Bibliographic Details
| Title: |
Sex-specific associations between body composition and depression among U.S. adults: a cross-sectional study. |
| Authors: |
Li, Yijing1 (AUTHOR), Li, Juan1 (AUTHOR), Sun, Tianning1 (AUTHOR), He, Zhigang1 (AUTHOR), Liu, Cheng1 (AUTHOR), Li, Zhixiao1 (AUTHOR), Wu, Yanqiong1 (AUTHOR) wuyanqiong-iap@taihehospital.com, Xiang, Hongbing1,2 (AUTHOR) xhbtj2004@163.com |
| Source: |
Lipids in Health & Disease. 1/18/2025, Vol. 24 Issue 1, p1-17. 17p. |
| Subject Terms: |
*DUAL-energy X-ray absorptiometry, *BODY mass index, *LEAN body mass, *ADIPOSE tissues, *DEPRESSION in men, *MUSCLE mass, *BODY composition |
| Abstract: |
Background: Depression presents sexual dimorphism, and one important factor that increases the frequency of depression and contributes to sex-specific variations in its presentation is obesity. The conventional use of Body Mass Index (BMI) as an indicator of obesity is inherently limited due to its inability to distinguish between fat and lean mass, which limits its predictive utility for depression risk. Implementation of dual-energy X-ray absorptiometry (DXA) investigated sex-specific associations between body composition (fat mass, appendicular lean mass) and depression. Methods: Data from the NHANES cycles between 2011 and 2018 were analyzed, including 3,637 participants (1,788 males and 1,849 females). Four body composition profiles were identified in the subjects: low adiposity-low muscle (LA-LM), low adiposity-high muscle (LA-HM), high adiposity-low muscle (HA-LM) and high adiposity-high muscle (HA-HM). After accounting for confounding variables, the associations between fat mass index (FMI), appendicular skeletal muscle mass index (ASMI), body fat percentage (BFP), body composition phenotypes, and depression risk were assessed using restricted cubic spline (RCS) curves and multivariable logistic regression models. We further conducted interaction analyses for ASMI and FMI in females. Results: RCS curves indicated a U-shaped relationship between ASMI and the risk of depression in males. Logistic regression analysis revealed that in males, the second (OR = 0.43, 95%CI:0.22–0.85) and third (OR = 0.35, 95%CI:0.14–0.86) quartile levels of ASMI were significantly negatively associated with depression risk. In females, increases in BFP (OR = 1.06, 95%CI:1.03–1.09) and FMI (OR = 1.08, 95% CI:1.04–1.12) were significantly associated with an increased risk of depression. Additionally, compared to females with a low-fat high-muscle phenotype, those with LA-LM (OR = 3.97, 95%CI:2.16–7.30), HA-LM (OR = 5.40, 95%CI:2.34–12.46), and HA-HM (OR = 6.36, 95%CI:3.26–12.37) phenotypes were more likely to develop depression. Interestingly, further interaction analysis of ASMI and FMI in females revealed an interplay between height-adjusted fat mass and muscle mass (OR = 4.67, 95%CI: 2.04–10.71). Conclusion: The findings demonstrate how important it is to consider body composition when estimating the risk of depression, particularly in females. There is a substantial correlation between the LA-LM, HA-LM, and HA-HM phenotypes in females with a higher prevalence of depression. It is advised to use a preventative approach that involves gaining muscle mass and losing fat. [ABSTRACT FROM AUTHOR] |
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