{"paper_id":"2007b7fb-2b7b-47b5-b08e-0b3f89e3aa96","body_text":"Could the TyG index be a screening tool for postmenopausal osteoporosis? | 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 Article Could the TyG index be a screening tool for postmenopausal osteoporosis? Jingyi Yang, Daoming Xu, Di Zhang, Ling Bai, Zun Wang, Xin Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4941509/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 Purposes: This study aims to explore the sensitivity and efficacy of the TyG index in the screening of postmenopausal osteoporosis, and to provide an objective new method for the prevention and early screening of postmenopausal osteoporosis. Methods: This retrospective study selected 1032 subjects who completed bone mineral density examination in the Department of Nuclear Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine from January 2021 to December 2023 according to the inclusion and exclusion criteria. The baseline data include age, weight, height, BMI, lumbar spine T -value (LS T -value), total hip T -value (TH T -value), femoral neck T -value (FN T -value), fasting blood glucose (FBG), triglyceride (TG), the TyG index and OSTA. After grouping, the differences in postmenopausal osteoporosis were compared. The correlation of the TyG index and OSTA with baseline data was analyzed. The ROC curve results of the TyG index in the total population, 60-year-old stratification, FBG and TG stratification were analyzed, and the sensitivity and efficacy of the TyG index in the screening of postmenopausal osteoporosis were obtained. Results: In 1032 postmenopausal women, there were significant differences ( P < 0.001) in age, weight, height, BMI, and T -values of three different sites, the TyG index and OSTA. The results of correlation analysis showed that the TyG index and OSTA were positively correlated with weight, BMI, and T -values of three different sites in 1032 postmenopausal women and after 60-year-old stratification ( P <0.001). In the total population and after stratification by 60 years old, the TyG index was positively correlated with FBG and TG ( P <0.001), but not correlated with age and height. Meanwhile, OSTA was negatively correlated with age ( P <0.001) and positively correlated with height ( P <0.001). OSTA was not correlated with FBG and TG in the total population and in postmenopausal women aged <60, but was positively correlated with TG in postmenopausal women aged≥60 ( P <0.001). ROC curve analysis showed that the area under the curve of the TyG index and OSTA was close in postmenopausal women aged≥60 with abnormal FBG and/or TG. The cut-off value of the TyG index in postmenopausal women aged≥60 was higher than that in postmenopausal women aged<60, indicating that the risk of osteoporosis increased in postmenopausal women aged≥60 with increased TyG index. Conclusion: The TyG index has the potential to objectively screen osteoporosis in postmenopausal women aged≥60 and postmenopausal women aged≥60 with abnormal FBG and/or TG. Health sciences/Biomarkers/Predictive markers Health sciences/Health care/Disease prevention the TyG index OSTA Osteoporosis Postmenopausal women Figures Figure 1 Figure 2 Figure 3 Figure 4 Background According to the newly released \"World Population Prospects 2024\" [ 1 ] , the global aging trend is accelerating, and it is expected that by 2100, the elderly population will reach one-third of the global population. China's 7th national population census pointed out that the country has the largest elderly population in the world, with 264 million persons aged 60 to 264 million (accounts for about 18.7% of the total population), an increase of 5.4% over the previous census [ 2 ] . Osteoporosis (OP) is a systemic degenerative disease defined by a loss in bone mass, the breakdown of bone microstructure, and an increased risk of fracture [ 3 ] . The prevalence of osteoporosis worldwide was 18.3%, with women having almost twice as high prevalence (23.1%) than men (11.7%) [ 4 ] . The most hazardous side effect of osteoporosis is osteoporotic fractures, which are most common in the distal portions of the spine, hip, and forearm [ 5 ] and affect 18.9% of older persons in China. About 50% of postmenopausal women get osteoporotic fractures, and hip fractures double the risk of death within a year [ 6 ] . Therefore, osteoporosis is called the \"silent killer\", and screening is the premise of prevention and treatment of osteoporosis and prevention of fragility fractures. Osteoporosis Self-Assessment Tool for Asians (OSTA) is a tool recommended by guidelines [ 7 ] for early identification and screening of postmenopausal osteoporosis, and only weight and age are used as calculation variables. Weight is easily affected by diet, exercise, and diseases, and could have a large range of short-term fluctuations, which reduces the screening efficiency of OSTA. The data of OSTA calculation model come from postmenopausal women in eight Asian countries, and there are limitations in the use of gender and ethnicity [ 8 ] . There is an urgent need for an objective and widely applicable osteoporosis screening model to accurately and efficiently identify high-risk populations. In Muscle and fat cells, insulin-stimulated glucose uptake was flawed, and impaired insulin suppression of hepatic glucose output, which is known as insulin resistance (IR) [ 9 ] . The triglyceride-glucose (TyG) index is a reliable and emerging indicator for evaluating Insulin resistance (IR). A cross-sectional study by Zhuo M. et al [ 10 ] . verified that IR causes low bone mass and the risk of osteoporosis. The risk of osteoporosis increases with the increased level of IR in female patients [ 11 ] . The triglyceride-glucose (TyG) index is a reliable and emerging indicator for evaluating Insulin resistance (IR) [ 12 , 13 ] . Recent studies have shown that [ 14 , 15 ] the TyG index obtain the ability to predict osteoporosis, offering an objective and applicable approach to osteoporosis prevention and early screening. Methods Data collection and study population The retrospective analysis of the screening was conducted between January 2021 and December 2023 at, Affiliated Hospital of Nanjing University of Chinese Medicine, which involved nuclear medicine bone mineral density assessment of clinical data pertaining to 20568 patients. Exclusion criteria for this study are: (1) lack comprehensive blood index information; (2) a history of kidney disease and malignant tumors; (3) Male; and (4) Females who have not reached menopausal status. The ultimate analysis included 1032 participants in total (Fig. 1 ). This study has been registered in the Chinese Clinical Trial Registry (Registration number: ChiCTR2400085209), and approved by the Ethics Committee of the Affiliated Hospital of Nanjing University of Chinese Medicine (Ethics number: 2024NL-085-02). Since this study employed anonymized patient records, the need for informed consent to participate was waived by an Institutional Review Board (IRB). We confirm that all methods were performed in accordance with the relevant guidelines and regulations. The study population's medical history was retrieved from the clinical information system, and our hospital's laboratory department provided the FBG and TG blood test indices. Using dual-energy X-ray absorptiometry (Hologic Discovery ASY-03954), the density of bone was determined. Laboratory analysis Blood triglyceride (TG) and fasting blood glucose (FBG) levels were measured quantitatively through an automatic biochemical analyzer (Beckman Coulter AU5800, USA). Chromatography and the GPO-PAP method are employed by TG and FBG, respectively, for assessment. Definition of index BMI = Weight (kg) / Height (m)² TyG = Ln [ TG (mg/dL) × FBG (mg/dL) / 2] [ 12 ] OSTA = [ Weight (kg) - Age (years) ] × 0.2 [ 7 ] Data analysis SPSS 29.0 software was used for statistical analysis. The normal distribution was expressed as mean ± standard deviation, and the non-normal distribution was expressed as median and interquartile range M(P25, P75). The non-normal distribution of the Mann-Whitney U test, P < 0.05 is a significant difference. The Spearman correlation analysis, the analysis of the TyG index and OSTA, and the correlation of the indexes. Graphpad Prism10 software was used for correlation heat mapping. receiver operating characteristic (ROC) curves of the TyG index, OSTA, age, and FBG and TG stratification were drawn and the area under the curve (AUC) were analyzed. Sensitivity, specificity, and the AUC was compared by z-test. With P < 0.05, the difference was statistically significant. Results Baseline Characteristics This study enrolled 1032 postmenopausal women in total, including 492 with osteoporosis, 540 without osteoporosis, 315 with abnormal FBG and/or TG, and 717 with normal FBG and TG. Grouping by T -value ≤-2.5, there were significant differences in the enrolled population's age, weight, height, BMI, T -values of three sites, the TyG index, and OSTA (P < 0.001). Blood TG and FBG did not differ statistically (Table 1 ). Table 1 Population grouping of study participants Overall (n−1032) PMOP (n = 492) Non-PMOP (n = 540) Z P Age (years) 61.00(54.00,69.00) 66(59,72) 57(51,66) −11.166 < .001 Weight (kg) 57.00(51.50,63.00) 55(50,60) 60(55,65) −9.84 < .001 Height (cm) 159.00(155.00,163.00) 158(155,161) 160(156,163) −5.642 < .001 BMI(kg/m 2 ) 22.70(20.70,24.80) 22.04(20.03,24.03) 23.44(21.48,25.39) −8.109 < .001 LS T -value −2.10(−2.80,−1.10) −2.9(−3.4,−2.5) −1.2(−1.8,−0.4) −24.68 < .001 TH T -value −1.30(−2.00,−0.50) −1.9(−2.5,−1.4) −0.7(−1.2,−0.1) −21.07 < .001 FN T -value −1.70(−2.40,−0.90) −2.5(−2.9,−1.8) −1.1(−1.7,−0.5) −20.634 < .001 FBG(mmol/L) 5.41(5.05,6.04) 5.41(5.08,6.06) 5.40(5.03,6.00) −0.75 0.453 TG(mmol/L) 1.21(0.89,1.62) 1.21(0.89,1.57) 1.22(0.89,1.63) −0.699 0.485 TyG 1.45(1.09,1.91) 1.34(0.99,1.80) 1.55(1.16,1.99) −4.665 < .001 OSTA −0.60(−2.80,1.20) −2.2(−3.8,−0.4) 0.45(−1.2, 2.2) −14.138 < .001 医学 TyG index and OSTA with T -values of three different sites and age distribution trends In Fig. 2 (a), (b), and (c) diagrams represent a decrease in T -values of three sites and an increase in the TyG index with aging in the 1032 postmenopausal women. The T -values of three sites and OSTA decreased with aging in the 1032 postmenopausal women, as shown in Fig. 2 (d), (e), (f). Correlation analysis The results of the 60-year-old stratification of 1032 postmenopausal women showed the TyG index and OSTA were positively correlated with body weight, BMI, LS T -value, TH T -value and FN T -value ( P < 0.001)(Fig. 3 ). The TyG index's correlation coefficient was lower than OSTA's. The TyG index showed a positive correlation with TG and FBG but not with age or height ( P < 0.001). Age showed a negative correlation with OSTA ( P < 0.001), while height showed a positive correlation with OSTA ( P < 0.001). OSTA had a negative correlation with age and FBG ( P < 0.001) and a positive correlation with height and TG ( P < 0.001, P = 0.013). In postmenopausal women age < 60, there was no correlation found between OSTA and FBG and TG; however, in women over 60, there was a positive correlation between OSTA and FBG and TG. Analysis of Receiver Operating Characteristic (ROC) curves Taking DXA T -value≤-2.5 as the diagnostic criteria for osteoporosis, the AUC of OSTA was better than that of TyG index in 1032 postmenopausal women. The cut-off value of the TyG index was 1.41, and the cut-off value of OSTA was − 0.25. (Table 2 and Fig. 4 a) The results of the 60-year-old stratification showed that the AUC and sensitivity of the TyG index were inferior to that of OSTA, but the specificity was superior. The sensitivity and specificity of the TyG index and OSTA were comparable in postmenopausal women aged ≥ 60 and their area under the curves was similar. The TyG index’s cut-off value for postmenopausal women aged ≥ 60 was higher than for postmenopausal women aged < 60, suggesting that an increase in TyG index in postmenopausal women aged ≥ 60 increases their risk of osteoporosis. The cut-off value of OSTA in postmenopausal women aged ≥ 60 years (-2.1) was lower than that in postmenopausal women aged < 60 years (1.35), indicating that the reduction of OSTA in postmenopausal women aged ≥ 60 years increased the risk of osteoporosis. (Table 2 and Fig. 4 b) The results of FBG and TG stratification of 1032 postmenopausal women showed that the AUC and specificity of the TyG index were inferior to those of TyG index. The sensitivity of FBG was similar to that of TG, and the specificity of FBG and/or TG was higher than that of OSTA. The cut-off value of the TyG index with abnormal FBG and/or TG (1.66) is higher than that with normal FBG and TG (1.38), and the risk of osteoporosis was increased. The cutoff value of OSTA with abnormal FBG and/or TG (-0.35) were larger than those with normal FBG and TG (-1.1). This indicated that postmenopausal women with abnormal FBG and/or TG have a higher risk of osteoporosis than those with normal FBG and TG. (Table 2 and Fig. 4 c) The stratification results of FBG and TG values in 451 postmenopausal women aged < 60 showed that the AUC and sensitivity of the TyG index with normal FBG and TG were inferior to those of OSTA, but the specificity was superior to that of OSTA. The AUC of the TyG index with abnormal FBG and/or TG was superior to that of OSTA, but the sensitivity and specificity were inferior to those of OSTA, and the P value of the TyG index was 0.430, which was not statistically significant. In postmenopausal women aged < 60, the OSTA cut-off values with normal FBG and TG (1.35) were higher than those with abnormal FBG and/or TG (0.35). The results indicated that The risk of osteoporosis decreases with the increase of OSTA in postmenopausal women aged < 60. (Table 2 and Fig. 4 d) The results of 581 postmenopausal women aged ≥ 60 stratified by FBG and TG showed that the AUC and specificity of the TyG index with normal FBG and TG values were inferior to those of OSTA, but the sensitivity was similar to that of OSTA. The AUC of the TyG index with abnormal FBG and/or TG values was similar to that of OSTA, but the sensitivity was inferior and the specificity was superior to that of OSTA. The similar TyG index cut-off values for FBG and TG value stratification indicated that the TyG index was not affected by FBG and TG in postmenopausal women aged ≥ 60. The OSTA cut-off value with normal FBG and TG (-1.95) was higher than that with abnormal FBG and/or TG (-2.10), indicating that abnormal FBG and/or TG may increase the risk of osteoporosis in postmenopausal women aged ≥ 60. (Table 2 and Fig. 4 e) Table 2 ROC curve analysis of the TyG index and OSTA AUC (95%CI) Cut-off value Sensitivity(%) Specificity(%) Youden index P The TyG index overall 0.584(0.549,0.619) 1.41 54.1 61.9 0.159 0.000 Age Age<60 0.564(0.506,0.621) 1.27 49.7 65.4 0.15 0.029 Age ≥ 60 0.629(0.583,0.676) 1.53 62.0 62.4 0.244 0.000 FBG and TG Normal FBG and TG 0.580(0.538,0.621) 1.38 64.8 50.1 0.15 0.000 Abnormal FBG and/or TG 0.601(0.538,0.664) 1.66 40.5 79.0 0.196 0.002 Age < 60 Normal FBG and TG 0.580(0.520,0.643) 1.00 40.7 78.5 0.192 0.004 Abnormal FBG and/or TG 0.623(0.550,0.697) 1.88 51.4 66.7 0.18 0.430 Age ≥ 60 Normal FBG and TG 0.588(0.530,0.648) 1.48 72.5 44.9 0.173 0.002 Abnormal FBG and/or TG 0.640(0.560,0.719) 1.49 34.2 86.9 0.212 0.001 OSTA overall 0.754(0.725,0.784) −0.25 75.8 62.6 0.384 0.000 Age Age<60 0.749(0.700,0.798) 1.35 79.3 60.5 0.398 0.000 Age ≥ 60 0.689(0.645,0.733) −2.10 68.9 62.8 0.317 0.000 FBG and TG Normal FBG and TG 0.767(0.733,0.802) −1.10 63.7 78.0 0.417 0.000 Abnormal FBG and/or TG 0.728(0.674,0.783) −0.35 79.7 54.5 0.342 0.000 Age < 60 Normal FBG and TG 0.719(0.660,0.777) 1.35 75.0 61.8 0.368 0.000 Abnormal FBG and/or TG 0.593(0.530,0.656) 0.35 81.1 81.7 0.627 0.000 Age ≥ 60 Normal FBG and TG 0.675(0.620,0.734) −1.95 73.3 57.5 0.308 0.000 Abnormal FBG and/or TG 0.700(0.630,0.769) −2.10 65.8 66.4 0.321 0.000 Discussion Early screening and risk assessment of osteoporosis are essential. Decreased secretion of estrogen in postmenopausal women leads to increased secretion of Receptor Activator of Nuclear Kappa-B Ligand (RANKL). The competitive binding of Osteoprotegerin (OPG) secreted by osteoblasts to RANKL is inhibited, and the formation and bone resorption of osteoclasts are enhanced, resulting in the decrease of bone mineral density and bone strength [ 16 ] . The proliferation of osteoblasts induced by the Wnt/β-catenin signaling pathway and the differentiation of pre-osteoblasts into osteoblasts promoted by the BMP signaling pathway are inhibited by estrogen deficiency. Estrogen deficiency can also increase the secretion of proinflammatory factors such as IL-1, IL-6 and tumor necrosis factor α (TNFα), and promote osteoclast formation [ 17 ] . Both osteocytes and osteoclasts contain insulin and IGF-1 receptors. Deficiency in bone development and maturation produces systemic IR and bone-specific IR, which in turn regulates glucose homeostasis and energy metabolism through Osteocalcin (OC) [ 18 ] . Insulin can stimulate bone formation and resorption through mitogen-activated protein kinase (MAPK) and Phosphatidylinositol 3-Kinase (PI3K) signaling pathways. Thus, it increases the growth, proliferation and survival of osteoblasts, which in turn increases bone mass. IR inhibits OC production by increasing insulin secretion and hyperinsulinism, which in turn affects BMD [ 19 ] . The TyG index, as an emerging assessment method of IR, is negatively correlated with BMD [ 10 , 20 ] . A cross-sectional study in the United States showed that TyG index had a nonlinear relationship with bone mineral density, and the risk of osteoporosis increased with the increase of TyG index, and was not affected by gender and race [14]. A large Chinese population cross-sectional study showed that TyG index can effectively and objectively predict the risk of osteoporosis in women and people aged ≥ 60 and < 60 [ 21 ] . The results of our team further confirmed that TyG index had a nonlinear relationship with T-values of three different sites, and the TyG index had a better predictive effect on osteoporosis in people aged ≥ 60 and those aged ≥ 60 with abnormal FBG and/or TG. A 6-year follow-up, which used the TyG index as the best predictor of fragility fracture endpoint events in postmenopausal patients with type 2 diabetes and postmenopausal osteoporosis, found that type 2 diabetes patients with normal bone mineral density had a higher risk of fracture [ 22 ] . This prospective study lays the foundation for the TyG index to be used as an independent or auxiliary predictor in clinical research and opens up a new perspective for predicting osteoporotic fractures. ROC analysis is a statistical method that shows the performance of classification models by drawing curves, which is widely used in clinical screening, diagnosis, and treatment [ 23 ] . The UK Health system [ 24 ] screened the prevalence of colorectal cancer (CRC) in the UK by drawing the ROC curve model of fecal hemoglobin concentration. ROC analysis used AUC as the main measure of accuracy, and sensitivity and specificity as auxiliary criteria [ 25 ] . AUC represents the area under the ROC curve, and the superiority and inferiority of the model are judged by comparing the size of the AUC. In this study, the maximum AUC of the TyG index screening was presented in postmenopausal women aged ≥ 60 with abnormal FBG and/or TG, indicating that such people have a higher risk of osteoporosis. Sensitivity represents the true positive rate, which is the rate at which actual patients are detected. The TyG index has the best sensitivity in postmenopausal women aged ≥ 60 with normal FBG and TG, and it is close to OSTA. The TyG index is similar to OSTA in the detection of osteoporosis in this population. Specificity, however, represents the false positive rate, which is the proportion of nonpatients in the negative population. The best specificity region of the TyG index was in postmenopausal women aged ≥ 60 with abnormal FBG and/or TG. In conclusion, the TyG index had the best performance in screening for osteoporosis in postmenopausal women aged ≥ 60. The maximum value of Youden's index, also known as the correct classification rate, corresponds to the best diagnostic critical value of the model, namely the Cut-off value. In this study, the TyG index cut-off value of postmenopausal women aged ≥ 60 was higher than that of postmenopausal women aged < 60, and the AUC, sensitivity, and specificity were better, indicating that the TyG index increased with age, and the risk of osteoporosis increased. In this retrospective study, with OSTA as contrast, ROC analysis was used to comprehensively and objectively compare the efficacy of the TyG index in screening postmenopausal osteoporosis. Despite the innovative use of the TyG index for postmenopausal osteoporosis screening in this study, there are certain limitations. ① The participants in this study were all from Nanjing, Jiangsu Province, which could not represent the population characteristics of China and the world. ② To objectively compare the screening efficacy of the TyG index and OSTA, the population included in this study was postmenopausal osteoporosis. ③ Gender and finer age stratification were not included in the comparison of screening models. In the future, our research group will verify the efficacy of the TyG index in screening osteoporosis from GHD, NHANES, KNHANES and other public medical databases. At the same time, the objectivity of the TyG index will be further verified in the multi-center clinical study undertaken by our team. Conclusion In conclusion, TyG index is not affected by age and height, and postmenopausal women with abnormal FBG and/or TG have a greater risk of osteoporosis. The TyG index has a great prospect in screening osteoporosis in postmenopausal women aged ≥ 60 and aged ≥ 60 with abnormal FBG and/or TG. Declarations Acknowledgment The authors would love to show their appreciation to all the contributors. Funding Key project of TCM science and technology development plan of Jiangsu Province (ZD202313) Natural Fund of Nanjing University of Chinese Medicine (XZR2021006) Nanjing Traditional Chinese Medicine Science and Technology Project (ZYYB202220) Jiangsu Provincial Administration of Traditional Chinese Medicine (MS2022021) Graduate student scientific research innovation projects in Jiangsu province (SJCX24_0997) Data availability The datasets generated and analyzed during the current study can be obtained by contacting the corresponding author upon request. Conflict of interest All authors affirm that they possess no competing interests. Author contributions DMX and BYZ provide guidance for research ideas and quality control of the whole paper. LB, SW and XZ made substantial contributions to providing and collating data. ZW and DZ carried out a significant portion of data analysis. The preliminary version of this paper was authored by JYY and JL with subsequent contributors enhancing ideas, conducting additional analyses, and finally completing the final manuscript. All authors meticulously checked the paper. Authors details: Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China. School of Acupuncture-Moxibustion and Tuina of Nanjing University of Chinese Medicine·School of Health Preservation and Rehabilitation of Nanjing University of Chinese Medicine, Nanjing 210023, China. References United Nations Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024: Summary of Results (UN DESA/POP/2024/TR/NO.9). Main Data of the Seventh National Population Census[EB/OL]．http://www.stats.gov.cn/ztjc/zdtjgz/zgrkpc /dqcrkpc/ggl/202105/t20210519_1817698.html Ensrud KE, Crandall CJ. Osteoporosis [published correction appears in Ann Intern Med. 2017;167(03):ITC17-ITC32. doi:10.7326/AITC201708010 Salari N, Ghasemi H, Mohammadi L, et al. The global prevalence of osteoporosis in the world: a comprehensive systematic review and meta-analysis. J Orthop Surg Res. 2021;16(01):609. doi:10.1186/s13018-021-02772-0 Meng S, Tong M, Yu Y, et al. The prevalence of osteoporotic fractures in the elderly in China: a systematic review and meta-analysis. J Orthop Surg Res. 2023;18(1):536. doi:10.1186/s13018-023-04030-x Walker MD, Shane E. Postmenopausal Osteoporosis. N Engl J Med. 2023;389(21):1979-1991. doi:10.1056/NEJMcp2307353 Workgroup of Chinese Guideline for the Diagnosis and Treatment of Senile Osteoporosis(2023), Osteoporosis Society of China Association of Gerontology and Geriatrics, Osteporosis Society of China International Exchange and Promotive Association for Medical and Health Care,et al. China guideline for diagnosis and treatment of senile osteoporosis (2023). Chin J Bone Joint Surg, 2023,16 (10): 865-885. doi: 10.3969/j.issn.2095-9958.2023.10.01 Koh LK, Sedrine WB, Torralba TP, et al. A simple tool to identify asian women at increased risk of osteoporosis. Osteoporos Int. 2001;12(08):699-705. doi:10.1007/s001980170070 Li M, Chi X, Wang Y, et al. Trends in insulin resistance: insights into mechanisms and therapeutic strategy. Signal Transduct Target Ther. 2022;7(01):216. doi:10.1038/s41392-022-01073-0 Zhuo M, Chen Z, Zhong ML, et al. Association of insulin resistance with bone mineral density in a nationwide health check-up population in China. Bone. 2023;170:116703. doi:10.1016/j.bone.2023.116703 Wang X, Jiang L, Shao X. Association Analysis of Insulin Resistance and Osteoporosis Risk in Chinese Patients with T2DM. Ther Clin Risk Manag. 2021;17:909-916. doi:10.2147/TCRM.S328510 Ramdas Nayak VK, Satheesh P, Shenoy MT, et al. Triglyceride Glucose (TyG) Index: A surrogate biomarker of insulin resistance. J Pak Med Assoc. 2022;72(05):986-988. doi:10.47391/JPMA.22-63 Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(07):3347-3351. doi:10.1210/jc.2010-0288 Zhan H, Liu X, Piao S, et al. Association between triglyceride-glucose index and bone mineral density in US adults: a cross sectional study. J Orthop Surg Res. 2023;18(01):810. doi:10.1186/s13018-023-04275-6 Tian N, Chen S, Han H, et al. Association between triglyceride glucose index and total bone mineral density: a cross-sectional study from NHANES 2011-2018. Sci Rep. 2024;14(01):4208. doi:10.1038/s41598-024-54192-9 Liu Q. OPG-RANKL-RANK pathway: important mechanism of postmenopausal osteoporosis. Chin J Orthop, 2021,41(10):668-674. doi: 10.3760/cma.j.cn121113-20210407-00286. Cheng CH, Chen LR, Chen KH. Osteoporosis Due to Hormone Imbalance: An Overview of the Effects of Estrogen Deficiency and Glucocorticoid Overuse on Bone Turnover. Int J Mol Sci. 2022;23(03):1376. doi:10.3390/ijms23031376 Greere DII, Grigorescu F, Manda D, et al. Insulin Resistance and pathogenesis of postmenopausal osteoporosis. Acta Endocrinol (Buchar). 2023;19(03):349-363. doi:10.4183/aeb.2023.349 Conte C, Epstein S, Napoli N. Insulin resistance and bone: a biological partnership. Acta Diabetol. 2018;55(04):305-314. doi:10.1007/s00592-018-1101-7 Yoon JH, Hong AR, Choi W, et al. Association of Triglyceride-Glucose Index with Bone Mineral Density in Non-diabetic Koreans: KNHANES 2008-2011. Calcif Tissue Int. 2021;108(02):176-187. doi:10.1007/s00223-020-00761-9 Yong J, Zhu F, Chen H, et al. The correlation between triglyceride glucose index and osteoporosis: a cross-sectional study based on natural population. J Clin Med Pract, 2023,27(06):29-32+38. doi:10.7619/jcmp.20230223. Pan J, Huang X, Wang Q, et al. Triglyceride Glucose Index is Strongly Associated with a Fragility Fracture in Postmenopausal Elderly Females with Type 2 Diabetes Mellitus Combined with Osteoporosis: A 6-Year Follow-Up Study. Clin Interv Aging. 2023;18:1841-1849. doi:10.2147/CIA.S434194 Niu Z, Shen J, Zhang Z, et al. Application of prediction models in clinical research. Shanghai J Prevent Med, 2023,35(01):56-65. doi:10.19428/j.cnki.sjpm.2023.22770. D'Souza N, Georgiou Delisle T, Chen M, et al. Faecal immunochemical test is superior to symptoms in predicting pathology in patients with suspected colorectal cancer symptoms referred on a 2WW pathway: a diagnostic accuracy study. Gut. 2021;70(06):1130-1138. doi:10.1136/gutjnl-2020-321956 Zou KH, O'Malley AJ, Mauri L. Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation. 2007;115(05):654-657. doi:10.1161/CIRCULATIONAHA.105.594929 Additional Declarations No competing interests reported. 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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-4941509\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":359726251,\"identity\":\"1e65b8a0-b889-42ce-b3f6-a5b7e5d58294\",\"order_by\":0,\"name\":\"Jingyi Yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jingyi\",\"middleName\":\"\",\"lastName\":\"Yang\",\"suffix\":\"\"},{\"id\":359726252,\"identity\":\"cd495dd3-53b8-4eec-ada7-70a337706a3c\",\"order_by\":1,\"name\":\"Daoming Xu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Daoming\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"},{\"id\":359726253,\"identity\":\"dead93c7-141c-47af-a213-a645d006e1c0\",\"order_by\":2,\"name\":\"Di Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese 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Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zun\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":359726256,\"identity\":\"3978a607-1889-4178-93d2-2a3fe5371838\",\"order_by\":5,\"name\":\"Xin Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xin\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":359726257,\"identity\":\"251d9ea4-2c77-45f9-8fac-7c877cb8d9f8\",\"order_by\":6,\"name\":\"Sheng Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese 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Liu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYFCCww0HGHgkGBjYmw8c+PCDKC0HoVp4jiUenNlDlBbGBggtkWN8mIONCA3mjAcbD/PIWOTJ+5z5cJiBh0GeX+wAfi2WDQcbDvPwSBQbHu/dcLjAgsFw5uwE/FoMDkC0JG7sObvh8AwehgSD20RrmZHz4DAPGyla5kvkMBCv5eAcoJYNPMcMgIEsQYRfbhw+/OFtT13i/Pbmxx8+/LCR55cmoIVB4gAwanpA1kG4BJSDAH8DkACmE/kGIhSPglEwCkbByAQA/C9Q2FnlZawAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Jing\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-08-20 02:39:09\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4941509/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4941509/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":65635390,\"identity\":\"541ea112-fd9a-4e89-9d7a-5fab6e1ed797\",\"added_by\":\"auto\",\"created_at\":\"2024-09-30 18:03:21\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":326154,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlowchart of participants’ selection\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4941509/v1/e01f1ebd6bc0737ac0ad97d3.png\"},{\"id\":65635061,\"identity\":\"997798b0-4692-4400-ba02-070693716d76\",\"added_by\":\"auto\",\"created_at\":\"2024-09-30 17:55:21\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":926479,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eScatter diagram of age distribution trend and \\u003cem\\u003eT\\u003c/em\\u003e-values of three different sites of TyG index and OSTA\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4941509/v1/73da895a39d887b39fc4d32d.png\"},{\"id\":65635391,\"identity\":\"06f0bb64-1943-4c78-924d-fab5b6d58a9e\",\"added_by\":\"auto\",\"created_at\":\"2024-09-30 18:03:21\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":748698,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorrelation between the TyG index, OSTA and other clinical features\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4941509/v1/d25d557398ffb71171e7df7b.png\"},{\"id\":65635064,\"identity\":\"460721e8-7a30-4edf-ae9c-510204a32f8b\",\"added_by\":\"auto\",\"created_at\":\"2024-09-30 17:55:21\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":586589,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eROC curve of the TyG index and OSTA\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4941509/v1/712a29167916789fdedc8b77.png\"},{\"id\":70276634,\"identity\":\"7819bdc5-4614-4ad2-92ff-3a1b18906525\",\"added_by\":\"auto\",\"created_at\":\"2024-12-01 12:31:47\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3976001,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4941509/v1/0f651d51-eb71-472f-9b78-567e547f2cc6.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Could the TyG index be a screening tool for postmenopausal osteoporosis?\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eAccording to the newly released \\\"World Population Prospects 2024\\\" \\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]\\u003c/sup\\u003e, the global aging trend is accelerating, and it is expected that by 2100, the elderly population will reach one-third of the global population. China's 7th national population census pointed out that the country has the largest elderly population in the world, with 264\\u0026nbsp;million persons aged 60 to 264\\u0026nbsp;million (accounts for about 18.7% of the total population), an increase of 5.4% over the previous census \\u003csup\\u003e[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e. Osteoporosis (OP) is a systemic degenerative disease defined by a loss in bone mass, the breakdown of bone microstructure, and an increased risk of fracture \\u003csup\\u003e[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e. The prevalence of osteoporosis worldwide was 18.3%, with women having almost twice as high prevalence (23.1%) than men (11.7%) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e. The most hazardous side effect of osteoporosis is osteoporotic fractures, which are most common in the distal portions of the spine, hip, and forearm \\u003csup\\u003e[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/sup\\u003e and affect 18.9% of older persons in China. About 50% of postmenopausal women get osteoporotic fractures, and hip fractures double the risk of death within a year \\u003csup\\u003e[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e. Therefore, osteoporosis is called the \\\"silent killer\\\", and screening is the premise of prevention and treatment of osteoporosis and prevention of fragility fractures. Osteoporosis Self-Assessment Tool for Asians (OSTA) is a tool recommended by guidelines \\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e for early identification and screening of postmenopausal osteoporosis, and only weight and age are used as calculation variables. Weight is easily affected by diet, exercise, and diseases, and could have a large range of short-term fluctuations, which reduces the screening efficiency of OSTA. The data of OSTA calculation model come from postmenopausal women in eight Asian countries, and there are limitations in the use of gender and ethnicity \\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e. There is an urgent need for an objective and widely applicable osteoporosis screening model to accurately and efficiently identify high-risk populations.\\u003c/p\\u003e \\u003cp\\u003eIn Muscle and fat cells, insulin-stimulated glucose uptake was flawed, and impaired insulin suppression of hepatic glucose output, which is known as insulin resistance (IR) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]\\u003c/sup\\u003e. The triglyceride-glucose (TyG) index is a reliable and emerging indicator for evaluating Insulin resistance (IR). A cross-sectional study by Zhuo M. et al \\u003csup\\u003e[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e. verified that IR causes low bone mass and the risk of osteoporosis. The risk of osteoporosis increases with the increased level of IR in female patients \\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]\\u003c/sup\\u003e. The triglyceride-glucose (TyG) index is a reliable and emerging indicator for evaluating Insulin resistance (IR) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/sup\\u003e. Recent studies have shown that \\u003csup\\u003e[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]\\u003c/sup\\u003e the TyG index obtain the ability to predict osteoporosis, offering an objective and applicable approach to osteoporosis prevention and early screening.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData collection and study population\\u003c/h2\\u003e \\u003cp\\u003eThe retrospective analysis of the screening was conducted between January 2021 and December 2023 at, Affiliated Hospital of Nanjing University of Chinese Medicine, which involved nuclear medicine bone mineral density assessment of clinical data pertaining to 20568 patients. Exclusion criteria for this study are: (1) lack comprehensive blood index information; (2) a history of kidney disease and malignant tumors; (3) Male; and (4) Females who have not reached menopausal status. The ultimate analysis included 1032 participants in total (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). This study has been registered in the Chinese Clinical Trial Registry (Registration number: ChiCTR2400085209), and approved by the Ethics Committee of the Affiliated Hospital of Nanjing University of Chinese Medicine (Ethics number: 2024NL-085-02). Since this study employed anonymized patient records, the need for informed consent to participate was waived by an Institutional Review Board (IRB). We confirm that all methods were performed in accordance with the relevant guidelines and regulations. The study population's medical history was retrieved from the clinical information system, and our hospital's laboratory department provided the FBG and TG blood test indices. Using dual-energy X-ray absorptiometry (Hologic Discovery ASY-03954), the density of bone was determined.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLaboratory analysis\\u003c/h2\\u003e \\u003cp\\u003eBlood triglyceride (TG) and fasting blood glucose (FBG) levels were measured quantitatively through an automatic biochemical analyzer (Beckman Coulter AU5800, USA). Chromatography and the GPO-PAP method are employed by TG and FBG, respectively, for assessment.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDefinition of index\\u003c/h2\\u003e \\u003cp\\u003eBMI\\u0026thinsp;=\\u0026thinsp;Weight (kg) / Height (m)\\u0026sup2;\\u003c/p\\u003e \\u003cp\\u003eTyG\\u0026thinsp;=\\u0026thinsp;Ln [ TG (mg/dL) \\u0026times; FBG (mg/dL) / 2] \\u003csup\\u003e[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eOSTA = [ Weight (kg) - Age (years) ] \\u0026times; 0.2 \\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData analysis\\u003c/h2\\u003e \\u003cp\\u003eSPSS 29.0 software was used for statistical analysis. The normal distribution was expressed as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation, and the non-normal distribution was expressed as median and interquartile range M(P25, P75). The non-normal distribution of the Mann-Whitney U test, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 is a significant difference. The Spearman correlation analysis, the analysis of the TyG index and OSTA, and the correlation of the indexes. Graphpad Prism10 software was used for correlation heat mapping. receiver operating characteristic (ROC) curves of the TyG index, OSTA, age, and FBG and TG stratification were drawn and the area under the curve (AUC) were analyzed. Sensitivity, specificity, and the AUC was compared by z-test. With \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, the difference was statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBaseline Characteristics\\u003c/h2\\u003e \\u003cp\\u003eThis study enrolled 1032 postmenopausal women in total, including 492 with osteoporosis, 540 without osteoporosis, 315 with abnormal FBG and/or TG, and 717 with normal FBG and TG. Grouping by \\u003cem\\u003eT\\u003c/em\\u003e-value \\u0026le;-2.5, there were significant differences in the enrolled population's age, weight, height, BMI, \\u003cem\\u003eT\\u003c/em\\u003e-values of three sites, the TyG index, and OSTA (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Blood TG and FBG did not differ statistically (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePopulation grouping of study participants\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOverall (n\\u0026minus;1032)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePMOP (n\\u0026thinsp;=\\u0026thinsp;492)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNon-PMOP (n\\u0026thinsp;=\\u0026thinsp;540)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eZ\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge (years)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e61.00(54.00,69.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e66(59,72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e57(51,66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;11.166\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eWeight (kg)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e57.00(51.50,63.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e55(50,60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60(55,65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;9.84\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHeight (cm)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e159.00(155.00,163.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e158(155,161)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e160(156,163)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;5.642\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBMI(kg/m\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sup\\u003e\\u003cb\\u003e)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22.70(20.70,24.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.04(20.03,24.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.44(21.48,25.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;8.109\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLS\\u003c/b\\u003e \\u003cb\\u003eT\\u003c/b\\u003e\\u003cb\\u003e-value\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026minus;2.10(\\u0026minus;2.80,\\u0026minus;1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;2.9(\\u0026minus;3.4,\\u0026minus;2.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026minus;1.2(\\u0026minus;1.8,\\u0026minus;0.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;24.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTH\\u003c/b\\u003e \\u003cb\\u003eT\\u003c/b\\u003e\\u003cb\\u003e-value\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026minus;1.30(\\u0026minus;2.00,\\u0026minus;0.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;1.9(\\u0026minus;2.5,\\u0026minus;1.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.7(\\u0026minus;1.2,\\u0026minus;0.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;21.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFN\\u003c/b\\u003e \\u003cb\\u003eT\\u003c/b\\u003e\\u003cb\\u003e-value\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026minus;1.70(\\u0026minus;2.40,\\u0026minus;0.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;2.5(\\u0026minus;2.9,\\u0026minus;1.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026minus;1.1(\\u0026minus;1.7,\\u0026minus;0.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;20.634\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFBG(mmol/L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.41(5.05,6.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.41(5.08,6.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.40(5.03,6.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.453\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTG(mmol/L)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.21(0.89,1.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.21(0.89,1.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.22(0.89,1.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.699\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.485\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTyG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.45(1.09,1.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.34(0.99,1.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.55(1.16,1.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;4.665\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOSTA\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.60(\\u0026minus;2.80,1.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;2.2(\\u0026minus;3.8,\\u0026minus;0.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.45(\\u0026minus;1.2, 2.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026minus;14.138\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e医学\\u003c/h2\\u003e \\u003cp\\u003e \\u003cb\\u003eTyG index and OSTA with\\u003c/b\\u003e \\u003cb\\u003eT\\u003c/b\\u003e\\u003cb\\u003e-values of three different sites and age distribution trends\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eIn Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e (a), (b), and (c) diagrams represent a decrease in \\u003cem\\u003eT\\u003c/em\\u003e-values of three sites and an increase in the TyG index with aging in the 1032 postmenopausal women. The \\u003cem\\u003eT\\u003c/em\\u003e-values of three sites and OSTA decreased with aging in the 1032 postmenopausal women, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e (d), (e), (f).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCorrelation analysis\\u003c/h2\\u003e \\u003cp\\u003eThe results of the 60-year-old stratification of 1032 postmenopausal women showed the TyG index and OSTA were positively correlated with body weight, BMI, LS \\u003cem\\u003eT\\u003c/em\\u003e-value, TH \\u003cem\\u003eT\\u003c/em\\u003e-value and FN \\u003cem\\u003eT\\u003c/em\\u003e-value (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001)(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). The TyG index's correlation coefficient was lower than OSTA's. The TyG index showed a positive correlation with TG and FBG but not with age or height (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Age showed a negative correlation with OSTA (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), while height showed a positive correlation with OSTA (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). OSTA had a negative correlation with age and FBG (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and a positive correlation with height and TG (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.013). In postmenopausal women age\\u0026thinsp;\\u0026lt;\\u0026thinsp;60, there was no correlation found between OSTA and FBG and TG; however, in women over 60, there was a positive correlation between OSTA and FBG and TG.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAnalysis of Receiver Operating Characteristic (ROC) curves\\u003c/h2\\u003e \\u003cp\\u003eTaking DXA \\u003cem\\u003eT\\u003c/em\\u003e-value\\u0026le;-2.5 as the diagnostic criteria for osteoporosis, the AUC of OSTA was better than that of TyG index in 1032 postmenopausal women. The cut-off value of the TyG index was 1.41, and the cut-off value of OSTA was \\u0026minus;\\u0026thinsp;0.25. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea)\\u003c/p\\u003e \\u003cp\\u003eThe results of the 60-year-old stratification showed that the AUC and sensitivity of the TyG index were inferior to that of OSTA, but the specificity was superior. The sensitivity and specificity of the TyG index and OSTA were comparable in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 and their area under the curves was similar. The TyG index\\u0026rsquo;s cut-off value for postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 was higher than for postmenopausal women aged\\u0026thinsp;\\u0026lt;\\u0026thinsp;60, suggesting that an increase in TyG index in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 increases their risk of osteoporosis. The cut-off value of OSTA in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 years (-2.1) was lower than that in postmenopausal women aged\\u0026thinsp;\\u0026lt;\\u0026thinsp;60 years (1.35), indicating that the reduction of OSTA in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 years increased the risk of osteoporosis. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb)\\u003c/p\\u003e \\u003cp\\u003eThe results of FBG and TG stratification of 1032 postmenopausal women showed that the AUC and specificity of the TyG index were inferior to those of TyG index. The sensitivity of FBG was similar to that of TG, and the specificity of FBG and/or TG was higher than that of OSTA. The cut-off value of the TyG index with abnormal FBG and/or TG (1.66) is higher than that with normal FBG and TG (1.38), and the risk of osteoporosis was increased. The cutoff value of OSTA with abnormal FBG and/or TG (-0.35) were larger than those with normal FBG and TG (-1.1). This indicated that postmenopausal women with abnormal FBG and/or TG have a higher risk of osteoporosis than those with normal FBG and TG. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ec)\\u003c/p\\u003e \\u003cp\\u003eThe stratification results of FBG and TG values in 451 postmenopausal women aged\\u0026thinsp;\\u0026lt;\\u0026thinsp;60 showed that the AUC and sensitivity of the TyG index with normal FBG and TG were inferior to those of OSTA, but the specificity was superior to that of OSTA. The AUC of the TyG index with abnormal FBG and/or TG was superior to that of OSTA, but the sensitivity and specificity were inferior to those of OSTA, and the \\u003cem\\u003eP\\u003c/em\\u003e value of the TyG index was 0.430, which was not statistically significant. In postmenopausal women aged\\u0026thinsp;\\u0026lt;\\u0026thinsp;60, the OSTA cut-off values with normal FBG and TG (1.35) were higher than those with abnormal FBG and/or TG (0.35). The results indicated that The risk of osteoporosis decreases with the increase of OSTA in postmenopausal women aged\\u0026thinsp;\\u0026lt;\\u0026thinsp;60. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ed)\\u003c/p\\u003e \\u003cp\\u003eThe results of 581 postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 stratified by FBG and TG showed that the AUC and specificity of the TyG index with normal FBG and TG values were inferior to those of OSTA, but the sensitivity was similar to that of OSTA. The AUC of the TyG index with abnormal FBG and/or TG values was similar to that of OSTA, but the sensitivity was inferior and the specificity was superior to that of OSTA. The similar TyG index cut-off values for FBG and TG value stratification indicated that the TyG index was not affected by FBG and TG in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60. The OSTA cut-off value with normal FBG and TG (-1.95) was higher than that with abnormal FBG and/or TG (-2.10), indicating that abnormal FBG and/or TG may increase the risk of osteoporosis in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ee)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eROC curve analysis of the TyG index and OSTA\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAUC\\u003c/p\\u003e \\u003cp\\u003e(95%CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCut-off value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSensitivity(%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eSpecificity(%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYouden index\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eThe TyG index\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eoverall\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.584(0.549,0.619)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e54.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e61.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.159\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u0026lt;60\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.564(0.506,0.621)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e49.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e65.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.029\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u0026thinsp;\\u0026ge;\\u0026thinsp;60\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.629(0.583,0.676)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e62.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e62.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.244\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFBG and TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNormal FBG and TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.580(0.538,0.621)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e64.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e50.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAbnormal FBG and/or TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.601(0.538,0.664)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e40.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e79.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.196\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u0026thinsp;\\u0026lt;\\u0026thinsp;60\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNormal FBG and TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.580(0.520,0.643)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e40.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e78.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.192\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAbnormal FBG and/or TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.623(0.550,0.697)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e51.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e66.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.430\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u0026thinsp;\\u0026ge;\\u0026thinsp;60\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNormal FBG and TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.588(0.530,0.648)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e72.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e44.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.173\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAbnormal FBG and/or TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.640(0.560,0.719)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e34.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e86.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.212\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOSTA\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eoverall\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.754(0.725,0.784)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e75.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e62.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.384\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u0026lt;60\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.749(0.700,0.798)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e79.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e60.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.398\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u0026thinsp;\\u0026ge;\\u0026thinsp;60\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.689(0.645,0.733)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026minus;2.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e68.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e62.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.317\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFBG and TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNormal FBG and TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.767(0.733,0.802)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026minus;1.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e63.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e78.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.417\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAbnormal FBG and/or TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.728(0.674,0.783)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e79.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e54.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.342\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u0026thinsp;\\u0026lt;\\u0026thinsp;60\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNormal FBG and TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.719(0.660,0.777)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e75.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e61.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.368\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAbnormal FBG and/or TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.593(0.530,0.656)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e81.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.627\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u0026thinsp;\\u0026ge;\\u0026thinsp;60\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNormal FBG and TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.675(0.620,0.734)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026minus;1.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e73.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e57.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.308\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAbnormal FBG and/or TG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.700(0.630,0.769)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026minus;2.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e65.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e66.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.321\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eEarly screening and risk assessment of osteoporosis are essential. Decreased secretion of estrogen in postmenopausal women leads to increased secretion of Receptor Activator of Nuclear Kappa-B Ligand (RANKL). The competitive binding of Osteoprotegerin (OPG) secreted by osteoblasts to RANKL is inhibited, and the formation and bone resorption of osteoclasts are enhanced, resulting in the decrease of bone mineral density and bone strength \\u003csup\\u003e[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e. The proliferation of osteoblasts induced by the Wnt/β-catenin signaling pathway and the differentiation of pre-osteoblasts into osteoblasts promoted by the BMP signaling pathway are inhibited by estrogen deficiency. Estrogen deficiency can also increase the secretion of proinflammatory factors such as IL-1, IL-6 and tumor necrosis factor α (TNFα), and promote osteoclast formation \\u003csup\\u003e[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]\\u003c/sup\\u003e. Both osteocytes and osteoclasts contain insulin and IGF-1 receptors. Deficiency in bone development and maturation produces systemic IR and bone-specific IR, which in turn regulates glucose homeostasis and energy metabolism through Osteocalcin (OC) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e. Insulin can stimulate bone formation and resorption through mitogen-activated protein kinase (MAPK) and Phosphatidylinositol 3-Kinase (PI3K) signaling pathways. Thus, it increases the growth, proliferation and survival of osteoblasts, which in turn increases bone mass. IR inhibits OC production by increasing insulin secretion and hyperinsulinism, which in turn affects BMD \\u003csup\\u003e[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]\\u003c/sup\\u003e. The TyG index, as an emerging assessment method of IR, is negatively correlated with BMD \\u003csup\\u003e[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003c/sup\\u003e. A cross-sectional study in the United States showed that TyG index had a nonlinear relationship with bone mineral density, and the risk of osteoporosis increased with the increase of TyG index, and was not affected by gender and race [14]. A large Chinese population cross-sectional study showed that TyG index can effectively and objectively predict the risk of osteoporosis in women and people aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 and \\u0026lt;\\u0026thinsp;60 \\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e. The results of our team further confirmed that TyG index had a nonlinear relationship with T-values of three different sites, and the TyG index had a better predictive effect on osteoporosis in people aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 and those aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 with abnormal FBG and/or TG. A 6-year follow-up, which used the TyG index as the best predictor of fragility fracture endpoint events in postmenopausal patients with type 2 diabetes and postmenopausal osteoporosis, found that type 2 diabetes patients with normal bone mineral density had a higher risk of fracture \\u003csup\\u003e[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]\\u003c/sup\\u003e. This prospective study lays the foundation for the TyG index to be used as an independent or auxiliary predictor in clinical research and opens up a new perspective for predicting osteoporotic fractures.\\u003c/p\\u003e \\u003cp\\u003eROC analysis is a statistical method that shows the performance of classification models by drawing curves, which is widely used in clinical screening, diagnosis, and treatment \\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e. The UK Health system \\u003csup\\u003e[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]\\u003c/sup\\u003e screened the prevalence of colorectal cancer (CRC) in the UK by drawing the ROC curve model of fecal hemoglobin concentration. ROC analysis used AUC as the main measure of accuracy, and sensitivity and specificity as auxiliary criteria \\u003csup\\u003e[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/sup\\u003e. AUC represents the area under the ROC curve, and the superiority and inferiority of the model are judged by comparing the size of the AUC. In this study, the maximum AUC of the TyG index screening was presented in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 with abnormal FBG and/or TG, indicating that such people have a higher risk of osteoporosis. Sensitivity represents the true positive rate, which is the rate at which actual patients are detected. The TyG index has the best sensitivity in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 with normal FBG and TG, and it is close to OSTA. The TyG index is similar to OSTA in the detection of osteoporosis in this population. Specificity, however, represents the false positive rate, which is the proportion of nonpatients in the negative population. The best specificity region of the TyG index was in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 with abnormal FBG and/or TG. In conclusion, the TyG index had the best performance in screening for osteoporosis in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60. The maximum value of Youden's index, also known as the correct classification rate, corresponds to the best diagnostic critical value of the model, namely the Cut-off value. In this study, the TyG index cut-off value of postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 was higher than that of postmenopausal women aged\\u0026thinsp;\\u0026lt;\\u0026thinsp;60, and the AUC, sensitivity, and specificity were better, indicating that the TyG index increased with age, and the risk of osteoporosis increased.\\u003c/p\\u003e \\u003cp\\u003eIn this retrospective study, with OSTA as contrast, ROC analysis was used to comprehensively and objectively compare the efficacy of the TyG index in screening postmenopausal osteoporosis. Despite the innovative use of the TyG index for postmenopausal osteoporosis screening in this study, there are certain limitations. ① The participants in this study were all from Nanjing, Jiangsu Province, which could not represent the population characteristics of China and the world. ② To objectively compare the screening efficacy of the TyG index and OSTA, the population included in this study was postmenopausal osteoporosis. ③ Gender and finer age stratification were not included in the comparison of screening models. In the future, our research group will verify the efficacy of the TyG index in screening osteoporosis from GHD, NHANES, KNHANES and other public medical databases. At the same time, the objectivity of the TyG index will be further verified in the multi-center clinical study undertaken by our team.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn conclusion, TyG index is not affected by age and height, and postmenopausal women with abnormal FBG and/or TG have a greater risk of osteoporosis. The TyG index has a great prospect in screening osteoporosis in postmenopausal women aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 and aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 with abnormal FBG and/or TG.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors would love to show their appreciation to all the contributors.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003col\\u003e\\n \\u003cli\\u003eKey project of TCM science and technology development plan of Jiangsu Province (ZD202313)\\u003c/li\\u003e\\n \\u003cli\\u003eNatural Fund of Nanjing University of Chinese Medicine (XZR2021006)\\u003c/li\\u003e\\n \\u003cli\\u003eNanjing Traditional Chinese Medicine Science and Technology Project (ZYYB202220)\\u003c/li\\u003e\\n \\u003cli\\u003eJiangsu Provincial Administration of Traditional Chinese Medicine (MS2022021)\\u003c/li\\u003e\\n \\u003cli\\u003eGraduate student scientific research innovation projects in Jiangsu province (SJCX24_0997)\\u003c/li\\u003e\\n\\u003c/ol\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and analyzed during the current study can be obtained by contacting the corresponding author upon request.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors affirm that they possess no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDMX and BYZ provide guidance for research ideas and quality control of the whole paper. LB, SW and XZ made substantial contributions to providing and collating data. ZW and DZ carried out a significant portion of data analysis. The preliminary version of this paper was authored by JYY and JL with subsequent contributors enhancing ideas, conducting additional analyses, and finally completing the final manuscript. All authors meticulously checked the paper.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors details:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003col\\u003e\\n \\u003cli\\u003eJiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.\\u003c/li\\u003e\\n \\u003cli\\u003eSchool of Acupuncture-Moxibustion and Tuina of Nanjing University of Chinese Medicine·School of Health Preservation and Rehabilitation of Nanjing University of Chinese Medicine, Nanjing 210023, China.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eUnited Nations Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024: Summary of Results (UN DESA/POP/2024/TR/NO.9).\\u003c/li\\u003e\\n\\u003cli\\u003eMain Data of the Seventh National Population Census[EB/OL]．http://www.stats.gov.cn/ztjc/zdtjgz/zgrkpc /dqcrkpc/ggl/202105/t20210519_1817698.html\\u003c/li\\u003e\\n\\u003cli\\u003eEnsrud KE, Crandall CJ. Osteoporosis [published correction appears in Ann Intern Med. 2017;167(03):ITC17-ITC32. doi:10.7326/AITC201708010 \\u003c/li\\u003e\\n\\u003cli\\u003eSalari N, Ghasemi H, Mohammadi L, et al. The global prevalence of osteoporosis in the world: a comprehensive systematic review and meta-analysis. J Orthop Surg Res. 2021;16(01):609. doi:10.1186/s13018-021-02772-0 \\u003c/li\\u003e\\n\\u003cli\\u003eMeng S, Tong M, Yu Y, et al. The prevalence of osteoporotic fractures in the elderly in China: a systematic review and meta-analysis. J Orthop Surg Res. 2023;18(1):536. doi:10.1186/s13018-023-04030-x\\u003c/li\\u003e\\n\\u003cli\\u003eWalker MD, Shane E. Postmenopausal Osteoporosis. N Engl J Med. 2023;389(21):1979-1991. doi:10.1056/NEJMcp2307353 \\u003c/li\\u003e\\n\\u003cli\\u003eWorkgroup of Chinese Guideline for the Diagnosis and Treatment of Senile Osteoporosis(2023), Osteoporosis Society of China Association of Gerontology and Geriatrics, Osteporosis Society of China International Exchange and Promotive Association for Medical and Health Care,et al. China guideline for diagnosis and treatment of senile osteoporosis (2023). Chin J Bone Joint Surg, 2023,16 (10): 865-885. doi: 10.3969/j.issn.2095-9958.2023.10.01\\u003c/li\\u003e\\n\\u003cli\\u003eKoh LK, Sedrine WB, Torralba TP, et al. A simple tool to identify asian women at increased risk of osteoporosis. Osteoporos Int. 2001;12(08):699-705. doi:10.1007/s001980170070\\u003c/li\\u003e\\n\\u003cli\\u003eLi M, Chi X, Wang Y, et al. Trends in insulin resistance: insights into mechanisms and therapeutic strategy. Signal Transduct Target Ther. 2022;7(01):216. doi:10.1038/s41392-022-01073-0\\u003c/li\\u003e\\n\\u003cli\\u003eZhuo M, Chen Z, Zhong ML, et al. Association of insulin resistance with bone mineral density in a nationwide health check-up population in China. Bone. 2023;170:116703. doi:10.1016/j.bone.2023.116703\\u003c/li\\u003e\\n\\u003cli\\u003eWang X, Jiang L, Shao X. Association Analysis of Insulin Resistance and Osteoporosis Risk in Chinese Patients with T2DM. Ther Clin Risk Manag. 2021;17:909-916. doi:10.2147/TCRM.S328510 \\u003c/li\\u003e\\n\\u003cli\\u003eRamdas Nayak VK, Satheesh P, Shenoy MT, et al. Triglyceride Glucose (TyG) Index: A surrogate biomarker of insulin resistance. J Pak Med Assoc. 2022;72(05):986-988. doi:10.47391/JPMA.22-63\\u003c/li\\u003e\\n\\u003cli\\u003eGuerrero-Romero F, Simental-Mend\\u0026iacute;a LE, Gonz\\u0026aacute;lez-Ortiz M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(07):3347-3351. doi:10.1210/jc.2010-0288 \\u003c/li\\u003e\\n\\u003cli\\u003eZhan H, Liu X, Piao S, et al. Association between triglyceride-glucose index and bone mineral density in US adults: a cross sectional study. J Orthop Surg Res. 2023;18(01):810. doi:10.1186/s13018-023-04275-6\\u003c/li\\u003e\\n\\u003cli\\u003eTian N, Chen S, Han H, et al. Association between triglyceride glucose index and total bone mineral density: a cross-sectional study from NHANES 2011-2018. Sci Rep. 2024;14(01):4208. doi:10.1038/s41598-024-54192-9\\u003c/li\\u003e\\n\\u003cli\\u003eLiu Q. OPG-RANKL-RANK pathway: important mechanism of postmenopausal osteoporosis. Chin J Orthop, 2021,41(10):668-674. doi: 10.3760/cma.j.cn121113-20210407-00286.\\u003c/li\\u003e\\n\\u003cli\\u003eCheng CH, Chen LR, Chen KH. Osteoporosis Due to Hormone Imbalance: An Overview of the Effects of Estrogen Deficiency and Glucocorticoid Overuse on Bone Turnover. Int J Mol Sci. 2022;23(03):1376. doi:10.3390/ijms23031376\\u003c/li\\u003e\\n\\u003cli\\u003eGreere DII, Grigorescu F, Manda D, et al. Insulin Resistance and pathogenesis of postmenopausal osteoporosis. Acta Endocrinol (Buchar). 2023;19(03):349-363. doi:10.4183/aeb.2023.349\\u003c/li\\u003e\\n\\u003cli\\u003eConte C, Epstein S, Napoli N. Insulin resistance and bone: a biological partnership. Acta Diabetol. 2018;55(04):305-314. doi:10.1007/s00592-018-1101-7\\u003c/li\\u003e\\n\\u003cli\\u003eYoon JH, Hong AR, Choi W, et al. Association of Triglyceride-Glucose Index with Bone Mineral Density in Non-diabetic Koreans: KNHANES 2008-2011. Calcif Tissue Int. 2021;108(02):176-187. doi:10.1007/s00223-020-00761-9\\u003c/li\\u003e\\n\\u003cli\\u003eYong J, Zhu F, Chen H, et al. The correlation between triglyceride glucose index and osteoporosis: a cross-sectional study based on natural population. J Clin Med Pract, 2023,27(06):29-32+38. doi:10.7619/jcmp.20230223.\\u003c/li\\u003e\\n\\u003cli\\u003ePan J, Huang X, Wang Q, et al. Triglyceride Glucose Index is Strongly Associated with a Fragility Fracture in Postmenopausal Elderly Females with Type 2 Diabetes Mellitus Combined with Osteoporosis: A 6-Year Follow-Up Study. Clin Interv Aging. 2023;18:1841-1849. doi:10.2147/CIA.S434194\\u003c/li\\u003e\\n\\u003cli\\u003eNiu Z, Shen J, Zhang Z, et al. Application of prediction models in clinical research. Shanghai J Prevent Med, 2023,35(01):56-65. doi:10.19428/j.cnki.sjpm.2023.22770.\\u003c/li\\u003e\\n\\u003cli\\u003eD\\u0026apos;Souza N, Georgiou Delisle T, Chen M, et al. Faecal immunochemical test is superior to symptoms in predicting pathology in patients with suspected colorectal cancer symptoms referred on a 2WW pathway: a diagnostic accuracy study. Gut. 2021;70(06):1130-1138. doi:10.1136/gutjnl-2020-321956\\u003c/li\\u003e\\n\\u003cli\\u003eZou KH, O\\u0026apos;Malley AJ, Mauri L. Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation. 2007;115(05):654-657. doi:10.1161/CIRCULATIONAHA.105.594929\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"the TyG index, OSTA, Osteoporosis, Postmenopausal women\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4941509/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4941509/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003ePurposes:\\u003c/strong\\u003e This study aims to explore the sensitivity and efficacy of the TyG index in the screening of postmenopausal osteoporosis, and to provide an objective new method for the prevention and early screening of postmenopausal osteoporosis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods: \\u003c/strong\\u003eThis retrospective study selected 1032 subjects who completed bone mineral density examination in the Department of Nuclear Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine from January 2021 to December 2023 according to the inclusion and exclusion criteria. The baseline data include age, weight, height, BMI, lumbar spine \\u003cem\\u003eT\\u003c/em\\u003e-value (LS \\u003cem\\u003eT\\u003c/em\\u003e-value), total hip \\u003cem\\u003eT\\u003c/em\\u003e-value (TH \\u003cem\\u003eT\\u003c/em\\u003e-value), femoral neck \\u003cem\\u003eT\\u003c/em\\u003e-value (FN \\u003cem\\u003eT\\u003c/em\\u003e-value), fasting blood glucose (FBG), triglyceride (TG), the TyG index and OSTA. After grouping, the differences in postmenopausal osteoporosis were compared. The correlation of the TyG index and OSTA with baseline data was analyzed. The ROC curve results of the TyG index in the total population, 60-year-old stratification, FBG and TG stratification were analyzed, and the sensitivity and efficacy of the TyG index in the screening of postmenopausal osteoporosis were obtained.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults: \\u003c/strong\\u003eIn 1032 postmenopausal women, there were significant differences (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt; 0.001) in age, weight, height, BMI, and \\u003cem\\u003eT\\u003c/em\\u003e-values of three different sites, the TyG index and OSTA. The results of correlation analysis showed that the TyG index and OSTA were positively correlated with weight, BMI, and \\u003cem\\u003eT\\u003c/em\\u003e-values of three different sites in 1032 postmenopausal women and after 60-year-old stratification (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.001). In the total population and after stratification by 60 years old, the TyG index was positively correlated with FBG and TG (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.001), but not correlated with age and height. Meanwhile, OSTA was negatively correlated with age (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.001) and positively correlated with height (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.001). OSTA was not correlated with FBG and TG in the total population and in postmenopausal women aged \\u0026lt;60, but was positively correlated with TG in postmenopausal women aged≥60 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.001). ROC curve analysis showed that the area under the curve of the TyG index and OSTA was close in postmenopausal women aged≥60 with abnormal FBG and/or TG. The cut-off value of the TyG index in postmenopausal women aged≥60 was higher than that in postmenopausal women aged\\u0026lt;60, indicating that the risk of osteoporosis increased in postmenopausal women aged≥60 with increased TyG index.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion:\\u003c/strong\\u003e The TyG index has the potential to objectively screen osteoporosis in postmenopausal women aged≥60 and postmenopausal women aged≥60 with abnormal FBG and/or TG.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Could the TyG index be a screening tool for postmenopausal osteoporosis?\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-09-30 17:55:16\",\"doi\":\"10.21203/rs.3.rs-4941509/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"826a5410-bd25-4050-b2fd-1e1c6fc79d91\",\"owner\":[],\"postedDate\":\"September 30th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":38279053,\"name\":\"Health sciences/Biomarkers/Predictive markers\"},{\"id\":38279054,\"name\":\"Health sciences/Health care/Disease prevention\"}],\"tags\":[],\"updatedAt\":\"2024-12-01T12:23:38+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-09-30 17:55:16\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4941509\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4941509\",\"identity\":\"rs-4941509\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}