Next Article in Journal
Ictal Fear or Panic Attack, This Is the Question—A Video–EEG Study
Previous Article in Journal
Endocannabinoid System Changes throughout Life: Implications and Therapeutic Potential for Autism, ADHD, and Alzheimer’s Disease
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Brain Sciences
Volume 14
Issue 6
10.3390/brainsci14060593
brainsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Bidirectional Association between Sarcopenia and Depressive Symptoms among Chinese Middle- and Older-Aged Adults: Longitudinal Observational Study

by
Na Zeng
1,2,
Chao Li
3,
Huan Mei
1,2,
Shuilin Wu
1,2,
Chang Liu
1,2,
Xiaokun Wang
1,2,
Jie Shi
2,
Lin Lu
2,4,5,* and
Yanping Bao
1,2,*
1
School of Public Health, Peking University, Beijing 100191, China
2
National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China
3
Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
4
National Clinical Research Center for Mental Disorders, Key of Mental Health, Ministry of Health (Peking University), Peking University Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
5
Peking-Tsinghua Centre for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
Brain Sci. 2024, 14(6), 593; https://doi.org/10.3390/brainsci14060593
Submission received: 10 April 2024 / Revised: 20 May 2024 / Accepted: 23 May 2024 / Published: 11 June 2024
(This article belongs to the Section Neuropsychiatry)
Browse Figures
Versions Notes

Abstract

:
Background: The study aimed to examine the bidirectional relationship between sarcopenia and depressive symptoms in a national, community-based cohort study, despite the unclear temporal sequence demonstrated previously. Methods: Data were derived from four waves (2011 baseline and 2013, 2015, and 2018 follow-ups) of the China Health and Retirement Longitudinal Study (CHARLS). A total of 17,708 participants aged 45 years or older who had baseline data on both sarcopenia status and depressive symptoms in 2011 were included in the study. For the two cohort analyses, a total of 8092 adults without depressive symptoms and 11,292 participants without sarcopenia in 2011 were included. Sarcopenia status was defined according to the Asian Working Group for Sarcopenia 2019 (AWGS 2019) criteria. Depressive symptoms were defined as a score of 20 or higher on the 10-item Center for Epidemiologic Studies Depressive Scale (CES-D-10). Cox proportional hazard regression models were conducted to examine the risk of depressive symptoms and sarcopenia risk, while cross-lagged panel models were used to examine the temporal sequence between depressive symptoms and sarcopenia over time. Results: During a total of 48,305.1 person-years follow-up, 1262 cases of incident depressive symptoms were identified. Sarcopenia exhibited a dose–response relationship with a higher risk of depressive symptoms (HR = 1.7, 95%CI: 1.2–2.3 for sarcopenia, and HR = 1.5, 95%CI: 1.2–1.8 for possible sarcopenia, p trend < 0.001). In the second cohort analysis, 240 incident sarcopenia cases were identified over 39,621.1 person-years. Depressive symptoms (HR = 1.5, 95%CI: 1.2–2.0) are significantly associated with a higher risk of developing sarcopenia after multivariable adjustment (p < 0.001, Cross-lagged panel analyses demonstrated that depressive symptoms were associated with subsequent sarcopenia (β = 0.003, p < 0.001). Simultaneously, baseline sarcopenia was also associated with subsequent depressive symptoms (β = 0.428, p < 0.001). Conclusion: This study identified a bidirectional relationship between depressive symptoms and sarcopenia. It seems more probable that baseline sarcopenia is associated with subsequent depressive symptoms in a stronger pattern than the reverse pathway. The interlinkage indicated that maintaining normal muscle mass and strength may serve as a crucial intervention strategy for alleviating mood disorders.

1. Introduction

Sarcopenia and depressive symptoms are two major contributors to disease burden worldwide, limiting functional abilities and diminishing quality of life in the elderly [1,2,3,4]. Both conditions were common, with major depressive disorder having a life-time prevalence of an estimated 15.0–18.0% and 4.6–9.3% in the elderly population [5,6,7,8]. Conversely, the prevalence of sarcopenia ranged from 8% to 36% in middle-aged and 11.5% to 40% in older adults [9,10,11,12]. Both depressive symptoms and sarcopenia are associated with a substantial risk of negative outcomes, resulting in personal suffering, family disruption, and imposing a substantial economic burden on society [13,14,15]. Previous studies have reported that these two conditions have the potential to engender significant health burdens and considerable adverse consequences in conjunction with the global aging population trend [16,17].
Irrespective of temporality, the co-occurrence of sarcopenia and depressive symptoms is common [17,18]. Overall, 28% of sarcopenia patients had depressive symptoms, while 32.8% of patients with depressive symptoms had comorbid sarcopenia [18,19]. Sarcopenia is associated with an 80% higher risk of depressive symptoms, and depressive symptoms increased sarcopenia risk by 24–113% [17,18,20]. Emerging evidence supports a possible association between sarcopenia and depressive symptoms due to the shared plausible pathogenetic mechanisms, including physical inactivity, the upregulation of inflammatory factors (i.e., elevated level of interleukin (IL)-6, C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-alpha)), the alteration of gut microbiota composition, insulin resistance (IR), and hormonal imbalances in the hypothalamic–pituitary–adrenal axis [17,18,21,22,23].
According to previous studies, adults with sarcopenia and depressive symptoms had lower cognitive performance [24,25] and physical activity levels [26,27] and higher risk of falls [28,29] than those without these conditions. Other studies revealed that older adults with both sarcopenia and depressive symptoms had a higher risk of falls and hospitalization than those without these conditions [18]. Additionally, it has been reported that both sarcopenia and depressive symptoms serve as risk factors for cardiovascular disease and are linked to long-term mortality associated with it [30,31,32]. In summary, the co-occurrence of the two conditions results in impaired functioning and incapacity, which diminishes overall well-being and raises the risk of illness and death.
However, while the majority of previous studies primarily focused on establishing a correlation between sarcopenia and depressive symptoms using cross-sectional data [19,33], more recent longitudinal studies have begun to explore the prospective effects of depression on sarcopenia. Nevertheless, the bidirectional and temporal sequence between sarcopenia and depressive symptoms remains unclear. Understanding the potential temporal sequence between sarcopenia and depressive symptoms is crucial for early detection and intervention, ultimately facilitating effective disease management and improving overall quality of life. Thus, to address these knowledge gaps, our study aimed to investigate the bidirectional association between sarcopenia and depressive symptoms using data from a large Chinese national community-based prospective cohort study with an extensive long-term follow-up.

2. Methods

2.1. Study Population

All of data in this study were collected from the 2011, 2013, 2015, and 2018 waves of the China Health and Retirement Longitudinal Study (CHARLS), including the assessment of depression and sarcopenia. CHARLS constitutes a continuous survey program characterized by periodic examinations recurring at intervals of 2 to 3 years. Within this framework, participants undergo face-to-face interviews within the confines of their own residences, facilitated by the deployment of computer-assisted personal interviewing (CAPI) technology. Detailed information regarding the study design of the CHARLS have been previously reported [34]. The Institute of Social Science Survey at Peking University maintains all CHARLS-collected data, which has been made available to the public via the CHARLS website (http://charls.pku.edu.cn/en, accessed on 27 May 2023).
In this present study, we analyzed data from the CHARLS 2011, 2013, 2015, and 2018. A total of 17,708 respondents were recruited in the 2011 survey. The inclusion criteria for the cohort 1 (cohort for the onset of depressive symptoms) were as follows: (1) individuals who were 45 years of age or older in 2011; (2) individuals who had baseline data available for assessing sarcopenia status; (3) individuals who neither received a diagnosis of mood disorders from medical professionals nor took any medication for addressing emotional, nervous, or mental issues; and (4) those who obtained CES-D-10 scores ≤ 10 in 2011. The exclusion criteria were as follows: (1) individuals who did not engage in the assessment of depressive symptoms in 2013, 2015, or 2018; and (2) individuals with missing data on all demographic characteristics and confounders. Ultimately, 8092 participants were included in the first cohort analysis. For cohort 2, focused on the onset of sarcopenia, inclusion criteria were (1) individuals aged 45 years or older in 2011; and (2) individuals who had baseline data on the assessment of depressive symptoms in 2011, either through participation in the CES-D-10 evaluation or by reporting a diagnosis of mood disorders by doctors or taking medication for mood disorders. The exclusion criteria comprised the following: (1) absence of data pertaining to all components associated with sarcopenia in the years 2013, 2015, or 2018; (2) participants who already had sarcopenia at baseline; (3) lack of information concerning demographic variables and covariates. Finally, 11,292 participants were included in the second cohort analysis (Table A1). Within cohort 1, approximately half of the participants were female (3717/8092, 45.93%), with a median age of 57.0 years. Cohort 2 comprised 47.33% females (5345/11,292), with a median age of 55.0 years.

2.2. Assessment of Sarcopenia

The assessment of sarcopenia was conducted in accordance with the 2019 Asian Working Group for Sarcopenia (AWGS) criteria, which included evaluations of physical performance, appendicular skeletal muscle mass (ASM), and muscle strength. The hands of both the dominant and non-dominant individuals were assessed for handgrip strength, which was quantified in kilograms, using a YuejianTM WL-1000 dynamometer (Nantong, China). Participants were instructed to exert maximal force while squeezing the dynamometer, with two measurements taken for each hand while holding the dynamometer at a right angle (90°). In analyses, the mean value of the maximal strength data was utilized. When measurement of one hand was impossible because of any reason, the greatest score obtained from the other hand was documented. Low handgrip strength was defined as less than 28 kg for males and less than 18 kg for females, in accordance with the AWGS 2019 criteria. A validated anthropometric equation was employed to estimate the ASM of the Chinese population, as reported by Chinese residents [30,35].
The weight and height of the subjects were determined utilizing an OmronTM HN-286 scale (Kyoto, Japan) and a SecaTM213 height meter (Hamburg, Germany), respectively. A former study has illustrated that the ASM computed through this formula strongly correlates with measurements derived from dual-energy X-ray absorptiometry (DXA) [36]. The threshold for defining low muscle mass was determined by identifying the sex-specific lowest 20th percentile of height-adjusted muscle mass (ASM/height2) within the study cohort, with values of <5.29 kg/m2 for women and <6.99 kg/m2 for men, consistent with previous studies [37].
Physical performance evaluation encompassed measures of gait speed, the five-time chair stand test, and the Short Physical Performance Battery (SPPB) [38]. In order to evaluate gait speed, participants were directed to walk a distance of 2.5 m at their typical speed, and their durations of completion were documented over the course of two trials. The five-time chair stand test documented the amount of time necessary to ascend consecutively from a chair with a cross-chest fold, measuring 47 cm in height. The SPPB assessment consisted of three balance tests, each lasting 10 s: (1) side-by-side stance; (2) semi-tandem stance (with the heel of one foot placed next to the big toe of the other foot); and (3) tandem stance (with the heel of one foot contacting the toes of the other foot). Each person’s examination was given a maximum of four points, resulting in a cumulative score of twelve. Per the AWGS 2019 guidelines, low physical performance was characterized by a gait speed below 1.0 m/s, a five-time chair stand test taking 12 s or longer to complete, or an SPPB score falling below 9.
Possible sarcopenia can be identified by reduced muscular strength or poor physical performance. Sarcopenia is characterized by an accompanying reduction in muscle mass, strength, or physical performance. Consequently, participants were categorized into three groups: non-sarcopenia, possible sarcopenia, and sarcopenia.

2.3. Depressive Symptoms Ascertainment

This study assessed depressive symptoms using the 10-item Center for Epidemiologic Studies Depressive Scale (CES-D-10), which is a refined version derived from the original 20-item CES-D. Participants were queried about the frequency of experiencing ten listed symptoms during the preceding week. Responses were categorized as follows: rarely or never (<1 day), sometimes (1–2 days), occasionally (3–4 days), and most or all of the time (5–7 days). Each item was assigned a score from 0 (rarely) to 3 (most or all of the time), with items 5 and 8 reverse-scored before score summation. The total CES-D-10 score ranged from 0 to 30. Consistent with prior research, participants scoring 20 or higher were classified into the depressive symptoms group, those with a score of ≥10 were categorized similarly, while those scoring <10 were designated as the no depressive symptoms group. [39,40]. The CES-D-10 has undergone comprehensive validation for application in general populations and has exhibited sufficient levels of reliability and validity when applied to senior adults residing in the community in China [41].

2.4. Covariates

Covariates were chosen in consideration of prior epidemiological evidence and the availability of data at baseline [10,17,24,30]. Potential confounding variables encompassed age (continuous), sex (male and female), BMI (<25 kg/m2 and ≥25 kg/m2), residential location (urban and rural), educational attainment (from illiterate to primary school, middle school, and high school to college or higher enrolled), average household income (CNY <1000, 1000, 5000, 10,000, and 20,000), marital status (including married, separated, and unmarried/divorced/widowed), smoking status (including never, current, and previous), alcohol intake (including never, current, and previous), daily sleep time, presence of comorbid physical diseases (including cancer, chronic lung diseases, heart disease, stroke, arthritis, dyslipidaemia, hepatic disease, kidney disease, digestive system disease, asthma, and memory-related diseases such as dementia, brain atrophy, and Parkinson’s disease, as well as hypertension and diabetes), and cognitive function. The presence of comorbidities was assessed through the CHARLS questionnaire, which inquired about participants’ diagnosed conditions and treatments by asking, “Have you been diagnosed with this condition by a doctor?” and “Are you currently undergoing any treatment for this condition?” Participants who affirmed either having the condition or undergoing treatment were categorized as having the condition in our study. Cognitive function was assessed by Mini-Mental State Examination (MMSE) results [42].

2.5. Statistical Analysis

In order to summarize continuous variables, means (standard deviations (SDs)) or medians (interquartile range (IQR)) were utilized. Subsequently, to compare differences between groups, we conducted independent t tests, one-way analysis of variance (ANOVA) analyses, Wilcoxon tests, and Kruskal–Wallis tests. χ2 tests were performed to compare categorical or ordinal variables represented as numbers (proportions). The cumulative incidence rate was determined using the Kaplan–Meier method. Subsequently, a Cox proportional hazards model was employed to investigate the relationship between sarcopenia and depressive symptoms. The follow-up duration started from the baseline assessment in 2011 and continued to the occurrence of the first reported outcome (sarcopenia or depressive symptoms). For participants who did not develop sarcopenia or depressive symptoms during the study period, the follow-up duration was determined from the baseline to the censoring date at the end of the study (2018), the date of death, or to the loss of follow-up.
In order to explore the reciprocal relationship between sarcopenia and depressive symptoms, three models were performed: (1) model 1, adjusted for age and sex; (2) model 2, additionally adjusted for residential region, education level, average household income, BMI, marital status, status of smoking, and alcohol intake; (3) model 3, including comorbidities, sleep duration, and MMSE score. A trend test was conducted by allocating a score value (0, 1, 2) to each sarcopenia status (non-sarcopenia, possible sarcopenia, and sarcopenia) and modeling this value as a continuous variable. The Wald test was then used to determine the statistical significance of this value.
Furthermore, a subgroup analysis was conducted to examine potential variations in the association between sarcopenia and depressive symptoms based on age, gender, sleep duration (≤6 h, >6 h), and physical disease comorbidity. In order to examine effect modification, interaction terms between stratified variables were incorporated. In the sensitivity analysis, participants who disclosed incident outcomes within a two-year period following recruitment were removed. Second, a competing risk model was implemented, which incorporated mortality and loss-of-follow-up as competing events, given that the participants in that model might subsequently develop outcomes.
Participants from the prospective cohort analysis who had comprehensive data on both depressive symptoms and sarcopenia at baseline (time 1) and who also completed at least one follow-up assessment were included in the cross-lagged path analysis. At the follow-up (time 2), depressive symptoms and sarcopenia were assessed using data collected from the participant with the longest duration of complete follow-up. Before conducting the cross-lagged path analysis, regression residual analyses were employed to modify the baseline and subsequent values of depressive symptoms and sarcopenia status for a set of covariates. Subsequently, the values were standardized using Z-transformation (mean 0, SD 1). Coefficients that were standardized were documented for each model. Age, gender, location of residence, marital status, level of education, smoking, and alcohol consumption have been adjusted for in all models. To demonstrate that the models were well-fitting, a comparative fit index (CFI) greater than 0.90 and a standardized root-mean-square residual (SRMR) less than 0.05 were used [43]. Analyses were conducted using SAS software version 9.4 and R version 4.0.2, with a p value < 0.05 indicating statistical significance.

3. Results

A total of 25,586 participants were interviewed in CHARLS 2011. Subsets of 13,210 and 7791 participants were excluded from the analysis due to the absence of data pertaining to the assessment of sarcopenia or depressive symptoms at both the baseline and follow-up for the two cohorts. In the longitudinal analysis, an additional 4284 participants with a mood disorder diagnosed by a psychiatrist at baseline were excluded from the depressive symptoms cohort, and 6503 participants with sarcopenia at baseline were excluded from the sarcopenia cohort. Our final analytic sample consisted of 8092 and 11,292 participants for the two prospective cohorts, respectively (Figure 1).

3.1. Prospective Effect of Sarcopenia Status on the Onset of Depressive Symptoms

Among 8092 participants (median age 57.0, 51.0–64.0), 54.1% were male. Overall, 2379 (29.4%) and 631 (7.9%) were possible sarcopenia and sarcopenia at baseline. There were 1205 (27.5%) cases of possible sarcopenia and 307 (7.0%) cases of sarcopenia in male participants, whereas there were 1174 (31.6%) cases of possible sarcopenia and 324 (8.7%) cases of sarcopenia in female participants. Participants with possible sarcopenia and sarcopenia were more likely to be female, live in a rural area, have a lower education level, have a lower level of household income, be divorced or separated, and have a lower BMI and lower cognitive score (Table 1 and Table A2). The median follow-up duration of this study was 7.0 (interquartile range: 5.0–7.0) years.
During a total of 48,305.1 person-years follow-up, 1262 cases of incident depressive symptoms were identified. The 7-year cumulative incidence of depressive symptoms was 15.6% (95%CI: 12.9–18.2) in the possible sarcopenia group and 14.3% (95%CI: 10.0–18.7) in the sarcopenia group versus 8.3% (95%CI: 7.0–9.5) in the non-sarcopenia group (Figure 2A). Possible sarcopenia (HR = 1.5, 95%CI: 1.2–1.8) and sarcopenia (HR = 1.7, 95%CI: 1.2–2.3) are significantly associated with a higher risk of developing depressive symptoms after multivariable adjustment (ptrend < 0.001, Table 2). Moreover, in the older population, a total of 191 cases developed incident depressive symptoms during 16,466.3 person-years of follow-up. The 7-year cumulative incidence of depressive symptoms was 12.4% (95%CI: 8.9–15.9) in the possible sarcopenia group and 15.2% (95%CI: 10.1–20.4) in the sarcopenia group versus 8.7% (95%CI: 6.2–11.1) in the non-sarcopenia group (Figure 2B). Compared with non-sarcopenia individuals, those with possible sarcopenia and sarcopenia participants showed 1.3- (95%CI: 1.0–1.9) and 1.9-fold (95%CI: 1.3–2.9) higher risk of incident depressive symptoms (ptrend = 0.0016, Table 2). Among middle-aged participants, 312 incident depressive symptoms cases occurred during 30,006.9 person-years’ follow-up. The 7-year cumulative incidence of depressive symptoms was 8.3% (95%CI: 3.0–13.5) in sarcopenia, 17.4% (95%CI: 13.6–21.1) in possible sarcopenia, and 8.3% (95%CI: 6.8–9.7) in the non-sarcopenia group (Figure 2C). Compared with non-sarcopenia, possible sarcopenia participants were associated with a 1.5-fold (95%CI: 1.2–2.0) higher risk of incident depressive symptoms (Table 2).

3.2. Prospective Relationship between Depressive Symptoms and Sarcopenia

The prospective cohort sample consisted of 11,292 participants who had no sarcopenia at baseline and who had at least one follow-up sarcopenia assessment (Figure 1). Among 11,292 participants (median age 55.0, 49.0–61.0), 52.67% were male. Overall, 3277 (29.0%) reported depressive symptoms at baseline. There were 1389 (23.36%) cases of depressive symptoms in male participants and 1888 (35.32%) cases of depressive symptoms in female. Participants with depressive symptoms were more likely to be female, live in a rural area, have a lower education level, have a medium level of household income, be non-married, and have a lower BMI and lower cognitive score (Table 1). The median follow-up duration of this study was 4.0 (interquartile range: 3.9–4.0) years.
During a total of 39,621.1 person-years follow-up, 240 cases of incident sarcopenia were identified. The 4-year cumulative incidence of sarcopenia was 6.5% (95%CI: 4.9–8.2) in the depressive symptoms group versus 2.9% (95%CI: 2.3–3.5) in the non-depressive symptoms group (p < 0.001, Figure 2D). Depressive symptoms (HR = 1.5, 95%CI: 1.2–2.0) were significantly associated with a higher risk of developing sarcopenia after multivariable adjustment (p < 0.001, Table 3). In the older population, a total of 183 cases developed incident sarcopenia symptoms during 11,781.8 person-years follow-up. The 4-year cumulative incidence of sarcopenia was 14.6% (95%CI: 10.7–18.6) in the depressive symptoms group versus 8.3% (95%CI: 6.3–10.2) in the non-depressive symptoms group (p = 0.0010, Figure 2F). Compared with non-depressive symptoms individuals, depressive symptoms participants showed a 1.4-fold (95%CI: 1.0–1.9) higher risk of incident sarcopenia (p = 0.0409, Table 3). Among middle-aged participants, 57 incident sarcopenia cases occurred during 27,839.3 person-years’ follow-up. The 4-year cumulative incidence of sarcopenia was 2.5% (95%CI: 1.3–3.6) in the depressive symptoms group and 0.6% (95%CI: 0.4–0.9) in the non-depressive symptoms group (p < 0.001, Figure 2E). Compared with non-depressive symptoms, depressive symptoms participants were associated with a 1.5-fold (95%CI: 1.2–2.0) higher risk of incident sarcopenia (Table 4).

3.3. Subgroup Analysis

The higher risk of depressive symptoms associated with possible sarcopenia and sarcopenia was observed in overall and in older participants; depressive symptoms were not associated with sarcopenia in the middle-aged group but were significantly associated with possible sarcopenia in this age group (Figure 3). Sarcopenia and possible sarcopenia were significantly associated with an increased risk of depressive symptoms in individuals who were male and older and had more than one type of physical illness. Female, middle-aged participants and participants without a physical illness were more likely to have possible sarcopenia than sarcopenia (Figure 3, Table 2 and Table A3). Independently of participants’ sleep duration, depressive symptoms were associated with sarcopenia and possible sarcopenia.
On the other hand, the increased risk of sarcopenia associated with depressive symptoms was consistently observed across various subgroups, including age, sex, sleep duration, and comorbidities (Table 3, Figure 4). Furthermore, significant interaction across sex and depressive symptoms and was observed, with a higher risk of sarcopenia in females (HR = 1.2, 95%CI: 1.5–3.2, Figure 4).

3.4. Sensitivity Analysis

Results of sensitivity analysis by each single status of sarcopenia and each specific depressive symptoms were similar to primary findings (Figure A1 and Figure A2). Sensitivity analysis excluding incident depressive symptoms cases within 2 years after baseline by performing a competing risk model and a further adjustment of major chronic physical diseases did not markedly affect the results, which indicated the robustness of our results (Table A4).

3.5. Bidirectional Association of Depressive Symptoms and Sarcopenia

Figure 5A illustrates the cross-lagged model exploring the bidirectional relationship between depressive symptoms and sarcopenia (model 1). After controlling for covariates, model 1 fitted the data adequately (CFI = 0.999; SRMR = 0.034). Depressive symptoms at time point 1 were positively related to themselves over time, as was sarcopenia. Furthermore, significant cross-lagged effects emerged, with preceding depressive symptoms significantly predicting sarcopenia at T2 (β = 0.003; p < 0.001) and vice versa, with sarcopenia predicting subsequent depressive symptoms at T2 (β = 0.428; p < 0.001). The standardized path coefficient from sarcopenia to depressive symptoms was significantly larger than that of the reverse pathway (χ2 = 28.516, p for difference < 0.001). These findings provide empirical support for our hypothesis positing a bidirectional relationship between depressive symptoms and sarcopenia.
Figure 5B delineates the cross-lagged path analysis examining the interplay between sarcopenia and depressive symptoms within a middle-aged population (model 2). Notably, a significant cross-lagged pathway was observed. Specifically, the standardized path coefficient from sarcopenia at time 1 to depressive symptoms at time 2 (β = 0.411; p < 0.001) exhibited notable strength compared to the corresponding pathway from depressive symptoms at time 1 to sarcopenia at time 2 (β = 0.003; p < 0.001; χ2 = 13.201, p for difference = 0.0002798). The fitting indicators (CFI = 0.999 and SRMR = 0.030) underscored the favorable model fit. Figure 5C illustrates the cross-lagged path analysis of sarcopenia and depressive symptoms in the older population (model 3). The cross-lagged pathway was evident, and the standardized path coefficient from sarcopenia at time 1 to depressive symptoms at time 2 (β = 0.486; p < 0.001) was markedly stronger than that from depressive symptoms at time 1 to sarcopenia at time 2 (β = 0.005; p < 0.001; χ2 = 18.936, p for difference < 0.001). Fitting indicators (CFI = 0.995 and SRMR = 0.048) suggested model 3 to be a good model fit.

4. Discussion

This study aimed to examine the bidirectional association between sarcopenia and depressive symptoms using data from a large Chinese national community-based prospective cohort study with an extensive long-term follow-up. Throughout the seven-year follow-up period, a longitudinal bidirectional association was observed between sarcopenia status and depressive symptoms among Chinese middle-aged and older adults. Our findings revealed a 50% and 70% elevated risk of depressive symptoms among participants with possible sarcopenia and sarcopenia, respectively, along with a 50% increased risk of sarcopenia in individuals experiencing depressive symptoms at the outset of the study. The increased risk of depressive symptoms was significantly identified in male, older participants with sarcopenia, whereas sarcopenia risk was higher in female and middle-aged individuals who experienced depressive symptoms. In addition, an examination of standardized cross-lagged coefficients indicated that the temporal connection between initial depressive symptoms and sarcopenia may be more pronounced than the inverse relationship. These results highlight the intricate interplay between sarcopenia and depressive symptoms, underscoring the importance of considering both conditions in comprehensive health management strategies for high-risk adults.
Our study provided new insights into the nature of the relationship between sarcopenia and depressive symptoms. To the best of our knowledge, there have been no studies elucidating the longitudinal reciprocal association between sarcopenia and depression. Similar to cross-sectional studies and meta-analysis, we also found a significant association between depression and sarcopenia [17,18]. However, longitudinal research may offer more robust insights regarding the temporal direction of the observed associations. Recently, two observational studies reported a longitudinal effect of depression on sarcopenia onset [20,44] utilizing two-sample Mendelian randomization and reported evidence suggesting that depression symptoms could potentially result in a decrease in appendicular skeletal muscle mass but not vice versa [20]. In contrast to previous studies, our study not only identified depressive symptoms as a precursor to sarcopenia but also uncovered a notably stronger temporal sequence effect of sarcopenia leading to depressive symptoms. Importantly, this finding held true for both middle-aged and older adults. In terms of sex disparities, while males comprised a majority of the China community population due to socioeconomic and traditional cultural factors, females were more susceptible to developing sarcopenia and depressive symptoms, which is consistent with previous studies [44,45,46].
Diabetes, metabolic syndrome, cardiovascular diseases, and physical inactivity, all of which are associated with an increased risk of depression, have been linked to sarcopenia [47,48]. Neurotrophins, chronic inflammation, and oxidative stress might play a role in the shared pathophysiological mechanisms bridging sarcopenia and depressive symptoms [49,50,51]. Previous studies had shown that decreased muscle mass and strength have significant immune and redox effects by affecting the expression of neurotrophins (such as BDNF) and pro-inflammatory cytokines (CRP, IL-6, TNF-α), which consequently affect the development of depressive symptoms [51,52,53,54,55]. Another plausible explanation might be that the loss of skeletal muscle mass and function leads to less physical activity and malnutrition, which accordingly increases the risk of occurrence of depressive symptoms [27,56].
Increased evidence has demonstrated that a lack of physical activity and lower nutrition intake (e.g., protein, fruit, vegetables, and vitamin D) are associated with the upregulation of chronic inflammation and oxidative stress, which in turn lead to depressive symptoms and sarcopenia [57,58,59,60,61,62,63]. To date, several feasible interventions used to improve sarcopenia have been proven effective in older adults, such as resistance training, protein supplement, and pharmacologic intervention [64,65,66,67]. Given the absence of primary prevention guidelines for sarcopenia in China as of yet, implementing early depression screening in community-based care services and encouraging individuals to maintain a healthy lifestyle could aid in the identification of those at increased risk of developing sarcopenia [44]. Assessing sarcopenia in male and older participants may facilitate the early detection and treatment of depressive symptoms and, accordingly, could reduce the incidence of major depressive symptoms disorder. While this study may have focused on sarcopenia and depression prevention in China, its implications extend to populations globally. By advocating for the adoption of healthy lifestyle practices and the implementation of early screening protocols for depression, this discovery enriches the broader discourse on proactive strategies aimed at mitigating sarcopenia, combating depression, and fostering healthy aging on a global scale.
While our study highlighted the stronger directional influence of early sarcopenia on subsequent depressive symptoms compared to the reverse pathway, we acknowledge the significance of addressing the impact of depressive symptoms on sarcopenia. Our findings suggest that early screening for depressive symptoms may also be beneficial in mitigating the occurrence of sarcopenia, particularly among females and middle-aged individuals. Given that both sarcopenia and depressive symptoms could independently predict cardiovascular disease and mortality, the early identification and intervention of these conditions may be helpful to prevent overall poor outcomes [10,30,68].
This research possesses several strengths. To begin with, individuals who were 45 years of age or older were selected as participants from the CHARLS study, a population-based prospective cohort that is nationally representative. In this study, well-validated measures were used to assess depressive symptoms and sarcopenia status. Furthermore, to clarify the temporal bidirectional relationships between these two factors, our investigation employed a cross-lagged panel model, compared to other prospective studies that followed a unidirectional approach. Finally, it is important to note that this research is the first endeavor to investigate the possible temporal progression of alterations in depressive symptoms and sarcopenia in the Chinese population. As such, our results merit additional verification and investigation.
Potential limitations also need to be addressed. First, the research was an observational study, and recall bias in questionnaire surveys was unavoidable. Furthermore, it should be noted that although the statistical significance of the reciprocal associations between depressive symptoms and sarcopenia was preserved even after controlling for numerous potential confounding variables, there might be additional confounding factors that were not accounted for in our research. Additional potential confounding variables, including but not limited to dietary intake, body fat mass, and other psychiatric disorders like eating disorders and substance use disorder, as well as inflammation factors such as IL-6 and TNF-α, should be considered in future research. Third, it is important to highlight that the evaluation of gait speed was conducted at a distance of 2.5 m, which deviates from the traditional 6 m standard. However, an earlier investigation indicated that the gait speed measurements acquired by CHARLS were consistent with those obtained in China, suggesting that the distance discrepancy did not have an effect on the gait speed results [68]. Fourth, the data utilized spanned from 2011 to 2018, potentially limiting its relevance to current circumstances. Factors such as the COVID-19 pandemic in 2020 could have influenced the prevalence of both sarcopenia and depressive symptoms, necessitating updated data to accurately depict the current state of conditions. Finally, a significant proportion of individuals lacked initial or subsequent assessments for depressive symptoms and sarcopenia; this incomplete follow-up may introduce potential bias into our findings, despite the substantial number of eligible participants involved in the study.

5. Conclusions

In conclusion, this study identified a longitudinal reciprocal association between sarcopenia and depressive symptoms. Individuals who are male and older than 60 are more sensitivity to sarcopenia and develop an increased risk of depressive symptoms in later life. Female and middle-aged adults are vulnerable to depressive symptoms and exhibited a higher likelihood to develop sarcopenia. Cross-lagged path coefficients suggest that the association between initial sarcopenia and subsequent depressive symptoms is more likely to be significant than the reverse pathway. The early assessment and improvement of sarcopenia and depressive symptoms may be beneficial to reduce the risk of poor overall health and foster the process of healthy aging within the Asian population. Further research is necessary to unravel this relationship and elucidate the underlying mechanism. Given the established association between sarcopenia and depressive symptoms, it is imperative to develop further cost-effective therapies.

Author Contributions

N.Z., Y.B. and L.L. proposed the topic and main idea; N.Z. and C.L. (Chao Li) analyzed the data and wrote the initial draft of the manuscript; N.Z., C.L. (Chang Liu) and H.M. explained the results; X.W., S.W., J.S. and Y.B. commented on and revised the manuscript; L.L., Y.B. and J.S. finalized the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by a grant from the National Key Research and Development Program of China (2019YFA0706200, 2021YFC0863700), National Programs for Brain Science and Brain-like Intelligence Technology of China (STI2030-Major Projects, 2021ZD0200800, 2021ZD0200700), the National Natural Science Foundation of China (no. 82288101, 82171514), and the Fundamental Research Funds for the Central Universities (Peking University Medicine Fund for world’s leading discipline or discipline cluster development, BMU2022DJXK007). The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be constructed as a potential conflict of interests.

Institutional Review Board Statement

The data for this study were obtained from the 2018 Wave 4 survey of the China Health and Retirement Longitudinal Study (CHARLS). The Institutional Review Board at Peking University granted ethical approval for all CHARLS waves (IRB00001052–11015), approval was obtained in January 2011. Readers can find the details of the CHARLS study in the following published article: [34].

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.

Data Availability Statement

The China Health and Retirement Longitudinal Study (CHARLS) data set is publicly available at http://charls.pku.edu.cn/en (accessed on 27 May 2023). The data analyzed in our study can be obtained from the corresponding author upon request.

Acknowledgments

We thanked Ying Qian for contributing to the manuscript revision and proofreading.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Brainsci 14 00593 g0a1
Figure A1. Association between sarcopenia status and each single depressive symptom.
Figure A1. Association between sarcopenia status and each single depressive symptom.
Brainsci 14 00593 g0a1
Brainsci 14 00593 g0a2
Figure A2. Association between sarcopenia indicators and depressive symptoms.
Figure A2. Association between sarcopenia indicators and depressive symptoms.
Brainsci 14 00593 g0a2
Table A1. Inclusion and exclusion criteria for the two prospective cohorts.
Table A1. Inclusion and exclusion criteria for the two prospective cohorts.
Cohort 1: Depressive Symptoms CohortCohort 2: Sarcopenia Cohort
Inclusion CriteriaExclusion CriteriaInclusion CriteriaExclusion Criteria
Individuals aged 45 years or older in 2011Missing data on all depressive symptoms in 2013, 2015, or 2018Individuals aged 45 years or older in 2011Absence of data pertaining to all components associated with sarcopenia in the years 2013, 2015, or 2018
Possessing baseline data concerning both sarcopenia statusAbsence of data pertaining to all demographic variables and covariatesIndividuals who had baseline data on the assessment of depressive symptoms in 2011, either through participation in the CES-D-10 evaluation or by reporting a diagnosis of mood disorders by doctors or taking medication for mood disorders.Participants who had sarcopenia at baseline
Individuals who neither received a diagnosis of mood disorders from doctors nor took any medication to treat emotional, nervous, or mental problems, and those with CES-D-10 scores ≤ 10 at baseline Lack of information concerning demographic variables and covariates
Table A2. Chronic diseases of all participants at baseline by sarcopenia.
Table A2. Chronic diseases of all participants at baseline by sarcopenia.
VariablesTotal
(N = 8092)		Non-Sarcopenia
(N = 5082)		Possible Sarcopenia
(N = 2379)		Sarcopenia
(N = 631)		p-Value
Co-morbidities
   Cancer49 (0.61)27 (0.53)18 (0.76)4 (0.63)0.5017
   Chronic lung disease621 (7.68)317 (6.24)212 (8.92)92 (14.58)<0.001
   Heart disease756 (9.34)402 (7.91)302 (12.7)52 (8.24)<0.001
   Stroke131 (1.62)55 (1.08)65 (2.73)11 (1.74)<0.001
   Arthritis2088 (25.81)1203 (23.67)727 (30.57)158 (25.04)<0.001
   Dyslipidaemia663 (8.21)413 (8.13)236 (9.96)14 (2.22)<0.001
   Hepatic disease190 (2.35)112 (2.21)65 (2.74)13 (2.06)0.3275
   Kidney disease303 (3.75)181 (3.56)100 (4.21)22 (3.49)0.3689
   Digestive system disease1340 (16.56)828 (16.3)389 (16.36)123 (19.49)0.1192
   Asthma520 (6.45)260 (5.14)185 (7.8)75 (11.94)<0.001
   Memory-related disease43 (0.53)12 (0.24)26 (1.1)5 (0.8)<0.001
   Hypertension1929 (23.85)1056 (20.79)764 (32.13)109 (17.27)<0.001
   Diabetes504 (6.23)295 (5.81)195 (8.2)14 (2.22)<0.001
Table A3. Chronic disease of all participants at baseline by depressive symptoms.
Table A3. Chronic disease of all participants at baseline by depressive symptoms.
VariablesTotal (N = 11,292)Non-Depression (N = 8015)Depression (N = 3277)p-Value
Co-morbidities
   Cancer90 (0.8)60 (0.76)30 (0.92)0.3818
   Chronic lung disease920 (8.23)529 (6.68)391 (11.99)<0.001
   Heart disease1138 (10.18)700 (8.84)438 (13.45)<0.001
   Stroke191 (1.71)108 (1.36)83 (2.55)<0.001
   Arthritis3170 (28.33)1829 (23.07)1341 (41.11)<0.001
   Dyslipidemia1055 (9.45)745 (9.41)310 (9.54)0.8354
   Hepatic disease346 (3.1)202 (2.55)144 (4.42)<0.001
   Kidney disease537 (4.8)300 (3.79)237 (7.28)<0.001
   Digestive system disease2280 (20.38)1321 (16.66)959 (29.41)<0.001
   Asthma743 (7.92)385 (5.92)358 (12.45)<0.001
   Memory-related disease55 (0.49)30 (0.38)25 (0.77)0.0073
   Hypertension2570 (22.98)1788 (22.56)782 (24.02)0.0952
   Diabetes791 (7.17)532 (6.81)259 (8.05)0.0224
Table A4. Sensitivity analysis regarding risk of depressive symptoms with baseline sarcopenia status.
Table A4. Sensitivity analysis regarding risk of depressive symptoms with baseline sarcopenia status.
Sarcopenia StatusIncident DepressionParticipantsHR (95%CI)p Valuep for Trend
Overall population
Sensitivity analysis 1: excluding depression participants diagnosed within 2 years after baseline (N = 7352) <0.001
   Sarcopenia455421.7 (1.2–2.5)0.0021
   Possible sarcopenia15519491.5 (1.3–1.9)<0.001
   Non-Sarcopenia2264861reference
Sensitivity analysis 2: competing risk model (N = 7803, No. of competing events = 110)
   Sarcopenia566291.7 (1.3–1.8)0.0007<0.001
   Possible sarcopenia17921221.5 (1.2–1.8)<0.001
   Non-Sarcopenia2645052reference

References

  1. Cruz-Jentoft, A.J.; Sayer, A.A. Sarcopenia. Lancet 2019, 393, 2636–2646. [Google Scholar] [CrossRef] [PubMed]
  2. Alexopoulos, G.S. Depression in the elderly. Lancet 2005, 365, 1961–1970. [Google Scholar] [CrossRef] [PubMed]
  3. Malhi, G.S.; Mann, J.J. Depression. Lancet 2018, 392, 2299–2312. [Google Scholar] [CrossRef] [PubMed]
  4. Zhu, C.; Zhang, T.; Li, Q.; Chen, X.; Wang, K. Depression and Anxiety During the COVID-19 Pandemic: Epidemiology, Mechanism, and Treatment. Neurosci. Bull. 2023, 39, 675–684. [Google Scholar] [CrossRef] [PubMed]
  5. Kessler, R.C.; Bromet, E.J. The epidemiology of depression across cultures. Annu. Rev. Public. Health 2013, 34, 119–138. [Google Scholar] [CrossRef] [PubMed]
  6. Bromet, E.; Andrade, L.H.; Hwang, I.; Sampson, N.A.; Alonso, J.; de Girolamo, G.; de Graaf, R.; Demyttenaere, K.; Hu, C.; Iwata, N.; et al. Cross-national epidemiology of DSM-IV major depressive episode. BMC Med. 2011, 9, 90. [Google Scholar] [CrossRef] [PubMed]
  7. Luppa, M.; Sikorski, C.; Luck, T.; Ehreke, L.; Konnopka, A.; Wiese, B.; Weyerer, S.; König, H.H.; Riedel-Heller, S.G. Age- and gender-specific prevalence of depression in latest-life--systematic review and meta-analysis. J. Affect. Disord. 2012, 136, 212–221. [Google Scholar] [CrossRef] [PubMed]
  8. Chen, S.; Conwell, Y.; Vanorden, K.; Lu, N.; Fang, Y.; Ma, Y.; Fan, H.; Jin, T.; Chiu, H. Prevalence and natural course of late-life depression in China primary care: A population based study from an urban community. J. Affect. Disord. 2012, 141, 86–93. [Google Scholar] [CrossRef] [PubMed]
  9. Petermann-Rocha, F.; Balntzi, V.; Gray, S.R.; Lara, J.; Ho, F.K.; Pell, J.P.; Celis-Morales, C. Global prevalence of sarcopenia and severe sarcopenia: A systematic review and meta-analysis. J. Cachexia Sarcopenia Muscle 2022, 13, 86–99. [Google Scholar] [CrossRef] [PubMed]
  10. Kitamura, A.; Seino, S.; Abe, T.; Nofuji, Y.; Yokoyama, Y.; Amano, H.; Nishi, M.; Taniguchi, Y.; Narita, M.; Fujiwara, Y.; et al. Sarcopenia: Prevalence, associated factors, and the risk of mortality and disability in Japanese older adults. J. Cachexia Sarcopenia Muscle 2021, 12, 30–38. [Google Scholar] [CrossRef]
  11. Chen, L.K.; Woo, J.; Assantachai, P.; Auyeung, T.W.; Chou, M.Y.; Iijima, K.; Jang, H.C.; Kang, L.; Kim, M.; Kim, S.; et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J. Am. Med. Dir. Assoc. 2020, 21, 300–307.e2. [Google Scholar] [CrossRef] [PubMed]
  12. Sosiawati, A.F.; Irwan, A.M.; Isnah, W.O.N. Identifying sarcopenia among post-stroke older people. Enferm. Clin. 2021, 31, S847–S850. [Google Scholar] [CrossRef]
  13. Collaborators, G.M.D. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet. Psychiatry 2022, 9, 137–150. [Google Scholar] [CrossRef] [PubMed]
  14. Cruz-Jentoft, A.J.; Bahat, G.; Bauer, J.; Boirie, Y.; Bruyère, O.; Cederholm, T.; Cooper, C.; Landi, F.; Rolland, Y.; Sayer, A.A.; et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 2019, 48, 16–31. [Google Scholar] [CrossRef] [PubMed]
  15. Franklin, B.A.; Eijsvogels, T.M.H. A Narrative Review on Exercise and Cardiovascular Disease: Physical Activity Thresholds for Optimizing Health Outcomes. Heart Mind 2023, 7, 34–39. [Google Scholar] [CrossRef]
  16. Yang, J.; Jiang, F.; Yang, M.; Chen, Z. Sarcopenia and nervous system disorders. J. Neurol. 2022, 269, 5787–5797. [Google Scholar] [CrossRef] [PubMed]
  17. Chang, K.V.; Hsu, T.H.; Wu, W.T.; Huang, K.C.; Han, D.S. Is sarcopenia associated with depression? A systematic review and meta-analysis of observational studies. Age Ageing 2017, 46, 738–746. [Google Scholar] [CrossRef]
  18. 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, 128–144. [Google Scholar] [CrossRef]
  19. Olgun Yazar, H.; Yazar, T. Prevalence of sarcopenia in patients with geriatric depression diagnosis. Ir. J. Med. Sci. 2019, 188, 931–938. [Google Scholar] [CrossRef]
  20. Zhong, Q.; Jiang, L.; An, K.; Zhang, L.; Li, S.; An, Z. Depression and risk of sarcopenia: A national cohort and Mendelian randomization study. Front. Psychiatry 2023, 14, 1263553. [Google Scholar] [CrossRef]
  21. Hallgren, M.; Herring, M.P.; Owen, N.; Dunstan, D.; Ekblom, Ö.; Helgadottir, B.; Nakitanda, O.A.; Forsell, Y. Exercise, Physical Activity, and Sedentary Behavior in the Treatment of Depression: Broadening the Scientific Perspectives and Clinical Opportunities. Front. Psychiatry 2016, 7, 36. [Google Scholar] [CrossRef] [PubMed]
  22. Li, C.W.; Yu, K.; Shyh-Chang, N.; Jiang, Z.; Liu, T.; Ma, S.; Luo, L.; Guang, L.; Liang, K.; Ma, W.; et al. Pathogenesis of sarcopenia and the relationship with fat mass: Descriptive review. J. Cachexia Sarcopenia Muscle 2022, 13, 781–794. [Google Scholar] [CrossRef] [PubMed]
  23. Cleasby, M.E.; Jamieson, P.M.; Atherton, P.J. Insulin resistance and sarcopenia: Mechanistic links between common co-morbidities. J. Endocrinol. 2016, 229, R67–R81. [Google Scholar] [CrossRef] [PubMed]
  24. Hu, Y.; Peng, W.; Ren, R.; Wang, Y.; Wang, G. Sarcopenia and mild cognitive impairment among elderly adults: The first longitudinal evidence from CHARLS. J. Cachexia Sarcopenia Muscle 2022, 13, 2944–2952. [Google Scholar] [CrossRef] [PubMed]
  25. Culpepper, L.; Lam, R.W.; McIntyre, R.S. Cognitive Impairment in Patients With Depression: Awareness, Assessment, and Management. J. Clin. Psychiatry 2017, 78, 1383–1394. [Google Scholar] [CrossRef] [PubMed]
  26. Zhao, G.; Lu, Z.; Sun, Y.; Kang, Z.; Feng, X.; Liao, Y.; Sun, J.; Zhang, Y.; Huang, Y.; Yue, W. Dissecting the causal association between social or physical inactivity and depression: A bidirectional two-sample Mendelian Randomization study. Transl. Psychiatry 2023, 13, 194. [Google Scholar] [CrossRef]
  27. Pearce, M.; Garcia, L.; Abbas, A.; Strain, T.; Schuch, F.B.; Golubic, R.; Kelly, P.; Khan, S.; Utukuri, M.; Laird, Y.; et al. Association Between Physical Activity and Risk of Depression: A Systematic Review and Meta-analysis. JAMA Psychiatry 2022, 79, 550–559. [Google Scholar] [CrossRef] [PubMed]
  28. Gambaro, E.; Gramaglia, C.; Azzolina, D.; Campani, D.; Molin, A.D.; Zeppegno, P. The complex associations between late life depression, fear of falling and risk of falls. A systematic review and meta-analysis. Ageing Res. Rev. 2022, 73, 101532. [Google Scholar] [CrossRef]
  29. Kinoshita, K.; Satake, S.; Matsui, Y.; Arai, H. Association between Sarcopenia and Fall Risk According to the Muscle Mass Adjustment Method in Japanese Older Outpatients. J. Nutr. Health Aging 2021, 25, 762–766. [Google Scholar] [CrossRef]
  30. Gao, K.; Cao, L.F.; Ma, W.Z.; Gao, Y.J.; Luo, M.S.; Zhu, J.; Li, T.; Zhou, D. Association between sarcopenia and cardiovascular disease among middle-aged and older adults: Findings from the China health and retirement longitudinal study. EClinicalMedicine 2022, 44, 101264. [Google Scholar] [CrossRef]
  31. Bucciarelli, V.; Caterino, A.L.; Bianco, F.; Caputi, C.G.; Salerni, S.; Sciomer, S.; Maffei, S.; Gallina, S. Depression and cardiovascular disease: The deep blue sea of women’s heart. Trends Cardiovasc. Med. 2020, 30, 170–176. [Google Scholar] [CrossRef]
  32. Meng, R.; Yu, C.; Liu, N.; He, M.; Lv, J.; Guo, Y.; Bian, Z.; Yang, L.; Chen, Y.; Zhang, X.; et al. Association of Depression With All-Cause and Cardiovascular Disease Mortality Among Adults in China. JAMA Netw. Open 2020, 3, e1921043. [Google Scholar] [CrossRef] [PubMed]
  33. Szlejf, C.; Suemoto, C.K.; Brunoni, A.R.; Viana, M.C.; Moreno, A.B.; Matos, S.M.A.; Lotufo, P.A.; Benseñor, I.M. Depression is Associated With Sarcopenia Due to Low Muscle Strength: Results From the ELSA-Brasil Study. J. Am. Med. Dir. Assoc. 2019, 20, 1641–1646. [Google Scholar] [CrossRef] [PubMed]
  34. Zhao, Y.; Hu, Y.; Smith, J.P.; Strauss, J.; Yang, G. Cohort profile: The China Health and Retirement Longitudinal Study (CHARLS). Int. J. Epidemiol. 2014, 43, 61–68. [Google Scholar] [CrossRef]
  35. Wen, X.; Wang, M.; Jiang, C.M.; Zhang, Y.M. Anthropometric equation for estimation of appendicular skeletal muscle mass in Chinese adults. Asia Pac. J. Clin. Nutr. 2011, 20, 551–556. [Google Scholar] [PubMed]
  36. Yang, M.; Hu, X.; Wang, H.; Zhang, L.; Hao, Q.; Dong, B. Sarcopenia predicts readmission and mortality in elderly patients in acute care wards: A prospective study. J. Cachexia Sarcopenia Muscle 2017, 8, 251–258. [Google Scholar] [CrossRef]
  37. Delmonico, M.J.; Harris, T.B.; Lee, J.S.; Visser, M.; Nevitt, M.; Kritchevsky, S.B.; Tylavsky, F.A.; Newman, A.B. Alternative definitions of sarcopenia, lower extremity performance, and functional impairment with aging in older men and women. J. Am. Geriatr. Soc. 2007, 55, 769–774. [Google Scholar] [CrossRef]
  38. Huang, X.; Liu, X.; Yu, Y. Depression and Chronic Liver Diseases: Are There Shared Underlying Mechanisms? Front. Mol. Neurosci. 2017, 10, 134. [Google Scholar] [CrossRef]
  39. Andresen, E.M.; Malmgren, J.A.; Carter, W.B.; Patrick, D.L. Screening for depression in well older adults: Evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am. J. Prev. Med. 1994, 10, 77–84. [Google Scholar] [CrossRef]
  40. Zhou, L.; Ma, X.; Wang, W. Relationship between Cognitive Performance and Depressive Symptoms in Chinese Older Adults: The China Health and Retirement Longitudinal Study (CHARLS). J. Affect. Disord. 2021, 281, 454–458. [Google Scholar] [CrossRef]
  41. Chen, H.; Mui, A.C. Factorial validity of the Center for Epidemiologic Studies Depression Scale short form in older population in China. Int. Psychogeriatr. 2014, 26, 49–57. [Google Scholar] [CrossRef] [PubMed]
  42. Tombaugh, T.N.; McIntyre, N.J. The mini-mental state examination: A comprehensive review. J. Am. Geriatr. Soc. 1992, 40, 922–935. [Google Scholar] [CrossRef]
  43. Zhang, T.; Zhang, H.; Li, Y.; Sun, D.; Li, S.; Fernandez, C.; Qi, L.; Harville, E.; Bazzano, L.; He, J.; et al. Temporal Relationship Between Childhood Body Mass Index and Insulin and Its Impact on Adult Hypertension: The Bogalusa Heart Study. Hypertension 2016, 68, 818–823. [Google Scholar] [CrossRef] [PubMed]
  44. Liu, Y.; Cui, J.; Cao, L.; Stubbendorff, A.; Zhang, S. Association of depression with incident sarcopenia and modified effect from healthy lifestyle: The first longitudinal evidence from the CHARLS. J. Affect. Disord. 2024, 344, 373–379. [Google Scholar] [CrossRef] [PubMed]
  45. Luo, H.; Li, J.; Zhang, Q.; Cao, P.; Ren, X.; Fang, A.; Liao, H.; Liu, L. Obesity and the onset of depressive symptoms among middle-aged and older adults in China: Evidence from the CHARLS. BMC Public Health 2018, 18, 909. [Google Scholar] [CrossRef]
  46. Yan, Y.; Du, Y.; Li, X.; Ping, W.; Chang, Y. Physical function, ADL, and depressive symptoms in Chinese elderly: Evidence from the CHARLS. Front. Public Health 2023, 11, 1017689. [Google Scholar] [CrossRef]
  47. Hashimoto, Y.; Takahashi, F.; Okamura, T.; Hamaguchi, M.; Fukui, M. Diet, exercise, and pharmacotherapy for sarcopenia in people with diabetes. Metab. Clin. Exp. 2023, 144, 155585. [Google Scholar] [CrossRef] [PubMed]
  48. Nishikawa, H.; Asai, A.; Fukunishi, S.; Nishiguchi, S.; Higuchi, K. Metabolic Syndrome and Sarcopenia. Nutrients 2021, 13, 3519. [Google Scholar] [CrossRef]
  49. Chen, G.Q.; Wang, G.P.; Lian, Y. Relationships between depressive symptoms, dietary inflammatory potential, and sarcopenia: Mediation analyses. Front. Nutr. 2022, 9, 844917. [Google Scholar] [CrossRef]
  50. Picca, A.; Calvani, R.; Sirago, G.; Coelho-Junior, H.J.; Marzetti, E. Molecular routes to sarcopenia and biomarker development: Per aspera ad astra. Curr. Opin. Pharmacol. 2021, 57, 140–147. [Google Scholar] [CrossRef]
  51. Pasco, J.A.; Williams, L.J.; Jacka, F.N.; Stupka, N.; Brennan-Olsen, S.L.; Holloway, K.L.; Berk, M. Sarcopenia and the Common Mental Disorders: A Potential Regulatory Role of Skeletal Muscle on Brain Function? Curr. Osteoporos. Rep. 2015, 13, 351–357. [Google Scholar] [CrossRef] [PubMed]
  52. Zhang, L.; Tao, S.; Lu, N.; Pan, R.; Liu, M. Anti-Inflammation Relieving Heart Dysfunction and Depressive Behaviors of Mice Under Myocardial Infarction and Chronic Stress. Heart Mind 2022, 6, 159–166. [Google Scholar] [CrossRef]
  53. Mousavi, K.; Jasmin, B.J. BDNF is expressed in skeletal muscle satellite cells and inhibits myogenic differentiation. J. Neurosci. Off. J. Soc. Neurosci. 2006, 26, 5739–5749. [Google Scholar] [CrossRef]
  54. Chung, H.Y.; Cesari, M.; Anton, S.; Marzetti, E.; Giovannini, S.; Seo, A.Y.; Carter, C.; Yu, B.P.; Leeuwenburgh, C. Molecular inflammation: Underpinnings of aging and age-related diseases. Ageing Res. Rev. 2009, 8, 18–30. [Google Scholar] [CrossRef]
  55. Kipinoinen, T.; Toppala, S.; Rinne, J.O.; Viitanen, M.H.; Jula, A.M.; Ekblad, L.L. Association of Midlife Inflammatory Markers With Cognitive Performance at 10-Year Follow-up. Neurology 2022, 99, e2294–e2302. [Google Scholar] [CrossRef]
  56. Jacka, F.N.; Pasco, J.A.; Williams, L.J.; Mann, N.; Hodge, A.; Brazionis, L.; Berk, M. Red meat consumption and mood and anxiety disorders. Psychother. Psychosom. 2012, 81, 196–198. [Google Scholar] [CrossRef]
  57. Ganapathy, A.; Nieves, J.W. Nutrition and Sarcopenia-What Do We Know? Nutrients 2020, 12, 1755. [Google Scholar] [CrossRef]
  58. Hurst, C.; Robinson, S.M.; Witham, M.D.; Dodds, R.M.; Granic, A.; Buckland, C.; De Biase, S.; Finnegan, S.; Rochester, L.; Skelton, D.A.; et al. Resistance exercise as a treatment for sarcopenia: Prescription and delivery. Age Ageing 2022, 51, afac003. [Google Scholar] [CrossRef] [PubMed]
  59. Nascimento, C.M.; Ingles, M.; Salvador-Pascual, A.; Cominetti, M.R.; Gomez-Cabrera, M.C.; Viña, J. Sarcopenia, frailty and their prevention by exercise. Free Radic. Biol. Med. 2019, 132, 42–49. [Google Scholar] [CrossRef]
  60. Anglin, R.E.; Samaan, Z.; Walter, S.D.; McDonald, S.D. Vitamin D deficiency and depression in adults: Systematic review and meta-analysis. Br. J. Psychiatry 2013, 202, 100–107. [Google Scholar] [CrossRef]
  61. Kim, J.; Lee, Y.; Kye, S.; Chung, Y.S.; Kim, K.M. Association of vegetables and fruits consumption with sarcopenia in older adults: The Fourth Korea National Health and Nutrition Examination Survey. Age Ageing 2015, 44, 96–102. [Google Scholar] [CrossRef] [PubMed]
  62. Lu, L.; Mao, L.; Feng, Y.; Ainsworth, B.E.; Liu, Y.; Chen, N. Effects of different exercise training modes on muscle strength and physical performance in older people with sarcopenia: A systematic review and meta-analysis. BMC Geriatr. 2021, 21, 708. [Google Scholar] [CrossRef] [PubMed]
  63. Papadopoulou, S.K. Sarcopenia: A Contemporary Health Problem among Older Adult Populations. Nutrients 2020, 12, 1293. [Google Scholar] [CrossRef] [PubMed]
  64. Mølmen, K.S.; Hammarström, D.; Pedersen, K.; Lian Lie, A.C.; Steile, R.B.; Nygaard, H.; Khan, Y.; Hamarsland, H.; Koll, L.; Hanestadhaugen, M.; et al. Vitamin D(3) supplementation does not enhance the effects of resistance training in older adults. J. Cachexia Sarcopenia Muscle 2021, 12, 599–628. [Google Scholar] [CrossRef] [PubMed]
  65. Lu, Y.; Niti, M.; Yap, K.B.; Tan, C.T.Y.; Zin Nyunt, M.S.; Feng, L.; Tan, B.Y.; Chan, G.; Khoo, S.A.; Chan, S.M.; et al. Assessment of Sarcopenia Among Community-Dwelling At-Risk Frail Adults Aged 65 Years and Older Who Received Multidomain Lifestyle Interventions: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw. Open 2019, 2, e1913346. [Google Scholar] [CrossRef] [PubMed]
  66. Martínez-Arnau, F.M.; Fonfría-Vivas, R.; Buigues, C.; Castillo, Y.; Molina, P.; Hoogland, A.J.; van Doesburg, F.; Pruimboom, L.; Fernández-Garrido, J.; Cauli, O. Effects of Leucine Administration in Sarcopenia: A Randomized and Placebo-controlled Clinical Trial. Nutrients 2020, 12, 932. [Google Scholar] [CrossRef] [PubMed]
  67. Kilsby, A.J.; Sayer, A.A.; Witham, M.D. Selecting Potential Pharmacological Interventions in Sarcopenia. Drugs Aging 2017, 34, 233–240. [Google Scholar] [CrossRef]
  68. Wu, X.; Li, X.; Xu, M.; Zhang, Z.; He, L.; Li, Y. Sarcopenia prevalence and associated factors among older Chinese population: Findings from the China Health and Retirement Longitudinal Study. PLoS ONE 2021, 16, e0247617. [Google Scholar] [CrossRef]
Brainsci 14 00593 g001
Figure 1. Flow chart of the study population. CHARLS: China Health and Retirement Longitudinal Study; CES-D-10: 10-item Center for Epidemiologic Studies Depressive Scale.
Figure 1. Flow chart of the study population. CHARLS: China Health and Retirement Longitudinal Study; CES-D-10: 10-item Center for Epidemiologic Studies Depressive Scale.
Brainsci 14 00593 g001
Brainsci 14 00593 g002
Figure 2. Survival curves of cumulative probabilities of outcomes. (A) Overall population for depressive symptoms. (B) Middle-aged population for depressive symptoms. (C) Old population for depressive symptoms. (D) Overall population for sarcopenia. (E) Middle-aged population for sarcopenia. (F) Old population for sarcopenia.
Figure 2. Survival curves of cumulative probabilities of outcomes. (A) Overall population for depressive symptoms. (B) Middle-aged population for depressive symptoms. (C) Old population for depressive symptoms. (D) Overall population for sarcopenia. (E) Middle-aged population for sarcopenia. (F) Old population for sarcopenia.
Brainsci 14 00593 g002
Brainsci 14 00593 g003
Figure 3. Association between sarcopenia status and depressive symptoms by sex, age, sleep duration, and physical comorbidities. All HRs were calculated by adjusting age, sex, residential region, education level, average household income, BMI, marital status, smoking status, alcohol intake, physical comorbidities, sleep duration, and MMSE score. CI, confidence interval; HR, hazard ratio.
Figure 3. Association between sarcopenia status and depressive symptoms by sex, age, sleep duration, and physical comorbidities. All HRs were calculated by adjusting age, sex, residential region, education level, average household income, BMI, marital status, smoking status, alcohol intake, physical comorbidities, sleep duration, and MMSE score. CI, confidence interval; HR, hazard ratio.
Brainsci 14 00593 g003
Brainsci 14 00593 g004
Figure 4. Association between depressive symptoms status and incident sarcopenia by sex, sleep duration, and physical comorbidities. All HRs were calculated by adjusting age, sex, residential region, education level, average household income, BMI, marital status, smoking status, alcohol intake, physical comorbidities, sleep duration, and MMSE score. CI, confidence interval; HR, hazard ratio.
Figure 4. Association between depressive symptoms status and incident sarcopenia by sex, sleep duration, and physical comorbidities. All HRs were calculated by adjusting age, sex, residential region, education level, average household income, BMI, marital status, smoking status, alcohol intake, physical comorbidities, sleep duration, and MMSE score. CI, confidence interval; HR, hazard ratio.
Brainsci 14 00593 g004
Brainsci 14 00593 g005
Figure 5. Cross-lagged panel models applied to assess depression symptoms and sarcopenia status, and mediating effect of healthy lifestyle among Chinese adults. (A) In the overall population; (B) in the middle-aged population; (C) in the older-aged population. ** means p < 0.01, *** means p < 0.001.
Figure 5. Cross-lagged panel models applied to assess depression symptoms and sarcopenia status, and mediating effect of healthy lifestyle among Chinese adults. (A) In the overall population; (B) in the middle-aged population; (C) in the older-aged population. ** means p < 0.01, *** means p < 0.001.
Brainsci 14 00593 g005
Table 1. Characteristics of all participants at baseline by sarcopenia.
Table 1. Characteristics of all participants at baseline by sarcopenia.
VariablesTotal
(N = 8092)		Non-Sarcopenia (N = 5082)Possible Sarcopenia (N = 2379)Sarcopenia
(N = 631)		p-Value
Age (Median, Q25–Q75)57.0 (51.0–64.0)55.0 (49.0–61.0)59.0 (53.0–66.0)68.0 (61.0–75.0)<0.001
Gender
   Male4375 (54.07)2863 (56.34)1205 (50.65)307 (48.65)<0.001
   Female3717 (45.93)2219 (43.66)1174 (49.35)324 (51.35)
Residential area <0.001
   Urban3278 (40.51)2069 (40.71)1024 (43.04)185 (29.32)
   Rural4814 (59.49)3013 (59.29)1355 (56.96)446 (70.68)
Education <0.001
   Illiterate1818 (22.47)905 (17.81)640 (26.9)273 (43.26)
   Primary school3182 (39.32)1984 (39.04)953 (40.06)245 (38.83)
   Middle school1904 (23.53)1357 (26.7)479 (20.13)68 (10.78)
   High school/vocational high school1018 (12.58)725 (14.27)252 (10.59)41 (6.5)
   Junior college or above170 (2.1)111 (2.18)55 (2.31)4 (0.63)
Average household income (CNY) <0.001
   <10003978 (49.16)2590 (50.96)1114 (46.83)274 (43.42)
   1000810 (10.01)436 (8.58)279 (11.73)95 (15.06)
   5000492 (6.08)291 (5.73)153 (6.43)48 (7.61)
   10,000691 (8.54)408 (8.03)209 (8.79)74 (11.73)
   20,0002121 (26.21)1357 (26.7)624 (26.23)140 (22.19)
Marital status
   Married6749 (83.4)4430 (87.17)1889 (79.4)430 (68.15)<0.001
   Separated359 (4.44)221 (4.35)124 (5.21)14 (2.22)
   Unmarried/divorced/widowed984 (12.16)431 (8.48)366 (15.38)187 (29.64)
Smoking status <0.001
   No4170 (51.54)2540 (49.99)1305 (54.85)325 (51.51)
   Yes2405 (29.72)1614 (31.77)612 (25.73)179 (28.37)
   Previous 1516 (18.74)927 (18.24)462 (19.42)127 (20.13)
Alcohol drinking <0.001
   No4177 (51.62)2536 (49.9)1294 (54.39)347 (54.99)
   Yes2984 (36.88)2043 (40.2)747 (31.4)194 (30.74)
   Previous931 (11.51)503 (9.9)338 (14.21)90 (14.26)
   Daily sleep time (hours)7.00 (6.00–8.00)7.00 (6.00–8.00)7.00 (6.00–8.00)7.00 (5.00–8.00)0.151
   MMSE score, (Median, Q25–Q75)16.0 (12.0–18.0)16.00 (13.00–19.00)15.00 (12.00–18.00)14.00 (10.00–15.00)<0.001
   BMI (kg/m2; Median, Q25–Q75)23.4 (21.1–26.0)23.61 (21.37–26.05)24.25 (22.25–26.80)19.08 (18.00–20.14)<0.001
Sarcopenia indicators
   Low muscle mass1244 (15.85)610 (12)3 (0.14)631 (100)<0.001
   Low muscle strength908 (11.56)0 (0)614 (28.64)294 (46.67)<0.001
   Low physical performance2284 (29.18)0 (0)1775 (83.81)509 (81.18)<0.001
CNY, Chinese Yuan; MMSE, Mini-Mental State Examination; BMI, body mass index.
Table 2. Risk of depression associated with baseline sarcopenia status.
Table 2. Risk of depression associated with baseline sarcopenia status.
Non-SarcopeniaPossible SarcopeniaSarcopeniap for Trend
Overall population (N = 8092)
   No. of participants50822379631
   No. of incident depression26719356
   Follow-up, person-years31,708.7013,332.503263.90
   Follow-up, years
Median (IQR)		7.0 (6.9–7.0)6.9 (4.0–7.0)6.9 (4.0–7.0)
Hazard ratio for incident depression (95%CI, p value)
   Unadjustedreference1.8 (1.5–2.1)2.1 (1.6–2.9)<0.001
   Adjusted model 1reference1.7 (1.4–2.1)2.0 (1.5–2.8)<0.001
   Adjusted model 2reference1.6 (1.4–2.0)1.8 (1.3–2.4)<0.001
   Adjusted model 3reference1.5 (1.2–1.8)1.7 (1.2–2.3)<0.001
Older adults, age ≥ 60 (N = 2910)
   No. of participants13891047474
   No. of incident depression747344
   Follow-up, person-years8469.605653.702343
   Follow-up, years
Median (IQR)		7.0 (5.0–7.0)7.0 (4.0–7.0)4.0 (4.0–7.0)
Hazard ratio for incident depression (95%CI, p value)
   Unadjustedreference1.5 (1.1–2.1)2.3 (1.6–3.3)<0.001
   Adjusted model 1reference1.5 (1.1–2.1)2.3 (1.5–3.4)<0.001
   Adjusted model 2reference1.4 (1.0–1.9)1.9 (1.3–2.9)<0.001
   Adjusted model 3reference1.3 (1.0–1.9)1.9 (1.3–2.9)0.0016
Middle-aged adults, age < 60 (N = 4893)
   No. of participants35261230137
   No. of incident depression1911129
   Follow-up, person-years22,154.207060.80791.9
   Follow-up, years
Median (IQR)		7.0 (6.9–7.0)7.0 (4.0–7.0)7.0 (4.0–7.0)
Hazard ratio for incident depression (95%CI, p value)
   Unadjustedreference1.8 (1.5–2.3)1.3 (0.7–2.6)<0.001
   Adjusted model 1reference1.8 (1.4–2.2)1.2 (0.6–2.4)<0.001
   Adjusted model 2reference1.7 (1.3–2.1)1.0 (0.5–2.0)<0.001
   Adjusted model 3reference1.5 (1.2–2.0)1.0 (0.5–2.0)0.0114
No. of participants, number of participants; IQR, interquartile range; 95%CI, 95% confidence interval.
Table 3. Characteristics of all participants at baseline by depressive symptoms.
Table 3. Characteristics of all participants at baseline by depressive symptoms.
VariablesTotal (N = 11,292)Non-Depression (N = 8015)Depression (N = 3277)p-Value
Age (Median, Q25–Q75)55.0 (49.0–61.0)55.0 (49.0–61.0)56.0 (49.0–61.0)<0.001
Gender
   Male5947 (52.67)4558 (56.87)1389 (42.39)<0.001
   Female5345 (47.33)3457 (43.13)1888 (57.61)
Residential area
   Urban3787 (40.49)2850 (43.8)937 (32.93)<0.001
   Rural5565 (59.51)3657 (56.2)1908 (67.07)
Education
   Illiterate2267 (20.08)1382 (17.24)885 (27.01)<0.001
   Primary school4608 (40.81)3142 (39.2)1466 (44.75)
   Middle school2697 (23.89)2052 (25.6)645 (19.69)
   High school/vocational high school1445 (12.8)1193 (14.88)252 (7.69)
   Junior college or above274 (2.43)246 (3.07)28 (0.85)
Average household income (CNY)
   <10006757 (59.84)4969 (62)1788 (54.56)<0.001
   1000–5000883 (7.82)506 (6.31)377 (11.5)
   5000–10,000584 (5.17)356 (4.44)228 (6.96)
   10,000–20,000763 (6.76)488 (6.09)275 (8.39)
   >20,0002305 (20.41)1696 (21.16)609 (18.58)
Marital status
   Married9494 (84.08)6911 (86.23)2583 (78.82)<0.001
   Separated644 (5.7)422 (5.27)222 (6.77)
   Unmarried/divorced/widowed1154 (10.22)682 (8.51)472 (14.4)
Never/current smoker
   No5941 (52.64)4037 (50.39)1904 (58.14)<0.001
   Yes3493 (30.95)2569 (32.07)924 (28.21)
   Never smoked1852 (16.41)1405 (17.54)447 (13.65)
Never/current alcohol
   No5726 (50.71)3915 (48.85)1811 (55.26)<0.001
   Yes4361 (38.62)3313 (41.33)1048 (31.98)
   Never drank alcohol1205 (10.67)787 (9.82)418 (12.76)
Daily sleep time (hours)7.00 (5.00–8.00)7.00 (6.00–8.00)6.00 (5.00–7.00)<0.001
MMSE score, (Median, Q25–Q75)16.00 (13.00–18.00)16.00 (13.00–19.00)15.00 (12.00–16.00)<0.001
BMI (kg/m2; Median, Q25–Q75)24.2 (21.3–26.0)23.8 (21.5–26.2)22.9 (20.7–25.5)<0.001
CNY, Chinese Yuan; MMSE, Mini-Mental State Examination; BMI, body mass index; Q25, 25th percentile; Q75, 75th percentile.
Table 4. Risk of sarcopenia associated with baseline depression.
Table 4. Risk of sarcopenia associated with baseline depression.
Non-DepressionDepressionp Value
Overall population (N = 11,292)
No. of participants80153277
No. of incident depression131109
Follow-up, person-years27,833.5 11,787.6
Follow-up, years
Median (IQR)		4.0 (3.9–4.0)4.0 (3.9–4.0)
Hazard ratio for incident depression (95%CI, p value)
Unadjustedreference2.0 (1.5–2.5)<0.001
Adjusted model 1reference1.8 (1.4–2.4)<0.001
Adjusted model 2reference1.6 (1.3–2.1)<0.001
Adjusted model 3reference1.5 (1.2–2.0)0.0032
Older adults, age ≥ 60 (N = 3303)
No. of participants22931010
No. of incident depression10578
Follow-up, person-years8127.473654.31
Follow-up, years
Median (IQR)		4.0 (3.9–4.0)4.0 (3.9–4.0)
Hazard ratio for incident depression (95%CI, p value)
Unadjustedreference1.6 (1.2–2.2)0.0012
Adjusted model 1reference1.6 (1.2–2.1)0.0022
Adjusted model 2reference1.5 (1.1–2.0)0.0164
Adjusted model 3reference1.4 (1.0–1.9)0.0409
Middle-aged adults, age < 60 (N = 7989)
No. of participants57222267
No. of incident sarcopenia2631
Follow-up, person-years19,705.9 8133.3
Follow-up, years
Median (IQR)		4.0 (3.9–4.0)4.0 (3.9–4.0)
Hazard ratio for incident depression (95%CI, p value)
Unadjustedreference2.9 (1.7–4.9)<0.001
Adjusted model 1reference2.7 (1.6–4.5)<0.001
Adjusted model 2reference2.4 (1.4–4.2)0.0021
Adjusted model 3reference2.2 (1.3–4.0)0.0059
IQR, interquartile range; 95%CI, 95% confidence interval.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zeng, N.; Li, C.; Mei, H.; Wu, S.; Liu, C.; Wang, X.; Shi, J.; Lu, L.; Bao, Y. Bidirectional Association between Sarcopenia and Depressive Symptoms among Chinese Middle- and Older-Aged Adults: Longitudinal Observational Study. Brain Sci. 2024, 14, 593. https://doi.org/10.3390/brainsci14060593

AMA Style

Zeng N, Li C, Mei H, Wu S, Liu C, Wang X, Shi J, Lu L, Bao Y. Bidirectional Association between Sarcopenia and Depressive Symptoms among Chinese Middle- and Older-Aged Adults: Longitudinal Observational Study. Brain Sciences. 2024; 14(6):593. https://doi.org/10.3390/brainsci14060593

Chicago/Turabian Style

Zeng, Na, Chao Li, Huan Mei, Shuilin Wu, Chang Liu, Xiaokun Wang, Jie Shi, Lin Lu, and Yanping Bao. 2024. "Bidirectional Association between Sarcopenia and Depressive Symptoms among Chinese Middle- and Older-Aged Adults: Longitudinal Observational Study" Brain Sciences 14, no. 6: 593. https://doi.org/10.3390/brainsci14060593

APA Style

Zeng, N., Li, C., Mei, H., Wu, S., Liu, C., Wang, X., Shi, J., Lu, L., & Bao, Y. (2024). Bidirectional Association between Sarcopenia and Depressive Symptoms among Chinese Middle- and Older-Aged Adults: Longitudinal Observational Study. Brain Sciences, 14(6), 593. https://doi.org/10.3390/brainsci14060593

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop