Abstract
Purpose
Muscle Quality Index (MQI), defined as the muscle strength per unit of muscle mass, is considered an emerging indicator of health and physical function. This study aims to explore the relationship between MQI and the risk of depression among non-elderly US adults.
Methods
This cross-sectional study collected data from participants aged between 20 and 59 years old, utilizing the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014. The MQI was calculated by dividing the handgrip strength (HGS, kg) by the arm and appendicular skeletal muscle mass (ASM, kg). Depression assessments were conducted using the Patient Health Questionnaire (PHQ-9). The relationship between MQI and the risk of depression was explored by weighted logistic regression, smooth curve fitting, and subgroup analyses.
Results
A total of 4773 participants were included in this study. After adjusting for confounding factors, low MQI levels were identified as an independent risk factor for depression (OR = 0.800, 95%CI:0.668–0.957, P = 0.015). Smooth curve fitting analysis indicated a nonlinear relationship. Subgroup analysis did not identify any specific populations.
Conclusions
Higher MQI levels were closely associated with a lower risk of depression among non-elderly US adults. MQI could enhance our understanding of the link between muscle and depression and might serve as a simple functional measure for evaluating and predicting depression.
Keywords: Depression, Muscle quality index, NHANES, Nonlinear relationship, Cross-sectional study
Introduction
Mental disorders are a major cause of the global health-related burden [1]. Depression, a prevalent mental disorder, often manifests as sustained low mood, lack of interest, and suicidal ideation, accompanying some potential complications like diabetes, obesity, and cardiovascular diseases [2]. Depression significantly affects individuals across various age groups, including the non-elderly population [3]. Statistics indicate that the incidence of depression among non-elderly individuals has been steadily increasing, highlighting the urgent need for effective management strategies [3, 4]. At present, the management of depression is generally confined to pharmacotherapy, psychotherapy, electroconvulsive therapy, and integrative therapy, neither of which is completely effective [5]. Pharmacotherapy often causes adverse reactions in clinical applications, and other therapies usually require more time and financial investment, posing challenges to patient compliance [5]. Therefore, predicting and preventing depression is particularly crucial. Currently, there is an urgent need for accurate and easily accessible indicators to evaluate the onset and progression of depression in clinical practice.
Sarcopenia is a syndrome characterized by a continuous decrease in skeletal muscle mass, strength, and function, and is closely associated with an increased risk of depression [6]. Handgrip strength (HGS) measures arm strength and provides an indirect indication of overall muscle strength [7]. Studies suggest that HGS is a potential indicator of mortality, functional disabilities, fractures, mental health issues, and chronic diseases [8–11]. Nevertheless, muscle strength tends to decrease more rapidly than muscle mass, so the declines in muscle strength and muscle mass do not happen simultaneously [12]. Therefore, a thorough assessment of muscle functional status necessitates consideration of both muscle strength and muscle mass. The Muscle Quality Index (MQI), calculated by dividing the HGS by the arm and appendicular skeletal muscle mass (ASM), and defined as muscle strength per unit of muscle mass, is emerging as a significant indicator of health and physical function [12, 13]. Current evidence indicates that a low MQI level is linked with trouble sleeping, metabolic syndrome, cardiovascular diseases, chronic kidney disease and a higher risk of mortality [13–16].
At present, to our knowledge, there is a lack of research on the relationship between MQI and depression in non-elderly adult population. To fill this gap, this research uses data from the National Health and Nutrition Examination Survey (NHANES) to investigate the link between MQI and depression in non-elderly adult population.
Materials and methods
Study population
Data for this study were derived from NHANES, a comprehensive survey conducted by the National Center for Health Statistics of the Centers for Disease Control and Prevention [17]. The survey utilizes a stratified, multi-stage random sampling approach to represent the national demographic accurately. Study participants underwent extensive physical exams, completed detailed health and nutrition surveys, and were involved in laboratory testing. The NHANES study protocol was approved by the Ethics Review Board of the National Center for Health Statistics. Detailed designs and data from this study are available at https://www.cdc.gov/nchs/nhanes/. This research combined participants from the NHANES 2011–2012 and 2013–2014 cycles, enrolling a total of 4773 participants (Fig. 1). Exclusion criteria included participants under 20 or over 59 years old, those lacking depression or MQI data, and those missing information on other covariates.
Fig. 1.
Flow chart
Exposure and outcome definitions
MQI was calculated by dividing the HGS (from both the dominant and non-dominant hands) by the ASM [12, 13]. HGS was measured using a hand dynamometer, while ASM was evaluated using dual-energy X-ray absorptiometry (DXA). For ASM calculation, the total lean soft tissue mass of the four limbs was determined through body composition analysis conducted by DXA. On the other hand, depression was assessed using the Patient Health Questionnaire (PHQ-9), a nine-item instrument that measures the frequency of depressive symptoms over the previous two weeks [18]. Each response is scored from 0 to 3, which represent “not at all,” “several days,” “more than half the days,” and “nearly every day,” respectively. The questionnaire is consistent with DSM-IV standards for diagnosing depression, with total scores ranging from 0 to 27. Individuals scoring 10 or above were considered to have major depression [18].
Covariate definitions
Based on prior research, several confounding factors that might influence the relationship between MQI and the risk of depression were considered, including sociodemographic characteristics, lifestyle habits, and health conditions [19–21]. Sociodemographic aspects encompassed age, gender, race, marital status, household income, and educational level. Races were classified into Mexican American, Non-Hispanic White, Non-Hispanic Black, Other Hispanic, and Other Races. Marital status was categorized as either married or unmarried, while household income was divided into below $20,000 annually or not. Educational levels were distinguished by whether individuals had graduated from high school or not. Lifestyle factors included smoking status, categorized into former, current, and never smokers; alcohol consumption, defined as having twelve or more drinks per year; and physical activity, which was classified as engaging in moderate or vigorous activities. Health conditions considered were diabetes, hypertension, cardiovascular diseases, chronic kidney disease (CKD), body mass index (BMI, kg/m2), and glycated hemoglobin (HbA1c, %). Both diabetes and hypertension were self-reported (DIQ010-Doctor told you have diabetes; BPQ020-Ever told you had high blood pressure). Cardiovascular diseases were identified through self-reports of conditions like heart attacks, strokes, heart failure, coronary artery disease, or angina. The diagnosis of chronic kidney disease uses the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula to calculate an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2 or an albumin-to-creatinine ratio ≥ 30 mg/g [22].
Statistical analysis
Appropriate weighting methods were utilized to address the complex sampling design, ensuring nationally representative results in accordance with NHANES guidelines [23]. Continuous variables were presented with means and standard errors (SE), and categorical variables were shown as percentages. Differences between groups were assessed using weighted t-tests for continuous data and weighted chi-square tests for categorical data. Three weighted logistic regression models were developed to explore the relationship between MQI and depression risk. Model 1 was unadjusted. Model 2 included adjustments for age, gender, and race. Model 3 added further adjustments for marital status, household annual income, educational level, smoking status, alcohol consumption, physical activity, diabetes, hypertension, cardiovascular disease, CKD, BMI, and HbA1c. Additionally, weighted linear regression analyses examined the link between MQI and PHQ-9 scores. The presence of collinearity was checked using the Variance Inflation Factor (VIF). Weighted smooth curve fitting with generalized additive models (GAMs) was employed to explore the potential nonlinear relationship. GAMs are effective tools for exploring nonlinear relationships in data. They extend linear models by incorporating nonlinear functions for predictor variables while maintaining their additive structure [24]. A two-piecewise linear regression model was applied to identify thresholds. Subgroup analyses were performed based on the covariates. All statistical analyses were carried out using R software and EmpowerStats (http://www.empowerstats.com), with a two side P value < 0.05 considered statistically significant.
Results
Baseline characteristics of study population
This study included 4773 participants, with an average age of 39.31 years. The total sample comprised 51.68% males and 48.32% females. We compared the clinical characteristics across different quartiles of MQI, as shown in Table 1. Compared to the low MQI group, the high MQI group was younger, more likely to be male, and had a higher educational level (P < 0.01). In terms of lifestyle habits, the high MQI group had lower rates of smoking and alcohol consumption and were more physically active (P < 0.05). Additionally, fewer individuals in this group were diagnosed with hypertension, diabetes, cardiovascular diseases, and CKD (P < 0.01). They also showed higher levels of HGS, lower levels of ASM, BMI, and HbA1c (P < 0.001). There were also significant differences in racial distribution among the groups (P < 0.001). Notably, compared to the lowest MQI group, the higher MQI group had lower PHQ-9 scores and a smaller proportion of individuals with depression (12.03% vs. 7.32% vs. 6.49% vs. 7.00%, P < 0.001).
Table 1.
Baseline characteristics according to MQI quartiles, weighted
| Overall (n = 4773) |
Quartile 1 (1.12–2.95) | Quartile 2 (2.95–3.38) |
Quartile 3 (3.38–3.81) |
Quartile 4 (3.81–5.83) |
P value | |
|---|---|---|---|---|---|---|
| Age (years) | 39.31 ± 0.39 | 41.18 ± 0.65 | 39.89 ± 0.54 | 38.77 ± 0.56 | 37.60 ± 0.50 | 0.001 |
| Gender, % | < 0.001 | |||||
| Female | 48.32 | 58.88 | 48.86 | 45.94 | 40.73 | |
| Male | 51.68 | 41.12 | 51.14 | 54.06 | 59.27 | |
| Race, % | < 0.001 | |||||
| Mexican American | 9.50 | 9.51 | 9.24 | 8.84 | 10.45 | |
| Non-Hispanic Black | 10.79 | 18.36 | 10.59 | 8.21 | 6.89 | |
| Non-Hispanic White | 65.49 | 60.00 | 66.08 | 68.65 | 66.50 | |
| Other Hispanic | 6.20 | 5.90 | 7.48 | 5.98 | 5.41 | |
| Other Races | 8.02 | 6.23 | 6.61 | 8.32 | 10.75 | |
| Married, % | 0.117 | |||||
| No | 48.15 | 51.86 | 46.14 | 46.29 | 48.80 | |
| Yes | 51.85 | 48.14 | 53.86 | 53.71 | 51.20 | |
| Household annual income (below $20,000), % | 0.305 | |||||
| No | 87.01 | 85.07 | 87.47 | 87.49 | 87.78 | |
| Yes | 12.99 | 14.93 | 12.53 | 12.51 | 12.22 | |
| Educational level (above high school), % | < 0.001 | |||||
| No | 33.66 | 38.08 | 29.90 | 30.27 | 36.96 | |
| Yes | 66.34 | 61.92 | 70.10 | 69.73 | 63.04 | |
| Smoking status, % | 0.008 | |||||
| Never | 58.28 | 55.96 | 57.99 | 58.86 | 60.51 | |
| Current | 22.69 | 27.49 | 21.55 | 19.32 | 22.48 | |
| Former | 19.04 | 16.55 | 20.46 | 21.82 | 17.02 | |
| Alcohol consumption, % | < 0.001 | |||||
| No | 17.09 | 15.33 | 15.67 | 16.48 | 21.37 | |
| Yes | 82.91 | 84.67 | 84.33 | 83.52 | 78.63 | |
| Physical activity, % | 0.048 | |||||
| Inactive | 54.74 | 58.85 | 54.53 | 54.94 | 51.02 | |
| Active | 45.26 | 41.15 | 45.47 | 45.06 | 48.98 | |
| Diabetes, % | < 0.001 | |||||
| No | 94.42 | 87.87 | 93.90 | 96.86 | 98.30 | |
| Yes | 5.58 | 12.13 | 6.10 | 3.14 | 1.70 | |
| Hypertension, % | < 0.001 | |||||
| No | 76.93 | 67.87 | 75.01 | 80.95 | 82.78 | |
| Yes | 23.07 | 32.13 | 24.99 | 19.05 | 17.22 | |
| Cardiovascular disease, % | 0.003 | |||||
| No | 96.87 | 94.81 | 97.33 | 97.19 | 97.94 | |
| Yes | 3.13 | 5.19 | 2.67 | 2.81 | 2.06 | |
| CKD, % | < 0.001 | |||||
| No | 92.13 | 88.27 | 92.76 | 94.20 | 92.76 | |
| Yes | 7.87 | 11.73 | 7.24 | 5.80 | 7.24 | |
| BMI (kg/m2) | 28.64 ± 0.18 | 34.44 ± 0.29 | 29.34 ± 0.23 | 26.83 ± 0.16 | 24.59 ± 0.11 | < 0.001 |
| HbA1c (%) | 5.50 ± 0.02 | 5.84 ± 0.04 | 5.51 ± 0.03 | 5.37 ± 0.02 | 5.32 ± 0.02 | < 0.001 |
| HGS (kg) | 77.62 ± 0.39 | 66.15 ± 0.80 | 75.79 ± 0.74 | 80.41 ± 0.85 | 86.92 ± 0.87 | < 0.001 |
| ASM (kg) | 23.13 ± 0.16 | 25.63 ± 0.32 | 23.86 ± 0.23 | 22.38 ± 0.23 | 20.93 ± 0.22 | < 0.001 |
| PHQ-9 score | 2.92 ± 0.09 | 3.77 ± 0.20 | 2.78 ± 0.14 | 2.68 ± 0.13 | 2.55 ± 0.15 | < 0.001 |
| Depression, % | < 0.001 | |||||
| No | 91.91 | 87.97 | 92.68 | 93.51 | 93.00 | |
| Yes | 8.09 | 12.03 | 7.32 | 6.49 | 7.00 |
Note: Values for categorical variables are given as weighted percentage; for continuous variables, as weighted mean ± standard error
Associations between MQI and depression
Negative correlations between MQI levels and the prevalence of depression are consistently observed across various logistic regression adjustments, including Model 1, Model 2, and Model 3 (P < 0.05) (Table 2). After full adjustment, an increase of one unit in MQI level is associated with a 20% lower risk of depression (OR = 0.800, 95%CI:0.668–0.957, P = 0.015). The quartile analysis of MQI levels shows that the risk of depression is reduced in the second, third, and fourth quartiles compared to the lowest quartile (P < 0.05). Linear regression results also confirm a significant link between MQI and PHQ-9 scores after adjusting for confounding factors (β=-0.524, 95%CI: -0.718–0.331, P < 0.001), as depicted in Table 3. Smooth curve fitting indicates a nonlinear relationship between MQI levels and depression (Fig. 2). A two-segment linear regression model identifies a breakpoint at 3.419 for the overall population’s MQI level (Table 4). Below this threshold, higher MQI levels are linked to a lower risk of depression, with an OR of 0.569 and a 95% CI between 0.426 and 0.759. Above this threshold, the association between MQI levels and depression risk seems to level off.
Table 2.
Logistic regression analysis results of MQI and depression
| OR (95%CI) P value | |||
|---|---|---|---|
| Depression | Model 1 | Model 2 | Model 3 |
| Continuous | |||
| MQI | 0.649 (0.551, 0.765) < 0.001 | 0.712 (0.600, 0.846) < 0.001 | 0.800 (0.668, 0.957) 0.015 |
| Categories | |||
| Quantile 1 | reference | reference | reference |
| Quantile 2 | 0.616 (0.468, 0.810) < 0.001 | 0.635 (0.480, 0.839) 0.001 | 0.725 (0.540, 0.973) 0.032 |
| Quantile 3 | 0.524 (0.393, 0.697) < 0.001 | 0.566 (0.422, 0.759) < 0.001 | 0.660 (0.484, 0.902) 0.009 |
| Quantile 4 | 0.558 (0.422, 0.740) < 0.001 | 0.650 (0.485, 0.871) 0.004 | 0.754 (0.552, 1.031) 0.077 |
| P for trend | < 0.001 | 0.001 | 0.047 |
OR: odds ratio
95% CI: 95% confidence interval
Model 1: no adjusted
Model 2: adjusted for age, gender, and race
Model 3: adjusted for age, gender, and race, marital status, household annual income, educational level, smoking status, alcohol consumption, physical activity, diabetes, hypertension, cardiovascular disease, CKD, BMI, and HbA1c
Table 3.
Linear regression analysis results of MQI and PHQ-9 score
| β (95%CI) P value | |||
|---|---|---|---|
| PHQ-9 score | Model 1 | Model 2 | Model 3 |
| Continuous | |||
| MQI | -0.805 (-0.996, -0.614) < 0.001 | -0.685 (-0.881, -0.489) < 0.001 | -0.524 (-0.718, -0.331) < 0.001 |
| Categories | |||
| Quantile 1 | reference | reference | reference |
| Quantile 2 | -0.865 (-1.203, -0.527) < 0.001 | -0.805 (-1.143, -0.467) < 0.001 | -0.555 (-0.881, -0.229) < 0.001 |
| Quantile 3 | -1.118 (-1.456, -0.780) < 0.001 | -0.999 (-1.340, -0.659) < 0.001 | -0.690 (-1.022, -0.358) < 0.001 |
| Quantile 4 | -1.238 (-1.576, -0.900) < 0.001 | -1.033 (-1.379, -0.688) < 0.001 | -0.776 (-1.115, -0.438) < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 |
Model 1: no adjusted
Model 2: adjusted for age, gender, and race
Model 3: adjusted for age, gender, and race, marital status, household annual income, educational level, smoking status, alcohol consumption, physical activity, diabetes, hypertension, cardiovascular disease, CKD, BMI, and HbA1c
Fig. 2.
The results of smooth curve fitting analysis
Table 4.
Threshold effect analysis of MQI on depression
| Model | OR (95%CI) P value |
|---|---|
| Depression | |
| Breakpoint | 3.419 |
| OR1 (< 3.419) | 0.569 (0.426, 0.759) < 0.001 |
| OR2 (> 3.419) | 1.273 (0.896, 1.808) 0.178 |
| OR2/OR1 | 2.239 (1.303, 3.847) 0.004 |
| P for logarithmic likelihood ratio | 0.004 |
Subgroup analyses
Based on factors such as age, gender, race, hypertension, diabetes, cardiovascular disease, and CKD, subgroup analyses were conducted to explore the relationship between MQI and depression prevalence (Fig. 3). Based on the results of the interaction test, we found that MQI and the risk of depression remain stable across various subgroups (P for interaction > 0.05).
Fig. 3.
The results of subgroup analysis
Discussion
To the best of our knowledge, this innovative study is the first to investigate the association between MQI and depression risk in non-elderly population. Our findings indicate that higher MQI levels are associated with a reduced risk of depression, even when considering confounding factors (OR = 0.800, 95%CI:0.668–0.957, P = 0.015). Smooth curve fitting analysis points to a nonlinear relationship, with a saturation threshold of 3.419.
Previous studies have primarily focused on the relationship between HGS as an indicator of muscle strength and the risk of depression. Based on the China Health and Retirement Longitudinal Study (CHARLS), there is a negative correlation between handgrip strength and depression among elderly community residents [25]. A prospective cohort study based on 162,167 participants from the UK Biobank demonstrates a negative correlation between HGS and incident depression [11]. Evidence from a meta-analysis suggests a significant inverse link between HGS and depression, evidenced by an OR of 0.74 (95% CI: 0.65–0.85), which supports the beneficial effects of handgrip strength on depression [26]. However, most studies have primarily targeted the elderly population and focused on the impact of muscle strength on depression, often neglecting non-elderly groups and overlooking the influence of muscle mass. Muscle mass is also a significant indicator of muscle condition. Recent Mendelian randomization research utilizing the FinnGen cohort has identified an independent causal relationship between appendicular lean mass and depression risk, offering genetic evidence [27]. A cross-sectional study from a middle-aged Korean population confirmed an independent association between ASM, assessed through bioelectrical impedance analysis, and depressive symptoms in the male population [28]. However, the decline in muscle strength is partly accompanied by a reduction in muscle mass, and standalone HGS measurements only partially reflect muscle mass [29]. Previous scholars have also suggested that relative HGS may better reflect the true muscle condition and physical function [30, 31]. We further emphasized the link between muscle condition and depression risk in the non-elderly group. Increasing MQI levels through active physical activity may be an effective way to prevent and treat depressive symptoms [32]. It is noteworthy that there is a nonlinear relationship between MQI and the risk of depression. Higher levels of MQI are associated with a reduced risk of depression, and as MQI levels increase, this relationship tends to saturate. In practical terms, understanding the connection between muscle status and mental health can guide depression prevention and treatment. Programs that promote muscle-enhancing physical activities may reduce depression risk in those with lower MQI. Regular MQI evaluations could be incorporated into mental health screenings for depression-prone individuals. Health professionals could adopt the identified MQI threshold to identify individuals at risk of depression, particularly within groups prone to muscle loss. Public health initiatives might also include muscle training as an accessible way to improve mental health. Should an individual’s MQI be below this critical threshold, healthcare practitioners might recommend interventions that emphasize physical exercise and muscle building as components of a holistic depression treatment strategy.
Several potential findings help explain the association of MQI and depression. Sarcopenia, characterized by a decline in muscle strength and mass, is recognized as an independent risk factor for depression [6]. This condition is often linked to severe complications, extended hospital stays, and a diminished quality of life, all of which can increase the risk of depression [33, 34]. Additionally, muscle condition is a crucial reflection of physical activity [35]. Engaging in physical activity or strength training can help treat and prevent symptoms of depression; however, individuals with depression tend to be less physically active, creating a negative feedback loop [32, 36]. Muscle condition also could impact the socio-psychological dimensions of depression. Individuals with low muscle mass may develop a negative self-perception regarding their health and body, exacerbating depressive symptoms [6]. Muscles can secrete myokines that travel through the bloodstream to the brain, regulating brain function and potentially reducing the risk of depression [37]. Previous research has identified specific myokines, such as brain-derived neurotrophic factor and irisin, which can mediate the signaling pathways of depressive symptoms [38, 39].
However, this study still has several limitations. Firstly, the study employed a cross-sectional design, which does not allow for the determination of causality; clinical intervention and cohort studies are essential. Secondly, this study attempted to control for potential confounding factors, but based on the available data, it was not possible to adjust for some potential confounders. Thirdly, the reliance on participants’ self-reported medical history might have skewed our results to some extent. Lastly, our conclusions are based on the non-elderly US population, so caution should be exercised when interpreting these results for other regions and elderly populations.
Conclusion
In conclusion, our study utilized a nationally representative cohort of non-elderly adults in the United States, revealing a robust nonlinear inverse relationship between MQI and depression. These findings suggest that maintaining appropriate MQI levels may help in preventing and treating depression risk.
Acknowledgements
We want to acknowledge all participants of this study and the support provided by the Xuzhou Medical University and the Jiangsu University.
Author contributions
Z.W. and M.W. wrote the main manuscript text. X.S. prepared figures and tables. Q.Y. reviewed the manuscript.
Funding
The study is funded by the Science and Technology Project of Changzhou Health Commission (WZ202226) and the Young Talent Development Plan of Changzhou Health Commission (CZQM2022029).
Data availability
The datasets generated and/or analyzed during the current study are available in the NHANES database (https://www.cdc.gov/nchs/nhanes/).
Declarations
Ethics approval and consent to participation
The study analyzed data obtained from the public database of the National Health and Nutrition Examination Survey (NHANES). Ethics approval was granted by the National Center for Health Statistics Ethics Review Committee. The research was conducted in accordance with relevant guidelines and regulations (Declaration of Helsinki). All individuals provided written informed consent before participating in the study. Details are available at https://www.cdc.gov/nchs/nhanes/irba98.htm.
Consent for publication
Not applicable.
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.
Zhaoxiang Wang and Menghuan Wu contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available in the NHANES database (https://www.cdc.gov/nchs/nhanes/).