Association Between Muscle Quality Index and Depression Symptoms in Women with Breastfeeding History: A NHANES-Based Cross-Sectional Study

母乳喂养史女性肌肉质量指数与抑郁症状的关联:一项基于NHANES的横断面研究

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Abstract

BACKGROUND: Postpartum depression (PPD) remains a significant challenge in women's health. While breastfeeding has been widely recognized as a protective factor against PPD, women with a breastfeeding history are still at risk for depression symptoms. This study aims to explore the relationship between muscle quality index (MQI) and depression symptoms in women with a breastfeeding history. METHODS: This cross-sectional study used data from 481 women with a breastfeeding history from the 2011-2012 National Health and Nutrition Examination Survey (NHANES). MQI was computed as the ratio of handgrip strength to appendicular skeletal muscle mass. Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9). Logistic regression models were applied to assess the relationship between MQI and depression, controlling for potential confounders, including sociodemographic factors, health status, and lifestyle behaviors. Restricted cubic spline (RCS) analysis was used to examine potential nonlinear associations. RESULTS: After adjusting for all covariates, lower MQI value was significantly associated with increased depression risk in women with breastfeeding history [P: 0.002, odds ratio (OR) = 0.25, 95% confidential interval (CI): 0.10-0.65]. Women in the highest MQI quartile had reduced odds of depression compared to those in the lowest quartile (P: 0.003, OR = 0.12, 95% CI: 0.03-0.44). A nonlinear relationship between MQI and depression was observed, with a critical threshold identified at MQI 3.32. Subgroup analyses revealed a consistent inverse association between MQI and depression across strata of factors such as BMI, smoking status, and income levels. CONCLUSION: MQI is associated with depression symptoms in women with a breastfeeding history. This association underscores the importance of integrating physical health markers into mental health risk assessments for more effective early intervention and care.

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