Brain age gap as a predictive biomarker that links aging, lifestyle, and neuropsychiatric health – Nature

 

Report on Brain Age Gap as a Predictive Biomarker for Sustainable Health Outcomes

Introduction

Context: Global Health and Sustainable Development Goal 3

The global increase in age-associated neurodegenerative and psychiatric disorders presents a significant challenge to achieving Sustainable Development Goal 3 (SDG 3), which aims to ensure healthy lives and promote well-being for all at all ages. Addressing the mechanisms of cerebral aging is critical for meeting Target 3.4, which calls for a reduction in premature mortality from non-communicable diseases and the promotion of mental health. This report details an investigation into the Brain Age Gap (BAG)—the divergence between an individual’s predicted brain age and chronological age—as a biomarker to support these global health objectives.

Objective

This study evaluates the clinical utility of BAG as a predictive biomarker for neuropsychiatric disorders, cognitive decline, and mortality. A primary objective is to assess its potential for risk stratification and to determine the modifiability of brain aging through lifestyle interventions, thereby providing actionable insights for public health strategies aligned with SDG 3.

Methodology

Study Design and Population

A multi-cohort approach was utilized to ensure robust and generalizable findings, contributing to evidence-based health policies as advocated by the SDGs.

• Primary Cohort: Data from 38,967 participants (ages 45–82) from the UK Biobank were used to develop and validate the brain age model.
• Validation Cohorts: Generalizability was tested on two independent datasets: 1,402 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 1,182 from the Parkinson’s Progression Markers Initiative (PPMI).

Brain Age Estimation Model

An innovative 3D Vision Transformer (3D-ViT) deep learning model was developed for whole-brain age estimation from T1-weighted MRI scans. This advanced analytical approach enhances the precision of the biomarker, a crucial step for its potential integration into clinical practice to support SDG 3.

Analytical Approach

The study employed a comprehensive statistical framework to link BAG with health outcomes relevant to the SDGs.

1. Survival Analysis: Cox proportional hazards models were used to assess the association between BAG and the incidence of neuropsychiatric disorders and all-cause mortality.
2. Cognitive Association: Linear regression and restricted cubic splines were used to model the relationship between BAG and performance across eight cognitive domains.
3. Lifestyle Interaction Analysis: The moderating effect of seven modifiable lifestyle factors on BAG was examined to identify preventive strategies that promote healthy aging (SDG 3).

Key Findings and Their Relevance to Sustainable Development

Model Performance and Accuracy

The 3D-ViT model demonstrated high accuracy in estimating brain age, a foundational requirement for a reliable health biomarker.

• UK Biobank: Mean Absolute Error (MAE) of 2.68 years.
• ADNI/PPMI Cohorts: MAE between 2.99 and 3.20 years.

This level of precision supports the use of BAG for early risk identification, a key component of preventive healthcare strategies under SDG 3.

BAG as a Predictor of Non-Communicable Diseases and Mortality

The findings establish a strong link between an elevated BAG and adverse health outcomes, directly informing SDG Target 3.4 (reduce premature mortality from NCDs).

• Neurodegenerative Risk: Each one-year increase in BAG was associated with a 16.5% increased risk of Alzheimer’s disease and a 4.0% increased risk of mild cognitive impairment.
• Mortality Risk: Each one-year increase in BAG raised the risk of all-cause mortality by 12%.
• High-Risk Stratification: Individuals in the highest BAG quartile (Q4) faced a 2.8-fold increased risk of Alzheimer’s disease, a 6.4-fold risk of multiple sclerosis, and a 2.4-fold higher mortality risk compared to the lowest quartile.

Association with Cognitive Decline

A higher BAG was significantly associated with poorer cognitive performance, particularly in domains crucial for maintaining independence and quality of life in older age. This aligns with the broader SDG 3 goal of promoting well-being across the lifespan.

• Cognitive decline was most evident in the highest BAG quartile (Q4).
• The most affected domains were reaction time and processing speed.
• Non-linear modeling identified critical thresholds where BAG begins to have a more pronounced negative effect on cognition.

Impact of Lifestyle Interventions on Brain Health

The study provides critical evidence that lifestyle modifications can mitigate accelerated brain aging, highlighting a pathway for prevention that empowers individuals and informs public health policy in line with SDG 3.

• Lifestyle interventions significantly slowed BAG progression, especially in individuals with advanced neurodegeneration.
• Key protective factors identified were:
  1. Smoking cessation
  2. Moderate alcohol consumption
  3. Regular physical activity
• These findings underscore the importance of promoting healthy lifestyles to achieve sustainable health outcomes and reduce the burden of non-communicable diseases.

Implications for Sustainable Development Goals

SDG 3: Good Health and Well-being

The research directly supports multiple targets within SDG 3. By providing a tool for early risk detection (BAG), the study contributes to the prevention of non-communicable diseases (Target 3.4). The demonstrated impact of lifestyle changes offers a clear, evidence-based strategy for public health initiatives aimed at promoting mental health and well-being throughout the life course.

SDG 10: Reduced Inequalities

By identifying modifiable risk factors and enabling early intervention, the use of BAG can help reduce health disparities. Promoting healthy aging ensures that older persons can maintain cognitive function and participate fully in society, contributing to Target 10.2, which aims to empower and promote the social inclusion of all, irrespective of age or disability.

SDG 11: Sustainable Cities and Communities

The finding that physical activity is a significant factor in slowing brain aging reinforces the importance of Target 11.7, which calls for universal access to safe and inclusive green and public spaces. Designing communities that encourage physical activity is a direct investment in the long-term brain health of residents.

Conclusion

The Brain Age Gap (BAG) is a robust and clinically relevant biomarker that effectively predicts accelerated brain aging, risk of neuropsychiatric disorders, cognitive decline, and mortality. Its utility is enhanced by its sensitivity to lifestyle modifications, positioning it as a valuable tool for public health strategies aimed at achieving Sustainable Development Goal 3. By enabling early risk stratification and highlighting the efficacy of preventive measures, the integration of BAG into clinical and public health frameworks can help promote healthy aging, reduce the burden of non-communicable diseases, and support the overall well-being of global populations.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

SDG 3: Good Health and Well-being

• The article is fundamentally centered on health, specifically addressing the challenges of an aging global population and the associated increase in age-related diseases. It investigates the “Brain Age Gap (BAG)” as a biomarker for cerebral health, linking it directly to neuropsychiatric disorders (e.g., Alzheimer’s disease, depression), cognitive decline, and all-cause mortality. The study’s objective to find methods for “risk stratification and prevention” and to guide “public health strategies to preserve brain health” aligns perfectly with the core mission of SDG 3.

SDG 9: Industry, Innovation, and Infrastructure (Secondary Connection)

• The research methodology relies heavily on technological innovation to address a health challenge. The development and application of a novel “3D Vision Transformer (3D-ViT) deep learning framework” for brain age estimation represents an advancement in scientific research and technology. This connects to SDG 9’s goal of fostering innovation and enhancing scientific research.

2. What specific targets under those SDGs can be identified based on the article’s content?

SDG 3: Good Health and Well-being

1. Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.

   The article directly addresses this target by:

   • Investigating Mortality from NCDs: The study performs a survival analysis linking elevated BAG to a higher risk of “all-cause mortality.” It quantifies this risk, stating that “each one-year increase in BAG… raises all-cause mortality by 12%.” The neuropsychiatric conditions studied, such as Alzheimer’s disease, stroke, and multiple sclerosis, are all non-communicable diseases.
   • Focusing on Prevention: A significant portion of the research is dedicated to prevention. It concludes that BAG is “modifiable through lifestyle changes” and that interventions like “smoking cessation, moderate alcohol consumption, and physical activity, significantly slow BAG progression.” This emphasizes prevention as a key strategy to mitigate the risks associated with accelerated brain aging.
   • Promoting Mental Health and Well-being: The article explicitly examines the link between BAG and psychiatric disorders, including major depressive disorder (MDD) and anxiety. It finds that BAG can predict the risk of these conditions, thereby contributing to the understanding and potential early intervention for mental health issues.

2. Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol.

   The study supports this target by providing evidence for the benefits of avoiding harmful substance use. It identifies “never smoking” and “moderate alcohol consumption” as key components of a healthy lifestyle that have a neuroprotective effect. The results show that these behaviors “significantly slow BAG progression,” with moderate alcohol consumption being the “strongest protective factor” in the highest-risk group. This reinforces public health messaging on the prevention of harmful substance use to maintain brain health.

3. Target 3.d: Strengthen the capacity of all countries… for early warning, risk reduction and management of national and global health risks.

   The entire premise of using the Brain Age Gap (BAG) as a biomarker aligns with this target. The article proposes BAG as a tool for “early detection of at-risk individuals,” “risk stratification,” and “personalized risk assessment.” By identifying individuals with accelerated brain aging before the clinical onset of disease, the BAG model serves as an early warning system, enabling “targeted preventive strategies” and better management of the health risks associated with neurodegenerative and psychiatric disorders on a population level.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Indicators for Target 3.4 (Reduce mortality from NCDs and promote mental health)

• Mortality Rate from NCDs: The article uses “all-cause mortality” as a primary outcome. The Hazard Ratio (HR) for mortality (adjusted HR of 1.12 for every 1-year increase in BAG) serves as a direct quantitative indicator of risk associated with poor brain health, a proxy for NCD burden.
• Risk of Specific Neuropsychiatric Disorders: The study provides specific risk metrics that can be used as indicators. For example, it states that a one-year increase in BAG raises “Alzheimer’s risk by 16.5%” and that the highest-risk group has a “6.4-fold risk of multiple sclerosis.” These quantifiable risk levels for specific NCDs can be tracked.
• Measures of Cognitive Decline: The article uses scores from various cognitive tests, such as the “Reaction Time Test (RTT)” and “Symbol Digit Substitution Test (SDST),” to measure cognitive performance. The finding that reaction time was “significantly slower in Q4” (the highest BAG quartile) implies that these test scores can serve as indicators of brain health and cognitive well-being in a population.

Indicators for Target 3.5 (Strengthen prevention of substance abuse)

• Impact of Smoking and Alcohol Use on Brain Health: The article provides a quantifiable indicator of the benefits of avoiding harmful substance use. It states that in the highest-risk group (Q4), “never smoking” was associated with a “-0.11 years” reduction in BAG and “moderate alcohol consumption” with a “-0.20 years” reduction. This change in BAG can be used as an indicator of the effectiveness of prevention strategies.

Indicators for Target 3.d (Strengthen early warning and risk management)

• The Brain Age Gap (BAG) as a Predictive Biomarker: The BAG itself is presented as a novel indicator. Its value (the difference between predicted brain age and chronological age) serves as a metric for early warning. The model’s accuracy, measured by the “mean error of 2.68 years,” is an indicator of the reliability of this early warning tool.

4. Table of SDGs, Targets, and Indicators

SDGs	Targets	Indicators (Mentioned or Implied in the Article)
SDG 3: Good Health and Well-being	Target 3.4: Reduce premature mortality from NCDs and promote mental health and well-being.	All-cause mortality risk (Hazard Ratio of 1.12 per 1-year increase in BAG). Incidence risk for specific NCDs (e.g., 16.5% increased Alzheimer’s risk per BAG year). Scores on cognitive function tests (e.g., Reaction Time, Symbol Digit Substitution).
	Target 3.5: Strengthen the prevention of substance abuse and harmful use of alcohol.	Reduction in BAG associated with healthy behaviors (e.g., -0.11 years for never smoking, -0.20 years for moderate alcohol consumption in the high-risk group).
	Target 3.d: Strengthen capacity for early warning and risk reduction of health risks.	The Brain Age Gap (BAG) value as a predictive biomarker for risk stratification. Accuracy of the BAG estimation model (Mean Absolute Error of 2.68 years).

Source: nature.com