Abstract
Background
Previous studies had indicated that sarcopenia and depressive symptoms were associated with increased risk of cardiovascular disease (CVD). The aim of present study was to evaluate the combined effect of sarcopenia and depressive symptoms on the CVD risk.
Methods
A total of 11,011 participants from the China Health and Retirement Longitudinal Study 2011–2020 were included. Multivariate Cox proportional hazards regression model was used to explore the associations between sarcopenia, depressive symptoms and new-onset CVD, stroke and cardiac events.
Results
During the 7-year follow-up, a total of 2,388 respondents experienced CVD (including 812 stroke and 1,831 cardiac events). There is a significant additive and multiplicative interactions of sarcopenia and depressive symptoms on risk of CVD, stroke and cardiac events. Compared with those without sarcopenia and depressive symptoms, individuals with depressive sarcopenia had the highest risk of CVD, stroke and cardiac events, with the corresponding hazard ratios (95% confidence interval) were 1.43 (1.26–1.63), 1.45 (1.15–1.82) and 1.50 (1.29−1.74), respectively.
Conclusion
Our study indicated that there was a combined effect of sarcopenia and depressive symptoms on the risk of CVD, stroke and cardiac events. Our findings highlighted the importance of identifying sarcopenia and depressive symptoms, and intervening much earlier both in older and younger population.
Keywords: Sarcopenia, Depressive symptoms, CVD, CHARLS
1. Introduction
As we all known, cardiovascular disease (CVD) is the most common cause of death worldwide, accounting for 31% of all deaths globally [1]. In addition to death, CVD poses a huge economic burden and accounts for a considerable proportion of health care spending and productivity loss worldwide [2]. Nowadays, the pace of population aging around the world is increasing dramatically, and the population over 65 years of age will reach to 22% in 2040 [3,4]. With the increase of the aging population, the number of CVD patients and the disease burden will continue to increase.
Sarcopenia, first recognizing as an independent condition with an International Classification of Diseases-10 code in 2016 [[5]], is a public health concern with the physiological aging process [6], and the reported estimated prevalence of sarcopenia among the Asia elderly ranges from 5.5% to 25.7% [7]]. Mounting evidence indicated that sarcopenia is strongly associated with increased risk of falls, frailty, mortality in general population and adverse clinical outcomes in patients with several diseases [[8], [9], [10]]. Similarly, several studies also reported that both possible sarcopenia and sarcopenia associated with CVD independent of other well-documented risk factors [[11], [12], [13]]. Depression and depressive symptoms, a common mental disorder, are highly prevalent in China and worldwide, and it is estimated that more than 300 million people in the world suffer from depression [[14], [15], [16]]. Epidemiologic studies had reported that both short- and long-term depressive symptoms are associated with increased risks of stroke, coronary heart disease and cardiovascular mortality [[17], [18], [19]].
In recent years, several studies have shown a correlation between sarcopenia and depression. Evidence from systematic review and meta-analysis studies indicated that sarcopenia was independently associated with depression [20], and the prevalence of depression in patients with sarcopenia was high relatively [21]. Furthermore, Chen et al. found sarcopenia may contribute substantially to the development of mild cognitive impairment in the elderly via depressive symptoms [22]. All of the above studies have proved that there was a significant correlation between sarcopenia and depression. However, no previous study has specifically evaluated the cumulative effect of sarcopenia and depression on risk of CVD in the general population.
Therefore, the aim of the present study was to evaluate the combined effect of sarcopenia and depression on the development of CVD among the middle-aged and elderly Chinese people, based on the data from the China Health and Retirement Longitudinal Study (CHARLS).
2. Methods
2.1. Study population
The CHARLS is an ongoing nationally representative and population-based study, that uses a multistage clustering sample method to select participants and conducted to collect a series of data regarding demographics, economic status, social networks, physical and psychological health in China [23]. The first visit was accomplished in 2011–2012 (Wave 1) of 17,708 patients, subsequently four follow-up visits carried out after that, each nearly two years apart among survivors (2013–2014: Wave 2, 2015–2016: Wave 3, 2017–2018: Wave 4, 2019–2020: Wave 5). The ethics application for collecting data on human subjects in CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015), and all CHARLS participants provided written informed consent. The details of the CHARLS data are available at its website (http://charls.pku.edu.cn/en).
In present study, we conducted a longitudinal analysis using data from the five waves of CHARLS for 2011 to 2020. In the longitudinal analysis, we included participants according to the following criteria: (1) individuals ≥45 years old, (2) individuals with complete information about sarcopenia and depression in 2011, (3) individuals without reported CVD in 2011, (4) individuals who were successfully followed-up and with complete information about CVD. Finally, a total of 11,011 individuals were eligible for subsequent analysis (Fig. 1).
Fig. 1.
Flow chart of sample selection and the exclusion criteria.
2.2. Assessment of sarcopenia and depression status
The handgrip strength (kg) was measured in the dominant hand and non-dominant hand, with the participant squeezing a YuejianTM WL-1000 dynamometer (Nantong Yuejian Physical Measure-ment Instrument Co., Ltd., Nantong, China) as hard as possible. The cut-off points for low grip strength for men and women were <28 and <18 kg, respectively [24]. The muscle mass was estimated by the appendicular skeletal muscle mass (ASM) using a previously validated anthropometric equation in a Chinese population [25], and the ASM/Ht2 values of <5.63 kg/m2 in women and <7.05 kg/m2 in men were considered as low muscle mass. For physical performance, the gait speed and the chair stand test were performed to evaluate individuals’ physical performance. The detail about sarcopenia evaluation and definition in CHARLS study has been described in previous study [25]. Sarcopenia status in current study was assessed according to the recommended diagnostic algorithm of AWGS 2019 [26]. Sarcopenia is diagnosed when low muscle mass plus low muscle strength or low physical performance. When low muscle strength, low muscle mass and low physical performance are all detected, severe sarcopenia will be considered. Possible sarcopenia is defined by low muscle strength with or without reduced physical performance. In current study, we merged subjects with possible sarcopenia into the sarcopenia group
The CES-D 10 was administrated to measure depressive symptoms at baseline and each follow-up visit of the CHARLS [27], which has been proved to be a reliable and valid approach to detect depression in Chinese adults [19]. The CES-D 10 scale incorporates depressed mood and positively affected parts and consists of ten items. Its sum scores range from 0 to 30, with higher scores indicating higher degrees of depressive symptoms. A cutoff of 12 was used to identify depression individuals [28].
2.3. Assessment of CVD events
The study outcome was CVD events, including heart disease and stroke. Similar to previous studies [29,30], CVD events were assessed by the following questions: “Have you been told by a doctor that you have been diagnosed with a heart attack, angina, coronary heart disease, heart failure, or other heart problems?” or “Have you been told by a doctor that you have been diagnosed with a stroke?”. Participants who reported heart disease or stroke were defined as having CVD [29].
2.4. Covariates assessments
The covariates were collected at baseline including age, sex, place of residence (rural vs. urban), smoking status (ever smoking vs. never smoking), educational level (illiteracy; primary school; middle school; high school or above), drinking status (ever drinking vs. never drinking), the presence or absence of other chronic diseases (dyslipidemia, diabetes mellitus, chronic lung disease and stroke) and medications (anti-hypertensive and anti-dyslipidemic).
2.5. Statistical analysis
In current study, all participants were divided into four groups according to sarcopenia and depressive symptoms: robust (no sarcopenia and depression); sarcopenia only; depression only and depressive sarcopenia.
Participants’ baseline characteristics are presented as percentages for categorical variables, as the means with standard deviation for normally distributed variables and as medians with interquartile range for nonnormally distributed variables. Demographic and clinical characteristics were compared between four subgroups by ANOVA or Kruskal–Wallis test for continuous variables and χ2 test for categorical variables. Multivariable Cox proportional hazards models were applied to calculate the hazard ratios (HRs) and 95% CIs for CVD, stroke and cardiac events, with those with robust (no sarcopenia and depression) as the reference. Potential covariates, such as age, sex, place of residence, education level, smoking, drinking, BMI, physical activity, systolic blood pressure, physical activity, chronic diseases (kidney disease, dyslipidemia and diabetes mellitus) and medications (anti-hypertensive, anti-dyslipidemic and anti-diabetic) were included in the multivariable models. Furthermore, we analyzed both the additive and multiplicative interactions between the sarcopenia and depressive symptoms with study outcomes.
In sensitivity analyses, we defined depressive symptoms via the cut-off value of CESD-10 of 10 to check the robustness of our findings [31]. In addition, we evaluate the association between the sarcopenia and depressive symptoms with study outcomes, separately. The subgroup analyses were further performed to evaluate the association between the combined effect of sarcopenia and depressive symptoms with risk of CVD according to sex, age, living place, smoking, drinking, diabetes, hypertension and BMI. Two tailed P < 0.05 was considered statistical significance. All statistical analyses were conducted using SAS statistical software (version 9.4, Cary, NC).
3. Results
In the current study, a total of 11,011 participants (5,361 men and 5,650 women) were included in the analysis, and the average age was 58.61 ± 9.46 years. Four groups were divided according to sarcopenia and depressive symptoms: neither sarcopenia or depression (n = 6,003; 54.52%), sarcopenia only (n = 2,130; 19.34%), depression only (n = 1,767; 16.05%) and depressive sarcopenia (n = 1,111; 54.52%). As shown in Table 1, baseline characteristics such as age, sex, living place, education level, history of diabetes mellitus, smoking, drinking, BMI, systolic and diastolic blood pressure were significantly different among the four subgroups (Table 1).
Table 1.
Baseline characteristics of the study participants according to sarcopenia and depression status in baseline.
| Variable | Robust | Sarcopenia only | Depression only | Depressive sarcopenia | P trend |
|---|---|---|---|---|---|
| No. of subjects | 6003 | 2130 | 1767 | 1111 | |
| Age, years | 56.29 ± 8.15 | 63.35 ± 10.44 | 57.43 ± 8.45 | 63.93 ± 10.11 | <0.001 |
| Sex, n (%) | <0.001 | ||||
| Male | 3235 (53.89) | 1027 (48.22) | 675 (38.20) | 424 (38.16) | |
| Female | 2768 (46.11) | 1103 (51.78) | 1092 (61.80) | 687 (61.84) | |
| Living place, n (%) | <0.001 | ||||
| Urban | 2405 (40.06) | 732 (34.37) | 487 (27.56) | 285 (25.65) | |
| Rural | 3598 (59.94) | 1398 (65.63) | 1280 (72.44) | 826 (74.35) | |
| Education level, n (%) | <0.001 | ||||
| Illiteracy | 1232 (20.52) | 832 (39.06) | 571 (32.31) | 507 (45.63) | |
| Primary school | 2365 (39.40) | 851 (39.95) | 783 (44.31) | 450 (40.50) | |
| Middle school | 1510 (25.15) | 302 (14.18) | 318 (18.00) | 118 (10.62) | |
| High school or above | 896 (14.93) | 145 (6.81) | 95 (5.38) | 36 (3.24) | |
| Medical history, n (%) | |||||
| Hypertension | 1234 (20.56) | 487 (28.86) | 326 (18.45) | 244 (21.96) | 0.983 |
| Dyslipidemia | 476 (7.93) | 134 (6.29) | 123 (6.96) | 77 (6.93) | 0.074 |
| Diabetes mellitus | 280 (4.66) | 127 (5.96) | 93 (5.26) | 72 (6.48) | 0.011 |
| Smoking, n (%) | 2598 (43.28) | 844 (39.62) | 603 (34.13) | 403 (36.27) | <0.001 |
| Drinking, n (%) | 2610 (44.48) | 791 (37.14) | 618 (34.97) | 373 (33.57) | <0.001 |
| BMI (kg/m [2]) | 23.36 (21.19−25.80) | 22.56 (20.25−25.33) | 22.42 (20.30−25.11) | 22.13 (19.74−24.72) | <0.001 |
| FBG (mg/dL) | 102.60 (94.32−113.22) | 102.06 (94.50−113.40) | 101.52 (93.60−112.14) | 102.60 (94.86−114.12) | 0.192 |
| SBP, mmHg | 128.79 ± 19.98 | 132.98 ± 23.47 | 127.63 ± 21.42 | 132.35 ± 23.48 | <0.001 |
| DBP, mmHg | 76.28 ± 11.72 | 75.55 ± 12.20 | 74.50 ± 11.26 | 74.73 ± 11.53 | <0.001 |
BMI: body mass index; FBG: Fasting blood glucose; SBP: systolic blood pressure; DBP: diastolic blood pressure.
Continuous variables are expressed as mean ± standard deviation, or as median (interquartile range). Categorical variables are expressed as frequency (percent).
During the 9-year follow-up, a total of 1,854 respondents experienced CVD (including 614 stroke and 1,393 cardiac events). The cumulative incidence of CVD, stroke and cardiac events were significantly different among four subgroups (Fig. 1). Compared to participants without sarcopenia and depression, those with depression only and depressive sarcopenia were associated with higher risk of CVD after adjusting for age and sex, with the corresponding HRs (95% CIs) were 1.29 (1.15−1.45) and 1.40 (1.22−1.62), respectively (Table 2). When place of residence, education level, smoking, drinking, BMI, systolic blood pressure, physical activity, chronic diseases (dyslipidemia and diabetes mellitus) and medications (anti-hypertensive, anti-dyslipidemic and anti-diabetic) were further adjusted, individuals with sarcopenia only (HR = 1.12; 95%CI 1.01−1.26), depression only (HR = 1.32; 95%CI 1.18–1.48) and depressive sarcopenia (HR = 1.43; 95%CI 1.26–1.63) were significant associated with increased risk of CVD (Table 2). Similarly, individuals with depression only and depressive sarcopenia were significantly associated with increased risk of stroke and cardiac events (Table 2). As shown in Table 2, we founded significant additive and/or multiplicative interactions of sarcopenia and depressive symptoms on risk of CVD, stroke and cardiac events.
Table 2.
Association of sarcopenia and depression with new-onset CVD, stroke and cardiac events.
| Variable | Robust | Sarcopenia only | Depression only | Depressive sarcopenia | P trend |
|---|---|---|---|---|---|
| CVD† | |||||
| Case, n (%) | 1148 (19.12) | 485 (22.77) | 438 (24.79) | 317 (28.53) | |
| Model 1a | 1.00 (Ref) | 1.10 (0.97−1.25) | 1.32 (1.16−1.49) | 1.45 (1.24−1.68) | <0.001 |
| Model 2b | 1.00 (Ref) | 1.13 (1.01−1.26) | 1.35 (1.21−1.52) | 1.47 (1.29−1.67) | <0.001 |
| Model 3c | 1.00 (Ref) | 1.16 (1.04−1.30) | 1.31 (1.18−1.46) | 1.41 (1.25−1.60) | <0.001 |
| Stroke | |||||
| Case, n (%) | 375 (6.25) | 179 (8.40) | 149 (8.43) | 109 (9.81) | |
| Model 1a | 1.00 (Ref) | 1.20 (0.99−1.45) | 1.35 (1.12−1.64) | 1.40 (1.12−1.75) | <0.001 |
| Model 2b | 1.00 (Ref) | 1.22 (1.01−1.48) | 1.42 (1.17−1.73) | 1.47 (1.17−1.84) | <0.001 |
| Model 3c | 1.00 (Ref) | 1.30 (1.08−1.56) | 1.45 (1.19−1.76) | 1.56 (1.24−1.95) | <0.001 |
| Cardiac events | |||||
| Case, n (%) | 886 (14.76) | 359 (16.85) | 329 (18.62) | 257 (23.13) | |
| Model 1a | 1.00 (Ref) | 1.05 (0.92–1.19) | 1.20 (1.06−1.37) | 1.43 (1.24−1.65) | <0.001 |
| Model 2b | 1.00 (Ref) | 1.08 (0.95–1.23) | 1.25 (1.10−1.42) | 1.48 (1.28−1.72) | <0.001 |
| Model 3c | 1.00 (Ref) | 1.10 (0.97–1.26) | 1.26 (1.11−1.44) | 1.53 (1.32−1.78) | <0.001 |
Measure of interaction on additive scale: for CVD, RERI (95% CI) = 1.29(0.94, 1.64), AP (95% CI) = 0.44(0.37, 0.51), and S = 3.02(2.24, 4.07); for stroke, RERI (95% CI) = 1.35(0.61, 2.08), AP (95% CI) = 0.43(0.31, 0.56), and S = 2.78(1.92, 4.00); for cardiac events, RERI (95% CI) = 1.13(0.76, 1.50), AP (95% CI) = 0.43(0.35, 0.51), and S = 3.30(2.24, 4.87).
Measure of interaction on multiplicative scale: for CVD, hazard ratios (HR) (95% CI) = 1.51(1.33, 1.91), P < 0.001; for stroke, HR (95% CI) =1.42(1.17, 1.62), P = 0.001; for cardiac events, hazard ratios (HR) (95% CI) =1.59(1.33, 1.87), P < 0.001.
CVD including stroke and cardiac events.
Model 1 adjusted for age, sex.
Model 2 further adjusted for living place, education level, smoking status, drinking status and BMI based on model 1.
Model 3 further adjusted for physical activity, systolic blood pressure, medical history (kidney disease, dyslipidemia and diabetes) and medications (anti-hypertensive, anti-dyslipidemic, and anti-diabetic) based on model 2.
In sensitivity analysis, there were robust combined effect of sarcopenia and depressive symptoms on risk of CVD, stroke and cardiac events, when cut-off of depressive symptoms was defined as more than 10 score of CESD-10 (Table 3). When we evaluate the association between the sarcopenia and depressive symptoms with study outcomes separately, those with sarcopenia or depression were significantly associated with increased risk of CVD, stroke and cardiac events (Table S1; Table S2). The stratified analyses for the combined effects of sarcopenia and depressive symptoms on the risk of CVD were shown in Table S3. Stratified analyses included sex, age, living place, smoking, drinking, diabetes, hypertension and BMI, and the significant interactions between sarcopenia/depressive symptoms groups and subgroups were observed in sex, age, smoking, diabetes, hypertension and BMI subgroups (Table 4). In the subgroup analysis, the significant associations between depressive sarcopenia with risk of CVD were observed in all subgroups (Table 4).
Table 3.
Sensitivity analysis the association of sarcopenia and depression with new-onset CVD, stroke and cardiac events.
| Variable | Robust | Sarcopenia only | Depression only | Depressive sarcopenia | P trend |
|---|---|---|---|---|---|
| CVD† | |||||
| Case, n (%) | 1002 (18.81) | 412 (22.58) | 584 (23.91) | 390 (27.54) | |
| Model 1a | 1.00 (Ref) | 1.12(0.98−1.43) | 1.29(1.15−1.45) | 1.40 (1.22−1.62) | <0.001 |
| Model 2b | 1.00 (Ref) | 1.14(1.01−1.29) | 1.34(1.21−1.49) | 1.44 (1.27−1.64) | <0.001 |
| Model 3c | 1.00 (Ref) | 1.18(1.05−1.33) | 1.34(1.20−1.48) | 1.46 (1.29−1.65) | <0.001 |
| Stroke | |||||
| Case, n (%) | 326 (6.12) | 149 (8.16) | 198 (8.10) | 139 (9.82) | |
| Model 1a | 1.00 (Ref) | 1.19 (0.97−1.46) | 1.33 (1.11−1.59) | 1.43 (1.16−1.77) | <0.001 |
| Model 2b | 1.00 (Ref) | 1.22 (0.99−1.49) | 1.40 (1.17−1.67) | 1.50 (1.22−1.86) | <0.001 |
| Model 3c | 1.00 (Ref) | 1.31 (1.07−1.60) | 1.43 (1.19−1.71) | 1.58 (1.28−1.95) | <0.001 |
| Cardiac events | |||||
| Case, n (%) | 772 (14.49) | 305 (16.71) | 443 (18.13) | 311 (21.96) | |
| Model 1a | 1.00 (Ref) | 1.06 (0.92−1.22) | 1.21 (1.07−1.36) | 1.38 (1.20−1.58) | <0.001 |
| Model 2b | 1.00 (Ref) | 1.09 (0.95−1.26) | 1.25 (1.11−1.41) | 1.43 (1.25−1.65) | <0.001 |
| Model 3c | 1.00 (Ref) | 1.13 (0.98−1.29) | 1.28 (1.13−1.44) | 1.49 (1.29−1.71) | <0.001 |
In sensitivity analysis, depression was defined as CES-D score ≥10.
CVD including stroke and cardiac events.
Model 1 adjusted for age, sex.
Model 2 further adjusted for living place, education level, smoking status, drinking status and BMI based on model 1.
Model 3 further adjusted for physical activity, systolic blood pressure, medical history (kidney disease, dyslipidemia and diabetes) and medications (anti-hypertensive, anti-dyslipidemic, and anti-diabetic) based on model 2.
Table 4.
Subgroup analysis of HRs (95% CI) of sarcopenia and depression isolation for CVD.
| Characteristics | Robust | Sarcopenia only | Depression only | Depressive sarcopenia | P value | P-interaction |
|---|---|---|---|---|---|---|
| Sex | 0.003 | |||||
| Male | 1.00 (Ref) | 1.27 (1.07−1.50) | 1.44 (1.20−1.73) | 1.50 (1.20−1.87) | <0.001 | |
| Female | 1.00 (Ref) | 1.08 (0.93−1.26) | 1.27 (1.10−1.47) | 1.45 (1.23−1.71) | <0.001 | |
| Age | <0.001 | |||||
| <65 | 1.00 (Ref) | 1.15 (1.01−1.33) | 1.37 (1.20−1.55) | 1.57 (1.33−1.85) | <0.001 | |
| ≥65 | 1.00 (Ref) | 1.20 (0.99−1.46) | 1.22 (0.95−1.56) | 1.39 (1.12−1.74) | <0.001 | |
| Living place | 0.749 | |||||
| Urban | 1.00 (Ref) | 1.20 (1.00−1.44) | 1.31 (1.07−1.61) | 1.41 (1.10−1.82) | <0.001 | |
| Rural | 1.00 (Ref) | 1.14 (0.99−1.31) | 1.35 (1.17−1.54) | 1.51 (1.29−1.77) | <0.001 | |
| Smoking | 0.039 | |||||
| No | 1.00 (Ref) | 1.12 (0.97−1.29) | 1.21 (1.05−1.40) | 1.40 (1.19−1.65) | <0.001 | |
| Yes | 1.00 (Ref) | 1.23 (1.02−1.47) | 1.59 (1.32−1.91) | 1.60 (1.28−2.00) | <0.001 | |
| Drinking | 0.531 | |||||
| No | 1.00 (Ref) | 1.15 (0.99−1.32) | 1.27 (1.10−1.46) | 1.54 (1.31−1.81) | <0.001 | |
| Yes | 1.00 (Ref) | 1.18 (0.98−1.42) | 1.48 (1.23−1.79) | 1.36 (1.08−1.72) | 0.011 | |
| Diabetes | <0.001 | |||||
| No | 1.00 (Ref) | 1.17 (1.04−1.31) | 1.33 (1.18−1.49) | 1.47 (1.28−1.69) | <0.001 | |
| Yes | 1.00 (Ref) | 0.87 (0.55−1.38) | 1.47 (0.89−2.44) | 1.58 (1.04−2.41) | 0.035 | |
| Hypertension | 0.007 | |||||
| No | 1.00 (Ref) | 1.23 (1.08−1.40) | 1.36 (1.19−1.55) | 1.43 (1.23−1.67) | <0.001 | |
| Yes | 1.00 (Ref) | 0.97 (0.77−1.22) | 1.30 (1.02−1.66) | 1.67 (1.29−2.15) | <0.001 | |
| BMI, kg/m2 | <0.001 | |||||
| <24 | 1.00 (Ref) | 1.21 (1.03−1.41) | 1.50 (1.29−1.74) | 1.54 (1.29−1.83) | <0.001 | |
| ≥24 | 1.00 (Ref) | 1.13 (0.96−1.32) | 1.17 (0.98−1.40) | 1.49 (1.22−1.83) | 0.002 |
In the multivariate models, confounding factors such as age, sex, living place, education level, smoking status, drinking status, BMI, physical activity, systolic blood pressure, medical history (kidney disease, dyslipidemia and diabetes) and medications (anti-hypertensive, anti-dyslipidemic, and anti-diabetic) were included unless the variable was used as a subgroup variable.
4. Discussion
In current study, we first examined the cumulative effect of sarcopenia and depressive symptoms on CVD risk among Chinese middle-aged and elderly adults. Our findings suggested that there was a potential joint effect of sarcopenia and depressive symptoms on CVD risk, and those with depressive sarcopenia had the highest risk of CVD, stroke and cardiac events. Furthermore, the combined effects were independent of age, sex and other covariates. These findings suggested that a combination of sarcopenia and depressive symptoms could provide more predictive value for CVD, stroke and cardiac events risk.
Several previous reported cross-sectional and longitudinal studies had suggested that the presence of sarcopenia was correlated with higher risk of heart disease, stroke and CVD. Xia et al. found that sarcopenia was significantly associated with increased risk of carotid atherosclerosis, myocardial infarction and atrial fibrillation among middle-aged and elderly adults [32]. Another study from CHARLS also found both possible sarcopenia and sarcopenia, assessed using the AWGS 2019 algorithm, were independently and positively associated with CVD prevalence and incidence [13]. In contrast with previous reported studies, our study also indicated that individuals with sarcopenia (including possible sarcopenia, sarcopenia and severe sarcopenia, assessed using the AWGS 2019 algorithm) was positively associated with a 17%, 25% and 17% higher risk of new onset CVD, stroke and cardiac events, respectively, by using data from CHARLS from 2011 to 2020. A large body of epidemiological evidence had indicated that depressive symptoms was a risk factor for cardiovascular morbidity and mortality [17,29]. Furthermore, there is a graded relationship that the more severe the depression, the higher the subsequent risk of mortality and other cardiovascular events [33]. Thus, the United States Preventive Services Task Force (USPSTF) issued recommendations to screen for depression in the general adult population while emphasizing that patients with CVD are at an increased risk of depression [34]. Similarly, our finding suggested that those with depressive symptoms (by CES-D 10) had a 33%, 37% and 32% higher risk of CVD, stroke and cardiac events, respectively.
Actually, both sarcopenia and depression are common and often coexist in the elderly, and recent studies have suggested a closed relationship between sarcopenia and depression. Results from a two-sample Mendelian randomization study found low muscle mass independently increases the risk of depression after being adjusted by BMI [35]. Similarly, a national cohort and Mendelian randomization study found that depression can lead to sarcopenia [36]. Furthermore, a significant mediating effect of depressive symptoms on the correlation between sarcopenia and mild cognitive impairment was observed [22]. Our previous reported studies found preliminary depressive sarcopenia appears to increase the risk of cognitive decline, mainly manifested in word recall ability and verbal fluency [37]. In current study, we founded a significant additive and multiplicative interactions of sarcopenia and depressive symptoms on risk of CVD, stroke and cardiac events, which indicated that there may be a potential joint effect of sarcopenia and depressive symptoms on CVD risk. Those with depressive sarcopenia had the highest cumulative incidence and highest risk of CVD, with the corresponding OR was 1.41 (1.25−1.60).
Muscle mass decreases with age, leading to low relative muscle mass in elderly people. Thus, sarcopenia has often been thought to be associated with aging [38]. Similarly, the depression prevalence falls in early adulthood and rises in late life [39]. In present study, those with depressive sarcopenia were significantly associated with increased risk of CVD among those with age less or more than 65 years. In addition, we found significant associations between depressive sarcopenia and CVD in all subgroups, including sex (male and female), living place (urban and rural), smoking (no and yes), drinking (no and yes), diabetes (no and yes), hypertension (no and yes) and BMI (<24 and ≥24 kg/m2). Therefore, our results provided more valid evidence about the depressive sarcopenia on CVD risk.
The precise mechanisms linking sarcopenia and depression with risk of CVD are incompletely understood but several potential explanations could be proposed. Both CVD, sarcopenia and depression have some similarities in clinical and etiology, such as physical inactive, smoking and improper diet [40]. Both sarcopenia and depression are a hyper-inflammation status and associated with muscle mitochondrial dysfunction, which may produce reactive oxygen species through oxidative stress, thus damaging vascular endothelium and cardiomyocytes [33,41]. Future large-scale studies are needed to explore the association of sarcopenia, depression and CVD risk and the exact mechanisms.
Considering the high prevalence of sarcopenia and depression in elderly population, and the major negative impact on morbidity and mortality of CVD, it is of clinical interest for clinicians to screen and intervene sarcopenia and depression to reduce CVD risk. Several interventions like use of oral energy and protein supplementation combined with supervised physical resistance exercise may help to reverse sarcopenia [42]. However, randomized controlled trials show controversial results in reestablishing muscle mass and strength, and in ameliorating physical performance, mobility and quality of life. A meta-analysis of 22 observational studies founded that serotonin and norepinephrine reuptake inhibitors use significantly increased cerebrovascular disease risk, whilst tricyclic antidepressants did not, when compared to no antidepressant treatment [43]. Therefore, future research should focus on more accurate and personalized interventions in both sarcopenia and depression.
The current study was based on the data from the CHARLS study, which is a large nationally representative cohort study with a high response rate, and potential confounders were collected and controlled in the multivariable models. However, several potential limitations of present study warrant mentioning. First, the present study used observational data from CHARLS. Although, we had adjusted a series of confounders. This observational analysis could be influenced by potential biases and confounding factors. Second, the diagnosis of CVD was self-reported physician-diagnosed and lack of detailed classification of CVD due to medical records were not available in the CHARLS, thus the use of self-reported measures of CVD may have some degree of bias. However, previous study had reported that 77.5% of self-reported incident coronary heart disease were confirmed by the English Longitudinal Study of Ageing researchers according to medical records [30]. Third, the muscle mass in sarcopenia evaluation was measured using BIA instead of the gold standard of dual-energy X-ray absorptiometry. Finally, the depressive symptoms were assessed by CESD-10, which may cause some bias in depression diagnosis.
In conclusion, our findings indicated that there is a significant additive and multiplicative interactions of sarcopenia and depressive symptoms on risk of CVD, stroke and cardiac events. Those with depressive sarcopenia had the highest risk of CVD. As a simple and cost-saving tool, sarcopenia and depression has great potential to enhance primary prevention. Our findings indicated a need to identify sarcopenia and depression, and intervene much earlier. Incorporate sarcopenia and depression screening into routine physical examinations may help identify patients with CVD and promote innovations in health management.
Authors' contributions
Yu Chen conceived and designed the research; Tongneng Xue and Yang Gu wrote the manuscript; and Tongneng Xue and Hai Xu performed the data analysis. All authors reviewed the manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Availability of data and materials
This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version C as of April 2018 developed by the Gateway to Global Aging Data.
Sponsor's role
The sponsors had no role in the design, methods, data collection, analysis, or preparation of the manuscript.
Competing interests
The authors declare that they have no competing interests.
Acknowledgements
This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version C as of April 2018 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Ageing (R01 AG030153, RC2 AG036619, R03 AG043052). For more information, please refer to www.g2aging.org.
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2024.100259.
Appendix A. Supplementary data
The following is Supplementary data to this article:
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Associated Data
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Supplementary Materials
Data Availability Statement
This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version C as of April 2018 developed by the Gateway to Global Aging Data.