Brain Age Index Methodology
What this tool measures
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 derived from twelve self-reported lifestyle and clinical inputs, calibrated to the same modifiable factors that the world's leading dementia research consortium has identified as causally linked to cognitive aging.
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.
Why it matters
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.
The validated framework we implement
Our tool implements the modifiable risk factor framework articulated in the 2024 Lancet Commission report (Livingston et al., 2024). Of the fourteen factors the Commission identified, our tool captures the twelve that are reliably self-reportable. We exclude two that require clinical or environmental measurement: untreated hearing loss diagnosed by audiometry (we use a validated subjective substitute), and ambient air pollution exposure as measured by environmental sensors (no honest self-report substitute exists).
Each factor carries a population attributable fraction derived from the Commission's meta-analytic synthesis. Less education in early life carries a 5% PAF. Hearing loss in midlife carries 7%. Hypertension carries 2%. The full PAF profile across all twelve factors we implement sums to approximately 38% — meaningfully close to the Commission's 45% global figure when the two clinically-measured factors we exclude are added back.
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.
How we measure it
The tool produces a single output: an estimated brain age expressed in years, with a confidence band. The algorithm proceeds in four steps.
Step one: chronological baseline. The user's chronological age becomes the starting point. In the absence of any modifiable risk factors and with all protective factors at their maximum, the estimated brain age would equal the chronological age.
Step two: factor-by-factor adjustment. Each of the twelve risk factors carries a population-attributable weight derived from the 2024 Commission's meta-analytic estimates. Each factor adjusts the estimate upward (if the user reports the risk) or holds it neutral (if they do not). The weights are not the user's personal risk; they are population-level associations. They provide a calibrated way to combine many imperfect signals into a single estimate.
Step three: protective factor offsets. Several of the twelve factors also have protective ranges. High educational attainment, regular physical activity above WHO thresholds, low alcohol consumption, and active social engagement each pull the estimate downward from the chronological baseline. The downward adjustments are bounded: no combination of protective factors reduces the estimated brain age below 80% of chronological age, because the underlying research does not support claims of dramatically reversed biological aging from lifestyle alone.
Step four: confidence band construction. The output is presented with an explicit uncertainty range of plus or minus 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 the typical margin of error in cohort-based brain age estimators when validated against clinical outcomes. We display the band rather than a point estimate to discourage over-interpretation of small differences.
Key variables
Each variable below maps directly to a 2024 Lancet Commission factor. The "weight" column reflects the Commission's published population attributable fraction for that factor at the global level. Where source instruments are well-known validated measures, we have shortened them while preserving construct validity.
| Variable | What it captures | Source instrument | PAF |
|---|---|---|---|
| Education | Early-life cognitive reserve | Years of formal schooling (OECD ISCED) | 5% |
| Hearing | Mid-life hearing difficulty | Adapted from HHIE-S | 7% |
| Hypertension | Mid-life vascular risk | Self-reported diagnosis & treatment | 2% |
| Smoking | Current/former tobacco exposure | WHO STEPS substance use module | 2% |
| Obesity | Mid-life BMI ≥ 30 | Self-reported height & weight → BMI | 1% |
| Depression history | Lifetime treated depression | Self-reported clinical diagnosis | 3% |
| Physical inactivity | Below WHO activity threshold | WHO 2020 guidelines (150 min/wk moderate) | 2% |
| Diabetes | Type 2 diabetes diagnosis | Self-reported clinical diagnosis | 2% |
| Social isolation | Loneliness & meaningful contact | UCLA Loneliness Scale (3-item) | 5% |
| Excess alcohol | ≥ 21 units/week | 2024 Commission threshold | 1% |
| TBI history | Concussion(s) with LOC | Self-reported lifetime history | 3% |
| Vision loss | Uncorrected visual impairment (2024 add) | Self-reported uncorrected vision | 2% |
Two factors from the 2024 framework are not captured by this tool: untreated hearing loss requiring audiometry (we use a self-report substitute, included above), and ambient air pollution exposure requiring environmental sensors. The 2024 Commission also adds elevated LDL cholesterol as a fourteenth factor (7% PAF); we plan to incorporate it in a future tool version once we have validated a self-report screening proxy.
The reference data we benchmark against
The PAF weights are drawn 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. Independent replication studies in low- and middle-income countries have found higher total PAFs — 56% in Latin America, 51% in Ethiopia, 41% in India, 40% in China — reflecting higher baseline prevalence of risk factors rather than different underlying biology.
We have applied the global weights 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.
Limitations
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 composite score also collapses twelve distinct risk factors 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. The factor-by-factor breakdown displayed alongside the composite is the more actionable output, because it identifies which specific behaviors most warrant attention. Treat the composite as a conversation starter and the per-factor view 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.
Independent analytical review
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 cited peer-reviewed methodology, 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.
Version log
- v1.0 (May 2, 2026) — Initial public release. Implements the twelve self-reportable factors from the 2024 Lancet Commission framework. The fourteenth factor (LDL cholesterol) is planned for v1.1 once a validated screening proxy is in place.
Selected references
- 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.
Key terms
The constructs measured by this tool, defined in the LifeByLogic glossary: