Abstract
Objectives
Sarcopenic obesity (SO) has been found to increase the risk of metabolic disorders, however, its relationship with cardiometabolic multimorbidity (CMM) remains unexplored. This study aims to investigate the potential association between SO and CMM in the middle-aged and older population.
Methods
Our study subjects were from CHARLS. SO was defined as the combination of impaired grip strength (grip strength <28 kg for men and <18 kg for women) and increased body mass index (BMI ≥25 kg/m2). CMM was defined as having two or more cardiometabolic diseases, including diabetes mellitus, stroke, and heart disease. The participants were divided into four groups according to their sarcopenia and obesity status, and logistic regression analysis was used to examine the association between SO and CMM.
Results
A total of 15,252 study subjects were included in the cross-sectional study, with an average age of 60.6 years and a male proportion of 47.4%. In the cross-sectional analysis conducted in 2015, the prevalence of CMM was highest in the SO group (9.1%), followed by the obesity (3.7%) and sarcopenia (3.5%) group. After adjustment for confounding factors, SO [OR (95%CI): 2.453 (1.742−3.455)], sarcopenia [OR (95% CI): 1.601 (1.157−2.217)], obesity [OR (95% CI): 1.446 (1.107−1.888)] were all observed to be associated with CMM, with the strongest association in the SO group. Furthermore, in the longitudinal analysis, only the SO group demonstrated a significant risk for developing CMM [OR (95% CI): 2.302 (1.239−4.228)].
Conclusions
SO was independently and positively associated with CMM in middle-aged and older population.
Keywords: Sarcopenic obesity, Cardiometabolic multimorbidity, Diabetes mellitus, Stroke, Heart disease
1. Introduction
The prevalence of cardiometabolic multimorbidity (CMM) is rapidly increasing in the middle-aged and older population, posing a global health challenge [1]. CMM refers to the presence of more than two cardiometabolic diseases (CMDs) simultaneously, such as diabetes mellitus (DM), stroke, and heart disease [[2], [3], [4]]. A population-based survey of 500,000 individuals aged 30–79 years in China showed that the prevalence of CMM was 6.0% [5]. Previous studies indicate that multimorbidity is associated with lower quality of life [6], higher healthcare utilization [7], as well as increased risk of disability [8] and mortality [9]. Numerous studies have shown that the risk associated with CMM is greater than that of any individual CMD. Di Angelantonio et al. [10] found that CMM is associated with a higher risk of death compared to having only one CMD, resulting in a reduction in life expectancy of 12–15 years at the age of 60. Therefore, early detection and intervention of CMM are crucial in preventing adverse outcomes.
As individuals age, there is a gradual decline in skeletal muscle mass and strength, leading to sarcopenia [11]. At the same time, the proportion of visceral fat and intramuscular fat increases, resulting in sarcopenic obesity (SO) [12]. This combination exacerbates the adverse effects. A review suggests that SO has a greater impact on CMDs compared to sarcopenia alone [13]. Chung et al. [14] also found that the SO group was more strongly associated with insulin resistance, metabolic syndrome, and CVD risk factors than the sarcopenia group or obese group. A systematic review conducted in 2021 revealed that the global prevalence of SO in older adults could reach 11% [15], making it a significant public health concern [16]. Both sarcopenia [17] and obesity [[18], [19], [20]] have been found to be associated with CMM, but no studies have explored the relationship between SO and CMM.
Therefore, this study aims to investigate the association between SO and CMM in middle-aged and older population.
2. Methods
2.1. Study population
The China Health and Retirement Longitudinal Study (CHARLS) [21] is a nationally representative longitudinal survey that collects high-quality data from individuals in China aged 45 and above. The participants are selected through multistage probability sampling. Every two years, face-to-face interviews and physical measurements are conducted to follow up with the participants.
For our study, we analyzed data collected from the CHARLS in 2015 and 2018. Initially, there were 21,095 participants in CHARLS 2015, but 5,843 participants were excluded based on specific criteria. The exclusion criteria for the study were: (1) participants without handgrip strength measurement; (2) participants without BMI data; (3) participants with age less than 45 years old; (4) participants without CMM data. The remaining 15,252 participants were included in the cross-sectional analysis. For the longitudinal analysis, we included 10,833 subjects. Fig. 1 provides a detailed overview of the selection procedure.
Fig. 1.
Study flowchart.
Written consent was obtained from all participants, and the study adhered to the principles outlined in the Declaration of Helsinki. The Ethics Committee of Peking University approved this study.
2.2. Assessment of SO status
SO was defined as a state of sarcopenia (possible) and obesity (possible). The diagnostic criteria for sarcopenia, as defined by the Asian Working Group for Sarcopenia (AWGS) [22], include low muscle mass, low muscle strength, and low physical performance. However, for the purpose of screening SO, simplicity and cost-effectiveness are important. Therefore, in this study, the diagnostic criteria for “sarcopenia” were based on the 2019 AWGS criteria for “possible sarcopenia,” which is determined by impaired grip strength: grip strength <28 kg for men and <18 kg for women. Grip strength was measured twice for both hands, and the average of the maximum grip strength was used as the final value. Obesity (possible) was defined as body mass index (BMI) ≥25 kg/m2 [23]. Abdominal obesity is defined as a waist circumference (WC) ≥ 85 cm for men and ≥80 cm for women [24,25].
The participants were classified into four groups based on their sarcopenia (possible) and obesity (possible) status. The normal group consisted of individuals who were neither obese nor had sarcopenia. The obesity group included individuals who were obese but did not have sarcopenia. The sarcopenia group comprised individuals who had sarcopenia but were not obese. And the SO group consisted of individuals who were both obese and had sarcopenia.
2.3. Assessment of CMM events
CMM events were defined as the presence of two or more CMDs, including DM, stroke, and heart disease. The presence of these conditions was determined based on self-reported medical history, with DM defined as hemoglobin A1C (HbA1C) ≥6.5% and fasting blood glucose (FBG) ≥7 mmol/L [26].
2.4. Follow-up
Baseline characteristics of all subjects were collected in 2015. Subjects with CMD at baseline status were removed. After 3 years of follow-up, we reassessed CMM status of subjects. New-onset CMM was defined as two or more new CMDs.
2.5. Covariates
The selection of covariates was based on variables from previous studies that may influence the outcomes of CMM. These variables included age, sex (male/female), urban residence (yes/no), education level (low/middle/high), marital status (married and others), smoking and drinking status (yes/no), regular exercise (yes/no), comorbidities (hypertension, hypercholesterolemia, and kidney disease), use of medications for hypertension and diabetes, lipid-lowering therapy, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Regular exercise was specifically defined as engaging in more than 20 min of heavy physical activity on three or more days per week, or more than 30 min of moderate physical activity on five or more days per week [27]. The classification of hypertension, hypercholesterolemia, and kidney disease was based on self-reported conditions. Low, middle, and high education levels were defined as follows: low (elementary school and below), middle (middle school and high school, including vocational high school), and high (college and above).
2.6. Statistical analysis
Continuous variables with normal distribution were expressed as mean ± standard deviation (SD), while those with non-normal distribution were presented as median. Categorical variables were presented as percentages. Differences between continuous variables were assessed using analysis of variance (ANOVA), and differences between categorical variables were assessed using chi-square tests. Further the Bonferroni test was used for multiple comparisons. In both cross-sectional and longitudinal analysis, logistic regression analysis was used to estimate the association between obesity, sarcopenia, sarcopenia-obesity and CMM and its components.
To account for potential confounding factors, four models were used in this assessment. Model 1 was unadjusted. Model 2 was adjusted for age and male sex. Model 3 was further adjusted for urban residence, education level, marital status, smoking status, drinking status, and regular exercise. Model 4 was additionally adjusted for hypertension, hypercholesterolemia, kidney disease, medications for hypertension and diabetes, lipid-lowering therapy, SBP, DBP.
The study subjects were divided into different subgroups based on their characteristics of age, gender, smoking status, marital status, and urban residence. The effect sizes were estimated separately for each subgroup and compared between subgroups.
To further assess the association between sarcopenic obesity and CMM, we performed a sensitivity analyse using WC-assessed sarcopenic obesity. Data analysis was conducted using SPSS 26.0, Stata 17.0, and R 4.2.1 software. A bilateral P value of <0.05 was considered statistically significant.
3. Results
3.1. Characteristics of participants in the cross‑sectional study
Among the 15,252 study subjects, the percentages of the normal group, obesity group, sarcopenia group, and SO group were 49.9% (7,616/15,252), 30.0% (4,568/15,252), 14.5% (2,219/15,252), and 5.6% (849/15,252), respectively (Table 1). The subjects in the SO group were older than those in the normal group (64.8 vs. 59.6 years), but younger than those in the sarcopenia group (64.8 vs. 68.1 years). Compared to the normal group, the SO group had a lower proportion of males (35.2% vs. 50.8%), a higher prevalence of hypertension (42.4% vs. 14.7%) and hypercholesterolemia (16.7% vs. 6.4%), a higher usage of medications for hypertension (47.8% vs. 15.5%) and diabetes (13.4% vs. 4.1%), a higher rate of lipid-lowering therapy (14.1% vs. 4.0%), and higher SBP (134.2 vs. 124.0) (all P < 0.001).
Table 1.
Baseline characteristics of groups according to sarcopenia and obesity classification.
| Variables | Overall | Normal | Obesity | Sarcopenia | Sarcopenic-obesity | P value |
|---|---|---|---|---|---|---|
| n = 15252 | n = 7616 | n = 4568 | n = 2219 | n = 849 | ||
| Age, y | 60.6 ± 9.9 | 59.6 ± 9.2c | 57.7 ± 8.5d | 68.1 ± 10.4a | 64.8 ± 10.0b | <0.001 |
| Male sex, n (%) | 7246 (47.4) | 3870 (50.8)a | 1949 (42.7)b | 1128 (50.8)a | 299 (35.2)c | <0.001 |
| Urban, n (%) | 5645 (37.02) | 2689 (35.4)b | 2041 (45.0)a | 589 (26.6)c | 326 (38.6)b | <0.001 |
| Education, n (%) | <0.001 | |||||
| Low | 9640 (63.1) | 4673 (66.8)c | 2526 (61.4)d | 1800 (84.3)a | 641 (80.2)b | |
| Middle | 4150 (27.2) | 2192 (31.3)b | 1487 (36.2)a | 322 (15.1)c | 149 (18.6)c | |
| High | 252 (1.6) | 131 (1.9)a | 100 (2.4)a | 12 (0.6)b | 9 (1.1)ab | |
| Married, n (%) | 13265 (86.9) | 6740 (88.5)b | 4173 (91.4)a | 1686 (76.0)c | 666 (78.4)c | <0.001 |
| Smoking, n (%) | 6232 (40.8) | 3402 (44.7)a | 1571 (34.4)b | 1001 (45.2)a | 258 (30.5)b | <0.001 |
| Drinking, n (%) | 5358 (35.1) | 2925 (38.4)a | 1543 (33.8)b | 681 (30.7)b | 209 (24.7)c | <0.001 |
| BMI, kg/m2 | 23.9 ± 3.7 | 21.9 ± 2.1b | 27.8 ± 2.5a | 21.0 ± 2.4c | 27.8 ± 2.5a | <0.001 |
| WC, cm | 85.5 ± 13.0 | 80.4 ± 11.0bc | 95.1 ± 10.8a | 79.8 ± 10.8c | 95.6 ± 10.8a | <0.001 |
| Regular exercise, n (%) | 3713 (24.3) | 2020 (26.5)a | 1124 (24.6)a | 410 (18.5)bc | 159 (18.7)d | <0.001 |
| Comorbidities, n (%) | ||||||
| Hypertension, n (%) | 3354 (22.0) | 1121 (14.7)d | 1350 (29.6)b | 523 (23.6)c | 360 (42.4)a | <0.001 |
| Hypercholesterolemia, n (%) | 1411 (9.3) | 485 (6.4)b | 630 (13.8)a | 154 (6.9)b | 142 (16.7)a | <0.001 |
| Kidney disease, n (%) | 987 (6.5) | 468 (6.1)b | 268 (5.9)b | 164 (7.4)ab | 87 (10.2)a | <0.001 |
| Hypertension medications, n (%) | 3643 (23.8) | 1184 (15.5)c | 1510 (33.1)b | 543 (24.5)c | 406 (47.8)a | <0.001 |
| Diabetes medications, n (%) | 912 (5.9) | 312 (4.1)c | 374 (8.2)b | 112 (5.0)c | 114 (13.4)a | <0.001 |
| Lipid-lowering therapy | 982 (6.4) | 303 (4.0)c | 451 (9.9)b | 108 (4.9)c | 120 (14.1)a | <0.001 |
| Triglycerides, mg/dL | 184.1 ± 36.3 | 102.7 (77.0, 149.6)b | 145.1 (102.7, 211.5)a | 98.2 (75.2, 141.6)c | 143.4 (101.3, 202.7)a | <0.001 |
| Total cholesterol, mg/dL | 143.3 ± 91.4 | 182.4 ± 35.4bc | 188.6 ± 36.6a | 179.0 ± 37.7d | 187.0 ± 35.5a | <0.001 |
| LDL-C, mg/dL | 102.3 ± 28.7 | 101.1 ± 28.3b | 105.1 ± 29.2a | 99.5 ± 29.2b | 104.7 ± 27.8ab | <0.001 |
| HDL-C, mg/dL | 51.2 ± 11.59 | 53.3 ± 11.9a | 47.8 ± 9.4bc | 52.6 ± 13.5a | 47.8 ± 10.0c | <0.001 |
| Handgrip strength, kg | 29.1 ± 10.2 | 31.7 ± 8.3b | 32.9 ± 9.2a | 17.5 ± 6.2c | 16.3 ± 5.6d | <0.001 |
| SBP, mmHg | 127.4 ± 19.8 | 124.0 ± 19.2d | 131.3 ± 18.7b | 128.5 ± 21.5c | 134.2 ± 21.0a | <0.001 |
| DBP, mmHg | 74.9 ± 11.5 | 73.3 ± 11.1c | 78.4 ± 11.2a | 72.3 ± 11.2d | 76.9 ± 11.5b | <0.001 |
BMI, body mass index; DBP, diastolic blood pressure; HDL-C, high density lipoprotein-cholesterol; LDL-C, low density lipoprotein-cholesterol; SBP, systolic blood pressure; WC, waist circumference.
Different lowercase letters indicate significant differences between the three groups (p < 0.05). Bonferroni test for multiple comparisons between the three groups.
3.2. Associations of sarcopenia and obesity status with incident CMM by subgroup
The results of the subgroup analysis are shown in Fig. 2. The association was found to be more significant in populations younger than 60 years, female, non-smokers, unmarried, and non-urban (Fig. 2). Additionally, the association between SO and CMM was stronger in SO populations who are younger than 60 years, female, and unmarried. There was a slight interaction in the age-stratified group, suggesting that older individuals are more likely to develop sarcopenia due to aging and muscle loss.
Fig. 2.
The odds ratios for developing CMM in subgroup analysis and the interaction of stratified factors with CMM.
3.3. Associations between sarcopenia and obesity status with CMM in the cross-sectional and longitudinal study
The prevalence of CMM and its components in each group in 2015 is shown in Fig. 3. Compared to the other three groups, the prevalence of CMM (9.1%) and its components, DM (20.7%), heart disease (22.7%), and stroke (6.0%), were highest in the SO group.
Fig. 3.
Prevalence rate of CMM and its components in groups by sarcopenia and obesity status.
In the cross-sectional study, compared with normal group, obesity [OR (95% CI): 2.380 (1.879−3.014)], sarcopenia [2.257 (1.691−3.012)], and SO [6.178 (4.599−8.299)] were all associated with CMM in the unadjusted model (Table 2). After adjustment for confounding factors, this association persisted in SO [2.453 (1.742−3.455)], obesity [1.446 (1.107−1.888)] and sarcopenia [1.601 (1.157−2.217)]. In addition, obesity, sarcopenia, and SO were all found to be associated with the components of CMM and SO was found to be most strongly associated with the CMM components: DM [1.581 (1.254−1.993)], heart disease [1.575 (1.286−1.930)], and stroke [2.439 (1.680−3.539)]. In addition, using groups based on WC and sarcopenia assessments to explore the relationship with CMM and its components yielded similar findings (Table S1).
Table 2.
Association between different groups and CMM and its components in cross-sectional analyses.
| Normal | Obesity | Sarcopenia | Sarcopenic-obesity | |
|---|---|---|---|---|
| CMM | ||||
| Model 1 | 1.000 (Reference) | 2.380 (1.879−3.014) | 2.257 (1.691−3.012) | 6.178 (4.599−8.299) |
| Model 2 | 1.000 (Reference) | 2.696 (2.124−4.424) | 1.412 (1.043−1.912) | 4.723 (3.495−6.383) |
| Model 3 | 1.000 (Reference) | 2.495 (1.959−3.178) | 1.529 (1.141−2.102) | 4.711 (3.472−6.391) |
| Model 4 | 1.000 (Reference) | 1.446 (1.107−1.888) | 1.601 (1.157−2.217) | 2.453 (1.742−3.455) |
| DM | ||||
| Model 1 | 1.000 (Reference) | 1.844 (1.639−2.075) | 1.391 (1.188−1.629) | 3.014 (2.504−3.629) |
| Model 2 | 1.000 (Reference) | 1.989 (1.764−2.243) | 0.966 (0.843−1.175) | 2.435 (2.014−2.944) |
| Model 3 | 1.000 (Reference) | 1.899 (1.678−2.148) | 1.020 (0.861−1.207) | 2.472 (2.037−2.998) |
| Model 4 | 1.000 (Reference) | 1.370 (1.186−1.582) | 1.036 (0.859−1.249) | 1.581 (1.254−1.993) |
| Heart disease | ||||
| Model 1 | 1.000 (Reference) | 1.534 (1.363−1.726) | 1.598 (1.380−1.850) | 3.091 (2.583−3.697) |
| Model 2 | 1.000 (Reference) | 1.651 (1.464−1.821) | 1.088 (0.932−1.271) | 2.369 (1.970−2.848) |
| Model 3 | 1.000 (Reference) | 1.611 (1.426−1.799) | 1.159 (0.990−1.356) | 2.461 (2.040−2.967) |
| Model 4 | 1.000 (Reference) | 1.165 (1.020−1.330) | 1.136 (0.965−1.930) | 1.575 (1.286−1.930) |
| Stroke | ||||
| Model 1 | 1.000 (Reference) | 1.285 (0.951−1.736) | 2.766 (2.048−3.736) | 4.853 (3.435−6.856) |
| Model 2 | 1.000 (Reference) | 1.408 (1.040−1.907) | 1.914 (1.391−2.632) | 3.895 (2.739−5.538) |
| Model 3 | 1.000 (Reference) | 1.434 (1.056−1.948) | 1.945 (1.411−2.678) | 4.083 (2.859−5.832) |
| Model 4 | 1.000 (Reference) | 0.909 (0.668−1.264) | 1.866 (1.349−2.581) | 2.439 (1.680−3.539) |
Model 1 was unadjusted. Model 2 was adjusted by age, male sex. Model3 was further adjusted by urban, education, married, smoking, drinking, regular exercise. Model4 was further adjusted by hypertension, hypercholesterolemia, kidney disease, hypertension medications, diabetes medications, Lipid-lowering therapy, SBP, DBP. DM, diabetes mellitus; CMM, cardiometabolic multimorbidity.
In the longitudinal study, compared with normal group, SO [4.542 (2.569−8.029)] and obesity [2.150 (1.482−3.121)] were associated with CMM, with similar results for CMD in the unadjusted model (Table 3). No association was observed between sarcopenia and either CMD or CMM. In the fully adjusted model, SO [1.627 (1.247−2.124)] and obesity [1.217 (1.052−1.408)] remained associated with an increased risk of developing CMD and only the SO group [2.302 (1.239−4.228)] showed an increased risk of developing CMM. Similarly, using groups based on WC and sarcopenia assessments to explore the relationship with CMM and its components had the similar outcome in the longitudinal study (Table S2).
Table 3.
Association between different groups and CMM and its components in longitudinal follow-up.
| CMD | CMM | |
|---|---|---|
| Model 1 | ||
| Normal | 1.000 (Reference) | 1.000 (Reference) |
| Obesity | 1.396 (1.226−1.590) | 2.150 (1.482−3.121) |
| Sarcopenia | 1.111 (0.927−1.332) | 1.545 (0.920−2.594) |
| Sarcopenic-obesity | 2.430 (1.915−3.084) | 4.542 (2.569−8.029) |
| Model 2 | ||
| Normal | 1.000 (Reference) | 1.000 (Reference) |
| Obesity | 1.461 (1.281−1.665) | 2.284 (1.568−3.327) |
| Sarcopenia | 0.921 (0.762−1.113) | 1.157 (0.674−1.983) |
| Sarcopenic-obesity | 2.165 (1.700−2.757) | 3.721 (2.084−6.643) |
| Model 3 | ||
| Normal | 1.000 (Reference) | 1.000 (Reference) |
| Obesity | 1.486 (1.293−1.707) | 2.132 (1.430−3.177) |
| Sarcopenia | 0.942 (0.776−1.144) | 1.176 (0.682−2.028) |
| Sarcopenic-obesity | 2.055 (1.589−2.659) | 3.676 (2.017−6.698) |
| Model 4 | ||
| Normal | 1.000 (Reference) | 1.000 (Reference) |
| Obesity | 1.217 (1.052−1.408) | 1.443 (0.952−2.187) |
| Sarcopenia | 0.955 (0.785−1.161) | 0.932 (0.516−1.686) |
| Sarcopenic-obesity | 1.627 (1.247−2.124) | 2.302 (1.239−4.228) |
Model 1 was unadjusted. Model 2 was adjusted by age, male sex. Model 3 was further adjusted by urban, education, married, smoking, drinking, regular exercise. Model 4 was further adjusted by hypertension, hypercholesterolemia, kidney disease, hypertension medications, diabetes medications, Lipid-lowering therapy, SBP, DBP. CMD, cardiometabolic disease; CMM, cardiometabolic multimorbidity.
4. Discussion
In our cross-sectional analysis of middle-aged and older adults, we observed that SO was significantly associated with CMM in middle-aged and older adults. This association was more significant in participants who were younger than 60 years, female, and unmarried. Furthermore, in the longitudinal study, the association between SO and CMM still existed.
Several studies have explored the relationship between SO and the components of CMM, whose results are generally consistent with our findings. In Korea, several large cohort studies have demonstrated that individuals with SO have a higher risk of developing metabolic syndrome compared to those without [28,29]. Dima et al. [30] conducted a meta-analysis and found that the presence of SO increased the risk of type 2 diabetes by 38% compared to those without SO. Additionally, SO has been associated with higher levels of cardiovascular risk factors [[31], [32], [33]]. However, unlike some previous studies and the results of our cross-sectional study, our longitudinal analysis showed no association between sarcopenia or obesity and CMM. There could be several explanations for this discrepancy. Individuals who developed obesity or sarcopenia during the three-year period, although at increased risk of developing CMM, may not have exhibited it within three years due to the chronic nature of CMM. On the other hand, individuals with SO were found to have more than twice the risk of developing CMM, possibly because SO accelerates the process of CMM development.
There are a few studies that have come to a different conclusion [[34], [35], [36]]. Aubertin-Leheudre and colleagues [34] reported that SO was associated with lower risk factors for cardiovascular diseases (CVD) in postmenopausal women. Another cohort study [35] that followed older men aged 60–79 years for 37 years found that although all-cause mortality was highest in obese men with sarcopenia, CVD mortality was not high. It is worth noting that, like many previous studies, both of these studies measured sarcopenia based on a decrease in muscle mass, which may have led to misjudgment of the patients’ conditions. Since obese individuals not only have a large fat mass, but also a large lean body mass, their absolute muscle mass remains within the normal range despite the fact that their muscle function is not adapted to their body size [36]. This implies that obesity can conceal the presence of sarcopenia and that using muscle mass alone to define SO may underestimate its occurrence. Thus, for our study, it is more appropriate to use muscle quality as a measure of sarcopenia. Furthermore, unlike our investigation of the middle-aged and older population as a whole, the two aforementioned studies focused exclusively on one gender. Also, these two studies were conducted in the Western population, while our study subjects were from the Eastern population, and there are significant differences in characteristics among different ethnic groups. Consequently, the inconsistent findings could be mainly attributed to varying definitions of SO and the heterogeneity of the populations involved in the studies.
There are several possible mechanisms that could explain the association that exists between SO and CMM [[37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47]]. Aging, also considered immune aging, is characterized by increased levels of pro-inflammatory cytokines in the blood in the absence of obvious induction [37]. Hotamisligil et al. [38] found increased concentrations of inflammatory biomarkers such as TNF, IL-6 and CRP in individuals with insulin resistance and obesity, concluding that inflammation plays a significant role in the pathogenesis of insulin resistance. Inflammation can also be found in obesity and sarcopenia [[39], [40], [41], [42]]. Kalinkovich et al. [43] suggested that adipose tissue and skeletal muscle inflammation are important factors in SO. In addition, because insulin plays the key role in regulating muscle protein metabolism, insulin resistance may be associated with age-related muscle protein loss that gradually leads to sarcopenia (44). In summary, it can be concluded that insulin resistance and SO are closely related and interact with each other. Insulin resistance is considered a major risk factor for DM and CVD [[45], [46], [47]], which are the components of CMM. Therefore, we can hypothesis that SO is very likely to be associated with CMM, which was confirmed in our present study.
According to our findings, individuals with SO had a significantly higher risk of developing CMM than those without in middle-aged and older populations. As the older population increases, the prevalence of SO and CMM also rises, making it a significant concern in geriatric health. Moreover, the medical sequelae associated with SO are greater than those associated with sarcopenia or obesity alone, resulting in prominently higher medical costs [48]. Additionally, CMM not only increases the disease burden and reduces life expectancy, but also accelerates the progression of cognitive decline and dementia [49], thus lowering the quality of life of patients. Therefore, it is necessary to implement targeted public health interventions, especially focusing on screening for SO among the older population. Particularly for those already suffering from CMD, once signs of SO are detected, targeted preventive measures should be taken to reduce the risk of CMM. These personalized health monitoring and intervention measures contribute to improving the quality of life among middle-aged and older populations, alleviating the burden of diseases, extending lifespan, and promoting overall health.
Growing evidence suggests a strong association between abdominal obesity and cardiometabolic diseases, indicating that waist circumference may be equally or even more important than BMI [50,51]. Therefore, we also assessed the relationship between SO which defined by waist circumference and muscle strength impairment, and CMM. We found a similar relationship between abdominal obesity sarcopenia and CMM and its components.
This study does have some limitations. Firstly, we defined SO as a state of ‘grip strength <28 kg for men and <18 kg for women’ and ‘BMI ≥25 kg/m2’. Similarly, ESPEN and EASO [52] recommend employing grip strength and BMI or waist circumference to evaluate SO when screening in the population. However, they applied validated cutoff points for grip strength across various age groups. They believe that the standard used in our study is more suitable for people aged 65 and above in the Asian population, while there is no corresponding standard for people aged below 65. Considering that muscle function may still be relatively preserved in younger patients with lower muscle mass, utilizing the same standard for different age groups may result in misclassification of SO. Secondly, using anthropometric data such as BMI or waist circumference to measure obesity is not as accurate as measuring body fat percentage. However, considering the convenience of implementing screening in a population, BMI or waist circumference is easier to obtain. Thirdly, to ensure an adequate number of study subjects and for screening purposes, we used the definition of possible sarcopenia, potentially leading to an overestimation or underestimation of the relationship between SO and CMM. Lastly, self-reported information on DM, heart disease, and stroke may introduce information bias.
5. Conclusion
SO, assessed by BMI and handgrip strength, was independently and positively associated with CMM in middle-aged and older population. Moreover, of the CMM components, SO was associated with DM, heart disease, and stroke, respectively.
Funding
This research was supported by National Key Research and Development Program of China (No. 2022YFC2407406), National Natural Science Foundation of China (No. 82373529 and82170259), Department of Science and Technology of Guangdong Province, China (No. 2024A1515012943), Science and technology program of Tibet Grant (No. XZ202201ZY0051G) and Science and Technology Fundation of Guangzhou Health (No. 2023A031004).
Ethical statement
The CHARLS study has approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015), and the informed consent was required from the respondents.
Conflict of interests
The authors have no conflicts of interest to disclose.
Availability of data and materials
The data sets analyzed in this study can be accessed in the China Health and Retirement Longitudinal Study (CHARLS; http://www.charls.pku.edu.cn/index.htm).
Acknowledgements
We thank the clinical research coordinators of each participating center for their dedication during patient recruitment and data acquisition.
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2024.100353.
Contributor Information
Haojian Dong, Email: donghaojian@gdph.org.cn.
Yanqiu Ou, Email: ouyanqiu@gdph.org.cn.
Appendix A. Supplementary data
The following is Supplementary data to this article:
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data sets analyzed in this study can be accessed in the China Health and Retirement Longitudinal Study (CHARLS; http://www.charls.pku.edu.cn/index.htm).