{"id":862126,"date":"2025-07-12T23:12:20","date_gmt":"2025-07-13T04:12:20","guid":{"rendered":"https:\/\/newsycanuse.com\/index.php\/2025\/07\/12\/these-5-hidden-health-risks-are-aging-your-brain-faster\/"},"modified":"2025-07-12T23:12:20","modified_gmt":"2025-07-13T04:12:20","slug":"these-5-hidden-health-risks-are-aging-your-brain-faster","status":"publish","type":"post","link":"https:\/\/newsycanuse.com\/index.php\/2025\/07\/12\/these-5-hidden-health-risks-are-aging-your-brain-faster\/","title":{"rendered":"These 5 Hidden Health Risks Are Aging Your Brain Faster"},"content":{"rendered":"
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\"Human<\/a>
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.<\/figcaption><\/figure>\n

Hypertension and other health risks accelerate brain aging, as shown in a 16-year study using MRI data and predictive modeling.<\/strong><\/p>\n

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.<\/p>\n

Background<\/h4>\n

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.<\/p>\n

\"Overview
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.<\/figcaption><\/figure>\n

Most existing brain age prediction models use a single neuroimaging method. However, multi-modal brain imaging offers a more detailed view of how individuals\u2019 brains age and improves prediction accuracy<\/div>\n

A measure of how close a result or measurement is to the true value.<\/div>\n

” data-gt-translate-attributes=”[{“attribute”:”data-cmtooltip”, “format”:”html”}]” tabindex=”0″ role=”link”>accuracy<\/span>. There is growing evidence that health-related risk factors such as hypertension, high blood sugar, and smoking may influence brain structure. Still, the exact relationship between these risk factors and brain aging is not well understood. Identifying the specific factors that speed up brain aging is therefore critical for supporting long-term brain health.<\/p>\n

Research progress<\/h4>\n

This study, utilizing a 16-year clinical follow-up cohort of the Kailuan population, elucidated that long-term adverse lifestyle, metabolic abnormalities, and other risk factors significantly accelerate brain aging.<\/p>\n

First, the authors constructed a matrix dataset integrating multi-dimensional health risk factors and multi-modal brain imaging features. By applying correlation analysis, correcting for multiple comparisons, they investigated the associations between multi-dimensional risk factors and multi-modal brain imaging features. They further identified the five risk factors most strongly associated with brain imaging features:<\/p>\n

    \n
  1. Hypertension<\/li>\n
  2. Hyperglycemia<\/li>\n
  3. Hypercreatinemia<\/li>\n
  4. Smoking<\/li>\n
  5. Relatively low educational level<\/li>\n<\/ol>\n

    This finding preliminarily provides key insights into the risk factors that accelerate brain aging.<\/p>\n

    \"Correlation
    Correlation analysis of multidimensional health risk factors and multimodal brain imaging features. Credit: Jing Sun et al.<\/figcaption><\/figure>\n

    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.<\/p>\n

    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.<\/p>\n

    \"Comparison
    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.<\/figcaption><\/figure>\n

    Suggestions for Future Research<\/h4>\n

    This study, based on a long-term longitudinal follow-up of a large population, reveals that the five risk factors\u2014hypertension, hyperglycemia, hypercreatinemia, smoking, and low educational attainment\u2014accelerate brain aging, with hypertension causing the most significant brain damage.<\/p>\n

    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.<\/p>\n

    \"Hypertension
    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.<\/figcaption><\/figure>\n

    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.<\/p>\n

    Reference: \u201cDiscovery of High-Risk Clinical Factors That Accelerate Brain Aging in Adults: A Population-Based Machine Learning Study\u201d by Jing Sun, Luyao Wang, Yiwen Gao, Ying Hui, Shuohua Chen, Shouling Wu, Zhenchang Wang, Jiehui Jiang and Han Lv, 21 October 2024, Research<\/i>.
    \nDOI: 10.34133\/research.0500<\/a><\/p>\n

    Funding: National Natural Science Foundation of China, Science and Technology Innovation 2030 \u2013 Major Projects, Shanghai Industrial Collaborative Innovation Project, Beijing Municipal Natural Science Foundation<\/p>\n

    Never miss a breakthrough: Join the SciTechDaily newsletter.<\/a><\/b><\/p>\n<\/p><\/div>\n

    \t\t\t Research
    Read More<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    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<\/p>\n","protected":false},"author":1,"featured_media":862127,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22895,43],"tags":[12095,5160],"class_list":{"0":"post-862126","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-hidden","8":"category-these","9":"tag-hidden","10":"tag-these"},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/862126","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/comments?post=862126"}],"version-history":[{"count":0,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/862126\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media\/862127"}],"wp:attachment":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media?parent=862126"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/categories?post=862126"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/tags?post=862126"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}