{"paper_id":"14b40432-5fe4-4858-b0e8-7036b3bb60dc","body_text":"Lipoprotein profile as a predictor of type 2 diabetes with sarcopenia: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lipoprotein profile as a predictor of type 2 diabetes with sarcopenia: A cross-sectional study Ting Tang, Junjie Hao, Qingyan Yang, Guodan Bao, Zhong-Ping Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5409255/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Endocrine → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose This study investigated the relationship between lipoprotein profiles and sarcopenia in patients with type 2 diabetes mellitus (T2DM). The objective is to provide a solid theoretical foundation and treatment strategies for clinical prevention and management of diabetes, particularly in individuals with concurrent sarcopenia. Methods In this study, we selected inpatients aged over 60 years diagnosed with T2DM who were admitted to the Department of Geriatrics at Qinghai University Affiliated Hospital from July 2023 to June 2024 as research subjects. We collected general patient data, including gender, age, ethnicity, height, weight, and calculated body mass index (BMI). Key indices measured included glycated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoproteins A and B (ApoA and ApoB), phospholipids, lipoprotein(a) [Lp(a)], very low-density lipoprotein (VLDL), and free fatty acids (FFA). Additionally, we assessed limb skeletal muscle mass, grip strength, walking speed, and calculated the appendicular skeletal muscle mass index (ASMI). Based on Asian diagnostic criteria for sarcopenia, patients were categorized into a non-sarcopenic group or a group with T2DM combined with sarcopenia. Baseline laboratory data along with ASMI measurements, grip strength assessments, and walking speeds were statistically analyzed for both groups. Results Compared with T2DM patients without sarcopenia, the levels of HbA1c, Lp(a), FFA, serum albumin, TC, TG, HDL-C, ApoA and VLDL in type 2 diabetic patients with sarcopenia were statistically significant (all P < 0.05). When multivariate adjustments were made for these clinical features, age (OR = 1.18, 95%CI: 1.11–1.25, P < 0.001), BMI (OR = 0.81, 95%CI: 0.72–0.91, P < 0.001), ApoA (OR = 0.04, 95%CI: 0.00-0.98, P = 0.048), Lp(a) > 15.5 mg/dL (OR = 3.27, 95%CI: 1.58–6.80, P = 0.001) and FFA > 0.48 g/L (OR = 4.06, 95%CI: 1.96–8.43, P < 0.001) were independent predictors of diabetes mellitus with sarcopenia. ROC curve analysis showed that free fatty acids (AUC = 0.721, 95%CI: 0.660–0.782, P < 0.001) in T2DM with sarcopenia has good predictive value judgment. Conclusion Age, BMI, ApoA, Lp(a), and FFA were independent predictors of T2DM with sarcopenia. Serum free fatty acids have a good predictive value in the judgment of T2DM complicated with sarcopenia. Type 2 diabetes Sarcopenia Lipoprotein profile Risk factors Figures Figure 1 1 Introduction Globally, the prevalence of diabetes has surged dramatically over the past three decades, with increased incidence and mortality making it the ninth leading cause of death impacting human health [ 1 ]. According to the 2024 International Diabetes Federation ( https://www.diabetesatlas.org ), an estimated 537 million individuals worldwide are diagnosed with diabetes, with approximately three-quarters residing in low- and middle-income countries; this figure is projected to rise to 783 million by 2045. Notably, 90% of these patients have type 2 diabetes mellitus (T2DM). T2DM is a chronic metabolic disorder characterized by insulin resistance and a persistent elevation in blood glucose levels. The disruption of blood glucose homeostasis significantly contributes to muscle mass loss and functional decline among elderly patients suffering from T2DM [ 2 ]. Research indicates that T2DM adversely affects protein metabolism, vascular integrity, and mitochondrial function through mechanisms such as insulin resistance, inflammation, accumulation of advanced glycation end products (AGEs), and heightened oxidative stress. These factors collectively impair various aspects of muscle health including mass, strength, and functionality thereby increasing the risk of sarcopenia in individuals with T2DM [ 3 ]. Sarcopenia is recognized as a major complication associated with diabetes, particularly among older adults afflicted by this condition. The coexistence of sarcopenia in T2DM patients not only diminishes their quality of life but also elevates mortality rates while imposing substantial social and economic burdens on healthcare systems globally [ 4 ]. The lipoprotein profile encompasses the composition and distribution of diverse lipoproteins within circulation that play crucial roles in lipid transport, metabolism, and disease pathogenesis. Lipoproteins can be classified into high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein (VLDL), and chylomicrons based on their density[ 5 ]. These entities not only facilitate lipid transport but also engage actively in lipid metabolism significantly influencing cardiovascular health as well as systemic functions throughout the body[ 6 ]. Concurrently, there is growing evidence suggesting that fatty acids along with their derived lipid intermediates are pivotal for regulating skeletal muscle function[ 7 ]. However, the precise relationship between specific components within the lipoprotein profile and sarcopenia among older adults living with T2DM remains inadequately understood. This study aims to investigate correlations between lipoprotein profiles in individuals with T2DM experiencing sarcopenia while providing a robust theoretical foundation for clinical strategies aimed at preventing or treating diabetic-related degeneration accompanied by sarcopenia. 2 Materials and methods 2.1 Patients A total of 297 T2DM patients >60 years old who were hospitalized in the Department of Geriatrics, Affiliated Hospital of Qinghai University from July 2023 to June 2024 were selected as the study subjects. All patients met the 2023 American Diabetes Association criteria for diabetes diagnosis [8]. Exclusion criteria: (1) Type 1 diabetes mellitus, specific type diabetes mellitus. (2) Diabetic ketoacidosis, diabetic hyperosmolar hyperglycemia syndrome, diabetic nephropathy. (3) There are infections or other systemic diseases, such as tumors, serious liver and kidney diseases, systemic immune diseases, etc. (4) Patients with a history of gastrointestinal surgery (5) who were unable to perform grip strength measurements or 6-meter pace tests. (6) Taking glucocorticoid drugs, vitamin D, estrogen, antiepileptic drugs, etc. (7) hyperthyroidism and subclinical hyperthyroidism. This study has been approved by the Research Ethics Committee of the Affiliated Hospital of Qinghai University (approval document No. P-SL-2023-452), and all participants signed informed consent. 2.2 Data collection 2.2.1 General data collection: All subjects fasted for 10 hours, and their height and weight were measured in the morning after fasting, bareheaded, undressed, and taking off shoes. body mass index (BMI) was calculated. 2.2.2 Testing of laboratory indicators: All subjects fasted for 10 hours, fasting venous blood was collected in the morning, and automatic biochemical analyzer was used (Roche, Switzerland) Fasting plasma glucose (FPG), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL‑C), high-density lipoprotein cholesterol (HDL‑C), apolipoprotein A (ApoA), apolipoprotein B (ApoB), phospholipid, lipoprotein(a) [LP(a)], very low-density lipoprotein (VLDL), and free fatty acid (FFA) were measured. HbA1c was determined by liquid chromatography-tandem mass spectrometry. 2.2.3 Test and Diagnostic Criteria for Sarcopenia: (1) A human body composition analyzer (InBody770 model, Korean BioSpace Company) was employed to evaluate appendicular skeletal muscle mass (ASM), defined as the cumulative skeletal muscle mass of both upper and lower limbs. The appendicular skeletal muscle mass index (ASMI) is computed as ASM (kg)/height² (m²). (2) Grip strength was assessed using a JAMAR grip dynamometer (Patterson Medical, Warrenville, IL, USA): All participants were seated in an upright position with their feet resting naturally on the ground, knees flexed at 90°, elbows bent at 90°, upper arms positioned against their chest, forearms oriented neutrally, and wrists extended between 0° to 30°. Maximum grip strength was recorded three times for each side in kilograms. (3) Physical function assessment was conducted utilizing the 6-meter walking test: A straight line measuring 12 meters was marked on flat ground with precise indicators at the starting point, the 3-meter mark, the 9-meter mark, and the endpoint. Participants commenced walking from the starting point; timing initiated upon reaching the 3-meter mark and concluded at the 9-meter mark. Each participant underwent three trials with only their fastest time being considered as the final result. According to the diagnostic criteria for Asian sarcopenia[9], sarcopenia is defined as low ASMI and/or low grip strength and low pace. Low ASMI: male ASMI <7.0 kg/m 2 , female ASMI <5.7 kg/m 2 ; Low grip strength: male grip strength <28kg, female grip strength <18kg; Low step speed: <1 m/s. 2.2.4 Grouping: According to the diagnostic criteria for sarcopenia, the subjects were divided into diabetic non-sarcopenia group (n=208, of which 36.06% were female) and diabetic combined sarcopenia group (n=89, of which 52.81% were female). 2.3 Statistical analysis Data from a normal distribution are presented as mean ± standard deviation (x̅ ± s) alongside the sample size (percentage), whereas data from a skewed distribution are represented by median values (P25, P75) and sample size (percentage). Independent samples t-tests and non-parametric tests were employed to compare measurement data conforming to normal and skewed distributions, respectively. Categorical variables were analyzed using chi-square tests. Furthermore, both univariate and multivariate logistic regression analyses were conducted to assess the impact of relevant variables on diabetes in individuals with sarcopenia. Biased distribution variables were transformed into binary variables for logistic regression analysis, with cutoff values determined through receiver operating characteristic (ROC) curve analysis. Statistical significance was defined as P < 0.05, with all P- values being two-tailed. All statistical analyses were performed using SPSS software version 25 (IBM Corporation, Armonk, NY, United States). 3 Results 3.1 Comparison of general data of T2DM patients with non-sarcopenia and T2DM patients with sarcopenia A total of 297 T2DM patients over 60 years old were included in this study, with an average age of (70.28 ± 6.66) years, including 208 cases in the non-sarcopenia group with a composition ratio of 70% (208/297) and 87 cases in the T2DM combined sarcopenia group with a composition ratio of 30% (87/297). Compared with T2DM non-sarcopenia group, T2DM patients with sarcopenia had higher age, lower BMI, longer diabetes course, and a higher proportion of women and hypertension ( P < 0.05, Table 1 ). Compared with T2DM non-sarcopenia group, the glycated hemoglobin, Lp(a) and FFA of T2DM patients with sarcopenia were increased, while the serum albumin, TC, TG, HDL, ApoA and VLDL were decreased ( P < 0.05, Table 1 ). Table 1 Clinical characteristics of the study participants Variables T2DM-NSM (n = 208) T2DM-SM (n = 89) P value Age (years) 68.23 ± 5.81 75.07 ± 6.08 < .001 Female (%) 75 (36.06) 47 (52.81) 0.007 The Han nationality (%) 143 (68.75) 67 (75.28) 0.257 BMI (kg/m 2 ) 24.82 ± 2.95 22.60 ± 3.23 < .001 Smoking (%) 73 (35.10) 19 (21.35) 0.019 Drinking (%) 76 (36.54) 25 (28.09) 0.159 Hypertension (%) 89 (42.79) 50 (56.18) 0.034 Diabetes course (years) 5.00 (1.00, 10.00) 10.00 (2.00, 17.00) < .001 FPG (mmol/L) 8.14 ± 2.93 7.66 ± 3.01 0.206 HbA1c (%) 7.72 ± 1.40 8.26 ± 1.41 0.003 Total protein (g/L) 67.18 ± 5.98 65.91 ± 7.69 0.126 Serum albumin (g/L) 41.40 ± 4.21 38.22 ± 5.08 < .001 TC (mmol/L) 4.13 ± 0.99 3.77 ± 0.98 0.004 TG (mmol/L) 2.04 ± 1.16 1.56 ± 0.86 < .001 HDL-C (mmol/L) 0.97 ± 0.20 0.90 ± 0.18 0.005 LDL-C (mmol/L) 2.55 ± 0.88 2.42 ± 0.87 0.247 ApoA (g/L) 1.06 ± 0.17 0.97 ± 0.16 < .001 ApoB (g/L) 0.75 ± 0.18 0.74 ± 0.29 0.834 Phospholipid (mmol/L) 2.81 ± 0.89 2.83 ± 0.91 0.876 Lp(a) (mg/dL) 11.00 (6.00, 18.00) 17.00 (9.00, 32.00) < .001 VLDL (mmol/L) 0.89 ± 0.42 0.70 ± 0.44 < .001 FFA (g/L) 0.42 (0.32, 0.58) 0.61 (0.45, 0.75) < .001 Step speed (m/s) 1.10 ± 0.31 1.76 ± 0.54 < .001 ASMI (kg/m 2 ) 7.57 ± 0.89 5.70 ± 0.70 < .001 Grip strength (kg) 25.31 ± 7.90 15.56 ± 6.02 < .001 T2DM-NSM, T2DM patients with non-sarcopenia group; T2DM-SM, T2DM patients with sarcopenia group; BMI, body mass index; FPG, Fasting plasma glucose; HbA1c, glycated hemoglobin; TC, total cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; ApoA, apolipoprotein A; ApoB, apolipoprotein B; Lp(a), Lipoprotein(a); VLDL, very low-density lipoprotein; FFA, free fatty acid. 3.2 Diagnostic efficacy of independent predictors of diabetes mellitus with sarcopenia The ROC curve analysis was employed to evaluate the diagnostic efficacy of ApoA, TG, TC, VLDL, Lp(a), and FFA in patients with diabetes mellitus accompanied by sarcopenia. (Fig. 1 ). Among them, the area under the TG level curve (AUC) in Fig. 1 (a) was the largest. The sensitivity and specificity of 1.73 mmol/L TG level to distinguish diabetic patients with non-sarcopenia and diabetic patients with sarcopenia were 70.8% and 53.4% (AUC = 0.642, 95%CI: 0.574–0.701, P < 0.001). Figure 1 (b) shows the largest area under the FFA level curve (AUC), with a sensitivity of 70.8% and specificity of 60.1% for 0.48 g/L FFA level to distinguish diabetic patients with non-sarcopenia from diabetic patients with sarcopenia (AUC = 0.721, 95%CI: 0.660–0.782, P < 0.001). The relevant parameters of ROC curve analysis are shown in Table 2 . Table 2 ROC curve information of continuous predictors of diabetes combined with sarcopenia Variables AUC(95% CI ) P value Optimal cutoff Sensitivity Specificity Youden index Age 0.792(0.734–0.851) < 0.001 71.50 0.775 0.721 0.496 BMI 0.705(0.639–0.772) < 0.001 23.94 0.719 0.649 0.368 TC 0.612(0.542–0.682) 0.002 4.31 0.764 0.413 0.178 TG 0.642(0.574–0.710) < 0.001 1.73 0.708 0.534 0.242 ApoA 0.634(0.565–0.703) < 0.001 1.06 0.730 0.452 0.182 VLDL 0.650(0.579–0.720) < 0.001 0.63 0.539 0.707 0.246 Lp(a) 0.655(0.589–0.721) < 0.001 15.50 0.551 0.697 0.248 FFA 0.721(0.660–0.782) < 0.001 0.48 0.708 0.601 0.309 Diabetes course 0.643(0.575–0.712) < 0.001 9.50 0.551 0.673 0.224 3.3 Binary logistic regression analysis of influencing factors of T2DM with sarcopenia For variables with statistical significance between basic data and laboratory test results ( P < 0.05), univariate and multivariate logistic regression analysis was used for descriptive analysis, and the results were shown in Table 3 . Univariate logistic regression analysis showed that age, sex, BMI, smoking, hypertension, diabetes duration, HBA1c, serum albumin, TC, TG, HDL, ApoA, Lp(a), VLDL, and FFA were associated with an increased or decreased risk of diabetes with sarcopenia. After multivariate adjustment, it was found that age (OR = 1.18, 95%CI: 1.11–1.25, P < 0.001), BMI (OR = 0.81, 95%CI: 0.72–0.91, P < 0.001), ApoA (OR = 0.04, 95%CI: 0.00-0.98, P = 0.048), Lp(a) > 15.5 mg/dL (OR = 3.27, 95%CI: 1.58–6.80, P = 0.001) and FFA > 0.48 g/L (OR = 4.06, 95%CI: 1.96–8.43, P < 0.001) were independent predictors of diabetes with sarcopenia. (Table 3 ). Table 3 Odds ratios for the presence of T2DM-SM derived from univariate and multivariate logistic regression analyses Variables Univariate analysis Multivariate analysis OR (95%CI) P value OR (95%CI) P value Age (years) 1.20 (1.14 ~ 1.26) < .001 1.18 (1.11 ~ 1.25) < .001 Female sex 1.98 (1.20 ~ 3.28) 0.008 1.71 (0.73 ~ 3.99) 0.217 The Han nationality (%) 0.72 (0.41 ~ 1.27) 0.258 BMI (kg/m²) 0.78 (0.72 ~ 0.86) < .001 0.81 (0.72 ~ 0.91) < .001 Smoking (%) 0.50 (0.28 ~ 0.90) 0.020 0.79 (0.31 ~ 2.02) 0.621 Drinking (%) 0.68 (0.39 ~ 1.17) 0.160 Hypertension (%) 1.71 (1.04 ~ 2.83) 0.035 1.38 (0.67 ~ 2.82) 0.382 Diabetes course > 9.5years 2.52 (1.52 ~ 4.19) < .001 2.00 (0.95 ~ 4.21) 0.066 FPG (mmol/L) 0.94 (0.86 ~ 1.03) 0.206 HbA1c (%) 1.29 (1.08 ~ 1.53) 0.004 1.20 (0.95 ~ 1.53) 0.132 Total protein (g/L) 0.97 (0.93 ~ 1.01) 0.129 Serum albumin (g/L) 0.86 (0.81 ~ 0.91) < .001 0.99 (0.92 ~ 1.08) 0.896 TC (mmol/L) 0.68 (0.51 ~ 0.89) 0.005 0.99 (0.60 ~ 1.65) 0.973 TG (mmol/L) 0.60 (0.44 ~ 0.81) 0.001 0.82 (0.51 ~ 1.31) 0.400 HDL-C (mmol/L) 0.14 (0.03 ~ 0.56) 0.006 0.90 (0.04 ~ 19.46) 0.946 LDL-C (mmol/L) 0.84 (0.63 ~ 1.13) 0.246 ApoA (g/L) 0.03 (0.01 ~ 0.17) < .001 0.04 (0.00 ~ 0.98) 0.048 ApoB (g/L) 0.88 (0.28 ~ 2.77) 0.833 Phospholipid (mmol/L) 1.02 (0.78 ~ 1.35) 0.876 Lp(a) > 15.5 mg/dL 2.82 (1.69 ~ 4.70) < .001 3.27 (1.58 ~ 6.80) 0.001 VLDL (mmol/L) 0.31 (0.16 ~ 0.60) < .001 0.76 (0.26 ~ 2.18) 0.605 FFA > 0.48 g/L 3.91 (2.26 ~ 6.77) < .001 4.06 (1.96 ~ 8.43) < .001 4 Discussion T2DM with sarcopenia is a disease characterized by hyperglycemia and poor skeletal muscle mass function, which seriously affects the general health and quality of life of the elderly. Its pathophysiology involves inflammation, oxidative stress, mitochondrial and endothelial dysfunction, and lipid and glucose metabolism abnormalities. Previous studies on T2DM with sarcopenia mostly focused on inflammation, oxidative stress, mitochondrial and endothelial dysfunction and glucose metabolism, while few studies on lipid metabolism were reported. Studies have shown that lipoproteins also play an important role in the onset and development of diabetes as well as sarcopenia [ 10 , 11 ]. Therefore, understanding the relationship between the subcomponents of lipoprotein spectrum and T2DM with sarcopenia is of great significance for the prevention and treatment of diabetes with sarcopenia. A total of 297 T2DM patients over 60 years old (67.7 ± 6.99) years old were included in this study, including 208 T2DM patients with non-sarcopenia (70%) and 87 T2DM patients with sarcopenia (30%). Compared with T2DM non-sarcopenia group, T2DM patients with sarcopenia had higher age, lower BMI, longer duration of diabetes, and a higher proportion of women and hypertension. This study also found that in the single-factor study, compared with the T2DM non-sarcopenia group, the glycated hemoglobin, Lp(a) and FFA of T2DM patients with sarcopenia were increased, and the serum albumin, TC, TG, HDL, apolipoprotein A and very low-density lipoprotein were decreased. Among them, the age difference in this study was consistent with the results of Lu L et al. [ 12 ], the gender difference was consistent with the conclusions of Bi B et al. [ 13 ], and the differences in TG and HDL were consistent with the results of Fu Q et al. [ 14 ]. It should be noted that the results of TG and BMI studies conducted by Jiang Y et al. [ 15 ] were consistent with ours, but the results of HDL were different from ours. In addition, the difference of very low-density lipoprotein we found was also consistent with the results of significant reduction of very low-density lipoprotein concentration in patients with sarcopenia found by Yin M et al. [ 16 ]. After multivariate adjustment for multiple factors, we found that age, BMI, apolipoprotein A, Lp(a), and FFA were independent predictors of T2DM with sarcopenia. Among them, age as an independent risk factor for diabetes combined with sarcopenia was consistent with previous studies by Chen, H et al.[ 17 ]. T2DM is most common in the elderly and is associated with age-related diseases, including the skeletal muscle disease sarcopenia[ 18 ]. Sarcopenia is an age-related disease characterized by a decline in muscle mass and/or strength. With age, there is an increase in adipose tissue buried within muscle and bone marrow, which changes body composition and leads to a decrease in muscle mass and bone density [ 19 ]. This is due to the fact that with the growth of age, the ability of mitochondria in muscle to catalyze fatty acid metabolism decreases, leading to the acceleration of insulin resistance and the disorder of lipid and sugar metabolism [ 20 ]. The breakdown and release of lipids is also affected by age-related inflammation. Al Saedi A et al. [ 21 ] found that the gradual decline of fatty triglyceride lipase in the skeletal muscle of the elderly is the driving factor of oxidation/inflammation in sarcopenia. In situations of energy need, such as fasting, catecholamines are sent by the sympathetic nervous system to begin the lipolysis of triglycerides stored in fat cells. However, in elderly visceral white adipose tissue (vWAT), the expression of catecholamine-degrading enzymes in adipose macrophages is elevated, preventing the signal from reaching adipose cells[ 22 ]. Altered gene expression in macrophages is driven by inflammasome containing pyrin domain 3 (NLRP3), an innate immune sensor that can be activated by a variety of pathogenic or endogenous molecules. NLRP3 deletion can save fasting induced vWAT lipolysis in elderly mice [ 22 ]. The exact trigger of the NLRP3 inflammasome in senile WAT is unknown, but a range of factors, including circulating fatty acids and ceramides, intracellular organelles dysfunction, and uncontrolled post-translational acetylation, may contribute to its activation[ 23 ]. Notably, our study found that FFA is also a critical independent risk factor for diabetes with sarcopenia. After analyzing the characteristics of 160 adult T2MD patients, Fu J et al. [ 24 ]found that FFAs was negatively correlated with muscle indexes of adult T2DM. FFAs may play an important role in the pathological process of muscle dysfunction in adult T2MD patients. FFA plays a vital role as an energy substrate and forms a key component of cell membranes. There is growing evidence that FFA and other intermediates in triglyceride lipid metabolism play an important role in regulating skeletal muscle mass and function. Sarcopenia is characterized by a reduction in stem cells and terminally differentiated muscle fibers, replaced by fat and fibrous tissue. Lipids and their derivatives accumulate in muscle cells and the intercellular chamber, further promoting lipid toxicity and inducing oxidative stress, mitochondrial dysfunction, inflammation, and insulin resistance. The study of Love KM et al. [ 25 ] also proved that clinically relevant increase in plasma FFA concentration could induce arterial insulin resistance, which would lead to reduced muscle microvascular perfusion and thus sarcopenia. In addition, free fatty acid receptors are widely expressed in the human body and have been shown to regulate a variety of biological processes. FFAR mediated signal transduction is related to metabolic processes such as insulin secretion by pancreatic beta cells, incretin secretion by intestinal endocrine cells, food intake regulation and adipose tissue biology. These receptors are considered attractive therapeutic targets for metabolic disorders such as obesity and T2DM [ 26 ]. The reduction of fatty acid catabolism affects the production of adenosine triphosphate (ATP) in mitochondria by reducing the activity of the tricarboxylic acid cycle, and may cause abnormal accumulation of adipose tissue with high oxidation and ectopic lipid deposition, which in turn is harmful to skeletal muscle [ 27 ]. It can be seen that the treatment of FFA can be a new target for diabetes complicated with sarcopenia [ 28 ]. This study found that compared with the T2DM non-sarcopenia group, the BMI of the T2DM combined sarcopenia group was lower, which was consistent with the findings of Yamada T et al. [ 29 ]. Obesity (as assessed by BMI) is associated with lipid metabolism in older adults. A higher BMI is often considered a risk factor for many diseases; However, a recent cross-sectional study suggests that an increase in BMI, a parameter related to lipid metabolism, within the normal reference range, may have a protective effect against sarcopenia [ 10 ], suggesting that obesity may lead to a better prognosis, which is known as the \"obesity paradox\". In addition, this study also found that Lp(a) is an independent risk factor for diabetes combined with sarcopenia, which is consistent with the conclusion of the study of Li X et al. [ 30 ]. They analyzed 461 elderly T2DM patients (34 patients with Asian sarcopenia diagnosis), 427 patients with no symptoms of sarcopenia) Using univariate/multivariate logistic regression analysis of the influencing factors of muscle loss in elderly T2MD patients, it was found that Lp(a) was closely related to the occurrence and development of muscle loss in elderly T2DM patients, and Lp(a) was a risk factor for muscle loss in elderly T2DM patients. The study of Gong H[ 31 ] also confirmed that high level of Lp(a) was closely related to the occurrence of sarcopenia. Lp(a) can cause loss of muscle mass, strength, and function by regulating inflammation. Chronic inflammation mainly acts on muscle loss by accelerating catabolism, decreasing appetite, increasing insulin resistance, and decreasing growth factor and insulin-like growth factor-1 levels[ 30 ]. This study used single-center data to analyze the proportion of elderly T2DM patients complicated with sarcopenia and analyzed the relationship between each component of lipoprotein spectrum and diabetes mellitus complicated with sarcopenia and made a beneficial discussion on T2DM complicated with sarcopenia and its causes and influencing factors. However, this study still has some limitations: (1) As this study is a single-center study, whether the results can be widely extrapolated to other regions and populations needs further verification; (2) The patients in this study were plateau residents, so the differences in lipoprotein profiles caused by plateau environment and diet should be considered; (3) The patients in this study were older and less educated, and they could not clearly describe the use of related hypoglycemic drugs before admission, resulting in the lack of use of hypoglycemic drugs before admission; Taken together, the results of this study show that age, BMI, apolipoprotein A, Lp(a), and FFA are independently associated with the risk of sarcopenia in elderly patients with T2DM. This study has enriched our understanding of sarcopenia in elderly patients with T2DM and revealed the correlation between lipoprotein profile and sarcopenia and its components, providing a new target and a new direction for the treatment of sarcopenia. Abbreviations BMI body mass index HbA1c glycated hemoglobin TC total cholesterol TG triglyceride HDL-C high-density lipoprotein cholesterol LDL-C low density lipoprotein cholesterol ApoA apolipoprotein A ApoB apolipoprotein B Lp(a) lipoprotein(a) VLDL very low-density lipoprotein FFA free fatty acid CI confidence interval OR odds ratio Declarations Compliance with ethical standards Conflict of interest The authors declare no competing interests. Ethical approval Ethics approval was obtained from the Affiliated Hospital of Qinghai University (No. P-SL-2023-452). The study was performed in accordance with Declaration of Helsinki. Informed consent This study has been approved by the Research Ethics Committee of the Affiliated Hospital of Qinghai University (approval document No. P-SL-2023-452), and all participants signed informed consent. Funding This study was funded by the National Natural Science Foundation of China (82260795). Author Contribution T.T., J.H., and Z.W. participated in the design of the study. T.T. wrote the manuscript. T.T., Q.Y., and G.B. collected and analyzed the data. J.H., Q.Y., and Z.W. contributed to interpretation of data and preparation of the manuscript. All authors read and approved the final manuscript. Data Availability Data is provided within the manuscript or supplementary information files References Watkins, D.A. and M.K. Ali, Measuring the global burden of diabetes: implications for health policy, practice, and research . Lancet, 2023. 402(10397): p. 163–165. Demir, S., et al., Emerging Targets in Type 2 Diabetes and Diabetic Complications . 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Fu, Q., et al., The correlation of triglyceride/high-density lipoprotein cholesterol ratio with muscle mass in type 2 diabetes patients . BMC Endocr Disord, 2023. 23(1): p. 93. Lee, Y.A., H.N. Kim, and S.W. Song, Associations between Hair Mineral Concentrations and Skeletal Muscle Mass in Korean Adults . J Nutr Health Aging, 2022. 26(5): p. 515–520. Yin, M., et al., Determination of skeletal muscle mass by aspartate aminotransferase / alanine aminotransferase ratio, insulin and FSH in Chinese women with sarcopenia . BMC Geriatr, 2022. 22(1): p. 893. Chen, H., et al., Utilize multi-metabolic parameters as determinants for prediction of skeletal muscle mass quality in elderly type2 diabetic Chinese patients . BMC Geriatr, 2024. 24(1): p. 325. Terada, T., et al., Sex-specific associations of fat mass and muscle mass with cardiovascular disease risk factors in adults with type 2 diabetes living with overweight and obesity: secondary analysis of the Look AHEAD trial . Cardiovasc Diabetol, 2022. 21(1): p. 40. Von Bank, H., C. Kirsh, and J. Simcox, Aging adipose: Depot location dictates age-associated expansion and dysfunction . Ageing Res Rev, 2021. 67: p. 101259. Carcelén-Fraile, M.D.C., et al., Does an Association among Sarcopenia and Metabolic Risk Factors Exist in People Older Than 65 Years? A Systematic Review and Meta-Analysis of Observational Studies . Life (Basel), 2023. 13(3). Al Saedi, A., et al., Lipid metabolism in sarcopenia . Bone, 2022. 164: p. 116539. Camell, C.D., et al., Inflammasome-driven catecholamine catabolism in macrophages blunts lipolysis during ageing . Nature, 2017. 550(7674): p. 119–123. He, M., et al., An Acetylation Switch of the NLRP3 Inflammasome Regulates Aging-Associated Chronic Inflammation and Insulin Resistance . Cell Metab, 2020. 31(3): p. 580–591.e5. Fu, J., et al., Free fatty acids are associated with muscle dysfunction in Chinese adults with type 2 diabetes . Endocrine, 2022. 77(1): p. 41–47. Love, K.M., et al., Impact of Free Fatty Acids on Vascular Insulin Responses Across the Arterial Tree: A Randomized Crossover Study . J Clin Endocrinol Metab, 2024. 109(4): p. 1041–1050. Al Mahri, S., et al., Free Fatty Acid Receptors (FFARs) in Adipose: Physiological Role and Therapeutic Outlook . Cells, 2022. 11(4). Takada, S., H. Sabe, and S. Kinugawa, Abnormalities of Skeletal Muscle, Adipocyte Tissue, and Lipid Metabolism in Heart Failure: Practical Therapeutic Targets . Front Cardiovasc Med, 2020. 7: p. 79. Shalit, A., et al., Nutrition of aging people with diabetes mellitus: Focus on sarcopenia . Maturitas, 2024. 185: p. 107975. Yamada, T., et al., Obesity and risk for its comorbidities diabetes, hypertension, and dyslipidemia in Japanese individuals aged 65 years . Sci Rep, 2023. 13(1): p. 2346. Li, X., X. Kong, and R. Li, Correlation between lipoprotein(a), albuminuria, myostatin and sarcopenia in elderly patients with type 2 diabetes . J Diabetes Complications, 2023. 37(1): p. 108382. Gong, H., et al., Lipoprotein subfractions in patients with sarcopenia and their relevance to skeletal muscle mass and function . Exp Gerontol, 2022. 159: p. 111668. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Endocrine → Version 1 posted Editorial decision: Revision requested 14 Jan, 2025 Reviews received at journal 03 Jan, 2025 Reviews received at journal 25 Dec, 2024 Reviewers agreed at journal 14 Dec, 2024 Reviewers agreed at journal 12 Dec, 2024 Reviewers invited by journal 02 Dec, 2024 Editor assigned by journal 07 Nov, 2024 Submission checks completed at journal 07 Nov, 2024 First submitted to journal 07 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5409255\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":381790267,\"identity\":\"1e503cb6-75ee-43f2-ae03-0de8ba471a47\",\"order_by\":0,\"name\":\"Ting Tang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Affiliated Hospital of Qinghai University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ting\",\"middleName\":\"\",\"lastName\":\"Tang\",\"suffix\":\"\"},{\"id\":381790268,\"identity\":\"9f3ed8d6-809b-4c71-8594-3645ed914ddc\",\"order_by\":1,\"name\":\"Junjie Hao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yunnan University of Traditional Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Junjie\",\"middleName\":\"\",\"lastName\":\"Hao\",\"suffix\":\"\"},{\"id\":381790269,\"identity\":\"42a55607-d020-4e83-91ea-676b09bf6893\",\"order_by\":2,\"name\":\"Qingyan Yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Affiliated Hospital of Qinghai University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qingyan\",\"middleName\":\"\",\"lastName\":\"Yang\",\"suffix\":\"\"},{\"id\":381790270,\"identity\":\"ee7b67e7-5683-483c-99e4-6897f817d910\",\"order_by\":3,\"name\":\"Guodan Bao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qinghai University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Guodan\",\"middleName\":\"\",\"lastName\":\"Bao\",\"suffix\":\"\"},{\"id\":381790271,\"identity\":\"423ea41d-9357-4bad-8c31-55c9aa2d1682\",\"order_by\":4,\"name\":\"Zhong-Ping Wang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACef7+D4d/VPyXsz/eQKQWwxkHDB8znGE2ZjhzgFhrDiQYGzO2MCcy3EggUgdjw4E06cIGtgTGmY833mCosYkmqIWdueGY9MwdPHnM0mnFFgzH0nIbCNtysE2C94xEMZt0jpkEY8NhwloYDiSzSfC2GST2SJ4hWksaszFvW0LiDAkeIrUYzjjD+HDGmQPGBjxAvyQQ4xd5/h6GAx8qDsgZsB/eeONDjQ0RDkMCBhIJpCiHaCFVxygYBaNgFIwMAADt4EHyGtR4PwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Qinghai University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Zhong-Ping\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-11-07 10:53:26\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5409255/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5409255/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s12020-025-04226-7\",\"type\":\"published\",\"date\":\"2025-04-15T15:57:24+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":70925510,\"identity\":\"60b9d996-3a5e-46bf-a1fe-d4824c04dc44\",\"added_by\":\"auto\",\"created_at\":\"2024-12-09 09:09:08\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":243716,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eReceive operating characteristic analysis of serum apolipoprotein A (ApoA), triglycerides (TG), total cholesterol (TC) (a); very low-density lipoprotein (VLDL), lipoprotein(a) [Lp(a)], free fatty acids (FFA) (b) in predicting T2DM-SM\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5409255/v1/f51f45dc8a62fa87b9d0830a.png\"},{\"id\":81050807,\"identity\":\"4427d834-039e-43c3-82b4-1a5696ebe829\",\"added_by\":\"auto\",\"created_at\":\"2025-04-21 16:05:24\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1123796,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5409255/v1/a62219e3-91f3-4ea5-9c4d-0b70dde5daca.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Lipoprotein profile as a predictor of type 2 diabetes with sarcopenia: A cross-sectional study\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eGlobally, the prevalence of diabetes has surged dramatically over the past three decades, with increased incidence and mortality making it the ninth leading cause of death impacting human health [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. According to the 2024 International Diabetes Federation (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.diabetesatlas.org\\u003c/span\\u003e\\u003cspan address=\\\"https://www.diabetesatlas.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), an estimated 537\\u0026nbsp;million individuals worldwide are diagnosed with diabetes, with approximately three-quarters residing in low- and middle-income countries; this figure is projected to rise to 783\\u0026nbsp;million by 2045. Notably, 90% of these patients have type 2 diabetes mellitus (T2DM). T2DM is a chronic metabolic disorder characterized by insulin resistance and a persistent elevation in blood glucose levels. The disruption of blood glucose homeostasis significantly contributes to muscle mass loss and functional decline among elderly patients suffering from T2DM [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Research indicates that T2DM adversely affects protein metabolism, vascular integrity, and mitochondrial function through mechanisms such as insulin resistance, inflammation, accumulation of advanced glycation end products (AGEs), and heightened oxidative stress. These factors collectively impair various aspects of muscle health including mass, strength, and functionality thereby increasing the risk of sarcopenia in individuals with T2DM [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Sarcopenia is recognized as a major complication associated with diabetes, particularly among older adults afflicted by this condition. The coexistence of sarcopenia in T2DM patients not only diminishes their quality of life but also elevates mortality rates while imposing substantial social and economic burdens on healthcare systems globally [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. The lipoprotein profile encompasses the composition and distribution of diverse lipoproteins within circulation that play crucial roles in lipid transport, metabolism, and disease pathogenesis. Lipoproteins can be classified into high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein (VLDL), and chylomicrons based on their density[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. These entities not only facilitate lipid transport but also engage actively in lipid metabolism significantly influencing cardiovascular health as well as systemic functions throughout the body[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Concurrently, there is growing evidence suggesting that fatty acids along with their derived lipid intermediates are pivotal for regulating skeletal muscle function[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. However, the precise relationship between specific components within the lipoprotein profile and sarcopenia among older adults living with T2DM remains inadequately understood. This study aims to investigate correlations between lipoprotein profiles in individuals with T2DM experiencing sarcopenia while providing a robust theoretical foundation for clinical strategies aimed at preventing or treating diabetic-related degeneration accompanied by sarcopenia.\\u003c/p\\u003e\"},{\"header\":\"2 Materials and methods\",\"content\":\"\\u003cp\\u003e2.1 Patients\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 297 T2DM patients \\u0026gt;60 years old who were hospitalized in the Department of Geriatrics, Affiliated Hospital of Qinghai University from July 2023 to June 2024 were selected as the study subjects. All patients met the 2023 American Diabetes Association criteria for diabetes diagnosis\\u0026nbsp;[8]. Exclusion criteria: (1) Type 1 diabetes mellitus, specific type diabetes mellitus. (2) Diabetic ketoacidosis, diabetic hyperosmolar hyperglycemia syndrome, diabetic nephropathy. (3) There are infections or other systemic diseases, such as tumors, serious liver and kidney diseases, systemic immune diseases, etc. (4) Patients with a history of gastrointestinal surgery (5) who were unable to perform grip strength measurements or 6-meter pace tests. (6) Taking glucocorticoid drugs, vitamin D, estrogen, antiepileptic drugs, etc. (7) hyperthyroidism and subclinical hyperthyroidism. This study has been approved by the Research Ethics Committee of the Affiliated Hospital of Qinghai University (approval document No. P-SL-2023-452), and all participants signed informed consent.\\u003c/p\\u003e\\n\\u003cp\\u003e2.2 Data collection\\u003c/p\\u003e\\n\\u003cp\\u003e2.2.1 General data collection: All subjects fasted for 10 hours, and their height and weight were measured in the morning after fasting, bareheaded, undressed, and taking off shoes. body mass index (BMI) was calculated.\\u003c/p\\u003e\\n\\u003cp\\u003e2.2.2 Testing of laboratory indicators: All subjects fasted for 10 hours, fasting venous blood was collected in the morning, and automatic biochemical analyzer was used (Roche, Switzerland) Fasting plasma glucose (FPG), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL‑C), high-density lipoprotein cholesterol (HDL‑C), apolipoprotein A (ApoA), apolipoprotein B (ApoB), phospholipid, lipoprotein(a) [LP(a)], very low-density lipoprotein (VLDL), and free fatty acid (FFA) were measured. HbA1c was determined by liquid chromatography-tandem mass spectrometry.\\u003c/p\\u003e\\n\\u003cp\\u003e2.2.3 Test and Diagnostic Criteria for Sarcopenia: (1) A human body composition analyzer (InBody770 model, Korean BioSpace Company) was employed to evaluate appendicular skeletal muscle mass (ASM), defined as the cumulative skeletal muscle mass of both upper and lower limbs. The appendicular skeletal muscle mass index (ASMI) is computed as ASM (kg)/height\\u0026sup2; (m\\u0026sup2;). (2) Grip strength was assessed using a JAMAR grip dynamometer (Patterson Medical, Warrenville, IL, USA): All participants were seated in an upright position with their feet resting naturally on the ground, knees flexed at 90\\u0026deg;, elbows bent at 90\\u0026deg;, upper arms positioned against their chest, forearms oriented neutrally, and wrists extended between 0\\u0026deg; to 30\\u0026deg;. Maximum grip strength was recorded three times for each side in kilograms. (3) Physical function assessment was conducted utilizing the 6-meter walking test: A straight line measuring 12 meters was marked on flat ground with precise indicators at the starting point, the 3-meter mark, the 9-meter mark, and the endpoint. Participants commenced walking from the starting point; timing initiated upon reaching the 3-meter mark and concluded at the 9-meter mark. Each participant underwent three trials with only their fastest time being considered as the final result. According to the diagnostic criteria for Asian sarcopenia[9], sarcopenia is defined as low ASMI and/or low grip strength and low pace. Low ASMI: male ASMI \\u0026lt;7.0 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, female ASMI \\u0026lt;5.7 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e; Low grip strength: male grip strength \\u0026lt;28kg, female grip strength \\u0026lt;18kg; Low step speed: \\u0026lt;1 m/s.\\u003c/p\\u003e\\n\\u003cp\\u003e2.2.4 Grouping: According to the diagnostic criteria for sarcopenia, the subjects were divided into diabetic non-sarcopenia group (n=208, of which 36.06% were female) and diabetic combined sarcopenia group (n=89, of which 52.81% were female).\\u003c/p\\u003e\\n\\u003cp\\u003e2.3 Statistical analysis\\u003c/p\\u003e\\n\\u003cp\\u003eData from a normal distribution are presented as mean \\u0026plusmn; standard deviation (x̅ \\u0026plusmn; s) alongside the sample size (percentage), whereas data from a skewed distribution are represented by median values (P25, P75) and sample size (percentage). Independent samples t-tests and non-parametric tests were employed to compare measurement data conforming to normal and skewed distributions, respectively. Categorical variables were analyzed using chi-square tests. Furthermore, both univariate and multivariate logistic regression analyses were conducted to assess the impact of relevant variables on diabetes in individuals with sarcopenia. Biased distribution variables were transformed into binary variables for logistic regression analysis, with cutoff values determined through receiver operating characteristic (ROC) curve analysis. Statistical significance was defined as\\u003cem\\u003e\\u0026nbsp;P\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.05, with all \\u003cem\\u003eP-\\u003c/em\\u003evalues being two-tailed. All statistical analyses were performed using SPSS software version 25 (IBM Corporation, Armonk, NY, United States).\\u003c/p\\u003e\"},{\"header\":\"3 Results\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Comparison of general data of T2DM patients with non-sarcopenia and T2DM patients with sarcopenia\\u003c/h2\\u003e \\u003cp\\u003eA total of 297 T2DM patients over 60 years old were included in this study, with an average age of (70.28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.66) years, including 208 cases in the non-sarcopenia group with a composition ratio of 70% (208/297) and 87 cases in the T2DM combined sarcopenia group with a composition ratio of 30% (87/297). Compared with T2DM non-sarcopenia group, T2DM patients with sarcopenia had higher age, lower BMI, longer diabetes course, and a higher proportion of women and hypertension (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Compared with T2DM non-sarcopenia group, the glycated hemoglobin, Lp(a) and FFA of T2DM patients with sarcopenia were increased, while the serum albumin, TC, TG, HDL, ApoA and VLDL were decreased (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eClinical characteristics of the study participants\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT2DM-NSM\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;208)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eT2DM-SM\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;89)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e68.23\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75.07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e75 (36.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e47 (52.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eThe Han nationality (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e143 (68.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67 (75.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.257\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m \\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24.82\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSmoking (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e73 (35.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19 (21.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.019\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDrinking (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e76 (36.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25 (28.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.159\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e89 (42.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50 (56.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.034\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes course (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.00 (1.00, 10.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.00 (2.00, 17.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFPG (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8.14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.93\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.66\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.206\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHbA1c (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.72\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal protein (g/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e67.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.98\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e65.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.126\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum albumin (g/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41.40\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTC (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.13\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.99\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.77\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.98\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTG (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.04\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHDL-C (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL-C (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.55\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.87\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.247\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eApoA (g/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.06\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eApoB (g/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.75\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.74\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.834\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePhospholipid (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.81\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.83\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.876\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLp(a) (mg/dL)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11.00 (6.00, 18.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.00 (9.00, 32.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVLDL (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.89\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.70\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFFA (g/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.42 (0.32, 0.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.61 (0.45, 0.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStep speed (m/s)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.10\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eASMI (kg/m \\u003csup\\u003e2\\u003c/sup\\u003e )\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.70\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGrip strength (kg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25.31\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eT2DM-NSM, T2DM patients with non-sarcopenia group; T2DM-SM, T2DM patients with sarcopenia group; BMI, body mass index; FPG, Fasting plasma glucose; HbA1c, glycated hemoglobin; TC, total cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; ApoA, apolipoprotein A; ApoB, apolipoprotein B; Lp(a), Lipoprotein(a); VLDL, very low-density lipoprotein; FFA, free fatty acid.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Diagnostic efficacy of independent predictors of diabetes mellitus with sarcopenia\\u003c/h2\\u003e \\u003cp\\u003eThe ROC curve analysis was employed to evaluate the diagnostic efficacy of ApoA, TG, TC, VLDL, Lp(a), and FFA in patients with diabetes mellitus accompanied by sarcopenia. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Among them, the area under the TG level curve (AUC) in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e (a) was the largest. The sensitivity and specificity of 1.73 mmol/L TG level to distinguish diabetic patients with non-sarcopenia and diabetic patients with sarcopenia were 70.8% and 53.4% (AUC\\u0026thinsp;=\\u0026thinsp;0.642, 95%CI: 0.574\\u0026ndash;0.701, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e (b) shows the largest area under the FFA level curve (AUC), with a sensitivity of 70.8% and specificity of 60.1% for 0.48 g/L FFA level to distinguish diabetic patients with non-sarcopenia from diabetic patients with sarcopenia (AUC\\u0026thinsp;=\\u0026thinsp;0.721, 95%CI: 0.660\\u0026ndash;0.782, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The relevant parameters of ROC curve analysis are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eROC curve information of continuous predictors of diabetes combined with sarcopenia\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAUC(95%\\u003cem\\u003eCI\\u003c/em\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOptimal cutoff\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSensitivity\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eSpecificity\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYouden\\u003c/p\\u003e \\u003cp\\u003eindex\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.792(0.734\\u0026ndash;0.851)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e71.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.775\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.721\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.496\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.705(0.639\\u0026ndash;0.772)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.94\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.719\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.649\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.368\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.612(0.542\\u0026ndash;0.682)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.764\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.413\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.178\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.642(0.574\\u0026ndash;0.710)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.708\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.534\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.242\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eApoA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.634(0.565\\u0026ndash;0.703)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.730\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.452\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.182\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVLDL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.650(0.579\\u0026ndash;0.720)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.539\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.707\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.246\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLp(a)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.655(0.589\\u0026ndash;0.721)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.551\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.697\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.248\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFFA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.721(0.660\\u0026ndash;0.782)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.708\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.601\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.309\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes course\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.643(0.575\\u0026ndash;0.712)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.551\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.673\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.224\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Binary logistic regression analysis of influencing factors of T2DM with sarcopenia\\u003c/h2\\u003e \\u003cp\\u003eFor variables with statistical significance between basic data and laboratory test results (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), univariate and multivariate logistic regression analysis was used for descriptive analysis, and the results were shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. Univariate logistic regression analysis showed that age, sex, BMI, smoking, hypertension, diabetes duration, HBA1c, serum albumin, TC, TG, HDL, ApoA, Lp(a), VLDL, and FFA were associated with an increased or decreased risk of diabetes with sarcopenia. After multivariate adjustment, it was found that age (OR\\u0026thinsp;=\\u0026thinsp;1.18, 95%CI: 1.11\\u0026ndash;1.25, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), BMI (OR\\u0026thinsp;=\\u0026thinsp;0.81, 95%CI: 0.72\\u0026ndash;0.91, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), ApoA (OR\\u0026thinsp;=\\u0026thinsp;0.04, 95%CI: 0.00-0.98, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.048), Lp(a)\\u0026thinsp;\\u0026gt;\\u0026thinsp;15.5 mg/dL (OR\\u0026thinsp;=\\u0026thinsp;3.27, 95%CI: 1.58\\u0026ndash;6.80, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001) and FFA\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.48 g/L (OR\\u0026thinsp;=\\u0026thinsp;4.06, 95%CI: 1.96\\u0026ndash;8.43, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) were independent predictors of diabetes with sarcopenia. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eOdds ratios for the presence of T2DM-SM derived from univariate and multivariate logistic regression analyses\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eUnivariate analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eMultivariate analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOR (95%CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eOR (95%CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.20 (1.14\\u0026thinsp;~\\u0026thinsp;1.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.18 (1.11\\u0026thinsp;~\\u0026thinsp;1.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale sex\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.98 (1.20\\u0026thinsp;~\\u0026thinsp;3.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.71 (0.73\\u0026thinsp;~\\u0026thinsp;3.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.217\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eThe Han nationality (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.72 (0.41\\u0026thinsp;~\\u0026thinsp;1.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.258\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m\\u0026sup2;)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.78 (0.72\\u0026thinsp;~\\u0026thinsp;0.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.81 (0.72\\u0026thinsp;~\\u0026thinsp;0.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSmoking (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.50 (0.28\\u0026thinsp;~\\u0026thinsp;0.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.020\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.79 (0.31\\u0026thinsp;~\\u0026thinsp;2.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.621\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDrinking (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.68 (0.39\\u0026thinsp;~\\u0026thinsp;1.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.160\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.71 (1.04\\u0026thinsp;~\\u0026thinsp;2.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.035\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.38 (0.67\\u0026thinsp;~\\u0026thinsp;2.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.382\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes course\\u0026thinsp;\\u0026gt;\\u0026thinsp;9.5years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.52 (1.52\\u0026thinsp;~\\u0026thinsp;4.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.00 (0.95\\u0026thinsp;~\\u0026thinsp;4.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.066\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFPG (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.94 (0.86\\u0026thinsp;~\\u0026thinsp;1.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.206\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHbA1c (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.29 (1.08\\u0026thinsp;~\\u0026thinsp;1.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.20 (0.95\\u0026thinsp;~\\u0026thinsp;1.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.132\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal protein (g/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.97 (0.93\\u0026thinsp;~\\u0026thinsp;1.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.129\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum albumin (g/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.86 (0.81\\u0026thinsp;~\\u0026thinsp;0.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.99 (0.92\\u0026thinsp;~\\u0026thinsp;1.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.896\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTC (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.68 (0.51\\u0026thinsp;~\\u0026thinsp;0.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.99 (0.60\\u0026thinsp;~\\u0026thinsp;1.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.973\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTG (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.60 (0.44\\u0026thinsp;~\\u0026thinsp;0.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.82 (0.51\\u0026thinsp;~\\u0026thinsp;1.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.400\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHDL-C (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.14 (0.03\\u0026thinsp;~\\u0026thinsp;0.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.90 (0.04\\u0026thinsp;~\\u0026thinsp;19.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.946\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL-C (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.84 (0.63\\u0026thinsp;~\\u0026thinsp;1.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.246\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eApoA (g/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.03 (0.01\\u0026thinsp;~\\u0026thinsp;0.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.04 (0.00\\u0026thinsp;~\\u0026thinsp;0.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.048\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eApoB (g/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.88 (0.28\\u0026thinsp;~\\u0026thinsp;2.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.833\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePhospholipid (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.02 (0.78\\u0026thinsp;~\\u0026thinsp;1.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.876\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLp(a)\\u0026thinsp;\\u0026gt;\\u0026thinsp;15.5 mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.82 (1.69\\u0026thinsp;~\\u0026thinsp;4.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.27 (1.58\\u0026thinsp;~\\u0026thinsp;6.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVLDL (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.31 (0.16\\u0026thinsp;~\\u0026thinsp;0.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.76 (0.26\\u0026thinsp;~\\u0026thinsp;2.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.605\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFFA\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.48 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.91 (2.26\\u0026thinsp;~\\u0026thinsp;6.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.06 (1.96\\u0026thinsp;~\\u0026thinsp;8.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4 Discussion\",\"content\":\"\\u003cp\\u003eT2DM with sarcopenia is a disease characterized by hyperglycemia and poor skeletal muscle mass function, which seriously affects the general health and quality of life of the elderly. Its pathophysiology involves inflammation, oxidative stress, mitochondrial and endothelial dysfunction, and lipid and glucose metabolism abnormalities. Previous studies on T2DM with sarcopenia mostly focused on inflammation, oxidative stress, mitochondrial and endothelial dysfunction and glucose metabolism, while few studies on lipid metabolism were reported. Studies have shown that lipoproteins also play an important role in the onset and development of diabetes as well as sarcopenia [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Therefore, understanding the relationship between the subcomponents of lipoprotein spectrum and T2DM with sarcopenia is of great significance for the prevention and treatment of diabetes with sarcopenia. A total of 297 T2DM patients over 60 years old (67.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.99) years old were included in this study, including 208 T2DM patients with non-sarcopenia (70%) and 87 T2DM patients with sarcopenia (30%). Compared with T2DM non-sarcopenia group, T2DM patients with sarcopenia had higher age, lower BMI, longer duration of diabetes, and a higher proportion of women and hypertension. This study also found that in the single-factor study, compared with the T2DM non-sarcopenia group, the glycated hemoglobin, Lp(a) and FFA of T2DM patients with sarcopenia were increased, and the serum albumin, TC, TG, HDL, apolipoprotein A and very low-density lipoprotein were decreased. Among them, the age difference in this study was consistent with the results of Lu L et al. [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e], the gender difference was consistent with the conclusions of Bi B et al. [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e], and the differences in TG and HDL were consistent with the results of Fu Q et al. [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. It should be noted that the results of TG and BMI studies conducted by Jiang Y et al. [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e] were consistent with ours, but the results of HDL were different from ours. In addition, the difference of very low-density lipoprotein we found was also consistent with the results of significant reduction of very low-density lipoprotein concentration in patients with sarcopenia found by Yin M et al. [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAfter multivariate adjustment for multiple factors, we found that age, BMI, apolipoprotein A, Lp(a), and FFA were independent predictors of T2DM with sarcopenia. Among them, age as an independent risk factor for diabetes combined with sarcopenia was consistent with previous studies by Chen, H et al.[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. T2DM is most common in the elderly and is associated with age-related diseases, including the skeletal muscle disease sarcopenia[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Sarcopenia is an age-related disease characterized by a decline in muscle mass and/or strength. With age, there is an increase in adipose tissue buried within muscle and bone marrow, which changes body composition and leads to a decrease in muscle mass and bone density [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. This is due to the fact that with the growth of age, the ability of mitochondria in muscle to catalyze fatty acid metabolism decreases, leading to the acceleration of insulin resistance and the disorder of lipid and sugar metabolism [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. The breakdown and release of lipids is also affected by age-related inflammation. Al Saedi A et al. [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e] found that the gradual decline of fatty triglyceride lipase in the skeletal muscle of the elderly is the driving factor of oxidation/inflammation in sarcopenia. In situations of energy need, such as fasting, catecholamines are sent by the sympathetic nervous system to begin the lipolysis of triglycerides stored in fat cells. However, in elderly visceral white adipose tissue (vWAT), the expression of catecholamine-degrading enzymes in adipose macrophages is elevated, preventing the signal from reaching adipose cells[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Altered gene expression in macrophages is driven by inflammasome containing pyrin domain 3 (NLRP3), an innate immune sensor that can be activated by a variety of pathogenic or endogenous molecules. NLRP3 deletion can save fasting induced vWAT lipolysis in elderly mice [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. The exact trigger of the NLRP3 inflammasome in senile WAT is unknown, but a range of factors, including circulating fatty acids and ceramides, intracellular organelles dysfunction, and uncontrolled post-translational acetylation, may contribute to its activation[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eNotably, our study found that FFA is also a critical independent risk factor for diabetes with sarcopenia. After analyzing the characteristics of 160 adult T2MD patients, Fu J et al. [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]found that FFAs was negatively correlated with muscle indexes of adult T2DM. FFAs may play an important role in the pathological process of muscle dysfunction in adult T2MD patients. FFA plays a vital role as an energy substrate and forms a key component of cell membranes. There is growing evidence that FFA and other intermediates in triglyceride lipid metabolism play an important role in regulating skeletal muscle mass and function. Sarcopenia is characterized by a reduction in stem cells and terminally differentiated muscle fibers, replaced by fat and fibrous tissue. Lipids and their derivatives accumulate in muscle cells and the intercellular chamber, further promoting lipid toxicity and inducing oxidative stress, mitochondrial dysfunction, inflammation, and insulin resistance. The study of Love KM et al. [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e] also proved that clinically relevant increase in plasma FFA concentration could induce arterial insulin resistance, which would lead to reduced muscle microvascular perfusion and thus sarcopenia. In addition, free fatty acid receptors are widely expressed in the human body and have been shown to regulate a variety of biological processes. FFAR mediated signal transduction is related to metabolic processes such as insulin secretion by pancreatic beta cells, incretin secretion by intestinal endocrine cells, food intake regulation and adipose tissue biology. These receptors are considered attractive therapeutic targets for metabolic disorders such as obesity and T2DM [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. The reduction of fatty acid catabolism affects the production of adenosine triphosphate (ATP) in mitochondria by reducing the activity of the tricarboxylic acid cycle, and may cause abnormal accumulation of adipose tissue with high oxidation and ectopic lipid deposition, which in turn is harmful to skeletal muscle [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. It can be seen that the treatment of FFA can be a new target for diabetes complicated with sarcopenia [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThis study found that compared with the T2DM non-sarcopenia group, the BMI of the T2DM combined sarcopenia group was lower, which was consistent with the findings of Yamada T et al. [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Obesity (as assessed by BMI) is associated with lipid metabolism in older adults. A higher BMI is often considered a risk factor for many diseases; However, a recent cross-sectional study suggests that an increase in BMI, a parameter related to lipid metabolism, within the normal reference range, may have a protective effect against sarcopenia [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e], suggesting that obesity may lead to a better prognosis, which is known as the \\\"obesity paradox\\\". In addition, this study also found that Lp(a) is an independent risk factor for diabetes combined with sarcopenia, which is consistent with the conclusion of the study of Li X et al. [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. They analyzed 461 elderly T2DM patients (34 patients with Asian sarcopenia diagnosis), 427 patients with no symptoms of sarcopenia) Using univariate/multivariate logistic regression analysis of the influencing factors of muscle loss in elderly T2MD patients, it was found that Lp(a) was closely related to the occurrence and development of muscle loss in elderly T2DM patients, and Lp(a) was a risk factor for muscle loss in elderly T2DM patients. The study of Gong H[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e] also confirmed that high level of Lp(a) was closely related to the occurrence of sarcopenia. Lp(a) can cause loss of muscle mass, strength, and function by regulating inflammation. Chronic inflammation mainly acts on muscle loss by accelerating catabolism, decreasing appetite, increasing insulin resistance, and decreasing growth factor and insulin-like growth factor-1 levels[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThis study used single-center data to analyze the proportion of elderly T2DM patients complicated with sarcopenia and analyzed the relationship between each component of lipoprotein spectrum and diabetes mellitus complicated with sarcopenia and made a beneficial discussion on T2DM complicated with sarcopenia and its causes and influencing factors. However, this study still has some limitations: (1) As this study is a single-center study, whether the results can be widely extrapolated to other regions and populations needs further verification; (2) The patients in this study were plateau residents, so the differences in lipoprotein profiles caused by plateau environment and diet should be considered; (3) The patients in this study were older and less educated, and they could not clearly describe the use of related hypoglycemic drugs before admission, resulting in the lack of use of hypoglycemic drugs before admission; Taken together, the results of this study show that age, BMI, apolipoprotein A, Lp(a), and FFA are independently associated with the risk of sarcopenia in elderly patients with T2DM. This study has enriched our understanding of sarcopenia in elderly patients with T2DM and revealed the correlation between lipoprotein profile and sarcopenia and its components, providing a new target and a new direction for the treatment of sarcopenia.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eBMI body mass index\\u003c/p\\u003e \\u003cp\\u003eHbA1c glycated hemoglobin\\u003c/p\\u003e \\u003cp\\u003eTC total cholesterol\\u003c/p\\u003e \\u003cp\\u003eTG triglyceride\\u003c/p\\u003e \\u003cp\\u003eHDL-C high-density lipoprotein cholesterol\\u003c/p\\u003e \\u003cp\\u003eLDL-C low density lipoprotein cholesterol\\u003c/p\\u003e \\u003cp\\u003eApoA apolipoprotein A\\u003c/p\\u003e \\u003cp\\u003eApoB apolipoprotein B\\u003c/p\\u003e \\u003cp\\u003eLp(a) lipoprotein(a)\\u003c/p\\u003e \\u003cp\\u003eVLDL very low-density lipoprotein\\u003c/p\\u003e \\u003cp\\u003eFFA free fatty acid\\u003c/p\\u003e \\u003cp\\u003eCI confidence interval\\u003c/p\\u003e \\u003cp\\u003eOR odds ratio\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\" \\u003ch2\\u003eCompliance with ethical standards\\u003c/h2\\u003e \\u003cp\\u003e \\u003cb\\u003eConflict of interest\\u003c/b\\u003e The authors declare no competing interests.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e \\u003cp\\u003e Ethics approval was obtained from the Affiliated Hospital of Qinghai University (No. P-SL-2023-452). The study was performed in accordance with Declaration of Helsinki.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eInformed consent\\u003c/strong\\u003e \\u003cp\\u003eThis study has been approved by the Research Ethics Committee of the Affiliated Hospital of Qinghai University (approval document No. P-SL-2023-452), and all participants signed informed consent.\\u003c/p\\u003e \\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis study was funded by the National Natural Science Foundation of China (82260795).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eT.T., J.H., and Z.W. participated in the design of the study. T.T. wrote the manuscript. T.T., Q.Y., and G.B. collected and analyzed the data. J.H., Q.Y., and Z.W. contributed to interpretation of data and preparation of the manuscript. All authors read and approved the final manuscript.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eData is provided within the manuscript or supplementary information files\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eWatkins, D.A. and M.K. Ali, \\u003cem\\u003eMeasuring the global burden of diabetes: implications for health policy, practice, and research\\u003c/em\\u003e. Lancet, 2023. 402(10397): p. 163\\u0026ndash;165.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDemir, S., et al., \\u003cem\\u003eEmerging Targets in Type 2 Diabetes and Diabetic Complications\\u003c/em\\u003e. Adv Sci (Weinh), 2021. 8(18): p. e2100275.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMesinovic, J., et al., \\u003cem\\u003eSarcopenia and type 2 diabetes mellitus: a bidirectional relationship\\u003c/em\\u003e. Diabetes Metab Syndr Obes, 2019. 12: p. 1057\\u0026ndash;1072.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMajety, P., et al., \\u003cem\\u003ePharmacological approaches to the prevention of type 2 diabetes mellitus\\u003c/em\\u003e. Front Endocrinol (Lausanne), 2023. 14: p. 1118848.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSakai, R., et al., \\u003cem\\u003eImpact of triglyceride-rich lipoproteins on early in-stent neoatherosclerosis formation in patients undergoing statin treatment\\u003c/em\\u003e. J Clin Lipidol, 2023. 17(2): p. 281\\u0026ndash;290.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAlbitar, O., C.M. D'Souza, and E.A. Adeghate, Effects of Lipoproteins on Metabolic Health. Nutrients, 2024. 16(13).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCui, G., et al., \\u003cem\\u003eTC and LDL-C are negatively correlated with bone mineral density in patients with osteoporosis\\u003c/em\\u003e. Am J Transl Res, 2024. 16(1): p. 163\\u0026ndash;178.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eElSayed, N.A., et al., \\u003cem\\u003e2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023\\u003c/em\\u003e. Diabetes Care, 2023. 46(Suppl 1): p. S19-s40.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen, L.K., et al., \\u003cem\\u003eAsian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment\\u003c/em\\u003e. J Am Med Dir Assoc, 2020. 21(3): p. 300\\u0026ndash;307.e2.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJiang, Y., et al., \\u003cem\\u003eThe association of lipid metabolism and sarcopenia among older patients: a cross-sectional study\\u003c/em\\u003e. Sci Rep, 2023. 13(1): p. 17538.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLin, Y., S. Zhong, and Z. Sun, \\u003cem\\u003eAssociation between serum triglyceride to high-density lipoprotein cholesterol ratio and sarcopenia among elderly patients with diabetes: a secondary data analysis of the China Health and Retirement Longitudinal Study\\u003c/em\\u003e. BMJ Open, 2023. 13(8): p. e075311.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLu, L., B. Liu, and F. Yin, \\u003cem\\u003eAlternative skeletal muscle index for sarcopenia diagnosis in elderly patients with type 2 diabetes mellitus: A pilot study\\u003c/em\\u003e. Front Endocrinol (Lausanne), 2023. 14: p. 1083722.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBi, B., et al., \\u003cem\\u003eDyslipidemia is associated with sarcopenia of the elderly: a meta-analysis\\u003c/em\\u003e. BMC Geriatr, 2024. 24(1): p. 181.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFu, Q., et al., \\u003cem\\u003eThe correlation of triglyceride/high-density lipoprotein cholesterol ratio with muscle mass in type 2 diabetes patients\\u003c/em\\u003e. BMC Endocr Disord, 2023. 23(1): p. 93.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLee, Y.A., H.N. Kim, and S.W. Song, \\u003cem\\u003eAssociations between Hair Mineral Concentrations and Skeletal Muscle Mass in Korean Adults\\u003c/em\\u003e. J Nutr Health Aging, 2022. 26(5): p. 515\\u0026ndash;520.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYin, M., et al., \\u003cem\\u003eDetermination of skeletal muscle mass by aspartate aminotransferase / alanine aminotransferase ratio, insulin and FSH in Chinese women with sarcopenia\\u003c/em\\u003e. BMC Geriatr, 2022. 22(1): p. 893.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen, H., et al., \\u003cem\\u003eUtilize multi-metabolic parameters as determinants for prediction of skeletal muscle mass quality in elderly type2 diabetic Chinese patients\\u003c/em\\u003e. BMC Geriatr, 2024. 24(1): p. 325.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTerada, T., et al., \\u003cem\\u003eSex-specific associations of fat mass and muscle mass with cardiovascular disease risk factors in adults with type 2 diabetes living with overweight and obesity: secondary analysis of the Look AHEAD trial\\u003c/em\\u003e. 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Front Cardiovasc Med, 2020. 7: p. 79.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShalit, A., et al., \\u003cem\\u003eNutrition of aging people with diabetes mellitus: Focus on sarcopenia\\u003c/em\\u003e. Maturitas, 2024. 185: p. 107975.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYamada, T., et al., \\u003cem\\u003eObesity and risk for its comorbidities diabetes, hypertension, and dyslipidemia in Japanese individuals aged 65 years\\u003c/em\\u003e. Sci Rep, 2023. 13(1): p. 2346.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi, X., X. Kong, and R. Li, \\u003cem\\u003eCorrelation between lipoprotein(a), albuminuria, myostatin and sarcopenia in elderly patients with type 2 diabetes\\u003c/em\\u003e. J Diabetes Complications, 2023. 37(1): p. 108382.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGong, H., et al., \\u003cem\\u003eLipoprotein subfractions in patients with sarcopenia and their relevance to skeletal muscle mass and function\\u003c/em\\u003e. Exp Gerontol, 2022. 159: p. 111668.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"endocrine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"endo\",\"sideBox\":\"Learn more about [Endocrine](https://www.springer.com/journal/12020)\",\"snPcode\":\"12020\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12020/3\",\"title\":\"Endocrine\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Type 2 diabetes, Sarcopenia, Lipoprotein profile, Risk factors\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5409255/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5409255/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003ePurpose\\u003c/h2\\u003e \\u003cp\\u003eThis study investigated the relationship between lipoprotein profiles and sarcopenia in patients with type 2 diabetes mellitus (T2DM). The objective is to provide a solid theoretical foundation and treatment strategies for clinical prevention and management of diabetes, particularly in individuals with concurrent sarcopenia.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eIn this study, we selected inpatients aged over 60 years diagnosed with T2DM who were admitted to the Department of Geriatrics at Qinghai University Affiliated Hospital from July 2023 to June 2024 as research subjects. We collected general patient data, including gender, age, ethnicity, height, weight, and calculated body mass index (BMI). Key indices measured included glycated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoproteins A and B (ApoA and ApoB), phospholipids, lipoprotein(a) [Lp(a)], very low-density lipoprotein (VLDL), and free fatty acids (FFA). Additionally, we assessed limb skeletal muscle mass, grip strength, walking speed, and calculated the appendicular skeletal muscle mass index (ASMI). Based on Asian diagnostic criteria for sarcopenia, patients were categorized into a non-sarcopenic group or a group with T2DM combined with sarcopenia. Baseline laboratory data along with ASMI measurements, grip strength assessments, and walking speeds were statistically analyzed for both groups.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eCompared with T2DM patients without sarcopenia, the levels of HbA1c, Lp(a), FFA, serum albumin, TC, TG, HDL-C, ApoA and VLDL in type 2 diabetic patients with sarcopenia were statistically significant (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). When multivariate adjustments were made for these clinical features, age (OR\\u0026thinsp;=\\u0026thinsp;1.18, 95%CI: 1.11\\u0026ndash;1.25, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), BMI (OR\\u0026thinsp;=\\u0026thinsp;0.81, 95%CI: 0.72\\u0026ndash;0.91, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), ApoA (OR\\u0026thinsp;=\\u0026thinsp;0.04, 95%CI: 0.00-0.98, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.048), Lp(a)\\u0026thinsp;\\u0026gt;\\u0026thinsp;15.5 mg/dL (OR\\u0026thinsp;=\\u0026thinsp;3.27, 95%CI: 1.58\\u0026ndash;6.80, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001) and FFA\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.48 g/L (OR\\u0026thinsp;=\\u0026thinsp;4.06, 95%CI: 1.96\\u0026ndash;8.43, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) were independent predictors of diabetes mellitus with sarcopenia. ROC curve analysis showed that free fatty acids (AUC\\u0026thinsp;=\\u0026thinsp;0.721, 95%CI: 0.660\\u0026ndash;0.782, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) in T2DM with sarcopenia has good predictive value judgment.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eAge, BMI, ApoA, Lp(a), and FFA were independent predictors of T2DM with sarcopenia. Serum free fatty acids have a good predictive value in the judgment of T2DM complicated with sarcopenia.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Lipoprotein profile as a predictor of type 2 diabetes with sarcopenia: A cross-sectional study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-12-09 09:08:20\",\"doi\":\"10.21203/rs.3.rs-5409255/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-01-14T11:48:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-01-03T16:36:53+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-12-25T11:56:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"129533200532729160159578478216908040594\",\"date\":\"2024-12-14T16:49:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"153346832899180076182323685784344668361\",\"date\":\"2024-12-12T11:29:17+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-12-03T02:54:30+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-11-08T01:49:32+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-11-08T01:49:03+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Endocrine\",\"date\":\"2024-11-07T10:51:55+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"endocrine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"endo\",\"sideBox\":\"Learn more about [Endocrine](https://www.springer.com/journal/12020)\",\"snPcode\":\"12020\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12020/3\",\"title\":\"Endocrine\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"fecf4360-42e0-46c0-82e2-c9aa33befdb2\",\"owner\":[],\"postedDate\":\"December 9th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-04-21T16:00:07+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-5409255\",\"link\":\"https://doi.org/10.1007/s12020-025-04226-7\",\"journal\":{\"identity\":\"endocrine\",\"isVorOnly\":false,\"title\":\"Endocrine\"},\"publishedOn\":\"2025-04-15 15:57:24\",\"publishedOnDateReadable\":\"April 15th, 2025\"},\"versionCreatedAt\":\"2024-12-09 09:08:20\",\"video\":\"\",\"vorDoi\":\"10.1007/s12020-025-04226-7\",\"vorDoiUrl\":\"https://doi.org/10.1007/s12020-025-04226-7\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5409255\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5409255\",\"identity\":\"rs-5409255\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}