| Title: | Metabolism-related brain morphology accelerates aging and predicts neurodegenerative diseases and stroke: a UK Biobank study |
| Journal: | Translational Psychiatry |
| Published: | 29 Jun 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37385998/ |
| DOI: | https://doi.org/10.1038/s41398-023-02515-1 |
| URL: | https://www.nature.com/articles/s41398-023-02515-1.pdf |
Publication 10919
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Abstract
AbstractsMetabolic syndrome (MetS) is characterized by a constellation of metabolic risk factors, including obesity, hypertriglyceridemia, low high-density lipoprotein (HDL) levels, hypertension, and hyperglycemia, and is associated with stroke and neurodegenerative diseases. This study capitalized on brain structural images and clinical data from the UK Biobank and explored the associations of brain morphology with MetS and brain aging due to MetS. Cortical surface area, thickness, and subcortical volumes were assessed using FreeSurfer. Linear regression was used to examine associations of brain morphology with five MetS components and the MetS severity in a metabolic aging group (N = 23,676, age 62.8 ± 7.5 years). Partial least squares (PLS) were employed to predict brain age using MetS-associated brain morphology. The five MetS components and MetS severity were associated with increased cortical surface area and decreased thickness, particularly in the frontal, temporal, and sensorimotor cortex, and reduced volumes in the basal ganglia. Obesity best explained the variation of brain morphology. Moreover, participants with the most severe MetS had brain age 1-year older than those without MetS. Brain age in patients with stroke (N = 1042), dementia (N = 83), Parkinson's (N = 107), and multiple sclerosis (N = 235) was greater than that in the metabolic aging group. The obesity-related brain morphology had the leading discriminative power. Therefore, the MetS-related brain morphological model can be used for risk assessment of stroke and neurodegenerative diseases. Our findings suggested that prioritizing adjusting obesity among the five metabolic components may be more helpful for improving brain health in aging populations.</p>
3 Authors
- Chenye Shen
- Chaoqiang Liu
- Anqi Qiu
1 Application
| Application ID | Title |
|---|---|
| 57831 | Predicting incident dementia Based on Neuroimaging and Genetics |
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