Objective:To investigate the association of skeletal muscle function(including muscle strength and physical performance) and insulin resistance as well as type 2 diabetes mellitus(T2DM).Methods:The retrospective study included 942 patients who visited the Department of Geriatrics at Renji Hospital, Shanghai Jiao Tong University School of Medicine, between October 2020 and July 2024. Low skeletal muscle function was defined as either reduced muscle strength or impaired physical performance. Muscle strength was assessed by grip strength, while physical performance was evaluated using the 5-time chair stand test. The associations between muscle function, insulin resistance, and T2DM were analyzed.Results:A significant association was observed between decreased skeletal muscle function and a higher prevalence of T2DM( P=0.001). Further analysis revealed that decreased physical performance was significantly associated with increased T2DM prevalence( P<0.001), whereas reduced muscle strength showed no significant association with T2DM prevalence( P=0.331). Linear regression analysis indicated that both the homeostasis model assessment of insulin resistance(HOMA2-IR) and fasting blood glucose levels increased significantly with longer chair stand times( P<0.05). Restrictive cubic spline(RCS) analysis demonstrated a nonlinear relationship between chair-rising time and HOMA2-IR. Notably, when the cumulative chair-rising time exceed 8.1 s, HOMA2-IR increased significantly with prolonged chair stand time. Logistic regression analysis showed that compared with patients with normal physical performance, those with decreased physical performance had significantly higher odds of T2DM( OR=2.64, P<0.001) and insulin resistance( OR=2.34, P=0.002). Conclusion:Decline in physical performance is significantly positively associated with insulin resistance and the risk of T2DM. Morever, when the cumulative chair stand time exceed 8.1 s, HOMA2-IR increases progressively with further prolongation of chair stand time.

Sarcopenia is characterized by a decline in both skeletal muscle mass and function. Since skeletal muscle is the primary organ responsible for insulin-stimulated glucose uptake, sarcopenia, which occurs with aging, is believed to promote insulin resistance in skeletal muscle, leading to disruptions in glucose metabolism. Growing clinical evidence indicates that decreased muscle function may contribute to the development of type 2 diabetes, while a reduction in muscle mass alone does not appear to be significantly associated with its onset. This review, based on the latest research, summarizes the clinical evidence linking sarcopenia to glucose metabolism abnormalities and potential mechanisms through which impaired muscle function affects glucose metabolism. The goal is to inspire early prevention, intervention, and management of type 2 diabetes by targeting skeletal muscle.

Objective:To investigate the association of skeletal muscle function(including muscle strength and physical performance) and insulin resistance as well as type 2 diabetes mellitus(T2DM).Methods:The retrospective study included 942 patients who visited the Department of Geriatrics at Renji Hospital, Shanghai Jiao Tong University School of Medicine, between October 2020 and July 2024. Low skeletal muscle function was defined as either reduced muscle strength or impaired physical performance. Muscle strength was assessed by grip strength, while physical performance was evaluated using the 5-time chair stand test. The associations between muscle function, insulin resistance, and T2DM were analyzed.Results:A significant association was observed between decreased skeletal muscle function and a higher prevalence of T2DM( P=0.001). Further analysis revealed that decreased physical performance was significantly associated with increased T2DM prevalence( P<0.001), whereas reduced muscle strength showed no significant association with T2DM prevalence( P=0.331). Linear regression analysis indicated that both the homeostasis model assessment of insulin resistance(HOMA2-IR) and fasting blood glucose levels increased significantly with longer chair stand times( P<0.05). Restrictive cubic spline(RCS) analysis demonstrated a nonlinear relationship between chair-rising time and HOMA2-IR. Notably, when the cumulative chair-rising time exceed 8.1 s, HOMA2-IR increased significantly with prolonged chair stand time. Logistic regression analysis showed that compared with patients with normal physical performance, those with decreased physical performance had significantly higher odds of T2DM( OR=2.64, P<0.001) and insulin resistance( OR=2.34, P=0.002). Conclusion:Decline in physical performance is significantly positively associated with insulin resistance and the risk of T2DM. Morever, when the cumulative chair stand time exceed 8.1 s, HOMA2-IR increases progressively with further prolongation of chair stand time.

Sarcopenia is characterized by a decline in both skeletal muscle mass and function. Since skeletal muscle is the primary organ responsible for insulin-stimulated glucose uptake, sarcopenia, which occurs with aging, is believed to promote insulin resistance in skeletal muscle, leading to disruptions in glucose metabolism. Growing clinical evidence indicates that decreased muscle function may contribute to the development of type 2 diabetes, while a reduction in muscle mass alone does not appear to be significantly associated with its onset. This review, based on the latest research, summarizes the clinical evidence linking sarcopenia to glucose metabolism abnormalities and potential mechanisms through which impaired muscle function affects glucose metabolism. The goal is to inspire early prevention, intervention, and management of type 2 diabetes by targeting skeletal muscle.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Objective:To investigate the association of skeletal muscle fat index(SMFI) with insulin resistance and type 2 diabetes mellitus.Methods:This retrospective study included 1 100 patients from Department of Geriatric, Renji Hospital, Shanghai Jiao Tong University School of Medicine from October 2020 to October 2023. The SMFI measured by chest computed tomography(CT) is used as an indicator to evaluate the quality of skeletal muscle. Spearman and linear regression analysis were used to evaluate the association between SMFI and homoeostasis model assessment for insulin resistance(HOMA-IR). Multivariate logistic regression analysis and restricted cubic spline(RCS) were used to analyze the association of HOMA-IR with the prevalence of type 2 diabetes mellitus. Finally, interaction and stratified analyses were conducted according to age, sex, body mass index, estimated glomerular filtration rate, uric acid, skeletal muscle index(SMI), smoking, alcohol drinking, hypertension, and dyslipidemia.Results:SMFI showed a significant positive correlation with HOMA-IR( r=0.385, P<0.001). Each 1-unit increase in SMFI led to 0.009, 0.011, 0.004, 0.004, and 0.004 rise in HOMA-IR across adjusted models(all P<0.05). Then subjects were stratified into tertiles( T1, T2, and T3) according to SMFI. Logistic regression analysis revealed that the prevalence of type 2 diabetes mellitus significantly increased in T2( OR=2.37, 95% CI 1.30-4.31, P=0.005) and T3( OR=2.85, 95% CI 1.39-5.86, P=0.004) compared to T1. RCS analysis showed that the prevalence of type 2 diabetes mellitus increased with the increase of SMFI when SMFI<157.195. The results of subgroup analysis showed that female, under 60 years old, body mass index<24 kg/m 2, uric acid<420 μmol/L, low SMI, non-smoking, non-alcoholic drinking, and non-hypertensive individuals with higher SMFI were more likely to develop type 2 diabetes mellitus. Conclusions:Elevated SMFI is positively associated with insulin resistance and type 2 diabetes mellitus risk. Reducing SMFI below 157.195 may significantly lower the risk of type 2 diabetes mellitus.

Objective·To explore the correlation between comorbidities of chronic non-communicable diseases(chronic diseases),phase angle(PhA),and muscle mass decline associated with sarcopenia in the elderly,and the predictive value of chronic disease comorbidities and PhA in muscle mass decline in the elderly.Methods·By retrospectively screening inpatients aged≥60 years who were admitted to the Department of Geriatrics,Renji Hospital,Shanghai Jiao Tong University School of Medicine from August 1,2018 to July 31,2019,basic information and medical history of the patients(gender,age,number of medications used,number of comorbidities,presence of osteoporosis,smoking history,etc.)were collected,as well as laboratory examination indicators(hemoglobin,albumin,serum creatinine,serum uric acid,ferritin,vitamin D,triacylglycerol,total cholesterol,high-density lipoprotein,low-density lipoprotein,etc.).The age-adjusted Charlson comorbidity index(aCCI)was calculated.The InBody S10 bioelectrical impedance body composition detector was used to test the body composition.Body mass index(BMI),skeletal muscle mass index(SMI),and PhA were collected.Some patients underwent measurement of grip strength.Muscle mass decline was diagnosed by using the SMI values recommended by the 2019 Asian Working Group for Sarcopenia(AWGS)(≤7.0 kg/m2 for males and≤5.7 kg/m2 for females).According to the measured SMI values,patients were divided into a group with normal muscle mass and a group with muscle mass decline.Univariate and multivariate Logistic analyses were employed to investigate the risk factors associated with muscle mass decline related to sarcopenia in the elderly.Additionally,the receiver operator characteristic(ROC)curve and the area under the curve were utilized to predict the significance of these factors in muscle mass decline.Results·A total of 359 chronic disease patients were enrolled,including 226 males and 133 females.There were 241 cases in the normal muscle mass group and 118 cases in the muscle mass decline group.The incidence of muscle mass decline related to sarcopenia in the elderly was 32.9%.The univariate Logistic regression analysis showed that age(OR=1.036,95%CI 1.013?1.060),comorbidities(OR=1.117,95%CI 1.025?1.217),aCCI(OR=1.123,95%CI 1.031?1.222),and high-density lipoprotein(OR=3.688,95%CI 2.065?6.622)were positively correlated with the risk of muscle mass decline in the elderly.BMI(OR=0.514,95%CI 0.443?0.597),PhA(OR=0.195,95%CI 0.126?0.303),hemoglobin(OR=0.984,95%CI 0.972?0.996)and triacylglycerol(OR=0.606,95%CI 0.424?0.866)were negatively correlated with the risk of muscle mass decline in the elderly.Multivariate Logistic regression model indicated that PhA(OR=0.338,95%CI 0.119?0.959)and BMI(OR=0.634,95%CI 0.476?0.844)were negatively correlated with the risk of muscle mass decline in elderly.The area under the ROC curve for predicting muscle mass decline related to sarcopenia in elderly by using BMI and PhA was 0.893(95%CI 0.855?0.931)and 0.786(95%CI 0.736?0.837),respectively.The sensitivity was 0.724 and 0.676,respectively.The specificity was 0.916 and 0.762,respectively.When BMI combined with PhA predicted muscle mass decline in the elderly,the area under the ROC curve was 0.917(95%CI 0.883?0.951).The sensitivity was 0.867,and the specificity was 0.860.Conclusion·aCCI is correlated with muscle mass decline associated with sarcopenia in the elderly.As BMI and PhA decrease,the risk of muscle mass decline in the elderly increases.The combination of BMI and PhA has a high predictive value in muscle mass decline in the elderly.No predictive value of chronic diseases comorbidities in muscle mass decline related to sarcopenia in the elderly is found.

Objective·To investigate the current situation and distribution characteristics of chronic comorbidities,and to provide reference for further improving the self-management of comorbidities and implementing the whole course and all-round management of comorbidity.Methods·Two thousand and forty-five inpatients in the Department of Geriatrics,Renji Hospital,Shanghai Jiao Tong University School of Medicine were enrolled in this study from December 2020 to February 2023.The general vital signs,routine laboratory examination and disease status were collected.The epidemiological distribution characteristics of chronic diseases and comorbidities were analyzed.Results·The incidence of chronic diseases in the surveyed population was 99.6％,and the incidence of comorbidities was 94.2％.The top 5 chronic diseases were hypertension(43.68％),diabetes mellitus(24.81％),malignant tumor(21.48％),hyperlipidemia(18.38％)and coronary heart disease(11.99％).The detection rates of hypertension,diabetes mellitus,coronary heart disease,chronic obstructive pulmonary disease,stoke and chronic kidney disease in males were significantly higher than those in females(P＜0.05).The proportion of patients with 5 chronic diseases was the highest(11.99％),followed by 7 chronic diseases(10.26％)and 6 chronic diseases(10.04％).Among the patients of different ages,the comorbidity rate was the highest in the patients aged 50-59 years(27.78％).In different age groups,patients aged 50 to 59 with 2 chronic diseases had the highest incidence of comorbidity,which was as high as 40.82％.Although the overall proportion of comorbidities among male patients(95.37％)was higher than that among females(93.77％),there was no statistically significant difference(P=0.125).However,the proportions of male patients with 2 and 5 chronic diseases were 70.41％and 60.63％,respectively,which were significantly higher than those of female patients(29.59％and 39.37％).The correlations between coronary heart disease and diabetes mellitus,hypertension and coronary heart disease,hypertension and diabetes mellitus were higher(r=0.24,r=0.27,r=0.35,all P＜0.05).Conclusion·The prevalence of chronic diseases and comorbidities is high in the middle-aged and elderly population,and the number of comorbidities increases significantly with the increase of age.

Objective:Exploring the role and mechanism of gremlin-1 in lipotoxicity-mediated pancreatic β-cell dysfunction.Methods:The model of lipid toxicity-mediated pancreatic β-cell dysfunction was constructed using palmitic acid(PA) to treat mouse pancreatic β-cells(MIN6). Initially, to clarify the effects of lipotoxicity on islet β-cells, the cellular lipid deposition and changes in the levels of insulin caused by PA were detected. The effects of PA on gremlin-1 expression and its downstream signaling pathway BMPs/Smads were further investigated using qPCR and Western Blot assay. Subsequently, recombinant mouse gremlin-1 protein and BMP signaling pathway inhibitor LDN193189 were used to intervene the cells to explore the effects of gremlin-1 and its downstream signaling pathway BMPs/Smads on pancreatic islet β-cells.Results:PA could reduce pancreatic β-cell viability and insulin secretion capacity( P<0.05). Meanwhile, PA inhibited the expression and secretion of cell gremlin-1 and upregulated BMP-4 and its downstream Smad-1 and Smad-5( P<0.05). Intervention of cells with recombinant mouse gremlin-1 protein resulted in a significant elevation of insulin secretion and a concomitant decrease in the expression of key molecules in the BMP4/Smads signaling pathway( P<0.05). And inhibition of the BMP4/Smads signaling pathway ameliorated PA-induced pancreatic β-cell dysfunction. Conclusion:Gremlin-1 is involved in the regulation of lipotoxicity-mediated pancreatic islet β-cell dysfunction, and this effect may be associated with activation of BMP4/Smads signaling pathway.

Objective:To investigate the association of skeletal muscle fat index(SMFI) with insulin resistance and type 2 diabetes mellitus.Methods:This retrospective study included 1 100 patients from Department of Geriatric, Renji Hospital, Shanghai Jiao Tong University School of Medicine from October 2020 to October 2023. The SMFI measured by chest computed tomography(CT) is used as an indicator to evaluate the quality of skeletal muscle. Spearman and linear regression analysis were used to evaluate the association between SMFI and homoeostasis model assessment for insulin resistance(HOMA-IR). Multivariate logistic regression analysis and restricted cubic spline(RCS) were used to analyze the association of HOMA-IR with the prevalence of type 2 diabetes mellitus. Finally, interaction and stratified analyses were conducted according to age, sex, body mass index, estimated glomerular filtration rate, uric acid, skeletal muscle index(SMI), smoking, alcohol drinking, hypertension, and dyslipidemia.Results:SMFI showed a significant positive correlation with HOMA-IR( r=0.385, P<0.001). Each 1-unit increase in SMFI led to 0.009, 0.011, 0.004, 0.004, and 0.004 rise in HOMA-IR across adjusted models(all P<0.05). Then subjects were stratified into tertiles( T1, T2, and T3) according to SMFI. Logistic regression analysis revealed that the prevalence of type 2 diabetes mellitus significantly increased in T2( OR=2.37, 95% CI 1.30-4.31, P=0.005) and T3( OR=2.85, 95% CI 1.39-5.86, P=0.004) compared to T1. RCS analysis showed that the prevalence of type 2 diabetes mellitus increased with the increase of SMFI when SMFI<157.195. The results of subgroup analysis showed that female, under 60 years old, body mass index<24 kg/m 2, uric acid<420 μmol/L, low SMI, non-smoking, non-alcoholic drinking, and non-hypertensive individuals with higher SMFI were more likely to develop type 2 diabetes mellitus. Conclusions:Elevated SMFI is positively associated with insulin resistance and type 2 diabetes mellitus risk. Reducing SMFI below 157.195 may significantly lower the risk of type 2 diabetes mellitus.