Foret, JT;
Dekhtyar, M;
Cole, JH;
Gourley, DD;
Caillaud, M;
Tanaka, H;
Haley, AP;
(2021)
Network Modeling Sex Differences in Brain Integrity and Metabolic Health.
Frontiers in Aging Neuroscience
, 13
, Article 691691. 10.3389/fnagi.2021.691691.
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Abstract
Hypothesis-driven studies have demonstrated that sex moderates many of the relationships between brain health and cardiometabolic disease, which impacts risk for later-life cognitive decline. In the present study, we sought to further our understanding of the associations between multiple markers of brain integrity and cardiovascular risk in a midlife sample of 266 individuals by using network analysis, a technique specifically designed to examine complex associations among multiple systems at once. Separate network models were constructed for male and female participants to investigate sex differences in the biomarkers of interest, selected based on evidence linking them with risk for late-life cognitive decline: all components of metabolic syndrome (obesity, hypertension, dyslipidemia, and hyperglycemia); neuroimaging-derived brain-predicted age minus chronological age; ratio of white matter hyperintensities to whole brain volume; seed-based resting state functional connectivity in the Default Mode Network, and ratios of N-acetyl aspartate, glutamate and myo-inositol to creatine, measured through proton magnetic resonance spectroscopy. Males had a sparse network (87.2% edges = 0) relative to females (69.2% edges = 0), indicating fewer relationships between measures of cardiometabolic risk and brain integrity. The edges in the female network provide meaningful information about potential mechanisms between brain integrity and cardiometabolic health. Additionally, Apolipoprotein ϵ4 (ApoE ϵ4) status and waist circumference emerged as central nodes in the female model. Our study demonstrates that network analysis is a promising technique for examining relationships between risk factors for cognitive decline in a midlife population and that investigating sex differences may help optimize risk prediction and tailor individualized treatments in the future.
| Type: | Article |
|---|---|
| Title: | Network Modeling Sex Differences in Brain Integrity and Metabolic Health |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fnagi.2021.691691 |
| Publisher version: | https://doi.org/10.3389/fnagi.2021.691691 |
| Language: | English |
| Additional information: | © 2021 Foret, Dekhtyar, Cole, Gourley, Caillaud, Tanaka and Haley. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | Sex differences, metabolic syndrome, network model, white matter hyper intensities, brain-predicted age, functional connectivity, APOE, magnetic resonance spectroscopy |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10132204 |
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