Abstract
Objectives
We aimed to investigate longitudinal associations of overall social support and its sub-domains with risk of sarcopenia and its related traits in community-dwelling Chinese aged ≥ 50 years. We also explored interaction effects of potential factors on such associations.
Design
A prospective cohort study.
Setting
Community-based setting in western China.
Participants
We included participants aged ≥50 years with complete information necessary for analysis from the WCHAT study who did not have sarcopenia at baseline (2018) and had sufficient data for sarcopenia assessment during 2021–2023.
Measurements
Exposures included overall social support, subjective support, objective support and support utilization, which were assessed with the Social Support Rating Scale. Outcomes included sarcopenia, low muscle mass (LMM), low muscle strength and low physical performance, which were diagnosed with the 2019 AWGS consensus. Longitudinal associations between the exposures and outcomes were estimated by logistic regression, with generalized estimating equations (GEE) as sensitivity analyses. Subgroup analyses by potential covariates were conducted to detect interaction effects.
Results
A total of 1905 participants were finally included in the analytic sample, of whom 326 (17.1%) developed incident sarcopenia during 5-year follow-up. After controlling for confounders, higher degree of overall social support (OR = 0.87, 95%CI 0.76−0.99), subjective support (OR = 0.88, 95%CI 0.77−0.99) and support utilization (OR = 0.87, 95%CI 0.77−0.99) correlated with lower sarcopenia risk, among which higher support utilization degree was indicative of lower risk for LMM (OR = 0.88, 95%CI 0.79−0.98). GEE further revealed that overall support degree was negatively associated with risk for sarcopenia (OR = 0.86, 95%CI 0.76−0.98) and LMM (OR = 0.87, 95%CI 0.77−0.99). Objective support was neither significantly associated with sarcopenia nor its traits. No significant interaction effect was observed between overall support and the concerned confounders on sarcopenia (interaction P-value > 0.05).
Conclusion
Overall social support degree was negatively associated with sarcopenia risk, possibly primarily through affecting muscle mass rather than muscle strength or physical performance, and such an association remained robust across subgroups with distinct characteristics. This holds implications for policymakers to conduct population-based risk assessment, and supportive strategies against sarcopenia should focus on enhancing subjective support and support utilization rather than objective support alone.
Keywords: Sarcopenia, Social support, Prospective cohort study
1. Introduction
Sarcopenia is a progressive skeletal muscle disease characterized by gradual decline in skeletal muscle mass and function [1,2]. As a common geriatric condition with a prevalence of 10%–27% in adults [3], sarcopenia represents a major public health issue due to various resultant clinical and societal consequences, including reduced quality of life, falls, fractures, frailty, physical limitation, loss of independence, high health care cost and mortality [[4], [5], [6], [7]], thus importance of its early identification and adequate intervention should be underscored.
Exploring biological mechanisms is vital for understanding disease pathogenesis and subsequently developing effective treatments. However, it is equally important to consider the role of socio-cultural factors, which may affect disease development by regulating internal biochemical processes. As one of those crucial factors, social support refers to material and spiritual support from multiple sources [8,9]. It encompasses comprehensive dimensions covering subjective support from emotional networks, objective support from living materials, and support utilization implying the capacity to obtain others’ support [10,11]. Social support has been shown to correlate with many health conditions or outcomes in the aging population, such as frailty, cognition, psychological symptoms and sleep quality [8,9,[11], [12], [13], [14], [15]]. This naturally prompts the hypothesis that it may also affect sarcopenia, given its influence on factors including inflammation levels [16], oral function, nutritional or psychological status [17], which are implicated in the multifactorial nature of sarcopenia. However, how social support correlates with sarcopenia has not been well elucidated. Several studies examined associations between social factors and sarcopenia, but most of them either lacked longitudinal analyses or narrowly focused on aspects representing only certain dimensions of social support like social networks, social engagement or organizational participation [17,18].
In this study, we aimed to provide insights into longitudinal associations of overall social support and its sub-domains with risk of sarcopenia and its related traits in community-based Chinese adults aged ≥50 years. We also explored interaction effects of potential factors on such associations.
Our preliminary hypothesis was that overall social support or its sub-domains might be negatively associated with risk of sarcopenia or its related traits, and such associations might be affected by potential confounders including sex, age, education level, marital status, physical activity level, ethnicity, history of smoking or alcohol consumption, depressive symptoms, cognitive impairment and number of comorbidities.
2. Methods
2.1. Study design and participants
The present study belonged to the ongoing project West China Health and Aging Trend (WCHAT) study, which was initiated in 2008 and registered on the Chinese Clinical Trial Registry (ChiCTR1800018895). In brief, community-dwelling participants aged ≥50 years were recruited from various areas of west China at baseline (in 2018) based on pre-established criteria, and they were followed up annually either through on-site visits (in 2019, 2021, 2022 and 2023) or by telephone (in 2020). Questionnaire survey, physical examinations and laboratory examinations were conducted for the on-site follow-ups. The project received approval from the Ethical Committee of Sichuan University West China Hospital and adhered to the principles of the Declaration of Helsinki, with written informed consent obtained from all participants or their guardians prior to project initiation. Additional details on the cohort profile can be found in previous publications [19].
Regarding the analytic sample of this 5-year prospective cohort study, inclusion criteria: (1) participants completing assessment for social support in 2018; (2) participants completing sarcopenia assessment in 2018. Exclusion criteria: (1) participants with missing demographic or anthropometric data in 2018; (2) participants confirmed to have sarcopenia in 2018; (3) participants without available data for sarcopenia assessment at any follow-up point during 2021–2023. The follow-up data for analyses were drawn from 2021 onwards, since only questionnaire-based information obtained by telephone was available in 2020 due to the severe coronavirus disease pandemic.
2.2. Exposure-social support
The exposures of interest included the overall social support and its three sub-domains (subjective support, objective support and support utilization).
Social support was assessed by the Social Support Rating Scale (SSRS), a widely-adopted instrument in measuring social support degree adapted to Chinese environmental and cultural conditions [10]. It comprises three sub-domains: (1) subjective support: the number of friends offering assistance, relationship with neighbors, relationship with colleagues, and the support level from family members; (2) objective support: living conditions in the past year, channels of economic support or practical problem-solving help during emergencies, and comfort or caring sources when encountering difficulties; (3) support utilization: the way of expressing themselves when facing trouble, the way of seeking assistance when in trouble, and the willingness to engage in group activities. Overall social support and each sub-domain were quantified by their respective scores (see Appendix S1 for details) [20]. Currently, there remains no consensus on the optimal cut-off value for the SSRS score [21,22], so we mainly treated the score as a continuous variable in subsequent analyses, with a higher score indicating higher social support degree.
2.3. Outcome-sarcopenia and its related traits
The primary outcome was defined as the first, newly diagnosed sarcopenia (incident sarcopenia) among the given follow-up timepoints. As recommended by the 2019 Asian Working Group for Sarcopenia (AWGS) consensus, sarcopenia diagnosis required low muscle mass (LMM) plus low muscle strength (LMS) and/or low physical performance (LPP) [23]. The secondary outcomes were sarcopenia-related traits including LMM, LMS and LPP.
LMM was indicated by an appendicular skeletal muscle mass index below 7.0 kg/m2 for men and below 5.7 kg/m2 for women, obtained from the bioelectrical impedance analysis equipment Inbody 770 (BioSpace, Seoul, Korea). LMS was assessed by handgrip strength below 28 kg for men and below 18 kg for women, measured by the dynamometer EH101 (Camry, Zhongshan, China). Subjects were asked to grip the dynamometer handle with the dominant hand to their full capacity, and testing was performed on two independent occasions with the largest value recorded for analysis. LPP was identified by a gait speed of <1.0 m/s in the 6-m walking test or a time of ≥12 s in the 5-time chair stand test.
2.4. Covariates
Height and weight were each measured twice with the average value used for analysis, and BMI was calculated as weight (kg) divided by the square of height (m2). Other covariates were derived from questionnaire survey, including age, sex, ethnicity, marital status, education level, smoking history, alcohol consumption history, activities of daily living (ADL), instrumental ADL (IADL), physical activity level, cognitive function, anxiety level, depression level, sleep quality, number of comorbidities, medical accessibility, and routine physical examination awareness.
ADL or IADL each represents daily self-care activities to support fundamental functioning or independent living [24], with ADL or IADL impairment identified by a total Barthel Index score below 100 or Lawton IADL Scale score below 14, respectively [25,26]. Physical activity level was determined by the validated China Leisure Time Physical Activity Questionnaire (CLTPAQ) [27], which was a modified version of the Minnesota Leisure Time Physical Activity Questionnaire (MLTPAQ) [28] adapted to Chinese lifestyle and cultural background. As previously detailed, CLTPAQ measured the total amount of energy (kcal) per week spent on a series of commonly performed physical activities, and the sex-specific threshold for low physical was the lowest 20th sex-specific percentile value of total energy consumption [29]. Cognitive function was assessed by the Short Portable Mental Status Questionnaire (SPMSQ), with ≥5 errors considered as moderate to severe cognitive impairment [30]. Anxiety or depression level was each assessed by the Generalized Anxiety Disorder 7-item (GAD-7) or 15-item Geriatric Depression scale (GDS-15), with a score of ≥10 or ≥9 indicating moderate to severe anxiety [31] or depression [32], respectively. Sleep quality was assessed by the Pittsburgh Sleep Quality Index (PSQI) score, with a score of >10 considered to be poor [33]. Number of comorbidities referred to the total number of self-reported chronic diseases among hypertension, diabetes, coronary heart disease, chronic obstructive pulmonary disease, osteoarthrosis, digestive disease and renal disease. Medical accessibility was assessed by response to the question “can you receive timely treatment in case of illness?”, and participants who answered “yes” were considered to have favorable medical accessibility. Routine physical examination awareness was assessed by response to the question “have you undergone routine physical examinations unrelated to any specific illness in the past year?”, and those answering “yes” were considered to have strong awareness.
3. Statistical analysis
Descriptive statistics were presented as mean with standard deviation or median with lower and upper quartile (Q1, Q3) for normally or non-normally distributed continuous variables, and number with percentage (%) for categorical variables. Data comparison between groups was performed by the Student t test or Mann-Whitney U test for continuous variables in normal or skewness distribution, and by the Chi-squared test or Fisher’s exact test for categorical variables.
Since the affirmatory occurrence timepoints of our concerned outcomes were actually unavailable, we assessed longitudinal associations between the exposures and outcomes by logistic regression models. To capture the changing trends of sarcopenia at multiple follow-up timepoints with potential coexistence of both progression and reversal [34,35], we visualized observed transitions between normal and sarcopenia during 2021–2023 by the Sankey diagram using Python’s Plotly library (version 5.4.0). To account for the dynamic nature of sarcopenia and repeated measurements during follow-up, we used the generalized estimating equations (GEE) to assess associations as sensitivity analyses, which can capture the average effects among variables over time. This approach enhances analytical power by leveraging the augmented number of observations [36,37]. Estimates by logistic regression and GEE were provided as the odds ratio (OR) and 95% confidence interval (CI) with three models applied. Model 1 was unadjusted for any factors; model 2 was adjusted for age and sex; model 3 was adjusted for age, sex, ethnicity, BMI, marital status, education level, smoking history, alcohol consumption history, physical activity level, cognitive function, depressive status, sleep quality, number of comorbidities, medical accessibility, and routine physical examination awareness. We further conducted subgroup analyses by potential covariates and calculated the interaction P-values by the Wald test, which represent the statistical significance of interaction effects between each individual covariate and the primary independent variable (overall social support) in relation to the dependent variable (e.g. risk of sarcopenia).
We used Python (version 3.10.10) and R (version 4.3.1) for all statistical analyses. P values < 0.05 were considered statistically significant.
4. Results
A total of 1905 participants were included in the final analytic sample (Fig. 1), and Table S1 presents their baseline characteristics grouped by the SSRS score for overall social support as a dichotomous variable, with 1.5 standard deviations below the age-specific mean values of the analytic sample being the age-specific thresholds for low SSRS score. Baseline characteristics between participants excluded due to insufficient information and those included in the analytic sample were presented in Table S2.
Fig. 1.
The flowchart of inclusion and exclusion of participants.
Abbreviations: WCHAT, West China Health and Aging Trend.
During 5-year follow-up, 326 (17.1%) participants developed incident sarcopenia, at baseline they were more likely to be older, less educated, physically inactive and have smoking history, lower BMI, lower scores for overall social support, subjective support and support utilization, while they were less likely to be married compared with those who remained normal during the follow-up process (Table 1).
Table 1.
Baseline characteristics in the analytic sample of 1905 participants without sarcopenia at entry grouped by developing incident sarcopenia and remaining normal during follow-up.
| Normal n = 1579 | Sarcopenia n = 326 | P value | |
|---|---|---|---|
| Age | 60.78 (7.10) | 65.51 (7.54) | <0.001 |
| Sex, n (%) | 0.317 | ||
| Male | 490 (31.03) | 111 (34.05) | |
| Female | 1089 (68.97) | 215 (65.95) | |
| Ethnicity, n (%) | <0.001 | ||
| Han | 743 (47.06) | 188 (57.67) | |
| Qiang | 574 (36.35) | 94 (28.83) | |
| Tibetan | 203 (12.86) | 26 (7.98) | |
| Yi | 38 (2.41) | 15 (4.60) | |
| Other minorities | 21 (1.33) | 3 (0.92) | |
| BMI (kg/m2) | 26.16 (3.26) | 23.25 (2.67) | <0.001 |
| Marital status: married, n (%) | 1377 (87.21) | 261 (80.06) | 0.001 |
| Education: high school or above, n (%) | 128 (17.90) | 17 (9.29) | 0.001 |
| Smoking history, n (%) | 166 (10.51) | 55 (16.87) | 0.002 |
| Alcohol consumption history, n (%) | 349 (22.10) | 83 (25.46) | 0.213 |
| Low physical activity, n (%) | 336 (21.28) | 90 (27.61) | 0.015 |
| ADL impairment, n (%) | 132 (8.36) | 27 (8.28) | 1.000 |
| IADL impairment, n (%) | 263 (16.66) | 66 (20.25) | 0.139 |
| Moderate to severe cognitive impairment, n (%) | 153 (9.69) | 38 (11.66) | 0.329 |
| Moderate to severe anxiety, n (%) | 57 (3.61) | 13 (3.99) | 0.866 |
| Moderate to severe depression, n (%) | 69 (4.37) | 15 (4.60) | 0.970 |
| Poor sleep quality, n (%) | 198 (12.54) | 39 (11.96) | 0.845 |
| Number of comorbidities, n (%) | 0.392 | ||
| <2 | 901 (57.06) | 177 (54.29) | |
| ≥2 | 678 (42.94) | 149 (45.71) | |
| Medical accessibility, n (%) | 1472 (93.22) | 301 (92.33) | 0.647 |
| Routine physical examination awareness, n (%) | 612 (38.76) | 119 (36.50) | 0.484 |
| SSRS scores | |||
| Overall social support | 43.53 (6.73) | 41.60 (7.13) | <0.001 |
| Subjective support | 26.91 (4.03) | 25.69 (4.35) | <0.001 |
| Objective support | 9.19 (3.03) | 8.94 (3.09) | 0.185 |
| Social utilization | 7.43 (2.43) | 6.97 (2.42) | 0.002 |
| ASMI (kg/m2) | 6.88 (0.82) | 6.08 (0.72) | <0.001 |
| Handgrip strength (kg) | 23.80 (8.63) | 20.76 (7.50) | <0.001 |
| Time consumed in the 6-meter walking test (s) | 4.81 (1.54) | 5.00 (1.43) | 0.031 |
| Time consumed in the 5-time chair stand test (s) | 10.92 (2.73) | 11.17 (2.55) | 0.121 |
Note: data were presented as mean (standard deviation) or n (%) as appropriate (continuous variables here were all in normal distribution). P value indicated the significance level for comparison between groups.
Abbreviations: BMI, body mass index; ADL, Activities of Daily Living; IADL, Instrumental ADL; SSRS, Social Support Rating Scale; ASMI, appendicular skeletal muscle mass index.
As suggested by logistic regression, in all the three models, higher degree of overall social support (ORadjusted = 0.87, 95% CI 0.76−0.99), subjective support (ORadjusted = 0.88, 95% CI 0.77−0.99) and support utilization (ORadjusted = 0.87, 95% CI 0.77−0.99) correlated with lower sarcopenia risk, among which higher support utilization degree was also indicative of reduced risk for LMM (ORadjusted = 0.88, 95% CI 0.79−0.98 (Table 2).
Table 2.
Longitudinal associations of overall social support and its sub-domains with sarcopenia and its related traits through logistic regression in different models.
| Model 1a | Model 2b | Model 3c | |
|---|---|---|---|
| OR (95%CI), P value | OR (95%CI), P value | OR (95%CI), P value | |
| Overall social supportd | |||
| Sarcopenia | 0.76 (0.67−0.85), <0.001 | 0.84 (0.74−0.95), 0.005 | 0.87 (0.76−0.99), 0.030 |
| LMM | 0.83 (0.75−0.93), 0.001 | 0.89 (0.80−0.99), 0.035 | 0.90 (0.81−1.01), 0.076 |
| LMS | 0.82 (0.74−0.90), <0.001 | 0.90 (0.82−0.99), 0.040 | 0.96 (0.87−1.07), 0.468 |
| LPP | 0.83 (0.76−0.91), <0.001 | 0.92 (0.84−1.02), 0.117 | 0.97 (0.87−1.07), 0.519 |
| Subjective supportd | |||
| Sarcopenia | 0.76 (0.68−0.85), <0.001 | 0.85 (0.75−0.95), 0.006 | 0.88 (0.77−0.99), 0.038 |
| LMM | 0.83 (0.75−0.92), <0.001 | 0.89 (0.80−0.99), 0.030 | 0.91 (0.81−1.01), 0.082 |
| LMS | 0.81 (0.74−0.89), <0.001 | 0.91 (0.82−1.00), 0.042 | 0.96 (0.86−1.06), 0.402 |
| LPP | 0.83 (0.75−0.91), <0.001 | 0.95 (0.86−1.05), 0.301 | 0.99 (0.89−1.10), 0.841 |
| Objective supportd | |||
| Sarcopenia | 0.92 (0.81−1.04), 0.179 | 0.98 (0.86−1.11), 0.731 | 0.98 (0.86−1.11), 0.753 |
| LMM | 0.98 (0.88−1.09), 0.700 | 1.02 (0.92−1.13), 0.720 | 1.01 (0.91−1.13), 0.858 |
| LMS | 0.91 (0.83−1.00), 0.053 | 0.97 (0.88−1.06), 0.498 | 0.99 (0.90−1.09), 0.832 |
| LPP | 0.94 (0.86−1.03), 0.176 | 1.00 (0.91−1.10), 0.993 | 1.02 (0.92−1.13), 0.691 |
| Support utilizationd | |||
| Sarcopenia | 0.83 (0.73−0.93), 0.002 | 0.84 (0.75−0.95), 0.007 | 0.87 (0.77−0.99), 0.034 |
| LMM | 0.84 (0.76−0.94), 0.002 | 0.86 (0.78−0.96), 0.007 | 0.88 (0.79−0.98), 0.026 |
| LMS | 0.91 (0.83−0.99), 0.034 | 0.93 (0.84−1.02), 0.135 | 0.98 (0.89−1.08), 0.737 |
| LPP | 0.89 (0.81−0.97), 0.009 | 0.88 (0.80−0.97), 0.009 | 0.91 (0.82−1.00), 0.056 |
Note: Bold entries represent Odds Ratios (ORs) with statistical significance (p < 0.05).
aModel 1 was unadjusted for any factors.
bModel 2 was adjusted for age and sex.
cModel 3 was adjusted for age, body mass index, sex (male vs. female), ethnicity (non-Han vs. Han Chinese), marital status (single, divorced or widowed vs. married), education level (high school or above vs. middle school or lower), smoking history (yes vs. no), alcohol consumption history (yes vs. no), physical activity level (low vs. normal), cognitive function (moderate to severe impairment vs. normal), depression level (moderate to severe vs. normal), sleep quality (low vs. normal), number of comorbidities (≥2 vs. <2), medical accessibility (favorable vs. unfavorable), and routine physical examination awareness (strong vs. weak).
dEstimates were provided with per standard deviation increase in the corresponding Social Support Rating Scale scores.
Abbreviations: OR, odds ratio; CI, confidence interval; LMM, low muscle mass; LMS, low muscle strength; LPP, low physical performance.
Considering the dynamic nature of sarcopenia depicted by inflows and outflows of status at multiple follow-up timepoints during 2021–2023 (Figure S1), sensitivity analyses by GEE were additionally performed, which revealed that overall support degree was negatively associated with risk for sarcopenia (ORadjusted = 0.86, 95% CI 0.76−0.98) and LMM (ORadjusted = 0.87, 95% CI 0.77−0.99) in all the three models (Table 3). However, neither logistic regression nor GEE revealed significant associations between objective support and sarcopenia or its traits in any models (Table 2, Table 3).
Table 3.
Longitudinal associations of overall social support and its sub-domains with sarcopenia and its related traits through GEE in different models.
| Model 1a | Model 2b | Model 3c | |
|---|---|---|---|
| OR (95%CI), P value | OR (95%CI), P value | OR (95%CI), P value | |
| Overall social supportd | |||
| Sarcopenia | 0.76 (0.68−0.86), <0.001 | 0.86 (0.76−0.96), 0.010 | 0.86 (0.76−0.98), 0.027 |
| LMM | 0.84 (0.75−0.93), 0.001 | 0.90 (0.80−1.00), 0.044 | 0.87 (0.77−0.99), 0.038 |
| LMS | 0.86 (0.79−0.94), <0.001 | 0.96 (0.88−1.04), 0.309 | 1.03 (0.94−1.12), 0.570 |
| LPP | 0.88 (0.81−0.94), <0.001 | 0.98 (0.91−1.06), 0.613 | 1.03 (0.95−1.13), 0.425 |
| Subjective supportd | |||
| Sarcopenia | 0.76 (0.68−0.85), <0.001 | 0.86 (0.76−0.97), 0.013 | 0.89 (0.78−1.01), 0.083 |
| LMM | 0.83 (0.75−0.93), 0.001 | 0.89 (0.80−1.00), 0.052 | 0.92 (0.81−1.05), 0.234 |
| LMS | 0.82 (0.76−0.90), <0.001 | 0.92 (0.84−1.00), 0.063 | 0.99 (0.91−1.08), 0.863 |
| LPP | 0.87 (0.81−0.94), <0.001 | 1.00 (0.93−1.08), 0.971 | 1.04 (0.96−1.14), 0.335 |
| Objective supportd | |||
| Sarcopenia | 0.91 (0.80−1.03), 0.128 | 0.96 (0.86−1.08), 0.521 | 0.89 (0.78−1.02), 0.093 |
| LMM | 0.96 (0.86−1.08), 0.512 | 1.00 (0.90−1.11), 0.990 | 0.89 (0.78−1.01), 0.076 |
| LMS | 0.96 (0.88−1.04), 0.334 | 1.01 (0.93−1.10), 0.800 | 1.03 (0.94−1.13), 0.493 |
| LPP | 0.95 (0.88−1.03), 0.198 | 1.01 (0.93−1.09), 0.890 | 1.05 (0.96−1.13), 0.270 |
| Support utilizationd | |||
| Sarcopenia | 0.84 (0.74−0.95), 0.004 | 0.86 (0.76−0.97), 0.016 | 0.93 (0.82−1.05), 0.253 |
| LMM | 0.84 (0.75−0.94), 0.002 | 0.86 (0.77−0.96), 0.008 | 0.91 (0.80−1.03), 0.153 |
| LMS | 0.96 (0.88−1.04), 0.340 | 0.99 (0.91−1.08), 0.787 | 1.04 (0.95−1.14), 0.351 |
| LPP | 0.94 (0.87−1.01), 0.090 | 0.94 (0.87−1.02), 0.135 | 0.97 (0.90−1.05), 0.510 |
Note: GEE models were fitted using the exchangeable correlation structure with robust estimation of the standard errors. The binomial response was selected for the distribution and link function. Bold entries represent Odds Ratios (ORs) with statistical significance (p < 0.05).
aModel 1 was unadjusted for any factors.
bModel 2 was adjusted for age and sex.
cModel 3 was adjusted for age, body mass index, sex (male vs. female), ethnicity (non-Han vs. Han Chinese), marital status (single, divorced or widowed vs. married), education level (high school or above vs. middle school or lower), smoking history (yes vs. no), alcohol consumption history (yes vs. no), physical activity level (low vs. normal), cognitive function (moderate to severe impairment vs. normal), depression level (moderate to severe vs. normal), sleep quality (low vs. normal), number of comorbidities (≥ 2 vs. <2), medical accessibility (favorable vs. unfavorable), and routine physical examination awareness (strong vs. weak).
dEstimates were provided with per standard deviation increase in the corresponding Social Support Rating Scale scores.
Abbreviations: GEE, generalized estimating equations; OR, odds ratio; CI, confidence interval; LMM, low muscle mass; LMS, low muscle strength; LPP, low physical performance.
As indicated by subgroup analyses, no significant interaction effect was observed between overall support and the concerned confounders on sarcopenia or LMM (interaction P-value > 0.05) (Fig. 2 and S2), while the influence of overall support on LMS or LPP was respectively moderated by number of comorbidities (interaction P-value = 0.028) or smoking history (interaction P-value = 0.047) (Figure S3−4).
Fig. 2.
Subgroup analyses to detect interaction effect between overall social support and the concerned confounders on sarcopenia in model 3.
Abbreviations: OR, odds ratio; CI, confidence interval.
Note: For each confounder investigated in subgroup analyses, estimates were obtained based on model 3 but without adjustment for the relevant confounder.
Model 3 was adjusted for age, body mass index, sex (male vs. female), ethnicity (non-Han vs. Han Chinese), marital status (single, divorced or widowed vs. married), education level (high school or above vs. middle school or lower), smoking history (yes vs. no), alcohol consumption history (yes vs. no), physical activity level (low vs. normal), cognitive function (moderate to severe impairment vs. normal), depression level (moderate to severe vs. normal), sleep quality (low vs. normal), number of comorbidities (≥2 vs. <2), medical accessibility (favorable vs. unfavorable), and routine physical examination awareness (strong vs. weak).
5. Discussion
In this study, we revealed that after controlling for the confounders, degree of overall social support was negatively associated with sarcopenia risk, possibly primarily through affecting muscle mass rather than muscle strength or physical performance, and such an association remained robust across subgroups with distinct characteristics. Subjective support and support utilization might also be protective against incident sarcopenia, while lack of objective support alone seemed not to contribute to sarcopenia development.
Although social support can exert positive effects on various health-related outcomes, it can also be a double-edged sword, because inappropriate social support may not benefit people's wellbeing and can even be harmful despite good intentions [38,39]. Therefore, how social support and sarcopenia are mutually correlated remains to be clarified. Our study supports the protective role of overall social support in sarcopenia with robustness across subgroups. Several socio-behavioral foundations may act as potential explanations. High-quality social support and social participation can help to prevent sarcopenia by promoting healthy lifestyles including balanced diet and regular exercise [40]. Social support, reflected by interaction and collaboration with peers, social encouragement, professional knowledge provision and social bond retention can motivate people to persevere in nutrition and resistance exercise intervention for sarcopenia [41]. Biophysiological factors may also play important roles. Higher social isolation degree is accompanied by increased proinflammatory activity [16], and support from family, friends, or spouse can modestly protect against inflammation risk [42], while chronic inflammation is a contributing factor to sarcopenia [43]. Moreover, social support may exert positive effects on sarcopenia by directly or indirectly affecting physical activity, oral function, nutritional status and psychological status [17].
Bian D et al. [21] revealed that high-level social support was negatively associated with sarcopenia, but besides the limitation of a cross-sectional design, the investigation did not consider sub-domains of social support separately, and it was confined to community-dwelling older people in Shanghai, who generally possessed relatively more abundant social resources and higher socio-economic level living context. However, the appropriateness of social support depends on cultural context, life events, individual characteristics and relationships between providers and recipients [44], so previous findings may not necessarily be generalizable to all conditions. Our study was conducted in the western region of China, where the population was mainly from rural areas with relatively poor medical conditions, limited resources and less-developed living context. It has become a trend for young people in those regions to migrate elsewhere for work, leaving behind plenty of older people locally. Most of them actually share closer connections with their spouse, neighbors and peers compared with children away from home, who primarily provide financial and emotional support. Some of them do not have pensions and keep doing odd jobs or farm work for livelihoods even at an old age; it is also common for some to look after their grandchildren, while the Chinese traditional concept valuing 'family happiness' implies that providing care itself may bring them comfort and self-esteem [45]. These socio-cultural factors have complex effects on the relationship between social support and sarcopenia, which necessitates further elucidation in future research.
Our study also suggested that the negative associations of social support with sarcopenia might primarily be exerted through improving muscle mass instead of muscle strength or physical performance. A possible explanation is that the association of social support with muscle mass was less susceptible to confounders compared with that with the other two traits, which was supported by our subgroup analyses regarding sarcopenia-related traits (Figures S2−4). Our participants with multimorbidity were more likely to benefit from social support to maintain muscle strength, which is analogous to previous findings that physical activity, an indication of social support [46], showed stronger associations with health indicators including muscle strength in those with multimorbidity. This may be explained by potential selection bias that multimorbidity itself could have hindered people from participating the project or adhering to follow-up [47]. The free examinations and health consultations we provided were actually a form of social support, and those with multimorbidity who remained in the study might represent more resilient ones with stronger initiative to take advantage of such support for health promotion. Our participants with no smoking history were more likely to benefit from social support for improving physical performance. Regarding possible reasons, we speculated that long-lasting effects on biological alterations may lead to insignificant improvement in physical performance among participants with smoking history when subjected to supportive interventions, and smoking-related psychological health issues may also reduce smokers' acceptance and engagement of supportive interventions [48]. There has been controversy regarding how social support-related factors influence sarcopenia-related traits [17,[49], [50], [51]], and the reason for different interpretations may lie in disparity in ethnicity, age distribution, socio-economic levels, lifestyles and measurement methods.
An unexpected finding was that despite sharp changes in family structure and social environment occurring at a more advanced age, the influence of social support on sarcopenia or LMM was relatively homogeneous across different age groups (<65 and ≥65 years old), as indicated by the insignificant interaction effect of age. Of course, interaction between age and social support might be modulated or masked by other variables considering the multifactorial nature of sarcopenia, which were unable to be fully accounted for in the interaction analyses due to limited sample size. However, this still holds implications for public health: since social support may equally benefit people below or over 65 years, supportive strategies could focus on enhancing social support as a universal intervention for people aged over 50 years, rather than tailoring it specifically for certain age groups, and resources could be allocated more evenly across different age populations, which could simplify the implementation of public health policies and programs.
Considering that social support may act differently depending on its specific type, we also divided overall support into three sub-domains to disentangle their specific roles in affecting sarcopenia, with subjective support and support utilization instead of objective support found to be protective against sarcopenia. Such discrepancies in support types were consistent with some research on other health conditions [8,52,53]. Subjective support may function by providing emotional reassurance and enhancing a feeling of control over the future, attributed to perceptions of support being satisfactory and available when needed; support utilization focuses on predisposing individuals to actively take advantage of available resources and seek outside help[52,54]. The two aspects may be more crucial for sarcopenia prevention as suggested by our study. However, objective support failed to exert protective effects in sarcopenia. Compared with actively obtaining support which is truly needed (the sub-domain of support utilization), passively receiving objective support, especially unnecessary instrumental support, may increase perceived dependence, helplessness and diminish sense of self-efficacy or control over life in older adults [55]. These factors may weaken or counteract the positive role of objective support in sarcopenia. The findings indicate that it is beneficial to maximize the perception and utilization of existing support to prevent sarcopenia, even when external objective support is lacking, which is particularly applicable to economically underdeveloped areas with scarce resources and inadequate healthcare.
To our knowledge, this is the first longitudinal study to comprehensively unveil how social support and its sub-domains correlate with sarcopenia and its related traits based on community-dwelling Chinese throughout a 5-year observation. It overcame previous limitation of cross-section designs or studying only certain dimensions of social support [17,18,21], and as discussed above, it may serve as a supplement for existing evidence focused on participants aged ≥50 years in less-developed areas, which may help to guide tailored supportive strategies against sarcopenia. For example, to enhance subjective support, cognitive behavioral therapy can be applied to help older individuals shape positive visions on received support and correct cognitive distortions towards health issues so that they can better accept and persist in interventions against sarcopenia [53]; to enhance support utilization, older adults can be provided with more education on effectively seeking external assistance such as how to engage with the community or organizations, and family members or friends can encourage older adults to express their needs when facing difficulties [56].
Despite the strengths, some limitations should also be recognized. First, social support assessment lacked objective methods or criteria and was based on self-reported questionnaires, which might introduce potential social desirability bias or response bias. However, the SSRS has been widely used with high reliability and validity [10], and our standardized quality control procedures may maximize accuracy and authenticity of collected data. Second, despite efforts to control for an adequate number of confounders to minimize their interference with results, there might still be other residual factors that have not been considered, including socio-economic status. However, socio-economic levels of our study population were relatively balanced due to generally subpar local economic development, and we expect to optimize acquisition of socio-economic-related information in the subsequent follow-ups. Third, participants recruited in the WCHAT were comparatively younger residents (aged ≥50 year) in western China, limiting extrapolation of our findings in older populations of other regions or even countries. However, considering progressive decline in muscle mass and strength after the fourth decade of life [57], sarcopenia can occur at an early age, and therefore our findings are valuable for such younger population whose social support issues and sarcopenia identification, prevention or intervention are increasingly emphasized. Besides, the excluded participants were found to have generally poorer health status than the included ones at baseline (Table S2). A possible explanation was that those experiencing more physical or functional limitations reasonably seemed less likely to attend the follow-up center to complete data collection. Therefore, our findings may be more conservative estimates which should be treated with caution. Despite the differences, the included sample still aligns with the general population prone to sarcopenia regarding diversity in sociodemographic, lifestyle-related and health-related characteristics.
6. Conclusion
Overall social support degree is negatively associated with sarcopenia risk, and supportive strategies against sarcopenia should focus on enhancing subjective support and support utilization rather than objective support alone. Tailored supportive strategies should be implemented. Further evidence from prospective cohorts with larger sample size, longer observation length and more representative population is needed to validate our findings; biophysiological mechanisms and exact interplays of socio-cultural factors in the relationship between social support and sarcopenia remain to be clarified in future research.
Author contribution
Yuxiao Li and Qiao Xiang contributed to the conception of the study, performed the data analyses, wrote the main manuscript text, as well as prepared the tables and figures. Rui Liang, Quhong Song and Linghui Deng contributed to data collection and database construction. Jirong Yue and Birong Dong supervised the project, provided instructions on the study design and revised the manuscript. All authors reviewed the manuscript.
Funding statement
This study was supported by the following grants: Chinese National Science & Technology Pillar Program (2020YFC2005600);Sichuan Science and Technology Program (2021YFS0136);National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University (Z2023LC008).
1.3.5 project for disciplines of excellence–Clinical Research Incubation Project, West China Hospital, Sichuan University (19HXFH012);
1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC21005).
Ethical statements
The research complied with the current laws of China. The study was approved by the Ethical Committee of Sichuan University West China Hospital and adhered to the principles of the Declaration of Helsinki.
Conflict of interest
The authors declare that they have no conflicts of interest to this work.
Acknowledgement
We thank all the participants for their contribution in the WCHAT study.
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2023.100014.
Contributor Information
Birong Dong, Email: birongdong123@outlook.com.
Jirong Yue, Email: yuejirong11@hotmail.com.
Appendix A. Supplementary data
The following is Supplementary data to this article:
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