Association between Sarcopenia and the Risk of Depression: the mediating effects of Obesity

doi:10.21203/rs.3.rs-4207542/v1

Background

The increased incidence of depression in people with sarcopenia may further increase the disease burden in middle-aged and older adults. We aimed to explore whether obesity factors mediate the associations of sarcopenia with the risk of depression.

Methods

We included 5341 participants aged ≥ 45 years old in 2011–2012 and followed up until 2018. Sarcopenia consists of three factors as proxies, including muscle strength, appendicular skeletal muscle mass (ASM), and physical performance. The degree of obesity was reflected by BMI and WC. Cox proportional hazards regression models were used to examine the associations between sarcopenia, obesity and the risk of depression. The mediation effects of obesity indicators on the association between sarcopenia and depression were analyzed.

Results

Compared with no-sarcopenic group, the sarcopenic group had a higher risk of depression (HR, 1.238; 95%CI, 1.091–1.403). Only the interaction between gender and sarcopenia on depression was found in all subgroups. The results showed a significant association between sarcopenia and the risk of depression in the male population (HR, 1.224; 95%CI, 1.007–1.488). The association between sarcopenia and depression was partially mediated by BMI (mediated proportion = 31.81%) and WC (proportional mediation = 20.28%) in the Chinese middle-aged and elderly male population.

Conclusion

The Chinese middle-aged and elderly male population with sarcopenia had a higher risk of depression, and obesity partially mediated this association. Therefore, we suggested that appropriate weight gain in middle-aged and elderly male population might serve as an intervention to reduce future incidence of depression.

Health sciences/Risk factors

Health sciences/Medical research/Epidemiology

Health sciences/Diseases/Nutrition disorders/Obesity

Sarcopenia

Depression

Mediating effect

Obesity

CHARLS

Depression, a common mental disorder and the most common cause of emotional distress in the elderly population, and is associated with the risk of mental illness in future(Chen et al., 2021; Eurelings et al., 2018). Approximately 280 million people worldwide are impacted by this mood disorder, as reported by the World Health Organization(WHO). Therefore, early screening and intervention in the early stages of depression, identifying risk factors and their interactions with depression, especially potentially modifiable symptoms, were of paramount importance. Depression could be caused by a variety of factors, including stressful life events, interpersonal associations, and the metabolic syndrome and so on(Hammen, 2018). In recent years, some studies found that depression was associated with skeletal muscle mass, strength, and function, as well as body composition(Gariballa and Alessa, 2020; Moon et al., 2018).

In addition to the role of sarcopenia, there have been studies quantifying the association between body mass index (BMI) and depression (Chang and Yen, 2012; Johnston et al., 2004), with a study pointing to an inverse association between depression and obesity risk in middle-aged and elderly Chinese populations(Luo et al., 2018). But the association between BMI and depression remained controversial. And there was a strong correlation between sarcopenia and changes in BMI(Curtis et al., 2023; Lee et al., 2007). Given these findings regarding the strong association between sarcopenia and BMI and between BMI and depression, it was reasonable to hypothesize that sarcopenia affected depression through the indirect effect of the mediating variable “BMI”. No studies have currently explored the potential mediation pathways among sarcopenia, BMI, and depression. And the association between BMI and sarcopenia, and between BMI and depression, remained controversial (Ishii et al., 2016; Noh et al., 2015; Xiang and An, 2015). Furthermore, since BMI cannot differentiate between adipose tissue and lean body mass, it is commonly used as a substitute for general obesity, whereas waist circumference (WC) is considered a better indicator for assessing abdominal obesity, may be more useful at assessing obesity risk(Zhang et al., 2021). Therefore, BMI and WC were used to comprehensively reflect the degree of obesity.

Therefore, using a large population 7-year prospective cohort of middle-aged and elderly Chinese populations, our study aimed to analyze the longitudinal association between sarcopenia and depression, and to explore the possible mediating role of obesity indicators in this association. Our finding will provide a scientific basis and intervention direction for further research on depression in middle-aged and elderly population.

Study population

The data in this study was obtained from the China Health and Retirement Longitudinal Study (CHARLS), which enrolled a nationally representative sample of community-dwellers aged ≥ 45 years from 28 provinces in China. The details about the design of CHARLS were published elsewhere(Zhao et al., 2014). This study used the baseline data from 2011 and follow-up data from 2013, 2015 and 2018. All participants provided written informed consent, and the ethical approval for data collection in CHARLS was approved by The Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). The ethical approval of CHARLS was from the Human Research Ethics Committee of the University of Newcastle (H-2015-0290).

After excluding the participants who had no depression at baseline and participated in at least one follow-up survey, who were missing anthropometric parameters, basic demographic and chronic disease data at baseline, a total of 5341 subjects were involved in this study (Figure 1).

Sarcopenia

Sarcopenia was assessed according to the AWGS 2019 algorithm, which included muscle strength, appendicular skeletal muscle mass (ASM), and physical performance(Wu et al., 2021).

Sarcopenia is diagnosed when low muscle mass combined with low muscle strength or low physical performance is detected. Handgrip strength (kg) was measured in the dominant and non-dominant hand, with participants squeezing a Yuejian™ WL-1000 dynamometer as hard as possible(Zhao et al., 2014). The cut-off points for low grip strength were < 28 kg and < 18 kg for male and female, respectively(Wu et al., 2021). ASM was estimated by a validated anthropometric equation among Chinese population(Wen et al., 2011):

$$\text{ASM=0.193×weight}\left(\text{kg}\right)\text{+0.107×height}\left(\text{cm}\right)\text{-4.157×gender-0.037×age}\left(\text{years}\right)\text{-2.631}$$

The weight and height were measured by Omron™ HN-286 scale and SecaTM213 stadiometer, in kilograms and centimeters, respectively(Zhao et al., 2014). The cut-off for low muscle mass was based on the gender-specific lowest 20% of height-adjusted muscle mass (ASM/height²)(Delmonico et al., 2007). The ASM/height² values of < 5.49 kg/m² in female and < 7.11 kg/m² in male were considered as low muscle mass. For physical performance, the gait speed and the chair stand test were performed as described by Wu et al(Wu et al., 2021). Low physical performance was defined as a five-time chair stand test ≥ 12 s, or gait speed on a 6-m walk < 1.0 m/s, or a Short Physical Performance Battery (SPPB) score < 9.

Depression

The presence of depression was assessed by using the 10-item Center for Epidemiological Studies-Depression Scale (CES-D10). CES-D10 consists of 10 items, each with four answers, as follows: "rarely (< 1 day/week);" "some days (1–2 days per week);" "occasionally (3–4 days);" "Mostly (5–7 days a week)." For negative items, "rarely", "some days", "occasionally" and "mostly" were scored as 0, 1, 2 and 3 points; while for positive items, "rarely", "some days", "occasionally" and "mostly" were scored 3, 2, 1 and 0 points, respectively. The total score ranged from 0 to 30, with lower scores indicating lower levels of depression. The study participants were classified as depression with a CES-D10 score ≥ 10. Previous studies found that the CES-D10 had sufficient reliability and validity in assessing depression in a community-dwelling Chinese elderly population(Boey, 1999).

Obesity Indicators

Anthropometric measurements were performed by trained staff. Weight was quantified without shoes to the nearest 0.1 kg, vertical height meter was used to measure the height, and the measurement was accurate to 0.1 cm. BMI was defined as weight (kg) divided by the square of height (m) and was used to describe general obesity. Participants were categorized as underweight (BMI < 18.5 kg/m²), normal weight (18.5 ≤ BMI < 24.0 kg/m²), overweight (24.0 ≤ BMI < 28.0 kg/m²), or obese (BMI ≥ 28.0 kg/m²). WC was measured horizontally around the subject at the umbilical position and to the nearest 0.1 cm and was used to describe abdominal obesity. WC ≥ 85 cm in males and ≥ 80 cm in females was used to define abdominal obesity(Sun et al., 2019).

Covariates

Covariates included sociodemographic characteristics and health-related factors assessed at baseline. Socio-demographic variables included age, gender, marital status (married and separated/single/divorced/widowed), educational level (primary school or below, secondary school or above). Health-related factors included self-reported smoking and drinking status (yes or no), sleep duration (< 6h, 6-8h, >8h). Chronic conditions included hypertension, dyslipidemia, diabetes, cardiovascular diseases, kidney diseases, arthritis, or rheumatism (yes or no).

Statistical Analysis

Participant characteristics were described using mean ± standard deviation (SD) or counts (percentage). Student’s t-test and Pearson’s Chi-square test were used for comparison between the two groups. Cox proportional hazards regression models were used to calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of sarcopenia and obesity indicators with the risk of depression. Model I was a single factor model. Model II adjusted for age and gender. Model III further adjusted for smoking, drinking, educational level, marital status, sleep duration, hypertension, dyslipidemia, diabetes, cardiovascular diseases, kidney diseases, arthritis or rheumatism. Model IV further adjusted for BMI based on model III. Model V further adjusted for WC based on model III. The sensitivity analysis of this study was achieved through stratified analysis. The interaction of sarcopenia and stratification factors on the risk of depression was examined by adding product terms in multivariate adjusted cox models by gender, smoking, drinking, educational level, marital status and sleep duration.

The mediating effect of obesity indicators on the relationship between sarcopenia and depression was assessed using mediation package in R(Geng et al., 2023), and the indirect, direct and total effects of each mediator were calculated by combining mediation and outcome models with adjustment for all covariates in model III. The specific steps of the mediation analysis are as follows: (1) the total effect of sarcopenia on depression; (2) the effect of sarcopenia on BMI and WC; (3) the direct effect of sarcopenia on depression and the indirect effect of BMI and WC on this association. The formula for the proportion of intermediaries was indirect effect / (indirect effect + direct effect) × 100%.

We used R version 4.3.1 (R Foundation for Statistical Computing) for all statistical analyses. A two-tailed p < 0.05 was considered to be statistically significant, and p-interaction less than 0.1 was considered significant(Li et al., 2022b).

Baseline characteristics

A total of 5341 participants were included in the study, of whom 2558 developed depression at follow-up. Table 1 summarized the baseline characteristics of the participants according to the presence or absence of depression at follow-up. The average age of the whole cohort was 57.2 years old, and 52.6% of them were male. Compared with participants without depression, depressed participants were older and unmarried at baseline, more likely to be female, had lower educational levels, smoked and drank less, had irregular sleep duration, had a lower BMI and WC, and were more likely to have hypertension, kidney diseases, arthritis or rheumatism and sarcopenia (p < 0.05).

Table 1

Demographic characteristics of the study population was classified by depression at follow up.
	Total (n = 5341)	Depression (n = 2558)	Without depression (n = 2783)	p-value
Age	57.2 ± 8.2	57.7 ± 8.4	56.8 ± 8.1	< 0.001
Gender				< 0.001
Male	2812(52.6)	1159(45.3)	1653(59.4)
Female	2529(47.4)	1399(54.7)	1130(40.6)
BMI (kg/m²)	23.9 ± 3.8	23.7 ± 3.9	24.1 ± 3.8	0.002
WC (cm)	86.1 ± 9.8	85.6 ± 9.9	86.5 ± 9.7	0.001
Smoking				< 0.001
Yes	2251(42.1)	993(38.8)	1258(45.2)
No	3090(57.9)	1655(61.2)	1525(54.8)
Drinking				< 0.001
Yes	1952(36.5)	825(32.3)	1127(40.5)
No	3389(63.5)	1733(67.7)	1656(59.5)
Educational level				< 0.001
Primary school or below	3222(60.3)	1741(68.1)	1481(53.2)
Secondary school or above	2119(39.7)	817(31.9)	1302(46.8)
Marital status				< 0.001
Married	4709(88.2)	2206(86.2)	2503(89.9)
Separated/unmarried /divorced/widowed	632(11.8)	352(13.8)	280(10.1)
Sleep duration				< 0.001
< 6h	846(15.8)	525(20.5)	321(11.5)
6-8h	3050(57.1)	1395(54.6)	1655(59.5)
> 8h	1445(27.1)	638(24.9)	807(29.0)
Hypertension				0.036
Yes	1131(21.2)	573(22.4)	558(20.1)
No	4210(78.8)	1985(77.6)	2225(79.9)
Diabetes				0.252
Yes	263(4.9)	135(5.3)	128(4.6)
No	5078(95.1)	2423(94.7)	2655(95.4)
Dyslipidemia				0.006
Yes	451(8.4)	188(7.3)	263(9.5)
No	4890(91.6)	2370(92.7)	2520(90.5)
Cardiovascular diseases				0.694
Yes	509(9.5)	248(9.7)	261(9.4)
No	4832(90.5)	2310(90.3)	2522(90.6)
Kidney diseases				0.001
Yes	253(4.7)	147(5.7)	106(3.8)
No	5088(95.3)	2411(94.3)	2677(96.2)
Arthritis or rheumatism				< 0.001
Yes	1436(26.9)	815(31.9)	621(22.3)
No	3905(73.1)	1743(68.1)	2162(77.7)
Sarcopenia				< 0.001
Yes	557(10.4)	322(12.6)	235(8.4)
No	4784(89.6)	2236(87.4)	2548(91.6)

Association between sarcopenia, BMI and depression

Based on the results of Table 2, we found that sarcopenia was positively associated with the risk of depression (Model I). After controlling for covariates (Model II and Model III), the association of sarcopenia with depression still persisted. In Model III, compared with the non-sarcopenia group, the sarcopenic group had a higher risk of depression (HR, 1.238; 95%CI, 1.091–1.403). And when the model adjusted for BMI and WC separately, the association between sarcopenia and depression became smaller (p < 0.05).

Table 2

Cox proportional hazard models for the association between sarcopenia and depression
	Sarcopenia (reference = Non-Sarcopenia)	p-value
	HR (95%CI)	p-value
Model I	1.401(1.246, 1.574)	< 0.001
Model II	1.274(1.125, 1.443)	< 0.001
Model III	1.238(1.091, 1.403)	< 0.001
Model IV	1.153(1.007, 1.320)	0.040
Model V	1.171(1.025, 1.337)	0.020

Abbreviations: BMI, Body mass index; WC, Waist Circumference

Model I = unadjusted model

Model II = Model I + age + gender

Model III = Model II + smoking + drinking + educational level + marital status + sleep duration + hypertension + diabetes + dyslipidemia + cardiovascular diseases + kidney diseases + arthritis or rheumatism

Model IV = Model III + BMI

Model V = Model III + WC

Subgroup analyses

Figure 2 presented results from subgroup analyses. Except for gender (p-interaction < 0.1), the association between sarcopenia and depression remained robust in other subgroups. There was a significant association between sarcopenia and the risk of depression in the male population, (HR, 1.224; 95%CI, 1.007–1.488).

Mediation analysis

We explored the mediating role of BMI and WC on the association between sarcopenia and depression scores in the male population, as shown in Fig. 3. First, controlling for all covariates, a linear regression analysis was performed with sarcopenia as the independent variable and BMI, WC as the dependent variables. The results showed that sarcopenia was negatively correlated with BMI and WC (p < 0.05) (S1 and S2 Table). Second, the cox regression analysis was performed with BMI and WC as independent variables and the incidence of depression as the dependent variable. The results showed that with the increase of BMI and WC, the risk of depression decreased (p < 0.05). Compared with the normal weight group, the risk of depression in the underweight group increased 1.375-fold (S3 and S4 Table).

The mediation analysis found that participants with sarcopenia might contribute to depression by influencing BMI or WC, especially for BMI (p < 0.05). Obesity might ameliorate the association between sarcopenia and depression. Specifically, BMI-mediated efficacy accounted for 31.81% of the association between sarcopenia and depression (IE, 0.589; 95% CI, 0.140–1.025). WC-mediated efficacy accounted for 20.28% of the association between sarcopenia and depression (IE, 0.378; 95% CI, 0.010–0.756).

In this nationwide prospective cohort study, we explored the associations between sarcopenia, obesity, and depression in the middle-aged and elderly Chinese population based on CHARLS. Our results reconfirmed that sarcopenia was associated with increased risk of depression, and that low BMI and WC was associated with increased risk of depression. And in the male population, the association was partially mediated through obesity biomarkers of BMI and WC.

Previous studies confirmed an association between sarcopenia and mental health, but results remained controversial. Hsu et al. reported an independent positive relationship between sarcopenia and depression(Hsu et al., 2014). However, the study by Bertoni et al. pointed no significant association between muscle weakness at baseline and incidence of depression at follow-up(Bertoni et al., 2018). Sarcopenia combined indicators of grip strength, muscle mass and physical performance. There was growing evidence that grip strength and muscle mass were associated with mental health conditions(Kim et al., 2011; Marques et al., 2020). A UK study found that grip strength was negatively correlated with depression and anxiety, and a significant interaction between grip strength and walking speed was found(Cabanas-Sánchez et al., 2022). Skeletal muscle mass was also inversely associated with the risk of depression in the elderly male population(Kim et al., 2011). Although most studies pointed to the possible adverse effects of sarcopenia on mental function through metabolic and endocrine mechanisms, a reverse pathogenic pathway was possible(Li et al., 2022c). Depression might contribute to sarcopenia by affecting social mobility. Therefore, we demonstrated the causal association between sarcopenia and the onset of depression in a prospective cohort study.

Our finding confirmed that sarcopenia was a risk factor for the onset of depression in a middle-aged and elderly Chinese population. Not only expanding the existing evidence, but our results further suggest that this association exists only in the male population. And a previous cross-sectional study in South Korea had similar results that depression was associated with sarcopenia and low body mass, especially in the male population(Kim et al., 2011). One possible reason for the gender difference is the effect of sex steroids (eg, estrogen, testosterone), which played an important role in the growth and maintenance of muscle mass and strength, and the metabolic function of skeletal muscle(McClung et al., 2006). Research pointed to the sex difference in muscle homeostasis. Low testosterone concentrations lead to a reduction in muscle mass and strength in men(Bhasin et al., 1996). And low testosterone levels were associated with incident depression(Ford et al., 2016). Furthermore, men were more likely than women to notice the loss of muscle mass and associated weakness, which might contribute to the development of depression(Ida et al., 2018).

Previous studies have analyzed sarcopenia and obesity in conjunction with depression. For example, a cross-sectional study in Japan found that participants with sarcopenic obesity had a significantly increased risk of depression and that sarcopenia and obesity had a synergistic effect(Ishii et al., 2016). The results of a study in China showed that the association between sarcopenia and depression varied among different BMI subgroups(Gao et al., 2021). However, these studies restricted the population to individuals with different obesity and did not investigate the contribution of obesity to the association between sarcopenia and depression. But some studies have pointed out the relationship between sarcopenia and depression, sarcopenia and obesity, obesity and depression(Ida et al., 2018; Kim et al., 2011; Lee et al., 2007). From this, it can be suggested that abdominal obesity and general obesity play an important role in the pathway between the two. Sarcopenia might adversely affect mental function through metabolic and endocrine mechanisms. Our study was the first to evaluate the mediating role of obesity between sarcopenia and depression. We found that BMI and WC significantly mediated the association between sarcopenia and the onset of depression, especially for BMI. Underweight might increase the risk of depression in population with sarcopenia.

At the population level, BMI and WC were reliable measures that captured the biological roles of adipose tissue, including inflammatory processes and the effects of leptin(Li et al., 2022a), which explained the mediating role of BMI and WC in our study. Previous studies showed that the severity of sarcopenia decreased linearly with BMI(Lee et al., 2007), which was consistent with our research results. This was also consistent with typical hallmarks of malnutrition in older adults, with low BMI, low skeletal muscle mass, or muscle strength being one of the criteria for malnutrition phenotypes recommended by the Global Leadership Initiative on the Malnutrition working group(Cederholm et al., 2019). Our study also pointed to an inverse association between obesity and depression. Similar results have been obtained in previous studies(Kim et al., 2011). A possible reason for this phenomenon was that people with a low BMI might still have as much fat as people with a high BMI, which could be explained by the obesity paradox. The obesity paradox is the unexpected phenomenon of reduced mortality in overweight or mildly obese individuals(Flegal et al., 2013). Furthermore, the severity of obesity may be underestimated due to the inability of BMI to distinguish adipose tissue from lean body mass(Romero-Corral et al., 2008). Therefore, this study included WC, which could reflect the accumulation degree of abdominal obesity, into the study. The results showed that WC still could regulate the association between sarcopenia and depression, which further indicated that malnutrition in the middle and elderly population will increase the risk of depression in the sarcopenia population.

There are several strengths of our study. First, this is the first mediation study to assess the effect of obesity on the association between sarcopenia and depression, which could help develop targeted intervention strategies to prevent and treat depression. Second, the present study was based on a long-term follow-up of a large, prospective national cohort of middle-aged and elderly Chinese populations, which allowed us to comprehensively assess the underlying mechanisms of the observed associations and to illustrate the causal associations of the study variables with outcomes. However, some limitations should be considered in the interpretation of our findings. First, we excluded all missing values instead of imputation, which might reduce the representativeness of the sample. Second, the CES-D10 was used to examine the number of self-reported depression in the past week, which could introduce recall bias. But previous studies also proved that CES-D10 had high reliability and validity in Chinese population(Boey, 1999). Given that the etiology of depression is complex and multifactorial, it cannot necessarily be assumed that overweight or obese middle-aged and elderly adults are protected from sarcopenia and depression.

The Chinese middle-aged and elderly male population with sarcopenia had a higher risk of depression, and obesity partially mediated this association. Therefore, we suggested that appropriate weight gain in middle-aged and elderly male population might serve as an intervention to reduce future incidence of depression.

Ethics approval and consent to participate:

Not applicable. The manuscript does not contain clinical studies or patient data.

Consent for publication:

Not applicable

Conflicts of Interest:

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Funding:

This work was supported by the National Natural Science Foundation of China (No.81973129).

Authors’ Contributions:

BL, XL, and RG made the study design; XL, RG, ZW, SW conducted the study; XL, RG, and FW analyzed the data and wrote the manuscript; YW, XW, JL and ZX attended original manuscript revision; BL, MS provided guidance in the revision of the manuscript. All authors agreed with the final manuscript.

Acknowledgements:

The authors thank the staff and participants of the China Health and Retirement Longitudinal Study (CHARLS).

Bertoni, M., Maggi, S., Manzato, E., Veronese, N., Weber, G., 2018. Depressive symptoms and muscle weakness: A two-way relation? Experimental gerontology 108, 87-91.
Bhasin, S., Storer, T.W., Berman, N., Callegari, C., Clevenger, B., Phillips, J., Bunnell, T.J., Tricker, R., Shirazi, A., Casaburi, R., 1996. The effects of supraphysiologic doses of testosterone on muscle size and strength in normal men. The New England journal of medicine 335, 1-7.
Boey, K.W., 1999. Cross-validation of a short form of the CES-D in Chinese elderly. International journal of geriatric psychiatry 14, 608-617.
Cabanas-Sánchez, V., Esteban-Cornejo, I., Parra-Soto, S., Petermann-Rocha, F., Gray, S.R., Rodríguez-Artalejo, F., Ho, F.K., Pell, J.P., Martínez-Gómez, D., Celis-Morales, C., 2022. Muscle strength and incidence of depression and anxiety: findings from the UK Biobank prospective cohort study. Journal of cachexia, sarcopenia and muscle 13, 1983-1994.
Cederholm, T., Jensen, G.L., Correia, M., Gonzalez, M.C., Fukushima, R., Higashiguchi, T., Baptista, G., Barazzoni, R., Blaauw, R., Coats, A.J.S., Crivelli, A.N., Evans, D.C., Gramlich, L., Fuchs-Tarlovsky, V., Keller, H., Llido, L., Malone, A., Mogensen, K.M., Morley, J.E., Muscaritoli, M., Nyulasi, I., Pirlich, M., Pisprasert, V., de van der Schueren, M.A.E., Siltharm, S., Singer, P., Tappenden, K., Velasco, N., Waitzberg, D., Yamwong, P., Yu, J., Van Gossum, A., Compher, C., 2019. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. Journal of cachexia, sarcopenia and muscle 10, 207-217.
Chang, H.H., Yen, S.T., 2012. Association between obesity and depression: evidence from a longitudinal sample of the elderly in Taiwan. Aging & mental health 16, 173-180.
Chen, X., Han, P., Yu, X., Zhang, Y., Song, P., Liu, Y., Jiang, Z., Tao, Z., Shen, S., Wu, Y., Zhao, Y., Zheng, J., Chu, L., Guo, Q., 2021. Relationships between sarcopenia, depressive symptoms, and mild cognitive impairment in Chinese community-dwelling older adults. Journal of affective disorders 286, 71-77.
Curtis, M., Swan, L., Fox, R., Warters, A., O'Sullivan, M., 2023. Associations between Body Mass Index and Probable Sarcopenia in Community-Dwelling Older Adults. Nutrients 15.
Delmonico, M.J., Harris, T.B., Lee, J.S., Visser, M., Nevitt, M., Kritchevsky, S.B., Tylavsky, F.A., Newman, A.B., 2007. Alternative definitions of sarcopenia, lower extremity performance, and functional impairment with aging in older men and women. Journal of the American Geriatrics Society 55, 769-774.
Eurelings, L.S., van Dalen, J.W., Ter Riet, G., Moll van Charante, E.P., Richard, E., van Gool, W.A., Almeida, O.P., Alexandre, T.S., Baune, B.T., Bickel, H., Cacciatore, F., Cooper, C., de Craen, T.A., Degryse, J.M., Di Bari, M., Duarte, Y.A., Feng, L., Ferrara, N., Flicker, L., Gallucci, M., Guaita, A., Harrison, S.L., Katz, M.J., Lebrão, M.L., Leung, J., Lipton, R.B., Mengoni, M., Ng, T.P., Østbye, T., Panza, F., Polito, L., Sander, D., Solfrizzi, V., Syddall, H.E., van der Mast, R.C., Vaes, B., Woo, J., Yaffe, K., 2018. Apathy and depressive symptoms in older people and incident myocardial infarction, stroke, and mortality: a systematic review and meta-analysis of individual participant data. Clinical epidemiology 10, 363-379.
Flegal, K.M., Kit, B.K., Orpana, H., Graubard, B.I., 2013. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. Jama 309, 71-82.
Ford, A.H., Yeap, B.B., Flicker, L., Hankey, G.J., Chubb, S.A., Handelsman, D.J., Golledge, J., Almeida, O.P., 2016. Prospective longitudinal study of testosterone and incident depression in older men: The Health In Men Study. Psychoneuroendocrinology 64, 57-65.
Gao, K., Ma, W.Z., Huck, S., Li, B.L., Zhang, L., Zhu, J., Li, T., Zhou, D., 2021. Association Between Sarcopenia and Depressive Symptoms in Chinese Older Adults: Evidence From the China Health and Retirement Longitudinal Study. Frontiers in medicine 8, 755705.
Gariballa, S., Alessa, A., 2020. Associations between low muscle mass, blood-borne nutritional status and mental health in older patients. BMC nutrition 6, 6.
Geng, T., Zhu, K., Lu, Q., Wan, Z., Chen, X., Liu, L., Pan, A., Liu, G., 2023. Healthy lifestyle behaviors, mediating biomarkers, and risk of microvascular complications among individuals with type 2 diabetes: A cohort study. PLoS medicine 20, e1004135.
Hammen, C., 2018. Risk Factors for Depression: An Autobiographical Review. Annual review of clinical psychology 14, 1-28.
Hsu, Y.H., Liang, C.K., Chou, M.Y., Liao, M.C., Lin, Y.T., Chen, L.K., Lo, Y.K., 2014. Association of cognitive impairment, depressive symptoms and sarcopenia among healthy older men in the veterans retirement community in southern Taiwan: a cross-sectional study. Geriatrics & gerontology international 14 Suppl 1, 102-108.
Ida, S., Murata, K., Nakai, M., Ito, S., Malmstrom, T.K., Ishihara, Y., Imataka, K., Uchida, A., Monguchi, K., Kaneko, R., Fujiwara, R., Takahashi, H., 2018. Relationship between sarcopenia and depression in older patients with diabetes: An investigation using the Japanese version of SARC-F. Geriatrics & gerontology international 18, 1318-1322.
Ishii, S., Chang, C., Tanaka, T., Kuroda, A., Tsuji, T., Akishita, M., Iijima, K., 2016. The Association between Sarcopenic Obesity and Depressive Symptoms in Older Japanese Adults. PloS one 11, e0162898.
Johnston, E., Johnson, S., McLeod, P., Johnston, M., 2004. The relation of body mass index to depressive symptoms. Canadian journal of public health = Revue canadienne de sante publique 95, 179-183.
Kim, N.H., Kim, H.S., Eun, C.R., Seo, J.A., Cho, H.J., Kim, S.G., Choi, K.M., Baik, S.H., Choi, D.S., Park, M.H., Han, C., Kim, N.H., 2011. Depression is associated with sarcopenia, not central obesity, in elderly korean men. Journal of the American Geriatrics Society 59, 2062-2068.
Lee, J.S., Auyeung, T.W., Kwok, T., Lau, E.M., Leung, P.C., Woo, J., 2007. Associated factors and health impact of sarcopenia in older chinese men and women: a cross-sectional study. Gerontology 53, 404-410.
Li, J., Glenn, A.J., Yang, Q., Ding, D., Zheng, L., Bao, W., Beasley, J., LeBlanc, E., Lo, K., Manson, J.E., Philips, L., Tinker, L., Liu, S., 2022a. Dietary Protein Sources, Mediating Biomarkers, and Incidence of Type 2 Diabetes: Findings From the Women's Health Initiative and the UK Biobank. Diabetes care 45, 1742-1753.
Li, X., Sun, M., Yao, N., Liu, J., Wang, L., Hu, W., Yang, Y., Guo, R., Li, B., Liu, Y., 2022b. Association between patterns of eating habits and mental health problems in Chinese adolescents: A latent class analysis. Frontiers in nutrition 9, 906883.
Li, Z., Tong, X., Ma, Y., Bao, T., Yue, J., 2022c. Prevalence of depression in patients with sarcopenia and correlation between the two diseases: systematic review and meta-analysis. Journal of cachexia, sarcopenia and muscle 13, 128-144.
Luo, H., Li, J., Zhang, Q., Cao, P., Ren, X., Fang, A., Liao, H., Liu, L., 2018. Obesity and the onset of depressive symptoms among middle-aged and older adults in China: evidence from the CHARLS. BMC public health 18, 909.
Marques, A., Gaspar de Matos, M., Henriques-Neto, D., Peralta, M., Gouveia É, R., Tesler, R., Martins, J., Gomez-Baya, D., 2020. Grip Strength and Depression Symptoms Among Middle-Age and Older Adults. Mayo Clinic proceedings 95, 2134-2143.
McClung, J.M., Davis, J.M., Wilson, M.A., Goldsmith, E.C., Carson, J.A., 2006. Estrogen status and skeletal muscle recovery from disuse atrophy. Journal of applied physiology (Bethesda, Md. : 1985) 100, 2012-2023.
Moon, J.H., Kong, M.H., Kim, H.J., 2018. Low Muscle Mass and Depressed Mood in Korean Adolescents: a Cross-Sectional Analysis of the Fourth and Fifth Korea National Health and Nutrition Examination Surveys. Journal of Korean medical science 33, e320.
Noh, J.W., Kwon, Y.D., Park, J., Kim, J., 2015. Body mass index and depressive symptoms in middle aged and older adults. BMC public health 15, 310.
Romero-Corral, A., Somers, V.K., Sierra-Johnson, J., Thomas, R.J., Collazo-Clavell, M.L., Korinek, J., Allison, T.G., Batsis, J.A., Sert-Kuniyoshi, F.H., Lopez-Jimenez, F., 2008. Accuracy of body mass index in diagnosing obesity in the adult general population. International journal of obesity (2005) 32, 959-966.
Sun, M., Jiang, Y., Sun, C., Li, J., Guo, X., Lv, Y., Yu, Y., Yao, Y., Jin, L., 2019. The associations between smoking and obesity in northeast China: a quantile regression analysis. Scientific reports 9, 3732.
Wen, X., Wang, M., Jiang, C.M., Zhang, Y.M., 2011. Anthropometric equation for estimation of appendicular skeletal muscle mass in Chinese adults. Asia Pacific journal of clinical nutrition 20, 551-556.
WHO, WHO, 2023. Depression [WWW Document].URL https://www.who.int/news-room/fact-sheets/detail/depression (Accessed 19 March 2023).
Wu, X., Li, X., Xu, M., Zhang, Z., He, L., Li, Y., 2021. Sarcopenia prevalence and associated factors among older Chinese population: Findings from the China Health and Retirement Longitudinal Study. PloS one 16, e0247617.
Xiang, X., An, R., 2015. Obesity and onset of depression among U.S. middle-aged and older adults. Journal of psychosomatic research 78, 242-248.
Zhang, W., He, K., Zhao, H., Hu, X., Yin, C., Zhao, X., Shi, S., 2021. Association of body mass index and waist circumference with high blood pressure in older adults. BMC geriatrics 21, 260.
Zhao, Y., Hu, Y., Smith, J.P., Strauss, J., Yang, G., 2014. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). International journal of epidemiology 43, 61-68.

There is NO conflict of interest to disclose

Supplementarymaterials.docx

Association between Sarcopenia and the Risk of Depression: the mediating effects of Obesity

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study population

Sarcopenia

Depression

Obesity Indicators

Covariates

Statistical Analysis

Results

Baseline characteristics

Association between sarcopenia, BMI and depression

Subgroup analyses

Mediation analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1