Analysis of the predictive value of insulin resistance for osteoporosis in middle-aged and elderly non-type 2 diabetic population

Analysis of the predictive value of insulin resistance for osteoporosis in middle-aged and elderly non-type 2 diabetic population | 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 Analysis of the predictive value of insulin resistance for osteoporosis in middle-aged and elderly non-type 2 diabetic population Qian Zhu, Yan Zhou, Silu Sun, Simin Tao, Xiaoyan Xi, Tao Jiang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4082092/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 Background With the deepening of the aging of the population, the incidence of osteoporosis in the middle-aged and elderly people is increasing. As a degenerative disease with damaged bone microstructure, decreased bone mass and decreased bone density, osteoporosis is characterized by high disability rate and high mortality. Therefore, the early prediction and diagnosis of osteoporosis is particularly important. Previous studies have demonstrated a strong relationship between insulin resistance and bone mineral density and osteoporosis in type 2 diabetes mellitus; however, there is a lack of attention on the correlation between insulin resistance and bone metabolism in healthy populations. The aim of this study was to analyze the correlation between three insulin resistance measures and bone mineral density, and to compare their value in predicting middle-aged and elderly non-type 2 diabetes. Methods In this study, the general data, bone mineral density, blood routine, lipid metabolism and other clinical data of 700 Chinese middle-aged and elderly non-type 2 diabetes patients were collected, and the patients were divided into osteoporosis group (n = 149) and non-osteoporosis group (n = 551). spearman correlation analysis was used to explore the correlation between three insulin resistance metabolic indexes and bone mineral density. The relationship between insulin resistance and osteoporosis was analyzed by binary logstics regression. ROC curve was used to compare the predictive value of METS-IR, TyG-BMI index and TG/HDL-C Ratio in osteoporosis. Results Spearman correlation showed that METS-IR, TyG-BMI index and TG/HDL-C Ratio were positively correlated with L1-L4 BMD, femoral neck BMD and hip BMD. Binary logstics regression analysis showed that METS-IR was related to the occurrence of osteoporosis. After adjusting for age, sex, smoking, drinking, serum total protein, serum albumin, serum creatinine, uric acid and total cholesterol, the correlation between METS-IR and osteoporosis still existed. ROC curve analysis showed that these three indexes of insulin resistance metabolism had certain predictive value in osteoporosis, among which METS-IR had the highest diagnostic value in osteoporosis. Conclusions METS-IR, TyG-BMI index and TG/HDL-C Ratio were correlated with BMD at all sites.The predictive value of METS-IR was better than TG/HDL-C Ratio and TyG-BMI index in osteoporosis. Insulin resistance Bone mineral density osteoporosis Figures Figure 1 1. Introduction Osteoporosis (OP) is a systemic skeletal disease with decreased bone mass, decreased bone strength and increased risk of fracture [ 1 ] , which is affected by various factors such as age, gender, hormone levels, body mass index (BMI) and other factors, and is characterized by high morbidity, disability and mortality [ 2 ] . In China, the prevalence of osteoporosis in people aged 40–49, over 50 and over 65 is 3.2%, 19.2% and 32.0%, respectively [ 3 ] . With the aging of the global population, the prevalence of OP has shown a gradual increasing trend, and OP has become a public health problem of key concern in countries around the world. It is well known that OP not only reduces the quality of life of patients, but also imposes a huge economic burden on patients, families and society. It has been reported that the annual cost of treating osteoporosis and fractures in the European Union is as high as 55 billion euros [ 4 ] , and the systematic evaluation by Maubeni et al [ 5 ] pointed out that the average cost of a single hospitalization for osteoporotic fracture in China is as high as 27,561 yuan. Therefore, early diagnosis and early intervention of OP are particularly important. insulin resistance (IR), a state of reduced responsiveness of insulin-targeted tissues to physiological levels of insulin, is one of the key mechanisms of abnormal glucose-lipid metabolism, which leads to internal environmental disorders, including obesity, elevated blood glucose, dyslipidemia, and abnormal synthesis and secretion of a variety of cytokines, and all of these effects have a direct or indirect impact on bone metabolism [ 6 , 7 ] . Insulin has been shown to have a direct effect on osteoblasts, and IR leads to impaired bone regulation and imbalance of bone homeostasis, causing alterations in bone density, bone geometry, and bone microarchitecture, which further leads to osteoporosis and fragility fractures [ 7 , 8 ] . The high insulin-normal glucose clamp method is the gold standard for assessing insulin resistance, but it is not suitable for large-scale clinical screening due to its invasiveness, complexity, and high cost [ 9 ] , therefore, researchers have developed the triglyceride glucose-body mass index(TyG-BMI index), the insulin resistance Metabolic score the metabolic score of insulin resistance (METS-IR), TG/HDL-C Ratio and other tools to assess IR [ 10 – 13 ] . Numerous previous studies have demonstrated that insulin resistance assessment indices such as METS-IR, TyG-BMI index and TG/HDL-C Ratio showed different degrees of correlation with BMD, osteoporosis and fracture risk in type 2 diabetic patients [ 14 – 16 ] . However, most of the current studies have focused on patients with type 2 diabetes, and less attention has been paid to insulin resistance and osteoporosis in non-type 2 diabetic populations. The relationship between insulin resistance evaluation indexes and BMD in middle-aged and elderly non-type 2 diabetic populations is unclear, as is their predictive value for OP. Therefore, this study will analyze the correlation between three commonly used insulin metabolism evaluation indexes and BMD in middle-aged and elderly non-type 2 diabetic population, and explore their diagnostic value for OP. It aims to provide a theoretical basis for the screening, identification and prevention of OP in middle-aged and elderly non-type 2 diabetic population. 2. Materials and Methods 2.1 Study Population In this study, a cross-sectional survey was conducted on 700 middle-aged and elderly non-type 2 diabetic patients who underwent health checkups at the First Affiliated Hospital of Chengdu Medical College from August 2016 to June 2023, of which 449 were female and 251 were male. Inclusion criteria were (1) ≥ 40 years old; (2) patients with non-type 2 diabetes; (3) receiving DXA for BMD determination; exclusion criteria were (1) incomplete medical records; (2) patients with myocardial infarction, cerebral hemorrhage, severe liver or renal insufficiency, a history of acute infections, and a history of malignant tumors within the last three months; and (3) history of treatment with anti-osteoporosis drugs. 2.2 Clinical data We obtained important information such as age, sex, blood pressure, height, weight, blood biochemical parameters through the patient's e-case.BMI was calculated as weight kg / (height m)^2. Fasting blood samples were collected from the patients and laboratory parameters included serum total protein (TP), serum albumin (ALB), blood creatinine (Cr), blood uric acid (UA), fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL). Smoking was defined as continuous or cumulative smoking for more than 6 months, and alcohol consumption was considered to be drinking at least once a week for 6 months or more. 2.3 Bone mineral density The patient's lumbar spine BMD (LS BMD), femoral neck BMD (FN BMD), and total hip BMD (TH BMD), and the T-value of each site were measured using dual-energy X-ray absorptiometry (enCORE, General Electric Company, version 15). The patients were examined by removing any metal objects from their bodies, and the measurements were performed in a resting state, while the operator performed whole-body and hip scans in accordance with the instrument's operating procedures to obtain the patients' lumbar spine BMD, femoral neck BMD, and total hip BMD, and the patients' T-value ≤ -2.5 was defined as osteoporosis according to the diagnostic criteria for osteoporosis. 2.4 Assessment of insulin resistance METS-IR and TyG-BMI index, TG/HDL-C Ratio were calculated using the following: METS-IR = Ln[2 × FPG (mg/dL) + TG (mg/dL)] × BMI (kg/m2)/Ln[HDL-C (mg/dL)] TyG-BMI = Ln [TG (mg/dL) × FPG (mg/dL)/2]× BMI TG/HDL-C Ratio = TG(mmol/L)/HDL-C(mmol/L) 2.5 Statistical Analysis Patients were categorized into two groups according to the diagnostic criteria of osteoporosis: the osteoporosis group (T-score≤-2.5) and the non-osteoporosis group (T-score ≥ -2.5 ) [6] . Statistical analysis was performed using SPSS 25.0 software. Quantitative data were expressed as mean ± standard for normal distribution and mean (25th percentile, 75th percentile) for non-normally distributed continuous variables. Qualitative parameters were expressed as numbers and percentages. One-way ANOVA and Kruskal-Wallis test were used for between-group comparisons, respectively.Pearson or Spearman correlations were used to test associations between METS-IR, TyG-BMI index,TG/HDL-C Ratio, and BMD by site. Binary logistic regression was used to analyze the risk factors for osteoporosis in middle-aged and elderly non-type 2 diabetic patients, and ROC curves were used to analyze the predictive value of METS-IR, TyG-BMI index, and TG/HDL-C Ratio for osteoporosis. The test level α was set at 0. 05 unless otherwise stated. 2.6 Ethics Statement The Chengdu Medical College Ethics Committee approved the study and complied with the Declaration of Helsinki (CMCEC–2022N0.40). Patient informed consent was not required due to the retrospective design of this study. 3. Results 2.1 Clinical Characteristics of the Study Population Participants were selected as shown in Fig. 1 . A total of 789 patients met the inclusion criteria for this retrospective study and 89 patients were excluded due to exclusion criteria, resulting in the inclusion of 700 patients. The basic characteristics of the patients are shown in Table 1 .A total of 551 non-osteoporotic patients and 149 osteoporotic patients, of which 61.4% were female, 28.1% suffered from hypertension, 11.9% had a history of smoking, and 12.4% had a history of alcohol consumption. The mean age of the observed patients was 57.9 ± 9.79 years, serum total protein 72.50(69.90,75.48)(g/L), serum albumin 44.00(42.10,45.88)(g/L), endogenous creatinine clearance 68.63 ± 42.57 µmol/L, uric acid 325.91 ± 84.64 µmol/L, fasting blood glucose 5.35 ± 0.99 mmol/L, triglycerides 1.63 ± 1.12 mmol/L, total cholesterol 5.24(4.60,5.95)mmol/L, HDL was 1.53 ± 0.38 mmol/L, LDL 3.19 ± 0.89 mmol/L, and BMI was 24.07 ± 3.19 (kg/m 2 ). METS-IR was 39.89 ± 7.67, TyG-BMI index was 171.01 ± 30.34, and TG/HDL-C Ratio was 0.90(0.56,1.43).L1 ~ L4 BMD, femoral neck BMD, and total hip BMD were 0.96(0.85,1.07)(g/cm 2 ), 0.90 ± 0.14 (g/cm 2 ), and 0.89(0.81,0.98)(g/cm 2 ). Compared to patients in the non-osteoporotic group, patients in the osteoporotic group were older (62.42 ± 10.36 vs. 56.70 ± 9.27, p < 0.001), had lower uric acid (313.37 ± 77.72 vs. 329.3 ± 86.18, p = 0.031), and higher total cholesterol and high-density lipoprotein (5.49(4.72,6.11) vs. 5.13 (4.57,5.87), p = 0.009, 1.65 ± 0.43 vs 1.48 ± 0.36, p < 0.001), lower height (154.26 ± 7.19 vs 159.22 ± 7.73, p < 0.001), smaller mean weight (55.45 ± 9.68 vs 61.75 ± 10.03, p < 0.001), and lower BMI (23.24 ± 3.29 vs 24.19 ± 3.15, p < 0.001). Lumbar spine 1–4 BMD was lower (0.75(0.69,0.79) vs 0.99(0.92,1.09), p < 0.001), femoral neck BMD was lower (0.75 ± 0.15 vs 0.86 ± 0.12, p < 0.001), and total hip BMD was lower (0.80 (0.71,0.90) vs 0.91 (0.83, 1.00), p < 0.001). lower METS-IR, TyG-BMI and TG/HDL-C ratio (37.82 ± 7.85 vs 40.46 ± 7.53, p < 0.001), (165.20 ± 32.09 vs 172.58 ± 29.76, p = 0.009), (0.80(0.49, 1.37) vs 0.92(0.59,1.45)). The rest of the relevant indicators were not statistically different. Table 1 Patient characteristic All participants (n = 700) OP (n=149) Non-OP (n=551) P value Age(years) 57.9 ± 9.79 62.42 ± 10.36 56.70 ± 9.27 <0.001 Sex,female,%(n) 64.1(449) 81.9(122) 59.3(327) <0.001 Hypertension %(n) 28.1(197) 30.9(46) 27.4(151) 0.404 Smoking,%(n) 11.9(83) 7.4(11) 13.1(72) 0.057 Drinking,%(n) 12.4(87) 7.4(11) 13.8(76) 0.035 TP (g/L) 72.50(69.90,75.48) 72.9(69.80,76.35) 72.40(69.90,75.2) 0.482 ALB (g/L) 44.00(42.10,45.88) 43.6(41.90,45.60) 44.1(42.10,45.90) 0.286 Cr (µmol/L) 68.63 ± 42.57 64.76 ± 20.64 69.68 ± 46.73 0.211 UA (µmol/L) 325.91 ± 84.64 313.37 ± 77.72 329.3 ± 86.18 0.031 FBG (mmol/L) 5.35 ± 0.99 5.44 ± 1.15 5.32 ± 0.93 0.257 TG (mmol/L) 1.36(0.98,1,88) 1.34(0.95,1.91) 1.36(1.00,1.85) 0.766 TC (mmol/L) 5.24(4.60,5.95) 5.49(4.72,6.11) 5.13(4.57,5.87) 0.009 HDL-C (mmol/L) 1.53 ± 0.38 1.65 ± 0.43 1.48 ± 0.36 < 0.001 LDL-C (mmol/L) 3.19 ± 0.89 3.24 ± 0.92 3.17 ± 0.88 0.376 Height (cm) 158.17 ± 7.88 154.26 ± 7.19 159.22 ± 7.73 < 0.001 Weight (kg) 60.41 ± 10.28 55.45 ± 9.68 61.75 ± 10.03 <0.001 BMI (kg/m 2 ) 24.07 ± 3.19 23.24 ± 3.29 24.19 ± 3.15 <0.001 Lumbar spine BMD (g/cm 2 ) 0.96(0.85,1.07) 0.75(0.69,0.79) 0.99(0.92,1.09) < 0.001 Femoral Neck BMD (g/cm 2 ) 0.90 ± 0.14 0.75 ± 0.15 0.86 ± 0.12 <0.001 Total Hip BMD (g/cm 2 ) 0.89(0.81,0.98) 0.80(0.71,0.90) 0.91(0.83,1.00) < 0.001 METS-IR 39.89 ± 7.67 37.82 ± 7.85 40.46 ± 7.53 < 0.001 TyG-BMI index 171.01 ± 30.34 165.20 ± 32.09 172.58 ± 29.76 0.009 TG/HDL-C ratio 0.90(0.56,1.43) 0.80(0.49,1.37) 0.92(0.59,1.45) 0.969 TP Total serum protein,ALB Serum albumin,Cr Creatinine, UA Hematuria,FBG Fasting blood glucose, TG Triglycerides, TC Total cholesterol, HDL High-density lipoprotein, LDL Low-density lipoprotein, BMI Body mass index,BMD Bone mineral density,METS-IR Metabolic score for IR,TyG-BMI index Triglyceride Glucose-Body Mass Index Table 2 Association of each index with bone mineral density Index Lumbar Spine BMD Femoral neck BMD Total Hip BMD β P value β P value β P value Age(years) −0.222* < 0.001 −0.330* < 0.001 −0.249* < 0.001 TP(g/L) 0.039 0.304 −0.025 0.507 −0.029 0.446 ALB(g/L) 0.020 0.594 0.063 0.095 0.031 0.414 Cr (µmol/L) 0.150* < 0.001 −0.012 0.757 0.129* 0.001 UA(µmol/L) 0.171* < 0.001 0.139* < 0.001 0.203* < 0.001 FBG(mmol/L) 0.025 0.505 −0.014 0.706 0.013 0.740 TG(mmol/L) 0.061 0.105 0.066 0.081 0.113* 0.003 TC(mmol/L) −0.174* < 0.001 −0.106* 0.005 −0.122* 0.001 HDL-C(mmol/L) −0.244** < 0.001 −0.166* < 0.001 −0.201* < 0.001 LDL-C(mmol/L) −0.092* 0.015 −0.024 0.522 −0.063 0.096 Height(cm) 0.284** < 0.001 0.290* < 0.001 0.296* <0.001 Weight(kg) 0.316** < 0.001 0.335* < 0.001 0.416* <0.001 BMI(kg/m 2 ) 0.180* < 0.001 0.220* < 0.001 0.319* <0.001 METS-IR 0.246* < 0.001 0.215* < 0.001 0.314* <0.001 TyG-BMI index 0.171* < 0.001 0.197* < 0.001 0.291* <0.001 TG/HDL-C Ratio 0.140* < 0.001 0.083* 0.027 0.157* <0.001 TP Total serum protein,ALB Serum albumin,Cr Creatinine, UA Hematuria,FBG Fasting blood glucose, TG Triglycerides, TC Total cholesterol, HDL High-density lipoprotein, LDL Low-density lipoprotein, BMI Body mass index,BMD Bone mineral density,METS-IR Metabolic score for IR,TyG-BMI index Triglyceride Glucose-Body Mass Index 2.2 Association of insulin resistance with BMD Spearman correlation analysis showed that METS-IR, TyG-BMI index and TG/HDL-C ratio were positively correlated with BMD at all points.The correlation coefficients of METS-IR with L1 ~ L4 BMD, femoral neck BMD and total hip BMD were 0.246 (p < 0.001), 0.215 (p < 0.001) and 0.314 (p < 0.001).The correlation coefficients of TyG-BMI index with the three were 0.171 (p < 0.001), 0.197 (p < 0.001) and 0.291 (p < 0.001), respectively.The correlation coefficients of TG/HDL-C ratio with L1 ~ L4BMD, femoral neck BMD and total hip The correlation coefficients of BMD were 0.140 (p < 0.001), 0.083 (p = 0.027) and 0.157 (p < 0.001). 2.3 Binary logstic regression Binary logistic regression was used to analyze the relationship between insulin resistance score and osteoporosis, and the results showed that METS-IR was associated with osteoporosis, and TyG-BMI index and TG/HDL-C ratio were not associated with osteoporosis. After adjusting for age, sex, smoking, alcohol consumption, serum albumin (ALB), and with serum total protein (TP), blood creatinine (Cr), blood uric acid (UA), and total cholesterol (TC), the METS-IR was still associated with osteoporosis, while the TyG-BMI index, and TG/HDL-C ratio were still not associated with osteoporosis. Table 3 Binary logistics regression analysis of the relationship between insulin resistance and osteoporosis Model 1 Model 2 Model 3 OR 95%CI P value OR 95%CI P value OR 95%CI P value METS-IR 1.136 1.050–1.229 0.001 1.109 1.021–1.206 0.015 1.117 0.768–1.012 0.020 TyG-BMI index 0.984 0.963–1.006 0.151 0.990 0.968–1.012 0.380 0.989 0.965–1.014 0.375 TG/HDL-C ratio 0.857 0.685–1.073 0.178 0.833 0.657–1.057 0.133 0.867 0.692–1.085 0.212 Model 1: unadjusted model Model 2: Model1 + gender, hypertension, smoking, alcohol consumption Moedel 3: Model1 + total serum protein, serum albumin, creatinine, uric acid, total cholesterol 2.4 Receiver-operating characteristics (ROC) curves ROC curve analysis showed that METS-IR predicted an area under the ROC curve (AUC) of 0.609 (95% CI 0.557–0.662) for OP, with an optimal cutoff value of 36.62, a sensitivity of 52.3%, and a specificity of 67.9%.The TyG-BMI index predicted an area under the ROC curve of 0.579 (95% CI 0.526–0.632), with an optimal cutoff value of 154.63, a sensitivity of 42.3%, and a specificity of 72.4%.The TG/HDL-C ratio predicted an area under the ROC curve (AUC) of 0.553 (95% CI 0.498–0.607) for OP, with an optimal cutoff value of 0.85, a sensitivity of 54.4%, and a specificity of 57.5%. ROC Receiver operating characteristic, AUC area under the curve Table 4 Comparing the ability of Indicators for the evaluation of IR to predict osteoporosis AUC 95% CI Sensitivity Specificity The best value for diagnosis METS-IR 0.609 0.557–0.662 0.523 0.679 36.62 TyG-BMI 0.579 0.526–0.632 0.423 0.724 154.63 TG/HDL-C ratio 0.553 0.498–0.607 0.544 0.575 0.85 4. Discussion With the deepening of population aging, the prevalence of OP in the middle-aged and elderly population has been increasing year by year, and OP has become a public health problem that is closely watched globally, which not only causes physiological and psychological damages to the patients, but also aggravates the burdens of the patients' families and the society. Therefore, early diagnosis and prevention of OP are extremely important.IR is a state in which insulin is ineffective in peripheral tissues, leading to dyslipidemia and impaired insulin homeostasis, and it is a key causative factor for metabolic diseases such as obesity, hyperlipidemia, and diabetes mellitus [ 6 ] , and a number of studies have demonstrated that IR is associated with cardiovascular disease [ 17 ] , thyroid cancer [ 18 ] , and cognitive impairment [ 19 ] . In recent years, more and more studies have focused on the correlation between insulin resistance and bone, and in a prospective study by Napoli et al [ 15 ] , the relationship between IR and BMD and fracture risk in nondiabetic older adults was analyzed, and it was found that IR was positively correlated with BMD, while an increase in IR was associated with a decrease in fracture risk in an unadjusted model. In this study, we found that TG/HDL-C ratio, METS-IR and TyG-BMI index were positively correlated with BMD at different sites in non-type 2 diabetic population, and, our binary logstics regression model showed a significant correlation between METS-IR and osteoporosis, and after adjusting for age, sex, smoking, alcohol consumption, ALB, and TP, this correlation still existed. Consistent with our findings, a study by Pu et al [ 20 ] found that METS-IR was positively associated with BMD, with each one-unit increase in METS-IR in non-diabetic adults in the U.S. being associated with an increase in femoral BMD and spinal BMD of 0.005 g/cm3 and 0.005 g/cm3 respectively, and this positive association persisted regardless of whether METS-IR was used as a continuous variable or quartiles were converted to categorical variables, this positive association persisted. Similar conclusions have been reached in longitudinal studies, Zhang et al [ 21 ] showed a positive correlation between the rate of change in METS-IR and the rate of change in bone mineral density in a male population by following up a Chinese middle-aged and elderly health check-up population.The TyG-BMI index, which combines serum triglyceride, fasting glucose, and obesity, is more reliable than the TyG index.Riggs et al [ 22 ] found that a higher TyG-BMI index was more reliable than the TyG index, and that a higher TyG-BMI index was more reliable than the TyG index. found that higher TyG-BMI was associated with greater BMD at both weight-bearing and non-weight-bearing skeletal sites.Chuang et al [ 23 ] found that BMD at all measured sites was positively correlated with TG levels and negatively correlated with HDL-C levels in a survey study of Korean adults undergoing health checkups. Alkaline phosphatase (ALP) plays a crucial role in bone metabolism and studies have demonstrated that ALP activity is positively and independently correlated with TG/HDL-C ratio and TyG index [ 24 , 25 ] . In contrast to previous studies, our study only observed that the occurrence of OP was only associated with METS-IR and not with TyG-BMI index and TG/HDL-C ratio. We hypothesize that this is due to the heterogeneity of the study population, such as differences in underlying diseases, age, and ethnicity, resulting in different study results. Our study focused on a middle-aged and elderly non-type 2 diabetic population, and there have been studies confirming the association of other IR evaluation indexes with OP in which the study population was mainly type 2 diabetic patients [ 26 , 27 ] , which may be related to the fact that diabetic patients are associated with varying degrees of inflammation, which further affects bone metabolism through oxidative stress and estrogen metabolism, leading to the development of OP. In addition, this study was the first to compare the diagnostic value of these three commonly used metabolic evaluation indexes of insulin resistance in a non-type 2 diabetic population.The results of the ROC curves showed that the METS-IR, TyG-BMI index, and TG/HDL-C ratio all had a certain diagnostic value for OP, with the highest predictive value of the METS-IR, the TyG-BMI index was the next highest, and TG/HDL-C ratio was the lowest.METS-IR was first identified in 2018 as a reliable and intuitive indicator for predicting IR, which was calculated from FPG, TG, BMI, and HDL-C [ 14 ] . Compared with TG/HDL-C ratio, TyG index and HOMA-IR, METS-IR fully takes into account BMI, blood glucose and lipids, which is more comprehensive in assessing metabolic status and has predictive value for many chronic diseases [ 17 , 26 , 28 ] . The limitations of this study are, firstly, that it was a single-center retrospective study with possible geographical and ethnic limitations. Second, this study was cross-sectional and could not assess the longitudinal relationship between IR evaluation indicators and BMD and OP over time. Finally, this study could not demonstrate a causal relationship between IR evaluation indicators and OP. Therefore, larger, multicenter longitudinal studies are still needed to enrich the correlation between IR and OP. 5. Conclusions The strength of our study is that it is the first to analyze and compare the correlation and predictive value of three IR evaluation indexes with BMD and OP in a middle-aged and elderly non-type 2 diabetes population. Our study found that METS-IR, TyG-BMI index and TG/HDL-C ratio were correlated with BMD at all points and all had different diagnostic values for OP, with METS-IR having the highest predictive value, and lower METS-IR being associated with the occurrence of OP. Declarations 6.1 Ethics approval and consent to participate The study complied with the Declaration of Helsinki and was approved by the Chengdu Medical College Ethics Committee (CMCEC–2022N0.40). This was a retrospective study and therefore informed consent was not required. 6.2 Consent for publication Not applicable. 6.3 Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. 6.4 Competing interests There is no conflict of interest to be declared. 6.5 Funding This research was funded by Sichuan Collaborative Innovation Center for Aging and Geriatric Health Joint Fund Project, grant number YLKYYB2208. 6.6 Author Contributions Q.Z. and H.L. designed the study, wrote, reviewed and edited the manuscript Q.Z. and Y.Z. analyzed data; H.L. was involved in validation; S.S., S.T. and H.C. contributed to investigation; X.X. helped in resources; T.J. was involved in project administration; H.L.,Y.Z. and H.Z. reviewed and edited the manuscript. All authors approved the final version of the manuscript to be published. 6.7 Acknowledgements Not applicable. 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Shanbhogue VV, Finkelstein JS, Bouxsein ML, Yu EW. Association between insulin resistance and bone structure in nondiabetic postmenopausal women. J Clin Endocrinol Metab. 2016. 2016;101(8):3114-22. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=27243136&query_hl=1 10.1210/jc.2016-1726 . Imerb N, Thonusin C, Chattipakorn N, Chattipakorn SC. Aging, obese-insulin resistance, and bone remodeling. Mech Ageing Dev. 2020 2020;191:111335. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=32858037&query_hl=1 10.1016/j.mad.2020.111335 . Tao LC, Xu JN, Wang TT, Hua F, Li JJ. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovasc Diabetol. 2022. 2022;21(1):68. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=35524263&query_hl=1 10.1186/s12933-022-01511-x . Pantoja-Torres B, Toro-Huamanchumo CJ, Urrunaga-Pastor D, Guarnizo-Poma M, Lazaro-Alcantara H, Paico-Palacios S et al. High triglycerides to hdl-cholesterol ratio is associated with insulin resistance in normal-weight healthy adults. Diabetes Metab Syndr. 2019 2019 Jan-Feb;13(1):382 – 88. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=30641729&query_hl=1 10.1016/j.dsx.2018.10.006 . Simental-Mendia LE, Rodriguez-Moran M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008. 2008;6(4):299–304. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=19067533&query_hl=1 10.1089/met.2008.0034 . Lee J, Kim B, Kim W, Ahn C, Choi HY, Kim JG et al. Lipid indices as simple and clinically useful surrogate markers for insulin resistance in the u.s. Population. Sci Rep. 2021. 2021;11(1):2366. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=33504930&query_hl=1 10.1038/s41598-021-82053-2 . Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A et al. Mets-ir, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur J Endocrinol. 2018 2018;178(5):533 – 44. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=29535168&query_hl=1 10.1530/EJE-17-0883 . Zhou H, Li C, Song W, Wei M, Cui Y, Huang Q et al. Increasing fasting glucose and fasting insulin associated with elevated bone mineral density-evidence from cross-sectional and mr studies. Osteoporos Int. 2021. 2021;32(6):1153-64. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=33409590&query_hl=1 10.1007/s00198-020-05762-w . Napoli N, Conte C, Pedone C, Strotmeyer ES, Barbour KE, Black DM et al. Effect of insulin resistance on bmd and fracture risk in older adults. J Clin Endocrinol Metab. 2019 2019;104(8):3303-10. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=30802282&query_hl=1 10.1210/jc.2018-02539 . Kim YH, Nam GE, Cho KH, Choi YS, Kim SM, Han BD et al. Low bone mineral density is associated with dyslipidemia in south korean men: the 2008–2010 korean national health and nutrition examination survey. Endocr J. 2013 2013/1/1;60(10):1179-89. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=23877056&query_hl=1 10.1507/endocrj.ej13-0224 . Han KY, Gu J, Wang Z, Liu J, Zou S, Yang CX et al. Association between mets-ir and prehypertension or hypertension among normoglycemia subjects in japan: a retrospective study. Front Endocrinol (Lausanne). 2022 2022/1/1;13:851338. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=35370984&query_hl=1 10.3389/fendo.2022.851338 . Xu N, Liu H, Wang Y, Xue Y. Relationship between insulin resistance and thyroid cancer in chinese euthyroid subjects without conditions affecting insulin resistance. Bmc Endocr Disord. 2022 2022;22(1):58. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=35255873&query_hl=1 10.1186/s12902-022-00943-6 . Chen M, Zhang M, Wang S, Ding X, Lee Y, Jiang G. Association between insulin resistance and cognitive impairment. J Coll Physicians Surg Pak. 2022 2022;32(2):202-07. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=35108792&query_hl=1 10.29271/jcpsp.2022.02.202 . Pu B, Gu P, Yue D, Xin Q, Lu W, Tao J et al. The mets-ir is independently related to bone mineral density, frax score, and bone fracture among u.s. Non-diabetic adults: a cross-sectional study based on nhanes. Bmc Musculoskelet Disord. 2023. 2023;24(1):730. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=37705037&query_hl=1 10.1186/s12891-023-06817-9 . Zhang Y. Correlation analysis and one-year follow-up study of insulin resistance metabolic score with bone mineral density and sRANKL/OPG[D]. Dalian Medical Universit; 2023. 51 p. Available from: https://link.cnki.net/doi/10.26994/d.cnki.gdlyu.2023.000845 . Riggs BL, Melton ILR, Robb RA, Camp JJ, Atkinson EJ, Peterson JM et al. Population-based study of age and sex differences in bone volumetric density, size, geometry, and structure at different skeletal sites. J Bone Miner Res. 2004 2004;19(12):1945-54. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15537436&query_hl=1 10.1359/JBMR.040916 . Chuang TL, Lin JW, Wang YF. Bone mineral density as a predictor of atherogenic indexes of cardiovascular disease, especially in nonobese adults. Dis Markers. 2019 2019/1/1;2019:1045098. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=31565096&query_hl=1 10.1155/2019/1045098 . Son DH, Ha HS, Lee YJ. Association of serum alkaline phosphatase with the tg/hdl ratio and tyg index in korean adults. Biomolecules. 2021 2021;11(6). Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=34198561&query_hl=1 10.3390/biom11060882 . Cheng X, Zhao C. The correlation between serum levels of alkaline phosphatase and bone mineral density in adults aged 20 to 59 years. Medicine (Baltimore). 2023 2023;102(32):e34755. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=37565863&query_hl=1 10.1097/MD.0000000000034755 . Gu P, Pu B, Xin Q, Yue D, Luo L, Tao J et al. The metabolic score of insulin resistance is positively correlated with bone mineral density in postmenopausal patients with type 2 diabetes mellitus. Sci Rep. 2023. 2023;13(1):8796. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=37258550&query_hl=1 10.1038/s41598-023-32931-8 . Xia J, Zhong Y, Huang G, Chen Y, Shi H, Zhang Z. The relationship between insulin resistance and osteoporosis in elderly male type 2 diabetes mellitus and diabetic nephropathy. Ann Endocrinol (Paris). 2012 2012;73(6):546 – 51. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=23122575&query_hl=1 10.1016/j.ando.2012.09.009 . Qian T, Sheng X, Shen P, Fang Y, Deng Y, Zou G. Mets-ir as a predictor of cardiovascular events in the middle-aged and elderly population and mediator role of blood lipids. Front Endocrinol (Lausanne). 2023 2023/1/1;14:1224967. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=37534205&query_hl=1 10.3389/fendo.2023.1224967 . 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4082092","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278680592,"identity":"32b20446-b1d9-4a00-b291-2ace3d52ec3c","order_by":0,"name":"Qian Zhu","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhu","suffix":""},{"id":278680593,"identity":"fa032856-920b-4a3a-b95b-4048fda39b4e","order_by":1,"name":"Yan Zhou","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhou","suffix":""},{"id":278680594,"identity":"d2fe1529-68d5-4637-b058-e5d81e7a37aa","order_by":2,"name":"Silu Sun","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Silu","middleName":"","lastName":"Sun","suffix":""},{"id":278680595,"identity":"a9d7a479-47ad-4331-a976-b6e83fcc7e7c","order_by":3,"name":"Simin Tao","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Simin","middleName":"","lastName":"Tao","suffix":""},{"id":278680596,"identity":"c6ab0892-6968-4b16-a4c1-c1f1d23566e8","order_by":4,"name":"Xiaoyan Xi","email":"","orcid":"","institution":"First Affiliated Hospital of Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Xi","suffix":""},{"id":278680597,"identity":"602a61c6-d6c6-4231-b8b9-c26230d1d6e8","order_by":5,"name":"Tao Jiang","email":"","orcid":"","institution":"First Affiliated Hospital of Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Jiang","suffix":""},{"id":278680598,"identity":"d792cc21-9baf-48d1-81f4-3c19437be994","order_by":6,"name":"Haiyu Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital•Nuclear Industry 416 Hospital, Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Haiyu","middleName":"","lastName":"Zhang","suffix":""},{"id":278680599,"identity":"3e7e9126-376f-4cc0-a6b5-7baba9351269","order_by":7,"name":"Hang Cai","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Cai","suffix":""},{"id":278680600,"identity":"dae56ead-9b71-4620-86e5-2bef972be086","order_by":8,"name":"Hui Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3QsWrDMBCAYRmBtIh2PQi2X8FG4CymzyIR0JQ5eKtLQJPpbjrkFfwIKoJ2MZltsjiEeM7orcUdS7GdrYP+8bhvuEPI5fqHEYyNERkEIc1/Bl4+Rx6oll1XpzwuzEISsJrHZ61k3oiFhIBIQGrrvZSXazWg1K8M7rsZokAcLaYrlbQFUrwyZB3NkA8QO0u8ty05IWRlZRiBaSI1CGIZauuRfC0gzOJIaAWoYSMxCwjVXifqNIqL8ZZow0tLkkkS7h9v70MGzwdq+2bInvzXz30/SX41vgrfse9yuVyuv/sGEqFMmElfyq4AAAAASUVORK5CYII=","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-03-12 08:47:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4082092/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4082092/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52702484,"identity":"fed412cb-c94b-4d16-a42c-26189ca7d4ef","added_by":"auto","created_at":"2024-03-14 17:52:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147956,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant enrollment\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4082092/v1/de673ce2969ea1f782024918.jpeg"},{"id":52703421,"identity":"09c1942d-cc20-4eb4-bd22-fb6b9028b24b","added_by":"auto","created_at":"2024-03-14 18:08:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":445088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4082092/v1/0ec51942-e176-4ffc-8100-c991b7691e27.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of the predictive value of insulin resistance for osteoporosis in middle-aged and elderly non-type 2 diabetic population","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOsteoporosis (OP) is a systemic skeletal disease with decreased bone mass, decreased bone strength and increased risk of fracture \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, which is affected by various factors such as age, gender, hormone levels, body mass index (BMI) and other factors, and is characterized by high morbidity, disability and mortality \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In China, the prevalence of osteoporosis in people aged 40\u0026ndash;49, over 50 and over 65 is 3.2%, 19.2% and 32.0%, respectively \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. With the aging of the global population, the prevalence of OP has shown a gradual increasing trend, and OP has become a public health problem of key concern in countries around the world. It is well known that OP not only reduces the quality of life of patients, but also imposes a huge economic burden on patients, families and society. It has been reported that the annual cost of treating osteoporosis and fractures in the European Union is as high as 55\u0026nbsp;billion euros \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, and the systematic evaluation by Maubeni et al \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e pointed out that the average cost of a single hospitalization for osteoporotic fracture in China is as high as 27,561 yuan. Therefore, early diagnosis and early intervention of OP are particularly important.\u003c/p\u003e\n\u003cp\u003einsulin resistance (IR), a state of reduced responsiveness of insulin-targeted tissues to physiological levels of insulin, is one of the key mechanisms of abnormal glucose-lipid metabolism, which leads to internal environmental disorders, including obesity, elevated blood glucose, dyslipidemia, and abnormal synthesis and secretion of a variety of cytokines, and all of these effects have a direct or indirect impact on bone metabolism \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Insulin has been shown to have a direct effect on osteoblasts, and IR leads to impaired bone regulation and imbalance of bone homeostasis, causing alterations in bone density, bone geometry, and bone microarchitecture, which further leads to osteoporosis and fragility fractures \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The high insulin-normal glucose clamp method is the gold standard for assessing insulin resistance, but it is not suitable for large-scale clinical screening due to its invasiveness, complexity, and high cost \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, therefore, researchers have developed the triglyceride glucose-body mass index(TyG-BMI index), the insulin resistance Metabolic score the metabolic score of insulin resistance (METS-IR), TG/HDL-C Ratio and other tools to assess IR \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Numerous previous studies have demonstrated that insulin resistance assessment indices such as METS-IR, TyG-BMI index and TG/HDL-C Ratio showed different degrees of correlation with BMD, osteoporosis and fracture risk in type 2 diabetic patients \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, most of the current studies have focused on patients with type 2 diabetes, and less attention has been paid to insulin resistance and osteoporosis in non-type 2 diabetic populations. The relationship between insulin resistance evaluation indexes and BMD in middle-aged and elderly non-type 2 diabetic populations is unclear, as is their predictive value for OP. Therefore, this study will analyze the correlation between three commonly used insulin metabolism evaluation indexes and BMD in middle-aged and elderly non-type 2 diabetic population, and explore their diagnostic value for OP. It aims to provide a theoretical basis for the screening, identification and prevention of OP in middle-aged and elderly non-type 2 diabetic population.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003ch2\u003e2.1 Study Population\u003c/h2\u003e\n\u003cp\u003eIn this study, a cross-sectional survey was conducted on 700 middle-aged and elderly non-type 2 diabetic patients who underwent health checkups at the First Affiliated Hospital of Chengdu Medical College from August 2016 to June 2023, of which 449 were female and 251 were male. Inclusion criteria were (1)\u0026thinsp;\u0026ge;\u0026thinsp;40 years old; (2) patients with non-type 2 diabetes; (3) receiving DXA for BMD determination; exclusion criteria were (1) incomplete medical records; (2) patients with myocardial infarction, cerebral hemorrhage, severe liver or renal insufficiency, a history of acute infections, and a history of malignant tumors within the last three months; and (3) history of treatment with anti-osteoporosis drugs.\u003c/p\u003e\n\u003ch2\u003e2.2 Clinical data\u003c/h2\u003e\n\u003cp\u003eWe obtained important information such as age, sex, blood pressure, height, weight, blood biochemical parameters through the patient's e-case.BMI was calculated as weight kg / (height m)^2. Fasting blood samples were collected from the patients and laboratory parameters included serum total protein (TP), serum albumin (ALB), blood creatinine (Cr), blood uric acid (UA), fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL). Smoking was defined as continuous or cumulative smoking for more than 6 months, and alcohol consumption was considered to be drinking at least once a week for 6 months or more.\u003c/p\u003e\n\u003ch2\u003e2.3 Bone mineral density\u003c/h2\u003e\n\u003cp\u003eThe patient's lumbar spine BMD (LS BMD), femoral neck BMD (FN BMD), and total hip BMD (TH BMD), and the T-value of each site were measured using dual-energy X-ray absorptiometry (enCORE, General Electric Company, version 15). The patients were examined by removing any metal objects from their bodies, and the measurements were performed in a resting state, while the operator performed whole-body and hip scans in accordance with the instrument's operating procedures to obtain the patients' lumbar spine BMD, femoral neck BMD, and total hip BMD, and the patients' T-value \u0026le; -2.5 was defined as osteoporosis according to the diagnostic criteria for osteoporosis.\u003c/p\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2.4 Assessment of insulin resistance\u003c/h2\u003e\n\u003cp\u003eMETS-IR and TyG-BMI index, TG/HDL-C Ratio were calculated using the following:\u003c/p\u003e\n\u003cp\u003eMETS-IR\u0026thinsp;=\u0026thinsp;Ln[2 \u0026times; FPG (mg/dL)\u0026thinsp;+\u0026thinsp;TG (mg/dL)] \u0026times; BMI (kg/m2)/Ln[HDL-C (mg/dL)]\u003c/p\u003e\n\u003cp\u003eTyG-BMI\u0026thinsp;=\u0026thinsp;Ln [TG (mg/dL) \u0026times; FPG (mg/dL)/2]\u0026times; BMI\u003c/p\u003e\n\u003cp\u003eTG/HDL-C Ratio\u0026thinsp;=\u0026thinsp;TG(mmol/L)/HDL-C(mmol/L)\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\n\u003cp\u003ePatients were categorized into two groups according to the diagnostic criteria of osteoporosis: the osteoporosis group (T-score\u0026le;-2.5) and the non-osteoporosis group (T-score \u0026ge; -2.5 ) \u003csup\u003e[6]\u003c/sup\u003e. Statistical analysis was performed using SPSS 25.0 software. Quantitative data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard for normal distribution and mean (25th percentile, 75th percentile) for non-normally distributed continuous variables. Qualitative parameters were expressed as numbers and percentages. One-way ANOVA and Kruskal-Wallis test were used for between-group comparisons, respectively.Pearson or Spearman correlations were used to test associations between METS-IR, TyG-BMI index,TG/HDL-C Ratio, and BMD by site. Binary logistic regression was used to analyze the risk factors for osteoporosis in middle-aged and elderly non-type 2 diabetic patients, and ROC curves were used to analyze the predictive value of METS-IR, TyG-BMI index, and TG/HDL-C Ratio for osteoporosis. The test level \u0026alpha; was set at 0. 05 unless otherwise stated.\u003c/p\u003e\n\u003ch2\u003e2.6 Ethics Statement\u003c/h2\u003e\n\u003cp\u003eThe Chengdu Medical College Ethics Committee approved the study and complied with the Declaration of Helsinki (CMCEC\u0026ndash;2022N0.40). Patient informed consent was not required due to the retrospective design of this study.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Clinical Characteristics of the Study Population\u003c/h2\u003e\n\u003cp\u003eParticipants were selected as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 789 patients met the inclusion criteria for this retrospective study and 89 patients were excluded due to exclusion criteria, resulting in the inclusion of 700 patients. The basic characteristics of the patients are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.A total of 551 non-osteoporotic patients and 149 osteoporotic patients, of which 61.4% were female, 28.1% suffered from hypertension, 11.9% had a history of smoking, and 12.4% had a history of alcohol consumption. The mean age of the observed patients was 57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.79 years, serum total protein 72.50(69.90,75.48)(g/L), serum albumin 44.00(42.10,45.88)(g/L), endogenous creatinine clearance 68.63\u0026thinsp;\u0026plusmn;\u0026thinsp;42.57 \u0026micro;mol/L, uric acid 325.91\u0026thinsp;\u0026plusmn;\u0026thinsp;84.64 \u0026micro;mol/L, fasting blood glucose 5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99 mmol/L, triglycerides 1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12 mmol/L, total cholesterol 5.24(4.60,5.95)mmol/L, HDL was 1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 mmol/L, LDL 3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89 mmol/L, and BMI was 24.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19 (kg/m\u003csup\u003e2\u003c/sup\u003e). METS-IR was 39.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.67, TyG-BMI index was 171.01\u0026thinsp;\u0026plusmn;\u0026thinsp;30.34, and TG/HDL-C Ratio was 0.90(0.56,1.43).L1\u0026thinsp;~\u0026thinsp;L4 BMD, femoral neck BMD, and total hip BMD were 0.96(0.85,1.07)(g/cm\u003csup\u003e2\u003c/sup\u003e), 0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 (g/cm\u003csup\u003e2\u003c/sup\u003e), and 0.89(0.81,0.98)(g/cm\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eCompared to patients in the non-osteoporotic group, patients in the osteoporotic group were older (62.42\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36 vs. 56.70\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), had lower uric acid (313.37\u0026thinsp;\u0026plusmn;\u0026thinsp;77.72 vs. 329.3\u0026thinsp;\u0026plusmn;\u0026thinsp;86.18, p\u0026thinsp;=\u0026thinsp;0.031), and higher total cholesterol and high-density lipoprotein (5.49(4.72,6.11) vs. 5.13 (4.57,5.87), p\u0026thinsp;=\u0026thinsp;0.009, 1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43 vs 1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower height (154.26\u0026thinsp;\u0026plusmn;\u0026thinsp;7.19 vs 159.22\u0026thinsp;\u0026plusmn;\u0026thinsp;7.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smaller mean weight (55.45\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68 vs 61.75\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lower BMI (23.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29 vs 24.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Lumbar spine 1\u0026ndash;4 BMD was lower (0.75(0.69,0.79) vs 0.99(0.92,1.09), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), femoral neck BMD was lower (0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 vs 0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and total hip BMD was lower (0.80 (0.71,0.90) vs 0.91 (0.83, 1.00), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). lower METS-IR, TyG-BMI and TG/HDL-C ratio (37.82\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85 vs 40.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (165.20\u0026thinsp;\u0026plusmn;\u0026thinsp;32.09 vs 172.58\u0026thinsp;\u0026plusmn;\u0026thinsp;29.76, p\u0026thinsp;=\u0026thinsp;0.009), (0.80(0.49, 1.37) vs 0.92(0.59,1.45)). The rest of the relevant indicators were not statistically different.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePatient characteristic\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAll participants\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;700)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOP\u003c/p\u003e\n\u003cp\u003e(n=149)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNon-OP\u003c/p\u003e\n\u003cp\u003e(n=551)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.42\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.70\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex,female,%(n)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.1(449)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.9(122)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.3(327)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension %(n)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.1(197)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.9(46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.4(151)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.404\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking,%(n)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.9(83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.4(11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.1(72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.057\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinking,%(n)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.4(87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.4(11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.8(76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.035\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTP\u003c/p\u003e\n\u003cp\u003e(g/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.50(69.90,75.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.9(69.80,76.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.40(69.90,75.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.482\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALB\u003c/p\u003e\n\u003cp\u003e(g/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.00(42.10,45.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.6(41.90,45.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.1(42.10,45.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.286\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCr (\u0026micro;mol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.63\u0026thinsp;\u0026plusmn;\u0026thinsp;42.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.76\u0026thinsp;\u0026plusmn;\u0026thinsp;20.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.68\u0026thinsp;\u0026plusmn;\u0026thinsp;46.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.211\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUA\u003c/p\u003e\n\u003cp\u003e(\u0026micro;mol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e325.91\u0026thinsp;\u0026plusmn;\u0026thinsp;84.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e313.37\u0026thinsp;\u0026plusmn;\u0026thinsp;77.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e329.3\u0026thinsp;\u0026plusmn;\u0026thinsp;86.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFBG\u003c/p\u003e\n\u003cp\u003e(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.257\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG\u003c/p\u003e\n\u003cp\u003e(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.36(0.98,1,88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.34(0.95,1.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.36(1.00,1.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.766\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTC\u003c/p\u003e\n\u003cp\u003e(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.24(4.60,5.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.49(4.72,6.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.13(4.57,5.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C\u003c/p\u003e\n\u003cp\u003e(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLDL-C\u003c/p\u003e\n\u003cp\u003e(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.376\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeight\u003c/p\u003e\n\u003cp\u003e(cm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e154.26\u0026thinsp;\u0026plusmn;\u0026thinsp;7.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159.22\u0026thinsp;\u0026plusmn;\u0026thinsp;7.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeight\u003c/p\u003e\n\u003cp\u003e(kg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.41\u0026thinsp;\u0026plusmn;\u0026thinsp;10.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.45\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.75\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI\u003c/p\u003e\n\u003cp\u003e(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLumbar spine BMD\u003c/p\u003e\n\u003cp\u003e(g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96(0.85,1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75(0.69,0.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99(0.92,1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemoral Neck BMD\u003c/p\u003e\n\u003cp\u003e(g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal Hip BMD\u003c/p\u003e\n\u003cp\u003e(g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89(0.81,0.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80(0.71,0.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91(0.83,1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMETS-IR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.82\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTyG-BMI index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171.01\u0026thinsp;\u0026plusmn;\u0026thinsp;30.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165.20\u0026thinsp;\u0026plusmn;\u0026thinsp;32.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e172.58\u0026thinsp;\u0026plusmn;\u0026thinsp;29.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG/HDL-C ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90(0.56,1.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80(0.49,1.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.92(0.59,1.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.969\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eTP Total serum protein,ALB Serum albumin,Cr Creatinine, UA Hematuria,FBG Fasting blood glucose, TG Triglycerides, TC Total cholesterol, HDL High-density lipoprotein, LDL Low-density lipoprotein, BMI Body mass index,BMD Bone mineral density,METS-IR Metabolic score for IR,TyG-BMI index Triglyceride Glucose-Body Mass Index\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAssociation of each index with bone mineral density\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIndex\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLumbar Spine BMD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eFemoral neck BMD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTotal Hip BMD\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.222*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.330*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.249*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTP(g/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.304\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.446\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALB(g/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.414\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCr (\u0026micro;mol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.150*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.757\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.129*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUA(\u0026micro;mol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.171*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.139*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.203*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFBG(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.505\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.706\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.740\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.113*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTC(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.174*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.106*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.122*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.244**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.166*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.201*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLDL-C(mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.092*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.522\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026minus;0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.096\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeight(cm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.284**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.290*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.296*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeight(kg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.316**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.335*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.416*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.180*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.220*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.319*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMETS-IR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.246*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.215*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.314*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTyG-BMI index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.171*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.197*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.291*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG/HDL-C Ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.140*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.083*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.157*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eTP Total serum protein,ALB Serum albumin,Cr Creatinine, UA Hematuria,FBG Fasting blood glucose, TG Triglycerides, TC Total cholesterol, HDL High-density lipoprotein, LDL Low-density lipoprotein, BMI Body mass index,BMD Bone mineral density,METS-IR Metabolic score for IR,TyG-BMI index Triglyceride Glucose-Body Mass Index\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Association of insulin resistance with BMD\u003c/h2\u003e\n\u003cp\u003eSpearman correlation analysis showed that METS-IR, TyG-BMI index and TG/HDL-C ratio were positively correlated with BMD at all points.The correlation coefficients of METS-IR with L1\u0026thinsp;~\u0026thinsp;L4 BMD, femoral neck BMD and total hip BMD were 0.246 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.215 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 0.314 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).The correlation coefficients of TyG-BMI index with the three were 0.171 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.197 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 0.291 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively.The correlation coefficients of TG/HDL-C ratio with L1\u0026thinsp;~\u0026thinsp;L4BMD, femoral neck BMD and total hip The correlation coefficients of BMD were 0.140 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.083 (p\u0026thinsp;=\u0026thinsp;0.027) and 0.157 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Binary logstic regression\u003c/h2\u003e\nBinary logistic regression was used to analyze the relationship between insulin resistance score and osteoporosis, and the results showed that METS-IR was associated with osteoporosis, and TyG-BMI index and TG/HDL-C ratio were not associated with osteoporosis. After adjusting for age, sex, smoking, alcohol consumption, serum albumin (ALB), and with serum total protein (TP), blood creatinine (Cr), blood uric acid (UA), and total cholesterol (TC), the METS-IR was still associated with osteoporosis, while the TyG-BMI index, and TG/HDL-C ratio were still not associated with osteoporosis.\u003cbr /\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBinary logistics regression analysis of the relationship between insulin resistance and osteoporosis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMETS-IR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.050\u0026ndash;1.229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.109\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.021\u0026ndash;1.206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.768\u0026ndash;1.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTyG-BMI index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.984\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.963\u0026ndash;1.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.990\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.968\u0026ndash;1.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.380\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.989\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.965\u0026ndash;1.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.375\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG/HDL-C ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.857\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.685\u0026ndash;1.073\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.833\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.657\u0026ndash;1.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.133\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.867\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.692\u0026ndash;1.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.212\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003eModel 1: unadjusted model\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003eModel 2: Model1\u0026thinsp;+\u0026thinsp;gender, hypertension, smoking, alcohol consumption\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003eMoedel 3: Model1\u0026thinsp;+\u0026thinsp;total serum protein, serum albumin, creatinine, uric acid, total cholesterol\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Receiver-operating characteristics (ROC) curves\u003c/h2\u003e\n\u003cp\u003eROC curve analysis showed that METS-IR predicted an area under the ROC curve (AUC) of 0.609 (95% CI 0.557\u0026ndash;0.662) for OP, with an optimal cutoff value of 36.62, a sensitivity of 52.3%, and a specificity of 67.9%.The TyG-BMI index predicted an area under the ROC curve of 0.579 (95% CI 0.526\u0026ndash;0.632), with an optimal cutoff value of 154.63, a sensitivity of 42.3%, and a specificity of 72.4%.The TG/HDL-C ratio predicted an area under the ROC curve (AUC) of 0.553 (95% CI 0.498\u0026ndash;0.607) for OP, with an optimal cutoff value of 0.85, a sensitivity of 54.4%, and a specificity of 57.5%.\u003c/p\u003e\n\u003cp\u003eROC Receiver operating characteristic, AUC area under the curve\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparing the ability of Indicators for the evaluation of IR to predict osteoporosis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eThe best value for diagnosis\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMETS-IR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.609\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.557\u0026ndash;0.662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.523\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.679\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.62\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTyG-BMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.579\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.526\u0026ndash;0.632\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.423\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e154.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG/HDL-C ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.553\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.498\u0026ndash;0.607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.544\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith the deepening of population aging, the prevalence of OP in the middle-aged and elderly population has been increasing year by year, and OP has become a public health problem that is closely watched globally, which not only causes physiological and psychological damages to the patients, but also aggravates the burdens of the patients' families and the society. Therefore, early diagnosis and prevention of OP are extremely important.IR is a state in which insulin is ineffective in peripheral tissues, leading to dyslipidemia and impaired insulin homeostasis, and it is a key causative factor for metabolic diseases such as obesity, hyperlipidemia, and diabetes mellitus \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, and a number of studies have demonstrated that IR is associated with cardiovascular disease \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, thyroid cancer \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, and cognitive impairment \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In recent years, more and more studies have focused on the correlation between insulin resistance and bone, and in a prospective study by Napoli et al \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, the relationship between IR and BMD and fracture risk in nondiabetic older adults was analyzed, and it was found that IR was positively correlated with BMD, while an increase in IR was associated with a decrease in fracture risk in an unadjusted model.\u003c/p\u003e\n\u003cp\u003eIn this study, we found that TG/HDL-C ratio, METS-IR and TyG-BMI index were positively correlated with BMD at different sites in non-type 2 diabetic population, and, our binary logstics regression model showed a significant correlation between METS-IR and osteoporosis, and after adjusting for age, sex, smoking, alcohol consumption, ALB, and TP, this correlation still existed. Consistent with our findings, a study by Pu et al \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e found that METS-IR was positively associated with BMD, with each one-unit increase in METS-IR in non-diabetic adults in the U.S. being associated with an increase in femoral BMD and spinal BMD of 0.005 g/cm3 and 0.005 g/cm3 respectively, and this positive association persisted regardless of whether METS-IR was used as a continuous variable or quartiles were converted to categorical variables, this positive association persisted. Similar conclusions have been reached in longitudinal studies, Zhang et al\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e showed a positive correlation between the rate of change in METS-IR and the rate of change in bone mineral density in a male population by following up a Chinese middle-aged and elderly health check-up population.The TyG-BMI index, which combines serum triglyceride, fasting glucose, and obesity, is more reliable than the TyG index.Riggs et al\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e found that a higher TyG-BMI index was more reliable than the TyG index, and that a higher TyG-BMI index was more reliable than the TyG index. found that higher TyG-BMI was associated with greater BMD at both weight-bearing and non-weight-bearing skeletal sites.Chuang et al \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e found that BMD at all measured sites was positively correlated with TG levels and negatively correlated with HDL-C levels in a survey study of Korean adults undergoing health checkups. Alkaline phosphatase (ALP) plays a crucial role in bone metabolism and studies have demonstrated that ALP activity is positively and independently correlated with TG/HDL-C ratio and TyG index \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn contrast to previous studies, our study only observed that the occurrence of OP was only associated with METS-IR and not with TyG-BMI index and TG/HDL-C ratio. We hypothesize that this is due to the heterogeneity of the study population, such as differences in underlying diseases, age, and ethnicity, resulting in different study results. Our study focused on a middle-aged and elderly non-type 2 diabetic population, and there have been studies confirming the association of other IR evaluation indexes with OP in which the study population was mainly type 2 diabetic patients \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, which may be related to the fact that diabetic patients are associated with varying degrees of inflammation, which further affects bone metabolism through oxidative stress and estrogen metabolism, leading to the development of OP.\u003c/p\u003e\n\u003cp\u003eIn addition, this study was the first to compare the diagnostic value of these three commonly used metabolic evaluation indexes of insulin resistance in a non-type 2 diabetic population.The results of the ROC curves showed that the METS-IR, TyG-BMI index, and TG/HDL-C ratio all had a certain diagnostic value for OP, with the highest predictive value of the METS-IR, the TyG-BMI index was the next highest, and TG/HDL-C ratio was the lowest.METS-IR was first identified in 2018 as a reliable and intuitive indicator for predicting IR, which was calculated from FPG, TG, BMI, and HDL-C \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Compared with TG/HDL-C ratio, TyG index and HOMA-IR, METS-IR fully takes into account BMI, blood glucose and lipids, which is more comprehensive in assessing metabolic status and has predictive value for many chronic diseases \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe limitations of this study are, firstly, that it was a single-center retrospective study with possible geographical and ethnic limitations. Second, this study was cross-sectional and could not assess the longitudinal relationship between IR evaluation indicators and BMD and OP over time. Finally, this study could not demonstrate a causal relationship between IR evaluation indicators and OP. Therefore, larger, multicenter longitudinal studies are still needed to enrich the correlation between IR and OP.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe strength of our study is that it is the first to analyze and compare the correlation and predictive value of three IR evaluation indexes with BMD and OP in a middle-aged and elderly non-type 2 diabetes population. Our study found that METS-IR, TyG-BMI index and TG/HDL-C ratio were correlated with BMD at all points and all had different diagnostic values for OP, with METS-IR having the highest predictive value, and lower METS-IR being associated with the occurrence of OP.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.1 Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe study complied with the Declaration of Helsinki and was approved by the Chengdu Medical College Ethics Committee (CMCEC\u0026ndash;2022N0.40). This was a retrospective study and therefore informed consent was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4 Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest to be declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5 Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Sichuan Collaborative Innovation Center for Aging and Geriatric Health Joint Fund Project, grant number YLKYYB2208.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.6 Author Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ.Z. and H.L. designed the study, wrote, reviewed and edited the manuscript Q.Z. and Y.Z. analyzed data; H.L. was involved in validation; S.S., S.T. and H.C. contributed to investigation; X.X. helped in resources; T.J. was involved in project administration; H.L.,Y.Z. and H.Z. reviewed and edited the manuscript. All authors approved the final version of the manuscript to be published. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.7 Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCompston J, Cooper A, Cooper C, Gittoes N, Gregson C, Harvey N et al. Uk clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos. 2017 2017;12(1):43. 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Mets-ir as a predictor of cardiovascular events in the middle-aged and elderly population and mediator role of blood lipids. Front Endocrinol (Lausanne). 2023 2023/1/1;14:1224967. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\u0026amp;db=pubmed\u0026amp;dopt=Abstract\u0026amp;list_uids=37534205\u0026amp;query_hl=1\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\u0026amp;db=pubmed\u0026amp;dopt=Abstract\u0026amp;list_uids=37534205\u0026amp;query_hl=1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2023.1224967\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2023.1224967\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"Insulin resistance, Bone mineral density, osteoporosis","lastPublishedDoi":"10.21203/rs.3.rs-4082092/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4082092/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWith the deepening of the aging of the population, the incidence of osteoporosis in the middle-aged and elderly people is increasing. As a degenerative disease with damaged bone microstructure, decreased bone mass and decreased bone density, osteoporosis is characterized by high disability rate and high mortality. Therefore, the early prediction and diagnosis of osteoporosis is particularly important. Previous studies have demonstrated a strong relationship between insulin resistance and bone mineral density and osteoporosis in type 2 diabetes mellitus; however, there is a lack of attention on the correlation between insulin resistance and bone metabolism in healthy populations. The aim of this study was to analyze the correlation between three insulin resistance measures and bone mineral density, and to compare their value in predicting middle-aged and elderly non-type 2 diabetes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, the general data, bone mineral density, blood routine, lipid metabolism and other clinical data of 700 Chinese middle-aged and elderly non-type 2 diabetes patients were collected, and the patients were divided into osteoporosis group (n\u0026thinsp;=\u0026thinsp;149) and non-osteoporosis group (n\u0026thinsp;=\u0026thinsp;551). spearman correlation analysis was used to explore the correlation between three insulin resistance metabolic indexes and bone mineral density. The relationship between insulin resistance and osteoporosis was analyzed by binary logstics regression. ROC curve was used to compare the predictive value of METS-IR, TyG-BMI index and TG/HDL-C Ratio in osteoporosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSpearman correlation showed that METS-IR, TyG-BMI index and TG/HDL-C Ratio were positively correlated with L1-L4 BMD, femoral neck BMD and hip BMD. Binary logstics regression analysis showed that METS-IR was related to the occurrence of osteoporosis. After adjusting for age, sex, smoking, drinking, serum total protein, serum albumin, serum creatinine, uric acid and total cholesterol, the correlation between METS-IR and osteoporosis still existed. ROC curve analysis showed that these three indexes of insulin resistance metabolism had certain predictive value in osteoporosis, among which METS-IR had the highest diagnostic value in osteoporosis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMETS-IR, TyG-BMI index and TG/HDL-C Ratio were correlated with BMD at all sites.The predictive value of METS-IR was better than TG/HDL-C Ratio and TyG-BMI index in osteoporosis.\u003c/p\u003e","manuscriptTitle":"Analysis of the predictive value of insulin resistance for osteoporosis in middle-aged and elderly non-type 2 diabetic population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-14 17:52:25","doi":"10.21203/rs.3.rs-4082092/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":"46e95b62-7a08-46f0-89a9-95ac3a3f77b0","owner":[],"postedDate":"March 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-14T17:52:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-14 17:52:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4082092","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4082092","identity":"rs-4082092","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}