These 5 Hidden Health Risks Are Aging Your Brain Faster

Human Brain Neural Network Cerebral Cortex
A groundbreaking 16-year study has uncovered how lifestyle and metabolic factors may speed up brain aging. Using advanced brain imaging and machine learning, researchers identified five high-risk factors that significantly impact brain structure.

Hypertension and other health risks accelerate brain aging, as shown in a 16-year study using MRI data and predictive modeling.

Chinese scientists have conducted a population-based cohort study to examine the long-term impact of unhealthy lifestyles, metabolic abnormalities, and other risk factors on brain aging. The findings showed that these factors significantly accelerate brain aging, and the researchers proposed strategies to support brain health. Their study was published in Research.

Background

As people age, their brains experience a range of structural changes linked to aging, including cerebral atrophy, white matter microstructure damage, and increased white matter hyperintensities. These changes are strongly associated with the onset and progression of cognitive decline and neurodegenerative diseases. Brain age, which is estimated using magnetic resonance imaging (MRI) features, has become a key biomarker for assessing brain aging.

Overview of Study Design Integrating Clinical Data, Imaging, and Machine Learning
The schematic overview of the research design. (A) The data used in the study include brain imaging, demographic information, anthropometric measurements, and laboratory assessments. (B) Associations between clinical factors and neuroimaging metrics. (C) Brain age difference calculation based on machine learning. Credit: Jing Sun et al.

Most existing brain age prediction models use a single neuroimaging method. However, multi-modal brain imaging offers a more detailed view of how individuals’ brains age and improves prediction Correlation Patterns Between Health Risk Factors and Brain Imaging Features Across T1, DTI, and WMH Modalities

Correlation analysis of multidimensional health risk factors and multimodal brain imaging features. Credit: Jing Sun et al.

Subsequently, the participants were stratified into five groups based on the number of high-risk factors they exhibited: 0 (healthy control), 1, 2, 3, and 4-5 high-risk factor groups. The brain age prediction model was trained on the healthy group and subsequently applied to the five risk exposure groups to predict their brain ages and compare the differences in brain aging. The results indicate that individuals with 4-5 high-risk factors exhibit a significantly greater brain age gap (BAG) compared to the healthy group and other risk exposure groups. This suggests that a range of health factors across unhealthy lifestyles, metabolic abnormalities, and other risk factors may collectively contribute to the accelerated aging process of the brain.

A further in-depth analysis revealed that the BAG predicted by T1-weighted imaging was significantly higher in the hypertensive subjects compared to those normotensive subjects. This indicates that hypertension exerts a pivotal influence on the structural degeneration of brain tissue and is a key factor in accelerating brain aging.

Comparison of Predicted Brain Age Gap Across Varying Levels of Health Risk Exposure
The predicted brain age gap for individuals in different risk exposure groups. Individuals with 4-5 high-risk factors exhibit a significantly greater brain age gap (BAG) compared to the healthy group and other risk exposure groups. Credit: Jing Sun et al.

Suggestions for Future Research

This study, based on a long-term longitudinal follow-up of a large population, reveals that the five risk factors—hypertension, hyperglycemia, hypercreatinemia, smoking, and low educational attainment—accelerate brain aging, with hypertension causing the most significant brain damage.

Future research will incorporate longitudinal brain imaging data to assess the dynamic progression pattern of brain aging. In addition, future research is warranted to fully excavate high-dimensional information from multi-modal images, thereby enhancing the predictive and generalization capabilities of the models.

Hypertension Is Linked to Elevated Brain Age Gap in T1 Based Imaging
Estimated BAGs in normotensive and hypertensive groups. The estimated T1-based BAG was significantly higher in the hypertensive subjects compared to those normotensive subjects. Credit: Jing Sun et al.

In summary, this study elucidates that a range of health risk factors contribute to the acceleration of brain aging, and effective management of blood pressure, blood glucose, and creatinine levels, along with reduced smoking and improved educational attainment, are essential for promoting brain health.

Reference: “Discovery of High-Risk Clinical Factors That Accelerate Brain Aging in Adults: A Population-Based Machine Learning Study” by Jing Sun, Luyao Wang, Yiwen Gao, Ying Hui, Shuohua Chen, Shouling Wu, Zhenchang Wang, Jiehui Jiang and Han Lv, 21 October 2024, Research.
DOI: 10.34133/research.0500

Funding: National Natural Science Foundation of China, Science and Technology Innovation 2030 – Major Projects, Shanghai Industrial Collaborative Innovation Project, Beijing Municipal Natural Science Foundation

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