Related Experiment Video
Updated: May 23, 2025

Author Spotlight: Assessing the Cardiovascular Profile of Patients with Metabolic Syndrome
Published on: September 27, 2024
Cardiometabolic risk factors and brain age: a meta-analysis to quantify brain structural differences related to
Maya Selitser1, Lorielle M F Dietze1, Sean R McWhinney1
1From the Department of Psychiatry, Dalhousie University, Halifax, N.S. (Selitser, Dietze, McWhinney, Hajek) and the Charles University, Third Faculty of Medicine, Prague, Czech Republic (Hajek).
Insights
Cardiometabolic risk factors like diabetes, hypertension, and obesity are linked to brain aging. Diabetes showed the most significant impact on brain structure, suggesting it as a key target for preventing cognitive decline.
Area of Science:
- Neuroscience
- Cardiology
- Endocrinology
Background:
- Cardiometabolic risk factors (diabetes, hypertension, obesity) are linked to adverse health outcomes.
- Subtle brain changes, such as alterations in regional brain volumes and cortical thickness, associated with these risk factors are not well understood.
- Machine learning models can estimate brain age from MRI data, potentially revealing subtle brain changes related to cardiometabolic risk.
Purpose of the Study:
- To investigate the relationship between cardiometabolic risk factors and machine learning-predicted brain age.
- To quantify the effect size of diabetes, hypertension, and obesity on brain age gap.
Main Methods:
- Systematic search of PubMed and Scopus databases.
- Meta-analysis of the brain age gap (predicted age - chronological age) as an index of brain structural integrity.
- Calculation of Cohen's d statistic for mean differences in brain age gap across risk factor groups.
Main Results:
- 14 studies met inclusion criteria from 185 identified.
- Diabetes had the largest effect size (d = 0.275) on brain age gap, followed by hypertension (d = 0.113) and obesity (d = 0.112).
- Effects remained significant in sensitivity analyses controlling for other cardiometabolic risk factors.
Conclusions:
- Each cardiometabolic risk factor uniquely contributes to brain structure as indicated by brain age.
- Diabetes has a disproportionately larger effect on brain structure compared to hypertension and obesity.
- Diabetes is a primary target for interventions aimed at preventing brain structural changes and subsequent cognitive decline or dementia.
Background:
Cardiometabolic risk factors - including diabetes, hypertension, and obesity - have long been linked with adverse health outcomes such as strokes, but more subtle brain changes in regional brain volumes and cortical thickness associated with these risk factors are less understood. Computer models can now be used to estimate brain age based on structural magnetic resonance imaging data, and subtle brain changes related to cardiometabolic risk factors may manifest as an older-appearing brain in prediction models; thus, we sought to investigate the relationship between cardiometabolic risk factors and machine learning-predicted brain age.
Methods:
We performed a systematic search of PubMed and Scopus. We used the brain age gap, which represents the difference between one's predicted and chronological age, as an index of brain structural integrity. We calculated the Cohen d statistic for mean differences in the brain age gap of people with and without diabetes, hypertension, or obesity and performed random effects meta-analyses.
Results:
We identified 185 studies, of which 14 met inclusion criteria. Among the 3 cardiometabolic risk factors, diabetes had the highest effect size (12 study samples; d = 0.275, 95% confidence interval [CI] 0.198-0.352; n = 47 436), followed by hypertension (10 study samples; d = 0.113, 95% CI 0.063-0.162; n = 45 102) and obesity (5 study samples; d = 0.112, 95% CI 0.037-0.187; n = 15 678). These effects remained significant in sensitivity analyses that included only studies that controlled for confounding effects of the other cardiometabolic risk factors.
Limitations:
Our study tested effect sizes of only categorically defined cardiometabolic risk factors and is limited by inconsistencies in diabetes classification, a smaller pooled sample in the obesity analysis, and limited age range reporting.
Conclusion:
Our findings show that each of the cardiometabolic risk factors uniquely contributes to brain structure, as captured by brain age. The effect size for diabetes was more than 2 times greater than the independent effects of hypertension and obesity. We therefore highlight diabetes as a primary target for the prevention of brain structural changes that may lead to cognitive decline and dementia.
More Related Videos
05:58Mouse Electroacupuncture Fixation Device Fabrication for Electroacupuncture Pretreatment in Diabetic Cardiomyopathy Mouse Model
Published on: April 18, 2025
06:26Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
Published on: November 27, 2019
Related Concept Videos
Biological Influences on Intelligence
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Psychoneuroimmunology: Cardiovascular Disease
A key area of focus in PNI is the relationship between stress and coronary...
Obesity