The association between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus

The association between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus | 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 The association between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus Xinyuan Guo, Binjing Pan, Mei Han, Dengrong Ma, Xiaohui Zan, Jingfang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5667977/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To investigate the relationships between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus (T2DM). Methods From April 2022 to May 2023, 978 middle-aged and elderly T2DM patients diagnosed in the Department of Endocrinology of the First Hospital of Lanzhou University were divided into a low skeletal muscle mass group and a normal group according to the muscle mass index, compared the differences between the groups. The above immunoinflammatory markers were grouped according to the quartile levels, and the prevalences of muscle mass loss were compared among the groups; the relationship between the immunoinflammatory index and low skeletal muscle mass in T2DM patients was analysed. Results Compared with the normal group, the levels of all immunoinflammatory indices of low skeletal muscle mass group were significantly increased ( P < 0.05); the prevalences of skeletal muscle mass loss were progressively raised with increasing quartile levels of each immunoinflammatory marker. The levels of immunoinflammatory markers were independently and positively correlated with the risk of low skeletal muscle mass (NAR: OR = 2.148, 95% CI 1.225–3.766, P = 0.008; NLR: OR = 1.210, 95% CI 1.036–1.411, P = 0.016; MLR: OR = 1.282, 95% CI 1.068–1.540, P = 0.008; SII: OR = 1.001, 95% CI 1.000 -1.002, P = 0.009; SIRI: OR = 1.828, 95% CI 1.271–2.628, P = 0.001; SIRI: OR = 1.003, 95% CI 1.001–1.004, P = 0.010) . Conclusions The occurrence of low skeletal muscle mass may be closely related to immune inflammation in middle-aged and elderly T2DM patients. Monitoring immune inflammation markers is of clinical value for early screening and intervention of muscle mass loss in middle-aged and elderly T2DM patients. Type 2 diabetes mellitus middle-aged and elderly patients low skeletal muscle mass immunoinflammatory markers Figures Figure 1 Introduction As human life expectancy increases, age-related diseases are receiving increasing attention. Skeletal muscle is mainly composed of extracellular matrix with muscle fibres and their surrounding nerves, blood vessels and lymph. Skeletal muscle tissue is the largest and most malleable organ in the human body. Statistically, muscle mass begins to decline at a rate of 3–8% per decade after the age of 30 years[1], and the rate of decline increases to 15–25% per decade after the age of 70 years[2]; in addition to aging, a number of hereditary diseases (Duchenne muscular dystrophy, amyotrophic lateral sclerosis), metabolic diseases (type 2 diabetes mellitus, metabolic syndrome), cachexia, muscle wasting, loss of muscle innervation, dystrophies, and other disorders are also associated with muscle loss and loss of muscle tone., muscle denervation, and malnutrition also contribute to the progressive decline in muscle mass[3]. Low skeletal muscle mass is strongly associated with increased inpatient length of stay, increased infection rates, and the occurrence of fractures[4]. This suggests the importance and urgency of early assessment of skeletal muscle mass and appropriate countermeasures. Previously, skeletal muscle mass has been assessed by computed tomography (CT), bioelectrical impedance analysis (BIA), Dual-emission X-ray absorptiometry (DXA) [5], of which DXA is the most commonly used method for determining skeletal muscle mass in clinical practice because of its low cost and low radiation[6] . Activation of inflammatory response and inflammatory cells play an important role in the development of diabetes. Hyperglycaemia activates inflammatory signalling pathways such as NF-κB and NOD-like receptor protein 3 inflammasome (NLRP3 inflammasome), which activate inflammatory cells to produce cytokines, and hyperglycaemia also activates inflammatory end-product receptors such as the Receptor of Advanced Glycation Endproducts ,RAGE, and Toll-like receptors. Hyperglycaemia also activates inflammatory receptors such as the Receptor of Advanced Glycation Endproducts (RAGE) and Toll-like receptors, which promote inflammation[7, 8]. A growing body of research suggests that inflammation may play an important role in the development of skeletal muscle wasting [9]. Inflammatory cytokines lead to mitochondrial dysfunction, resulting in decreased ATP production and increased production of reactive oxygen species (ROS).Excessive production of ROS further exacerbates mitochondrial damage and subsequent metabolic abnormalities, and contributes to skeletal muscle atrophy by enhancing the ubiquitin-proteasome system [10]. Neutrophil-to-albumin (NAR), neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate inflammation systemic index (AISI) are novel immunoinflammatory markers that are fast and easy to obtain clinically; they are less susceptible to short-term changes in disease than single cell counts [11]. Immunoinflammatory markers have demonstrated high diagnostic and predictive value in tumours, cardiovascular diseases and other diseases at present[12], but there are relatively few studies on the relationships between immunoinflammatory markers and skeletal muscle mass in patients with T2DM, this study analyses the relationship between immunoinflammatory markers and skeletal muscle mass in middle-aged and elderly patients with T2DM, to provide some ideas for the clinical decline of muscle mass in middle-aged and elderly patients with T2DM. Subjects and methods Study subjects A total of 1389 middle-aged and elderly patients diagnosed with T2DM in the Department of Endocrinology at the First Hospital of Lanzhou University, Lanzhou, Gansu, China, from April 2022 to May 2023 were selected for the study. After screening based on the inclusion and exclusion criteria, 978 patients with T2DM were enrolled. The study was approved by the Ethics Committee of the First Hospital of Lanzhou University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards (No. LDYYLL2024-693). Inclusion criteria The inclusion criteria were the following: (1) age ≥ 45 years, Han nationality; (2) patients meeting the 1999 World Health Organization (WHO) diagnostic criteria for T2DM based on medical history, symptoms, and laboratory tests; (3) T2DM duration ≥ 1 year; and (4) patients with complete study data and information. Exclusion criteria The exclusion criteria were the following: (1) patients with type 1 diabetes, gestational diabetes, or other special types of diabetes; (2) patients with acute complications of diabetes such as diabetic ketoacidosis and diabetic hyperosmolar hyperglycemia; (3) patients with malignant tumors, immune diseases, and liver diseases (defined as aspartic acid aminotransaminase [AST]/alanine aminotransaminase [ALT] in whom levels were higher than three times the upper limit of normal), and kidney diseases (defined as patients with estimated glomerular filtration rate [eGFR] < 30 mL/(min*1.73m 2 ) or patients with renal insufficiency receiving renal replacement therapy); (4) patients with acute infections; (5)patients with acute cardiovascular and cerebrovascular disease; (6) patients with parkinson's disease, motor neurone disease, post-stroke sequelae, severe osteoarthritis, long-term bedridden, etc., which affects activity function; and (7) dementia, mental retardation, and cognitively impaired incapacitated patients. Methods Collection of general characteristics of the study subjects General information (age and sex), duration of diabetes, medication status, personal history (such as dietary habits; smoking, and drinking status), and any other history of disease and medication of each participant were collected. Height, weight, and blood pressure were measured and recorded. Laboratory examinations After fasting for 10 − 12 h, 5 ml of venous blood was extracted from each subject in the morning and serum was separated.Serum aspartic acid aminotransaminase (AST), alanine aminotransaminase (ALT), total bilirubin (TBIL), direct bilirubin (DBIL), alkaline phosphatase (ALP), albumin(ALB),triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), lactic dehydrogenase (LDH), calcium (Ca), phosphorus (P), magnesium (Mg), fasting plasma glucose (FPG) were measured using automatic biochemical analyzers. Platelet counts(PLT), neutrophil counts(NEUT),lymphocyte counts(LYM), monocyte counts(MONO) were determined using a Mindray automated blood cell analyzer (Mindray Medical International Limited, Shenzhen, China). Calculate the immunoinflammatory markers.Glycated Hb (HbA1c) was determined using HPLC (Bio-Rad D10; Bio-Rad Laboratories). Fasting insulin (FINS) levels were determined using a chemiluminescence immunoassay (CENTAUR-XP automat chemiluminescence immunoanalyzer; Siemens Healthineers). Dual-energy X-ray bone densitometry (Lunar iDXA, GE, USA) was used to determine skeletal muscle mass in the extremities. Body mass index(BMI) = Weighe(Kg)/Height 2 (m 2 ) Appendicular skeletal muscle mass index(ASMI) = Skeletal muscle mass (ASM)/ Height 2 (m 2 )[6] Immunoinflammatory markers: Neutrophil-to- albumin(NAR) = NEUT/ALB; Neutrophil-to-lymphocyte(NLR) = NEUT / LYM; Monocyte -to- lymphocyte ratio(MLR) = MONO/LYM; Systemic immune-inflammation index(SII) = PLT × NEUT /LYM; Systemic inflammation response index(SIRI) = NEUT×MONO/ LYM; Aggregate inflammation systemic index(AISI) = NEUT×MONO×PLT /LYM Diagnostic criteria Diagnostic criteria for T2DM, according to the WHO diagnostic criteria in 1999, are the following: FPG ≥ 7.0 mmol/L; 2h-PG ≥ 11.1 mmol/L; history of diagnosis of T2DM. Diagnostic criteria for low skeletal muscle mass, ASMI < 7 kg/m² for men and < 5.4kg/m² for women is diagnosed as low skeletal muscle mass [6]. Statistical methods All data were analyzed by IBM SPSS Statistics for Windows, version 25.0 (IBM Corp.). Normally distributed measurement data are expressed as means ± standard deviations (x¯± s ), while non-normally distributed measures are expressed as medians (p25, p75). One-way analysis of variance (ANOVA) and Kruskal-Whitney non-parametric tests for independent samples were used to analyze the measurement data according to the normal or non-normal distributions, and the Shapiro–Wilk test was used to test the normality of the sample distribution. Comparisons between the two groups were analyzed based on whether equal variances were satisfied using Bonferroni’s and Tamhane’s tests. The enumeration data were expressed as frequencies and percentages (%), and differences among groups were compared using the chi-square test. Comparisons between the two groups were analyzed using the Bonferroni method under the z test to adjust the P -values. Correlation analysis was performed using Pearson’s or Spearman’s rank correlation analyses. Univariate Logistic regression was used to analyse the influencing factors related to low skeletal muscle mass. Binary logistic regression was used to analyse the independent correlations between skeletal muscle mass reduction and the levels of novel immunoinflammatory markers in middle-aged and elderly T2DM patients. P < 0.05 was considered statistically significant. Results Baseline characteristics of the study population A total of 978 study subjects were finally included in this study, 348 patients (35.6%)with reduced muscle mass, including 276 males(79.3%) and 72 females(20.1%) . Compared with the normal skeletal muscle mass group, the levels of age, MONO, NAR, NLR, MLR, SII, SIRI, and SIRI were increased in the low skeletal muscle mass group (all P < 0.05), and the levels of BMI, TP, ALB, TC, TG, and LYM were decreased (all P 0.05). (Table 1 ). Table 1 Comparison of indicators in the normal skeletal muscle mass group and the low skeletal muscle mass group the normal skeletal muscle mass group(n = 630) the low skeletal muscle mass group (n = 348) Z/t P Age(years) 60.64 ± 7.51 62.96 ± 7.94 1.186 < 0.001 Sex 47.814 < 0.001 Men 361(57.3%) 276(79.3%) Women 269(42.7%) 72(20.7%) Hypertension 2.656 0.103 Yes 351(55.7%) 175(50.3%) No 279(44.3%) 173(49.7%) BMI(kg/m 2 ) 25.11 ± 2.99 22.11 ± 2.17 4.840 < 0.001 TP(g/L) 69.99 ± 6.92 69.01 ± 6.62 0.061 0.028 ALB(g/L) 42.97 ± 3.64 42.29 ± 4.05 1.139 0.007 FPG(mmol/L) 8.01(6.66, 10.20) 8.24(6.72, 10.97) -1.351 0.177 AST(U/L) 19.00(16.00, 24.00) 19.00(15.00, 24.25) -1.173 0.241 ALT(U/L) 19.00(14.00, 28.00) 19.00(14.00, 28.25) -0.749 0.454 TC(mmol/L) 4.26 ± 1.10 4.10 ± 1.01 0.095 0.032 TG(mmol/L) 1.56(1.13, 2.15) 1.37(0.96, 1.82) -4.275 < 0.001 HDL- C(mmol/L) 1.03(0.88, 1.21) 1.01(0.87, 1.18) -1.030 0.303 LDL-C(mmol/L) 2.73(2.24, 3.22) 2.69(2.08, 3.18) -1.531 0.126 WBC/(10^9/L) 5.68(4.89, 6.72) 5.75(4.82, 6.94) -1.082 0.279 Lypmh(10^9/L) 1.88(1.48, 2.34) 1.75(1.45, 2.15) -2.396 0.017 Neu(10^9/L) 3.24(2.61, 3.99) 3.35(2.74, 4.17) -1.953 0.051 MONO(10^9/L) 0.35(0.28, 0.42) 0.39(0.31, 0.47) -4.779 < 0.001 PLT(10^9/L) 181༎00(149.75, 215.00) 182.50(148.00, 218.50) -0.549 0.583 NAR 0.75(0.06,0.09) 0.79(0.64,0.10) -2.808 0.005 NLR 1.73(1.33,2.31) 1.93(1.46,2.49) -3.074 0.002 MLR 0.19(0.15,0.24) 0.22(0.17,0.29) -6.185 < 0.001 SII 309.66(233.90,431.04) 343.71(248.54,474.89) -2.948 0.003 SIRI 0.60(0.42,0.87) 0.74(0.51,1.07) -5.263 < 0.001 AISI 108.79(73.65,159.42) 134.15(86.45,198.69) -4.667 < 0.001 Note: SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; TP: total protein; ALB: albumin; FPG: fasting blood-glucose; AST: aspartate aminotransferase; ALT: alanine aminotransferase; TC: total cholesterol; TG: triglycerides; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol;WBC: white blood cell; Lypmh: lymphocyte;Neu:neutrophil; MONO: monocyte; PLT: blood platelet;NAR:neutrophil-to-albumin;NLR:neutrophil-to-lymphocyte;MLR:monocyte-to-lymphocyte ratio; SII:systemic immune-inflammation index, SIRI:systemic inflammation response index; AISI: aggregate inflammation systemic index Comparison of the prevalence of muscle mass loss in groups with different quartile levels of each immune-inflammatory marker The T2DM patients were divided into four groups (groups Q1 to Q4) according to quartile levels of each immune-inflammatory marker, respectively. The results showed that the prevalences of muscle mass loss were increased significantly with the increasing levels of each immune-inflammatory marker. In the NLR quartiles groups, the prevalence of muscle mass loss of the Q4 group (29.6%) was increased than that in the Q1 group (21.0%, P < 0.05) ( Fig. 1 B ) . In the MLR quartiles groups, the prevalences of muscle mass loss of the Q3 (25.3%) and Q4 groups (34.8%) were increased than those in the Q1 group (16.7%, both P < 0.05) (Fig. 1 B), and the prevalences of muscle mass loss of the Q4 groups (34.8%) were increased than those in the Q1 (16.7%) and Q2 groups (25.3%,both P < 0.05)( Fig. 1 C). In the SII quartiles groups, the prevalence of muscle mass loss of the Q4 group (29.9%) was increased than that in the Q1 group (21.8%, both P < 0.05) ( Fig. 1 D ) . In the SIRI quartiles groups, the prevalences of muscle mass loss of the Q4 groups (33.0%) were increased than those in the Q1 (18.1%) and Q2 groups (21.8%, both P < 0.05), the prevalences of muscle mass loss of the Q3 groups (27.0%) were increased than that in the Q1 (18.0%, P < 0.05) ( Fig. 1 E) In the AISI quartile groups, the prevalences of muscle mass loss of the Q4 groups (37.3%) were increased than those in the Q1 (20.1%) and Q2 groups (21.6%,both P < 0.05) (Fig. 1 F). Univariate logistic regression analysis of the factors influencing skeletal muscle mass in middle-aged and elderly patients with T2DM One-way logistic regression was used to analyse the factors influencing the occurrence of skeletal muscle mass loss in middle-aged and elderly T2DM patients, skeletal muscle mass loss as the dependent variable, and the variables with significant differences between the normal and reduced muscle mass groups in Table 1 . as independent variables. The results showed that age, diabetic duration, NAR, NLR, MLR, SII, SIRI, AISI, the, serum total protein, TC, and TG were the middle and old age independent influences on muscle mass loss, with females, older age, longer disease duration, and higher levels of immune inflammation being more likely to suffer from skeletal muscle mass loss, and lower levels of BMI,TP, TC, and TG being more likely to suffer from skeletal muscle mass loss. (Table 2 ). Table 2 Univariate logistic regression analysis of the factors influencing skeletal muscle mass in middle-aged and elderly T2DM patients β Waldχ༒ P OR(95%CI) Age(years) 0.040 20.335 < 0.001 1.041(1.023, 1.059) BMI(kg/m 2 ) -0.478 178.058 < 0.001 0.620(0.578, 0.665) Sex(Men vs Women) -1.050 45.900 < 0.001 0.350(0.258,, 0.474) Duration(years) 0.041 17.212 < 0.001 1.042(1.022, 1.063) TP(g/L) -0.021 4.791 0.029 0.979(0.960, 0.998) TC(mmol/L) -0.136 4.558 0.033 0.873(0.770, 0.989) TG(mmol/L) -0.166 8.553 0.003 0.847(0.758, 0.947) NAR 0.750 13.109 < 0.001 2.116(1.410, 3.176) NLR 0.289 18.732 < 0.001 1.335(1.171, 1.522) MLR 0.518 36.491 < 0.001 1.679(1.419, 1.986) SII 0.001 16.470 < 0.001 1.001(1.001, 1.002) SIRI 0.851 31.964 < 0.001 2.342(1.744, 3.146) AISI 0.003 22.872 < 0.001 1.003(1.002, 1.004) Note: BMI: body mass index; TP: total protein; TC: total cholesterol; TG: triglycerides; NAR:neutrophil-to-albumin;NLR:neutrophil-to-lymphocyte;MLR:monocyte-to-lymphocyte ratio; SII:systemic immune-inflammation index, SIRI:systemic inflammation response index; AISI: aggregate inflammation systemic index Multiple linear regression analysis of the relationships between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with T2DM The presence or absence of skeletal muscle mass loss as the dependent variable, each immunoinflammatory marker and the variables with P < 0.05 in the multiple linear regression model were separately included in the multivariate logistic regression model as the dependent variables, and the results showed that all immunoinflammatory markers were significantly negatively correlated with the risk of the prevalence of skeletal muscle mass loss before adjusting parameters ( P all < 0.05). After correcting for age, gender, BMI, TP, ALB, TC, TG, and other confounders, NAR, NLR, MLR, SII, SIRI, and AISI were independently and positively associated with skeletal muscle mass loss; for every 1 increase in the values of NAR, NLR, MLR, SII, SIRI, and AISI, the risk of prevalence of skeletal muscle mass loss was increased by 2.148, 1.21, 1.282, 1.001 1.828, and 1.003, respectively.. (Table 3 ). Table 3 Multiple linear regression analysis of the relationships between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus Model 1 Model 2 Model 3 OR (95%CI) P OR(95%CI) P OR(95%CI) P NAR 1.763(1.160,2.677) 0.008 2.146(1.223, 3.763) 0.008 2.148(1.225, 3.766) 0.008 NLR 1.244(1.079,1.433) 0.003 1.207(1.035, 1.407) 0.016 1.210(1.036, 1.411) 0.016 MLR 1.398(1.187,1.647) < 0.001 1.280(1.067, 1.535) 0.008 1.282(1.068,1.540) 0.008 SII 1.001(1.000,1.002) 0.003 1.001(1.000, 1.002) 0.009 1.001(1.000, 1.002) 0.009 SIRI 1.912(1.380,2.650) < 0.001 1.821(1.267, 2.617) 0.001 1.828(1.271, 2.628) 0.001 AISI 1.002(1.001,1.004) 0.001 1.003(1.001, 1.004) 0.001 1.003(1.001, 1.004) 0.010 Note: Model 1: Adjusted sex, age; Model 2: Adjusted Model 1 ་BMI་TP་ALB; Model 3: Model 2 + TC + TG Discussion In this study, we found that the levels of immunoinflammatory markers NAR, NLR, MLR, SII, SIRI and AISI were elevated in middle-aged and elderly T2DM patients with low skeletal muscle mass, and these immunoinflammatory markers were independently negatively correlated with muscle mass after adjusting for the effects of confounders such as age, sex and BMI, and with increasing levels of immunoinflammatory markers, the prevalences of muscle mass loss in T2DM patients were increased. The relationship between inflammatory markers and chronic complications of T2DM, such as diabetic nephropathy and diabetic retinopathy, is now gaining attention from researchers and clinicians alike[13, 14]. Low skeletal muscle mass likewise as a chronic complication of T2DM,several studies in recent years have found that the levels of some inflammatory markers are closely associated with alterations in muscle mass: a meta-analysis showed that serum CRP levels were elevated in patients with sarcopenia [15, 16]; however, there are relatively few studies analysing the relationship between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus. Similar to our results, In the cross-sectional study by Lin Shi et al. 10,367 individuals enrolled in their analysis. They found that subjects with higher SII levels showed an increased risk of low muscle mass. After adjusting all potential confounding factors, higher SII levels still independently increased the risk of low muscle mass[17]A retrospective analysis observed by Zeynel et al [18] in a Turkish population found that patients in the sarcopenia group had significantly higher levels of NLR than those in the non-sarcopenia group, and that NLR was an independent predictor of sarcopenia (OR = 1.31, P = 0.013). A cross-sectional study of the National Health and Nutrition Examination Survey (NHANES) Phase III in the United States of America [19]showed that PLR levels were positively associated with sarcopenia after adjusting for a number of conventional risk factors (uric acid, bilirubin, creatinine, albumin). It was also reported [20] that the levels of systemic inflammatory markers (WBC, NOMO, NEU, PLT, NLR, PLR, SII) were strongly associated with the development of sarcopenia in a middle-aged and elderly population in western China, and the prevalence of sarcopenia increased in patients with higher levels of NLR, PLR, and SII, suggesting that chronic low-grade inflammation may be associated with the development of sarcopenia. Yoshinaga Okugawa et al [21] found that to the colorectal cancer patients, sarcopenia was significantly associated with elevated levels of SII and SIRI. Of course, a few studies have failed to find a correlation between the immunoinflammatory markers PLR, NLR, LMR and muscle mass[22]. Differences in the basic characteristics of the included subjects, the number of sample sizes, the range of variability in the measurement of immunoinflammatory markers, and the fact that some confounders were not adjusted for may have contributed to the inconsistent results. Skeletal muscle is known to play an integral role in the maintenance of homeostasis in various organ systems. Skeletal muscle is plastic and changes dynamically with physical activity, loading, injury, disease, and aging[23] ; previous studies have shown that decreased and dysregulated immune function during aging leaves the body in a chronic low-grade inflammatory state [15]; and that it may be an important factor in triggering or accelerating age-related diseases, such as stroke, Alzheimer's disease, and osteoarthritis. In diabetic patients, chronic hyperglycaemia promotes glycosylation of lipids and proteins, which increases the production of glycosylation end products and triggers a number of adverse effects. Glycosylation end products can bind to the receptors of immune cells and influence the inflammatory response [24]. Prolonged hyperglycaemia-induced inflammation disrupts the balance between protein synthesis and catabolism and generates high levels of oxygen free radicals, leading to myocyte apoptosis. During muscle wasting, chronic inflammation overexpresses the ubiquitin-proteasome pathway, reduces insulin-like growth factor 1 and increases muscle cortisol synthesis, all of which promote skeletal muscle proteolysis[25]. Chronic low-grade inflammation plays a role in the progression of inflammation and sarcopenia through the regulation of pro-inflammatory cytokines such as TNF-α and IL-6[26].TNFα is a key endocrine factor for contractile dysfunction in chronic inflammation, short-term increases in TNF-α, considered a key mediator of the inflammatory responses and apoptosis, promote muscle repair [27]. However, continuous elevated levels of TNF-α lead to muscle damage[28],In addition myogenic reactive oxygen species (ROS) and nitric oxide (NO) are involved in inhibiting myofibre proportions, which can lead to muscle atrophy[29] .IL-6 can contribute to muscle atrophy by stimulating the protein hydrolysis pathway, leading to muscle degradation through ubiquitination [25]. Decreased levels of lipocalin also lead to increased secretion of pro-inflammatory cytokines[30]. And upon aging, adipose tissue is redistributed outside fat depots, accumulating viscerally. it can be found accumulated as ectopic fat depots or as intramuscular lipid droplets, local adiposity promotes the attraction of immune cells by the recruitment of macrophages[31]. In aging muscle, there is a reduced mitochondrial volume and reduced oxidative capacity leading to a state of oxidative stress capable of triggering an inflammatory response. Reduced autophagy with aging may be the cause underlying the age-associated decline in muscle stem cells pool. In the elderly, mitochondrial dysfunction together with insufficient autophagy may promote a pro-inflammatory environment, worsening sarcopenia outcomes[32, 33]. This study offers numerous advantages. Firstly, the study is based on a large sample population study in western China; secondly, the immunoinflammatory markers identified in this study are readily available in clinical practice and the test is cost-effective; finally, this study is a comparative analysis of the relationship between several novel immunoinflammatory indices and muscle mass. The results demonstrate that SII and AIAI, two novel and well-integrated inflammatory indexes, provide a more accurate reflection of the body's actual condition. This study validates the value of immune-inflammatory indexes in predicting muscle mass reduction, even when used in conjunction with traditional inflammatory indexes. It should be noted that this study is not without limitations. Firstly, as a retrospective study based on single-centre data, there is a possibility of bias in the results. Secondly, we did not adjust for some important potential confounding factors, such as activities of daily living and other related variables. Finally, the calculation of these inflammatory indices was based on a single measurement, which could have affected the accuracy of the results. In conclusion, the present study found that in middle-aged and elderly patients with type 2 diabetes mellitus, the level of the immunoinflammatory markers was significantly higher in the group with skeletal muscle mass loss, and the prevalence of skeletal muscle mass loss increased progressively with increasing quartile levels of each immunoinflammatory marker. The immunoinflammatory index was found to be independently and positively associated with the risk of skeletal muscle mass loss. Consequently, monitoring the levels of novel immunoinflammatory markers is clinically valuable for the early screening and intervention of muscle mass loss in middle-aged and elderly T2DM patients. Declarations Conflicts of interest No potential conflict of interest relevant to this article was reported. Acknowledgments We would like to thank the First Hospital of Lanzhou University for supporting the construction of the registry of data . Funding This study was funded by National Natural Science Foundation of China (No.81960155; No.82360161) Author contribution Xinyuan Guo and Jingfang Liu conceived and designed the study. Xinyuan Guo; Binjing Pan; Mei Han; Dengrong Ma; Xiaohui Zan collected clinical and biochemical data. Xinyuan Guo and Jingfang Liu contributed to the statistical analysis, results interpretation, drafting and revising the paper. All authors read and approved the final version of the manuscript. References Volpi E, Nazemi R, Fujita S. 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Reference values for white blood-cell-based inflammatory markers in the Rotterdam Study: a population-based prospective cohort study. Sci Rep. 2018 Jul 12;8(1):10566. Xiao S, Wang Z, Zuo R, Zhou Y, Yang Y, Chen T ,et al. Association of Systemic Immune Inflammation Index with All-Cause, Cardiovascular Disease, and Cancer-Related Mortality in Patients with Cardiovascular Disease: A Cross-Sectional Study. J Inflamm Res. 2023 Mar 6;16:941-961. Li X, Wang L, Liu M, Zhou H, Xu H. Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study. Front Endocrinol (Lausanne). 2024 Jan 4;14:1285509. He X, Qi S, Zhang X, Pan J. The relationship between the neutrophil-to-lymphocyte ratio and diabetic retinopathy in adults from the United States: results from the National Health and nutrition examination survey. BMC Ophthalmol. 2022 Aug 17;22(1):346. Hernández-Lepe MA, Ortiz-Ortiz M, Hernández-Ontiveros DA, Mejía-Rangel MJ. Inflammatory Profile of Older Adults in Response to Physical Activity and Diet Supplementation: A Systematic Review. Int J Environ Res Public Health. 2023 Feb 25;20(5):4111. Bano G, Trevisan C, Carraro S, Solmi M, Luchini C, Stubbs B,et al.Inflammation and sarcopenia: A systematic review and meta-analysis. Maturitas. 2017 Feb;96:10-15. Shi L, Zhang L, Zhang D, Chen Z. Association between systemic immune-inflammation index and low muscle mass in US adults: a cross-sectional study. BMC Public Health. 2023 Jul 24;23(1):1416. Öztürk ZA, Kul S, Türkbeyler İH, Sayıner ZA, Abiyev A. Is increased neutrophil lymphocyte ratio remarking the inflammation in sarcopenia? Exp Gerontol. 2018 Sep;110:223-229. Liaw FY, Huang CF, Chen WL, Wu LW, Peng TC, Chang YW,et al. Higher Platelet-to-Lymphocyte Ratio Increased the Risk of Sarcopenia in the Community-Dwelling Older Adults. Sci Rep. 2017 Nov 30;7(1):16609. Zhao WY, Zhang Y, Hou LS, Xia X, Ge ML, Liu XL,et al. The association between systemic inflammatory markers and sarcopenia: Results from the West China Health and Aging Trend Study (WCHAT). Arch Gerontol Geriatr. 2021 Jan-Feb;92:104262. Okugawa Y, Toiyama Y, Yamamoto A, Shigemori T, Kitamura A, Ichikawa T,et al.Close Relationship Between Immunological/Inflammatory Markers and Myopenia and Myosteatosis in Patients With Colorectal Cancer: A Propensity Score Matching Analysis. JPEN J Parenter Enteral Nutr. 2019 May;43(4):508-515. Tang T, Xie L, Tan L, Hu X, Yang M. Inflammatory indexes are not associated with sarcopenia in Chinese community-dwelling older people: a cross-sectional study. BMC Geriatr. 2020 Nov 7;20(1):457. Tang T, Xie L, Tan L, Hu X, Yang M. Inflammatory indexes are not associated with sarcopenia in Chinese community-dwelling older people: a cross-sectional study. BMC Geriatr. 2020 Nov 7;20(1):457. Papadopoulou SK. Sarcopenia: A Contemporary Health Problem among Older Adult Populations. Nutrients. 2020 May 1;12(5):1293. Calvani R, Joseph AM, Adhihetty PJ, Miccheli A, Bossola M, Leeuwenburgh C, Bernabei R, Marzetti E. Mitochondrial pathways in sarcopenia of aging and disuse muscle atrophy. Biol Chem. 2013 Mar;394(3):393-414. Custodero C, Anton SD, Beavers DP, Mankowski RT, Lee SA, McDermott MM,et al.ENRGISE study investigators. The relationship between interleukin-6 levels and physical performance in mobility-limited older adults with chronic low-grade inflammation: The ENRGISE Pilot study. Arch Gerontol Geriatr. 2020 Sep-Oct;90:104131. Sente T, Van Berendoncks AM, Fransen E, Vrints CJ, Hoymans VY. Tumor necrosis factor-α impairs adiponectin signalling, mitochondrial biogenesis, and myogenesis in primary human myotubes cultures. Am J Physiol Heart Circ Physiol. 2016 May 1;310(9):H1164-75. Chen SE, Jin B, Li YP. TNF-alpha regulates myogenesis and muscle regeneration by activating p38 MAPK. Am J Physiol Cell Physiol. 2007 May;292(5):C1660-71. Reid MB, Li YP. Tumor necrosis factor-alpha and muscle wasting: a cellular perspective. Respir Res. 2001;2(5):269-72. Tuttle CSL, Thang LAN, Maier AB. Markers of inflammation and their association with muscle strength and mass: A systematic review and meta-analysis. Ageing Res Rev. 2020 Dec;64:101185. Song J, Farris D, Ariza P, Moorjani S, Varghese M, Blin M,et al.Age-associated adipose tissue inflammation promotes monocyte chemotaxis and enhances atherosclerosis. Aging Cell. 2023 Feb;22(2):e13783. doi: 10.1111/acel.13783. Epub 2023 Jan 23. PMID: 36683460; PMCID: PMC9924943. Biferali B, Proietti D, Mozzetta C, Madaro L. Fibro-Adipogenic Progenitors Cross-Talk in Skeletal Muscle: The Social Network. Front Physiol. 2019 Aug 21;10:1074. González-Blanco L, Bermúdez M, Bermejo-Millo JC, Gutiérrez-Rodríguez J, Solano JJ, Antuña E,et al. Cell interactome in sarcopenia during aging. J Cachexia Sarcopenia Muscle. 2022 Apr;13(2):919-931. Liu R, Cui J, Sun Y, Xu W, Wang Z, Wu M, Dong H, Yang C, Hong S, Yin S, Wang H. Autophagy deficiency promotes M1 macrophage polarization to exacerbate acute liver injury via ATG5 repression during aging. Cell Death Discov. 2021 Dec 20;7(1):397. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5667977","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392405199,"identity":"be0e9119-a0ea-400e-b3f4-95730a64225b","order_by":0,"name":"Xinyuan Guo","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xinyuan","middleName":"","lastName":"Guo","suffix":""},{"id":392405200,"identity":"f81ed5ed-c387-441a-93ee-c4e2c41c2fa0","order_by":1,"name":"Binjing Pan","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Binjing","middleName":"","lastName":"Pan","suffix":""},{"id":392405201,"identity":"78fe4e26-8472-47a3-908e-570a8d5d0c00","order_by":2,"name":"Mei Han","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Han","suffix":""},{"id":392405202,"identity":"3bbe468c-43e3-4d53-a587-c9b1faa4ddeb","order_by":3,"name":"Dengrong Ma","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Dengrong","middleName":"","lastName":"Ma","suffix":""},{"id":392405203,"identity":"72c28d7a-1dde-4130-bac0-5502543667ad","order_by":4,"name":"Xiaohui Zan","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Zan","suffix":""},{"id":392405204,"identity":"699e7ddf-f004-4843-bab5-4ca265e40b35","order_by":5,"name":"Jingfang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACPoaDDx984JGQ42dvbHzwgRgtbAyHjQ1nyFgYS/YcbjacQZwWZjNpHpuKRIMb6W3SHERpYTzMbMCTI5EgOfNhgzQDg52cbgNhhzE+kDgjkccvndhgXMCQbGx2gKCW84cNDHskiiVnJzYkz2A4kLiNsJbDbBKJ/yQSN9w82HCYh2gtB3iAWm4wNjYTq4XZsIFHAhjIic2MMwyI8Au/xGHGx3946oBRefz5jw8VdnIEtTBIoKgwIKQcbE0DMapGwSgYBaNgRAMAuCRDY5m/SokAAAAASUVORK5CYII=","orcid":"","institution":"First Hospital of Lanzhou University","correspondingAuthor":true,"prefix":"","firstName":"Jingfang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-12-18 09:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5667977/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5667977/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72337432,"identity":"f2a8c19e-a319-4296-a9db-145a627af63c","added_by":"auto","created_at":"2024-12-25 16:03:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65255,"visible":true,"origin":"","legend":"\u003cp\u003eComparing the prevalence of muscle mass loss across quartiles of immunoinflammatory markers\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5667977/v1/201eeb748a081c4f6d1d8913.png"},{"id":72651534,"identity":"915588ba-11ae-4d6c-ab8e-ae36b158ccaa","added_by":"auto","created_at":"2024-12-30 18:46:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":756193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5667977/v1/bfec23e1-0ab0-4663-90d1-de78572b7031.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs human life expectancy increases, age-related diseases are receiving increasing attention. Skeletal muscle is mainly composed of extracellular matrix with muscle fibres and their surrounding nerves, blood vessels and lymph. Skeletal muscle tissue is the largest and most malleable organ in the human body. Statistically, muscle mass begins to decline at a rate of 3\u0026ndash;8% per decade after the age of 30 years[1], and the rate of decline increases to 15\u0026ndash;25% per decade after the age of 70 years[2]; in addition to aging, a number of hereditary diseases (Duchenne muscular dystrophy, amyotrophic lateral sclerosis), metabolic diseases (type 2 diabetes mellitus, metabolic syndrome), cachexia, muscle wasting, loss of muscle innervation, dystrophies, and other disorders are also associated with muscle loss and loss of muscle tone., muscle denervation, and malnutrition also contribute to the progressive decline in muscle mass[3]. Low skeletal muscle mass is strongly associated with increased inpatient length of stay, increased infection rates, and the occurrence of fractures[4]. This suggests the importance and urgency of early assessment of skeletal muscle mass and appropriate countermeasures.\u003c/p\u003e \u003cp\u003ePreviously, skeletal muscle mass has been assessed by computed tomography (CT), bioelectrical impedance analysis (BIA), Dual-emission X-ray absorptiometry (DXA) [5], of which DXA is the most commonly used method for determining skeletal muscle mass in clinical practice because of its low cost and low radiation[6] .\u003c/p\u003e \u003cp\u003eActivation of inflammatory response and inflammatory cells play an important role in the development of diabetes. Hyperglycaemia activates inflammatory signalling pathways such as NF-κB and NOD-like receptor protein 3 inflammasome (NLRP3 inflammasome), which activate inflammatory cells to produce cytokines, and hyperglycaemia also activates inflammatory end-product receptors such as the Receptor of Advanced Glycation Endproducts ,RAGE, and Toll-like receptors. Hyperglycaemia also activates inflammatory receptors such as the Receptor of Advanced Glycation Endproducts (RAGE) and Toll-like receptors, which promote inflammation[7, 8].\u003c/p\u003e \u003cp\u003eA growing body of research suggests that inflammation may play an important role in the development of skeletal muscle wasting [9]. Inflammatory cytokines lead to mitochondrial dysfunction, resulting in decreased ATP production and increased production of reactive oxygen species (ROS).Excessive production of ROS further exacerbates mitochondrial damage and subsequent metabolic abnormalities, and contributes to skeletal muscle atrophy by enhancing the ubiquitin-proteasome system [10].\u003c/p\u003e \u003cp\u003eNeutrophil-to-albumin (NAR), neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate inflammation systemic index (AISI) are novel immunoinflammatory markers that are fast and easy to obtain clinically; they are less susceptible to short-term changes in disease than single cell counts [11]. Immunoinflammatory markers have demonstrated high diagnostic and predictive value in tumours, cardiovascular diseases and other diseases at present[12], but there are relatively few studies on the relationships between immunoinflammatory markers and skeletal muscle mass in patients with T2DM, this study analyses the relationship between immunoinflammatory markers and skeletal muscle mass in middle-aged and elderly patients with T2DM, to provide some ideas for the clinical decline of muscle mass in middle-aged and elderly patients with T2DM.\u003c/p\u003e"},{"header":"Subjects and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy subjects\u003c/h2\u003e \u003cp\u003eA total of 1389 middle-aged and elderly patients diagnosed with T2DM in the Department of Endocrinology at the First Hospital of Lanzhou University, Lanzhou, Gansu, China, from April 2022 to May 2023 were selected for the study. After screening based on the inclusion and exclusion criteria, 978 patients with T2DM were enrolled. The study was approved by the Ethics Committee of the First Hospital of Lanzhou University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards (No. LDYYLL2024-693).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion criteria\u003c/h3\u003e\n\u003cp\u003eThe inclusion criteria were the following: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;45 years, Han nationality; (2) patients meeting the 1999 World Health Organization (WHO) diagnostic criteria for T2DM based on medical history, symptoms, and laboratory tests; (3) T2DM duration\u0026thinsp;\u0026ge;\u0026thinsp;1 year; and (4) patients with complete study data and information.\u003c/p\u003e\n\u003ch3\u003eExclusion criteria\u003c/h3\u003e\n\u003cp\u003eThe exclusion criteria were the following: (1) patients with type 1 diabetes, gestational diabetes, or other special types of diabetes; (2) patients with acute complications of diabetes such as diabetic ketoacidosis and diabetic hyperosmolar hyperglycemia; (3) patients with malignant tumors, immune diseases, and liver diseases (defined as aspartic acid aminotransaminase [AST]/alanine aminotransaminase [ALT] in whom levels were higher than three times the upper limit of normal), and kidney diseases (defined as patients with estimated glomerular filtration rate [eGFR]\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/(min*1.73m\u003csup\u003e2\u003c/sup\u003e) or patients with renal insufficiency receiving renal replacement therapy); (4) patients with acute infections; (5)patients with acute cardiovascular and cerebrovascular disease; (6) patients with parkinson's disease, motor neurone disease, post-stroke sequelae, severe osteoarthritis, long-term bedridden, etc., which affects activity function; and (7) dementia, mental retardation, and cognitively impaired incapacitated patients.\u003c/p\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCollection of general characteristics of the study subjects\u003c/h2\u003e \u003cp\u003eGeneral information (age and sex), duration of diabetes, medication status, personal history (such as dietary habits; smoking, and drinking status), and any other history of disease and medication of each participant were collected. Height, weight, and blood pressure were measured and recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory examinations\u003c/h2\u003e \u003cp\u003eAfter fasting for 10\u0026thinsp;\u0026minus;\u0026thinsp;12 h, 5 ml of venous blood was extracted from each subject in the morning and serum was separated.Serum aspartic acid aminotransaminase (AST), alanine aminotransaminase (ALT), total bilirubin (TBIL), direct bilirubin (DBIL), alkaline phosphatase (ALP), albumin(ALB),triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), lactic dehydrogenase (LDH), calcium (Ca), phosphorus (P), magnesium (Mg), fasting plasma glucose (FPG) were measured using automatic biochemical analyzers. Platelet counts(PLT), neutrophil counts(NEUT),lymphocyte counts(LYM), monocyte counts(MONO) were determined using a Mindray automated blood cell analyzer (Mindray Medical International Limited, Shenzhen, China). Calculate the immunoinflammatory markers.Glycated Hb (HbA1c) was determined using HPLC (Bio-Rad D10; Bio-Rad Laboratories). Fasting insulin (FINS) levels were determined using a chemiluminescence immunoassay (CENTAUR-XP automat chemiluminescence immunoanalyzer; Siemens Healthineers). Dual-energy X-ray bone densitometry (Lunar iDXA, GE, USA) was used to determine skeletal muscle mass in the extremities.\u003c/p\u003e \u003cp\u003eBody mass index(BMI)\u0026thinsp;=\u0026thinsp;Weighe(Kg)/Height\u003csup\u003e2\u003c/sup\u003e(m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eAppendicular skeletal muscle mass index(ASMI)\u0026thinsp;=\u0026thinsp;Skeletal muscle mass (ASM)/ Height\u003csup\u003e2\u003c/sup\u003e(m\u003csup\u003e2\u003c/sup\u003e)[6]\u003c/p\u003e \u003cp\u003eImmunoinflammatory markers:\u003c/p\u003e \u003cp\u003eNeutrophil-to- albumin(NAR)\u0026thinsp;=\u0026thinsp;NEUT/ALB;\u003c/p\u003e \u003cp\u003eNeutrophil-to-lymphocyte(NLR)\u0026thinsp;=\u0026thinsp;NEUT / LYM;\u003c/p\u003e \u003cp\u003eMonocyte -to- lymphocyte ratio(MLR)\u0026thinsp;=\u0026thinsp;MONO/LYM;\u003c/p\u003e \u003cp\u003eSystemic immune-inflammation index(SII)\u0026thinsp;=\u0026thinsp;PLT \u0026times; NEUT /LYM;\u003c/p\u003e \u003cp\u003eSystemic inflammation response index(SIRI)\u0026thinsp;=\u0026thinsp;NEUT\u0026times;MONO/ LYM;\u003c/p\u003e \u003cp\u003eAggregate inflammation systemic index(AISI)\u0026thinsp;=\u0026thinsp;NEUT\u0026times;MONO\u0026times;PLT /LYM\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiagnostic criteria\u003c/h3\u003e\n\u003cp\u003eDiagnostic criteria for T2DM, according to the WHO diagnostic criteria in 1999, are the following: FPG\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L; 2h-PG\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L; history of diagnosis of T2DM.\u003c/p\u003e \u003cp\u003eDiagnostic criteria for low skeletal muscle mass, ASMI\u0026thinsp;\u0026lt;\u0026thinsp;7 kg/m\u0026sup2; for men and \u0026lt;\u0026thinsp;5.4kg/m\u0026sup2; for women is diagnosed as low skeletal muscle mass [6].\u003c/p\u003e\n\u003ch3\u003eStatistical methods\u003c/h3\u003e\n\u003cp\u003eAll data were analyzed by IBM SPSS Statistics for Windows, version 25.0 (IBM Corp.). Normally distributed measurement data are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (x\u0026macr;\u0026plusmn;\u003cem\u003es\u003c/em\u003e), while non-normally distributed measures are expressed as medians (p25, p75). One-way analysis of variance (ANOVA) and Kruskal-Whitney non-parametric tests for independent samples were used to analyze the measurement data according to the normal or non-normal distributions, and the Shapiro\u0026ndash;Wilk test was used to test the normality of the sample distribution. Comparisons between the two groups were analyzed based on whether equal variances were satisfied using Bonferroni\u0026rsquo;s and Tamhane\u0026rsquo;s tests. The enumeration data were expressed as frequencies and percentages (%), and differences among groups were compared using the chi-square test. Comparisons between the two groups were analyzed using the Bonferroni method under the \u003cem\u003ez\u003c/em\u003e test to adjust the \u003cem\u003eP\u003c/em\u003e-values. Correlation analysis was performed using Pearson\u0026rsquo;s or Spearman\u0026rsquo;s rank correlation analyses. Univariate Logistic regression was used to analyse the influencing factors related to low skeletal muscle mass. Binary logistic regression was used to analyse the independent correlations between skeletal muscle mass reduction and the levels of novel immunoinflammatory markers in middle-aged and elderly T2DM patients. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 978 study subjects were finally included in this study, 348 patients (35.6%)with reduced muscle mass, including 276 males(79.3%) and 72 females(20.1%) .\u003c/p\u003e \u003cp\u003eCompared with the normal skeletal muscle mass group, the levels of age, MONO, NAR, NLR, MLR, SII, SIRI, and SIRI were increased in the low skeletal muscle mass group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the levels of BMI, TP, ALB, TC, TG, and LYM were decreased (all\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was no statistically difference in the prevalence of hypertension, FPG, AST, ALT, HDL, LDL, WBC, NEUT, and PLT levels between the groups (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\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\u003eComparison of indicators in the normal skeletal muscle mass group and the low skeletal muscle mass group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethe normal skeletal muscle mass group(n\u0026thinsp;=\u0026thinsp;630)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ethe low skeletal muscle mass group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;348)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ/t\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\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\u003e60.64\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.96\u0026thinsp;\u0026plusmn;\u0026thinsp;7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e361(57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276(79.3%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269(42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72(20.7%)\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 \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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351(55.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175(50.3%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e279(44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173(49.7%)\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 \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\u003e25.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.99\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.01\u0026thinsp;\u0026plusmn;\u0026thinsp;6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\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\u003e8.01(6.66, 10.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.24(6.72, 10.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.00(16.00, 24.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.00(15.00, 24.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.00(14.00, 28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.00(14.00, 28.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.454\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\u003e4.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\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\u003e1.56(1.13, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37(0.96, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e1.03(0.88, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01(0.87, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.303\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\u003e2.73(2.24, 3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.69(2.08, 3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC/(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.68(4.89, 6.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.75(4.82, 6.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLypmh(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88(1.48, 2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75(1.45, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeu(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.24(2.61, 3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.35(2.74, 4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMONO(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35(0.28, 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39(0.31, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181༎00(149.75, 215.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182.50(148.00, 218.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75(0.06,0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79(0.64,0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73(1.33,2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93(1.46,2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19(0.15,0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22(0.17,0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e309.66(233.90,431.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e343.71(248.54,474.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60(0.42,0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74(0.51,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108.79(73.65,159.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.15(86.45,198.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; TP: total protein; ALB: albumin; FPG: fasting blood-glucose; AST: aspartate aminotransferase; ALT: alanine aminotransferase; TC: total cholesterol; TG: triglycerides; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol;WBC: white blood cell; Lypmh: lymphocyte;Neu:neutrophil; MONO: monocyte; PLT: blood platelet;NAR:neutrophil-to-albumin;NLR:neutrophil-to-lymphocyte;MLR:monocyte-to-lymphocyte ratio; SII:systemic immune-inflammation index, SIRI:systemic inflammation response index; AISI: aggregate inflammation systemic index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison of the prevalence of muscle mass loss in groups with different quartile levels of each immune-inflammatory marker\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe T2DM patients were divided into four groups (groups Q1 to Q4) according to quartile levels of each immune-inflammatory marker, respectively. The results showed that the prevalences of muscle mass loss were increased significantly with the increasing levels of each immune-inflammatory marker.\u003c/p\u003e \u003cp\u003eIn the NLR quartiles groups, the prevalence of muscle mass loss of the Q4 group (29.6%) was increased than that in the Q1 group (21.0%,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn the MLR quartiles groups, the prevalences of muscle mass loss of the Q3 (25.3%) and Q4 groups (34.8%) were increased than those in the Q1 group (16.7%, both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), and the prevalences of muscle mass loss of the Q4 groups (34.8%) were increased than those in the Q1 (16.7%) and Q2 groups (25.3%,both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eIn the SII quartiles groups, the prevalence of muscle mass loss of the Q4 group (29.9%) was increased than that in the Q1 group (21.8%, both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn the SIRI quartiles groups, the prevalences of muscle mass loss of the Q4 groups (33.0%) were increased than those in the Q1 (18.1%) and Q2 groups (21.8%, both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the prevalences of muscle mass loss of the Q3 groups (27.0%) were increased than that in the Q1 (18.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE)\u003c/p\u003e \u003cp\u003eIn the AISI quartile groups, the prevalences of muscle mass loss of the Q4 groups (37.3%) were increased than those in the Q1 (20.1%) and Q2 groups (21.6%,both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eUnivariate logistic regression analysis of the factors influencing skeletal muscle mass in middle-aged and elderly patients with T2DM\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOne-way logistic regression was used to analyse the factors influencing the occurrence of skeletal muscle mass loss in middle-aged and elderly T2DM patients, skeletal muscle mass loss as the dependent variable, and the variables with significant differences between the normal and reduced muscle mass groups in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. as independent variables. The results showed that age, diabetic duration, NAR, NLR, MLR, SII, SIRI, AISI, the, serum total protein, TC, and TG were the middle and old age independent influences on muscle mass loss, with females, older age, longer disease duration, and higher levels of immune inflammation being more likely to suffer from skeletal muscle mass loss, and lower levels of BMI,TP, TC, and TG being more likely to suffer from skeletal muscle mass loss. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eUnivariate logistic regression analysis of the factors influencing skeletal muscle mass in middle-aged and elderly T2DM patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eWaldχ༒\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%CI)\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\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.041(1.023, 1.059)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.620(0.578, 0.665)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(Men \u003cem\u003evs\u003c/em\u003e Women)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.350(0.258,, 0.474)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.042(1.022, 1.063)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.979(0.960, 0.998)\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\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.873(0.770, 0.989)\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\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.847(0.758, 0.947)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.116(1.410, 3.176)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.335(1.171, 1.522)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.679(1.419, 1.986)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.001(1.001, 1.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.342(1.744, 3.146)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.003(1.002, 1.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: BMI: body mass index; TP: total protein; TC: total cholesterol; TG: triglycerides; NAR:neutrophil-to-albumin;NLR:neutrophil-to-lymphocyte;MLR:monocyte-to-lymphocyte ratio; SII:systemic immune-inflammation index, SIRI:systemic inflammation response index; AISI: aggregate inflammation systemic index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMultiple linear regression analysis of the relationships between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with T2DM\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe presence or absence of skeletal muscle mass loss as the dependent variable, each immunoinflammatory marker and the variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the multiple linear regression model were separately included in the multivariate logistic regression model as the dependent variables, and the results showed that all immunoinflammatory markers were significantly negatively correlated with the risk of the prevalence of skeletal muscle mass loss before adjusting parameters (\u003cem\u003eP\u003c/em\u003e all \u0026lt;\u0026thinsp;0.05). After correcting for age, gender, BMI, TP, ALB, TC, TG, and other confounders, NAR, NLR, MLR, SII, SIRI, and AISI were independently and positively associated with skeletal muscle mass loss; for every 1 increase in the values of NAR, NLR, MLR, SII, SIRI, and AISI, the risk of prevalence of skeletal muscle mass loss was increased by 2.148, 1.21, 1.282, 1.001 1.828, and 1.003, respectively.. (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\u003eMultiple linear regression analysis of the relationships between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.763(1.160,2.677)\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 \u003cp\u003e2.146(1.223, 3.763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.148(1.225, 3.766)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.244(1.079,1.433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.207(1.035, 1.407)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.210(1.036, 1.411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.398(1.187,1.647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.280(1.067, 1.535)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.282(1.068,1.540)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.001(1.000,1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.001(1.000, 1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.001(1.000, 1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.912(1.380,2.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.821(1.267, 2.617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.828(1.271, 2.628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.002(1.001,1.004)\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 \u003cp\u003e1.003(1.001, 1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.003(1.001, 1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Model 1: Adjusted sex, age; Model 2: Adjusted Model 1 ་BMI་TP་ALB; Model 3: Model 2\u0026thinsp;+\u0026thinsp;TC\u0026thinsp;+\u0026thinsp;TG\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found that the levels of immunoinflammatory markers NAR, NLR, MLR, SII, SIRI and AISI were elevated in middle-aged and elderly T2DM patients with low skeletal muscle mass, and these immunoinflammatory markers were independently negatively correlated with muscle mass after adjusting for the effects of confounders such as age, sex and BMI, and with increasing levels of immunoinflammatory markers, the prevalences of muscle mass loss in T2DM patients were increased.\u003c/p\u003e \u003cp\u003eThe relationship between inflammatory markers and chronic complications of T2DM, such as diabetic nephropathy and diabetic retinopathy, is now gaining attention from researchers and clinicians alike[13, 14]. Low skeletal muscle mass likewise as a chronic complication of T2DM,several studies in recent years have found that the levels of some inflammatory markers are closely associated with alterations in muscle mass: a meta-analysis showed that serum CRP levels were elevated in patients with sarcopenia [15, 16]; however, there are relatively few studies analysing the relationship between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus. Similar to our results, In the cross-sectional study by Lin Shi et al. 10,367 individuals enrolled in their analysis. They found that subjects with higher SII levels showed an increased risk of low muscle mass. After adjusting all potential confounding factors, higher SII levels still independently increased the risk of low muscle mass[17]A retrospective analysis observed by Zeynel \u003cem\u003eet al\u003c/em\u003e[18] in a Turkish population found that patients in the sarcopenia group had significantly higher levels of NLR than those in the non-sarcopenia group, and that NLR was an independent predictor of sarcopenia (OR\u0026thinsp;=\u0026thinsp;1.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). A cross-sectional study of the National Health and Nutrition Examination Survey (NHANES) Phase III in the United States of America [19]showed that PLR levels were positively associated with sarcopenia after adjusting for a number of conventional risk factors (uric acid, bilirubin, creatinine, albumin). It was also reported [20] that the levels of systemic inflammatory markers (WBC, NOMO, NEU, PLT, NLR, PLR, SII) were strongly associated with the development of sarcopenia in a middle-aged and elderly population in western China, and the prevalence of sarcopenia increased in patients with higher levels of NLR, PLR, and SII, suggesting that chronic low-grade inflammation may be associated with the development of sarcopenia. Yoshinaga Okugawa \u003cem\u003eet al\u003c/em\u003e[21] found that to the colorectal cancer patients, sarcopenia was significantly associated with elevated levels of SII and SIRI. Of course, a few studies have failed to find a correlation between the immunoinflammatory markers PLR, NLR, LMR and muscle mass[22]. Differences in the basic characteristics of the included subjects, the number of sample sizes, the range of variability in the measurement of immunoinflammatory markers, and the fact that some confounders were not adjusted for may have contributed to the inconsistent results.\u003c/p\u003e \u003cp\u003eSkeletal muscle is known to play an integral role in the maintenance of homeostasis in various organ systems. Skeletal muscle is plastic and changes dynamically with physical activity, loading, injury, disease, and aging[23] ; previous studies have shown that decreased and dysregulated immune function during aging leaves the body in a chronic low-grade inflammatory state [15]; and that it may be an important factor in triggering or accelerating age-related diseases, such as stroke, Alzheimer's disease, and osteoarthritis.\u003c/p\u003e \u003cp\u003eIn diabetic patients, chronic hyperglycaemia promotes glycosylation of lipids and proteins, which increases the production of glycosylation end products and triggers a number of adverse effects. Glycosylation end products can bind to the receptors of immune cells and influence the inflammatory response [24]. Prolonged hyperglycaemia-induced inflammation disrupts the balance between protein synthesis and catabolism and generates high levels of oxygen free radicals, leading to myocyte apoptosis.\u003c/p\u003e \u003cp\u003eDuring muscle wasting, chronic inflammation overexpresses the ubiquitin-proteasome pathway, reduces insulin-like growth factor 1 and increases muscle cortisol synthesis, all of which promote skeletal muscle proteolysis[25]. Chronic low-grade inflammation plays a role in the progression of inflammation and sarcopenia through the regulation of pro-inflammatory cytokines such as TNF-α and IL-6[26].TNFα is a key endocrine factor for contractile dysfunction in chronic inflammation, short-term increases in TNF-α, considered a key mediator of the inflammatory responses and apoptosis, promote muscle repair [27]. However, continuous elevated levels of TNF-α lead to muscle damage[28],In addition myogenic reactive oxygen species (ROS) and nitric oxide (NO) are involved in inhibiting myofibre proportions, which can lead to muscle atrophy[29] .IL-6 can contribute to muscle atrophy by stimulating the protein hydrolysis pathway, leading to muscle degradation through ubiquitination [25]. Decreased levels of lipocalin also lead to increased secretion of pro-inflammatory cytokines[30]. And upon aging, adipose tissue is redistributed outside fat depots, accumulating viscerally. it can be found accumulated as ectopic fat depots or as intramuscular lipid droplets, local adiposity promotes the attraction of immune cells by the recruitment of macrophages[31]. In aging muscle, there is a reduced mitochondrial volume and reduced oxidative capacity leading to a state of oxidative stress capable of triggering an inflammatory response. Reduced autophagy with aging may be the cause underlying the age-associated decline in muscle stem cells pool. In the elderly, mitochondrial dysfunction together with insufficient autophagy may promote a pro-inflammatory environment, worsening sarcopenia outcomes[32, 33].\u003c/p\u003e \u003cp\u003eThis study offers numerous advantages. Firstly, the study is based on a large sample population study in western China; secondly, the immunoinflammatory markers identified in this study are readily available in clinical practice and the test is cost-effective; finally, this study is a comparative analysis of the relationship between several novel immunoinflammatory indices and muscle mass. The results demonstrate that SII and AIAI, two novel and well-integrated inflammatory indexes, provide a more accurate reflection of the body's actual condition. This study validates the value of immune-inflammatory indexes in predicting muscle mass reduction, even when used in conjunction with traditional inflammatory indexes. It should be noted that this study is not without limitations. Firstly, as a retrospective study based on single-centre data, there is a possibility of bias in the results. Secondly, we did not adjust for some important potential confounding factors, such as activities of daily living and other related variables. Finally, the calculation of these inflammatory indices was based on a single measurement, which could have affected the accuracy of the results.\u003c/p\u003e \u003cp\u003eIn conclusion, the present study found that in middle-aged and elderly patients with type 2 diabetes mellitus, the level of the immunoinflammatory markers was significantly higher in the group with skeletal muscle mass loss, and the prevalence of skeletal muscle mass loss increased progressively with increasing quartile levels of each immunoinflammatory marker. The immunoinflammatory index was found to be independently and positively associated with the risk of skeletal muscle mass loss. Consequently, monitoring the levels of novel immunoinflammatory markers is clinically valuable for the early screening and intervention of muscle mass loss in middle-aged and elderly T2DM patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest relevant to this article was reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank\u0026nbsp;the First Hospital of Lanzhou University\u0026nbsp;for supporting the construction of the registry of data .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was funded by National Natural Science Foundation of China (No.81960155; No.82360161)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXinyuan Guo and Jingfang Liu conceived and designed the study. Xinyuan Guo; Binjing Pan; Mei Han; Dengrong Ma; Xiaohui Zan collected clinical and biochemical data. Xinyuan Guo and Jingfang Liu contributed to the statistical analysis, results interpretation, drafting and revising the paper. All authors read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVolpi E, Nazemi R, Fujita S. Muscle tissue changes with aging. Curr Opin Clin Nutr Metab Care. 2004;7(4):405-410.\u003c/li\u003e\n\u003cli\u003eIzzo A, Massimino E, Riccardi G, Della Pepa G. A Narrative Review on Sarcopenia in Type 2 Diabetes Mellitus: Prevalence and Associated Factors. Nutrients. 2021 Jan 9;13(1):183. \u003c/li\u003e\n\u003cli\u003eLiu J, Saul D, B\u0026ouml;ker KO, Ernst J, Lehman W, Schilling AF. Current Methods for Skeletal Muscle Tissue Repair and Regeneration. Biomed Res Int. 2018 Apr 16;2018:1984879. \u003c/li\u003e\n\u003cli\u003eDe Nardi P, Giani A, Maggi G, Braga M. 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Cell Death Discov. 2021 Dec 20;7(1):397. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes mellitus, middle-aged and elderly patients, low skeletal muscle mass, immunoinflammatory markers","lastPublishedDoi":"10.21203/rs.3.rs-5667977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5667977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo investigate the relationships between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus (T2DM).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFrom April 2022 to May 2023, 978 middle-aged and elderly T2DM patients diagnosed in the Department of Endocrinology of the First Hospital of Lanzhou University were divided into a low skeletal muscle mass group and a normal group according to the muscle mass index, compared the differences between the groups. The above immunoinflammatory markers were grouped according to the quartile levels, and the prevalences of muscle mass loss were compared among the groups; the relationship between the immunoinflammatory index and low skeletal muscle mass in T2DM patients was analysed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with the normal group, the levels of all immunoinflammatory indices of low skeletal muscle mass group were significantly increased ( \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); the prevalences of skeletal muscle mass loss were progressively raised with increasing quartile levels of each immunoinflammatory marker. The levels of immunoinflammatory markers were independently and positively correlated with the risk of low skeletal muscle mass (NAR: OR\u0026thinsp;=\u0026thinsp;2.148, 95% CI 1.225\u0026ndash;3.766, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008; NLR: OR\u0026thinsp;=\u0026thinsp;1.210, 95% CI 1.036\u0026ndash;1.411, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016; MLR: OR\u0026thinsp;=\u0026thinsp;1.282, 95% CI 1.068\u0026ndash;1.540, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008; SII: OR\u0026thinsp;=\u0026thinsp;1.001, 95% CI 1.000 -1.002, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009; SIRI: OR\u0026thinsp;=\u0026thinsp;1.828, 95% CI 1.271\u0026ndash;2.628, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; SIRI: OR\u0026thinsp;=\u0026thinsp;1.003, 95% CI 1.001\u0026ndash;1.004, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) .\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe occurrence of low skeletal muscle mass may be closely related to immune inflammation in middle-aged and elderly T2DM patients. Monitoring immune inflammation markers is of clinical value for early screening and intervention of muscle mass loss in middle-aged and elderly T2DM patients.\u003c/p\u003e","manuscriptTitle":"The association between low skeletal muscle mass and immunoinflammatory markers in middle-aged and elderly patients with type 2 diabetes mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-25 16:03:52","doi":"10.21203/rs.3.rs-5667977/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"74aa4130-9a7f-4ff8-830a-045b8520abde","owner":[],"postedDate":"December 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T18:38:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-25 16:03:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5667977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5667977","identity":"rs-5667977","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}