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Sex-specific associations between body composition and depression among U.S. adults: a cross-sectional study

Lipids in Health and Disease volume 24, Article number: 15 (2025) Cite this article

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.

Introduction

Depression is a widespread mental disorder globally, characterized by a high lifetime prevalence and a significant risk of chronic recurrence [1]. The risk of suicide mortality among depressed patients is more than 20 times higher than that of the general population [2]. Furthermore, depression is closely linked to adverse outcomes such as marital breakdown and decreased work efficiency, which impose a considerable burden on the socioeconomic landscape [2, 3]. In recent years, the relationship between obesity and depression has emerged as a focal point in the health domain [3, 4].

It is noteworthy that the prevalence rate of depression among females is twice that of males, with females more frequently reporting typical depressive symptoms such as low mood and changes in appetite [5]. Gender roles are socially constructed roles that align with societal expectations and stereotypes, and differing from biological sex [6]. Traditional gender roles often place disproportionate demands on women, including greater responsibilities for household chores, childbearing, and childcare [7,8,9]. Women continue to face discrimination in health and education, as well as in participation in the labor market and political arenas [10], which exacerbates stress and may contribute to gender differences in depression [11, 12].

Current research indicates that the association between depression and obesity appears to be more pronounced in females, as evidenced by higher obesity rates among females with depression compared to their male counterparts [13, 14]. The phenotypic relationship between obesity and depression partially stems from overlapping genetic foundations, with identified loci including neuronal growth regulator 1 (NEGR1) and kinase suppressors of ras2 (KSR2) [15, 16], playing a role in brain areas that regulate mood and appetite [17, 18]. Obese patients typically exhibit hyperactivation of the hypothalamic–pituitary–adrenal (HPA) axis [19], which may lead to neuronal damage in brain regions associated with depression [20] and exacerbate obesity by promoting appetite and inhibiting lipolysis [21]. Additionally, the chronic low-grade inflammatory state in obesity, characterized by immune cell infiltration of white adipose tissue (WAT) and increased expression of pro-inflammatory cytokines [22], may activate central inflammation pathways, influencing the pathophysiology of depression [23]. Energy homeostasis is another critical factor linking depression and obesity, where insulin resistance can exacerbate depressive symptoms through metabolic changes [24], and neuronal damage in specific brain regions [25,26,27]. Leptin, an adipokine synthesized and secreted primarily by WAT, binds to leptin receptors in the hypothalamus to mediate appetite suppression and energy expenditure [28]. However, impaired leptin signaling in obese individuals may aggravate depression [29, 30]. The comorbidity of obesity and depression can lead to the prolongation and adverse outcomes of depression [31], as well as reduced adherence to obesity-related treatments [32].

Existing studies primarily use body mass index (BMI) as a measure of obesity, which fails to differentiate between adipose and muscle tissue and does not accurately reflect the distribution of body fat within the body [33]. For adults with similar BMI, the proportions of muscle and adipose tissue in body composition may vary significantly [34, 35]. Moreover, sex differences in body fat percentage also indicate the limitations of BMI as a predictive indicator of depression risk [36]. While waist circumference (WC) is a useful indicator of abdominal obesity, it cannot differentiate between subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) [37]. Additionally, the high cost and technical complexity of CT and MRI limit their clinical application [38]. Dual-energy X-ray absorptiometry (DXA) is the clinical method of choice for measuring muscle mass in the diagnostic criteria for sarcopenia. The lean body mass detected by DXA has been shown to be a reliable method for estimating muscle mass using predictive equations, offering advantages such as low radiation exposure, low cost, and simplicity, as well as being quick to perform [39]. The variation in body composition phenotypes, particularly changes in fat mass and muscle mass levels, may be associated with the differential risk of depression between males and females. This study aims to explore the association between the risk of depression and the body composition phenotypes represented by fat mass and appendicular lean mass in male and female participants. The combination of these body composition phenotypes may provide further insights into the sex-specific risks of depression.

Methods

Participants

The study included the data from the National Health and Nutrition Examination Survey (NHANES) cycles spanning the years 2011 to 2018. NHANES is a nationally representative health and nutrition survey in the United States that collects various health and nutrition data through nationwide questionnaires, physical examinations, and laboratory tests (https://wwwn.cdc.gov/nchs/nhanes/default.aspx). Among the initially recruited participants aged 20 years and older (n = 39,156), individuals lacking relevant DXA and the Patient Health Questionnaire-9 (PHQ-9) data were first excluded (n = 22,972). Afterwards, individuals (n = 12,547) without covariate data were also eliminated from the study. In the end, 3,637 individuals were incorporated among the details of analysis, and these were parted into two cohorts based on sex: 1,788 males and 1,849 females (Fig. 1). Appropriate weights are selected according to NHANES guidelines. Every participant provided their consent, and the de-identified and anonymised data released on the NHANES website was authorised by the National Centre for Health Statistics (NCHS) Ethics Review Board. For the subsequent secondary analyses, neither informed permission nor further ethical approval was required.

Fig. 1
figure 1

Flowchart of the sample selection from NHANES 2011–2018

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Body composition measurement and defining body phenotypes

The DXA calibration process is detailed in the Body Composition Procedures Manual available on the NHANES website. Trained and qualified radiologic technicians performed the DXA tests utilising an Apex 3.2 software-equipped Hologic Discovery A densitometer (Hologic, Inc., Bedford, Massachusetts). Appendicular lean mass (kg), body fat percentage (BFP), and total body fat mass (kg) were the body composition variables that were measured. Participants were not permitted to take part in the study if they were pregnant, weighed more than 204.12 kg, had used radioactive contrast material within the preceding seven days, or were taller than 1.96 m. The WC of the individuals was measured by qualified experts.

The BMI, appendicular skeletal muscle mass index (ASMI) and fat mass index (FMI) were calculated by dividing each measurement (specifically, weight, appendicular lean mass [kg], and fat mass [kg]) by the square of height (m [2]). Additionally, we estimated the total skeletal muscle mass based on the appendicular lean mass obtained from DXA (SM (kg) = (appendicular lean mass (kg) × 1.19)- 1.01) [40]. We then calculated the skeletal muscle index (SMI) by dividing the total skeletal muscle mass by the square of height (m2). Based on the medians of our data, four distinct body composition types were defined using the medians of ASMI (8.68 kg/m2 for men and 6.67 kg/m2 for females) and FMI (7.60 kg/m2 for males and 10.83 kg/m2 for females), categorized as follows (Fig. 2) [41]:

Fig. 2
figure 2

Body composition phenotypes classified based on the median ASMI and FMI. ASMI, appendicular skeletal muscle mass index; FMI, fat mass index; HA-HM, high adiposity with high muscle mass; HA-LM, high adiposity with low muscle mass; LA-HM, low adiposity with high muscle mass; LA-LM, low adiposity with low muscle mass

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  1. (1)

    Low adiposity/Low muscle (LA-LM, FMI: 0–49.99 percentile; ASMI: 0–49.99 percentile).

  2. (2)

    Low adiposity/High muscle (LA-HM, FMI: 0–49.99 percentile; ASMI: 50–100 percentile).

  3. (3)

    High adiposity/Low muscle (HA-LM, FMI: 50–100 percentile; ASMI: 0–49.99 percentile).

  4. (4)

    High adiposity/High muscle (HA-HM, FMI: 50–100 percentile; ASMI: 50–100 percentile).

We designated the LA-HM group as the relatively healthy reference group, taking into account the criteria for muscle strength decline as defined by the International Working Group on Sarcopenia (IWGS), in which mass of appendicular skeletal muscle was measured by DXA is lower than 7.23 kg/m2 for males and under 5.67 kg/m2 for females [42].

Depression examination

The PHQ-9 is a brief self-report tool designed for assessing the severity of depression. It is based on the nine criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). The PHQ-9 consists of nine questions, each rated on a scale from 0 to 3, corresponding to the following frequencies: 0 (not at all), 1 (several days), 2 (more than half the days), and 3 (nearly every day). The scores are summed to yield a total score for each participant, ranging from 0 to 27. Consistent with prior studies, a total score of ≥ 10 on the PHQ-9 is indicative of depression, with the instrument demonstrating a sensitivity of 88% and specificity also at 88%, reflecting its high efficacy in diagnosing depressive disorders [43].

Covaries assessments

This study includes age, ethnicity, qualification, marital status, history of alcohol use, smoking habits, and the level of physical activeness as confounders. Based on the results of the NHANES questionnaires, specifically the variables SMQ020 and SMQ040 from the “Smoking—Cigarette Use” section (variable name prefix SMQ), the smoking status of participants was divided into three categories: past smoker, current smoker, and nonsmoker. Alcohol history was similarly categorized into three groups—former drinker, current drinker, and non-drinker—according to the Alcohol Use questionnaire. The NHANES physical activity questionnaire reports the types of activities, intensity, and duration of participants’ typical week. The metabolic equivalent (MET) for walking/cycling is 4.0, the MET for moderate-intensity activities is 4.0, and the MET for vigorous activities is 8.0. The MET minutes per week for work, transportation, and leisure activities were used to measure participants’ physical activity. The total of 500 MET minutes of moderate to vigorous physical activity (MVPA) per week aligns with public health guidelines for physical activity, such as 150 min of moderate-intensity activity, 75 min of vigorous-intensity activity, or an equivalent combination of both intensities [44]. If they reported 0 MET-min/week, they were considered inactive; if they reported 0 to 500 MET-min/week, they were considered somewhat active; and if they reported ≥ 500 MET-min/week, they were considered active [45]. Additionally, we considered several comorbidities, including hypertension, coronary heart disease (CHD), diabetes, and dyslipidemia. Hypertension was diagnosed based on the question response, “Ever been told by a doctor or other health professional that you had high blood pressure.” CHD was defined by self-reported diagnosis. Diabetes was diagnosed by the participants themselves, using insulin or oral hypoglycemic medications, having haemoglobin A1c (HbA1c) levels ≥ 6.5%, using insulin, or fasting blood glucose levels ≥ 126 mg/dL (7.0 mmol/L), with any of these criteria being sufficient to diagnose the disease [46]. Any of the following criteria might be used to identify dyslipidemia: Levels of low-density lipoprotein (LDL) > 100 mg/dL, triglycerides (TG) ≥ 150 mg/dL, total cholesterol (TC) > 200 mg/dL, or high-density lipoprotein (HDL) < 40 mg/dL for males and < 50 mg/dL for females. Participants with at least one lipid biomarker exceeding the specified thresholds were considered to have dyslipidemia [47].

Statistical analysis

In the preliminary analysis, the interaction effect of sex and body composition on depression was statistically significant (Supplementary Table S1), while age did not show a significant effect (Supplementary Table S2). Therefore, all analyses in this study were stratified by sex. All of our research work was based on sex stratification, dividing the final cohort of 3,637 participants into two groups according to sex. The intricate survey design and related weights were considered in the work. Baseline characteristics of the population were analyzed using Analysis of Variance (ANOVA) for continuous variables (reported as mean ± standard deviation) and Chi-square tests for categorical variables (reported as percentages). Depression was treated as the outcome variable, and the relationships between FMI, ASMI, BFP, SMI and body composition phenotypes (LA-LM, LA-HM, HA-LM and HA-HM) with depression were separately analyzed. Restricted Cubic Splines (RCS) combine spline functions with generalized linear models to describe the dose–response relationship between independent and dependent variables. Compared to traditional linear regression models, RCS can better capture the nonlinear relationships between these variables [48]. RCS analyses were conducted at the 5th, 50th, and 95th percentiles of the distribution of FMI, ASMI, and BFP, adjusting for the same variables as in Model 3. The ASMI, which showed a nonlinear association with depression risk, was subsequently categorized into four quartiles based on its distribution within the NHANES cohort. Multivariable logistic regression models were utilized to estimate the relationships between FMI, ASMI, BFP, SMI and body composition phenotypes with depression. Model 1 was a crude model that did not include confounding variables. In Model 2, we considered age, race, education level, marital status, smoking history, alcohol consumption, and physical activity as covariates. In Model 3, we further adjusted for CHD, hypertension, diabetes, and dyslipidemia based on the adjustments made in Model 2. Interaction analyses were performed in the modified Model 3 to investigate the relationship between FMI and ASMI while accounting for confounding variables. All statistical analyses were performed using R software version 4.1.0 (R Core Team, Vienna, Austria) and the Windows version of Statistical Product and Service Solutions software 26.0 (SPSS Inc., Chicago, IL, USA). All p-values were two-tailed, with a p-value of less than 0.05 indicating statistical significance.

Results

Baseline characteristics of participants and Body composition features

A total of 3,637 participants—1,788 males and 1,849 females—from the NHANES database covering the years 2011 to 2018 were eventually included in the research (Supplementary Table S3). Tables 1 and 2 gave the core attributes regarding the participants, categorised by body composition. Table 1 displayed the characteristics of the male participants, while Table 2 displayed the elements of the female participants. The prevalence of depressive episodes within the study population is illustrated separately by sex in Supplementary Table S4 and S5. Among male participants, the proportions of those classified into the LA-LM, LA-HM, HA-LM, and HA-HM phenotypes were 33.9%, 16.0%, 16.0%, and 34.1%, respectively (Table 1). Among the various male body morphologies, there were no appreciable variations in the incidence of depression (P = 0.393).

Table 1 Baseline characteristics of male participants by body composition
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Table 2 Baseline characteristics of female participants by body composition
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Conversely, the proportions of female participants within the LA-LM, LA-HM, HA-LM, and HA-HM phenotypes were 38.8%, 11.3%, 11.2%, and 38.7%, respectively (Table 2). Participants exhibiting the LA-HM phenotype demonstrated a significantly lower prevalence of depression compared to other phenotypes (P < 0.001).

Female participants with depression were more likely to exhibit higher BMI, WC, BFP, FMI, ASMI and SMI (Supplementary Table S5), whereas males did not display significant differences in body composition (Supplementary Table S4). Comprehensive summaries of the characteristics of other covariates are provided in Table 1, 2, and Supplementary Table S4 and S5.

Body composition indices and depression

In males, the RCS analyses of FMI, ASMI and BFP in relation to depression, were presented in Figs. 3A, 4A and 5A, respectively. In females, the RCS analyses of FMI, ASMI and BFP in relation to depression, were shown in the Figs. 3B, 4B and 5B. The associations between BFP (P non-linear = 0.500, Fig. 3A) and FMI with depression were not statistically significant (P non-linear = 0.084, Fig. 4A). In contrast, there was a statistically significant U-shaped correlation between ASMI and depression (P non-linear = 0.008, Fig. 5A). In the analysis of the nonlinear relationship between female body composition indices and depression risk, ASMI was found to exhibit a significant nonlinear association with depression (P non-linear = 0.007, Fig. 5B), whereas the relationships for BFP (P non-linear = 0.360, Fig. 3B) and FMI (P non-linear = 0.520, Fig. 4B) with depression risk were not statistically significant. Based on these findings, we divided the ASMI of male and female participants into quartiles to further investigate the relationship between depression and body composition. Logistic regression analyses were conducted separately for the associations between depression and FMI, ASMI, BFP, stratified by sex (Tables 3 and 4). Among males, the correlation between FMI and BFP with depression risk did not reach statistical significance (Table 3). However, in Model 1, compared to the first quartile of ASMI, the second (OR = 0.42, 95%CI: 0.23–0.79) and third (OR = 0.30, 95%CI: 0.13–0.71) quartiles of ASMI were negatively associated with the risk of depression in males. This association remained significant in Model 2, which adjusted for confounding variables (OR = 0.46, 95%CI:0.23–0.91 and OR = 0.37, 95%CI: 0.15–0.91, respectively). In Model 3, which controlled for the confounding effects of comorbidities based on Model 2, the second (OR = 0.43, 95%CI: 0.22–0.85) and third (OR = 0.35, 95%CI: 0.14–0.86) quartiles of ASMI continued to demonstrate a significant negative correlation with depression risk. In females, no significant associations were found between depression risk and the quartiles of ASMI (Table 4). In Model 1, increases in FMI (OR = 1.09, 95%CI: 1.06–1.12) and BFP (OR = 1.06, 95%CI: 1.04–1.09) were mainly related to the higher chances of depression. Furthermore, in Model 2 (OR = 1.09, 95%CI: 1.05–1.13 and OR = 1.07, 95%CI: 1.03–1.10, respectively) and Model 3 (OR = 1.08, 95%CI: 1.04–1.12 and OR = 1.06, 95%CI: 1.03–1.09, respectively), which adjusted for confounding factors, the positive correlation between FMI and BFP with depression risk remained significant. Furthermore, we analyzed the relationship between SMI and depression, as shown in Supplementary Table S6 and S7. Similar to the results with ASMI, the second and third levels of SMI were negatively associated with depression risk in males (Supplementary Table S6). In females, no significant association was found between SMI levels and depression risk (Supplementary Table S7).

Fig. 3
figure 3

RCS curves illustrate the association between BFP with depression in both males (3A) and females (3B). The red area represents the 95% confidence interval. The U-shaped trend in the RCS curves indicates a nonlinear relationship between body composition measures and depression risk. The spline analyses were adjusted for age, race, education level, marital status, smoking history, alcohol consumption, physical activity, CHD, hypertension, diabetes, and dyslipidemia. BFP, body fat percentage; CHD, coronary heart disease. * indicated statistical significance

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Fig. 4
figure 4

RCS curves illustrate the association between FMI with depression in both males (3A) and females (3B). The red area represents the 95% confidence interval. The U-shaped trend in the RCS curves indicates a nonlinear relationship between body composition measures and depression risk. The spline analyses were adjusted for age, race, education level, marital status, smoking history, alcohol consumption, physical activity, CHD, hypertension, diabetes, and dyslipidemia. FMI, fat mass index; CHD, coronary heart disease. * indicated statistical significance

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Fig. 5
figure 5

RCS curves illustrate the association between ASMI with depression in both males (3A) and females (3B). The red area represents the 95% confidence interval. The U-shaped trend in the RCS curves indicates a nonlinear relationship between body composition measures and depression risk. The spline analyses were adjusted for age, race, education level, marital status, smoking history, alcohol consumption, physical activity, CHD, hypertension, diabetes, and dyslipidemia. ASMI, appendicular skeletal muscle mass index; CHD, coronary heart disease. * indicated statistical significance

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Table 3 Weighted associations between the ASMI, FMI and BFP with depression in male cohort
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Table 4 Weighted associations between the ASMI, FMI and BFP with depression in female cohort
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Relationship between body composition phenotypes and depression

The association between depression risk and body composition of four various types of phenotypes was shown in Fig. 6, with the LA-HM phenotype acting as the reference group that is sex-stratified. The four body composition phenotypes were not actually interlinked with risk of depression in males (Fig. 6). In Model 3, after adjusting for covariates, the LA-LM group (OR = 0.79, 95%CI: 0.40–1.59), HA-LM group (OR = 0.45, 95%CI: 0.20–1.02), and HA-HM group (OR = 0.61, 95%CI:0.30–1.22) did not demonstrate any significant correlation with depression risk compared to the LA-HM group. In females, substituent to the modification for confounding factors in Model 2, the LA-LM (OR = 3.80, 95%CI: 2.07–7.01), HA-LM (OR = 5.44, 95%CI: 2.40–12.34), and HA-HM (OR = 6.87, 95%CI: 3.54–13.32) groups represented a huge increase in depression risk while comparing to the LA-HM group. Similarly, in Model 3, which controlled for comorbidities, the LA-LM (OR = 3.97, 95%CI: 2.16–7.30) group showed a higher prevalence of depression risk compared to the LA-HM group, while a significant positive correlation with depression risk was observed in the HA-LM (OR = 5.40, 95%CI: 2.34–12.46) group. Additionally, the HA-HM group (OR = 6.36, 95%CI: 3.26–12.37) also demonstrated an increased risk of depression. The logistic regression analysis of body composition phenotypes and depression risk, using the SMI median level to replace the ASMI median level as one of the classification criteria, was shown in Supplementary Table S8. Similarly, females with the LA-LM, HA-LM, and HA-HM body composition phenotypes exhibited a higher risk of depression compared to those with the LA-HM phenotype. In contrast, no significant differences were observed among body composition phenotypes in male participants.

Fig. 6
figure 6

The association between body composition phenotypes and depression. Note: Number(case): participants (participants with depression). Model 1 was an unadjusted crude model; Model 2 was built upon Model 1 by incorporating adjustments for age, race, education level, marital status, smoking history, alcohol consumption, and physical activity. Model 3 further extended Model 2 by incorporating adjustments for CHD, hypertension, diabetes, and dyslipidemia

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Interaction between fat and muscle tissue

To further explore the relationship between the quantity of adiposity and muscle mass, multiplicative interaction analyses were conducted (Table 5). Interaction analyses indicated that the interplay between FMI and ASMI did not significantly impact the risk of depression among male participants (OR = 1.29, 95%CI: 0.45–3.69, P = 0.631). Conversely, interaction analyses revealed a statistically significant synergistic effect between muscle mass and fat mass in females (OR = 4.67, 95%CI: 2.04–10.71, P = 0.001).

Table 5 Multiplicative interaction analysis for the interactive effect of adiposity mass and muscle mass on depression risk, NHANES 2011–2018
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Discussion

Our research shows that there are sex-specific differences in the associations between depression and ASMI, FMI, and body composition phenotypes as evaluated by DXA. Males with muscle mass at the second and third quartile levels were linked to a lower risk of depression after controlling for potential confounding variables, whereas females with greater FMI and BFP were substantially linked to higher incidence rates of depression. There was a substantial correlation found between depression risk and specific body composition phenotypes in females. Comparing to females with the LA-HM phenotype, with the ones with the LA-LM, HA-LM, and HA-HM phenotypes were greatly susceptible to depression, maintaining a positive correlation even after adjusting for confounding factors. In contrast, no significant differences were observed among body composition phenotypes in male participants with depression. Interaction study confirms that there was an interaction among fat mass and muscle mass in females. However, no interaction between FMI and ASMI was found in males.

The notion that there is a larger correlation between depression and obesity in females is supported by the link between female FMI and higher depression prevalence. Being overweight or obese significantly increases the prevalence of depression, with the obesity-depression relationship being significant only among females when stratified population analyses are conducted [49]. A clinical study has shown that only in females, increases in body composition, including BMI, BFP, and VAT, are significantly negatively correlated with the alleviation of depressive symptoms [50]. Similarly, sex differences may be a potential factor affecting the strength of the relationship between VAT, which is closely related to metabolism, and depression [51, 52]. Although the connection in obesity and depression has been confirmed, most early analyses primarily utilized BMI as the measure of obesity [51, 53]; however, BMI, which depends.

on height and weight, cannot accurately reflect the content of adipose and muscle tissue. Current research shows a strong link between depression risk and metabolic health. People with metabolically healthy obesity do not have an increased risk of depression, while those who are metabolically unhealthy but not obese have a 19%–60% higher risk of developing depression [53]. This suggests that obesity defined by BMI may inaccurately predict depression risk, potentially overlooking metabolically disordered participants who are not obese. The adiposity-muscle phenotype diagnosis was initially proposed based on the cumulative effect of abnormal muscle and fat tissue content on metabolic health and was refined by Prado as a tool for assessing nutritional status and predicting disease risk [41, 54]. Our research shows that females with the LA-LM, HA-HM, and HA-LM phenotypes exhibit a significant association with depression risk in comparison to those with the LA-HM phenotype. This further suggests that a low-fat, high-muscle phenotype may represent a beneficial body composition for lowering female depression risk. The sex differences in the association between depression risk and body composition may involve multiple mechanisms. Females have higher fat mass and body fat percentage [34], and estrogen may play a significant role in the causes and consequences of female obesity [55]. Estrogen contributes to increased body fat by reducing postprandial fatty acid oxidation [56] and plays a regulatory role in appetite fluctuations throughout the female menstrual cycle. During the follicular phase, estrogen reduces food intake [55], whereas in the luteal phase, progesterone promotes tendencies toward binge eating and emotional eating [57, 58]. In addition, the prevalence of binge eating and eating disorders is significantly higher in females compared to males [59]. Among individuals with depression, pronounced fluctuations in sex hormones have also been observed [60]. Stress-induced hyperactivation of the HPA axis alters estradiol sensitivity in female mammals, mediating emotional eating and the development of obesity [61]. Societal gender stereotypes and high expectations regarding physical appearance further exacerbate psychological stress and increase the risk of depression in women [62]. Leptin, primarily secreted by adipose tissue, regulates food intake, body weight, and insulin secretion by binding to central leptin receptors [63]. In obesity, leptin resistance impairs leptin signaling in Pro-opiomelanocortin (POMC) neurons, enhancing the disinhibition of neurons co-expressing orexigenic neuropeptide Y and agouti-related protein, thus stimulating food intake [64]. Studies in rats have demonstrated that leptin resistance in the hippocampus and hypothalamus is closely associated with depression-like behaviors [65]. Sex differences exist in serum leptin levels, with females typically having higher levels than males [66]. Circulating leptin levels are also elevated in females with depression, suggesting that leptin may serve as a sex-specific mediator in the relationship between obesity and depression [13]. Additionally, leptin regulates insulin sensitivity and pancreatic β-cell function through phosphatidylinositol-3 kinase (PI3K) signaling [67, 68]. Obesity-induced leptin resistance impairs both insulin sensitivity and secretion [69], potentially exacerbating the development of depression. Our study further examined the relationship between VAT and SAT with depression risk, showing that VAT and SAT remain risk factors for depression in females but not in males. WAT is classified into SAT and VAT based on fat function and location. SAT is located subcutaneously throughout the body, storing excess fat and accounting for 80%−90% of human total adipose tissue [70], while VAT is located around visceral organs and within the abdominal cavity, having significant impacts on metabolism-related diseases [71]. VAT accumulation is closely associated with insulin resistance, while SAT is positively correlated with circulating serum leptin levels [72]. In obesity characterized by insulin resistance, VAT exhibits impaired insulin sensitivity and enhanced lipolysis [73], releasing free fatty acids that are transported to the liver via the portal vein. This process further exacerbates hepatic and systemic insulin resistance [74].

The mechanisms underlying the association between depression and sarcopenia [75, 76], involve brain-derived neurotrophic factor (BDNF) [77], inflammation [78, 79] and lifestyle [36, 80]. In studies on variables related to sarcopenia diagnostic criteria and depression risk, evidence regarding the role of muscle mass remains contradictory. Two Mendelian randomization studies support a causal relationship between low muscle mass and depression, suggesting it independently increases the risk of depression [81, 82]. Another NHANES study demonstrates a nonlinear relationship between muscle mass and depression risk, with a stronger association observed in non-obese males [83]. Nonetheless, clinical randomized controlled trials have not found a correlation between muscle mass and improvement in treatment-resistant depression in late life [84]. Our findings indicate that, compared to excessively low or high skeletal muscle mass, an optimal level of muscle mass in males can possibly be associated with a lower risk of depression. Physical exercise benefits for depression are well-documented [85], with exercise-induced activation of PGC-1α1 promoting the conversion of kynurenine to kynurenic acid and reducing kynurenine levels, thereby mitigating stress’s negative effects on the central nervous system [86]. In hospitalized elderly patients, lower serum albumin levels related to low muscle mass indicate that poor nutrition may influence depressive symptoms [87]. Furthermore, sarcopenia, an age-related condition, disrupts mitochondrial homeostasis in muscle cells, leading to atrophy and systemic homeostasis issues [88]. Although there is currently insufficient direct evidence to establish a definitive role for muscle mass in the development of depression, studies on aging, nutritional status, and exercise therapy provide insights into how muscle mass might influence mental health. Future research is needed to explore the potential mechanisms underlying the relationship between muscle mass and depression.

Exercise interventions are increasingly recognized in the treatment of depression, with mechanisms potentially involving anti-inflammatory effects and improvements in self-esteem and self-worth [89]. Several meta-analyses and systematic reviews have examined the impact of exercise on the prevention and treatment of depression, indicating that exercise reduces the risk of depression across a wide range of ages in healthy populations and improves symptoms in depressed patients [89,90,91]. While exercise interventions are widely recognized for their therapeutic and preventive effects on depression, some studies have reported negative results, which may be attributed to the significant heterogeneity in exercise types [92]. Some research suggests that low-intensity exercise and high-intensity exercise have similar efficacy in preventing depression risk [90]. Gordon et al. reported that both aerobic exercise and resistance training showed significant therapeutic benefits [91]. In lifelong moderate-intensity continuous training or high-intensity interval training in mice, both exercise training modalities showed similar effects, reversing age-related muscle loss and maintaining lower fat mass, thereby improving depressive-like behaviors [93]. This suggests that exercise intensity may not be the key determinant in improving depressive symptoms. Due to the significant variability among exercise therapy studies, few studies have summarized the specific characteristics of exercise programs (type, intensity, duration, and frequency). Our findings suggest that a low-fat, high-muscle body phenotype is associated with a reduced risk of depression in females. Designing personalized exercise interventions based on patients’ baseline fat and muscle mass, or maintaining an appropriate body phenotype, may offer novel strategies for the prevention and treatment of depression.

Our study highlights the sex differences in depression from the perspective of body composition phenotypes, identifying a significant impact of high fat mass on depression risk in females using the DXA method. The study also underscores the complex interaction between muscle and fat tissue, suggesting that reducing fat and increasing muscle mass may be an effective strategy for lowering depression risk in females. However, our study has certain limitations. First, participants lacking covariate data and those with extreme height and weight due to DXA equipment limitations were excluded, and despite including an eight-year sample from the NHANES database, the presence of missing values reduces the population representativeness of the included samples, introducing potential bias. Further research should consider methods such as multiple imputation and mean substitution to mitigate the bias introduced by missing values. Second, the limitations of DXA technology prevent differentiation of lipid infiltration in muscle cells [94]. Third, due to sample size constraints, a more detailed classification of muscle and fat mass could not be performed. Further clinical studies with larger sample sizes are needed to validate our findings and to determine the optimal cutoff values for FMI and ASMI.

Fourth, as this study is cross-sectional in nature, it cannot establish causal relationships between depression and body composition. Future research should consider cohort studies to evaluate the causal relationship between depression and body composition, which may contribute to the prevention and treatment of depression.

Conclusion

Our study utilizing DXA technology for precise body composition measurement, reveals sex differences in the relationship between body composition and depression risk. According to the research, males who have an adequate amount of muscular mass are less likely to experience depression. Notably, distinct body composition phenotypes in females are significantly related to depression risk, with those displaying a low-adiposity, high-muscle phenotype experiencing a reduced risk.

Future research should involve larger sample sizes and datasets to further explore the relationship between body composition and depression, aiming to identify more precise body composition ratios for the prevention and management of depression, which may provide a framework for developing clinical exercise prescriptions. Tailoring personalized exercise interventions based on patients’ baseline fat and muscle mass may hold potential for depression prevention, necessitating further research to establish the importance of body composition assessment in exercise program design.

Data availability

All detailed NHANES study designs and data are publicly available at www.cdc.gov/nchs/nhanes/. The processed data are available from the corresponding author on reasonable request.

Abbreviations

FMI:

Fat mass index

ASMI:

Appendicular Skeletal Muscle Mass Index

BFP:

Body fat percentage

DXA:

Dual-energy X-ray absorptiometry

LA-LM:

Low adiposity-low muscle

LA-HM:

Low adiposity-high muscle

HA-LM:

High adiposity-low muscle

HA-HM:

High adiposity-high muscle

BMI:

Body Mass Index

WC:

Waist circumference

PHQ-9:

Patient Health Questionnaire-9

CHD:

Coronary heart disease

VAT:

Visceral adipose tissue

MET:

Metabolic equivalent

NHANES:

National Health and Nutrition Examination Survey

NCHS:

National Centre for Health Statistics

RCS:

Restricted cubic spline

DSM-IV:

Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition

LDL:

Low-density lipoprotein

HDL:

High-density lipoprotein

TG:

Triglycerides

HbA1C:

Haemoglobin A1c

IWGS:

International Working Group on Sarcopenia

ANOVA:

Analysis of Variance

OR:

Odds ratio

CI:

Confidence interval

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Acknowledgements

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Funding

This project was supported by the National Natural Science Foundation of China (No. 81873467).

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Authors and Affiliations

  1. Department of Anesthesiology, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan, Hubei, China

    Yijing Li, Juan Li, Tianning Sun, Zhigang He, Cheng Liu, Zhixiao Li, Yanqiong Wu & Hongbing Xiang

  2. Key Laboratory of Anesthesiology and Resuscitation , (Huazhong University of Science and Technology), Ministry of Education, Wuhan, China

    Hongbing Xiang

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  1. Yijing Li
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Contributions

YJ.L.: Design of the work, analysis, writing, J.L.: Manuscript edit, TN.S.: Data curation, supervision, ZG.H.: Data curation, C.L.: Investigation, ZX.L.: Investigation, YQ.W.: Supervision, HB.X.: Conception. All authors have approved the submitted version (and any substantially modified version that involves the author's contribution to the study) and to have agreed both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

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Li, Y., Li, J., Sun, T. et al. Sex-specific associations between body composition and depression among U.S. adults: a cross-sectional study. Lipids Health Dis 24, 15 (2025). https://doi.org/10.1186/s12944-025-02437-5

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X