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More comprehensive relationship between eGDR and sarcopenia in China: a nationwide cohort study with national representation

Diabetology & Metabolic Syndrome volume 17, Article number: 97 (2025) Cite this article

Abstract

Introduction

Although studies had shown that Insulin resistance (IR) was correlated with the occurrence of sarcopenia, there were still many controversial conclusions. Therefore, we conducted a more comprehensive study on the relationship between the estimated glucose disposal rate (eGDR), an alternative indicator of IR, and the risk of sarcopenia, muscle mass, and muscle strength to clarify their interactions.

Methods

The Study included individuals from The China Health and Retirement Longitudinal Study (CHARLS) who had complete eGDR data at baseline and did not develop low muscle mass and low muscle strength. The individuals were divided into four subgroups based on the quartile (Q) of the eGDR. The lowest quartile (Q1) of the eGDR was used as a reference. Logistic regression and linear regression were used to evaluate the relationship between eGDR and sarcopenia (low muscle mass, low muscle strength, possible sarcopenia, and sarcopenia) and sarcopenia related features (ASM/Ht2, grip, and RMS), respectively. In addition, we further evaluated the nonlinear relationship using smooth curve fitting and threshold effect analysis.

Results

The results showed that after adjusting for confounders, eGDR was negatively associated with the risk of sarcopenia and positively associated with sarcopenia related characteristics. In addition, men showed a more significant reduction in the likelihood of low muscle mass compared to women. But as eGDR levels rise, women gain more ASM/Ht2. Further nonlinear analysis revealed an inverse correlation between eGDR and ASM/Ht2 at the inflection point of 15.3893. Besides that, eGDR was positively correlated with grip (7.1862) and RMS (11.1042) before the inflection point.

Conclusions

The study found that higher levels of eGDR were associated with a lower risk of developing sarcopenia. However, the effects of eGDR on muscle mass and muscle strength need to be considered comprehensively. For muscle mass, it is recommended to maintain eGDR below 15.3893, and for muscle strength, it is recommended to maintain eGDR below 7.1862, with more potential benefits for early warning of sarcopenia.

Introduction

Sarcopenia was a systemic degenerative disease characterized by the loss of muscle mass, muscle strength, and physical function[1]. The condition had been found to be closely associated with the occurrence of adverse outcomes such as falls, fractures, cardiovascular diseases, and metabolic disorders [2,3,4,5]. Although there were various diagnostic methods and criteria for defining values, the global prevalence of sarcopenia could still reach as high as 10–27% [6]. A study in Asia found that the prevalence of sarcopenia was 11.5% in men and 16.7% in women [7]. The high disability and mortality rate of sarcopenia made it sufficient to be considered an important public health problem [8].

Insulin resistance (IR), diabetes, obesity, and lipid deposition were all considered to be associated with the development of sarcopenia [9, 10]. Based on research conducted within Asian population cohorts, it had been concluded that there was a significant negative correlation between skeletal muscle mass and IR [11]. IR had been confirmed to be correlated with sarcopenia [12] and might serve as a potential biomarker for sarcopenic in the elderly [13]. IR impaired glucose uptake in skeletal muscle, reducing the tissue's ability to utilize glucose and contributing to muscle mass loss [14]. It also inhibited glycogen synthase activity, decreasing glycogen synthesis, which deprived muscles of an essential energy source and increased their susceptibility to weakened strength [15]. Furthermore, IR disrupted muscle protein homeostasis by modulating energy metabolism and oxidative stress pathways, thereby promoting muscle atrophy [16].

The gold standard for identifying IR was the Hyperinsulinemic-euglycemic Clamp (HEGC) [17]. However, due to its complex procedures and high-cost nature, it was difficult to apply in large-scale epidemiological studies. The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was one of the alternative indicators to HEGC, calculated using fasting blood glucose and fasting insulin levels [18]. However, HOMA-IR was difficult to avoid the bias in conclusions caused by individuals in the study who had already used insulin. In recent years, the triglyceride-glucose (TyG) index and estimated glucose disposal rate (eGDR) had been widely used in epidemiological studies for assessing IR due to their convenience and cost-effectiveness. In some disease predictions, they had been shown to have better predictive capability compared to traditional indicators [19]. Compared to HOMA-IR, eGDR combined blood pressure and waist circumference, reflecting the long-term overall health status of the body. At the same time, the simultaneous use of HbA1c could reflect the long-term monitoring level of blood glucose, thereby reducing research bias [20]. Previous studies had demonstrated the relationship between eGDR and cardiovascular diseases [21, 22], various complications of diabetes [23,24,25], mortality [26, 27] and other related outcomes.

Recent viewpoints suggested that focusing on muscle strength is more valuable than muscle mass in predicting clinical outcomes for sarcopenia [1]. However, more previous studies had primarily focused on the relationship between IR and muscle mass [28, 29], with limited attention given to muscle strength. This had also made the relationship between IR and sarcopenia less clear. Therefore, our study, based on data from The China Health and Retirement Longitudinal Study (CHARLS), aimd to comprehensively explore the associations between eGDR, muscle mass, muscle strength, and sarcopenia.

Materials and methods

Study sample and data source

This study utilized data from the CHARLS, a nationally representative cohort comprising over 17,000 individuals from 28 provinces across China. CHARLS initiated its baseline survey in 2011 and conducted follow-up surveys in 2013, 2015, 2018, and 2020. Ethical approval was obtained from the Ethical Review Committee of Peking University (IRB00001052-11015), and all participants provided written informed consent. Detailed information regarding the study design of the CHARLS cohort can be found on its official website (http://charls.pku.edu.cn/).

A total of 3,872 participants were included in the analysis, using 2011 data as baseline and 2015 data as follow-up data. A further 13,833 individuals were excluded for the following reasons: (1) age below 45 years (n = 391); (2) missing eGDR data at baseline (n = 7,629); (3) incomplete data for grip strength (n = 137) or Relative Muscle Strength (RMS) (n = 106) or extreme outliers in height or weight measurements at baseline (n = 13); (4) pre-existing diagnoses of low grip strength (n = 560) and low muscle mass (n = 1,944) at baseline; (5) missing follow-up data for height (n = 3,045), weight (n = 2), or grip strength (n = 9). The participant selection process was illustrated in Fig. 1.

Fig. 1
figure 1

Flow chart of participant screening included in this study

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Data collection and variable definition

Assessment of eGDR

The calculation of eGDR was based on the formula: eGDR (mg/kg/min) = 21.158—(0.09 × Waist)—(3.407 × HTN)—(0.551 × HbA1c). [Waist (cm), hypertension (yes = 1/ no = 0), and HbA1c (%)]. Based on the quartiles of eGDR, participants were further divided into four groups: Q1(1.13–6.90), Q2(6.90–9.59), Q3(9.59–10.84), Q4(10.84–17.81).

Assessment of variables associated with sarcopenia

The assessment of sarcopenia is based on the 2019 sarcopenia criteria established by the Asian Working Group for Sarcopenia (AWGS), which encompass appendicular skeletal muscle mass (ASM), muscle strength, and physical performance [30]. Grip strength was measured as the maximum value of two consecutive measurements from both hands. Low grip strength was defined as < 28 kg for men and < 18 kg for women. ASM was estimated using a validated formula for the Chinese population: ASM = 0.193 × weight (kg) + 0.107 × height (cm)—4.157 × gender—0.037 × age (years)—2.631 [31]. If assigned a value of 1 for males and a value of 2 for females. The criterion for determining low muscle mass status was based on the sex-specific lowest 20% of height-adjusted muscle mass (ASM/Ht2). The ASM/Ht2 value for women < 5.27 kg/m2 and for men < 7.03 kg/m2, could be considered as low muscle mass status. Low physical performance is defined as one of the following: (1) Chair stand test time ≥ 12 s (5 repetitions), (2) 6-m walking speed < 1.0 m/s, (3) Short Physical Performance Battery (SPPB) score < 9.

According to the AWGS 2019 diagnostic criteria, individuals diagnosed with either low grip strength or low muscle mass could be diagnosed with probable sarcopenia. Those diagnosed with both low grip and low muscle mass could be diagnosed with sarcopenia. If low physical performance was also identified, the diagnosis can be classified as severe sarcopenia. In this study, participants diagnosed with sarcopenia were all simultaneously identified as having low physical performance, so the participants with sarcopenia and severe sarcopenia were combined into one group.

It is worth mentioning that, to highlight the importance of muscle strength and quality in the diagnosis of sarcopenia, we also calculated the Relative Muscle Strength (RMS) using the following formula: maximum grip strength(kg) / ASM [32].

Covariables

We collected demographic information of participants from the CHARLS database. Including age, gender, education, marital status, location, whether smoking or drinking, height (cm), weight (kg), waist (cm), HbA1c (%), grip (kg), ASM (kg), whether suffering from diabetes, kidney disease, hypertension, heart disease, arthritis.

The educational level includes four categories: Illiterate, Primary, High School, and College. The marital status includes four categories: Married, Married, Unmarried, and Widowed. Location includes two types: city and village. Whether the participant had diabetes or not combines the patient's self report, whether to use hypoglycemic drugs and the level of glycosylated hemoglobin. Whether or not they had hypertension was a combination of patients' self-reports, use of blood pressure lowering medications and blood pressure levels measured in the field. The determination of whether a patient has kidney disease, heart disease and arthritis is mainly based on their self-report.

Statistical analysis

All statistical analyses were based on R version 4.3.2, and when P was < 0.05, the difference was considered statistically significant. Continuous variables were represented by mean ± standard deviation (SD) based on data distribution. When comparing baselines, analysis of variance (ANOVA) was used for normally distributed variables, and Kruskal WallisH test was used for non normally distributed variables. Categorical data was represented by counts and percentages, and chi square tests are used to evaluate differences. Using logistic regression analysis to evaluate the relationship between eGDR and the risk of low muscle mass, low muscle strength, probable sarcopenia and sarcopenia. In addition, linear regression was used to evaluate the relationship between eGDR and ASM/Ht2, grip and RMS. Based on the STROBE declaration [33], the link was evaluated using three different models.Model 1 was unadjusted. Model 2 was adjusted according to age, gender, education, marital status and location. Model 3 was adjusted by adding smoking, drinking, height, weight, diabetes, kidney disease and arthritis on the basis of model 2. Subsequently, the subgroup analysis was stratified based on gender, age, education, location, smoking and drinking to examine potential interactions.

In addition, we further evaluated the nonlinear relationship between eGDR and ASM/Ht2, grip and RMS by using the generalized addition model (GAM) in smooth curve fitting. Following that, based on the piecewise regression model and the LRT test, threshold effect analysis was conducted to find the significant inflection point between the eGDR and the three outcomes.

Results

Characteristics of the participants

The baseline characteristics of participants stratified by quartiles of eGDR index were shown in Table 1. There were 3,872 participants, including 1,746 were men and 2,126 were women. The median age (mean ± SD) was 57.49 ± 8.1 years, and the mean eGDR (SD) was 8.97 ± 2.32.Compared with participants in the lower quartile of the eGDR index, those in the higher quartile were more likely to be younger, female, have a secondary or higher education, be married, live in rural areas, and not smoke or drink. Additionally, they were less likely to have diabetes, kidney disease, hypertension, heart disease, or arthritis. Participants in the higher eGDR quartiles also exhibited lower levels of weight, waist, HbA1c, and ASM.

Table 1 Basic characteristics of the study population based on eGDR quartile
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Relationship between eGDR and sarcopenia

We performed multiple logistic regression analyses based on quartile groupings of eGDR. According to Model 3, which adjusted for potential confounders, we identified negative associations between eGDR and low muscle mass, low muscle strength, possible sarcopenia, and sarcopenia. Specifically, the probability of low muscle mass in the highest quartile (Q4) was 0.08 times higher compared to the lowest quartile (Q1) with an odds ratio (OR) of 0.08 (95% CI 0.01 ~ 0.57). Similarly, the probability of low muscle strength of Q4 was 0.68 times that of Q1with an OR of 0.68 (95% CI 0.47 ~ 0.97). Additionally, participants in the highest quartile had a 0.64 times greater risk of possible sarcopenia (OR: 0.64, 95%CI [0.46 ~ 0.88]) and a 0.14 times greater risk of sarcopenia (OR: 0.14, 95%CI [0.04 ~ 0.47]) compared to those in the lowest quartile. All differences were statistically significant (Table 2).

Table 2 Association of eGDR with sarcopenia in different models
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Intriguingly, we also performed linear regression to explore the relationship between eGDR and sarcopenia related features. In Model 3, which adjusted for potential confounding factors,a positive correlation was observed between eGDR and ASM/Ht2, grip, and RMS. Compared to Q1, Q4 showed an increase of 0.02 units in ASM/Ht2 (β: 0.02, 95% CI [0.01 ~ 0.03]), 1.11 units in grip strength (β: 1.11, 95% CI [0.15 ~ 2.06]), and 0.18 units in RES (β: 0.18, 95% CI [0.14 ~ 0.23]). The differences were also statistically significant (Table 3).

Table 3 Association of eGDR with muscle mass and muscle strength in different models
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Subgroup analyses

Subgroup analyses were conducte based on gender, age, education, location, smoking, and drinking. Gender was identified as a significant factor influencing the relationship between eGDR and both low muscle strength (P for interaction < 0.05) and ASM/Ht2 (P for interaction < 0.0001). Specifically, males (OR: 0.27, 95% CI [0.05 ~ 1.37]) demonstrated a more significant reduction in the likelihood of low muscle mass compared to females (OR: 0.28, 95% CI [0.08 ~ 0.97]), as shown in Table S1. In contrast, the increase in ASM/Ht2 was more substantial in females (β: 0.06, 95% CI [0.04 ~ 0.07]) than in males (β: 0.02, 95% CI [0.01 ~ 0.03]), with further details provided in Table S2. No significant interactions were observed between other subgroup variables (P for interaction > 0.05).

Non-linear relationships

To more thoroughly explore the relationship between eGDR, muscle mass, and muscle strength, we employed smooth curve fitting to identify potential nonlinear associations. Following that, The threshold effect analysis was used to determine the inflection point.

According to the model adjusted for covariates, the relationship between eGDR and ASM/Ht2 shows roughly the Inverse U-shaped (Fig. 2). The threshold effect analysis identified an inflection point of 15.3893. Before the inflection point, eGDR was found to be positively correlated with ASM/Ht2 (β: 0.0170, 95% CI [0.0050 ~ 0.0291]). However, when eGDR > 15.3893, there was a negative correlation between eGDR and ASM/Ht2 (β: −0.0784, 95% CI [−0.1349 to −0.0219]) (Table 4). The relationship between eGDR and grip was inverted U-shaped (Fig. 3). The inflection point determined by threshold effect analysis was 7.1862. When eGDR was below this inflection point, a positive correlation between eGDR and grip was observed (β: 1.1156, 95% CI [0.5637 ~ 1.6676]). Beyond the inflection point, the association between eGDR and grip was no longer statistically significant. (β: −0.0456, 95% CI [−0.2363 to 0.1451]) (Table 5). In addition, the inverted U-shaped association was also found between eGDR and RMS (Fig. 4). There was an inflection point of 11.1042 between eGDR and RMS. When eGDR was below this inflection point, there was a positive correlation between eGDR and RMS (β: 0.0805, 95% CI [0.0510 ~ 0.1099]). Beyond the inflection point, the relationship between eGDR and RMS also losed statistical significance (β: −0.0246, 95% CI [−0.0518 to 0.0026]) (Table 6).

Fig. 2
figure 2

The relationship between eGDR and ASM/Ht2. eGDR estimated glucose disposal rate, ASM/Ht2 height-adjusted muscle mass. The solid red line represents a smooth curve fit between variables. The blue band represents the 95% confidence interval of the fit. Age, gender, education, marital, location, smoking, drinking, height, weight, diabetes, kidney disease and arthritis was adjusted

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Table 4 Analysis of threshold effect of eGDR on ASM/Ht2
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Fig. 3
figure 3

The relationship between eGDR and grip. eGDR estimated glucose disposal rate. The solid red line represents a smooth curve fit between variables. The blue band represents the 95% confidence interval of the fit. Age, gender, education, marital, location, smoking, drinking, height, weight, diabetes, kidney disease and arthritis was adjusted

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Table 5 Analysis of threshold effect of eGDR on grip
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Fig. 4
figure 4

The relationship between eGDR and RMS. eGDR estimated glucose disposal rate; RMS, relative muscle strength. The solid red line represents a smooth curve fit between variables. The blue band represents the 95% confidence interval of the fit. Age, gender, education, marital, location, smoking, drinking, height, weight, diabetes, kidney disease and arthritis was adjusted

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Table 6 Analysis of threshold effect of eGDR on RMS
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Discussion

Our study was the first to report the relationship between the succedaneous indicator of IR (eGDR) and sarcopenia, along with its related characteristics. We analyzed 4-year longitudinal follow-up data from 3,872 individuals in the CHARLS cohort. The results showed that eGDR was significantly associated with sarcopenia, as well as its characteristics. We observed a negative association between eGDR and low muscle mass, low muscle strength, possible sarcopenia, and sarcopenia. The positive correlation was also observed between eGDR and ASM/Ht2, grip, and RMS, and the nonlinear relationships among them were subsequently explored. The results suggested that monitoring eGDR levels may help enable early warning of sarcopenia.

The correlation between IR and sarcopenia has been validated in multiple population cohorts [34,35,36]. For instance, the TyG index, a surrogate marker of IR, has shown a positive association with sarcopenia onset, indicating that lower IR levels reduce the risk of sarcopenia [34], consistent with the results of our study. Furthermore, TyG had demonstrated strong predictive potential for sarcopenia and hold promise as a biomarker, with its validity already established in the Chinese population [12]. During the subgroup analysis, we identified the interaction between eGDR and gender, which was particularly evident in relation to muscle mass. While males showed a greater reduction in the likelihood of low muscle mass with increased eGDR levels, the effect on muscle mass gain was more pronounced in females. Study indicated that females exhibit higher insulin sensitivity [37], which may be influenced by estrogen levels. Similar conclusions had been observed in animal models, where ovariectomy [38] and estrogen receptor knockout both lead to increased IR levels [39]. Estrogen had been demonstrated to regulate glucose homeostasis by activating the estrogen receptor (ER) α-phosphatidylinositol 3-kinase (PI3K)-Akt-Foxo1 signaling pathway, which in turn helped reduce IR levels [40]. It was well known that there was a significant difference in skeletal muscle mass between different gender. Furthermore, postmenopausal changed in lipid metabolism and estrogen levels had a considerable impact on females, leading to a higher turnover rate of skeletal muscle mass. At the same time, estrogen deficiency led to a reduction in the expansion, differentiation and self-renewal of muscle satellite cells, resulting in loss of muscle mass [41].The underlying mechanism of this process may be that the loss of estrogen leads to the inhibition of mitochondrial fission, causing mitochondrial dysfunction [42]. This may also be one of the reasons why females face greater challenges than males in reducing the likelihood of low muscle mass [37].

Subsequently, we explored the nonlinear relationships between eGDR and ASM/Ht2, grip, and RMS, with the aim of providing a more comprehensive discussion on the relationship between eGDR, muscle mass, and muscle strength. An interesting finding was that the relationship between eGDR and muscle mass shows an opposing trend before and after the inflection point. The relationship between IR and muscle mass had been a subject of ongoing debate. A cross-sectional study involving 15,741 adults aged 19 which above in South Korea found that as the TyG index increased, the likelihood of IR also increased, while muscle mass tended to rise as well [43]. However, a study conducted in a Chinese population reached the opposite conclusion [36]. A bidirectional effect had been found between IR and muscle mass. Skeletal muscle was considered the primary site for glucose uptake in the body, a process that was mediated by insulin [44, 45]. The loss of skeletal muscle could disrupt glucose metabolism, increasing the risk of IR. Conversely, IR also affected skeletal muscle quality. In accordance with recent viewpoints, we had placed greater emphasis on muscle strength. The nonlinear relationship between eGDR and both grip and RMS, further validating and strengthening our findings. A study including 4,449 individuals aged over 50 found that low muscle strength, rather than muscle mass, was an independent risk factor for elevated all-cause mortality [46]. Insulin sensitivity had also been found to have a positive correlation with muscle strength [47]. Strength training could enhance muscle strength and skeletal muscle mass, activate insulin pathways in skeletal muscle, and thus mitigate the detrimental effects of IR on the muscular system [48].

Limitations and strengths

Our study was the first to establish a link between eGDR, an alternative indicator of IR, and sarcopenia. Besides logistic regression, linear regression was also employed to examine the relationship between eGDR and sarcopenia-related factors (muscle mass, muscle strength), thereby making our conclusions more thorough. In addition, we have placed greater emphasis on muscle strength and used a smoothing curve fitting approach to explore the nonlinear relationships between eGDR and ASM/Ht2, grip strength, and RMS. Through threshold effect analysis, we were able to pinpoint the inflection points, which further reinforced and confirmed our earlier findings.

Unfortunately, our study lacked focus on physical performance variables, which was due to the incompleteness of the CHARLS data. Secondly, although we took into account the impact of numerous covariates, factors such as medication use, insulin dependence, diet, and lifestyle habits, which can influence the muscular system, were not included in our analysis. The possibility of potential confounding variables cannot be ruled out. Moreover, our study primarily focused on the Chinese population. Previous studies have reported conflicting findings on the relationship between IR and sarcopenia across different ethnic groups. Whether our findings could be generalized to other ethnic populations warrants further consideration.

Conclusion

To summarize, our research indicated a negative relationship between eGDR and the risk of sarcopenia. It is worth mentioning that there is a positive correlation between eGDR and both muscle mass and muscle strength. Based on nonlinear association analysis, we believe that maintaining eGDR below 15.3893 for muscle mass and 7.1862 for muscle strength contributes to early warning of sarcopenia. Future prospective cohort studies based on other populations are needed to further validate our conclusions, with a greater focus on the relationship between insulin resistance and muscle strength.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

BMI:

Body mass index

BP:

Blood pressure

BUN:

Blood urea nitrogen

CHARLS:

China health and retirement longitudinal study

CI:

Confidence interval

CVD:

Cardiovascular diseases

Egdr:

Estimated glucose disposal rate

FBG:

Fasting blood glucose

HbAlc:

Glycosylated hemoglobin A1c

HDL:

High-density lipoprotein

HIEG:

Hyperinsulinemic-euglycemic

HOMA-R:

Homeostasis model assessment for insulin resistance

HR:

Hazard ratio

hsCRP:

High-sensitivity C-reactive protein

IDI:

Integrated discrimination improvement

LDL:

Low-density lipoprotein

NRI:

Net reclassification improvement

Q:

Quartiles

RAAS:

Renin–angiotensin–aldosterone system

RCS:

Restricted cubic spline

TC:

Total cholesterol

TG:

Triglyceride

TyG:

Triglyceride-glucose

UA:

Uric acid

WC:

Waist circumference

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Acknowledgements

All authors express their gratitude to all participants and personnel involved in the CHARLS.

Funding

This work was supported by National Natural Science Foundation of China (No.T2341023), Scientific and technological innovation project of China Academy of Chinese Medical Sciences (No. CI2024D003) and the Fundamental Research Funds for the Central Public Welfare Research Institutes (No. ZZ13-YQ-039, ZZ17-YQ-014).

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Author notes

  1. Zikai Jin, Liming Zheng, and Chuanrui Sun have contributed equally to this study.

Authors and Affiliations

  1. Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China

    Zikai Jin, Liming Zheng, Chuanrui Sun, Bo Xu, Linghui Li & Xu Wei

  2. Graduate School, Beijing University of Chinese Medicine, Beijing, 100029, China

    Zikai Jin

  3. School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China

    Xiangyun Guo & Yili Zhang

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  1. Zikai Jin
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  2. Liming Zheng
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  3. Chuanrui Sun
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  4. Bo Xu
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  5. Xiangyun Guo
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  6. Yili Zhang
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  7. Linghui Li
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  8. Xu Wei
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Contributions

KJ: Conceptualization, Study design, and Writing Original Draft. LZ: Investigation and Study design. CS: Writing Original Draft. BX: Data analysis. XG: Data analysis. YZ: Conceptualization and Study design. LL: Conceptualization, Writing—Review & Editing, Supervision. XW: Conceptualization, Writing—Review & Editing, Visualization, Supervision.

Corresponding authors

Correspondence to Linghui Li or Xu Wei.

Ethics declarations

Ethics approval and consent to participate

CHARLS was approved by the Institutional Review Board of Peking University (approval number: IRB00001052-11015), and all participants provided written consent.

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The authors declare no competing interests.

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Jin, Z., Zheng, L., Sun, C. et al. More comprehensive relationship between eGDR and sarcopenia in China: a nationwide cohort study with national representation. Diabetol Metab Syndr 17, 97 (2025). https://doi.org/10.1186/s13098-025-01657-0

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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