Brain Age Index Methodology

Brain age is a way of asking a simple question: at this moment in your life, do your lifestyle, vascular health, sensory functions, and social environment look more like the average person your chronological age, or more like someone older or younger? The answer is not a diagnosis. It is a structured way to look at the modifiable forces that shape how brains age, decade by decade.

The Brain Age Index does not scan your brain. The most rigorous brain age estimators in research use deep learning models trained on tens of thousands of structural MRI scans, predicting chronological age from grey matter morphology and reporting the gap between predicted and actual age. That methodology requires a scanner, a radiologist, and a validated machine-learning pipeline. Our tool computes something different and complementary: a risk-factor-weighted estimate of the modifiable brain-age gap, derived from twenty-five self-reported inputs organized into six brain-health domains, built around the potentially modifiable risk factors for dementia identified by the 2024 Lancet Commission on dementia prevention, intervention, and care.

The six domains are Cardiometabolic (blood pressure, LDL cholesterol, blood sugar, central adiposity), Lifestyle & Movement (physical activity, diet, smoking, alcohol), Sleep & Recovery (duration, quality, breathing-related disruption), Cognitive Reserve (education, occupational complexity, mental stimulation, multilingualism), Mind & Mood (mood, chronic stress, sense of purpose), and Sensory & Protective (hearing, vision, social connection, head injury, air pollution). Each domain is scored 0–100. The page shows an estimated brain age and a brain-health archetype; the radar of your six domain scores, your brain-age gap, your overall brain-health score, your recoverable years and your biggest levers, ranked, each with its name and worth, are in The Brain Blueprint report, and the Depleted Brain profile shows all of them on the page.

The result is an estimate, not a measurement. We surface that distinction in every output. The tool is designed for personal reflection on cognitive health behaviors and is never a substitute for evaluation by a licensed healthcare professional.

More than 57 million people live with dementia worldwide, a number projected to reach 152 million by 2050 absent meaningful intervention. The 2024 Lancet Commission on dementia prevention, intervention, and care — chaired by Gill Livingston of University College London with 27 international experts — concluded that approximately 45% of global dementia cases could be prevented or delayed by addressing fourteen modifiable risk factors across the life course. This is one of the most consequential findings in modern public health: roughly half of dementia is, in principle, avoidable.

The Commission's framework rests on twenty-five years of cohort and longitudinal evidence drawn from studies including the Whitehall II Study, the Cardiovascular Health Study, the Three-City Study, the Rotterdam Study, the Framingham Heart Study, and the Honolulu–Asia Aging Study — collectively following hundreds of thousands of adults across decades. The 2017, 2020, and 2024 iterations of the Commission report represent successive refinements as evidence has matured. The 2024 update added two new factors that 2020 lacked sufficient evidence for: vision loss and elevated low-density lipoprotein (LDL) cholesterol.

The Commission's headline number — 45% — is a population attributable fraction (PAF), meaning the proportional reduction in dementia cases that would occur if a specific risk factor were eliminated from a population, assuming causality. PAFs are population-level, not individual-level: your personal risk depends on how the factors apply to you. But the population number frames the public health stakes, and the per-factor breakdown frames the personal action surface.

Our tool implements the modifiable risk factor framework articulated in the 2024 Lancet Commission report (Livingston et al., 2024), and converts it into years using the brain-age-gap (BAG) neuroimaging literature. Version 2.0 captures all of the Commission's self-reportable factors — including the two added in 2024, elevated LDL cholesterol and untreated vision loss — with waist size standing in for obesity (waist-to-hip ratio outperformed BMI as a BAG predictor in causal-modeling work), and extends them with further items: sleep duration, sleep quality, breathing-related sleep disruption, diet quality, occupational complexity, mental stimulation, multilingualism, chronic stress, and sense of purpose. Two background items, biological sex and family history of dementia, shift the brain-age estimate but belong to no domain.

The Commission's population attributable fractions (PAFs) give each factor's estimated share of dementia cases across a population. Less education carries roughly a 5% PAF; hearing loss roughly 7%; high LDL roughly 7%; untreated vision loss roughly 2%; with hypertension, diabetes, obesity, smoking, depression, physical inactivity, alcohol, social isolation, traumatic brain injury, and air pollution making up the remainder of the ~45% total. They give context, not this tool's weights: the individual year-estimate comes from year values this tool sets, anchored to effect sizes in the brain-age-gap literature and scaled conservatively (see below), so it does not follow the PAFs one for one.

The Commission's methodology is itself transparent: each factor's PAF is derived from a meta-analysis of the cohort evidence and a separate estimate of the factor's prevalence in the population. The Commission does not claim that eliminating these factors would prevent 45% of dementia in any individual; it claims that doing so at the population level would reduce the population burden by approximately that amount. We carry that distinction forward into every result: this tool estimates the modifiable portion of brain aging, not a clinical brain age.

The tool produces a full profile: an estimated brain age and brain-age gap, six domain scores, an overall brain-health score, a ranked list of levers, and a brain-health archetype. The page shows your brain age and your archetype; the rest, with your recoverable years and the name and worth of each lever, is in The Brain Blueprint report, and the Depleted Brain profile shows all of it on the page. The algorithm proceeds as follows.

Step one: each answer maps to two values. Every response carries a signed year-contribution (some factors protect and subtract years, others accelerate and add them) and a 0–1 sub-score for its domain (1 = healthiest). The year-contributions are anchored to effect sizes in the brain-age-gap literature.

Step two: age-conditional salience. Each factor's year-contribution is scaled by a salience multiplier that reflects the Lancet Commission's life-course framing. Midlife-salient factors (blood pressure, LDL, waist size) carry their peak weight from 40 to 65; late-life-salient factors (hearing, vision, social connection) carry less weight before 50, more from 50, and their peak from 65. This is a refinement over a flat additive model: the same factor matters differently at different ages.

Step three: conservative aggregation. Because lifestyle and cardiometabolic factors explain no more than about 21% of the variance in the measured brain-age gap (Rotterdam Study), the summed year-contributions are scaled by a global conservatism factor and clamped to a defensible range (roughly −6 to +10 years). This keeps the headline estimate honest rather than overclaiming large swings from self-report alone.

Step four: domains, archetype, and recoverable years. Each domain score is the mean of its sub-scores, mapped to 0–100; the overall score is the mean of the six domains. The recoverable years figure sums the brain-aging years currently being added by factors you can change — the upper bound of what optimizing them could plausibly recover. Finally, an archetype is assigned: the Depleted Brain, with care-aware routing, when mood and sleep are both strained and recoverable years reach 3 or more; otherwise the Sensory-Gated Brain when the sensory and protective domain sits well below the overall score; otherwise one of four from the interaction of cognitive reserve and recoverable years.

Confidence band. Read the brain age and gap as approximate, give or take about three years. This range is not derived from a closed-form variance estimate — it would be misleading to imply that level of statistical precision from self-report data — but it reflects both the typical margin of error in MRI-based brain-age estimators (the 2025 Communications Medicine model the tool page cites was off by about 2.7 years on average in UK Biobank and about 3 years in two other cohorts) and the limited share of variance that self-reported factors explain. The tool shows whole-year figures, so treat differences of a year or two as within this range.

Variables with a PAF map to a 2024 Lancet Commission factor; those marked — sit outside the Commission's core fourteen (see the note below the table). The PAF column gives the Commission's global population attributable fraction for context; it is not the tool's weight. Each of the tool's items is a single self-report question, not a full published instrument.

PAF values are the 2024 Lancet Commission global population-attributable fractions where published. Factors marked — are evidence-based brain-aging contributors drawn from the brain-age-gap literature that sit outside the Commission's core fourteen and therefore have no Commission PAF. The PAF column is context, not the tool's weight: every factor's year value, with or without a PAF, is set by this tool and scaled conservatively.

end-table-note

Version 2.0 captures all of the Commission's self-reportable factors, including elevated LDL cholesterol and untreated vision loss (both added in 2024). For factors that clinically require instrumentation — audiometric hearing loss, sensor-measured air pollution — we use a simple self-report question in their place rather than excluding them. We additionally include items beyond the Commission's core fourteen: sleep duration, sleep quality, breathing-related sleep disruption, diet quality, occupational complexity, mental stimulation, multilingualism, chronic stress, and sense of purpose, plus biological sex and family history as background items.

The PAF figures quoted here come from the 2024 Commission's meta-analytic synthesis, which itself aggregates effect sizes from cohort studies conducted predominantly in high-income settings: the United States, the United Kingdom, the European Union, Australia, and parts of East Asia. This is a meaningful limitation for users outside those populations. A 2019 analysis of low- and middle-income countries (Mukadam et al., The Lancet Global Health), using the Commission's earlier list of nine factors, found higher totals than the 35% those nine explained worldwide — about 56% in Latin America, 41% in India and 40% in China — reflecting higher baseline prevalence of risk factors rather than different underlying biology.

The tool applies the same year values to everyone, without ethnic-specific adjustment. The within-population evidence for ethnic-specific weights is too thin to justify recalibration, though future versions may incorporate cohort-specific calibrations as more diverse longitudinal data becomes available, particularly from the UK Biobank, the Health and Retirement Study, and the SAGE cohort.

Several limitations are important enough to surface explicitly. The tool relies on self-report for every input. Self-report is subject to recall bias, social desirability bias, and the known tendency of healthier and more health-conscious users to disproportionately complete tools like this. The result is a snapshot of a single point in time, not a trajectory. Brain aging trajectories are by definition longitudinal; a single result tells you very little compared to two results six months apart.

The tool does not measure genetic risk factors such as APOE4 status, which has a substantial effect on dementia risk independent of modifiable factors. A 2025 replication of the Lancet model in the Wisconsin Longitudinal Study (5,526 participants, 70 years of data) found that risk factor associations diverged meaningfully by APOE4 carrier status — mid-life diabetes was the primary risk factor among carriers, while mid-life hearing loss was the strongest signal among non-carriers. This kind of gene-environment interaction is invisible to our tool.

The overall score also collapses twenty-five distinct inputs into a single number. This is a compression: a person with mild deficits across many factors may score similarly to a person with severe deficits in two or three. This is precisely why version 2.0 works out a six-domain radar and ranks your modifiable levers alongside the headline figure; the page shows the headline figure and your archetype, and the radar, the name and worth of each lever and your recoverable years are in The Brain Blueprint report (the Depleted Brain profile shows all of them on the page). The domain profile and the per-factor view are the more actionable outputs. Treat the single number as a conversation starter and the domain breakdown as the working surface.

Finally: a single result is not a diagnosis, a forecast, or a verdict. It is a structured way to think about cognitive health behaviors at a particular moment, in light of what the published research currently establishes as modifiable. If you have concerns about your cognitive health, please consult a qualified healthcare professional.

The analytical modeling and results-analysis logic of this tool is independently reviewed by a domain expert in statistical modeling and machine learning. The reviewer validates that tool outputs faithfully implement the method this page documents, tests edge cases at the boundaries of the input space, and confirms that results match the underlying mathematics. See our About page for reviewer credentials.

The reviewer's role is methodological, not editorial. Review covers the analytical model and how it converts inputs to outputs, not the framing of the prose surrounding it. The framing, including this methodology page, is the responsibility of the author.

• v2.0, corrected October 18, 2026 — The sleep-duration answers now read 7–9 hours and More than 9 hours (they read 7–8 and more than 9, leaving 8–9 hours without an answer); each answer keeps its score, so no estimate changes.
• v2.0 (May 2026) — Major rework. Reorganized into six brain-health domains scored 0–100 with a radar profile; added a brain-age-gap year-conversion calibrated to the neuroimaging literature; introduced age-conditional salience weighting, a global conservatism factor, recoverable-years estimation, six brain-health archetypes with care-aware routing. Expanded from twelve to twenty-five inputs; new inputs include LDL cholesterol, untreated vision loss, central adiposity, breathing-related sleep disruption, occupational complexity, multilingualism, and sense of purpose.
• v1.0 (May 2, 2026) — Initial public release. Implemented twelve self-reportable factors from the 2024 Lancet Commission framework as a single additive estimate.

• Livingston, G., et al. (2024). Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. The Lancet, 404(10452), 572–628.
• Livingston, G., et al. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet, 396(10248), 413–446.
• Stephan, B. C. M., et al. (2024). Population attributable fractions of modifiable risk factors for dementia: a systematic review and meta-analysis. The Lancet Healthy Longevity, 5(6), e406–e421.
• Williams, V. J., et al. (2025). Life Course Modifiable Risk Factors of Dementia: Replicating the 2024 Lancet Commission Model in a Single Longitudinal Cohort. Wisconsin Longitudinal Study analysis.
• Cole, J. H., & Franke, K. (2017). Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends in Neurosciences, 40(12), 681–690.
• World Health Organization (2020). WHO guidelines on physical activity and sedentary behaviour. Geneva: WHO.

The constructs measured by this tool, defined in the LifeByLogic glossary:

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