, this tool offers users something rare in health optimisation: a realistic, actionable roadmap backed by decades of research involving millions of participants.

At its core, the Triage Ultimate Health Assessment Tool is built on several foundational principles that distinguish it from generic health assessments. First and foremost, it prioritises healthspan over lifespan. Living to 95 doesn’t mean much if you spend the last 20 years in chronic pain, dependent on others, cognitively impaired, and unable to do the things you love. The goal isn’t just adding years to your life, it’s adding life to your years. The metrics included predict both longevity and functional capacity. High VO₂ max doesn’t just reduce mortality, it ensures you can hike, play with grandkids, travel, and remain independent into your 80s and beyond. Strong muscles don’t just predict survival, they prevent falls, maintain mobility, and preserve autonomy. We’re optimising for the period of life spent in good health, free from chronic disease and disability, because that’s what actually matters.

The calculator also emphasises prevention over treatment. Every metric included is a leading indicator, as it predicts disease years or decades before symptoms appear. Elevated ApoB predicts heart attacks 10-30 years before chest pain. Rising HbA1c signals diabetes risk 5-10 years before diagnosis. Declining VO₂ max forecasts cardiovascular events years in advance. Low muscle mass predicts frailty and functional decline a decade before it becomes obvious. By the time you have symptoms, damage is already done. The power of these metrics is that they give you a window to intervene early, when lifestyle changes are most effective and disease is still preventable or reversible. Treatment is expensive, reactive, and often too late. Prevention is cheap, proactive, and almost always more effective.

Understanding that health is multifactorial is crucial. There is no one metric that tells the whole story, no single intervention that fixes everything. You need good cardiovascular fitness, healthy metabolism, adequate strength and muscle mass, quality recovery, favourable body composition, and a strong psychosocial foundation. Neglect any major domain, and you introduce risk. Excel in all of them, and you’ve built a fortress of health resilience. This calculator forces a comprehensive view, as you can’t just optimise one category and ignore the others. Your overall score integrates all six domains because your body doesn’t compartmentalise. Everything affects everything else.

Perhaps the most empowering principle underlying this tool is that all metrics are modifiable. Every single one can be improved through your choices. You can’t change your genetics, your age, or your family history. But you can change your cardiovascular fitness through training, your strength through progressive resistance exercise, your blood pressure through diet and lifestyle, your glucose control through nutrition and activity, your lipid profile through diet quality and exercise, your sleep through better habits, your stress through mindfulness and social support, your body composition through training and nutrition, your social connections through intentional relationship-building, and your sense of purpose through self-reflection and life design. Health isn’t a curse of fate; it’s the cumulative result of thousands of decisions compounded over time. This calculator shows you which decisions matter most.

The personalised nature of the assessment makes it both fair and motivating. A 25-year-old and a 70-year-old aren’t held to the same standards. A man and a woman aren’t scored identically on metrics where biology differs. Age adjustments recognise that VO₂ max naturally declines with age, that menopause is normal for a 52-year-old woman but concerning for a 35-year-old, and that expectations shift across decades. Sex-specific standards acknowledge that women carry more essential body fat than men, that testosterone levels differ by an order of magnitude, that upper body strength differences reflect biology rather than effort, and that some health metrics are inherently sex-specific. These adjustments ensure the calculator doesn’t discourage people for biological realities they can’t change, while still maintaining rigorous standards that push everyone toward optimal health.

Every element of this calculator is evidence-based. Each metric has been validated in large-scale epidemiological studies. Every threshold is derived from mortality data. Every weight reflects demonstrated impact on health outcomes. This isn’t guesswork or opinion, it’s science, and the research citations are provided (below) so you can verify the evidence yourself. 

What makes this tool truly actionable is its clear prioritisation. You have limited time, energy, and willpower, so trying to optimise 40+ metrics simultaneously is a recipe for paralysis and failure. The calculator does the triage for you by weighting metrics by impact, calculating which gaps pose the greatest risk, surfacing the top priorities automatically, and telling you where to focus for maximum return on investment. Instead of spinning your wheels trying to improve everything at once, you get a focused action plan. This focus is what makes the calculator practically useful rather than just informationally overwhelming.

The calculator also provides quantified targets that make progress trackable. “Get healthier” is a terrible goal as it’s vague, unmeasurable, and demotivating. “Improve VO₂ max from 38 to 48 ml/kg/min over six months” is a great goal because it’s specific, measurable, and creates accountability. The calculator provides your current score, the optimal target, the gap between where you are and where you could be, and a clear timeframe for retesting. Measurement creates accountability. What gets measured gets managed. Progress you can see motivates continued effort.

Used correctly (as a screening and tracking tool, not a replacement for medical care) this calculator can help you make informed decisions about where to focus your limited time, energy, and resources for maximum health impact. Use it to identify your biggest health gaps, track progress over time, prioritise lifestyle interventions, have informed conversations with your doctor, and stay motivated through objective feedback. But don’t use it to self-diagnose medical conditions, replace comprehensive medical care, ignore symptoms because your score is good, or avoid medical care because your score is poor. The calculator is a tool. A powerful, evidence-based, intelligently designed tool, but a tool nonetheless. It complements medical care rather than replacing it.

Health optimisation isn’t a sprint, it’s a decades-long process of progressive improvement. You don’t need to get perfect fives across the board next quarter. You just need to improve. Move from 3.2 to 3.8. Then from 3.8 to 4.2. Over years, small improvements compound into transformative changes. The research is clear: improving these metrics extends your healthspan and lifespan. Every point increase in VO₂ max, every mmHg reduction in blood pressure, every improvement in HbA1c, every improvement in sleep quality, they all matter. They all reduce your risk. They all buy you more healthy time on Earth.

You have agency over your health trajectory. This calculator shows you how to exercise that agency most effectively. Use it well. Track your progress. Celebrate your wins. Stay consistent. And build a life that’s not just long, but vibrant, functional, and worth living to the very end. Your health is the foundation for everything else you want to accomplish in life. Invest in it accordingly.

If you want to understand what you should be prioritising, or you need help creating a plan of action, we can help you do this. You can reach out to us and get online coaching, or alternatively, you can interact with our free content. Our health habits assessment tool may also be useful for you too.

If you want more free information on nutrition and exercise, you can follow us on Instagram, YouTube or listen to the podcast, where we discuss all the little intricacies of exercise and nutrition. You can always stay up to date with our latest content by subscribing to our newsletter.

Finally, if you want to learn how to coach nutrition, then consider our Nutrition Coach Certification course, and if you want to learn to get better at exercise program design, then consider our course on exercise program design. We do have other courses available too. If you don’t understand something, or you just need clarification, you can always reach out to us on Instagram or via email.

 

References and Further Reading

Category 1: Blood Work & Hormonal Health

 

Cardiovascular Biomarkers – ApoB & LDL-C

Walldius G, Jungner I. Apolipoprotein B and apolipoprotein A-I: risk indicators of coronary heart disease and targets for lipid-modifying therapy. J Intern Med. 2004;255(2):188-205. doi:10.1046/j.1365-2796.2003.01276.x. https://pubmed.ncbi.nlm.nih.gov/14746556/

Sniderman AD, Islam S, Yusuf S, McQueen MJ. Discordance analysis of apolipoprotein B and non-high density lipoprotein cholesterol as markers of cardiovascular risk in the INTERHEART study. Atherosclerosis. 2012;225(2):444-449. doi:10.1016/j.atherosclerosis.2012.08.039. https://pubmed.ncbi.nlm.nih.gov/23068583/

Sniderman AD, Islam S, Yusuf S, McQueen MJ. Is the superiority of apoB over non-HDL-C as a marker of cardiovascular risk in the INTERHEART study due to confounding by related variables? J Clin Lipidol. 2013;7(6):626-631. doi:10.1016/j.jacl.2013.08.004. https://pubmed.ncbi.nlm.nih.gov/24314360/

Ference BA, Ginsberg HN, Graham I, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2017;38(32):2459-2472. doi:10.1093/eurheartj/ehx144. https://pubmed.ncbi.nlm.nih.gov/28444290/

Sniderman AD, Thanassoulis G, Glavinovic T, et al. Apolipoprotein B Particles and Cardiovascular Disease: A Narrative Review. JAMA Cardiol. 2019;4(12):1287-1295. doi:10.1001/jamacardio.2019.3780. Free full text: https://pmc.ncbi.nlm.nih.gov/articles/PMC7369156/

Lawler PR, Akinkuolie AO, Ridker PM, et al. Discordance between circulating atherogenic cholesterol mass and lipoprotein particle concentration in relation to future coronary events in women. Clin Chem. 2017;63(4):870-879. doi:10.1373/clinchem.2016.264515. Free full text: https://pmc.ncbi.nlm.nih.gov/articles/PMC5374022/

Welsh C, Celis-Morales CA, Brown R, et al. Comparison of conventional lipoprotein tests and apolipoproteins in the prediction of cardiovascular disease. Circulation. 2019;140(7):542-552. doi:10.1161/CIRCULATIONAHA.119.041149. Free full text: https://pmc.ncbi.nlm.nih.gov/articles/PMC6693929/

Kaneva AM, Potolitsyna NN, Bojko ER, Odland JØ. The apolipoprotein B/apolipoprotein A-I ratio as a potential marker of plasma atherogenicity. Dis Markers. 2015;2015:591454. doi:10.1155/2015/591454. Free full text: https://pmc.ncbi.nlm.nih.gov/articles/PMC4380097/

Thanassoulis G, Williams K, Ye K, et al. Relations of change in plasma levels of LDL-C, non-HDL-C and apoB with risk reduction from statin therapy: a meta-analysis of randomized trials. J Am Heart Assoc. 2014;3(2):e000759. doi:10.1161/JAHA.113.000759. https://pubmed.ncbi.nlm.nih.gov/24732920/

Sniderman AD, Williams K, Contois JH, et al. A meta-analysis of LDL cholesterol, non-HDL cholesterol, and apolipoprotein B as markers of cardiovascular risk. Circ Cardiovasc Qual Outcomes. 2011;4(3):337-345. doi:10.1161/CIRCOUTCOMES.110.959247. https://pubmed.ncbi.nlm.nih.gov/21487090/

Boekholdt SM, Hovingh GK, Mora S, et al. Very low levels of atherogenic lipoproteins and the risk for cardiovascular events: a meta-analysis of statin trials. J Am Coll Cardiol. 2014;64(5):485-494. doi:10.1016/j.jacc.2014.02.615. https://pubmed.ncbi.nlm.nih.gov/25082583/

Boekholdt SM, Arsenault BJ, Mora S, et al. Association of LDL cholesterol, non-HDL cholesterol, and apolipoprotein B levels with risk of cardiovascular events among patients treated with statins: a meta-analysis. JAMA. 2012;307(12):1302-1309. doi:10.1001/jama.2012.366. https://pubmed.ncbi.nlm.nih.gov/22453571/

Martin SS, Blaha MJ, Toth PP, et al. Apolipoprotein B but not LDL cholesterol is associated with coronary artery calcification in type 2 diabetic whites. Diabetes. 2009;58(8):1887-1892. Free full text: https://pmc.ncbi.nlm.nih.gov/articles/PMC2712798/

Contois JH, McConnell JP, Sethi AA, et al.; AACC Lipoproteins and Vascular Diseases Division Working Group on Best Practices. Apolipoprotein B and cardiovascular disease risk: position statement. Clin Chem. 2009;55(3):407-419. https://pubmed.ncbi.nlm.nih.gov/19168552/

Yun YM, Kim HS, Lee JI, et al. Apolipoprotein B, Non-HDL Cholesterol, and LDL Cholesterol as Markers for ASCVD Risk Assessment. Ann Lab Med. 2023;43(1):3-11. doi:10.3343/alm.2023.43.1.3. Free full text: https://pmc.ncbi.nlm.nih.gov/articles/PMC9791017/

Sniderman AD, Thanassoulis G, Glavinovic T, et al. Physiological Bases for the Superiority of Apolipoprotein B over LDL-C and non-HDL-C as a Marker of Cardiovascular Risk. J Am Heart Assoc. 2022;11(20):e025858. doi:10.1161/JAHA.122.025858. https://pubmed.ncbi.nlm.nih.gov/36216435/ PubMed

Wilkins JT, Li RC, Sniderman A, et al. Discordance between apolipoprotein B and LDL-cholesterol in young adults predicts coronary artery calcification: the CARDIA study. J Am Coll Cardiol. 2016;67(2):193-201. doi:10.1016/j.jacc.2015.10.055. https://pubmed.ncbi.nlm.nih.gov/26791067/

Sniderman AD, Furberg CD, Keech A, et al. ApoB versus cholesterol in estimating cardiovascular risk and in guiding therapy: report of the thirty-person/ten-country panel. J Am Coll Cardiol. 2006;47(4):837-845. https://pubmed.ncbi.nlm.nih.gov/16476102/ 

Richardson TG, Wang Q, Sanderson E, et al. Apolipoprotein B outperforms low-density lipoprotein particle number as a marker of cardiovascular risk in the UK Biobank. Eur Heart J. 2024;45(45):4567-4576. https://pubmed.ncbi.nlm.nih.gov/40887080/

Contois JH, Delatour V, Cole J, et al. Standardization of apolipoprotein B, LDL-cholesterol, and non-HDL-cholesterol. J Am Heart Assoc. 2023;12(20):e030405. doi:10.1161/JAHA.123.030405. https://pubmed.ncbi.nlm.nih.gov/37489721/ 

American College of Cardiology. Guidelines & Clinical Documents (Dyslipidemia and related). Accessed Oct 31, 2025. https://www.acc.org/Guidelines

European Society of Cardiology. ESC Guidelines (index). Accessed Oct 31, 2025. https://www.escardio.org/Guidelines 

 

HDL Cholesterol

Gordon DJ, Probstfield JL, Garrison RJ, et al. High-density lipoprotein cholesterol and cardiovascular disease. Four prospective American studies. Circulation. 1989;79(1):8-15. doi:10.1161/01.cir.79.1.8 https://pubmed.ncbi.nlm.nih.gov/2642759/

Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486-2497. doi:10.1001/jama.285.19.2486 https://pubmed.ncbi.nlm.nih.gov/11368702/

National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143-3421. https://pubmed.ncbi.nlm.nih.gov/12485966/

Lewis GF, Rader DJ. New insights into the regulation of HDL metabolism and reverse cholesterol transport. Circ Res. 2005;96(12):1221-1232. doi:10.1161/01.RES.0000170946.56981.5c https://pubmed.ncbi.nlm.nih.gov/15976321/

Khera AV, Cuchel M, de la Llera-Moya M, et al. Cholesterol efflux capacity, high-density lipoprotein function, and atherosclerosis. N Engl J Med. 2011;364(2):127-135. doi:10.1056/NEJMoa1001689 https://pubmed.ncbi.nlm.nih.gov/21226578/

Madsen CM, Varbo A, Nordestgaard BG. Extreme high high-density lipoprotein cholesterol is paradoxically associated with high mortality in men and women: two prospective cohort studies. Eur Heart J. 2017;38(32):2478-2486. doi:10.1093/eurheartj/ehx163 https://pubmed.ncbi.nlm.nih.gov/28419274/

Bowe B, Xie Y, Xian H, Balasubramanian S, Zayed MA, Al-Aly Z. High Density Lipoprotein Cholesterol and the Risk of All-Cause Mortality among U.S. Veterans. Clin J Am Soc Nephrol. 2016;11(10):1784-1793. doi:10.2215/CJN.00730116 https://pubmed.ncbi.nlm.nih.gov/27515591/

Kodama S, Tanaka S, Saito K, et al. Effect of aerobic exercise training on serum levels of high-density lipoprotein cholesterol: a meta-analysis. Arch Intern Med. 2007;167(10):999-1008. doi:10.1001/archinte.167.10.999 https://pubmed.ncbi.nlm.nih.gov/17533202/

Mensink RP, Zock PL, Kester AD, Katan MB. Effects of dietary fatty acids and carbohydrates on the ratio of serum total to HDL cholesterol and on serum lipids and apolipoproteins: a meta-analysis of 60 controlled trials. Am J Clin Nutr. 2003;77(5):1146-1155. doi:10.1093/ajcn/77.5.1146 https://pubmed.ncbi.nlm.nih.gov/12716665/

Mensink RP, Katan MB. Effect of dietary trans fatty acids on high-density and low-density lipoprotein cholesterol levels in healthy subjects. N Engl J Med. 1990;323(7):439-445. doi:10.1056/NEJM199008163230703 https://pubmed.ncbi.nlm.nih.gov/2374566/

Brien SE, Ronksley PE, Turner BJ, Mukamal KJ, Ghali WA. Effect of alcohol consumption on biological markers associated with risk of coronary heart disease: systematic review and meta-analysis of interventional studies. BMJ. 2011;342:d636. Published 2011 Feb 22. doi:10.1136/bmj.d636 https://pubmed.ncbi.nlm.nih.gov/21343206/

 

Triglycerides & TG:HDL Ratio

Toth PP. Triglycerides and Cardiovascular Risk: Getting to the Heart of the Matter. J Am Coll Cardiol. 2024;84(11):1007-1009. doi:10.1016/j.jacc.2024.07.027 https://pubmed.ncbi.nlm.nih.gov/39232627/

Miller M, Stone NJ, Ballantyne C, et al. Triglycerides and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2011;123(20):2292-2333. doi:10.1161/CIR.0b013e3182160726 https://pubmed.ncbi.nlm.nih.gov/21502576/

Aberra T, Peterson ED, Pagidipati NJ, et al. The association between triglycerides and incident cardiovascular disease: What is “optimal”?. J Clin Lipidol. 2020;14(4):438-447.e3. doi:10.1016/j.jacl.2020.04.009 https://pubmed.ncbi.nlm.nih.gov/32571728/

Patel A, Barzi F, Jamrozik K, et al. Serum triglycerides as a risk factor for cardiovascular diseases in the Asia-Pacific region. Circulation. 2004;110(17):2678-2686. doi:10.1161/01.CIR.0000145615.33955.83 https://pubmed.ncbi.nlm.nih.gov/15492305/

Sarwar N, Danesh J, Eiriksdottir G, et al. Triglycerides and the risk of coronary heart disease: 10,158 incident cases among 262,525 participants in 29 Western prospective studies. Circulation. 2007;115(4):450-458. doi:10.1161/CIRCULATIONAHA.106.637793 https://pubmed.ncbi.nlm.nih.gov/17190864/

Murguía-Romero M, Jiménez-Flores JR, Sigrist-Flores SC, et al. Plasma triglyceride/HDL-cholesterol ratio, insulin resistance, and cardiometabolic risk in young adults. J Lipid Res. 2013;54(10):2795-2799. doi:10.1194/jlr.M040584 https://pubmed.ncbi.nlm.nih.gov/23863983/

Giannini C, Santoro N, Caprio S, et al. The triglyceride-to-HDL cholesterol ratio: association with insulin resistance in obese youths of different ethnic backgrounds. Diabetes Care. 2011;34(8):1869-1874. doi:10.2337/dc10-2234 https://pubmed.ncbi.nlm.nih.gov/21730284/

Mederos-Torres CV, Díaz-Burke Y, Muñoz-Almaguer ML, García-Zapién AG, Uvalle-Navarro RL, González-Sandoval CE. Triglyceride/high-density cholesterol ratio as a predictor of cardiometabolic risk in young population. J Med Life. 2024;17(7):722-727. doi:10.25122/jml-2024-0117 https://pubmed.ncbi.nlm.nih.gov/39440341/

Flores-Guerrero JL, Been RA, Shalaurova I, Connelly MA, van Dijk PR, Dullaart RPF. Triglyceride/HDL cholesterol ratio and lipoprotein insulin resistance Score: Associations with subclinical atherosclerosis and incident cardiovascular disease. Clin Chim Acta. 2024;553:117737. doi:10.1016/j.cca.2023.117737 https://pubmed.ncbi.nlm.nih.gov/38142802/

Gong R, Luo G, Wang M, Ma L, Sun S, Wei X. Associations between TG/HDL ratio and insulin resistance in the US population: a cross-sectional study. Endocr Connect. 2021;10(11):1502-1512. Published 2021 Nov 15. doi:10.1530/EC-21-0414 https://pubmed.ncbi.nlm.nih.gov/34678755/

 

 

 

Blood Pressure

Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560-2572. doi:10.1001/jama.289.19.2560 https://pubmed.ncbi.nlm.nih.gov/12748199/

Ettehad D, Emdin CA, Kiran A, et al. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet. 2016;387(10022):957-967. doi:10.1016/S0140-6736(15)01225-8 https://pubmed.ncbi.nlm.nih.gov/26724178/

SPRINT Research Group, Wright JT Jr, Williamson JD, et al. A Randomized Trial of Intensive versus Standard Blood-Pressure Control. N Engl J Med. 2015;373(22):2103-2116. doi:10.1056/NEJMoa1511939 https://pubmed.ncbi.nlm.nih.gov/26551272/

SPRINT Research Group, Lewis CE, Fine LJ, et al. Final Report of a Trial of Intensive versus Standard Blood-Pressure Control. N Engl J Med. 2021;384(20):1921-1930. doi:10.1056/NEJMoa1901281 https://pubmed.ncbi.nlm.nih.gov/34010531/

Zhang W, Zhang S, Deng Y, et al. Trial of Intensive Blood-Pressure Control in Older Patients with Hypertension. N Engl J Med. 2021;385(14):1268-1279. doi:10.1056/NEJMoa2111437 https://pubmed.ncbi.nlm.nih.gov/34491661/

Whelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension. 2018;71(6):e13-e115. doi:10.1161/HYP.0000000000000065 https://pubmed.ncbi.nlm.nih.gov/29133356/

Colantonio LD, Booth JN 3rd, Bress AP, et al. 2017 ACC/AHA Blood Pressure Treatment Guideline Recommendations and Cardiovascular Risk. J Am Coll Cardiol. 2018;72(11):1187-1197. doi:10.1016/j.jacc.2018.05.074 https://pubmed.ncbi.nlm.nih.gov/30189994/

Rapsomaniki E, Timmis A, George J, et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1·25 million people. Lancet. 2014;383(9932):1899-1911. doi:10.1016/S0140-6736(14)60685-1 https://pubmed.ncbi.nlm.nih.gov/24881994/

Flint AC, Conell C, Ren X, et al. Effect of Systolic and Diastolic Blood Pressure on Cardiovascular Outcomes. N Engl J Med. 2019;381(3):243-251. doi:10.1056/NEJMoa1803180 https://pubmed.ncbi.nlm.nih.gov/31314968/

Haider AW, Larson MG, Franklin SS, Levy D; Framingham Heart Study. Systolic blood pressure, diastolic blood pressure, and pulse pressure as predictors of risk for congestive heart failure in the Framingham Heart Study. Ann Intern Med. 2003;138(1):10-16. doi:10.7326/0003-4819-138-1-200301070-00006 https://pubmed.ncbi.nlm.nih.gov/12513039/

Blood Pressure Lowering Treatment Trialists’ Collaboration. Pharmacological blood pressure lowering for primary and secondary prevention of cardiovascular disease across different levels of blood pressure: an individual participant-level data meta-analysis. Lancet. 2021;397(10285):1625-1636. doi:10.1016/S0140-6736(21)00590-0 https://pubmed.ncbi.nlm.nih.gov/33933205/

Bundy JD, Li C, Stuchlik P, et al. Systolic Blood Pressure Reduction and Risk of Cardiovascular Disease and Mortality: A Systematic Review and Network Meta-analysis. JAMA Cardiol. 2017;2(7):775-781. doi:10.1001/jamacardio.2017.1421 https://pubmed.ncbi.nlm.nih.gov/28564682/

Lewington S, Clarke R, Qizilbash N, Peto R, Collins R; Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360(9349):1903-1913. doi:10.1016/s0140-6736(02)11911-8 https://pubmed.ncbi.nlm.nih.gov/12493255/

 

Glucose Metabolism – Fasting Glucose

Coutinho M, Gerstein HC, Wang Y, Yusuf S. The relationship between glucose and incident cardiovascular events. A metaregression analysis of published data from 20 studies of 95,783 individuals followed for 12.4 years. Diabetes Care. 1999;22(2):233-240. doi:10.2337/diacare.22.2.233 https://pubmed.ncbi.nlm.nih.gov/10333939/

Park C, Guallar E, Linton JA, et al. Fasting glucose level and the risk of incident atherosclerotic cardiovascular diseases. Diabetes Care. 2013;36(7):1988-1993. doi:10.2337/dc12-1577 https://pubmed.ncbi.nlm.nih.gov/23404299/

Emerging Risk Factors Collaboration, Sarwar N, Gao P, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215-2222. doi:10.1016/S0140-6736(10)60484-9 https://pubmed.ncbi.nlm.nih.gov/20609967/

Balkau B, Bertrais S, Ducimetiere P, Eschwege E. Is there a glycemic threshold for mortality risk?. Diabetes Care. 1999;22(5):696-699. doi:10.2337/diacare.22.5.696 https://pubmed.ncbi.nlm.nih.gov/10332668/

Huang Y, Cai X, Mai W, Li M, Hu Y. Association between prediabetes and risk of cardiovascular disease and all cause mortality: systematic review and meta-analysis. BMJ. 2016;355:i5953. Published 2016 Nov 23. doi:10.1136/bmj.i5953 https://pubmed.ncbi.nlm.nih.gov/27881363/

Xu T, Liu W, Cai X, et al. Risk of Coronary Heart Disease in Different Criterion of Impaired Fasting Glucose: A Meta-Analysis. Medicine (Baltimore). 2015;94(40):e1740. doi:10.1097/MD.0000000000001740 https://pubmed.ncbi.nlm.nih.gov/26448033/

Levitzky YS, Pencina MJ, D’Agostino RB, et al. Impact of impaired fasting glucose on cardiovascular disease: the Framingham Heart Study. J Am Coll Cardiol. 2008;51(3):264-270. doi:10.1016/j.jacc.2007.09.038 https://pubmed.ncbi.nlm.nih.gov/18206734/

Kim HK, Kim CH, Kim EH, et al. Impaired fasting glucose and risk of cardiovascular disease in Korean men and women: the Korean Heart Study. Diabetes Care. 2013;36(2):328-335. doi:10.2337/dc12-0587 https://pubmed.ncbi.nlm.nih.gov/23002083/

Bjørnholt JV, Erikssen G, Aaser E, et al. Fasting blood glucose: an underestimated risk factor for cardiovascular death. Results from a 22-year follow-up of healthy nondiabetic men. Diabetes Care. 1999;22(1):45-49. doi:10.2337/diacare.22.1.45 https://pubmed.ncbi.nlm.nih.gov/10333902/

Kohansal K, Masrouri S, Khalili D, et al. Changes in Fasting plasma glucose status and risk of mortality events in individuals without diabetes over two decades of Follow-up: a pooled cohort analysis. Cardiovasc Diabetol. 2022;21(1):267. Published 2022 Dec 3. doi:10.1186/s12933-022-01709-z https://pubmed.ncbi.nlm.nih.gov/36463152/

Yi SW, Park S, Lee YH, Balkau B, Yi JJ. Fasting Glucose and All-Cause Mortality by Age in Diabetes: A Prospective Cohort Study. Diabetes Care. 2018;41(3):623-626. doi:10.2337/dc17-1872 https://pubmed.ncbi.nlm.nih.gov/29301823/

Yi SW, Park S, Lee YH, Park HJ, Balkau B, Yi JJ. Association between fasting glucose and all-cause mortality according to sex and age: a prospective cohort study. Sci Rep. 2017;7(1):8194. Published 2017 Aug 15. doi:10.1038/s41598-017-08498-6 https://pubmed.ncbi.nlm.nih.gov/28811570/

 

HbA1c

Rodgers LR, Hill AV, Dennis JM, et al. Choice of HbA1c threshold for identifying individuals at high risk of type 2 diabetes and implications for diabetes prevention programmes: a cohort study. BMC Med. 2021;19(1):184. Published 2021 Aug 20. doi:10.1186/s12916-021-02054-w https://pubmed.ncbi.nlm.nih.gov/34412655/

Wu S, Yi F, Zhou C, et al. HbA1c and the diagnosis of diabetes and prediabetes in a middle-aged and elderly Han population from northwest China (HbA1c). J Diabetes. 2013;5(3):282-290. doi:10.1111/1753-0407.12035 https://pubmed.ncbi.nlm.nih.gov/23452328/

Kaur G, Lakshmi PVM, Rastogi A, et al. Diagnostic accuracy of tests for type 2 diabetes and prediabetes: A systematic review and meta-analysis. PLoS One. 2020;15(11):e0242415. Published 2020 Nov 20. doi:10.1371/journal.pone.0242415 https://pubmed.ncbi.nlm.nih.gov/33216783/

Cavero-Redondo I, Peleteiro B, Álvarez-Bueno C, Rodriguez-Artalejo F, Martínez-Vizcaíno V. Glycated haemoglobin A1c as a risk factor of cardiovascular outcomes and all-cause mortality in diabetic and non-diabetic populations: a systematic review and meta-analysis. BMJ Open. 2017;7(7):e015949. Published 2017 Jul 31. doi:10.1136/bmjopen-2017-015949 https://pubmed.ncbi.nlm.nih.gov/28760792/

Scicali R, Giral P, Gallo A, et al. HbA1c increase is associated with higher coronary and peripheral atherosclerotic burden in non diabetic patients. Atherosclerosis. 2016;255:102-108. doi:10.1016/j.atherosclerosis.2016.11.003 https://pubmed.ncbi.nlm.nih.gov/27870948/

van ‘t Riet E, Rijkelijkhuizen JM, Alssema M, et al. HbA1c is an independent predictor of non-fatal cardiovascular disease in a Caucasian population without diabetes: a 10-year follow-up of the Hoorn Study. Eur J Prev Cardiol. 2012;19(1):23-31. doi:10.1097/HJR.0b013e32833b0932 https://pubmed.ncbi.nlm.nih.gov/20517157/

Ikeda F, Doi Y, Ninomiya T, et al. Haemoglobin A1c even within non-diabetic level is a predictor of cardiovascular disease in a general Japanese population: the Hisayama Study. Cardiovasc Diabetol. 2013;12:164. Published 2013 Nov 7. doi:10.1186/1475-2840-12-164 https://pubmed.ncbi.nlm.nih.gov/24195452/

Pai JK, Cahill LE, Hu FB, Rexrode KM, Manson JE, Rimm EB. Hemoglobin a1c is associated with increased risk of incident coronary heart disease among apparently healthy, nondiabetic men and women. J Am Heart Assoc. 2013;2(2):e000077. Published 2013 Mar 22. doi:10.1161/JAHA.112.000077 https://pubmed.ncbi.nlm.nih.gov/23537807/

Schöttker B, Rathmann W, Herder C, et al. HbA1c levels in non-diabetic older adults – No J-shaped associations with primary cardiovascular events, cardiovascular and all-cause mortality after adjustment for confounders in a meta-analysis of individual participant data from six cohort studies. BMC Med. 2016;14:26. Published 2016 Feb 11. doi:10.1186/s12916-016-0570-1 https://pubmed.ncbi.nlm.nih.gov/26867584/

Li FR, Zhang XR, Zhong WF, et al. Glycated Hemoglobin and All-Cause and Cause-Specific Mortality Among Adults With and Without Diabetes. J Clin Endocrinol Metab. 2019;104(8):3345-3354. doi:10.1210/jc.2018-02536 https://pubmed.ncbi.nlm.nih.gov/30896760/

Levitan EB, Liu S, Stampfer MJ, et al. HbA1c measured in stored erythrocytes and mortality rate among middle-aged and older women. Diabetologia. 2008;51(2):267-275. doi:10.1007/s00125-007-0882-y https://pubmed.ncbi.nlm.nih.gov/18043905/

Kianmehr H, Zhang P, Luo J, et al. Potential Gains in Life Expectancy Associated With Achieving Treatment Goals in US Adults With Type 2 Diabetes. JAMA Netw Open. 2022;5(4):e227705. Published 2022 Apr 1. doi:10.1001/jamanetworkopen.2022.7705 https://pubmed.ncbi.nlm.nih.gov/35435970/

 

Inflammation – hs-CRP

Bassuk SS, Rifai N, Ridker PM. High-sensitivity C-reactive protein: clinical importance. Curr Probl Cardiol. 2004;29(8):439-493. https://pubmed.ncbi.nlm.nih.gov/15258556/

Ridker PM, Rifai N, Rose L, Buring JE, Cook NR. Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events. N Engl J Med. 2002;347(20):1557-1565. doi:10.1056/NEJMoa021993 https://pubmed.ncbi.nlm.nih.gov/12432042/

Ridker PM, Danielson E, Fonseca FA, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med. 2008;359(21):2195-2207. doi:10.1056/NEJMoa0807646 https://pubmed.ncbi.nlm.nih.gov/18997196/

Ridker PM, Danielson E, Fonseca FA, et al. Reduction in C-reactive protein and LDL cholesterol and cardiovascular event rates after initiation of rosuvastatin: a prospective study of the JUPITER trial. Lancet. 2009;373(9670):1175-1182. doi:10.1016/S0140-6736(09)60447-5 https://pubmed.ncbi.nlm.nih.gov/19329177/

Emerging Risk Factors Collaboration, Kaptoge S, Di Angelantonio E, et al. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis. Lancet. 2010;375(9709):132-140. doi:10.1016/S0140-6736(09)61717-7 https://pubmed.ncbi.nlm.nih.gov/20031199/

Tian R, Tian M, Wang L, et al. C-reactive protein for predicting cardiovascular and all-cause mortality in type 2 diabetic patients: A meta-analysis. Cytokine. 2019;117:59-64. doi:10.1016/j.cyto.2019.02.005 https://pubmed.ncbi.nlm.nih.gov/30826600/

Buckley DI, Fu R, Freeman M, Rogers K, Helfand M. C-reactive protein as a risk factor for coronary heart disease: a systematic review and meta-analyses for the U.S. Preventive Services Task Force. Ann Intern Med. 2009;151(7):483-495. doi:10.7326/0003-4819-151-7-200910060-00009 https://pubmed.ncbi.nlm.nih.gov/19805771/

Hemingway H, Philipson P, Chen R, et al. Evaluating the quality of research into a single prognostic biomarker: a systematic review and meta-analysis of 83 studies of C-reactive protein in stable coronary artery disease. PLoS Med. 2010;7(6):e1000286. Published 2010 Jun 1. doi:10.1371/journal.pmed.1000286 https://pubmed.ncbi.nlm.nih.gov/20532236/

Genest J. C-reactive protein: risk factor, biomarker and/or therapeutic target?. Can J Cardiol. 2010;26 Suppl A:41A-44A. doi:10.1016/s0828-282x(10)71061-8 https://pubmed.ncbi.nlm.nih.gov/20386760/

Peikert A, Kaier K, Merz J, et al. Residual inflammatory risk in coronary heart disease: incidence of elevated high-sensitive CRP in a real-world cohort. Clin Res Cardiol. 2020;109(3):315-323. doi:10.1007/s00392-019-01511-0 https://pubmed.ncbi.nlm.nih.gov/31325043/

Prati F, Marco V, Paoletti G, Albertucci M. Coronary inflammation: why searching, how to identify and treat it. Eur Heart J Suppl. 2020;22(Suppl E):E121-E124. doi:10.1093/eurheartj/suaa076 https://pubmed.ncbi.nlm.nih.gov/32523455/

Romero-Cabrera JL, Ankeny J, Fernández-Montero A, Kales SN, Smith DL. A Systematic Review and Meta-Analysis of Advanced Biomarkers for Predicting Incident Cardiovascular Disease among Asymptomatic Middle-Aged Adults. Int J Mol Sci. 2022;23(21):13540. Published 2022 Nov 4. doi:10.3390/ijms232113540 https://pubmed.ncbi.nlm.nih.gov/36362325/

 

White Blood Cell Count

Seppä AMJ, Skrifvars MB, Vuopio H, Raj R, Reinikainen M, Pekkarinen PT. Association of white blood cell count with one-year mortality after cardiac arrest. Resusc Plus. 2024;20:100816. Published 2024 Nov 2. doi:10.1016/j.resplu.2024.100816 https://pubmed.ncbi.nlm.nih.gov/39554491/

Zhu Z, Zhou S. Leukocyte count and the risk of adverse outcomes in patients with HFpEF. BMC Cardiovasc Disord. 2021;21(1):333. Published 2021 Jul 7. doi:10.1186/s12872-021-02142-y https://pubmed.ncbi.nlm.nih.gov/34233611/

Kabat GC, Kim MY, Manson JE, et al. White Blood Cell Count and Total and Cause-Specific Mortality in the Women’s Health Initiative. Am J Epidemiol. 2017;186(1):63-72. doi:10.1093/aje/kww226 https://pubmed.ncbi.nlm.nih.gov/28369251/

Margolis KL, Rodabough RJ, Thomson CA, Lopez AM, McTiernan A; Women’s Health Initiative Research Group. Prospective study of leukocyte count as a predictor of incident breast, colorectal, endometrial, and lung cancer and mortality in postmenopausal women. Arch Intern Med. 2007;167(17):1837-1844. doi:10.1001/archinte.167.17.1837 https://pubmed.ncbi.nlm.nih.gov/17893304/

Lind L, Zanetti D, Högman M, Sundman L, Ingelsson E. Commonly used clinical chemistry tests as mortality predictors: Results from two large cohort studies. PLoS One. 2020;15(11):e0241558. Published 2020 Nov 5. doi:10.1371/journal.pone.0241558 https://pubmed.ncbi.nlm.nih.gov/33152050/

Arai Y, Kanda E, Iimori S, et al. Low white blood cell count is independently associated with chronic kidney disease progression in the elderly: the CKD-ROUTE study. Clin Exp Nephrol. 2018;22(2):291-298. doi:10.1007/s10157-017-1441-6 https://pubmed.ncbi.nlm.nih.gov/28699033/

de Labry LO, Campion EW, Glynn RJ, Vokonas PS. White blood cell count as a predictor of mortality: results over 18 years from the Normative Aging Study. J Clin Epidemiol. 1990;43(2):153-157. doi:10.1016/0895-4356(90)90178-r https://pubmed.ncbi.nlm.nih.gov/2303845/

Zhu B, Liu Y, Liu W, et al. Association of neutrophil-to-lymphocyte ratio with all-cause and cardiovascular mortality in CVD patients with diabetes or pre-diabetes. Sci Rep. 2024;14(1):24324. Published 2024 Oct 17. doi:10.1038/s41598-024-74642-8 https://pubmed.ncbi.nlm.nih.gov/39414853/

Shankar A, Mitchell P, Rochtchina E, Wang JJ. The association between circulating white blood cell count, triglyceride level and cardiovascular and all-cause mortality: population-based cohort study. Atherosclerosis. 2007;192(1):177-183. doi:10.1016/j.atherosclerosis.2006.04.029 https://pubmed.ncbi.nlm.nih.gov/16730736/

 

Kidney Function – eGFR

 

Chronic Kidney Disease Prognosis Consortium, Matsushita K, van der Velde M, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375(9731):2073-2081. doi:10.1016/S0140-6736(10)60674-5 https://pubmed.ncbi.nlm.nih.gov/20483451/

Mafham M, Emberson J, Landray MJ, Wen CP, Baigent C. Estimated glomerular filtration rate and the risk of major vascular events and all-cause mortality: a meta-analysis. PLoS One. 2011;6(10):e25920. doi:10.1371/journal.pone.0025920 https://pubmed.ncbi.nlm.nih.gov/22039429/

Ramezankhani A, Azizi F, Hadaegh F. Association between estimated glomerular filtration rate slope and cardiovascular disease among individuals with and without diabetes: a prospective cohort study. Cardiovasc Diabetol. 2023;22(1):270. Published 2023 Oct 4. doi:10.1186/s12933-023-02008-x https://pubmed.ncbi.nlm.nih.gov/37794456/

Matsushita K, Selvin E, Bash LD, Franceschini N, Astor BC, Coresh J. Change in estimated GFR associates with coronary heart disease and mortality. J Am Soc Nephrol. 2009;20(12):2617-2624. doi:10.1681/ASN.2009010025 https://pubmed.ncbi.nlm.nih.gov/19892932/

Oshima M, Jun M, Ohkuma T, et al. The relationship between eGFR slope and subsequent risk of vascular outcomes and all-cause mortality in type 2 diabetes: the ADVANCE-ON study. Diabetologia. 2019;62(11):1988-1997. doi:10.1007/s00125-019-4948-4 https://pubmed.ncbi.nlm.nih.gov/31302707/

Marx-Schütt K, Cherney DZI, Jankowski J, Matsushita K, Nardone M, Marx N. Cardiovascular disease in chronic kidney disease. Eur Heart J. 2025;46(23):2148-2160. doi:10.1093/eurheartj/ehaf167 https://pubmed.ncbi.nlm.nih.gov/40196891/

Barzilay JI, Davis BR, Ghosh A, et al. Rapid eGFR change as a determinant of cardiovascular and renal disease outcomes and of mortality in hypertensive adults with and without type 2 diabetes. J Diabetes Complications. 2018;32(9):830-832. doi:10.1016/j.jdiacomp.2018.07.003 https://pubmed.ncbi.nlm.nih.gov/30030011/

Delanaye P, Glassock RJ, Pottel H, Rule AD. An Age-Calibrated Definition of Chronic Kidney Disease: Rationale and Benefits. Clin Biochem Rev. 2016;37(1):17-26. https://pubmed.ncbi.nlm.nih.gov/27057075/

Glassock RJ, Winearls C. Ageing and the glomerular filtration rate: truths and consequences. Trans Am Clin Climatol Assoc. 2009;120:419-428. https://pubmed.ncbi.nlm.nih.gov/19768194/

van der Burgh AC, Rizopoulos D, Ikram MA, Hoorn EJ, Chaker L. Determinants of the Evolution of Kidney Function With Age. Kidney Int Rep. 2021;6(12):3054-3063. Published 2021 Oct 16. doi:10.1016/j.ekir.2021.10.006 https://pubmed.ncbi.nlm.nih.gov/34901574/

Waas T, Schulz A, Lotz J, et al. Distribution of estimated glomerular filtration rate and determinants of its age dependent loss in a German population-based study. Sci Rep. 2021;11(1):10165. Published 2021 May 13. doi:10.1038/s41598-021-89442-7 https://pubmed.ncbi.nlm.nih.gov/33986324/

Noronha IL, Santa-Catharina GP, Andrade L, Coelho VA, Jacob-Filho W, Elias RM. Glomerular filtration in the aging population. Front Med (Lausanne). 2022;9:769329. Published 2022 Sep 15. doi:10.3389/fmed.2022.769329 https://pubmed.ncbi.nlm.nih.gov/36186775/

 

Liver Function – ALT & AST

Xia MF, Yan HM, Lin HD, et al. Elevation of liver enzymes within the normal limits and metabolic syndrome. Clin Exp Pharmacol Physiol. 2011;38(6):373-379. doi:10.1111/j.1440-1681.2011.05519.x https://pubmed.ncbi.nlm.nih.gov/21418268/

Kang HS, Um SH, Seo YS, et al. Healthy range for serum ALT and the clinical significance of “unhealthy” normal ALT levels in the Korean population. J Gastroenterol Hepatol. 2011;26(2):292-299. doi:10.1111/j.1440-1746.2010.06481.x https://pubmed.ncbi.nlm.nih.gov/21261719/

Katzke V, Johnson T, Sookthai D, Hüsing A, Kühn T, Kaaks R. Circulating liver enzymes and risks of chronic diseases and mortality in the prospective EPIC-Heidelberg case-cohort study. BMJ Open. 2020;10(3):e033532. Published 2020 Mar 8. doi:10.1136/bmjopen-2019-033532 https://pubmed.ncbi.nlm.nih.gov/32152162/

Schmilovitz-Weiss H, Gingold-Belfer R, Boltin D, et al. Risk of mortality and level of serum alanine aminotransferase among community-dwelling elderly in Israel. Eur J Gastroenterol Hepatol. 2018;30(12):1428-1433. doi:10.1097/MEG.0000000000001225 https://pubmed.ncbi.nlm.nih.gov/30048334/

Hernaez R, Yeh HC, Lazo M, et al. Elevated ALT and GGT predict all-cause mortality and hepatocellular carcinoma in Taiwanese male: a case-cohort study. Hepatol Int. 2013;7(4):1040-1049. doi:10.1007/s12072-013-9476-6 https://pubmed.ncbi.nlm.nih.gov/26202033/

Ke P, Zhong L, Peng W, et al. Association of the serum transaminase with mortality among the US elderly population. J Gastroenterol Hepatol. 2022;37(5):946-953. doi:10.1111/jgh.15815 https://pubmed.ncbi.nlm.nih.gov/35233823/

Ekstedt M, Franzén LE, Mathiesen UL, et al. Long-term follow-up of patients with NAFLD and elevated liver enzymes. Hepatology. 2006;44(4):865-873. doi:10.1002/hep.21327 https://pubmed.ncbi.nlm.nih.gov/17006923/

Lonardo A. Alanine aminotransferase predicts incident steatotic liver disease of metabolic etiology: Long life to the old biomarker!. World J Gastroenterol. 2024;30(24):3016-3021. doi:10.3748/wjg.v30.i24.3016 https://pubmed.ncbi.nlm.nih.gov/38983954/

Williams AL, Hoofnagle JH. Ratio of serum aspartate to alanine aminotransferase in chronic hepatitis. Relationship to cirrhosis. Gastroenterology. 1988;95(3):734-739. doi:10.1016/s0016-5085(88)80022-2 https://pubmed.ncbi.nlm.nih.gov/3135226/

Nyblom H, Berggren U, Balldin J, Olsson R. High AST/ALT ratio may indicate advanced alcoholic liver disease rather than heavy drinking. Alcohol Alcohol. 2004;39(4):336-339. doi:10.1093/alcalc/agh074 https://pubmed.ncbi.nlm.nih.gov/15208167/

Sorbi D, Boynton J, Lindor KD. The ratio of aspartate aminotransferase to alanine aminotransferase: potential value in differentiating nonalcoholic steatohepatitis from alcoholic liver disease. Am J Gastroenterol. 1999;94(4):1018-1022. doi:10.1111/j.1572-0241.1999.01006.x https://pubmed.ncbi.nlm.nih.gov/10201476/

Noureddin N, Noureddin M, Singh A, Alkhouri N. Progression of Nonalcoholic Fatty Liver Disease-Associated Fibrosis in a Large Cohort of Patients with Type 2 Diabetes. Dig Dis Sci. 2022;67(4):1379-1388. doi:10.1007/s10620-021-06955-x https://pubmed.ncbi.nlm.nih.gov/33779880/

Mansoor S, Collyer E, Alkhouri N. A comprehensive review of noninvasive liver fibrosis tests in pediatric nonalcoholic Fatty liver disease. Curr Gastroenterol Rep. 2015;17(6):23. doi:10.1007/s11894-015-0447-z https://pubmed.ncbi.nlm.nih.gov/26031832/

Reddy YK, Marella HK, Jiang Y, et al. Natural History of Non-Alcoholic Fatty Liver Disease: A Study With Paired Liver Biopsies. J Clin Exp Hepatol. 2020;10(3):245-254. doi:10.1016/j.jceh.2019.07.002 https://pubmed.ncbi.nlm.nih.gov/32405181/

 

Thyroid – TSH

Rodondi N, den Elzen WP, Bauer DC, et al. Subclinical hypothyroidism and the risk of coronary heart disease and mortality. JAMA. 2010;304(12):1365-1374. doi:10.1001/jama.2010.1361 https://pubmed.ncbi.nlm.nih.gov/20858880/

Ochs N, Auer R, Bauer DC, et al. Meta-analysis: subclinical thyroid dysfunction and the risk for coronary heart disease and mortality. Ann Intern Med. 2008;148(11):832-845. doi:10.7326/0003-4819-148-11-200806030-00225 https://pubmed.ncbi.nlm.nih.gov/18490668/

Singh S, Duggal J, Molnar J, Maldonado F, Barsano CP, Arora R. Impact of subclinical thyroid disorders on coronary heart disease, cardiovascular and all-cause mortality: a meta-analysis. Int J Cardiol. 2008;125(1):41-48. doi:10.1016/j.ijcard.2007.02.027 https://pubmed.ncbi.nlm.nih.gov/17434631/

Moon S, Kim MJ, Yu JM, Yoo HJ, Park YJ. Subclinical Hypothyroidism and the Risk of Cardiovascular Disease and All-Cause Mortality: A Meta-Analysis of Prospective Cohort Studies. Thyroid. 2018;28(9):1101-1110. doi:10.1089/thy.2017.0414 https://pubmed.ncbi.nlm.nih.gov/29978767/

Zhu P, Lao G, Chen C, Luo L, Gu J, Ran J. TSH levels within the normal range and risk of cardiovascular and all-cause mortality among individuals with diabetes. Cardiovasc Diabetol. 2022;21(1):254. Published 2022 Nov 23. doi:10.1186/s12933-022-01698-z https://pubmed.ncbi.nlm.nih.gov/36419168/

Laclaustra M, Hurtado-Roca Y, Sendin M, et al. Lower-normal TSH is associated with better metabolic risk factors: A cross-sectional study on Spanish men. Nutr Metab Cardiovasc Dis. 2015;25(12):1095-1103. doi:10.1016/j.numecd.2015.09.007 https://pubmed.ncbi.nlm.nih.gov/26552743/

Giandalia A, Russo GT, Romeo EL, et al. Influence of high-normal serum TSH levels on major cardiovascular risk factors and Visceral Adiposity Index in euthyroid type 2 diabetic subjects. Endocrine. 2014;47(1):152-160. doi:10.1007/s12020-013-0137-2 https://pubmed.ncbi.nlm.nih.gov/24385267/

Boggio A, Muzio F, Fiscella M, Sommariva D, Branchi A. Is thyroid-stimulating hormone within the normal reference range a risk factor for atherosclerosis in women?. Intern Emerg Med. 2014;9(1):51-57. doi:10.1007/s11739-011-0743-z https://pubmed.ncbi.nlm.nih.gov/22203234/

Xu Y, Derakhshan A, Hysaj O, et al. The optimal healthy ranges of thyroid function defined by the risk of cardiovascular disease and mortality: systematic review and individual participant data meta-analysis. Lancet Diabetes Endocrinol. 2023;11(10):743-754. doi:10.1016/S2213-8587(23)00227-9 https://pubmed.ncbi.nlm.nih.gov/37696273/

Cappola AR, Fried LP, Arnold AM, et al. Thyroid status, cardiovascular risk, and mortality in older adults. JAMA. 2006;295(9):1033-1041. doi:10.1001/jama.295.9.1033 https://pubmed.ncbi.nlm.nih.gov/16507804/

Donangelo I, Braunstein GD. Update on subclinical hyperthyroidism. Am Fam Physician. 2011;83(8):933-938. https://pubmed.ncbi.nlm.nih.gov/21524033/

Gencer B, Collet TH, Virgini V, Auer R, Rodondi N. Subclinical thyroid dysfunction and cardiovascular outcomes among prospective cohort studies. Endocr Metab Immune Disord Drug Targets. 2013;13(1):4-12. doi:10.2174/1871530311313010003 https://pubmed.ncbi.nlm.nih.gov/23369133/

Donangelo I, Suh SY. Subclinical Hyperthyroidism: When to Consider Treatment. Am Fam Physician. 2017;95(11):710-716. https://pubmed.ncbi.nlm.nih.gov/28671443/

Floriani C, Gencer B, Collet TH, Rodondi N. Subclinical thyroid dysfunction and cardiovascular diseases: 2016 update. Eur Heart J. 2018;39(7):503-507. doi:10.1093/eurheartj/ehx050 https://pubmed.ncbi.nlm.nih.gov/28329380/

 

Testosterone (Men)

Laughlin GA, Barrett-Connor E, Bergstrom J. Low serum testosterone and mortality in older men. J Clin Endocrinol Metab. 2008;93(1):68-75. doi:10.1210/jc.2007-1792 https://pubmed.ncbi.nlm.nih.gov/17911176/

Muraleedharan V, Marsh H, Kapoor D, Channer KS, Jones TH. Testosterone deficiency is associated with increased risk of mortality and testosterone replacement improves survival in men with type 2 diabetes. Eur J Endocrinol. 2013;169(6):725-733. Published 2013 Oct 21. doi:10.1530/EJE-13-0321 https://pubmed.ncbi.nlm.nih.gov/23999642/

Yeap BB, Marriott RJ, Dwivedi G, et al. Associations of Testosterone and Related Hormones With All-Cause and Cardiovascular Mortality and Incident Cardiovascular Disease in Men : Individual Participant Data Meta-analyses. Ann Intern Med. 2024;177(6):768-781. doi:10.7326/M23-2781 https://pubmed.ncbi.nlm.nih.gov/38739921/

Khaw KT, Dowsett M, Folkerd E, et al. Endogenous testosterone and mortality due to all causes, cardiovascular disease, and cancer in men: European prospective investigation into cancer in Norfolk (EPIC-Norfolk) Prospective Population Study. Circulation. 2007;116(23):2694-2701. doi:10.1161/CIRCULATIONAHA.107.719005 https://pubmed.ncbi.nlm.nih.gov/18040028/

Malipatil NS, Yadegarfar G, Lunt M, et al. Male hypogonadism: 14-year prospective outcome in 550 men with type 2 diabetes. Endocrinol Diabetes Metab. 2019;2(3):e00064. Published 2019 Mar 27. doi:10.1002/edm2.64 https://pubmed.ncbi.nlm.nih.gov/31294081/

Yeap BB, Marriott RJ, Antonio L, et al. Serum Testosterone is Inversely and Sex Hormone-binding Globulin is Directly Associated with All-cause Mortality in Men. J Clin Endocrinol Metab. 2021;106(2):e625-e637. doi:10.1210/clinem/dgaa743 https://pubmed.ncbi.nlm.nih.gov/33059368/

Pye SR, Huhtaniemi IT, Finn JD, et al. Late-onset hypogonadism and mortality in aging men. J Clin Endocrinol Metab. 2014;99(4):1357-1366. doi:10.1210/jc.2013-2052 https://pubmed.ncbi.nlm.nih.gov/24423283/

Blackwell KM, Buckingham H, Paul KK, Uddin H, Jehle DVK, Blackwell TA. Benefits of Testosterone Replacement Therapy in Hypogonadal Males. J Am Board Fam Med. 2024;37(5):816-825. doi:10.3122/jabfm.2024.240025R1 https://pubmed.ncbi.nlm.nih.gov/39978846/

Araujo AB, Dixon JM, Suarez EA, Murad MH, Guey LT, Wittert GA. Clinical review: Endogenous testosterone and mortality in men: a systematic review and meta-analysis. J Clin Endocrinol Metab. 2011;96(10):3007-3019. doi:10.1210/jc.2011-1137 https://pubmed.ncbi.nlm.nih.gov/21816776/

Adelborg K, Rasmussen TB, Nørrelund H, Layton JB, Sørensen HT, Christiansen CF. Cardiovascular Outcomes and All-cause Mortality Following Measurement of Endogenous Testosterone Levels. Am J Cardiol. 2019;123(11):1757-1764. doi:10.1016/j.amjcard.2019.02.042 https://pubmed.ncbi.nlm.nih.gov/30928032/

Meyer EJ, Wittert G. Endogenous testosterone and mortality risk. Asian J Androl. 2018;20(2):115-119. doi:10.4103/aja.aja_70_17 https://pubmed.ncbi.nlm.nih.gov/29384142/

Araujo AB, Kupelian V, Page ST, Handelsman DJ, Bremner WJ, McKinlay JB. Sex steroids and all-cause and cause-specific mortality in men. Arch Intern Med. 2007;167(12):1252-1260. doi:10.1001/archinte.167.12.1252 https://pubmed.ncbi.nlm.nih.gov/17592098/

Feng Y, Jin X, Zhu J, et al. Association between endogenous estradiol, testosterone, and long-term mortality in adults with prediabetes and diabetes: Evidence from NHANES database. J Diabetes Investig. 2025;16(3):481-491. doi:10.1111/jdi.14367 https://pubmed.ncbi.nlm.nih.gov/39705158/

Wang C, Jackson G, Jones TH, et al. Low testosterone associated with obesity and the metabolic syndrome contributes to sexual dysfunction and cardiovascular disease risk in men with type 2 diabetes. Diabetes Care. 2011;34(7):1669-1675. doi:10.2337/dc10-2339 https://pubmed.ncbi.nlm.nih.gov/21709300/

Jones TH. Testosterone deficiency: a risk factor for cardiovascular disease?. Trends Endocrinol Metab. 2010;21(8):496-503. doi:10.1016/j.tem.2010.03.002 https://pubmed.ncbi.nlm.nih.gov/20381374/

Rao PM, Kelly DM, Jones TH. Testosterone and insulin resistance in the metabolic syndrome and T2DM in men. Nat Rev Endocrinol. 2013;9(8):479-493. doi:10.1038/nrendo.2013.122 https://pubmed.ncbi.nlm.nih.gov/23797822/

Spark RF. Testosterone, diabetes mellitus, and the metabolic syndrome. Curr Urol Rep. 2007;8(6):467-471. doi:10.1007/s11934-007-0050-4 https://pubmed.ncbi.nlm.nih.gov/18042326/

Rice D, Brannigan RE, Campbell RK, et al. Men’s health, low testosterone, and diabetes: individualized treatment and a multidisciplinary approach. Diabetes Educ. 2008;34 Suppl 5:97S-4S. doi:10.1177/0145721708327143 https://pubmed.ncbi.nlm.nih.gov/19020265/

García-Cruz E, Leibar-Tamayo A, Romero J, et al. Metabolic syndrome in men with low testosterone levels: relationship with cardiovascular risk factors and comorbidities and with erectile dysfunction. J Sex Med. 2013;10(10):2529-2538. doi:10.1111/jsm.12265 https://pubmed.ncbi.nlm.nih.gov/23898860/

Saad F, Gooren L. The role of testosterone in the metabolic syndrome: a review. J Steroid Biochem Mol Biol. 2009;114(1-2):40-43. doi:10.1016/j.jsbmb.2008.12.022 https://pubmed.ncbi.nlm.nih.gov/19444934/

Gucenmez S, Yildiz P, Donderici O, Serter R. The effect of testosterone level on metabolic syndrome: a cross-sectional study. Hormones (Athens). 2024;23(1):163-169. doi:10.1007/s42000-023-00507-w https://pubmed.ncbi.nlm.nih.gov/37981619/

Cheung KK, Luk AO, So WY, et al. Testosterone level in men with type 2 diabetes mellitus and related metabolic effects: A review of current evidence. J Diabetes Investig. 2015;6(2):112-123. doi:10.1111/jdi.12288 https://pubmed.ncbi.nlm.nih.gov/25802717/

Laaksonen DE, Niskanen L, Punnonen K, et al. Testosterone and sex hormone-binding globulin predict the metabolic syndrome and diabetes in middle-aged men. Diabetes Care. 2004;27(5):1036-1041. doi:10.2337/diacare.27.5.1036 https://pubmed.ncbi.nlm.nih.gov/15111517/

Zhu A, Andino J, Daignault-Newton S, Chopra Z, Sarma A, Dupree JM. What Is a Normal Testosterone Level for Young Men? Rethinking the 300 ng/dL Cutoff for Testosterone Deficiency in Men 20-44 Years Old. J Urol. 2022;208(6):1295-1302. doi:10.1097/JU.0000000000002928 https://pubmed.ncbi.nlm.nih.gov/36282060/

 

Menstrual Status (Women)

van der Schouw YT, van der Graaf Y, Steyerberg EW, Eijkemans JC, Banga JD. Age at menopause as a risk factor for cardiovascular mortality. Lancet. 1996;347(9003):714-718. doi:10.1016/s0140-6736(96)90075-6 https://pubmed.ncbi.nlm.nih.gov/8602000/

Muka T, Oliver-Williams C, Kunutsor S, et al. Association of Age at Onset of Menopause and Time Since Onset of Menopause With Cardiovascular Outcomes, Intermediate Vascular Traits, and All-Cause Mortality: A Systematic Review and Meta-analysis. JAMA Cardiol. 2016;1(7):767-776. doi:10.1001/jamacardio.2016.2415 https://pubmed.ncbi.nlm.nih.gov/27627190/

Ossewaarde ME, Bots ML, Verbeek AL, et al. Age at menopause, cause-specific mortality and total life expectancy. Epidemiology. 2005;16(4):556-562. doi:10.1097/01.ede.0000165392.35273.d4 https://pubmed.ncbi.nlm.nih.gov/15951675/

Lee GB, Nam GE, Kim W, et al. Association Between Premature Menopause and Cardiovascular Diseases and All-Cause Mortality in Korean Women. J Am Heart Assoc. 2023;12(22):e030117. doi:10.1161/JAHA.123.030117 https://pubmed.ncbi.nlm.nih.gov/37947103/

Wren BG. The effect of oestrogen on the female cardiovascular system. Med J Aust. 1992;157(3):204-208. doi:10.5694/j.1326-5377.1992.tb137091.x https://pubmed.ncbi.nlm.nih.gov/1635499/

Nabulsi AA, Folsom AR, White A, et al. Association of hormone-replacement therapy with various cardiovascular risk factors in postmenopausal women. The Atherosclerosis Risk in Communities Study Investigators. N Engl J Med. 1993;328(15):1069-1075. doi:10.1056/NEJM199304153281501 https://pubmed.ncbi.nlm.nih.gov/8384316/

Kaplan NM. Estrogen replacement therapy. Effect on blood pressure and other cardiovascular risk factors. J Reprod Med. 1985;30(10 Suppl):802-804. https://pubmed.ncbi.nlm.nih.gov/4078812/

Poitras M, Shearzad F, Qureshi AF, Blackburn C, Plamondon H. Bloody stressed! A systematic review of the associations between adulthood psychological stress and menstrual cycle irregularity. Neurosci Biobehav Rev. 2024;163:105784. doi:10.1016/j.neubiorev.2024.105784 https://pubmed.ncbi.nlm.nih.gov/38950686/

Popat VB, Prodanov T, Calis KA, Nelson LM. The menstrual cycle: a biological marker of general health in adolescents. Ann N Y Acad Sci. 2008;1135:43-51. doi:10.1196/annals.1429.040 https://pubmed.ncbi.nlm.nih.gov/18574207/

Liu J, Jin X, Liu W, et al. The risk of long-term cardiometabolic disease in women with premature or early menopause: A systematic review and meta-analysis. Front Cardiovasc Med. 2023;10:1131251. Published 2023 Mar 21. doi:10.3389/fcvm.2023.1131251 https://pubmed.ncbi.nlm.nih.gov/37025693/

Anagnostis P, Theocharis P, Lallas K, et al. Early menopause is associated with increased risk of arterial hypertension: A systematic review and meta-analysis. Maturitas. 2020;135:74-79. doi:10.1016/j.maturitas.2020.03.006 https://pubmed.ncbi.nlm.nih.gov/32252968/

Appiah D, Schreiner PJ, Demerath EW, Loehr LR, Chang PP, Folsom AR. Association of Age at Menopause With Incident Heart Failure: A Prospective Cohort Study and Meta-Analysis. J Am Heart Assoc. 2016;5(8):e003769. Published 2016 Jul 28. doi:10.1161/JAHA.116.003769 https://pubmed.ncbi.nlm.nih.gov/27468929/

Atsma F, Bartelink ML, Grobbee DE, van der Schouw YT. Postmenopausal status and early menopause as independent risk factors for cardiovascular disease: a meta-analysis. Menopause. 2006;13(2):265-279. doi:10.1097/01.gme.0000218683.97338.ea https://pubmed.ncbi.nlm.nih.gov/16645540/

Liu J, Jin X, Chen W, Wang L, Feng Z, Huang J. Early menopause is associated with increased risk of heart failure and atrial fibrillation: A systematic review and meta-analysis. Maturitas. 2023;176:107784. doi:10.1016/j.maturitas.2023.107784 https://pubmed.ncbi.nlm.nih.gov/37454569/

Zhu D, Chung HF, Dobson AJ, et al. Age at natural menopause and risk of incident cardiovascular disease: a pooled analysis of individual patient data. Lancet Public Health. 2019;4(11):e553-e564. doi:10.1016/S2468-2667(19)30155-0 https://pubmed.ncbi.nlm.nih.gov/31588031/

Okoth K, Chandan JS, Marshall T, et al. Association between the reproductive health of young women and cardiovascular disease in later life: umbrella review. BMJ. 2020;371:m3502. Published 2020 Oct 7. doi:10.1136/bmj.m3502 https://pubmed.ncbi.nlm.nih.gov/33028606/

Roeters van Lennep JE, Heida KY, Bots ML, Hoek A; collaborators of the Dutch Multidisciplinary Guideline Development Group on Cardiovascular Risk Management after Reproductive Disorders. Cardiovascular disease risk in women with premature ovarian insufficiency: A systematic review and meta-analysis. Eur J Prev Cardiol. 2016;23(2):178-186. doi:10.1177/2047487314556004 https://pubmed.ncbi.nlm.nih.gov/25331207/

Behboudi-Gandevani S, Arntzen EC, Normann B, Haugan T, Bidhendi-Yarandi R. Cardiovascular Events Among Women with Premature Ovarian Insufficiency: A Systematic Review and Meta-Analysis. Rev Cardiovasc Med. 2023;24(7):193. Published 2023 Jul 4. doi:10.31083/j.rcm2407193 https://pubmed.ncbi.nlm.nih.gov/39077000/

Daan NM, Muka T, Koster MP, et al. Cardiovascular Risk in Women With Premature Ovarian Insufficiency Compared to Premenopausal Women at Middle Age. J Clin Endocrinol Metab. 2016;101(9):3306-3315. doi:10.1210/jc.2016-1141 https://pubmed.ncbi.nlm.nih.gov/27300572/

Stevenson JC, Collins P, Hamoda H, et al. Cardiometabolic health in premature ovarian insufficiency. Climacteric. 2021;24(5):474-480. doi:10.1080/13697137.2021.1910232 https://pubmed.ncbi.nlm.nih.gov/34169795/

Gunning MN, Meun C, van Rijn BB, et al. The cardiovascular risk profile of middle age women previously diagnosed with premature ovarian insufficiency: A case-control study. PLoS One. 2020;15(3):e0229576. Published 2020 Mar 5. doi:10.1371/journal.pone.0229576 https://pubmed.ncbi.nlm.nih.gov/32134933/

Bast JA, Olubi O, Le DE, Parashar S, Dobrescu I, Kondapalli L. Cardiovascular Consequences of Premature Menopause. Curr Cardiol Rep. 2025;27(1):126. Published 2025 Aug 13. doi:10.1007/s11886-025-02265-0 https://pubmed.ncbi.nlm.nih.gov/40804564/

Yorgun H, Tokgözoğlu L, Canpolat U, et al. The cardiovascular effects of premature ovarian failure. Int J Cardiol. 2013;168(1):506-510. doi:10.1016/j.ijcard.2012.09.197 https://pubmed.ncbi.nlm.nih.gov/23073277/

Rezende GP, Dassie T, Gomes DAY, Benetti-Pinto CL. Cardiovascular Risk Factors in Premature Ovarian Insufficiency using Hormonal Therapy. Fatores de risco cardiovascular na insuficiência ovariana prematura em uso de terapia hormonal. Rev Bras Ginecol Obstet. 2023;45(6):312-318. doi:10.1055/s-0043-1770088 https://pubmed.ncbi.nlm.nih.gov/37494573/

Podfigurna A, Męczekalski B. Cardiovascular health in patients with premature ovarian insufficiency. Management of long-term consequences. Prz Menopauzalny. 2018;17(3):109-111. doi:10.5114/pm.2018.78551 https://pubmed.ncbi.nlm.nih.gov/30357009/

Hill K. The demography of menopause. Maturitas. 1996;23(2):113-127. doi:10.1016/0378-5122(95)00968-x https://pubmed.ncbi.nlm.nih.gov/8735350/

Peacock K, Carlson K, Ketvertis KM. Menopause. In: StatPearls. Treasure Island (FL): StatPearls Publishing; December 21, 2023. https://pubmed.ncbi.nlm.nih.gov/29939603/

Kalantaridou SN, Nelson LM. Premature ovarian failure is not premature menopause. Ann N Y Acad Sci. 2000;900:393-402. doi:10.1111/j.1749-6632.2000.tb06251.x https://pubmed.ncbi.nlm.nih.gov/10818427/

 

Morning Erections (Men)

Dong JY, Zhang YH, Qin LQ. Erectile dysfunction and risk of cardiovascular disease: meta-analysis of prospective cohort studies. J Am Coll Cardiol. 2011;58(13):1378-1385. doi:10.1016/j.jacc.2011.06.024 https://pubmed.ncbi.nlm.nih.gov/21920268/

Zhao B, Hong Z, Wei Y, Yu D, Xu J, Zhang W. Erectile Dysfunction Predicts Cardiovascular Events as an Independent Risk Factor: A Systematic Review and Meta-Analysis. J Sex Med. 2019;16(7):1005-1017. doi:10.1016/j.jsxm.2019.04.004 https://pubmed.ncbi.nlm.nih.gov/31104857/

Vlachopoulos CV, Terentes-Printzios DG, Ioakeimidis NK, Aznaouridis KA, Stefanadis CI. Prediction of cardiovascular events and all-cause mortality with erectile dysfunction: a systematic review and meta-analysis of cohort studies. Circ Cardiovasc Qual Outcomes. 2013;6(1):99-109. doi:10.1161/CIRCOUTCOMES.112.966903 https://pubmed.ncbi.nlm.nih.gov/23300267/

Guo W, Liao C, Zou Y, et al. Erectile dysfunction and risk of clinical cardiovascular events: a meta-analysis of seven cohort studies. J Sex Med. 2010;7(8):2805-2816. doi:10.1111/j.1743-6109.2010.01792.x https://pubmed.ncbi.nlm.nih.gov/20367771/

Jackson G. Erectile dysfunction and asymptomatic coronary artery disease: frequently detected by computed tomography coronary angiography but not by exercise electrocardiography. Int J Clin Pract. 2013;67(11):1159-1162. doi:10.1111/ijcp.12275 https://pubmed.ncbi.nlm.nih.gov/23981083/

Montorsi P, Ravagnani PM, Vlachopoulos C. Clinical significance of erectile dysfunction developing after acute coronary event: exception to the rule or confirmation of the artery size hypothesis?. Asian J Androl. 2015;17(1):21-25. doi:10.4103/1008-682X.139254 https://pubmed.ncbi.nlm.nih.gov/25337840/

Montorsi P, Ravagnani PM, Galli S, et al. The artery size hypothesis: a macrovascular link between erectile dysfunction and coronary artery disease. Am J Cardiol. 2005;96(12B):19M-23M. doi:10.1016/j.amjcard.2005.07.006 https://pubmed.ncbi.nlm.nih.gov/16387561/

Elesber AA, Solomon H, Lennon RJ, et al. Coronary endothelial dysfunction is associated with erectile dysfunction and elevated asymmetric dimethylarginine in patients with early atherosclerosis. Eur Heart J. 2006;27(7):824-831. doi:10.1093/eurheartj/ehi749 https://pubmed.ncbi.nlm.nih.gov/16434411/

Jackson G, Nehra A, Miner M, et al. The assessment of vascular risk in men with erectile dysfunction: the role of the cardiologist and general physician. Int J Clin Pract. 2013;67(11):1163-1172. doi:10.1111/ijcp.12200 https://pubmed.ncbi.nlm.nih.gov/23714173/

Carani C, Granata AR, Bancroft J, Marrama P. The effects of testosterone replacement on nocturnal penile tumescence and rigidity and erectile response to visual erotic stimuli in hypogonadal men. Psychoneuroendocrinology. 1995;20(7):743-753. doi:10.1016/0306-4530(95)00017-8 https://pubmed.ncbi.nlm.nih.gov/8848520/

Granata AR, Rochira V, Lerchl A, Marrama P, Carani C. Relationship between sleep-related erections and testosterone levels in men. J Androl. 1997;18(5):522-527. https://pubmed.ncbi.nlm.nih.gov/9349750/

Schiavi RC, White D, Mandeli J, Schreiner-Engel P. Hormones and nocturnal penile tumescence in healthy aging men. Arch Sex Behav. 1993;22(3):207-215. doi:10.1007/BF01541766 https://pubmed.ncbi.nlm.nih.gov/8494488/

Yaman O, Tokatli Z, Ozdiler E, Anafarta K. Effect of aging on quality of nocturnal erections: evaluation with NPTR testing. Int J Impot Res. 2004;16(2):150-153. doi:10.1038/sj.ijir.3901199 https://pubmed.ncbi.nlm.nih.gov/14973521/

Stranne J, Malmsten UGH, Areskoug B, Milsom I, Molander U, Peeker R. The rate of deterioration of erectile function increases with age: results from a longitudinal population based survey. Scand J Urol. 2019;53(2-3):161-165. doi:10.1080/21681805.2019.1596154 https://pubmed.ncbi.nlm.nih.gov/31023125/

Djordjevic D, Vukovic I, Milenkovic Petronic D, et al. Erectile dysfunction as a predictor of advanced vascular age. Andrology. 2015;3(6):1125-1131. doi:10.1111/andr.12105 https://pubmed.ncbi.nlm.nih.gov/26446405/

 

Category 2: Cardiovascular Fitness

VO₂ Max (Maximal Oxygen Uptake)

Strasser B, Burtscher M. Survival of the fittest: VO2max, a key predictor of longevity?. Front Biosci (Landmark Ed). 2018;23(8):1505-1516. Published 2018 Mar 1. doi:10.2741/4657 https://pubmed.ncbi.nlm.nih.gov/29293447/

Kodama S, Saito K, Tanaka S, et al. Cardiorespiratory fitness as a quantitative predictor of all-cause mortality and cardiovascular events in healthy men and women: a meta-analysis. JAMA. 2009;301(19):2024-2035. doi:10.1001/jama.2009.681 https://pubmed.ncbi.nlm.nih.gov/19454641/

Laukkanen JA, Zaccardi F, Khan H, Kurl S, Jae SY, Rauramaa R. Long-term Change in Cardiorespiratory Fitness and All-Cause Mortality: A Population-Based Follow-up Study. Mayo Clin Proc. 2016;91(9):1183-1188. doi:10.1016/j.mayocp.2016.05.014 https://pubmed.ncbi.nlm.nih.gov/27444976/

Mandsager K, Harb S, Cremer P, Phelan D, Nissen SE, Jaber W. Association of Cardiorespiratory Fitness With Long-term Mortality Among Adults Undergoing Exercise Treadmill Testing. JAMA Netw Open. 2018;1(6):e183605. Published 2018 Oct 5. doi:10.1001/jamanetworkopen.2018.3605 https://pubmed.ncbi.nlm.nih.gov/30646252/

Kokkinos P, Faselis C, Samuel IBH, et al. Cardiorespiratory Fitness and Mortality Risk Across the Spectra of Age, Race, and Sex. J Am Coll Cardiol. 2022;80(6):598-609. doi:10.1016/j.jacc.2022.05.031 https://pubmed.ncbi.nlm.nih.gov/35926933/

Scribbans TD, Vecsey S, Hankinson PB, Foster WS, Gurd BJ. The Effect of Training Intensity on VO2max in Young Healthy Adults: A Meta-Regression and Meta-Analysis. Int J Exerc Sci. 2016;9(2):230-247. Published 2016 Apr 1. doi:10.70252/HHBR9374 https://pubmed.ncbi.nlm.nih.gov/27182424/

Clausen JSR, Marott JL, Holtermann A, Gyntelberg F, Jensen MT. Midlife Cardiorespiratory Fitness and the Long-Term Risk of Mortality: 46 Years of Follow-Up. J Am Coll Cardiol. 2018;72(9):987-995. doi:10.1016/j.jacc.2018.06.045 https://pubmed.ncbi.nlm.nih.gov/30139444/

SPRINT Research Group, Wright JT Jr, Williamson JD, et al. A Randomized Trial of Intensive versus Standard Blood-Pressure Control. N Engl J Med. 2015;373(22):2103-2116. doi:10.1056/NEJMoa1511939 https://pubmed.ncbi.nlm.nih.gov/26551272/

Male V, Hughes T, McClory S, Colucci F, Caligiuri MA, Moffett A. Immature NK cells, capable of producing IL-22, are present in human uterine mucosa. J Immunol. 2010;185(7):3913-3918. doi:10.4049/jimmunol.1001637 https://pubmed.ncbi.nlm.nih.gov/20802153/

Lang JJ, Prince SA, Merucci K, et al. Cardiorespiratory fitness is a strong and consistent predictor of morbidity and mortality among adults: an overview of meta-analyses representing over 20.9 million observations from 199 unique cohort studies. Br J Sports Med. 2024;58(10):556-566. Published 2024 May 2. doi:10.1136/bjsports-2023-107849 https://pubmed.ncbi.nlm.nih.gov/38599681/

Laukkanen JA, Lakka TA, Rauramaa R, et al. Cardiovascular fitness as a predictor of mortality in men. Arch Intern Med. 2001;161(6):825-831. doi:10.1001/archinte.161.6.825 ​​https://pubmed.ncbi.nlm.nih.gov/11268224/

Harber MP, Kaminsky LA, Arena R, et al. Impact of Cardiorespiratory Fitness on All-Cause and Disease-Specific Mortality: Advances Since 2009. Prog Cardiovasc Dis. 2017;60(1):11-20. doi:10.1016/j.pcad.2017.03.001 https://pubmed.ncbi.nlm.nih.gov/28286137/

Wei M, Kampert JB, Barlow CE, et al. Relationship between low cardiorespiratory fitness and mortality in normal-weight, overweight, and obese men. JAMA. 1999;282(16):1547-1553. doi:10.1001/jama.282.16.1547 https://pubmed.ncbi.nlm.nih.gov/10546694/

Blair SN, Brodney S. Effects of physical inactivity and obesity on morbidity and mortality: current evidence and research issues. Med Sci Sports Exerc. 1999;31(11 Suppl):S646-S662. doi:10.1097/00005768-199911001-00025 https://pubmed.ncbi.nlm.nih.gov/10593541/

Myers J, Kaykha A, George S, et al. Fitness versus physical activity patterns in predicting mortality in men. Am J Med. 2004;117(12):912-918. doi:10.1016/j.amjmed.2004.06.047 https://pubmed.ncbi.nlm.nih.gov/15629729/

Koch LG, Britton SL. Theoretical and Biological Evaluation of the Link between Low Exercise Capacity and Disease Risk. Cold Spring Harb Perspect Med. 2018;8(1):a029868. Published 2018 Jan 2. doi:10.1101/cshperspect.a029868 https://pubmed.ncbi.nlm.nih.gov/28389512/

Grundy SM, Barlow CE, Farrell SW, Vega GL, Haskell WL. Cardiorespiratory fitness and metabolic risk. Am J Cardiol. 2012;109(7):988-993. doi:10.1016/j.amjcard.2011.11.031 https://pubmed.ncbi.nlm.nih.gov/22221951/

Davidson T, Vainshelboim B, Kokkinos P, Myers J, Ross R. Cardiorespiratory fitness versus physical activity as predictors of all-cause mortality in men. Am Heart J. 2018;196:156-162. doi:10.1016/j.ahj.2017.08.022 https://pubmed.ncbi.nlm.nih.gov/29421008/

Imboden MT, Harber MP, Whaley MH, et al. The Association between the Change in Directly Measured Cardiorespiratory Fitness across Time and Mortality Risk. Prog Cardiovasc Dis. 2019;62(2):157-162. doi:10.1016/j.pcad.2018.12.003 https://pubmed.ncbi.nlm.nih.gov/30543812/

Leeper NJ, Myers J, Zhou M, et al. Exercise capacity is the strongest predictor of mortality in patients with peripheral arterial disease. J Vasc Surg. 2013;57(3):728-733. doi:10.1016/j.jvs.2012.07.051 https://pubmed.ncbi.nlm.nih.gov/23044259/

Obrusnikova I, Firkin CJ, Farquhar WB. A systematic review and meta-analysis of the effects of aerobic exercise interventions on cardiorespiratory fitness in adults with intellectual disability. Disabil Health J. 2022;15(1):101185. doi:10.1016/j.dhjo.2021.101185 https://pubmed.ncbi.nlm.nih.gov/34452861/

Blair SN, Kohl HW 3rd, Paffenbarger RS Jr, Clark DG, Cooper KH, Gibbons LW. Physical fitness and all-cause mortality. A prospective study of healthy men and women. JAMA. 1989;262(17):2395-2401. doi:10.1001/jama.262.17.2395 https://pubmed.ncbi.nlm.nih.gov/2795824/

Lu Z, Woo J, Kwok T. The Effect of Physical Activity and Cardiorespiratory Fitness on All-Cause Mortality in Hong Kong Chinese Older Adults. J Gerontol A Biol Sci Med Sci. 2018;73(8):1132-1137. doi:10.1093/gerona/glx180 https://pubmed.ncbi.nlm.nih.gov/29029009/

Weeldreyer NR, De Guzman JC, Paterson C, Allen JD, Gaesser GA, Angadi SS. Cardiorespiratory fitness, body mass index and mortality: a systematic review and meta-analysis. Br J Sports Med. 2025;59(5):339-346. Published 2025 Feb 20. doi:10.1136/bjsports-2024-108748 https://pubmed.ncbi.nlm.nih.gov/39537313/

Solomon A, Borodulin K, Ngandu T, Kivipelto M, Laatikainen T, Kulmala J. Self-rated physical fitness and estimated maximal oxygen uptake in relation to all-cause and cause-specific mortality. Scand J Med Sci Sports. 2018;28(2):532-540. doi:10.1111/sms.12924 https://pubmed.ncbi.nlm.nih.gov/28543703/

Khan H, Kunutsor S, Rauramaa R, et al. Cardiorespiratory fitness and risk of heart failure: a population-based follow-up study. Eur J Heart Fail. 2014;16(2):180-188. doi:10.1111/ejhf.37 https://pubmed.ncbi.nlm.nih.gov/24464981/

Ferreira I, Twisk JW, Van Mechelen W, Kemper HC, Stehouwer CD; Amsterdam Growth and Health Longitudinal Study. Current and adolescent levels of cardiopulmonary fitness are related to large artery properties at age 36: the Amsterdam Growth and Health Longitudinal Study. Eur J Clin Invest. 2002;32(10):723-731. doi:10.1046/j.1365-2362.2002.01066.x https://pubmed.ncbi.nlm.nih.gov/12406019/

Fung E, Ting Lui L, Gustafsson F, et al. Predicting 10-year mortality in older adults using VO2max, oxygen uptake efficiency slope and frailty class. Eur J Prev Cardiol. 2021;28(10):1148-1151. doi:10.1177/2047487320914435 https://pubmed.ncbi.nlm.nih.gov/33611420/

 

Resting Heart Rate

Lau K, Malik A, Foroutan F, et al. Resting Heart Rate as an Important Predictor of Mortality and Morbidity in Ambulatory Patients With Heart Failure: A Systematic Review and Meta-Analysis. J Card Fail. 2021;27(3):349-363. doi:10.1016/j.cardfail.2020.11.003 https://pubmed.ncbi.nlm.nih.gov/33171294/

Zhang D, Shen X, Qi X. Resting heart rate and all-cause and cardiovascular mortality in the general population: a meta-analysis. CMAJ. 2016;188(3):E53-E63. doi:10.1503/cmaj.150535 https://pubmed.ncbi.nlm.nih.gov/26598376/

Aune D, Sen A, ó’Hartaigh B, et al. Resting heart rate and the risk of cardiovascular disease, total cancer, and all-cause mortality – A systematic review and dose-response meta-analysis of prospective studies. Nutr Metab Cardiovasc Dis. 2017;27(6):504-517. doi:10.1016/j.numecd.2017.04.004 https://pubmed.ncbi.nlm.nih.gov/28552551/

Li Y. Association between resting heart rate and cardiovascular mortality: evidence from a meta-analysis of prospective studies. Int J Clin Exp Med. 2015;8(9):15329-15339. Published 2015 Sep 15. https://pubmed.ncbi.nlm.nih.gov/26629022/

Cho W, Kim HS, Lee DH, et al. Elevated Resting Heart Rate is an Independent Risk Factor for Mortality in Patients with Colorectal Cancer: A Retrospective Cohort Study. Cancer Epidemiol Biomarkers Prev. Published online September 17, 2025. doi:10.1158/1055-9965.EPI-25-0704 https://pubmed.ncbi.nlm.nih.gov/40960855/

Wen CP, Chen CH, Nauman J, et al. Resting heart rate – The forgotten risk factor? Comparison of resting heart rate and hypertension as predictors of all-cause mortality in 692,217 adults in Asia and Europe. Prog Cardiovasc Dis. 2025;89:35-44. doi:10.1016/j.pcad.2025.01.007 https://pubmed.ncbi.nlm.nih.gov/39894380/

Pfister R, Michels G, Sharp SJ, Luben R, Wareham NJ, Khaw KT. Resting heart rate and incident heart failure in apparently healthy men and women in the EPIC-Norfolk study. Eur J Heart Fail. 2012;14(10):1163-1170. doi:10.1093/eurjhf/hfs104 https://pubmed.ncbi.nlm.nih.gov/22736739/

Jensen MT, Marott JL, Jensen GB. Elevated resting heart rate is associated with greater risk of cardiovascular and all-cause mortality in current and former smokers. Int J Cardiol. 2011;151(2):148-154. doi:10.1016/j.ijcard.2010.05.003 https://pubmed.ncbi.nlm.nih.gov/20605243/

Jensen MT, Suadicani P, Hein HO, Gyntelberg F. Elevated resting heart rate, physical fitness and all-cause mortality: a 16-year follow-up in the Copenhagen Male Study. Heart. 2013;99(12):882-887. doi:10.1136/heartjnl-2012-303375 https://pubmed.ncbi.nlm.nih.gov/23595657/

Jensen MT. Resting heart rate and relation to disease and longevity: past, present and future. Scand J Clin Lab Invest. 2019;79(1-2):108-116. doi:10.1080/00365513.2019.1566567 https://pubmed.ncbi.nlm.nih.gov/30761923/

Levine HJ. Rest heart rate and life expectancy. J Am Coll Cardiol. 1997;30(4):1104-1106. doi:10.1016/s0735-1097(97)00246-5 https://pubmed.ncbi.nlm.nih.gov/9316546/

Olshansky B, Ricci F, Fedorowski A. Importance of resting heart rate. Trends Cardiovasc Med. 2023;33(8):502-515. doi:10.1016/j.tcm.2022.05.006 https://pubmed.ncbi.nlm.nih.gov/35623552/

Jouven X, Empana JP, Schwartz PJ, Desnos M, Courbon D, Ducimetière P. Heart-rate profile during exercise as a predictor of sudden death. N Engl J Med. 2005;352(19):1951-1958. doi:10.1056/NEJMoa043012 https://pubmed.ncbi.nlm.nih.gov/15888695/

Chen XJ, Barywani SB, Hansson PO, et al. Impact of changes in heart rate with age on all-cause death and cardiovascular events in 50-year-old men from the general population. Open Heart. 2019;6(1):e000856. Published 2019 Apr 15. doi:10.1136/openhrt-2018-000856 https://pubmed.ncbi.nlm.nih.gov/31168369/

Jouven X, Zureik M, Desnos M, Guérot C, Ducimetière P. Resting heart rate as a predictive risk factor for sudden death in middle-aged men. Cardiovasc Res. 2001;50(2):373-378. doi:10.1016/s0008-6363(01)00230-9 https://pubmed.ncbi.nlm.nih.gov/11334841/

Serra-Grima R, Puig T, Doñate M, Gich I, Ramon J. Long-term follow-up of bradycardia in elite athletes. Int J Sports Med. 2008;29(11):934-937. doi:10.1055/s-2008-1038602 https://pubmed.ncbi.nlm.nih.gov/18512181/

Jensen-Urstad K, Saltin B, Ericson M, Storck N, Jensen-Urstad M. Pronounced resting bradycardia in male elite runners is associated with high heart rate variability. Scand J Med Sci Sports. 1997;7(5):274-278. doi:10.1111/j.1600-0838.1997.tb00152.x https://pubmed.ncbi.nlm.nih.gov/9338944/

Doyen B, Matelot D, Carré F. Asymptomatic bradycardia amongst endurance athletes. Phys Sportsmed. 2019;47(3):249-252. doi:10.1080/00913847.2019.1568769 https://pubmed.ncbi.nlm.nih.gov/30640577/

Boraita Pérez A, Serratosa Fernández L. “El corazón del deportista”: hallazgos electrocardiográficos más frecuentes [“The athlete’s heart”: most common electrocardiographic findings]. Rev Esp Cardiol. 1998;51(5):356-368. doi:10.1016/s0300-8932(98)74759-1 https://pubmed.ncbi.nlm.nih.gov/9644959/

Al-Othman S, Boyett MR, Morris GM, et al. Symptomatic bradyarrhythmias in the athlete-Underlying mechanisms and treatments. Heart Rhythm. 2024;21(8):1415-1427. doi:10.1016/j.hrthm.2024.02.050 https://pubmed.ncbi.nlm.nih.gov/38428449/

 

Heart Rate Variability (HRV)

Jarczok MN, Weimer K, Braun C, et al. Heart rate variability in the prediction of mortality: A systematic review and meta-analysis of healthy and patient populations. Neurosci Biobehav Rev. 2022;143:104907. doi:10.1016/j.neubiorev.2022.104907 https://pubmed.ncbi.nlm.nih.gov/36243195/

Fang SC, Wu YL, Tsai PS. Heart Rate Variability and Risk of All-Cause Death and Cardiovascular Events in Patients With Cardiovascular Disease: A Meta-Analysis of Cohort Studies. Biol Res Nurs. 2020;22(1):45-56. doi:10.1177/1099800419877442 https://pubmed.ncbi.nlm.nih.gov/31558032/

Stewart M, Chandra R, Chiu A, et al. The value of resident presentations at scientific meetings. Head Neck. 2013;35(1):1. doi:10.1002/hed.23222 https://pubmed.ncbi.nlm.nih.gov/23193051/

Tan JPH, Beilharz JE, Vollmer-Conna U, Cvejic E. Heart rate variability as a marker of healthy ageing. Int J Cardiol. 2019;275:101-103. doi:10.1016/j.ijcard.2018.08.005 https://pubmed.ncbi.nlm.nih.gov/30104034/

de Bruyne MC, Kors JA, Hoes AW, et al. Both decreased and increased heart rate variability on the standard 10-second electrocardiogram predict cardiac mortality in the elderly: the Rotterdam Study. Am J Epidemiol. 1999;150(12):1282-1288. doi:10.1093/oxfordjournals.aje.a009959 https://pubmed.ncbi.nlm.nih.gov/10604770/

Tsuji H, Venditti FJ Jr, Manders ES, et al. Reduced heart rate variability and mortality risk in an elderly cohort. The Framingham Heart Study. Circulation. 1994;90(2):878-883. doi:10.1161/01.cir.90.2.878 https://pubmed.ncbi.nlm.nih.gov/8044959/

Dekker JM, Schouten EG, Klootwijk P, Pool J, Swenne CA, Kromhout D. Heart rate variability from short electrocardiographic recordings predicts mortality from all causes in middle-aged and elderly men. The Zutphen Study. Am J Epidemiol. 1997;145(10):899-908. doi:10.1093/oxfordjournals.aje.a009049 https://pubmed.ncbi.nlm.nih.gov/9149661/

Osataphan N, Wongcharoen W, Phrommintikul A, Putchagarn P, Noppakun K. Predictive value of heart rate variability on long-term mortality in end-stage kidney disease on hemodialysis. PLoS One. 2023;18(2):e0282344. Published 2023 Feb 24. doi:10.1371/journal.pone.0282344 https://pubmed.ncbi.nlm.nih.gov/36827405/

Yang L, Zhao Y, Qiao B, et al. Heart Rate Variability and Prognosis in Hemodialysis Patients: A Meta-Analysis. Blood Purif. 2021;50(3):298-308. doi:10.1159/000511723 

https://pubmed.ncbi.nlm.nih.gov/33291108/

Buccelletti E, Gilardi E, Scaini E, et al. Heart rate variability and myocardial infarction: systematic literature review and metanalysis. Eur Rev Med Pharmacol Sci. 2009;13(4):299-307. https://pubmed.ncbi.nlm.nih.gov/19694345/

Ryan ML, Ogilvie MP, Pereira BM, et al. Heart rate variability is an independent predictor of morbidity and mortality in hemodynamically stable trauma patients. J Trauma. 2011;70(6):1371-1380. doi:10.1097/TA.0b013e31821858e6 https://pubmed.ncbi.nlm.nih.gov/21817974/

Palatini P. Heart rate as an independent risk factor for cardiovascular disease: current evidence and basic mechanisms. Drugs. 2007;67 Suppl 2:3-13. doi:10.2165/00003495-200767002-00002 https://pubmed.ncbi.nlm.nih.gov/17999559/

Kleiger RE, Stein PK, Bigger JT Jr. Heart rate variability: measurement and clinical utility. Ann Noninvasive Electrocardiol. 2005;10(1):88-101. doi:10.1111/j.1542-474X.2005.10101.x https://pubmed.ncbi.nlm.nih.gov/15649244/

Hynynen E, Uusitalo A, Konttinen N, Rusko H. Heart rate variability during night sleep and after awakening in overtrained athletes. Med Sci Sports Exerc. 2006;38(2):313-317. doi:10.1249/01.mss.0000184631.27641.b5 https://pubmed.ncbi.nlm.nih.gov/16531900/

Stein PK, Barzilay JI, Chaves PH, et al. Higher levels of inflammation factors and greater insulin resistance are independently associated with higher heart rate and lower heart rate variability in normoglycemic older individuals: the Cardiovascular Health Study. J Am Geriatr Soc. 2008;56(2):315-321. doi:10.1111/j.1532-5415.2007.01564.x https://pubmed.ncbi.nlm.nih.gov/18179502/

Carter JB, Banister EW, Blaber AP. The effect of age and gender on heart rate variability after endurance training. Med Sci Sports Exerc. 2003;35(8):1333-1340. doi:10.1249/01.MSS.0000079046.01763.8F https://pubmed.ncbi.nlm.nih.gov/12900687/

Levy WC, Cerqueira MD, Harp GD, et al. Effect of endurance exercise training on heart rate variability at rest in healthy young and older men. Am J Cardiol. 1998;82(10):1236-1241. doi:10.1016/s0002-9149(98)00611-0 https://pubmed.ncbi.nlm.nih.gov/9832101/

Rueda-Ochoa OL, Osorio-Romero LF, Sanchez-Mendez LD. Which indices of heart rate variability are the best predictors of mortality after acute myocardial infarction? Meta-analysis of observational studies. J Electrocardiol. 2024;84:42-48. doi:10.1016/j.jelectrocard.2024.03.006 https://pubmed.ncbi.nlm.nih.gov/38489897/

 

Category 3: Strength & Functional Fitness

Muscular Strength & Mortality

García-Hermoso A, Cavero-Redondo I, Ramírez-Vélez R, et al. Muscular Strength as a Predictor of All-Cause Mortality in an Apparently Healthy Population: A Systematic Review and Meta-Analysis of Data From Approximately 2 Million Men and Women. Arch Phys Med Rehabil. 2018;99(10):2100-2113.e5. doi:10.1016/j.apmr.2018.01.008 https://pubmed.ncbi.nlm.nih.gov/29425700/

Jochem C, Leitzmann M, Volaklis K, Aune D, Strasser B. Association Between Muscular Strength and Mortality in Clinical Populations: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc. 2019;20(10):1213-1223. doi:10.1016/j.jamda.2019.05.015 https://pubmed.ncbi.nlm.nih.gov/31331825/

Volaklis KA, Halle M, Meisinger C. Muscular strength as a strong predictor of mortality: A narrative review. Eur J Intern Med. 2015;26(5):303-310. doi:10.1016/j.ejim.2015.04.013 https://pubmed.ncbi.nlm.nih.gov/25921473/

Li R, Xia J, Zhang XI, et al. Associations of Muscle Mass and Strength with All-Cause Mortality among US Older Adults. Med Sci Sports Exerc. 2018;50(3):458-467. doi:10.1249/MSS.0000000000001448 https://pubmed.ncbi.nlm.nih.gov/28991040/

Ruiz JR, Sui X, Lobelo F, et al. Association between muscular strength and mortality in men: prospective cohort study. BMJ. 2008;337(7661):a439. Published 2008 Jul 1. doi:10.1136/bmj.a439 https://pubmed.ncbi.nlm.nih.gov/18595904/

Shailendra P, Baldock KL, Li LSK, Bennie JA, Boyle T. Resistance Training and Mortality Risk: A Systematic Review and Meta-Analysis. Am J Prev Med. 2022;63(2):277-285. doi:10.1016/j.amepre.2022.03.020 https://pubmed.ncbi.nlm.nih.gov/35599175/

Saeidifard F, Medina-Inojosa JR, West CP, et al. The association of resistance training with mortality: A systematic review and meta-analysis. Eur J Prev Cardiol. 2019;26(15):1647-1665. doi:10.1177/2047487319850718 https://pubmed.ncbi.nlm.nih.gov/31104484/

Momma H, Kawakami R, Honda T, Sawada SS. Muscle-strengthening activities are associated with lower risk and mortality in major non-communicable diseases: a systematic review and meta-analysis of cohort studies. Br J Sports Med. 2022;56(13):755-763. doi:10.1136/bjsports-2021-105061 https://pubmed.ncbi.nlm.nih.gov/35228201/

Sasaki H, Kasagi F, Yamada M, Fujita S. Grip strength predicts cause-specific mortality in middle-aged and elderly persons. Am J Med. 2007;120(4):337-342. doi:10.1016/j.amjmed.2006.04.018 https://pubmed.ncbi.nlm.nih.gov/17398228/

Petermann-Rocha F, Parra-Soto S, Cid V, et al. The association between walking pace and grip strength and all-cause mortality: A prospective analysis from the MAUCO cohort. Maturitas. 2023;168:37-43. doi:10.1016/j.maturitas.2022.11.004 https://pubmed.ncbi.nlm.nih.gov/36442346/

Lopez-Jaramillo P, Lopez-Lopez JP, Tole MC, Cohen DD. Muscular Strength in Risk Factors for Cardiovascular Disease and Mortality: A Narrative Review. Anatol J Cardiol. 2022;26(8):598-607. doi:10.5152/AnatolJCardiol.2022.1586 https://pubmed.ncbi.nlm.nih.gov/35924286/

Artero EG, Lee DC, Lavie CJ, et al. Effects of muscular strength on cardiovascular risk factors and prognosis. J Cardiopulm Rehabil Prev. 2012;32(6):351-358. doi:10.1097/HCR.0b013e3182642688 https://pubmed.ncbi.nlm.nih.gov/22885613/

Kim Y, White T, Wijndaele K, et al. The combination of cardiorespiratory fitness and muscle strength, and mortality risk. Eur J Epidemiol. 2018;33(10):953-964. doi:10.1007/s10654-018-0384-x https://pubmed.ncbi.nlm.nih.gov/29594847/

Bettariga F, Galvao DA, Taaffe DR, et al. Association of muscle strength and cardiorespiratory fitness with all-cause and cancer-specific mortality in patients diagnosed with cancer: a systematic review with meta-analysis. Br J Sports Med. 2025;59(10):722-732. Published 2025 May 2. doi:10.1136/bjsports-2024-108671 https://pubmed.ncbi.nlm.nih.gov/39837589/

 

Sex Differences in Strength

Nuzzo JL. Narrative Review of Sex Differences in Muscle Strength, Endurance, Activation, Size, Fiber Type, and Strength Training Participation Rates, Preferences, Motivations, Injuries, and Neuromuscular Adaptations. J Strength Cond Res. 2023;37(2):494-536. doi:10.1519/JSC.0000000000004329 https://pubmed.ncbi.nlm.nih.gov/36696264/

Kraemer WJ, Chaudhry NF, Graham JH, et al. Sex differences in upper-body strength, lean mass, and bone density across the adult lifespan: insights into musculoskeletal aging and strength preservation. J Appl Physiol (1985). 2025;139(4):1000-1009. doi:10.1152/japplphysiol.00544.2025 https://pubmed.ncbi.nlm.nih.gov/40875382/

Nuzzo JL, Pinto MD. Sex Differences in Upper- and Lower-Limb Muscle Strength in Children and Adolescents: A Meta-Analysis. Eur J Sport Sci. 2025;25(5):e12282. doi:10.1002/ejsc.12282 https://pubmed.ncbi.nlm.nih.gov/40186614/

Pérez MA, Urrejola-Contreras GP, Hernández J, Silva P, Torres-Banduc M. Sex differences in upper and lower strength and their association with body composition among university students. Phys Act Nutr. 2024;28(3):64-71. doi:10.20463/pan.2024.0025 https://pubmed.ncbi.nlm.nih.gov/39501696/

Roberts BM, Nuckols G, Krieger JW. Sex Differences in Resistance Training: A Systematic Review and Meta-Analysis. J Strength Cond Res. 2020;34(5):1448-1460. doi:10.1519/JSC.0000000000003521 https://pubmed.ncbi.nlm.nih.gov/32218059/

Miller AE, MacDougall JD, Tarnopolsky MA, Sale DG. Gender differences in strength and muscle fiber characteristics. Eur J Appl Physiol Occup Physiol. 1993;66(3):254-262. doi:10.1007/BF00235103 https://pubmed.ncbi.nlm.nih.gov/8477683/

Lu G, Duan Y. Sex differences in the adaptations in maximal strength and anaerobic power to upper body plyometric training. Sci Rep. 2024;14(1):21304. Published 2024 Sep 12. doi:10.1038/s41598-024-72234-0 https://pubmed.ncbi.nlm.nih.gov/39266662/

Refalo MC, Nuckols G, Galpin AJ, Gallagher IJ, Hamilton DL, Fyfe JJ. Sex differences in absolute and relative changes in muscle size following resistance training in healthy adults: a systematic review with Bayesian meta-analysis. PeerJ. 2025;13:e19042. Published 2025 Feb 25. doi:10.7717/peerj.19042 https://pubmed.ncbi.nlm.nih.gov/40028215/

Voskuil CC, Dudar MD, Carr JC. Sex differences in fatiguability during single-joint resistance exercise in a resistance-trained population. Eur J Appl Physiol. 2024;124(8):2261-2271. doi:10.1007/s00421-024-05445-y https://pubmed.ncbi.nlm.nih.gov/38441692/

Lewis MH, Siedler MR, Lamadrid P, et al. Sex Differences May Exist for Performance Fatigue but Not Recovery After Single-Joint Upper-Body and Lower-Body Resistance Exercise. J Strength Cond Res. 2022;36(6):1498-1505. doi:10.1519/JSC.0000000000004239 https://pubmed.ncbi.nlm.nih.gov/35333210/

Jones MD, Wewege MA, Hackett DA, Keogh JWL, Hagstrom AD. Sex Differences in Adaptations in Muscle Strength and Size Following Resistance Training in Older Adults: A Systematic Review and Meta-analysis. Sports Med. 2021;51(3):503-517. doi:10.1007/s40279-020-01388-4 https://pubmed.ncbi.nlm.nih.gov/33332016/

Lephart SM, Ferris CM, Riemann BL, Myers JB, Fu FH. Gender differences in strength and lower extremity kinematics during landing. Clin Orthop Relat Res. 2002;(401):162-169. doi:10.1097/00003086-200208000-00019 https://pubmed.ncbi.nlm.nih.gov/12151893/

Alonazi A, Alsunaid F, Alofaisan L, Ghassan Alqarni M, Alhumoud J, Kashoo F. Gender Differences in Lower Limb Strength and Endurance Among Saudi Adolescents: A Cross-Sectional Study on the Limited Role of Body Mass Index. Children (Basel). 2025;12(7):899. Published 2025 Jul 8. doi:10.3390/children12070899 https://pubmed.ncbi.nlm.nih.gov/40723092/

Nindl BC, Mahar MT, Harman EA, Patton JF. Lower and upper body anaerobic performance in male and female adolescent athletes. Med Sci Sports Exerc. 1995;27(2):235-241. https://pubmed.ncbi.nlm.nih.gov/7723647/

Janssen I, Heymsfield SB, Wang ZM, Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18-88 yr. J Appl Physiol (1985). 2000;89(1):81-88. doi:10.1152/jappl.2000.89.1.81 https://pubmed.ncbi.nlm.nih.gov/10904038/

Belzunce MA, Henckel J, Di Laura A, Horga LM, Hart AJ. Gender similarities and differences in skeletal muscle and body composition: an MRI study of recreational cyclists. BMJ Open Sport Exerc Med. 2023;9(3):e001672. Published 2023 Aug 24. doi:10.1136/bmjsem-2023-001672 https://pubmed.ncbi.nlm.nih.gov/37637483/

Bredella MA. Sex Differences in Body Composition. Adv Exp Med Biol. 2017;1043:9-27. doi:10.1007/978-3-319-70178-3_2 https://pubmed.ncbi.nlm.nih.gov/29224088/

Cureton KJ, Collins MA, Hill DW, McElhannon FM Jr. Muscle hypertrophy in men and women. Med Sci Sports Exerc. 1988;20(4):338-344. doi:10.1249/00005768-198808000-00003 https://pubmed.ncbi.nlm.nih.gov/3173042/

Tipton KD. Gender differences in protein metabolism. Curr Opin Clin Nutr Metab Care. 2001;4(6):493-498. doi:10.1097/00075197-200111000-00005 https://pubmed.ncbi.nlm.nih.gov/11706282/

Frontera WR, Hughes VA, Lutz KJ, Evans WJ. A cross-sectional study of muscle strength and mass in 45- to 78-yr-old men and women. J Appl Physiol (1985). 1991;71(2):644-650. doi:10.1152/jappl.1991.71.2.644 https://pubmed.ncbi.nlm.nih.gov/1938738/

Ferretti JL, Capozza RF, Cointry GR, et al. Gender-related differences in the relationship between densitometric values of whole-body bone mineral content and lean body mass in humans between 2 and 87 years of age. Bone. 1998;22(6):683-690. doi:10.1016/s8756-3282(98)00046-5 https://pubmed.ncbi.nlm.nih.gov/9626409/

Blaak E. Gender differences in fat metabolism. Curr Opin Clin Nutr Metab Care. 2001;4(6):499-502. doi:10.1097/00075197-200111000-00006 https://pubmed.ncbi.nlm.nih.gov/11706283/

Abernathy RP, Black DR. Healthy body weights: an alternative perspective. Am J Clin Nutr. 1996;63(3 Suppl):448S-451S. doi:10.1093/ajcn/63.3.448 https://pubmed.ncbi.nlm.nih.gov/8615340/

Goran MI, Allison DB, Poehlman ET. Issues relating to normalization of body fat content in men and women. Int J Obes Relat Metab Disord. 1995;19(9):638-643. https://pubmed.ncbi.nlm.nih.gov/8574274/

Ramos E, Frontera WR, Llopart A, Feliciano D. Muscle strength and hormonal levels in adolescents: gender related differences. Int J Sports Med. 1998;19(8):526-531. doi:10.1055/s-2007-971955 https://pubmed.ncbi.nlm.nih.gov/9877143/

Sgrò P, Antinozzi C, Duranti G, Dimauro I, Radak Z, Di Luigi L. Sex Differences in Human Myogenesis Following Testosterone Exposure. Biology (Basel). 2025;14(7):855. Published 2025 Jul 14. doi:10.3390/biology14070855 https://pubmed.ncbi.nlm.nih.gov/40723413/

Hunter SK, S Angadi S, Bhargava A, et al. The Biological Basis of Sex Differences in Athletic Performance: Consensus Statement for the American College of Sports Medicine. Med Sci Sports Exerc. 2023;55(12):2328-2360. doi:10.1249/MSS.0000000000003300 https://pubmed.ncbi.nlm.nih.gov/37772882/

Round JM, Jones DA, Honour JW, Nevill AM. Hormonal factors in the development of differences in strength between boys and girls during adolescence: a longitudinal study. Ann Hum Biol. 1999;26(1):49-62. doi:10.1080/030144699282976 https://pubmed.ncbi.nlm.nih.gov/9974083/

Lang TF. The bone-muscle relationship in men and women. J Osteoporos. 2011;2011:702735. doi:10.4061/2011/702735 https://pubmed.ncbi.nlm.nih.gov/22007336/

Reader M, Schwartz G, English AW. Brief exposure to testosterone is sufficient to induce sex differences in the rabbit masseter muscle. Cells Tissues Organs. 2001;169(3):210-217. doi:10.1159/000047884 https://pubmed.ncbi.nlm.nih.gov/11455116/

Vikmoen O, Teien HK, Raustøl M, et al. Sex differences in the physiological response to a demanding military field exercise. Scand J Med Sci Sports. 2020;30(8):1348-1359. doi:10.1111/sms.13689 https://pubmed.ncbi.nlm.nih.gov/32311789/

Bartolomei S, Grillone G, Di Michele R, Cortesi M. A Comparison between Male and Female Athletes in Relative Strength and Power Performances. J Funct Morphol Kinesiol. 2021;6(1):17. Published 2021 Feb 9. doi:10.3390/jfmk6010017 https://pubmed.ncbi.nlm.nih.gov/33572280/

Doré E, Martin R, Ratel S, Duché P, Bedu M, Van Praagh E. Gender differences in peak muscle performance during growth. Int J Sports Med. 2005;26(4):274-280. doi:10.1055/s-2004-821001 https://pubmed.ncbi.nlm.nih.gov/15795811/

Zhang W, Cui Z, Shen D, Gao L, Li Q. Testosterone levels positively linked to muscle mass but not strength in adult males aged 20-59 years: a cross-sectional study. Front Physiol. 2025;16:1512268. Published 2025 Apr 15. doi:10.3389/fphys.2025.1512268 https://pubmed.ncbi.nlm.nih.gov/40303597/

 

Strength Standards Research

Ferland PM, Laurier A, Comtois AS. Relationships Between Anthropometry and Maximal Strength in Male Classic Powerlifters. Int J Exerc Sci. 2020;13(4):1512-1531. Published 2020 Dec 1. doi:10.70252/WKTF5547 https://pubmed.ncbi.nlm.nih.gov/33414873/

van den Hoek DJ, Beaumont PL, van den Hoek AK, et al. Normative data for the squat, bench press and deadlift exercises in powerlifting: Data from 809,986 competition entries. J Sci Med Sport. 2024;27(10):734-742. doi:10.1016/j.jsams.2024.07.005 https://pubmed.ncbi.nlm.nih.gov/39060209/

Baker DG, Newton RU. An analysis of the ratio and relationship between upper body pressing and pulling strength. J Strength Cond Res. 2004;18(3):594-598. doi:10.1519/R-12382.1 https://pubmed.ncbi.nlm.nih.gov/15320678/

Dhahbi W, Padulo J, Russo L, et al. 4-6 Repetition Maximum (RM) and 1-RM Prediction in Free-Weight Bench Press and Smith Machine Squat Based on Body Mass in Male Athletes. J Strength Cond Res. 2024;38(8):1366-1371. doi:10.1519/JSC.0000000000004803 https://pubmed.ncbi.nlm.nih.gov/38888595/

Sanchez-Moreno M, Pareja-Blanco F, Diaz-Cueli D, González-Badillo JJ. Determinant factors of pull-up performance in trained athletes. J Sports Med Phys Fitness. 2016;56(7-8):825-833. https://pubmed.ncbi.nlm.nih.gov/26176615/

Negrete RJ, Hanney WJ, Kolber MJ, et al. Reliability, minimal detectable change, and normative values for tests of upper extremity function and power. J Strength Cond Res. 2010;24(12):3318-3325. doi:10.1519/JSC.0b013e3181e7259c https://pubmed.ncbi.nlm.nih.gov/21088548/

Bartolomei S, Grillone G, Di Michele R, Cortesi M. A Comparison between Male and Female Athletes in Relative Strength and Power Performances. J Funct Morphol Kinesiol. 2021;6(1):17. Published 2021 Feb 9. doi:10.3390/jfmk6010017 https://pubmed.ncbi.nlm.nih.gov/33572280/

Latella C, van den Hoek D, Wolf M, Androulakis-Korakakis P, Fisher JP, Steele J. Using Powerlifting Athletes to Determine Strength Adaptations Across Ages in Males and Females: A Longitudinal Growth Modelling Approach. Sports Med. 2024;54(3):753-774. doi:10.1007/s40279-023-01962-6 https://pubmed.ncbi.nlm.nih.gov/38060089/

Ervin RB, Wang CY, Fryar CD, Miller IM, Ogden CL. Measures of muscular strength in U.S. children and adolescents, 2012. NCHS Data Brief. 2013;(139):1-8. https://pubmed.ncbi.nlm.nih.gov/24331231/

https://exrx.net/WorkoutTools/StrengthStandards

https://strengthlevel.com/

 

Category 4: Recovery & Sleep

Sleep Quantity

Yin J, Jin X, Shan Z, et al. Relationship of Sleep Duration With All-Cause Mortality and Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies. J Am Heart Assoc. 2017;6(9):e005947. Published 2017 Sep 9. doi:10.1161/JAHA.117.005947 https://pubmed.ncbi.nlm.nih.gov/28889101/

Ungvari Z, Fekete M, Lehoczki A, et al. Inadequate sleep increases stroke risk: evidence from a comprehensive meta-analysis of incidence and mortality. Geroscience. 2025;47(3):4679-4695. doi:10.1007/s11357-025-01593-x https://pubmed.ncbi.nlm.nih.gov/40072786/

Wang H, Sun J, Sun M, Liu N, Wang M. Relationship of sleep duration with the risk of stroke incidence and stroke mortality: an updated systematic review and dose-response meta-analysis of prospective cohort studies. Sleep Med. 2022;90:267-278. doi:10.1016/j.sleep.2021.11.001 https://pubmed.ncbi.nlm.nih.gov/35245890/

Kurina LM, McClintock MK, Chen JH, Waite LJ, Thisted RA, Lauderdale DS. Sleep duration and all-cause mortality: a critical review of measurement and associations. Ann Epidemiol. 2013;23(6):361-370. doi:10.1016/j.annepidem.2013.03.015 https://pubmed.ncbi.nlm.nih.gov/23622956/

Cappuccio FP, D’Elia L, Strazzullo P, Miller MA. Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies. Sleep. 2010;33(5):585-592. doi:10.1093/sleep/33.5.585 https://pubmed.ncbi.nlm.nih.gov/20469800/

Xiao Q, Keadle SK, Hollenbeck AR, Matthews CE. Sleep duration and total and cause-specific mortality in a large US cohort: interrelationships with physical activity, sedentary behavior, and body mass index. Am J Epidemiol. 2014;180(10):997-1006. doi:10.1093/aje/kwu222 https://pubmed.ncbi.nlm.nih.gov/25281691/

Grandner MA, Hale L, Moore M, Patel NP. Mortality associated with short sleep duration: The evidence, the possible mechanisms, and the future. Sleep Med Rev. 2010;14(3):191-203. doi:10.1016/j.smrv.2009.07.006 https://pubmed.ncbi.nlm.nih.gov/19932976/

Gallicchio L, Kalesan B. Sleep duration and mortality: a systematic review and meta-analysis. J Sleep Res. 2009;18(2):148-158. doi:10.1111/j.1365-2869.2008.00732.x https://pubmed.ncbi.nlm.nih.gov/19645960/

Yang L, Xi B, Zhao M, Magnussen CG. Association of sleep duration with all-cause and disease-specific mortality in US adults. J Epidemiol Community Health. Published online January 13, 2021. doi:10.1136/jech-2020-215314 https://pubmed.ncbi.nlm.nih.gov/33441393/

Gu J, Wu H, Diao W, Ji Y, Li J, Huo J. Association of Sleep Duration with Risk of All-Cause and Cause-Specific Mortality Among American Adults: A Population-Based Cohort Study. Nat Sci Sleep. 2024;16:949-962. Published 2024 Jul 11. doi:10.2147/NSS.S469638 https://pubmed.ncbi.nlm.nih.gov/39011490/

Fondell E, Axelsson J, Franck K, et al. Short natural sleep is associated with higher T cell and lower NK cell activities. Brain Behav Immun. 2011;25(7):1367-1375. doi:10.1016/j.bbi.2011.04.004 https://pubmed.ncbi.nlm.nih.gov/21496482/

Irwin M, Mascovich A, Gillin JC, Willoughby R, Pike J, Smith TL. Partial sleep deprivation reduces natural killer cell activity in humans. Psychosom Med. 1994;56(6):493-498. doi:10.1097/00006842-199411000-00004 https://pubmed.ncbi.nlm.nih.gov/7871104/

Morales-Muñoz I, Marwaha S, Upthegrove R, Cropley V. Role of Inflammation in Short Sleep Duration Across Childhood and Psychosis in Young Adulthood. JAMA Psychiatry. 2024;81(8):825-833. doi:10.1001/jamapsychiatry.2024.0796 https://pubmed.ncbi.nlm.nih.gov/38717746/

Zhang Y, Zhao W, Liu K, et al. The causal associations of altered inflammatory proteins with sleep duration, insomnia and daytime sleepiness. Sleep. 2023;46(10):zsad207. doi:10.1093/sleep/zsad207 https://pubmed.ncbi.nlm.nih.gov/37535878/

Irwin MR, Olmstead R, Carroll JE. Sleep Disturbance, Sleep Duration, and Inflammation: A Systematic Review and Meta-Analysis of Cohort Studies and Experimental Sleep Deprivation. Biol Psychiatry. 2016;80(1):40-52. doi:10.1016/j.biopsych.2015.05.014 https://pubmed.ncbi.nlm.nih.gov/26140821/

Liu A, Kushida CA, Reaven GM. Habitual shortened sleep and insulin resistance: an independent relationship in obese individuals. Metabolism. 2013;62(11):1553-1556. doi:10.1016/j.metabol.2013.06.003 https://pubmed.ncbi.nlm.nih.gov/23849514/

Zuraikat FM, Laferrère B, Cheng B, et al. Chronic Insufficient Sleep in Women Impairs Insulin Sensitivity Independent of Adiposity Changes: Results of a Randomized Trial. Diabetes Care. 2024;47(1):117-125. doi:10.2337/dc23-1156 

https://pubmed.ncbi.nlm.nih.gov/37955852/

Donga E, van Dijk M, van Dijk JG, et al. A single night of partial sleep deprivation induces insulin resistance in multiple metabolic pathways in healthy subjects. J Clin Endocrinol Metab. 2010;95(6):2963-2968. doi:10.1210/jc.2009-2430 https://pubmed.ncbi.nlm.nih.gov/20371664/

Taheri S, Lin L, Austin D, Young T, Mignot E. Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med. 2004;1(3):e62. doi:10.1371/journal.pmed.0010062 https://pubmed.ncbi.nlm.nih.gov/15602591/

van Egmond LT, Meth EMS, Engström J, et al. Effects of acute sleep loss on leptin, ghrelin, and adiponectin in adults with healthy weight and obesity: A laboratory study. Obesity (Silver Spring). 2023;31(3):635-641. doi:10.1002/oby.23616 https://pubmed.ncbi.nlm.nih.gov/36404495/

Spiegel K, Tasali E, Penev P, Van Cauter E. Brief communication: Sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann Intern Med. 2004;141(11):846-850. doi:10.7326/0003-4819-141-11-200412070-00008 https://pubmed.ncbi.nlm.nih.gov/15583226/

Jackowska M, Hamer M, Carvalho LA, Erusalimsky JD, Butcher L, Steptoe A. Short sleep duration is associated with shorter telomere length in healthy men: findings from the Whitehall II cohort study. PLoS One. 2012;7(10):e47292. doi:10.1371/journal.pone.0047292 https://pubmed.ncbi.nlm.nih.gov/23144701/

Tempaku PF, D’Almeida V, Andersen ML, Tufik S. Sleep is associated with telomere shortening: A population-based longitudinal study. J Sleep Res. 2025;34(1):e14274. doi:10.1111/jsr.14274 https://pubmed.ncbi.nlm.nih.gov/39054789/

Kabat GC, Xue X, Kamensky V, et al. The association of sleep duration and quality with all-cause and cause-specific mortality in the Women’s Health Initiative. Sleep Med. 2018;50:48-54. doi:10.1016/j.sleep.2018.05.015 https://pubmed.ncbi.nlm.nih.gov/29982090/

Sabia S, Dugravot A, Léger D, Ben Hassen C, Kivimaki M, Singh-Manoux A. Association of sleep duration at age 50, 60, and 70 years with risk of multimorbidity in the UK: 25-year follow-up of the Whitehall II cohort study. PLoS Med. 2022;19(10):e1004109. Published 2022 Oct 18. doi:10.1371/journal.pmed.1004109 https://pubmed.ncbi.nlm.nih.gov/36256607/

Chen M, Lu C, Zha J. Long Sleep Duration Increases the Risk of All-Cause Mortality Among Community-Dwelling Older Adults With Frailty: Evidence From NHANES 2009-2014. J Appl Gerontol. 2023;42(5):1078-1088. doi:10.1177/07334648221147917 https://pubmed.ncbi.nlm.nih.gov/36560922/

Chaput JP, Dutil C, Featherstone R, et al. Sleep duration and health in adults: an overview of systematic reviews. Appl Physiol Nutr Metab. 2020;45(10 (Suppl. 2)):S218-S231. doi:10.1139/apnm-2020-0034 https://pubmed.ncbi.nlm.nih.gov/33054337/

Ungvari Z, Fekete M, Varga P, et al. Imbalanced sleep increases mortality risk by 14-34%: a meta-analysis. Geroscience. 2025;47(3):4545-4566. doi:10.1007/s11357-025-01592-y https://pubmed.ncbi.nlm.nih.gov/40072785/

Hirshkowitz M, Whiton K, Albert SM, et al. National Sleep Foundation’s updated sleep duration recommendations: final report. Sleep Health. 2015;1(4):233-243. doi:10.1016/j.sleh.2015.10.004 https://pubmed.ncbi.nlm.nih.gov/29073398/

Baranwal N, Yu PK, Siegel NS. Sleep physiology, pathophysiology, and sleep hygiene. Prog Cardiovasc Dis. 2023;77:59-69. doi:10.1016/j.pcad.2023.02.005 https://pubmed.ncbi.nlm.nih.gov/36841492/

 

Sleep Quality

Kwok CS, Kontopantelis E, Kuligowski G, et al. Self-Reported Sleep Duration and Quality and Cardiovascular Disease and Mortality: A Dose-Response Meta-Analysis. J Am Heart Assoc. 2018;7(15):e008552. doi:10.1161/JAHA.118.008552 https://pubmed.ncbi.nlm.nih.gov/30371228/

Lao XQ, Liu X, Deng HB, et al. Sleep Quality, Sleep Duration, and the Risk of Coronary Heart Disease: A Prospective Cohort Study With 60,586 Adults. J Clin Sleep Med. 2018;14(1):109-117. Published 2018 Jan 15. doi:10.5664/jcsm.6894 https://pubmed.ncbi.nlm.nih.gov/29198294/

Hoevenaar-Blom MP, Spijkerman AM, Kromhout D, van den Berg JF, Verschuren WM. Sleep duration and sleep quality in relation to 12-year cardiovascular disease incidence: the MORGEN study. Sleep. 2011;34(11):1487-1492. Published 2011 Nov 1. doi:10.5665/sleep.1382 https://pubmed.ncbi.nlm.nih.gov/22043119/

Aziz M, Ali SS, Das S, et al. Association of Subjective and Objective Sleep Duration as well as Sleep Quality with Non-Invasive Markers of Sub-Clinical Cardiovascular Disease (CVD): A Systematic Review. J Atheroscler Thromb. 2017;24(3):208-226. doi:10.5551/jat.36194 https://pubmed.ncbi.nlm.nih.gov/27840384/

Reinhard W, Plappert N, Zeman F, et al. Prognostic impact of sleep duration and sleep efficiency on mortality in patients with chronic heart failure. Sleep Med. 2013;14(6):502-509. doi:10.1016/j.sleep.2012.12.014 https://pubmed.ncbi.nlm.nih.gov/23628241/

Martin JL, Fiorentino L, Jouldjian S, Mitchell M, Josephson KR, Alessi CA. Poor self-reported sleep quality predicts mortality within one year of inpatient post-acute rehabilitation among older adults. Sleep. 2011;34(12):1715-1721. Published 2011 Dec 1. doi:10.5665/sleep.1444 https://pubmed.ncbi.nlm.nih.gov/22131610/

Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193-213. doi:10.1016/0165-1781(89)90047-4 https://pubmed.ncbi.nlm.nih.gov/2748771/

Muzni K, Groeger JA, Dijk DJ, Lazar AS. Self-reported sleep quality is more closely associated with mental and physical health than chronotype and sleep duration in young adults: A multi-instrument analysis. J Sleep Res. 2021;30(1):e13152. doi:10.1111/jsr.13152 https://pubmed.ncbi.nlm.nih.gov/32783404/

 

Daily Energy Level

Hardy SE, Studenski SA. Fatigue predicts mortality in older adults. J Am Geriatr Soc. 2008;56(10):1910-1914. doi:10.1111/j.1532-5415.2008.01957.x https://pubmed.ncbi.nlm.nih.gov/18811604/

Newman AB, Spiekerman CF, Enright P, et al. Daytime sleepiness predicts mortality and cardiovascular disease in older adults. The Cardiovascular Health Study Research Group. J Am Geriatr Soc. 2000;48(2):115-123. doi:10.1111/j.1532-5415.2000.tb03901.x https://pubmed.ncbi.nlm.nih.gov/10682939/

Alhashemi A, Jones JM, Goldstein DP, et al. An Exploratory Study of Fatigue and Physical Activity in Canadian Thyroid Cancer Patients. Thyroid. 2017;27(9):1156-1163. doi:10.1089/thy.2016.0541 https://pubmed.ncbi.nlm.nih.gov/28712348/

Denu MKI, Revoori R, Eghan C, et al. Association between chronic fatigue syndrome/myalgic encephalomyelitis and cardiovascular disease. Sci Rep. 2025;15(1):2294. Published 2025 Jan 17. doi:10.1038/s41598-025-86609-4 https://pubmed.ncbi.nlm.nih.gov/39833264/

Natelson BH, Brunjes DL, Mancini D. Chronic Fatigue Syndrome and Cardiovascular Disease: JACC State-of-the-Art Review. J Am Coll Cardiol. 2021;78(10):1056-1067. doi:10.1016/j.jacc.2021.06.045 https://pubmed.ncbi.nlm.nih.gov/34474739/

Nelesen R, Dar Y, Thomas K, Dimsdale JE. The relationship between fatigue and cardiac functioning. Arch Intern Med. 2008;168(9):943-949. doi:10.1001/archinte.168.9.943 https://pmc.ncbi.nlm.nih.gov/articles/PMC2633298/

Basu N, Yang X, Luben RN, et al. Fatigue is associated with excess mortality in the general population: results from the EPIC-Norfolk study. BMC Med. 2016;14(1):122. Published 2016 Aug 20. doi:10.1186/s12916-016-0662-y https://pmc.ncbi.nlm.nih.gov/articles/PMC4992307/

Li J, Covassin N, Bock JM, et al. Excessive Daytime Sleepiness and Cardiovascular Mortality in US Adults: A NHANES 2005-2008 Follow-Up Study. Nat Sci Sleep. 2021;13:1049-1059. Published 2021 Jul 6. doi:10.2147/NSS.S319675 https://pmc.ncbi.nlm.nih.gov/articles/PMC8273750/

 

Perceived Stress

Keller A, Litzelman K, Wisk LE, et al. Does the perception that stress affects health matter? The association with health and mortality. Health Psychol. 2012;31(5):677-684. doi:10.1037/a0026743 https://pubmed.ncbi.nlm.nih.gov/22201278/

Prior A, Fenger-Grøn M, Larsen KK, et al. The Association Between Perceived Stress and Mortality Among People With Multimorbidity: A Prospective Population-Based Cohort Study. Am J Epidemiol. 2016;184(3):199-210. doi:10.1093/aje/kwv324 https://pubmed.ncbi.nlm.nih.gov/27407085/

Epel ES, Blackburn EH, Lin J, et al. Accelerated telomere shortening in response to life stress. Proc Natl Acad Sci U S A. 2004;101(49):17312-17315. doi:10.1073/pnas.0407162101 https://pubmed.ncbi.nlm.nih.gov/15574496/

Lin J, Epel E. Stress and telomere shortening: Insights from cellular mechanisms. Ageing Res Rev. 2022;73:101507. doi:10.1016/j.arr.2021.101507 https://pubmed.ncbi.nlm.nih.gov/34736994/

Miller ES, Apple CG, Kannan KB, et al. Chronic stress induces persistent low-grade inflammation. Am J Surg. 2019;218(4):677-683. doi:10.1016/j.amjsurg.2019.07.006 https://pubmed.ncbi.nlm.nih.gov/31378316/

Burford NG, Webster NA, Cruz-Topete D. Hypothalamic-Pituitary-Adrenal Axis Modulation of Glucocorticoids in the Cardiovascular System. Int J Mol Sci. 2017;18(10):2150. Published 2017 Oct 16. doi:10.3390/ijms18102150 https://pmc.ncbi.nlm.nih.gov/articles/PMC5666832/

Kulkarni S, O’Farrell I, Erasi M, Kochar MS. Stress and hypertension. WMJ. 1998;97(11):34-38. https://pubmed.ncbi.nlm.nih.gov/9894438/

Yao BC, Meng LB, Hao ML, Zhang YM, Gong T, Guo ZG. Chronic stress: a critical risk factor for atherosclerosis. J Int Med Res. 2019;47(4):1429-1440. doi:10.1177/0300060519826820 https://pubmed.ncbi.nlm.nih.gov/30799666/

Li L, Li X, Zhou W, Messina JL. Acute psychological stress results in the rapid development of insulin resistance. J Endocrinol. 2013;217(2):175-184. Published 2013 Apr 15. doi:10.1530/JOE-12-0559 https://pubmed.ncbi.nlm.nih.gov/23444388/

Segerstrom SC, Miller GE. Psychological stress and the human immune system: a meta-analytic study of 30 years of inquiry. Psychol Bull. 2004;130(4):601-630. doi:10.1037/0033-2909.130.4.601 https://pmc.ncbi.nlm.nih.gov/articles/PMC1361287/

 

Category 5: Body Composition

Waist-to-Height Ratio (WHtR)

Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275-286. doi:10.1111/j.1467-789X.2011.00952.x https://pubmed.ncbi.nlm.nih.gov/22106927/

Abdi Dezfouli R, Mohammadian Khonsari N, Hosseinpour A, Asadi S, Ejtahed HS, Qorbani M. Waist to height ratio as a simple tool for predicting mortality: a systematic review and meta-analysis. Int J Obes (Lond). 2023;47(12):1286-1301. doi:10.1038/s41366-023-01388-0 https://pubmed.ncbi.nlm.nih.gov/37770574/

Patel AV, Faw K, Rees-Punia E, et al. Is waist to height ratio better at assessing cause-specific mortality risk than body mass index or waist circumference? A prospective analysis in a large U.S.-based cohort. PLoS One. 2025;20(8):e0328760. Published 2025 Aug 13. doi:10.1371/journal.pone.0328760 https://pubmed.ncbi.nlm.nih.gov/40802716/

Wang G, Luo Y, Yang T, et al. Association of waist-to-height ratio with all-cause and obesity-related mortality in adults: a prospective cohort study. Front Nutr. 2025;12:1614347. Published 2025 Aug 11. doi:10.3389/fnut.2025.1614347 https://pubmed.ncbi.nlm.nih.gov/40860487/

Jiang H, Li M, Yu H, et al. Body mass index and waist-to-height ratio effect on mortality in non-alcoholic fatty liver: revisiting the obesity paradox. Front Endocrinol (Lausanne). 2024;15:1419715. Published 2024 Dec 6. doi:10.3389/fendo.2024.1419715 https://pubmed.ncbi.nlm.nih.gov/39713050/

Yang Y, Zhang Y, Tian Z. Elevated Waist-to-Height Ratio Increases the Risk of Cardiovascular and Cerebrovascular Disease Mortality in Elderly Type 2 Diabetes Mellitus Populations. J Multidiscip Healthc. 2025;18:2681-2692. Published 2025 May 12. doi:10.2147/JMDH.S521758 https://pubmed.ncbi.nlm.nih.gov/40384814/

Zhang Y, Yao Y. The association between obesity indicators and mortality among individuals with hyperlipidemia: evidence from the NHANES 2003-2018. Lipids Health Dis. 2025;24(1):20. Published 2025 Jan 24. doi:10.1186/s12944-025-02442-8 https://pubmed.ncbi.nlm.nih.gov/39856680/

Hu H, Wang J, Han X, et al. BMI, Waist Circumference and All-Cause Mortality in a Middle-Aged and Elderly Chinese Population. J Nutr Health Aging. 2018;22(8):975-981. doi:10.1007/s12603-018-1047-z https://pubmed.ncbi.nlm.nih.gov/30272102/

Jayedi A, Soltani S, Zargar MS, Khan TA, Shab-Bidar S. Central fatness and risk of all cause mortality: systematic review and dose-response meta-analysis of 72 prospective cohort studies. BMJ. 2020;370:m3324. Published 2020 Sep 23. doi:10.1136/bmj.m3324 https://pubmed.ncbi.nlm.nih.gov/32967840/

Rådholm K, Chalmers J, Ohkuma T, et al. Use of the waist-to-height ratio to predict cardiovascular risk in patients with diabetes: Results from the ADVANCE-ON study. Diabetes Obes Metab. 2018;20(8):1903-1910. doi:10.1111/dom.13311 https://pubmed.ncbi.nlm.nih.gov/29603537/

Ashwell M, Mayhew L, Richardson J, Rickayzen B. Waist-to-height ratio is more predictive of years of life lost than body mass index. PLoS One. 2014;9(9):e103483. Published 2014 Sep 8. doi:10.1371/journal.pone.0103483 https://pubmed.ncbi.nlm.nih.gov/25198730/

Li S, Fu Z, Zhang W. Association of anthropometric measures with all-cause and cause-specific mortality in US adults: revisiting the obesity paradox. BMC Public Health. 2024;24(1):929. Published 2024 Apr 1. doi:10.1186/s12889-024-18418-9 https://pubmed.ncbi.nlm.nih.gov/38556859/

Wang P, Zhao Y, Wang D, et al. Relationship between waist-to-height ratio and heart failure outcome: A single-centre prospective cohort study. ESC Heart Fail. 2025;12(1):290-303. doi:10.1002/ehf2.15029 https://pubmed.ncbi.nlm.nih.gov/39287135/

Kim KJ, Kim S, Oh R, et al. High Waist-to-Height Ratio Increases the Risk of Cardiovascular Outcomes in Adults with Type 1 Diabetes Mellitus: A Nationwide Cohort Study. Diabetes Metab J. Published online September 4, 2025. doi:10.4093/dmj.2025.0179 https://pubmed.ncbi.nlm.nih.gov/40904015/

Zhang S, Fu X, Du Z, et al. Is waist-to-height ratio the best predictive indicator of cardiovascular disease incidence in hypertensive adults? A cohort study. BMC Cardiovasc Disord. 2022;22(1):214. Published 2022 May 11. doi:10.1186/s12872-022-02646-1 https://pubmed.ncbi.nlm.nih.gov/35545759/

Feng Q, Bešević J, Conroy M, et al. Waist-to-height ratio and body fat percentage as risk factors for ischemic cardiovascular disease: a prospective cohort study from UK Biobank. Am J Clin Nutr. 2024;119(6):1386-1396. doi:10.1016/j.ajcnut.2024.03.018 https://pubmed.ncbi.nlm.nih.gov/38839194/

 

Body Fat Percentage

Jayedi A, Khan TA, Aune D, Emadi A, Shab-Bidar S. Body fat and risk of all-cause mortality: a systematic review and dose-response meta-analysis of prospective cohort studies. Int J Obes (Lond). 2022;46(9):1573-1581. doi:10.1038/s41366-022-01165-5 https://pubmed.ncbi.nlm.nih.gov/35717418/

Yan KL, Liang I, Ravellette K, Gornbein J, Srikanthan P, Horwich TB. Body Composition Risk Assessment of All-Cause Mortality in Patients With Coronary Artery Disease Completing Cardiac Rehabilitation. J Am Heart Assoc. 2025;14(5):e035006. doi:10.1161/JAHA.124.035006 https://pubmed.ncbi.nlm.nih.gov/40008528/

Myint PK, Kwok CS, Luben RN, Wareham NJ, Khaw KT. Body fat percentage, body mass index and waist-to-hip ratio as predictors of mortality and cardiovascular disease. Heart. 2014;100(20):1613-1619. doi:10.1136/heartjnl-2014-305816 https://pubmed.ncbi.nlm.nih.gov/24966306/

Huang BT, Yang L, Yang BS, et al. Relationship of body fat and left ventricular hypertrophy with the risk of all-cause death in patients with coronary artery disease. J Geriatr Cardiol. 2022;19(3):218-226. doi:10.11909/j.issn.1671-5411.2022.03.002 https://pubmed.ncbi.nlm.nih.gov/35464645/

Si J, Kang L, Liu Y. Association between Body Fat Percentage and Cardiometabolic Diseases in General Population. Endocr Metab Immune Disord Drug Targets. 2024;24(12):1395-1400. doi:10.2174/0118715303274348231130052050 https://pubmed.ncbi.nlm.nih.gov/38173063/

Ortega-Loubon C, Fernández-Molina M, Singh G, Correa R. Obesity and its cardiovascular effects. Diabetes Metab Res Rev. 2019;35(4):e3135. doi:10.1002/dmrr.3135 https://pubmed.ncbi.nlm.nih.gov/30715772/

Macek P, Biskup M, Terek-Derszniak M, et al. Optimal Body Fat Percentage Cut-Off Values in Predicting the Obesity-Related Cardiovascular Risk Factors: A Cross-Sectional Cohort Study. Diabetes Metab Syndr Obes. 2020;13:1587-1597. Published 2020 May 12. doi:10.2147/DMSO.S248444 https://pmc.ncbi.nlm.nih.gov/articles/PMC7229792/

Tomlinson DJ, Erskine RM, Morse CI, Onambélé GL. Body Fat Percentage, Body Mass Index, Fat Mass Index and the Ageing Bone: Their Singular and Combined Roles Linked to Physical Activity and Diet. Nutrients. 2019;11(1):195. Published 2019 Jan 18. doi:10.3390/nu11010195 https://pmc.ncbi.nlm.nih.gov/articles/PMC6356293/

Potter AW, Chin GC, Looney DP, Friedl KE. Defining Overweight and Obesity by Percent Body Fat Instead of Body Mass Index. J Clin Endocrinol Metab. 2025;110(4):e1103-e1107. doi:10.1210/clinem/dgae341 https://pubmed.ncbi.nlm.nih.gov/38747476/

Mainous AG 3rd, Yin L, Wu V, et al. Body Mass Index vs Body Fat Percentage as a Predictor of Mortality in Adults Aged 20-49 Years. Ann Fam Med. 2025;23(4):337-343. Published 2025 Jul 28. doi:10.1370/afm.240330 https://pubmed.ncbi.nlm.nih.gov/40555527/

Lee DH, Giovannucci EL. Body composition and mortality in the general population: A review of epidemiologic studies. Exp Biol Med (Maywood). 2018;243(17-18):1275-1285. doi:10.1177/1535370218818161 https://pmc.ncbi.nlm.nih.gov/articles/PMC6348595/

Lee H, Chung HS, Kim YJ, et al. Association between body composition and the risk of mortality in the obese population in the United States. Front Endocrinol (Lausanne). 2023;14:1257902. Published 2023 Nov 24. doi:10.3389/fendo.2023.1257902 https://pmc.ncbi.nlm.nih.gov/articles/PMC10711108/

Lavie CJ, De Schutter A, Patel D, Artham SM, Milani RV. Body composition and coronary heart disease mortality–an obesity or a lean paradox?. Mayo Clin Proc. 2011;86(9):857-864. doi:10.4065/mcp.2011.0092 https://pmc.ncbi.nlm.nih.gov/articles/PMC3257992/

 

BMI (Body Mass Index)

 

Afghahi H, Nasic S, Svensson J, Rydell H, Wärme A, Peters B. The association between body mass index and mortality in diabetic patients with end-stage renal disease is different in hemodialysis and peritoneal dialysis. Ren Fail. 2025;47(1):2510549. doi:10.1080/0886022X.2025.2510549 https://pubmed.ncbi.nlm.nih.gov/40437975/

Mainous AG 3rd, Yin L, Wu V, et al. Body Mass Index vs Body Fat Percentage as a Predictor of Mortality in Adults Aged 20-49 Years. Ann Fam Med. 2025;23(4):337-343. Published 2025 Jul 28. doi:10.1370/afm.240330 https://pubmed.ncbi.nlm.nih.gov/40555527/

Genton L, Bertoni Maluf VA, Herrmann FR, et al. Obesity is associated with lower 30-day mortality in critically ill patients: A retrospective study of over 5400 patients. Clin Nutr ESPEN. 2025;69:37-44. doi:10.1016/j.clnesp.2025.06.046 https://pubmed.ncbi.nlm.nih.gov/40602605/

Yu W, Jiang W, Yuan J, et al. Association between BMI and outcomes in critically ill patients: an analysis of the MIMIC-III database. Sci Rep. 2024;14(1):31127. Published 2024 Dec 28. doi:10.1038/s41598-024-82424-5 https://pubmed.ncbi.nlm.nih.gov/39730662/

Hu J, Tang S, Zhu Q, Liao H. Predictive value of six anthropometric indicators for prevalence and mortality of obstructive sleep apnoea asthma and COPD using NHANES data. Sci Rep. 2025;15(1):16190. Published 2025 May 9. doi:10.1038/s41598-025-99490-y https://pubmed.ncbi.nlm.nih.gov/40346342/

Nowak MM, Niemczyk M, Gołębiewski S, Pączek L. Impact of Body Mass Index on All-Cause Mortality in Adults: A Systematic Review and Meta-Analysis. J Clin Med. 2024;13(8):2305. Published 2024 Apr 16. doi:10.3390/jcm13082305 https://pubmed.ncbi.nlm.nih.gov/38673577/

Visaria A, Setoguchi S. Body mass index and all-cause mortality in a 21st century U.S. population: A National Health Interview Survey analysis. PLoS One. 2023;18(7):e0287218. Published 2023 Jul 5. doi:10.1371/journal.pone.0287218 https://pubmed.ncbi.nlm.nih.gov/37405977/

Sophiea MK, Zaccardi F, Cheng YJ, Vamos EP, Holman N, Gregg EW. Trends in all-cause and cause-specific mortality by BMI levels in England, 2004-2019: a population-based primary care records study. Lancet Reg Health Eur. 2024;44:100986. Published 2024 Jul 2. doi:10.1016/j.lanepe.2024.100986 https://pubmed.ncbi.nlm.nih.gov/39049870/

Lv Y, Zhang Y, Li X, et al. Body mass index, waist circumference, and mortality in subjects older than 80 years: a Mendelian randomization study. Eur Heart J. 2024;45(24):2145-2154. doi:10.1093/eurheartj/ehae206 https://pubmed.ncbi.nlm.nih.gov/38626306/

Zhang T, Li S, Chang J, Qin Y, Li C. Impact of BMI on the survival outcomes of non-small cell lung cancer patients treated with immune checkpoint inhibitors: a meta-analysis. BMC Cancer. 2023;23(1):1023. Published 2023 Oct 23. doi:10.1186/s12885-023-11512-y https://pubmed.ncbi.nlm.nih.gov/37872469/

Bhaskaran K, Dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK. Lancet Diabetes Endocrinol. 2018;6(12):944-953. doi:10.1016/S2213-8587(18)30288-2 https://pubmed.ncbi.nlm.nih.gov/30389323/

Faeh D, Kaufmann M, Haile SR, Bopp M. BMI-mortality association: shape independent of smoking status but different for chronic lung disease and lung cancer. Int J Chron Obstruct Pulmon Dis. 2018;13:1851-1855. Published 2018 Jun 6. doi:10.2147/COPD.S157629 https://pubmed.ncbi.nlm.nih.gov/29922051/

Abramowitz MK, Hall CB, Amodu A, Sharma D, Androga L, Hawkins M. Muscle mass, BMI, and mortality among adults in the United States: A population-based cohort study. PLoS One. 2018;13(4):e0194697. Published 2018 Apr 11. doi:10.1371/journal.pone.0194697 https://pubmed.ncbi.nlm.nih.gov/29641540/

Wang Z, Peng Y, Dong B. Is body mass index associated with lowest mortality increasing over time?. Int J Obes (Lond). 2017;41(8):1171-1175. doi:10.1038/ijo.2017.107 https://pubmed.ncbi.nlm.nih.gov/28465606/

Global BMI Mortality Collaboration, Di Angelantonio E, Bhupathiraju ShN, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388(10046):776-786. doi:10.1016/S0140-6736(16)30175-1 https://pubmed.ncbi.nlm.nih.gov/27423262/

Klatsky AL, Zhang J, Udaltsova N, Li Y, Tran HN. Body Mass Index and Mortality in a Very Large Cohort: Is It Really Healthier to Be Overweight?. Perm J. 2017;21:16-142. doi:10.7812/TPP/16-142 https://pubmed.ncbi.nlm.nih.gov/28678695/

Nuttall FQ. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr Today. 2015;50(3):117-128. doi:10.1097/NT.0000000000000092 https://pubmed.ncbi.nlm.nih.gov/27340299/

Winter JE, MacInnis RJ, Wattanapenpaiboon N, Nowson CA. BMI and all-cause mortality in older adults: a meta-analysis. Am J Clin Nutr. 2014;99(4):875-890. doi:10.3945/ajcn.113.068122 https://pubmed.ncbi.nlm.nih.gov/24452240/

Berrington de Gonzalez A, Hartge P, Cerhan JR, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med. 2010;363(23):2211-2219. doi:10.1056/NEJMoa1000367 https://pubmed.ncbi.nlm.nih.gov/21121834/

 

Fat-Free Mass Index (FFMI)

Chen J, Cheng Z, Yao Y, Wang S. Variation of All-Cause Mortality with Fat-Free Mass Index (FFMI) and Fat Mass Index (FMI) in Individuals with Asthma: Results from the NHANES Database Retrospective Cohort Study. J Epidemiol Glob Health. 2024;14(4):1555-1568. doi:10.1007/s44197-024-00307-4 https://pubmed.ncbi.nlm.nih.gov/39347931/

Zhang X, Li X, Shi H, et al. Association of the fat-free mass index with mortality in patients with cancer: A multicenter observational study. Nutrition. 2022;94:111508. doi:10.1016/j.nut.2021.111508 https://pubmed.ncbi.nlm.nih.gov/34813982/

Sørensen TIA, Frederiksen P, Heitmann BL. Levels and changes in body mass index decomposed into fat and fat-free mass index: relation to long-term all-cause mortality in the general population. Int J Obes (Lond). 2020;44(10):2092-2100. doi:10.1038/s41366-020-0613-8 https://pubmed.ncbi.nlm.nih.gov/32518354/

Kyle UG, Schutz Y, Dupertuis YM, Pichard C. Body composition interpretation. Contributions of the fat-free mass index and the body fat mass index. Nutrition. 2003;19(7-8):597-604. doi:10.1016/s0899-9007(03)00061-3 https://pubmed.ncbi.nlm.nih.gov/12831945/

Hull HR, Thornton J, Wang J, et al. Fat-free mass index: changes and race/ethnic differences in adulthood. Int J Obes (Lond). 2011;35(1):121-127. doi:10.1038/ijo.2010.111 https://pubmed.ncbi.nlm.nih.gov/20531353/

Ying Z, Wen CP, Tu H, et al. Association of fat mass and fat-free mass with all-cause and cause-specific mortality in Asian individuals: A prospective cohort study. Obesity (Silver Spring). 2023;31(12):3043-3055. doi:10.1002/oby.23878 https://pubmed.ncbi.nlm.nih.gov/37731225/

Zhu S, Heo M, Plankey M, Faith MS, Allison DB. Associations of body mass index and anthropometric indicators of fat mass and fat free mass with all-cause mortality among women in the first and second National Health and Nutrition Examination Surveys follow-up studies. Ann Epidemiol. 2003;13(4):286-293. doi:10.1016/s1047-2797(02)00417-9 https://pubmed.ncbi.nlm.nih.gov/12684196/

Bigaard J, Frederiksen K, Tjønneland A, et al. Waist circumference and body composition in relation to all-cause mortality in middle-aged men and women. Int J Obes (Lond). 2005;29(7):778-784. doi:10.1038/sj.ijo.0802976 https://pubmed.ncbi.nlm.nih.gov/15917857/

Schutz Y, Kyle UU, Pichard C. Fat-free mass index and fat mass index percentiles in Caucasians aged 18-98 y. Int J Obes Relat Metab Disord. 2002;26(7):953-960. doi:10.1038/sj.ijo.0802037 https://pubmed.ncbi.nlm.nih.gov/12080449/

Narumi T, Watanabe T, Kadowaki S, et al. Sarcopenia evaluated by fat-free mass index is an important prognostic factor in patients with chronic heart failure. Eur J Intern Med. 2015;26(2):118-122. doi:10.1016/j.ejim.2015.01.008 https://pubmed.ncbi.nlm.nih.gov/25657117/

Bigaard J, Frederiksen K, Tjønneland A, et al. Body fat and fat-free mass and all-cause mortality. Obes Res. 2004;12(7):1042-1049. doi:10.1038/oby.2004.131 https://pubmed.ncbi.nlm.nih.gov/15292467/

Merchant RA, Seetharaman S, Au L, et al. Relationship of Fat Mass Index and Fat Free Mass Index With Body Mass Index and Association With Function, Cognition and Sarcopenia in Pre-Frail Older Adults. Front Endocrinol (Lausanne). 2021;12:765415. Published 2021 Dec 24. doi:10.3389/fendo.2021.765415 https://pubmed.ncbi.nlm.nih.gov/35002957/

Vestbo J, Prescott E, Almdal T, et al. Body mass, fat-free body mass, and prognosis in patients with chronic obstructive pulmonary disease from a random population sample: findings from the Copenhagen City Heart Study. Am J Respir Crit Care Med. 2006;173(1):79-83. doi:10.1164/rccm.200506-969OC https://pubmed.ncbi.nlm.nih.gov/16368793/

Zhang X, Zhang Q, Feng LJ, et al. The Application of Fat-Free Mass Index for Survival Prediction in Cancer Patients With Normal and High Body Mass Index. Front Nutr. 2021;8:714051. Published 2021 Aug 4. doi:10.3389/fnut.2021.714051 https://pubmed.ncbi.nlm.nih.gov/34422885/

Rutten EP, Calverley PM, Casaburi R, et al. Changes in body composition in patients with chronic obstructive pulmonary disease: do they influence patient-related outcomes?. Ann Nutr Metab. 2013;63(3):239-247. doi:10.1159/000353211 https://pubmed.ncbi.nlm.nih.gov/24216978/

Kouri EM, Pope HG Jr, Katz DL, Oliva P. Fat-free mass index in users and nonusers of anabolic-androgenic steroids. Clin J Sport Med. 1995;5(4):223-228. doi:10.1097/00042752-199510000-00003 https://pubmed.ncbi.nlm.nih.gov/7496846/

Obisesan TO, Aliyu MH, Bond V, Adams RG, Akomolafe A, Rotimi CN. Ethnic and age-related fat free mass loss in older Americans: the Third National Health and Nutrition Examination Survey (NHANES III). BMC Public Health. 2005;5:41. Published 2005 Apr 19. doi:10.1186/1471-2458-5-41 https://pubmed.ncbi.nlm.nih.gov/15840167/

Gómez-García M, Torrado J, Pereira M, Bia D, Zócalo Y. Fat-Free Mass Index, Visceral Fat Level, and Muscle Mass Percentage Better Explain Deviations From the Expected Value of Aortic Pressure and Structural and Functional Arterial Properties Than Body Fat Indexes. Front Nutr. 2022;9:856198. Published 2022 Apr 29. doi:10.3389/fnut.2022.856198 https://pubmed.ncbi.nlm.nih.gov/35571946/

Chang CS, Liu IT, Liang FW, et al. Effects of age and gender on body composition indices as predictors of mortality in middle-aged and old people. Sci Rep. 2022;12(1):7912. Published 2022 May 12. doi:10.1038/s41598-022-12048-0 https://pubmed.ncbi.nlm.nih.gov/35551227/

 

Category 6: Psychosocial Health

Social Connection & Relationships

Holt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci. 2015;10(2):227-237. doi:10.1177/1745691614568352 https://pubmed.ncbi.nlm.nih.gov/25910392/

Donovan NJ, Blazer D. Social Isolation and Loneliness in Older Adults: Review and Commentary of a National Academies Report. Am J Geriatr Psychiatry. 2020;28(12):1233-1244. doi:10.1016/j.jagp.2020.08.005 https://pubmed.ncbi.nlm.nih.gov/32919873/

Kanbay M, Tanriover C, Copur S, et al. Social isolation and loneliness: Undervalued risk factors for disease states and mortality. Eur J Clin Invest. 2023;53(10):e14032. doi:10.1111/eci.14032 https://pubmed.ncbi.nlm.nih.gov/37218451/

Holt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci. 2015;10(2):227-237. doi:10.1177/1745691614568352 https://pubmed.ncbi.nlm.nih.gov/25910392/

Lennartsson C, Rehnberg J, Dahlberg L. The association between loneliness, social isolation and all-cause mortality in a nationally representative sample of older women and men. Aging Ment Health. 2022;26(9):1821-1828. doi:10.1080/13607863.2021.1976723 https://pubmed.ncbi.nlm.nih.gov/34550832/

Stokes AC, Xie W, Lundberg DJ, Glei DA, Weinstein MA. Loneliness, social isolation, and all-cause mortality in the United States. SSM Ment Health. 2021;1:100014. doi:10.1016/j.ssmmh.2021.100014 https://pubmed.ncbi.nlm.nih.gov/36936717/

Beller J, Wagner A. Loneliness, social isolation, their synergistic interaction, and mortality. Health Psychol. 2018;37(9):808-813. doi:10.1037/hea0000605 https://pubmed.ncbi.nlm.nih.gov/30138019/

Czaja SJ, Moxley JH, Rogers WA. Social Support, Isolation, Loneliness, and Health Among Older Adults in the PRISM Randomized Controlled Trial. Front Psychol. 2021;12:728658. Published 2021 Oct 5. doi:10.3389/fpsyg.2021.728658 https://pubmed.ncbi.nlm.nih.gov/34675843/

Yu B, Steptoe A, Chen Y. Social isolation, loneliness, and all-cause mortality: A cohort study of 35,254 Chinese older adults. J Am Geriatr Soc. 2022;70(6):1717-1725. doi:10.1111/jgs.17708 https://pubmed.ncbi.nlm.nih.gov/35229887/

Leigh-Hunt N, Bagguley D, Bash K, et al. An overview of systematic reviews on the public health consequences of social isolation and loneliness. Public Health. 2017;152:157-171. doi:10.1016/j.puhe.2017.07.035 https://pubmed.ncbi.nlm.nih.gov/28915435/

Lieberz J, Shamay-Tsoory SG, Saporta N, et al. Loneliness and the Social Brain: How Perceived Social Isolation Impairs Human Interactions. Adv Sci (Weinh). 2021;8(21):e2102076. doi:10.1002/advs.202102076 https://pubmed.ncbi.nlm.nih.gov/34541813/

Crowe CL, Domingue BW, Graf GH, Keyes KM, Kwon D, Belsky DW. Associations of Loneliness and Social Isolation With Health Span and Life Span in the U.S. Health and Retirement Study. J Gerontol A Biol Sci Med Sci. 2021;76(11):1997-2006. doi:10.1093/gerona/glab128 https://pubmed.ncbi.nlm.nih.gov/33963758/

Nakou A, Dragioti E, Bastas NS, et al. Loneliness, social isolation, and living alone: a comprehensive systematic review, meta-analysis, and meta-regression of mortality risks in older adults. Aging Clin Exp Res. 2025;37(1):29. Published 2025 Jan 21. doi:10.1007/s40520-024-02925-1 https://pubmed.ncbi.nlm.nih.gov/39836319/

Holt-Lunstad J, Steptoe A. Social isolation: An underappreciated determinant of physical health. Curr Opin Psychol. 2022;43:232-237. doi:10.1016/j.copsyc.2021.07.012 https://pubmed.ncbi.nlm.nih.gov/34438331/

Long RM, Terracciano A, Sutin AR, et al. Loneliness, Social Isolation, and Living Alone Associations With Mortality Risk in Individuals Living With Cardiovascular Disease: A Systematic Review, Meta-Analysis, and Meta-Regression. Psychosom Med. 2023;85(1):8-17. doi:10.1097/PSY.0000000000001151 https://pubmed.ncbi.nlm.nih.gov/36441849/

Steptoe A, Shankar A, Demakakos P, Wardle J. Social isolation, loneliness, and all-cause mortality in older men and women. Proc Natl Acad Sci U S A. 2013;110(15):5797-5801. doi:10.1073/pnas.1219686110 https://pubmed.ncbi.nlm.nih.gov/23530191/

Holt-Lunstad J. Social connection as a critical factor for mental and physical health: evidence, trends, challenges, and future implications. World Psychiatry. 2024;23(3):312-332. doi:10.1002/wps.21224 https://pubmed.ncbi.nlm.nih.gov/39279411/

Lim MH, Manera KE, Owen KB, Phongsavan P, Smith BJ. The prevalence of chronic and episodic loneliness and social isolation from a longitudinal survey. Sci Rep. 2023;13(1):12453. Published 2023 Aug 1. doi:10.1038/s41598-023-39289-x https://pubmed.ncbi.nlm.nih.gov/37528108/

 

 

Purpose & Meaning

Hill PL, Turiano NA. Purpose in life as a predictor of mortality across adulthood. Psychol Sci. 2014;25(7):1482-1486. doi:10.1177/0956797614531799 https://pubmed.ncbi.nlm.nih.gov/24815612/

Cohen R, Bavishi C, Rozanski A. Purpose in Life and Its Relationship to All-Cause Mortality and Cardiovascular Events: A Meta-Analysis. Psychosom Med. 2016;78(2):122-133. doi:10.1097/PSY.0000000000000274 https://pubmed.ncbi.nlm.nih.gov/26630073/

Martela F, Laitinen E, Hakulinen C. Which predicts longevity better: Satisfaction with life or purpose in life?. Psychol Aging. 2024;39(6):589-598. doi:10.1037/pag0000802 https://pubmed.ncbi.nlm.nih.gov/38358729/

Sone T, Nakaya N, Ohmori K, et al. Sense of life worth living (ikigai) and mortality in Japan: Ohsaki Study. Psychosom Med. 2008;70(6):709-715. doi:10.1097/PSY.0b013e31817e7e64 https://pubmed.ncbi.nlm.nih.gov/18596247/

Krause N. Meaning in life and mortality. J Gerontol B Psychol Sci Soc Sci. 2009;64(4):517-527. doi:10.1093/geronb/gbp047 https://pubmed.ncbi.nlm.nih.gov/19515991/

Luo J, Hooker SA, Kroenke CH, et al. Purpose in life and mortality among breast cancer survivors. Health Psychol. Published online September 22, 2025. doi:10.1037/hea0001563 https://pubmed.ncbi.nlm.nih.gov/40991796/

Karppinen H, Laakkonen ML, Strandberg TE, Tilvis RS, Pitkälä KH. Will-to-live and survival in a 10-year follow-up among older people. Age Ageing. 2012;41(6):789-794. doi:10.1093/ageing/afs082 https://pubmed.ncbi.nlm.nih.gov/22762904/

Tanno K, Sakata K, Ohsawa M, et al. Associations of ikigai as a positive psychological factor with all-cause mortality and cause-specific mortality among middle-aged and elderly Japanese people: findings from the Japan Collaborative Cohort Study. J Psychosom Res. 2009;67(1):67-75. doi:10.1016/j.jpsychores.2008.10.018 https://pubmed.ncbi.nlm.nih.gov/19539820/

Koizumi M, Ito H, Kaneko Y, Motohashi Y. Effect of having a sense of purpose in life on the risk of death from cardiovascular diseases. J Epidemiol. 2008;18(5):191-196. doi:10.2188/jea.je2007388 https://pubmed.ncbi.nlm.nih.gov/18753736/

Miyazaki J, Shirai K, Kimura T, Ikehara S, Tamakoshi A, Iso H. Purpose in life (Ikigai) and employment status in relation to cardiovascular mortality: the Japan Collaborative Cohort Study. BMJ Open. 2022;12(10):e059725. Published 2022 Oct 10. doi:10.1136/bmjopen-2021-059725 https://pubmed.ncbi.nlm.nih.gov/36216422/

Marone S, Bloore K, Sebastiani P, et al. Purpose in Life Among Centenarian Offspring. J Gerontol B Psychol Sci Soc Sci. 2020;75(2):308-315. doi:10.1093/geronb/gby023 https://pubmed.ncbi.nlm.nih.gov/29522128/

Levy BR, Slade MD, Kunkel SR, Kasl SV. Longevity increased by positive self-perceptions of aging. J Pers Soc Psychol. 2002;83(2):261-270. doi:10.1037//0022-3514.83.2.261 https://pubmed.ncbi.nlm.nih.gov/12150226/

Okuzono SS, Shiba K, Kim ES, et al. Ikigai and subsequent health and wellbeing among Japanese older adults: Longitudinal outcome-wide analysis. Lancet Reg Health West Pac. 2022;21:100391. Published 2022 Feb 3. doi:10.1016/j.lanwpc.2022.100391 https://pubmed.ncbi.nlm.nih.gov/35141667/

Sutin AR, Luchetti M, Stephan Y, Terracciano A. Purpose in life and cognitive health: a 28-year prospective study. Int Psychogeriatr. 2024;36(10):956-964. doi:10.1017/S1041610224000383 https://pubmed.ncbi.nlm.nih.gov/38454883/

Musich S, Wang SS, Kraemer S, Hawkins K, Wicker E. Purpose in Life and Positive Health Outcomes Among Older Adults. Popul Health Manag. 2018;21(2):139-147. doi:10.1089/pop.2017.0063 https://pubmed.ncbi.nlm.nih.gov/28677991/

Kim ES, Shiba K, Boehm JK, Kubzansky LD. Sense of purpose in life and five health behaviors in older adults. Prev Med. 2020;139:106172. doi:10.1016/j.ypmed.2020.106172 https://pubmed.ncbi.nlm.nih.gov/32593729/

Zábó V, Lehoczki A, Fekete M, et al. The role of purpose in life in healthy aging: implications for the Semmelweis Study and the Semmelweis-EUniWell Workplace Health Promotion Model Program. Geroscience. 2025;47(3):2817-2833. doi:10.1007/s11357-025-01625-6 https://pubmed.ncbi.nlm.nih.gov/40155585/

Rush CL, Hooker SA, Ross KM, Frers AK, Peters JC, Masters KS. Brief report: Meaning in life is mediated by self-efficacy in the prediction of physical activity. J Health Psychol. 2021;26(5):753-757. doi:10.1177/1359105319828172 https://pubmed.ncbi.nlm.nih.gov/30791727/

Carmel S, Shrira A, Shmotkin D. The will to live and death-related decline in life satisfaction. Psychol Aging. 2013;28(4):1115-1123. doi:10.1037/a0034649 https://pubmed.ncbi.nlm.nih.gov/24364413/

Carmel S, Baron-Epel O, Shemy G. The will-to-live and survival at old age: gender differences. Soc Sci Med. 2007;65(3):518-523. doi:10.1016/j.socscimed.2007.03.034 https://pubmed.ncbi.nlm.nih.gov/17467131/

Araújo L, Teixeira L, Afonso RM, Ribeiro O. To Live or Die: What to Wish at 100 Years and Older. Front Psychol. 2021;12:726621. Published 2021 Sep 10. doi:10.3389/fpsyg.2021.726621 https://pubmed.ncbi.nlm.nih.gov/34566812/

Kim ES, Strecher VJ, Ryff CD. Purpose in life and use of preventive health care services. Proc Natl Acad Sci U S A. 2014;111(46):16331-16336. doi:10.1073/pnas.1414826111 https://pubmed.ncbi.nlm.nih.gov/25368165/

Carmel S. Gender differences and the will-to-live in old age. Przegl Lek. 2012;69(2):49-53. https://pubmed.ncbi.nlm.nih.gov/22768413/

Tsuzishita S, Wakui T. The Effect of High and Low Life Purpose on Ikigai (a Meaning for Life) among Community-Dwelling Older People-A Cross-Sectional Study. Geriatrics (Basel). 2021;6(3):73. Published 2021 Jul 24. doi:10.3390/geriatrics6030073 https://pubmed.ncbi.nlm.nih.gov/34449639/

 

Financial Resilience

Machado S, Kyriopoulos I, Orav EJ, Papanicolas I. Association between Wealth and Mortality in the United States and Europe. N Engl J Med. 2025;392(13):1310-1319. doi:10.1056/NEJMsa2408259 https://pubmed.ncbi.nlm.nih.gov/40174225/

Glei DA, Lee C, Weinstein M. Assessment of Mortality Disparities by Wealth Relative to Other Measures of Socioeconomic Status Among US Adults. JAMA Netw Open. 2022;5(4):e226547. Published 2022 Apr 1. doi:10.1001/jamanetworkopen.2022.6547 https://pubmed.ncbi.nlm.nih.gov/35394513/

Gugushvili A, Wiborg ØN. Wealth and mortality among late-middle-aged individuals in Norway: a nationwide register-based retrospective study. Lancet Reg Health Eur. 2024;48:101113. Published 2024 Nov 8. doi:10.1016/j.lanepe.2024.101113 https://pubmed.ncbi.nlm.nih.gov/39583942/

Demakakos P, Biddulph JP, Bobak M, Marmot MG. Wealth and mortality at older ages: a prospective cohort study. J Epidemiol Community Health. 2016;70(4):346-353. doi:10.1136/jech-2015-206173 https://pubmed.ncbi.nlm.nih.gov/26511887/

Pool LR, Burgard SA, Needham BL, Elliott MR, Langa KM, Mendes de Leon CF. Association of a Negative Wealth Shock With All-Cause Mortality in Middle-aged and Older Adults in the United States. JAMA. 2018;319(13):1341-1350. doi:10.1001/jama.2018.2055 https://pubmed.ncbi.nlm.nih.gov/29614178/

Hajat A, Kaufman JS, Rose KM, Siddiqi A, Thomas JC. Long-term effects of wealth on mortality and self-rated health status. Am J Epidemiol. 2011;173(2):192-200. doi:10.1093/aje/kwq348 https://pubmed.ncbi.nlm.nih.gov/21059808/

Schrage B, Lund LH, Benson L, et al. Lower socioeconomic status predicts higher mortality and morbidity in patients with heart failure. Heart. 2021;107(3):229-236. doi:10.1136/heartjnl-2020-317216 https://pubmed.ncbi.nlm.nih.gov/32769169/

DeVille NV, Iyer HS, Holland I, et al. Neighborhood socioeconomic status and mortality in the nurses’ health study (NHS) and the nurses’ health study II (NHSII). Environ Epidemiol. 2022;7(1):e235. Published 2022 Dec 14. doi:10.1097/EE9.0000000000000235 https://pubmed.ncbi.nlm.nih.gov/36777531/

Lusk JB, Hoffman MN, Clark AG, Bae J, Luedke MW, Hammill BG. Association Between Neighborhood Socioeconomic Status and 30-Day Mortality and Readmission for Patients With Common Neurologic Conditions. Neurology. 2023;100(17):e1776-e1786. doi:10.1212/WNL.0000000000207094 https://pubmed.ncbi.nlm.nih.gov/36792379/

Anderson RT, Sorlie P, Backlund E, Johnson N, Kaplan GA. Mortality effects of community socioeconomic status. Epidemiology. 1997;8(1):42-47. doi:10.1097/00001648-199701000-00007 https://pubmed.ncbi.nlm.nih.gov/9116094/

Yabroff KR, Han X, Song W, et al. Association of Medical Financial Hardship and Mortality Among Cancer Survivors in the United States. J Natl Cancer Inst. 2022;114(6):863-870. doi:10.1093/jnci/djac044 https://pubmed.ncbi.nlm.nih.gov/35442439/

Zheng Z, Hu X, Banegas MP, et al. Health-related social needs, medical financial hardship, and mortality risk among cancer survivors. Cancer. 2024;130(17):2938-2947. doi:10.1002/cncr.35342 https://pubmed.ncbi.nlm.nih.gov/38695561/

Samuel LJ, Abshire Saylor M, Choe MY, et al. Financial strain measures and associations with adult health: A systematic literature review. Soc Sci Med. 2025;364:117531. doi:10.1016/j.socscimed.2024.117531 https://pubmed.ncbi.nlm.nih.gov/39591796/

Osibogun O, Ogunmoroti O, Turkson-Ocran RA, et al. Financial strain is associated with poorer cardiovascular health: The multi-ethnic study of atherosclerosis. Am J Prev Cardiol. 2024;17:100640. Published 2024 Feb 13. doi:10.1016/j.ajpc.2024.100640 https://pubmed.ncbi.nlm.nih.gov/38419947/

Liu L, Wen W, Shrubsole MJ, et al. Impacts of Poverty and Lifestyles on Mortality: A Cohort Study in Predominantly Low-Income Americans. Am J Prev Med. 2024;67(1):15-23. doi:10.1016/j.amepre.2024.02.015 https://pubmed.ncbi.nlm.nih.gov/38417593/

Chetty R, Stepner M, Abraham S, et al. The Association Between Income and Life Expectancy in the United States, 2001-2014. JAMA. 2016;315(16):1750-1766. doi:10.1001/jama.2016.4226 https://pubmed.ncbi.nlm.nih.gov/27063997/

Bilal U, Cainzos-Achirica M, Cleries M, et al. Socioeconomic status, life expectancy and mortality in a universal healthcare setting: An individual-level analysis of >6 million Catalan residents. Prev Med. 2019;123:91-94. doi:10.1016/j.ypmed.2019.03.005 https://pubmed.ncbi.nlm.nih.gov/30853378/

Schwandt H, Currie J, von Wachter T, Kowarski J, Chapman D, Woolf SH. Changes in the Relationship Between Income and Life Expectancy Before and During the COVID-19 Pandemic, California, 2015-2021. JAMA. 2022;328(4):360-366. doi:10.1001/jama.2022.10952 https://pubmed.ncbi.nlm.nih.gov/35797033/

Kim JY, Park S, Park M, Kim NH, Kim SG. Income-Related Disparities in Mortality Among Young Adults With Type 2 Diabetes. JAMA Netw Open. 2024;7(11):e2443918. Published 2024 Nov 4. doi:10.1001/jamanetworkopen.2024.43918 https://pubmed.ncbi.nlm.nih.gov/39531234/

Bassuk SS, Berkman LF, Amick BC 3rd. Socioeconomic status and mortality among the elderly: findings from four US communities. Am J Epidemiol. 2002;155(6):520-533. doi:10.1093/aje/155.6.520 https://pubmed.ncbi.nlm.nih.gov/11882526/

Zhang YB, Chen C, Pan XF, et al. Associations of healthy lifestyle and socioeconomic status with mortality and incident cardiovascular disease: two prospective cohort studies. BMJ. 2021;373:n604. Published 2021 Apr 14. doi:10.1136/bmj.n604 https://pubmed.ncbi.nlm.nih.gov/33853828/

Jørgensen RE, Hovde Lyngstad T. Does local income and wealth inequality affect mortality? A register-based fixed effects study of 58 million person-years. Scand J Public Health. 2024;52(1):58-63. doi:10.1177/14034948221126264 https://pubmed.ncbi.nlm.nih.gov/36271601/

Shiba K, Kubzansky LD, Williams DR, VanderWeele TJ, Kim ES. Associations Between Purpose in Life and Mortality by SES. Am J Prev Med. 2021;61(2):e53-e61. doi:10.1016/j.amepre.2021.02.011 https://pubmed.ncbi.nlm.nih.gov/34020851/

Sart G, Bayar Y, Danilina M. Impact of education and income inequalities on life expectancy: insights from the new EU members. Front Public Health. 2024;12:1397585. Published 2024 Aug 21. doi:10.3389/fpubh.2024.1397585 https://pubmed.ncbi.nlm.nih.gov/39234080/