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3069 Characterizing the Neural Signature of Metabolic Syndrome

Published online by Cambridge University Press:  26 March 2019

Eithan Kotkowski
Affiliation:
University of Texas Health Science Center San Antonio: Research Imaging Institute
Larry R. Price
Affiliation:
University of Texas Health Science Center San Antonio: Research Imaging Institute
Crystal G. Franklin
Affiliation:
University of Texas Health Science Center San Antonio: Research Imaging Institute
Maximino Salazar
Affiliation:
University of Texas Health Science Center San Antonio: Research Imaging Institute
Ralph A. DeFronzo
Affiliation:
UTHSA: Diabetes Division, Texas Diabetes Institute
David Glahn
Affiliation:
Yale School of Medicine
John Blangero
Affiliation:
UTRGV School of Medicine
Peter T. Fox
Affiliation:
University of Texas Health Science Center San Antonio: Research Imaging Institute
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Abstract

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OBJECTIVES/SPECIFIC AIMS: Our objective is to understand the influence of the features comprising metabolic syndrome (central obesity, raised fasting plasma glucose, triglycerides, blood pressure, and decreased HDL cholesterol) on brain structure in men and women. With the understanding that MetS is a strong predictor of gray matter volume loss in specific brain regions, in this study we sought to quantify the influence of each of the metabolic syndrome biometric variables on the structures involved in the neural signature of metabolic syndrome. METHODS/STUDY POPULATION: We conducted multiple linear regression analyses on a cross-sectional sample of 800 individuals from the Genetics of Brian Structure (GOBS) image archive (352 men and 448 women). GOBS is an offshoot of the San Antonio Heart Study involving an extended pedigree of Mexican Americans from the greater San Antonio area. Its goal is to localize, identify, and characterize genes/quantitative trait loci associated with variations in brain structure and function (Winkler, 2010). The archive has continuously added participants from approximately 40 families since 2006. Neuroanatomic (T1-weighted MRI scans obtained on a Siemens 3T scanner and processed using FSL), neurocognitive, and biometric phenotypes have been obtained for each subject (including blood lipids). Linear regressions were run using SPSS and incorporated biometric and gray matter volume values obtained from 800 GOBS participants. RESULTS/ANTICIPATED RESULTS: Linear regressions incorporating metabolic syndrome variables as dependent variables and gray matter volume from regions involved in the neural signature of metabolic syndrome as predictors show significant predictive patterns that are largely similar between men and women, with some differences. Another linear regression conducted with gray matter volume from the neural signature of metabolic syndrome as the dependent variable and metabolic syndrome variables as predictors show that waist circumference and triglycerides are the greatest predictors of gray matter volume loss in men, and fasting plasma glucose and waist circumference are the greatest predictors of gray matter volume loss in women. DISCUSSION/SIGNIFICANCE OF IMPACT: Significant sex differences in the relationships between metabolic syndrome variables and gray matter volume changes between brain regions comprising the neural signature of metabolic syndrome were identified. waist circumference, fasting plasma glucose, and triglycerides are the most reliable predictors of gray matter volume loss. The variance in gray matter volume of the neural signature of metabolic syndrome in men is more significantly explained by waist circumference and triglycerides (when accounting for age) and in women is more significantly explained by waist circumference and fasting plasma glucose (when accounting for age). A model of metabolic syndrome that emphasizes a risk of neurodegeneration should focus on waist circumference for both men and women and weigh the remaining variables accordingly by sex (triglycerides in men and fasting plasma glucose in women).

Type
Basic/Translational Science/Team Science
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-ncnd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Association for Clinical and Translational Science 2019
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