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Major Articles

Sex-specific correlates of metabolic syndrome risk in college-aged young adults

, PhDORCID Icon, , , MS, , PhD, MPH, , PhD & , PhD
Pages 2527-2534 | Received 05 Aug 2020, Accepted 22 Dec 2020, Published online: 12 Feb 2021
 

Abstract

Objective: To identify the sex-specific prevalence of metabolic syndrome (MetS) risk factors and their physiological, psychosocial, and behavioral correlates in a college-aged population. Participants and methods: Cross-sectional assessment of MetS risk factors and potential correlates occurred in 379 first-year students (aged 18.34 ± 0.49 years, 67.3% female). Multivariable linear regression assessed the relationships between potential correlates and continuous MetS risk scores, derived from principal component analysis. Results: MetS risk factors were present in 58.4% of females and 68.5% of males, with 2.4% and 3.2% having defined MetS. In females, percent body fat (β = 0.46, p < 0.001), stress (β = 0.12, p = 0.031), % kcal from sugar (β = 0.18, p = 0.001), and moderate-to-vigorous physical activity (β=-0.12, p = 0.036) were associated with risk score. Whereas, correlates in males included percent body fat (β = 0.54, p < 0.001), C-reactive protein (β = 0.15, p = 0.045), and AUDIT alcohol consumption score (β = 0.15, p = 0.033). Conclusion: The sex-specific prevalence of MetS risk factors and correlates suggest that primary prevention strategies on college campuses should also follow a sex-specific approach.

Acknowledgments

This study was supported by U.S. Department of Agriculture under Grant no. 2008-55215-18825 to EME. Data collection was performed by BMD, MVF, and EME. Review of literature and statistical analyses were performed by SH and ANS. Manuscript preparation and revision were performed by SH, ANS, BMD, ERW, MVF, and EME. Preparation of the final document for submission was performed by SH and ANS.

Conflicts of interest disclosure

The authors declare no conflict of interest. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

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