Abstract
Background
Emerging evidence suggests that body composition, particularly the balance between fat and muscle mass, may influence mental health. However, the relationship between fat‑to‑muscle mass ratio (FMR) and depressive symptoms in adults remains unclear. This study explored associations between regional and total FMR and depressive symptoms in a nationally representative sample of US adults.
Methods
This cross-sectional study included 8767 adults from the 2011–2018 National Health and Nutrition Examination Survey (NHANES). Multivariable logistic regression was used to examine associations between arm, leg, trunk, and total FMR and depressive symptoms. Receiver operating characteristic (ROC) analysis, restricted cubic splines (RCS), subgroup analyses, and multiple imputation were performed to assess predictive performance, the shape of the association, and robustness.
Results
Among 8767 US adults, 697 exhibited depressive symptoms. After comprehensive adjustment, multivariable logistic regression analysis revealed that higher leg, trunk, and total FMR were independently associated with greater odds of depressive symptoms (all P < 0.05), while arm FMR was not (P > 0.05). Compared to the lowest quartile, individuals in the highest quartile of leg, trunk, and total FMR had 76% (OR: 1.76, 95% CI 1.06–2.92), 47% (OR: 1.47, 95% CI 1.06–2.04), and 66% (OR: 1.66, 95% CI 1.08–2.56) higher odds, respectively. ROC analysis revealed modest discriminatory accuracy, with AUCs ranging from 0.729 to 0.731. RCS models indicated a linear positive association (P for non-linearity > 0.05). The findings were consistent across subgroups and robust to sensitivity analyses (all P-interaction > 0.05).
Conclusion
Elevated leg, trunk, and total FMR are associated with an increased likelihood of depressive symptoms, positioning FMR as a comprehensive risk indicator that links body composition to mental health. Although useful for risk stratification, its modest predictive performance limits its utility as a standalone screening tool.
Keywords: Fat-to-muscle mass ratio, Depressive symptoms, Body composition, NHANES
Highlights
Elevated leg, trunk, and total body fat-to-muscle ratio (FMR) is independently associated with an increased likelihood of depressive symptoms in US adults.
FMR demonstrates modest predictive accuracy for depression, with area under the curve (AUC) values ranging from 0.729 to 0.731, underscoring its relevance for etiological research.
Integrating FMR with psychosocial and behavioral factors could enhance a tool for personalized depression prevention.
Introduction
Depression is a pervasive psychiatric disorder that constitutes a major contributor to the global burden of disease, affecting approximately 280 million individuals worldwide [1]. Each year, more than 700,000 lives are lost to suicide, underscoring the profound societal and healthcare impact of this condition [2]. The etiology of depression is complex, resulting from a multifaceted interplay among genetic, environmental, and psychosocial factors [3].
Recently, attention has shifted toward somatic determinants, particularly changes in body composition, as potential contributors to the onset and severity of depressive symptoms [4]. Emerging epidemiological evidence suggests that both increased adiposity and reduced skeletal muscle mass are associated with a heightened risk of depression [5]. Lower muscle mass and strength have been independently linked to higher odds of depressive symptoms [6], whereas a higher body fat percentage further amplifies the risk, particularly among certain demographic groups [7]. Moreover, novel anthropometric indices, such as the a body shape index (ABSI) [8], have demonstrated associations with depression severity that are not detected by traditional measures such as BMI, thereby underscoring the limitations of standard obesity metrics in reflecting the complexity of tissue distribution and metabolic function [9]. These findings suggest that adiposity and muscle deficits may have opposing effects on mental health. Standard anthropometric indices, including BMI and waist circumference, are insufficient to capture the combined and opposing influences of adipose and muscle tissue [10, 11].
The fat-to-muscle mass ratio (FMR) serves as a comprehensive anthropometric indicator, quantifying both the metabolic burden imposed by adiposity and the protective effects conferred by skeletal muscle mass [12]. Prior research has established FMR as a robust predictor of incident type 2 diabetes and all-cause mortality, irrespective of BMI [13, 14]. However, the potential involvement of FMR in the development of mood disorders remains largely unexamined. Notably, lower FMR values have been correlated with more advantageous lifestyle profiles, such as greater physical activity and healthier dietary patterns, as observed in large-scale population studies [15]. In contrast, a reduced skeletal muscle mass to visceral fat area ratio (SVR) has been associated with a heightened risk of depression, which suggests that disproportion between fat and muscle compartments may exert divergent influences on mental health [16].
Biological mechanisms plausibly underlie the association between elevated FMR and increased risk of depression. Excess adiposity is known to promote chronic low-grade inflammation, largely due to increased secretion of pro-inflammatory cytokines such as IL-6 and TNF-α, as well as altered adipokine profiles [17]. In contrast, skeletal muscle serves as a significant source of anti-inflammatory myokines, providing a counterbalance within the systemic inflammatory milieu [18]. Such an imbalance may contribute to the exacerbation of neuroinflammatory processes implicated in the pathophysiology of depression. Furthermore, dysregulation of the hypothalamic—pituitary—adrenal (HPA) axis, particularly in the context of both obesity and sarcopenia, may further increase vulnerability to mood disturbances [19].
Building on these findings, we hypothesized that an elevated FMR would be independently associated with an increased risk and greater severity of depressive symptoms. To evaluate this hypothesis, we performed a cross-sectional analysis utilizing data from the US National Health and Nutrition Examination Survey (NHANES), which included DXA-derived measures of body composition as well as PHQ-9 assessments of depressive symptoms. Elucidating the relationship between both total and regional FMR and depressive symptoms may provide valuable insights for the development of integrated prevention and intervention strategies targeting both metabolic and mental health outcomes.
Materials and methods
Data source and study population
This study utilized data from the 2011–2018 NHANES, which is a nationally representative, multistage probability sample of the non-institutionalized US population. NHANES collects a wide range of data, including demographic, socioeconomic, dietary, and health information, through structured interviews and physical examinations conducted at mobile examination centers [20]. The NHANES survey protocol was reviewed and approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. All participants provided written informed consent prior to participation in the study, ensuring compliance with ethical standards for research involving human subjects.
Among the 39,156 individuals enrolled in the 2011–2018 NHANES cycles, a total of 8767 participants were included in the final analytic sample. Participants were excluded if they had missing FMR data (n = 22,132), missing or invalid PHQ-9 scores (n = 6639), or missing information for key covariates (n = 1618).
Variable definitions
FMR
The primary exposure variable was total and regional FMR, measured by dual-energy X-ray absorptiometry (DXA), the gold standard for body composition assessment. Whole-body DXA scans were performed using a Hologic Discovery Model A densitometer and analyzed with Hologic APEX software (version 4.0) incorporating the NHANES Body Composition Analysis module. Fat mass (FM) and lean mass were quantified for the whole body, trunk, arms, and legs. For limb regions, bilateral FM and lean mass values were summed before calculating regional FMRs, using the formula: FMR equals fat mass divided by lean mass. Whole-body FMR was determined by dividing total FM by total lean mass after excluding bone mineral content [12, 21]. All FMR values were standardized as Z-scores and categorized into quartiles (Q1–Q4), from lowest to highest.
Depressive symptoms
Depressive symptoms were evaluated using the patient health questionnaire-9 (PHQ-9), a validated nine-item instrument derived from DSM-IV criteria for major depressive disorder. Each item assesses symptoms such as depressed mood, anhedonia, and sleep disturbances, with responses rated on a four-point scale from 0 (“not at all”) to 3 (“nearly every day”). The total PHQ-9 score ranges from 0 to 27, with higher scores reflecting greater severity of depressive symptoms [22]. Depression severity was categorized as follows: none (0–4), mild (5–9), moderate (10–14), moderately severe (15–19), and severe (≥ 20) [23]. A PHQ-9 score of 10 or higher is widely used as a clinical cutoff, demonstrating approximately 88% sensitivity and specificity for identifying major depressive disorder [24].
Covariates
Following previous research, we included covariates known to influence both FMR and depressive outcomes to adjust for potential confounders. Demographic variables comprised sex, age, race/ethnicity, classified as Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and multiracial/other; Education level was classified as less than high school, high school or general educational development (GED), or college and above. Marital status was grouped into married or cohabiting, widowed, divorced or separated, and never married [25]. Socioeconomic status was assessed using the poverty income ratio (PIR), with a PIR < 1.0 indicating below the poverty level and a PIR ≥ 1.0 indicating at or above it [26]. Moderate-intensity physical activity was defined by self-reports of engaging in at least ten consecutive minutes per week of activities such as brisk walking, cycling, swimming, or volleyball that slightly elevate breathing or heart rate. Similarly, vigorous-intensity activity was characterized as any sports, fitness, or recreational activity, such as running or basketball, performed continuously for at least ten minutes per session and causing substantial increases in breathing or heart rate.
We assessed comorbid conditions including diabetes mellitus, hypertension, and cardiovascular disease (CVD). Diabetes mellitus was identified by self‑reported physician diagnosis, current use of insulin or oral hypoglycemic agents, fasting plasma glucose ≥ 7.0 mmol/L, 2‑hour oral glucose tolerance test ≥ 11.1 mmol/L, random blood glucose ≥ 11.1 mmol/L, or glycated hemoglobin (HbA1c) ≥ 6.5% [27]. Hypertension was defined by self‑reported physician diagnosis, current use of antihypertensive medication, or measured systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg [28]. CVD encompassed self‑reported coronary heart disease, angina pectoris, myocardial infarction, or stroke [29].
Statistical analysis
Statistical analyses were conducted using R (version 4.4.3), incorporating NHANES sample weights to adjust for its stratified multistage design. Data from 2011 to 2018 were pooled, and 8-year weights were calculated by dividing 2-year weights by four. Continuous variables are reported as weighted means ± standard deviations (SD), and categorical variables as weighted percentages. Group differences were assessed using the Kruskal–Wallis and chi-square tests. Multivariable logistic regression was used to examine the association between FMR (both continuous and in quartiles) and depressive symptoms. Three models were employed: Model 1 (unadjusted), Model 2 (adjusted for demographic factors), and Model 3 (further adjusted for covariates in Sect. "Covariates"). Receiver operating characteristic (ROC) curves, along with the area under the curve (AUC), were used to evaluate model discrimination. Model calibration was assessed using the Hosmer–Lemeshow test. The optimal cut-off for each FMR indicator was established by maximizing Youden’s index. To improve clinical relevance, the absolute risk difference (ARD) in depressive symptoms between high-risk and low-risk groups, as defined by these cut-offs, was calculated. Subgroup and interaction analyses were conducted to examine consistency across population strata, while restricted cubic splines (RCS) were utilized to explore non-linear associations. Missing data were handled using multiple imputation (five chained-equation replications), and results were compared between imputed and original datasets. A two-sided P-value of < 0.05 was considered statistically significant.
Results
Weighted baseline characteristics
A total of 8767 individuals were included, among whom 697 were identified as exhibiting depressive symptoms (Table 1). Participants with depressive symptoms were more likely to be female, to have lower educational attainment, and to be widowed, divorced, separated, or never married. Furthermore, they had lower PIR, participated less frequently in recreational activity, and exhibited a higher prevalence of hypertension, diabetes, and CVD.
Table 1.
Baseline Characteristics
| Characteristic | N | Overall | No Depression | Depression | P-value |
|---|---|---|---|---|---|
| Sex | 8767 | < 0.001 | |||
| Male | 4501 (52%) | 4246 (53%) | 255 (37%) | ||
| Female | 4266 (48%) | 3824 (47%) | 442 (63%) | ||
| Age (years) | 8767 | 0.3 | |||
| 18–29 | 2325 (27%) | 2174 (28%) | 151 (25%) | ||
| 30–39 | 2194 (24%) | 2022 (24%) | 172 (24%) | ||
| 40–49 | 2166 (25%) | 2003 (25%) | 163 (22%) | ||
| 50–59 | 2082 (24%) | 1871 (24%) | 211 (28%) | ||
| Race/ethnicity, n (%) | 8767 | 0.017 | |||
| Mexican American | 1274 (10%) | 1205 (10%) | 69 (6.7%) | ||
| Other Hispanic | 863 (6.9%) | 769 (6.7%) | 94 (9.0%) | ||
| Non-Hispanic White | 3196 (63%) | 2884 (63%) | 312 (64%) | ||
| Non-Hispanic Black | 1783 (11%) | 1650 (11%) | 133 (11%) | ||
| Multiracial/other | 1651 (9.5%) | 1562 (9.5%) | 89 (9.4%) | ||
| Educational level, n (%) | 8767 | < 0.001 | |||
| Less than high school | 460 (3.3%) | 414 (3.1%) | 46 (4.9%) | ||
| High school or GED | 2937 (31%) | 2632 (30%) | 305 (42%) | ||
| College or above | 5370 (66%) | 5024 (67%) | 346 (53%) | ||
| Marital status, n (%) | 8767 | < 0.001 | |||
| Married or living with partner | 5237 (62%) | 4938 (63%) | 299 (46%) | ||
| Widowed/divorced/separated | 1180 (13%) | 993 (12%) | 187 (23%) | ||
| Never married | 2350 (25%) | 2139 (25%) | 211 (31%) | ||
| PIR | 8767 | < 0.001 | |||
| < 1 | 1938 (16%) | 1676 (15%) | 262 (30%) | ||
| ≥ 1 | 6829 (84%) | 6394 (85%) | 435 (70%) | ||
| Vigorous recreational activity | 8767 | < 0.001 | |||
| Yes | 2793 (34%) | 2681 (36%) | 112 (17%) | ||
| No | 5974 (66%) | 5389 (64%) | 585 (83%) | ||
| Moderate recreational activity | 8767 | < 0.001 | |||
| Yes | 3976 (50%) | 3759 (51%) | 217 (35%) | ||
| No | 4791 (50%) | 4311 (49%) | 480 (65%) | ||
| Diabetes | 8767 | < 0.001 | |||
| Yes | 626 (5.4%) | 541 (5.1%) | 85 (9.0%) | ||
| No | 8141 (95%) | 7529 (95%) | 612 (91%) | ||
| Hypertension | 8767 | < 0.001 | |||
| Yes | 1980 (22%) | 1730 (21%) | 250 (35%) | ||
| No | 6787 (78%) | 6340 (79%) | 447 (65%) | ||
| CVD | 8767 | < 0.001 | |||
| Yes | 302 (2.8%) | 233 (2.4%) | 69 (8.2%) | ||
| No | 8465 (97%) | 7837 (98%) | 628 (92%) |
The mean ± standard deviation (SD) for continuous variables was calculated, and the P-value was determined using the Kruskal–Wallis H test. Survey-weighted percentages for categorical variables were calculated, and the P-value was obtained using the chi-square test. GED general educational development, PIR poverty income ratio, CVD cardiovascular disease
Association between regional and total FMR and depressive symptoms
We conducted multivariable logistic regression analyses to investigate the association between regional and total FMR and depressive symptoms (Table 2). When FMR was considered as a continuous variable, elevated FMR values in the arm, leg, trunk, and total body were each significantly associated with an increased likelihood of depressive symptoms (all P < 0.05). In analyses based on FMR quartiles, a clear dose–response association emerged between leg, trunk, and total FMR and the risk of depression (P-trend < 0.05). In the fully adjusted Model 3, individuals in the highest quartile (Q4) of leg FMR had 1.76 times greater odds of experiencing depressive symptoms compared to those in the lowest quartile (Q1) (95% CI 1.06–2.92; P-trend = 0.026). Similarly, a trunk FMR in the highest quartile was linked to an odds ratio of 1.47 (95% CI 1.06–2.04; P-trend = 0.021), and total FMR in Q4 was associated with an odds ratio of 1.66 (95% CI 1.08–2.56; P-trend = 0.007). In contrast, arm FMR did not demonstrate a significant association after adjustment (P-trend = 0.20).
Table 2.
Assocoation between Regional and Total FMR and Depressive Symptoms
| Model 1 OR (95%CI) | Model 2 OR (95%CI) | Model 3 OR (95%CI) | ||
|---|---|---|---|---|
| Arm FMR | All | 1.50 (1.38–1.64) | 1.43 (1.27–1.62) | 1.25 (1.10–1.42) |
| P-value | < 0.001 | < 0.001 | 0.001 | |
| Q1 | Ref. | Ref. | Ref. | |
| Q2 | 1.02 (0.71–1.47) | 1.05 (0.72–1.54) | 0.90 (0.60–1.35) | |
| Q3 | 1.79 (1.28–2.49) | 1.57 (1.04–2.37) | 1.20 (0.79–1.83) | |
| Q4 | 2.38 (1.78–3.19) | 1.92 (1.26–2.94) | 1.27 (0.82–1.95) | |
| P-trend | < 0.001 | 0.002 | 0.201 | |
| Leg FMR | All | 1.44 (1.31–1.59) | 1.31 (1.13–1.53) | 1.20 (1.02–1.40) |
| P-value | < 0.001 | 0.001 | 0.024 | |
| Q1 | Ref. | Ref. | Ref. | |
| Q2 | 1.43 (0.97–2.11) | 1.41 (0.94–2.12) | 1.20 (0.80–1.82) | |
| Q3 | 2.17 (1.55–3.05) | 1.89 (1.23–2.90) | 1.54 (1.00–2.39) | |
| Q4 | 2.79 (2.00–3.89) | 2.33 (1.41–3.83) | 1.76 (1.06–2.92) | |
| P-trend | < 0.001 | 0.001 | 0.026 | |
| Trunk FMR | All | 1.48 (1.36–1.61) | 1.35 (1.22–1.51) | 1.19 (1.06–1.34) |
| P-value | < 0.001 | < 0.001 | 0.004 | |
| Q1 | Ref. | Ref. | Ref. | |
| Q2 | 1.07 (0.75–1.52) | 1.15 (0.81–1.64) | 1.00 (0.70–1.43) | |
| Q3 | 1.38 (1.02–1.87) | 1.34 (0.98–1.84) | 1.07 (0.77–1.47) | |
| Q4 | 2.55 (1.96–3.33) | 2.09 (1.54–2.84) | 1.47 (1.06–2.04) | |
| P-trend | < 0.001 | < 0.001 | 0.021 | |
| Total FMR | All | 1.51 (1.38–1.65) | 1.41 (1.25–1.60) | 1.24 (1.08–1.41) |
| P-value | < 0.001 | < 0.001 | 0.002 | |
| Q1 | Ref. | Ref. | Ref | |
| Q2 | 1.16 (0.82–1.66) | 1.19 (0.81–1.74) | 1.00 (0.68–1.47) | |
| Q3 | 1.60 (1.15–2.25) | 1.47 (0.98–2.22) | 1.15 (0.75–1.77) | |
| Q4 | 2.84 (2.08–3.87) | 2.43 (1.60–3.68) | 1.66 (1.08–2.56) | |
| P-trend | < 0.001 | < 0.001 | 0.007 | |
Model 1 represents the unadjusted analysis. Model 2 is adjusted for sex, age, race/ethnicity, educational level, marital status, and the poverty income ratio. Model 3 is adjusted for all covariates in Model 2, as well as for vigorous recreational activity, moderate recreational activity, diabetes, hypertension, and cardiovascular disease. OR odds ratio, CI confidence interval, FMR fat‑to‑muscle mass ratio
Predictive performance and shape of association
The discriminatory performance of all FMR indicators for identifying depressive symptoms (PHQ-9 ≥ 10) was modest but statistically significant, with AUC values ranging from 0.729 to 0.731 (all P < 0.001) (Fig. 1, Table 3). The Hosmer–Lemeshow test revealeded no evidence of poor calibration for any of the models (all P > 0.05). Youden’s index identified optimal FMR cut-offs of 0.068 (Arm), 0.071 (Leg), 0.069 (Trunk), and 0.075 (Total). At these thresholds, sensitivity ranged from 61.4% to 65.7% and specificity from 69.6% to 74.0% (Table 3). The corresponding ARD between classification groups ranged from 16.6% to 16.9%. Furthermore, RCS analyses revealed no significant departure from linearity (all P for non-linearity > 0.05), supporting a linear, positive association between each FMR metric and the odds of depressive symptoms (Fig. 2).
Fig. 1.
ROC Curves for the Discriminatory Performance of FMR in Predicting Depressive Symptoms. All analyses were adjusted for gender, age, race/ethnicity, educational level, marital status, poverty income ratio, vigorous recreational activity, moderate recreational activity, diabetes, hypertension, and cardiovascular disease. FMR fat‑to‑muscle mass ratio, ROC receiver operating characteristic, AUC area under the curve
Table 3.
Performance Metrics of FMR Indicators for Identifying Depressive Symptoms
| Overall Model Performance | Performance at Youden Index Cut-point | Clinical Risk Measure | ||||||
|---|---|---|---|---|---|---|---|---|
| AUC (95%CI) | HL test (P-value) | Cut-Off | Specificity (%) | Sensitivity (%) | PPV (%) | NPV (%) | ARD (%) | |
| Arm FMR | 0.731 (0.712, 0.751) | 0.395 | 0.068 | 74.000% | 61.375% | 67.023% | 67.590% | 16.838% |
| Leg FMR | 0.729 (0.710, 0.749) | 0.051 | 0.071 | 72.000% | 62.887% | 65.402% | 70.182% | 16.596% |
| Trunk FMR | 0.730 (0.711, 0.750) | 0.602 | 0.069 | 73.601% | 61.648% | 66.569% | 70.374% | 16.755% |
| Total FMR | 0.730 (0.711, 0.750) | 0.503 | 0.075 | 69.584% | 65.688% | 66.934% | 68.592% | 16.792% |
All analyses were adjusted for gender, age, race/ethnicity, educational level, marital status, poverty income ratio, vigorous recreational activity, moderate recreational activity, diabetes, hypertension, and cardiovascular disease. The ARD represents the difference in the probability of depressive symptoms between groups classified as high-risk and low-risk based on the optimal FMR cut-point. The Hosmer–Lemeshow test P > 0.05 indicates adequate model calibration. All AUCs were statistically significant (P < 0.001). AUC area under the curve, CI confidence interval, FMR fat-to-muscle ratio, PPV positive predictive value, NPV negative predictive value, HL Hosmer–Lemeshow, ARD absolute risk difference
Fig. 2.
RCS Analysis of the Association between FMR and Risk of Depressive Symptoms. All analyses were adjusted for gender, age, race/ethnicity, educational level, marital status, poverty income ratio, vigorous recreational activity, moderate recreational activity, diabetes, hypertension, and cardiovascular disease. OR odds ratio, CI confidence interval, FMR fat‑to‑muscle mass ratio
Subgroup analyses and sensitivity analyses
We further examined whether the association between FMR and depression varied across different strata by incorporating interaction terms for FMR subgroups. None of the interaction tests achieved statistical significance (all P‑interaction > 0.05), which indicates that the positive relationship between regional (arm, leg, trunk) and total FMR and depressive symptoms remained consistently observable across all examined demographic and clinical subgroups (Fig. 3). To verify the robustness of our findings, we generated five imputed datasets using various imputation methods. Subsequently, we conducted multivariable logistic regression analyses on each imputed dataset, and fully adjusted models yielded similar results, thereby confirming the positive correlation between leg, trunk, and total FMR and depression risk (Table 4).
Fig. 3.
Subgroup Analyses of the Association between FMR and Depressive Symptoms. All analyses were adjusted for gender, age, race/ethnicity, educational level, marital status, poverty income ratio, vigorous recreational activity, moderate recreational activity, diabetes, hypertension, and cardiovascular disease. OR odds ratio, CI confidence interval, FMR fat‑to‑muscle mass ratio, CVD cardiovascular disease
Table 4.
Sensitivity Analyses of the Association between FMR and Depressive Symptoms Using Imputed Datasets
| Imputation 1 OR (95%CI) | Imputation 2 OR (95%CI) | Imputation 3 OR (95%CI) | Imputation 4 OR (95%CI) | Imputation 5 OR (95%CI) | ||
|---|---|---|---|---|---|---|
| Arm FMR | All | 1.28 (1.13–1.44) | 1.28 (1.13–1.44) | 1.28 (1.13–1.44) | 1.28 (1.13–1.44) | 1.28 (1.14–1.44) |
| P-value | < 0.001 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | |
| Q1 | Ref. | Ref. | Ref. | Ref. | Ref. | |
| Q2 | 0.95 (0.66–1.37) | 0.94 (0.65–1.35) | 0.95 (0.66–1.37) | 0.94 (0.65–1.36) | 0.95 (0.66–1.37) | |
| Q3 | 1.23 (0.82–1.84) | 1.22 (0.81–1.83) | 1.24 (0.83–1.86) | 1.23 (0.82–1.84) | 1.23 (0.82–1.84) | |
| Q4 | 1.35 (0.89–2.03) | 1.34 (0.88–2.02) | 1.36 (0.90–2.06) | 1.35 (0.89–2.04) | 1.35 (0.89–2.05) | |
| P-trend | 0.099 | 0.111 | 0.092 | 0.097 | 0.100 | |
| Leg FMR | All | 1.21 (1.05–1.40) | 1.21 (1.05–1.39) | 1.21 (1.05–1.40) | 1.21 (1.05–1.39) | 1.21 (1.05–1.40) |
| P-value | 0.010 | 0.011 | 0.010 | 0.011 | 0.010 | |
| Q1 | Ref. | Ref. | Ref. | Ref. | Ref. | |
| Q2 | 1.17 (0.79–1.73) | 1.16 (0.78–1.72) | 1.17 (0.79–1.74) | 1.16 (0.78–1.72) | 1.16 (0.78–1.73) | |
| Q3 | 1.53 (1.00–2.34) | 1.53 (1.00–2.35) | 1.56 (1.02–2.38) | 1.54 (1.00–2.36) | 1.55 (1.01–2.37) | |
| Q4 | 1.80 (1.10–2.96) | 1.79 (1.08–2.95) | 1.83 (1.11–3.00) | 1.79 (1.09–2.95) | 1.81 (1.10–2.98) | |
| P-trend | 0.016 | 0.019 | 0.014 | 0.018 | 0.016 | |
| Trunk FMR | All | 1.20 (1.07–1.34) | 1.20 (1.07–1.34) | 1.20 (1.08–1.34) | 1.20 (1.07–1.34) | 1.20 (1.07–1.34) |
| P-value | 0.002 | 0.002 | 0.002 | 0.002 | 0.002 | |
| Q1 | Ref. | Ref. | Ref. | Ref. | Ref. | |
| Q2 | 1.05 (0.76–1.44) | 1.04 (0.76–1.43) | 1.07 (0.78–1.46) | 1.05 (0.76–1.44) | 1.05 (0.76–1.45) | |
| Q3 | 1.14 (0.81–1.60) | 1.13 (0.80–1.59) | 1.15 (0.82–1.62) | 1.13 (0.80–1.60) | 1.14 (0.81–1.61) | |
| Q4 | 1.57 (1.11–2.23) | 1.56 (1.10–2.22) | 1.59 (1.12–2.26) | 1.57 (1.11–2.23) | 1.58 (1.11–2.25) | |
| P-trend | 0.009 | 0.010 | 0.008 | 0.009 | 0.009 | |
| Total FMR | All | 1.25 (1.10–1.41) | 1.24 (1.10–1.41) | 1.25 (1.10–1.42) | 1.25 (1.10–1.41) | 1.25 (1.10–1.42) |
| P-value | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | |
| Q1 | Ref. | Ref. | Ref. | Ref. | Ref. | |
| Q2 | 0.95 (0.66–1.36) | 0.94 (0.66–1.35) | 0.96 (0.67–1.37) | 0.95 (0.66–1.36) | 0.95 (0.66–1.36) | |
| Q3 | 1.14 (0.75–1.75) | 1.14 (0.74–1.74) | 1.16 (0.76–1.77) | 1.14 (0.75–1.74) | 1.15 (0.75–1.76) | |
| Q4 | 1.59 (1.04–2.44) | 1.57 (1.02–2.42) | 1.61 (1.05–2.47) | 1.59 (1.03–2.44) | 1.60 (1.04–2.46) | |
| P-trend | 0.012 | 0.014 | 0.011 | 0.012 | 0.012 | |
All analyses were adjusted for gender, age, race/ethnicity, educational level, marital status, poverty income ratio, vigorous recreational activity, moderate recreational activity, diabetes, hypertension, and cardiovascular disease. OR odds ratio, CI confidence interval, FMR fat‑to‑muscle mass ratio
Discussion
This large, cross-sectional study of a nationally representative US cohort demonstrated a robust association between an elevated FMR in the leg, trunk, and total body and an increased likelihood of clinically significant depressive symptoms. This association persisted even after comprehensive adjustments for sociodemographic, lifestyle, and cardiometabolic confounders, and remained stable across all sensitivity analyses. These findings, therefore, suggest that FMR, an integrative body composition metric, plays a role in the underlying pathology of depression.
FMR, as a novel anthropometric parameter, captures changes in both adipose and skeletal muscle tissue metabolic function. While previous research has extensively examined the independent effects of adiposity and skeletal muscle mass on depression, studies directly exploring the integrative role of FMR are still lacking [6, 7]. Our findings reveal a significant positive correlation between elevated leg, trunk, and total FMR and depression, which may reflect the synergistic effect of adiposity accumulation and skeletal muscle loss on the individual’s risk of developing depression.
Skeletal muscle is not only essential for maintaining metabolic homeostasis but also acts as a dynamic endocrine organ, secreting myokines that improve insulin sensitivity, regulate energy metabolism, and influence neuroendocrine pathways [30]. Loss of muscle mass and function (sarcopenia) has been consistently associated with higher depression risk [31]. Recent studies have demonstrated that each additional kilogram of appendicular lean mass (ALM) corresponds to an approximate 5.5% reduction in depression risk [6]. Parallel findings emerged in a Korean cohort [32], while Mendelian randomization analyses from the FinnGen project furnish genetic evidence supporting a causal relationship between muscle mass and depression [33]. As a principal target of insulin action, skeletal muscle loss may exacerbate insulin resistance, a metabolic disturbance frequently observed in depression [34, 35]. Reduced contractile strength of skeletal muscle can diminish brain-derived neurotrophic factor (BDNF) levels, impair hippocampal neurogenesis, and weaken synaptic plasticity, thereby promoting the onset of depression [36, 37]. Oxidative stress and chronic low-grade inflammation constitute shared pathological mechanisms linking muscle loss and depression [38]. Skeletal muscle activity may bolster immune function, sustain redox homeostasis, and stabilize overall mood [39, 40]. A chronic inflammatory cascade characterized by elevated levels of tumor necrosis factor-alpha, C-reactive protein, and interleukin-6 can activate the HPA axis, increasing concentrations of norepinephrine, dopamine, and serotonin in brain regions implicated in depression [41]. Moreover, increasing lean mass may positively impact mood disorders by fostering social engagement and support, alleviating feelings of isolation, and enhancing self-confidence and body image [42].
Adiposity, especially the accumulation of visceral fat, represents the pathological hallmark of obesity and significantly increases the risk of both metabolic and psychiatric disorders. Excess adipose tissue drives a state of chronic inflammation and metabolic imbalance through heightened secretion of proinflammatory cytokines (e.g. TNF-α, IL-6), the development of leptin resistance, and dysregulation of the HPA axis [43]. These processes can cross the blood brain barrier, disrupt monoamine neurotransmission, suppress BDNF expression, and impair neural plasticity, each of which contributes to the neurobiology of depression [44, 45]. Concurrently, obesity-associated insulin and leptin resistance may weaken dopaminergic signaling within the nucleus accumbens and undermine prefrontal limbic connectivity, thereby altering reward processing and mood regulation [46]. Furthermore, obesity-induced oxidative stress and gut microbial dysbiosis exacerbate neuroimmune activation along the gut brain axis [47, 48]. Empirical evidence supports the association between this pathological cascade and depression. For example, Zhu et al. reported in a cross-sectional study that each standard deviation increase in relative fat mass (RFM) corresponded to a 3.3% higher risk of depression [49], while large-scale Mendelian randomization analyses have further established body fat mass as a causal risk factor [50].
This study demonstrates that the relationship between FMR and depressive symptoms varies by region. Specifically, FMR in the lower limbs, trunk, and total body is significantly associated with depressive symptoms, whereas arm FMR shows no such relationship. This difference may be due to several factors, including trunk fat, particularly visceral fat, which contributes to systemic inflammation and insulin resistance, while lower limb muscle plays a key role in maintaining glucose homeostasis [51, 52]. Consequently, fat accumulation and muscle loss in these regions have a greater impact on depression-related pathways. In contrast, the smaller fat and muscle mass in the arms has a limited effect on metabolic status, insufficient to yield a significant statistical association. Additionally, the low variability in arm FMR may contribute to reduced statistical power. Future research should employ precise imaging techniques, along with inflammatory biomarkers, to further clarify the biological mechanisms behind these region-specific associations.
Our analysis not only confirms significant associations but also quantifies the predictive utility of FMR for depressive symptoms, with AUC values reflecting modest discriminatory accuracy (0.729–0.731). Although this suggests that FMR captures meaningful variance in depression risk, its performance is unlikely to be adequate for standalone screening in clinical practice. The optimal cut-off values, which balanced sensitivity and specificity, along with a clinically meaningful absolute risk difference of approximately 17%, underscore FMR’s potential as an objective and informative risk indicator in population health contexts. These findings merit further investigation in prospective and interventional settings.
The relationship between FMR and depressive symptoms was best characterized by a monotonic, linear increase across all body regions, as demonstrated by restricted cubic spline analysis. This suggests that even modest increments in FMR are linked to a proportionally higher risk of depression, without evidence of a threshold effect. The consistency of these positive associations was reinforced by subgroup analyses, which revealed no significant effect modification by gender, age, ethnicity, socioeconomic status, marital status, physical activity, or cardiometabolic profile. The robustness of our primary findings was further validated through sensitivity analyses employing multiple imputation for missing data.
The consistent and generalizable relationship between FMR and depression underscores the importance of incorporating body composition into mental health screening and prevention efforts, rather than relying exclusively on weight. Nevertheless, several limitations must be acknowledged. This study employs a cross-sectional design, limiting causal inference and preventing the establishment of a temporal relationship between FMR and depressive symptoms. Depression was assessed using the PHQ-9, a commonly used tool in epidemiological studies, though less diagnostically accurate than structured clinical interviews, potentially leading to misclassification, particularly for mild depression. Although several confounders were adjusted for, residual confounding remains a concern. Notably, factors such as sleep quality, dietary habits, chronic pain, medication use, inflammatory biomarkers, and social support were not fully controlled in the NHANES dataset, which may influence the observed relationship between FMR and depressive symptoms. The predictive performance of FMR requires further evaluation. With an AUC of approximately 0.73, its discriminatory accuracy is modest but statistically significant, suggesting FMR’s potential as a risk indicator, though it may not suffice as a standalone screening tool in clinical practice. Moreover, the results are confined to US adults, limiting the generalizability to other ethnic and cultural populations.
Future research should utilize longitudinal designs to establish causality and clarify the temporal dynamics between FMR and depressive symptoms. Structured clinical interviews, such as the Structured Clinical Interview for DSM Disorders (SCID) or the Mini-International Neuropsychiatric Interview (MINI), are crucial for improving diagnostic accuracy and minimizing misclassification. Additionally, studies should account for a broader array of confounders to elucidate the biological mechanisms linking FMR and depression, incorporating inflammatory markers, myokines, and other relevant biomarkers. Intervention studies are critical, particularly those assessing the impact of resistance training and dietary modifications on FMR reduction. A promising direction would be the development of integrated risk models combining FMR with psychosocial and behavioral factors for more precise stratification and personalized prevention.
Conclusion
In summary, this study provides robust evidence of a positive, linear association between the FMR, particularly in the leg, trunk, and total body, and depressive symptoms among US adults. These findings position FMR as an objective and integrative risk indicator that physically embodies the link between metabolic health and mental well-being. Although its modest predictive accuracy limits its use as a standalone screening tool, FMR holds significant potential for improving risk stratification in public health. Future research should prioritize longitudinal designs to establish causality, explore underlying inflammatory and myokine-mediated pathways, and assess the efficacy of targeted interventions. Ultimately, integrating FMR with psychosocial and behavioral factors could create a more powerful and comprehensive tool for personalized depression prevention.
Acknowledgements
We acknowledge the use of publicly available data from the NHANES study.
Author contributions
Yuyao He: Writing—original draft, Conceptualization, Software, Methodology. Cheu Hiutung: Visualization, Investigation, Writing—review and editing (equal). Danfeng Tian: Conceptualization, Supervision, Writing—review and editing. Zhenyun Han: Writing—review and editing, Conceptualization, Funding acquisition (equal). Wenyue Hu: Writing—review and editing, Visualization, Supervision, Data curation (equal).
Funding
This work was supported by the Sanming Project of Medicine in Shenzhen (Grant No. SZZYSM202105010) and the National Natural Science Foundation of China (Grant No. 82274466).
Data availability
The datasets analyzed in this study are publicly available from NHANES (https://www.cdc.gov/nchs/nhanes).
Declarations
Ethics approval and consent to participate
This research was conducted using data from the NNHANES, which is publicly available and anonymized. The NHANES data collection is conducted in accordance with the ethical principles set out by the Declaration of Helsinki. Ethical approval for the survey was obtained from the National Center for Health Statistics (NCHS) Research Ethics Review Board (ERB). Since the data are anonymized and de-identified, no additional informed consent was required from individual participants for the use of these data in this study. The dataset is publicly accessible and can be freely used for research purposes, ensuring participant confidentiality and privacy.
Clinical trial registration
Not applicable. This study is a secondary analysis of data from the NHANES, an ongoing, publicly available observational cohort. As such, clinical trial registration was not required.
Informed consent statement
All participants provided informed consent prior to participation.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Zhenyun Han, Email: tohanzhenyun@sina.com.
Wenyue Hu, Email: maryhuyy@163.com.
References
- 1.Monroe SM, Harkness KL. Major depression and its recurrences: life course matters. Annu Rev Clin Psychol. 2022;18:329–57. 10.1146/annurev-clinpsy-072220-021440. [DOI] [PubMed] [Google Scholar]
- 2.World Health Organization. (2023). Depressive disorder (depression). March 31, 2023.https://www.who.int/zh/news-room/fact-sheets/detail/depression.
- 3.Ménard C, Hodes GE, Russo SJ. Pathogenesis of depression: insights from human and rodent studies. Neuroscience. 2016;321:138–62. 10.1016/j.neuroscience.2015.05.053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Milaneschi Y, Lamers F, Berk M, Penninx BWJH. Depression heterogeneity and its biological underpinnings: toward immunometabolic depression. Biol Psychiatry. 2020;88(5):369–80. 10.1016/j.biopsych.2020.01.014. [DOI] [PubMed] [Google Scholar]
- 5.Pilati I, Slee A, Frost R. Sarcopenic obesity and depression: a systematic review. J Frailty Aging. 2022;11(1):51–8. 10.14283/jfa.2021.39. [DOI] [PubMed] [Google Scholar]
- 6.Qiu L, Ren Y, Li J, Li M, Li W, Qin L, et al. Associations of muscle mass and strength with depression among US adults: a cross-sectional NHANES study. J Affect Disord. 2024;363:373–80. 10.1016/j.jad.2024.07.139. [DOI] [PubMed] [Google Scholar]
- 7.Gu W, Bao K, Li X, Xiang S, He J, He J, et al. Association between body fat percentage and depression: a cross-sectional study of NHANES. J Affect Disord. 2025;371:305–14. 10.1016/j.jad.2024.11.066. [DOI] [PubMed] [Google Scholar]
- 8.Zhang Z, Ruan XY, Ma W. The association between a body shape index and depressive symptoms: a cross-sectional study using NHANES data (2011–2018). Front Nutr. 2025;11:1510218. 10.3389/fnut.2024.1510218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes. 2008;32(6):959–66. 10.1038/ijo.2008.11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Sweatt K, Garvey WT, Martins C. Strengths and limitations of BMI in the diagnosis of obesity: What is the path forward? Curr Obes Rep. 2024;13(3):584–95. 10.1007/s13679-024-00580-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ravensbergen HR, Lear SA, Claydon VE. Waist circumference is the best index for obesity-related cardiovascular disease risk in individuals with spinal cord injury. J Neurotrauma. 2014;31(3):292–300. 10.1089/neu.2013.3042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Dong H, Liu Y, Wang X, Liu P. Associations between total and regional fat-to-muscle mass ratio with the prevalence of infertility: a cross-sectional study. J Multidiscip Healthc. 2025;18:2173–84. 10.2147/JMDH.S517307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Wang N, Sun Y, Zhang H, Chen C, Wang Y, Zhang J, et al. Total and regional fat-to-muscle mass ratio measured by bioelectrical impedance and risk of incident type 2 diabetes. J Cachexia Sarcopenia Muscle. 2021;12(6):2154–62. 10.1002/jcsm.12822. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Hoffmann U, Vasan RS, Irlbeck T, Meigs JB, Sutherland P, D’Agostino RB, Sr O’Donnell CJ, Fox CS. Abdominal subcutaneous and visceral adipose tissue and insulin resistance in the Framingham heart study. Obesity (Silver Spring, Md.), 2010;18(11):2191–2198. 10.1038/oby.2010.59. [DOI] [PMC free article] [PubMed]
- 15.Nishikori S, Fujita S. Association of fat-to-muscle mass ratio with physical activity and dietary protein, carbohydrate, sodium, and fiber intake in a cross-sectional study. Sci Rep. 2024;14(1):10631. 10.1038/s41598-024-61289-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ye C, Chen G, Huang W, Liu Y. Association between skeletal muscle mass to visceral fat area ratio and depression: a cross-sectional study based on the National Health and Nutrition Examination Survey. J Affect Disord. 2025;372:314–23. 10.1016/j.jad.2024.12.041. [DOI] [PubMed] [Google Scholar]
- 17.Gawlik-Kotelnicka O, Strzelecki D. Adiposity in depression or depression in adiposity? The role of immune-inflammatory-microbial overlap. Life (Basel, Switzerland). 2021;11(2):117. 10.3390/life11020117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Capuron L, Lasselin J, Castanon N. Role of adiposity-driven inflammation in depressive morbidity. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol. 2017;42(1):115–28. 10.1038/npp.2016.123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Milaneschi Y, Simmons WK, van Rossum EFC, Penninx BW. Depression and obesity: evidence of shared biological mechanisms. Mol Psychiatry. 2019;24(1):18–33. 10.1038/s41380-018-0017-5. [DOI] [PubMed] [Google Scholar]
- 20.Johnson CL, Dohrmann SM, Burt VL, Mohadjer LK. National health and nutrition examination survey: sample design, 2011–2014. Vital and health statistics. Series 2. Data Eval Methods Res. 2014;162:1–33. [PubMed] [Google Scholar]
- 21.Seo YG, Song HJ, Song YR. Fat-to-muscle ratio as a predictor of insulin resistance and metabolic syndrome in Korean adults. J Cachexia Sarcopenia Muscle. 2020;11(3):710–25. 10.1002/jcsm.12548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. 10.1046/j.1525-1497.2001.016009606.xI. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Ullah H, Di Minno A, Esposito C, El-Seedi HR, Khalifa SAM, Baldi A, et al. Efficacy of a food supplement based on S-adenosyl methionine and probiotic strains in subjects with subthreshold depression and mild-to-moderate depression: a monocentric, randomized, cross-over, double-blind, placebo-controlled clinical trial. Biomed Pharmacother Biomedec Pharmacother. 2022;156:113930. 10.1016/j.biopha.2022.113930. [DOI] [PubMed] [Google Scholar]
- 24.Liu X, Liu X, Wang Y, Zeng B, Zhu B, Dai F. Association between depression and oxidative balance score: National Health and Nutrition Examination Survey (NHANES) 2005–2018. J Affect Disord. 2023;337:57–65. [DOI] [PubMed] [Google Scholar]
- 25.Liu C, Yang J, Li H, Deng Y, Dong S, He P, et al. Association between life’s essential 8 and diabetic kidney disease: a population-based study. Ren Fail. 2025;47(1):2454286. 10.1080/0886022X.2025.2454286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Centers for Disease Control and Prevention. (2011–2012). National Health and Nutrition Examination Survey: Demographic data and related documentation. Retrieved February 1, 2020, from https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/DEMO_G.htm#INDFMPIR.
- 27.Dong G, Gan M, Xu S, Xie Y, Zhou M, Wu L. The neutrophil-lymphocyte ratio as a risk factor for all-cause and cardiovascular mortality among individuals with diabetes: evidence from the NHANES 2003–2016. Cardiovasc Diabetol. 2023;22(1):267. 10.1186/s12933-023-01998-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Yuan M, He J, Hu X, Yao L, Chen P, Wang Z, et al. Hypertension and NAFLD risk: insights from the NHANES 2017–2018 and Mendelian randomization analyses. Chin Med J. 2024;137(4):457–64. 10.1097/CM9.0000000000002753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Peng C, Fang MS. Association of dietary antioxidant intake with depression risk and all-cause mortality in people with prediabetes. Sci Rep. 2024;14(1):20009. 10.1038/s41598-024-71152-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Balakrishnan R, Thurmond DC. Mechanisms by which skeletal muscle myokines ameliorate insulin resistance. Int J Mol Sci. 2022;23(9):4636. 10.3390/ijms23094636. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Li Z, Tong X, Ma Y, Bao T, Yue J. Prevalence of depression in patients with sarcopenia and correlation between the two diseases: systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2022;13(1):128–44. 10.1002/jcsm.12908. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Heo JE, Shim JS, Song BM, Bae HY, Lee HJ, Lee E, et al. Association between appendicular skeletal muscle mass and depressive symptoms: review of the cardiovascular and metabolic diseases etiology research center cohort. J Affect Disord. 2018;238:8–15. 10.1016/j.jad.2018.05.012. [DOI] [PubMed] [Google Scholar]
- 33.Lv Z, Zhao Y, Cui J, Zhang J. Genetically proxied sarcopenia-related muscle traits and depression: evidence from the FinnGen cohort. Am J Geriatr Psychiatry. 2024;32(1):32–41. 10.1016/j.jagp.2023.08.001. [DOI] [PubMed] [Google Scholar]
- 34.Mai Z, Chen Y, Mao H, Wang L. Association between the skeletal muscle mass to visceral fat area ratio and metabolic dysfunction-associated fatty liver disease: a cross-sectional study of NHANES 2017–2018. J Diabetes. 2024;16(6):e13569. 10.1111/1753-0407.13569. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Fernandes BS, Salagre E, Enduru N, Grande I, Vieta E, Zhao Z. Insulin resistance in depression: a large meta-analysis of metabolic parameters and variation. Neurosci Biobehav Rev. 2022;139:104758. 10.1016/j.neubiorev.2022.104758. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Wang Z, Wu M, Shao X, Yang Q. Muscle quality index is associated with depression among non-elderly US adults. BMC Psychiatry. 2024;24(1):672. 10.1186/s12888-024-06136-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Zhang JC, Yao W, Hashimoto K. Brain-derived neurotrophic factor (BDNF)-TrkB signaling in inflammation-related depression and potential therapeutic targets. Curr Neuropharmacol. 2016;14(7):721–31. 10.2174/1570159x14666160119094646. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Gan D, Wu X, Zhang Y, Gao Y, Huang X, Han S. Dietary influence on the link between depression and muscle mass and muscle strength: exploring interaction and mediation effects. BMC Public Health. 2025;25(1):764. 10.1186/s12889-025-21975-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Pedersen BK, Febbraio MA. Muscles, exercise and obesity: skeletal muscle as a secretory organ. Nat Rev Endocrinol. 2012;8(8):457–65. 10.1038/nrendo.2012.49. [DOI] [PubMed] [Google Scholar]
- 40.Maes M, Ruckoanich P, Chang YS, Mahanonda N, Berk M. Multiple aberrations in shared inflammatory and oxidative & nitrosative stress (IO&NS) pathways explain the co-association of depression and cardiovascular disorder (CVD), and the increased risk for CVD and due mortality in depressed patients. Prog Neuropsychopharmacol Biol Psychiatry. 2011;35(3):769–83. 10.1016/j.pnpbp.2010.06.008. [DOI] [PubMed] [Google Scholar]
- 41.Miller AH, Maletic V, Raison CL. Inflammation and its discontents: the role of cytokines in the pathophysiology of major depression. Biol Psychiatry. 2009;65(9):732–41. 10.1016/j.biopsych.2008.11.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Alfaro-González S, Garrido-Miguel M, Pascual-Morena C, Pozuelo-Carrascosa DP, Fernández-Rodríguez R, Martínez-Hortelano JA, et al. The association between adherence to the Mediterranean diet and depression and anxiety symptoms in university students: the mediating role of lean mass and the muscle strength index. Nutrients. 2025;17(2):346. 10.3390/nu17020346. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Selman A, Dai J, Driskill J, Reddy AP, Reddy PH. Depression and obesity: focus on factors and mechanistic links. Biochim Biophys Acta Mol Basis Dis. 2025;1871(1):167561. 10.1016/j.bbadis.2024.167561. [DOI] [PubMed] [Google Scholar]
- 44.Schachter J, Martel J, Lin CS, Chang CJ, Wu TR, Lu CC, et al. Effects of obesity on depression: a role for inflammation and the gut microbiota. Brain Behav Immun. 2018;69:1–8. 10.1016/j.bbi.2017.08.026. [DOI] [PubMed] [Google Scholar]
- 45.Liao SF, Su CY, Su MH, Chen CY, Chen CY, Lin YF, et al. Associations of polygenic risks, depression, and obesity-related traits in Taiwan Biobank. J Affect Disord. 2023;320:397–403. 10.1016/j.jad.2022.09.149. [DOI] [PubMed] [Google Scholar]
- 46.Kroemer NB, Opel N, Teckentrup V, Li M, Grotegerd D, Meinert S, et al. Functional connectivity of the Nucleus Accumbens and changes in appetite in patients with depression. JAMA Psychiatr. 2022;79(10):993–1003. 10.1001/jamapsychiatry.2022.2464. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Bhatt S, Nagappa AN, Patil CR. Role of oxidative stress in depression. Drug Discov Today. 2020;25(7):1270–6. 10.1016/j.drudis.2020.05.001. [DOI] [PubMed] [Google Scholar]
- 48.Refisch A, Sen ZD, Klassert TE, Busch A, Besteher B, Danyeli LV, et al. Microbiome and immuno-metabolic dysregulation in patients with major depressive disorder with atypical clinical presentation. Neuropharmacology. 2023;235:109568. 10.1016/j.neuropharm.2023.109568. [DOI] [PubMed] [Google Scholar]
- 49.Zhu X, Yue Y, Li L, Zhu L, Cai Y, Shu Y. The relationship between depression and relative fat mass (RFM): a population-based study. J Affect Disord. 2024;356:323–8. 10.1016/j.jad.2024.04.031. [DOI] [PubMed] [Google Scholar]
- 50.Speed MS, Jefsen OH, Børglum AD, Speed D, Østergaard SD. Investigating the association between body fat and depression via Mendelian randomization. Transl Psychiatry. 2019;9(1):184. 10.1038/s41398-019-0516-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Lei J, Luo Y, Xie Y, Wang X. Visceral adiposity index is a measure of the likelihood of developing depression among adults in the United States. Front Psychol. 2022;13:772556. 10.3389/fpsyg.2022.772556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Yamamoto S, Matsushita Y, Nakagawa T, Honda T, Hayashi T, Noda M, et al. Visceral fat accumulation, insulin resistance, and elevated depressive symptoms in middle-aged Japanese men. PLoS ONE. 2016;11(2):e0149436. 10.1371/journal.pone.0149436. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets analyzed in this study are publicly available from NHANES (https://www.cdc.gov/nchs/nhanes).