Abstract
The prevalence of type 2 diabetes mellitus (T2DM) complicated with osteoporosis (OP) is increasing yearly. Early prevention, detection and treatment of OP are important in postmenopausal patients with T2DM. This study aimed to explore the correlation between insulin resistance and bone mineral density (BMD), and OP in postmenopausal patients with T2DM. In this study, postmenopausal patients with T2DM who visited our hospital from January 2021 to March 2022 were divided into the OP group (n = 91) and non-OP group (n = 119) according to whether they were complicated with OP or not. The general data of patients, BMD, blood routine, glucose metabolism, lipid metabolism, liver and kidney function indexes were collected, and the homeostatic model assessment for IR (HOMA-IR), the triglyceride-glucose (TyG) index and the metabolic score for IR (METS-IR) were calculated. A weighted multivariate linear regression model assessed the correlation between insulin resistance (IR) related indexes and lumbar spine, femoral neck, and hip BMD. A weighted logistic regression model assessed the odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association between the IR-related indexes and OP risk. The nonlinear relationship was also evaluated by smooth curve fitting (SCF) and a weighted generalized additive model (GAM). Moreover, the Receiver-operating characteristics (ROC) curve was used to analyze the predictive efficiency of METS-IR in postmenopausal patients with T2DM with OP. HOMA-IR, TyG, and METS-IR in the OP group were lower than those in the non-OP group (all P < 0.05). Weighted multiple linear regression after adjusting covariates showed that METS-IR was positively correlated with the lumbar spine, femoral neck, and hip BMD (βMETS-IR = 0.006,0.005,0.005, all P < 0.001). The results of weighted Logistic regression and GAM showed that when METS-IR < 44.5, each unit of increased METS-IR value was associated with a decreased OP risk of 12% (P = 0.002). When METS-IR ≥ 44.5, there was no significant correlation between METS-IR and the risk of OP (OR = 1.00, P = 0.934). Similar trends were not observed in HOMA-IR and TyG. The ROC suggested helpful discriminative power of the METS-IR index for T2DM. We confirmed that METS-IR, as a novel alternative marker of IR, had a positive association with BMD in postmenopausal patients with T2DM, and METS-IR was a protective factor for OP in a specific range.
Similar content being viewed by others
Introduction
Osteoporosis (OP) is a disease that occurs and develops with age. It is characterized by the progressive reduction of bone mass and destruction of bone microstructure and is prone to fragility fractures1. About 1.5 million cases of osteoporotic fractures are reported worldwide annually, most of which are postmenopausal women2. It has resulted in a vast social and economic burden and has become a significant public health problem worldwide3. Patients with type 2 diabetes mellitus (T2DM) have a higher risk of OP and fragility fractures. Some studies have put forward the concept of diabetic osteoporosis and considered OP a significant complication of diabetes in the skeletal system4,5.
Insufficiency of insulin secretion caused by islet β cell dysfunction and insulin resistance (IR) or relative decrease are the leading causes of T2DM. Insulin can directly affect the function of osteoblasts and osteoclasts through insulin receptors or indirectly affect osteocyte metabolism by regulating vitamin D and parathyroid hormone levels6,7,8. Previous studies have confirmed that diabetes-related indicators (fasting insulin, FINS, fasting plasma glucose, FPG, glycated hemoglobin, HbA1c, insulin resistance, IR) were related to the risk of bone mineral density (BMD) and OP9,10,11,12. Among them, we are very interested in the relationship between IR and bone metabolism.
IR is a pathophysiological marker of OP and many other metabolic diseases. The euglycemic–hyperinsulinemic clamp technique is the gold standard for evaluating IR in humans13. However, this tool is unsuitable for large-scale epidemiological studies because of its invasive, and complicated nature. Therefore, massive studies have developed non-invasive and easy-to-operate assessment indicators of IR, such as the homeostatic model assessment for IR (HOMA-IR), the triglyceride-glucose (TyG) index and the metabolic score for IR (METS-IR)13,14,15.
Based on these perspectives, we innovatively put forward that IR-related indexes (HOMA-IR, TyG, METS-IR) may be related to BMD. Therefore, this study collected the serological indexes of postmenopausal patients with T2DM, analyzed the correlation between IR markers and OP, and predicted the diagnostic efficacy, aiming to find a novel, safe and simple indicator for the prevention and diagnosis of OP in postmenopausal patients with T2DM.
Methods
Study design and population
This was a single-center retrospective study. Postmenopausal patients with T2DM who received treatment in the first affiliated Hospital of Guangzhou University of Traditional Chinese Medicine from January 2021 to March 2022 were selected. All participants received standardized medication treatment for T2DM during hospitalization. Inclusion criteria: (a) hospitalized patients who were diagnosed with T2DM and had natural menopause, (b) patients with complete BMD and serological data. We excluded: (a) patients who have received anti-OP or oral hormone therapy that may affect bone metabolism for a long time (> 6 months) (n = 16); (b) patients with any acute infection or diabetic crisis (n = 8); (c) patients with severe heart failure, lung disease and hepatorenal insufficiency (n = 23); (d) patients with abnormal thyroid function, malignant tumor and other diseases affecting BMD (n = 22). This study was approved by the Ethics Committee of the first affiliated Hospital of Guangzhou University of Traditional Chinese Medicine (batch number: NO.K [2020] 102). The collected data does not contain any private information identified as individuals. The patients who participated in the trial volunteered to participate, fully informed consent to the trial process, and signed the informed consent form to understand the treatment plan fully. The study was conducted in accordance with the Helsinki Declaration.
Clinical data
General data were obtained from the medical records of inpatients, which were collected separately by three researchers through the JiaHe medical record system. The patients' demographic data (age, height, and weight) and clinical features (history of previous and medication) were obtained through face-to-face interviews between residents and patients. Body mass index (BMI) is calculated by dividing weight by the square of height (kg/m2). From the laboratory examination on the second day of admission, we extracted information on serum calcium (Ca), serum phosphorus (P), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), uric acid (UA), HbA1c, FINS, FPG, fasting C-peptide (FCP), glomerular filtration rate (eGFR), serum creatinine (SCr), alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Blood samples were collected after ≥ 8 h of fasting and analyzed using Roche Covas701, Covas702 biochemical analyzer, Mindray BC-6800_A automatic five-classification blood cell analyzer.
Assessment of insulin resistance
HOMA-IR and TyG index, and METS-IR are calculated as follows13,15,16:
HOMA-IR = [FINS (µU/mL) × FPG (mg/dL)/405].
TyG index = ln [TG (mg/dL) × FPG (mg/dL)/2].
METS-IR = ln [2 × FPG (mg/dL) + TG (mg/dL)] × BMI (kg/m2)/ln [HDL-C (mg/dL)].
Assessment of BMD
The BMD of lumbar vertebrae L1-4, left femoral neck, and left hip were measured by dual-energy X-ray absorptiometry (Lunar company, American model: DPX- L type), and T values were recorded. According to the diagnostic criteria of OP put forward by the World Health Organization17, this study defined T < − 2.5 as the OP group, osteopenia (− 2.5 ≤ T ≤ − 1.0), and normal bone mass (T ≥ − 1.0) as the non-OP group. The baseline characteristics of all subjects were described by mean ± standard deviation (continuous variable) or rate (classified variable). T-test or X2 test was used for comparison between the two groups.
Statistical analysis
The linear relationship between clinical indexes and BMD of all participants was analyzed using single-factor linear regression, and the indexes with the linear relationship were taken as covariates. The regression coefficient β value, P value, and the corresponding 95% confidence interval (CI) between HOMA-IR, TyG, METS-IR, and BMD were determined by weighted multiple linear regression analysis. The association between HOMA-IR, TyG, METS-IR, and OP risk of all participants was assessed using the unadjusted and adjusted weighted Logistic regression. The adjusted variables were selected from the binary Logistic regression analysis between covariates and OP. We use a generalized additive model (GAM) and smooth curve fitting (SCF) to address nonlinearity. In addition, the two-piecewise binary logistic regression model was used to explain the nonlinearity further. The diagnostic efficacy of METS-IR in predicting the occurrence of OP in postmenopausal patients with T2DM was assessed using Receiver-operating characteristics (ROC). Sensitivity refers to the percentage of OP patients who are positive by METS-IR; specificity refers to the percentage of non-OP patients who are negative by METS-IR. The optimal critical value is calculated by using the Youden index (Youden index = sensitivity–specificity). The range is between 0 to 1, and the higher the index is, the higher the prediction efficiency is. When Youden’s index is maximum, the corresponding value is the best threshold.
All analyses were conducted using R (version 4.0.3) and EmpowerStats software. The figures were generated using GraphPad Prism 9.0.0 (121). A double-tailed P-value < 0.05 was considered statistically significant in all analyses.
Ethics approval and informed consent statement
The study was approved by the Institutional Ethics Committee of The First Affiliated Hospital, Guangzhou University of Chinese Medicine for retrospective analysis (ethics number: NO.K [2020] 102).
Results
Participant selection and baseline characteristics
The study included 210 postmenopausal patients with T2DM treated in our center (Fig. 1), who were divided into the OP group (n = 91) and non-OP group (n = 119).
The baseline characteristics of selected participants were compared between the OP and non-OP groups (Table 1). The average age of patients in the OP group was higher than that in the non-OP group (68.74 years vs. 61.94 years, P < 0.001). Patients in the OP group were thinner (22.55 ± 3.22 kg/m2 vs. 24.16 ± 3.55 kg/m2, P = 0.001). The average BMD of lumbar vertebrae, femoral neck, and hip in the OP group were 0.82, 0.66, and 0.72 g/cm3, respectively, and those in the non-OP group was 1.09, 0.85, and 0.92 g/cm3, respectively. Serum P, FPG, FINS, FCP, TYG, HOMA-IR, and METS-IR in the OP group was significantly lower than in the non-OP group (all P < 0.05).
Associations of HOMA-IR, TyG, METS-IR with BMD
Univariate correlation analysis showed that age, HDL-C, and BMD were negatively correlated (all P < 0.05); BMI, serum Ca, serum P, FPG, FINS, SUA, eGFR, TYG, HOMA-IR, METS-IR were positively correlated with BMD (all P < 0.05) (Table 2).
Weighted multiple linear regression showed that METS-IR was still significantly positively correlated with lumbar vertebrae, femoral neck, and hip BMD after adjusting the covariates (β = 0.006, 0.005, 0.005 respectively, P < 0.001). However, no correlation was observed between HOMA-IR, TyG, and BMD (Table 3).
Associations of HOMA-IR, TyG, METS-IR with OP
Binary Logistic regression analysis showed that age was an independent risk factor for OP in postmenopausal patients with T2DM (OR = 1.125, P < 0.05). BMI, serum P, FINS, FCP, HOMA-IR, TyG, and METS-IR were independent protective factors (OR = 0.866, 0.187, 0.956, 0.818, 0.642, 0.906, and 0.938, respectively; P < 0.05) (Table 4).
Weighted Logistic regression analysis showed that METS-IR was still a protective factor for OP in postmenopausal patients with T2DM after adjusting covariates (OR = 0.940, P < 0.05), but no correlation was observed between HOMA-IR, TyG, and OP (Table 5). To further confirm our conclusion, we sorted the METS-IR values of the participants from small to large and then divided them into three parts according to the number of people. The prevalence rate of OP was 58.57%, 37.14%, and 34.29%, respectively, and there was a significant difference among the three groups (P = 0.007) (Fig. 2).
Curve fitting and threshold effect analysis
After adjusting for age, FINS, FCP, and serum P, the results of GAM and SCF showed that the risk of osteoporosis changed with the increase of METS-IR value. It changed considerably initially, and after reaching a specific METS-IR value, the change of OP risk became smooth and showed a piecewise linear relationship (Fig. 3). By observing the fitting curve, we set the inflection point to 44.5 and used the two-piecewise logical regression model to evaluate the threshold effect of the fitting curve. The log-likelihood ratio test of METS-IR at inflection point 44.5 was statistically significant (P = 0.042), indicating that the two-piecewise regression model was suitable to describe the relationship between METS-IR and OP. When METS-IR < 44.5, each unit of increased METS-IR value was associated with a decreased OP risk of 12% (P = 0.002); When METS-IR ≥ 44.5, there was no significant correlation between METS-IR and the risk of OP (OR = 1.00, P = 0.934) (Table 6).
Predictive efficacy of METS-IR on OP
The ROC curve showed that the area under the curve, sensitivity, and specificity of METS-IR in predicting the occurrence of OP in postmenopausal patients with T2DM were 0.639, 64.7%, and 60.4%, respectively, and the best cutoff value was 42.35 (Fig. 4).
Discussion
This study provides new findings on the relationship between METS-IR, and BMD and OP in postmenopausal patients with T2DM. The METS-IR in the OP group was significantly lower than in the non-OP group. After adjusting the confounding factors, each unit of increased METS-IR value was associated with increased lumbar vertebrae, femoral neck, and hip BMD 0.006 g/cm3, 0.005 g/cm3, and 0.005 g/cm3, respectively (all P < 0.05). When METS-IR < 44.5, each unit of increased METS-IR value was associated with a decreased OP risk of 12%; When METS-IR ≥ 44.5, there was no significant correlation between METS-IR and the risk of OP. In addition, METS-IR has a certain predictive value for the risk of OP in postmenopausal patients with T2DM. In summary, it is helpful to measure and calculate METS-IR as an objective index to evaluate the risk of OP in the diagnosis and treatment of postmenopausal patients with T2DM.
Early diagnosis and risk assessment of OP in postmenopausal patients with T2DM is essential. In recent years, with the improvement of people's living standards and the change in living habits, the prevalence of OP in T2DM patients has increased yearly. Especially in postmenopausal women, due to the decrease of estrogen in the body, osteoclasts' inhibition, bone resorption enhanced, and massive bone loss led to the prevalence of OP significantly increasing18. At present, the clinical diagnosis of OP is mainly through dual-energy X-ray absorptiometry. The risk of OP can be evaluated by bone turnover markers, HDL-C, and BMI. Dual-energy X-ray is relatively expensive, has radiation and can only reflect the static, and local BMD of the patient19. Moreover, it is easy to underestimate the fracture risk in T2DM patients simply considering BMD alone20. Detecting bone turnover markers takes a long time, and many primary healthcare facilities lack relevant detection equipment. Using laboratory indexes such as HDL-C and BMI21 alone to predict the risk of OP has low sensitivity and specificity. Therefore, it is crucial to explore a more simple, economical, and accurate method to predict the risk of OP in postmenopausal T2DM.
IR is a state in which insulin is ineffective in peripheral tissues, leading to hyperinsulinemia and impaired lipid and glucose homeostasis. Among various methods for evaluating IR, the gold standard is the euglycemic–hyperinsulinemic clamp technique22, but this invasive method is unsuitable for the large-scale population. Some non-insulin indicators, such as TyG and METS-IR, combined with various serum biochemical indicators to evaluate IR, have attracted more and more attention. TyG is calculated from TG and FPG15, and METS-IR is calculated from HDL-C, TG, FPG, and BMI13. It is stated that these parameters are a non-insulin-based alternative to insulin-based methods to quantify peripheral insulin sensitivity. These indicators are easy to measure and calculate, so they are widely used in epidemiological studies and compared with traditional IR indicators. Cho et al.'s study23 of 1145 middle-aged people in Korea found that individuals with a high TyG correlation index are likelier to experience coronary artery calcification. Compared with HOMA-IR, TyG correlation index can better predict the progression of coronary artery calcification. In a study of 4,986 Korean adults, Lee et al.24 found that the TyG index has a better predictive power for NAFLD compared with HOMA-IR. A large cross-sectional study of 21,082 participants by Chen et al.25 found that the increase in METS-IR index was associated with a higher incidence of asthma and an earlier age of first asthma in American adults. Han et al.26 found a positive correlation between METS-IR and serum ferritin in a cross-sectional study of 4182 American women. This correlation was evident among participants ≥ 40 years old. Yoon et al.27 found that METS-IR was highly correlated with metabolic syndrome and cardiac metabolic risk, and METS-IR had better predictive value for ischemic heart disease than metabolic syndrome. Similarly, many studies have confirmed the correlation between IR and BMD, but the results are inconsistent, and we have not found any research on METS-IR and BMD. A cross-sectional study of postmenopausal women in Tunisia by Cherif et al.28 found that HOMA-IR was positively correlated with BMD of the left femur and total hip. Yoon et al.29 found that the TyG index negatively correlated with femoral neck BMD in non-diabetic men and postmenopausal women over 50 in a cohort study of 4810 non-diabetic Koreans. Zhou et al.12 found that the increase in HOMA-IR level was related to the increase of hip BMD in 7,170 American adults, but no causal relationship was found between IR and BMD in a Mendelian randomized study of European adults. In addition, numerous studies30,31,32,33 have proved that the indexes used to calculate METS-IR are significantly correlated with BMD. Therefore, this study collected serological indicators of postmenopausal patients with T2DM and evaluated the correlation between METS-IR and OP for the first time. The results showed that there was a significant positive correlation between METS-IR and BMD, and METS-IR was the protective factor of OP in postmenopausal patients with T2DM. However, we found that TyG and HOMA-IR had no significant correlation with BMD and OP.
These contradictory results may be due to different study populations or different assessment methods of IR. Based on the population of this study (postmenopausal patients with T2DM) and the IR assessment method (METS-IR), we believe that the possible mechanism of METS-IR affecting BMD and OP is as follows. Firstly, IR promotes insulin secretion, and hyperinsulinemia leads to an increase in BMD. Insulin can promote osteoblast proliferation, inhibit osteoclast activity, and act as an anabolic agent in bones34. In the state of IR, insulin secretion increases to compensate for the resistance of skeletal muscle, adipose tissue, and liver to insulin, which leads to hyperinsulinemia. Therefore, IR can promote insulin secretion and further increase bone mass. In addition, the synergistic effect of excessive insulin and other synthetic metabolic hormones (parathyroid hormone, insulin-like growth factor) can also lead to BMD increase7,35. Secondly, IR may further affect bone metabolism by affecting inflammatory response and estrogen levels. Wang et al.36 speculated that the relationship between IR and OP may not be linear and have a threshold effect. Our results confirm this view. In postmenopausal women with T2DM, when METS-IR < 44.5, the higher the IR, the lower the risk of OP. When METS-IR ≥ 44.5, the higher the IR, the greater the risk of OP. The reason may be that with the development of diabetes, the increase of pro-inflammatory cytokines and oxidative stress and the decrease of estrogen level has adverse effects on bone health, eliminating the protective effect of IR on the bone37,38. Finally, previous studies on IR and BMD mostly used TyG and HOMA-IR as evaluation indicators12,23,24. Our results showed that the BMD of lumbar vertebrae, femoral neck, and hip increased with the increasing TyG, HOMA-IR, and METS-IR in postmenopausal patients with T2DM. However, the association between TyG, HOMA-IR, and BMD lost significance after adjusting BMI. Napoli et al.39 found similar results in a prospective study of 2398 non-diabetic elderly. We think that compared with METS-IR, TyG, and HOMA-IR ignore the effects of BMI and other lipid types on bone metabolism. METS-IR is more comprehensive in evaluating metabolic status and is recognized as an effective index for IR estimation in the Chinese population40,41,42,43. Some chronic disease studies have also confirmed this view13,27,44.
The main advantage of this study is that METS-IR is used for the first time to evaluate the correlation of BMD and OP risk in postmenopausal patients with T2DM, which opens a new direction for the study of the correlation between IR and OP. To a certain extent, it can provide a breakthrough point for expanding OP-related predictive biological indicators and the screening, prevention, and treatment of OP in primary healthcare facilities. Despite the efforts made in this study, there are still some limitations. Firstly, this was a single-center cross-sectional study in which the sample size was small and the METS-IR was not repeatedly evaluated. The effectiveness of METS-IR changes over time in predicting OP risk was not obtained in postmenopausal patients with T2DM. Secondly, the population of this study is Chinese postmenopausal T2DM patients, and there are geographical and ethnic restrictions. The study results cannot be applied to healthy people or other races. Finally, the use of hypoglycemic drugs in inpatients was not collected in this study, so we cannot rule out the bias of hypoglycemic drugs on our results by affecting lipid metabolism. Therefore, large-scale, multicenter, high-level evidence-based research is still needed to confirm the relationship between METS-IR and OP in different populations.
Conclusions
We confirmed that METS-IR, as a novel alternative marker of IR, had a positive association with BMD in postmenopausal patients with T2DM, and METS-IR was a protective factor for OP in a specific range. Therefore, we cautiously suggest that the risk of OP may need to be evaluated when the METS-IR decreases in postmenopausal patients with T2DM.
Data availability
All data generated or analyzed during this study are available from the corresponding author upon reasonable request.
Abbreviations
- T2DM:
-
Type 2 diabetes mellitus
- BMD:
-
Bone mineral density
- OP:
-
Osteoporosis
- IR:
-
Insulin resistance
- FINS:
-
Fasting insulin
- FPG:
-
Fasting plasma glucose
- HbA1c:
-
Glycated hemoglobin
- HOMA-IR:
-
Homeostatic model assessment for IR
- TyG:
-
Triglyceride-glucose
- METS-IR:
-
Metabolic score for IR
- BMI:
-
Body mass index
- Ca:
-
Calcium
- P:
-
Phosphorus
- TC:
-
Total cholesterol
- TG:
-
Triglyceride
- HDL-C:
-
High-density lipoprotein cholesterol
- LDL-C:
-
Low-density lipoprotein cholesterol
- SUA:
-
Serum uric acid
- FCP:
-
Fasting C-peptide
- eGFR:
-
Glomerular filtration rate
- SCr:
-
Serum creatinine
- ALT:
-
Alanine aminotransferase
- AST:
-
Aspartate aminotransferase
- ROC:
-
Receiver-operating characteristics
- GAM:
-
Generalized additive model
- SCF:
-
Smooth curve fitting
- OR:
-
Odds ratio
- CI:
-
Confidence interval
References
Compston, J. E., McClung, M. R. & Leslie, W. D. Osteoporosis. Lancet 393(10169), 364–376. https://doi.org/10.1016/S0140-6736(18)32112-3 (2019).
Black, D. M. & Rosen, C. J. Postmenopausal osteoporosis. N. Engl. J. Med. 374(3), 254–262. https://doi.org/10.1056/NEJMcp1513724 (2016).
Rd, L. J. M. Who has osteoporosis? A conflict between clinical and public health perspectives. J. Bone Miner. Res. 15(12), 2309–2314. https://doi.org/10.1359/jbmr.2000.15.12.2309 (2000).
Oei, L., Rivadeneira, F., Zillikens, M. C. & Oei, E. H. G. Diabetes, diabetic complications, and fracture risk. Curr. Osteoporos. Rep. 13(2), 106–115. https://doi.org/10.1007/s11914-015-0260-5 (2015).
Shanbhogue, V. V. M., Mitchell, D. M. M., Rosen, C. J. P. & Bouxsein, M. L. D. Type 2 diabetes and the skeleton: New insights into sweet bones. Lancet Diabetes Endocrinol. 4(2), 159–173. https://doi.org/10.1016/S2213-8587(15)00283-1 (2016).
Sealand, R., Razavi, C. & Adler, R. A. Diabetes mellitus and osteoporosis. Curr. Diabetes Rep. 13(3), 411–418. https://doi.org/10.1007/s11892-013-0376-x (2013).
Thrailkill, K. M., Lumpkin, C. J., Bunn, R. C., Kemp, S. F. & Fowlkes, J. L. Is insulin an anabolic agent in bone? Dissecting the diabetic bone for clues. Am. J. Physiol. Endocrinol. Metab. 289(5), E735–E745. https://doi.org/10.1152/ajpendo.00159.2005 (2005).
Ma, L. et al. Association between bone mineral density and type 2 diabetes mellitus: A meta-analysis of observational studies. Eur. J. Epidemiol. 27(5), 319–332. https://doi.org/10.1007/s10654-012-9674-x (2012).
Ghodsi, M. et al. Mechanisms involved in altered bone metabolism in diabetes: A narrative review. J. Diabetes Metab. Disord. https://doi.org/10.1186/s40200-016-0275-1 (2016).
Arikan, S., Tuzcu, A., Bahceci, M., Ozmen, S. & Gokalp, D. Insulin resistance in type 2 diabetes mellitus may be related to bone mineral density. J. Clin. Densitom. 15(2), 186–190. https://doi.org/10.1016/j.jocd.2011.11.005 (2012).
Kim, C. J. et al. Relationship between body composition and bone mineral density (BMD) in perimenopausal Korean women. Clin. Endocrinol. 71(1), 18–26. https://doi.org/10.1111/j.1365-2265.2008.03452.x (2009).
Zhou, H. et al. Increasing fasting glucose and fasting insulin associated with elevated bone mineral density—Evidence from cross-sectional and MR studies. Osteoporosis Int. 32(6), 1153–1164. https://doi.org/10.1007/s00198-020-05762-w (2021).
Bello-Chavolla, O. Y. et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur. J. Endocrinol. 178(5), 533–544. https://doi.org/10.1530/EJE-17-0883 (2018).
Borai, A., Livingstone, C. & Ferns, G. A. A. The biochemical assessment of insulin resistance. Ann. Clin. Biochem. 44(4), 324–342. https://doi.org/10.1258/000456307780945778 (2007).
Simental-Mendía, L. E., Rodríguez-Morán, M. & Guerrero-Romero, F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab. Syndr. Relat. D. 6(4), 299–304. https://doi.org/10.1089/met.2008.0034 (2008).
Matthews, D. R. et al. Homeostasis model assessment: Insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28(7), 412–419. https://doi.org/10.1007/BF00280883 (1985).
Kanis, J. A., Melton, L. J., Christiansen, C., Johnston, C. C. & Khaltaev, N. The diagnosis of osteoporosis. J. Bone Miner. Res. 9(8), 1137–1141. https://doi.org/10.1002/jbmr.5650090802 (1994).
Geng, Q., Gao, H., Yang, R., Guo, K. & Miao, D. Pyrroloquinoline quinone prevents estrogen deficiency-induced osteoporosis by inhibiting oxidative stress and osteocyte senescence. Int. J. Biol. Sci. 15(1), 58–68. https://doi.org/10.7150/ijbs.25783 (2019).
Iconaru, L. et al. Does the prediction accuracy of osteoporotic fractures by BMD and clinical risk factors vary with fracture site?. JBMR Plus https://doi.org/10.1002/jbm4.10238 (2019).
Botushanov, N. P. & Orbetzova, M. M. Bone mineral density and fracture risk in patients with type 1 and type 2 diabetes mellitus. Folia Med. (Plovdiv) 51(17), 12–17 (2009).
Zhang, J. et al. Assessment risk of osteoporosis in Chinese people: Relationship among body mass index, serum lipid profiles, blood glucose, and bone mineral density. Clin. Interv. Aging 11, 887–895. https://doi.org/10.2147/CIA.S103845 (2016).
DeFronzo, R. A., Tobin, J. D. & Andres, R. Glucose clamp technique: A method for quantifying insulin secretion and resistance. Am. J. Physiol. 237(3), E214 (1979).
Cho, Y. K. et al. Triglyceride glucose-waist circumference better predicts coronary calcium progression compared with other indices of insulin resistance: A longitudinal observational study. J. Clin. Med. 10(1), 92. https://doi.org/10.3390/jcm10010092 (2021).
Lee, S. B. et al. Triglyceride glucose index is superior to the homeostasis model assessment of insulin resistance for predicting nonalcoholic fatty liver disease in Korean adults. Endocrinol. Metab. (Seoul) 34(2), 179–186. https://doi.org/10.3803/EnM.2019.34.2.179 (2019).
Chen, Y. et al. An elevated METS-IR index is associated with higher asthma morbidity and earlier age of first asthma in US adults: Results based on a cross-sectional study. Front. Endocrinol. https://doi.org/10.3389/fendo.2022.920322 (2022).
Hao, H. et al. The association between METS-IR and serum ferritin level in United States female: A cross-sectional study based on NHANES. Front. Med. https://doi.org/10.3389/fmed.2022.925344 (2022).
Yoon, J., Jung, D., Lee, Y. & Park, B. The metabolic score for insulin resistance (METS-IR) as a predictor of incident ischemic heart disease: A longitudinal study among Korean without diabetes. J. Personal. Med. 11(8), 742. https://doi.org/10.3390/jpm11080742 (2021).
Cherif, R. et al. Positive association of obesity and insulin resistance with bone mineral density in tunisian postmenopausal women. J. Clin. Densitom. 21(2), 163–171. https://doi.org/10.1016/j.jocd.2017.05.015 (2018).
Yoon, J. H. et al. Association of triglyceride-glucose index with bone mineral density in non-diabetic Koreans: KNHANES 2008–2011. Calcified Tissue Int. 108(2), 176–187. https://doi.org/10.1007/s00223-020-00761-9 (2021).
Sun, Q. et al. Osteopenia is associated with glycemic levels and blood pressure in Chinese postmenopausal women: A cross-sectional study. Clin. Exp. Med. 17(1), 85–91. https://doi.org/10.1007/s10238-015-0397-7 (2017).
Song, J. et al. The relationship between body mass index and bone mineral density: A Mendelian randomization study. Calcified Tissue Int. 107(5), 440–445. https://doi.org/10.1007/s00223-020-00736-w (2020).
Ackert-Bicknell, C. L. HDL cholesterol and bone mineral density: Is there a genetic link?. Bone 50(2), 525–533. https://doi.org/10.1016/j.bone.2011.07.002 (2012).
Tang, Y., Sheu, W. H., Liu, P., Lee, W. & Chen, Y. Positive associations of bone mineral density with body mass index, physical activity, and blood triglyceride level in men over 70 years old: A TCVGHAGE study. J. Bone Miner. Metab. 25(1), 54–59. https://doi.org/10.1007/s00774-006-0727-7 (2006).
Conte, C., Epstein, S. & Napoli, N. Insulin resistance and bone: A biological partnership. Acta Diabetol. 55(4), 305–314. https://doi.org/10.1007/s00592-018-1101-7 (2018).
Fornari, R. et al. Insulin growth factor-1 correlates with higher bone mineral density and lower inflammation status in obese adult subjects. Eat. Weight Disord. 23(3), 375–381. https://doi.org/10.1007/s40519-017-0362-4 (2018).
Wang, X., Jiang, L. & Shao, X. Association analysis of insulin resistance and osteoporosis risk in Chinese patients with T2DM. Ther. Clin. Risk Manag. 17, 909–916. https://doi.org/10.2147/TCRM.S328510 (2021).
Shin, D., Kim, S., Kim, K. H., Lee, K. & Park, S. M. Association between insulin resistance and bone mass in men. J. Clin. Endocrinol. Metab. 99(3), 988–995. https://doi.org/10.1210/jc.2013-3338 (2014).
Cao, J. J. Effects of obesity on bone metabolism. J. Orthop. Surg. Res. 6, 30. https://doi.org/10.1186/1749-799X-6-30 (2011).
Napoli, N. et al. Effect of insulin resistance on BMD and fracture risk in older adults. J. Clin. Endocrinol. Metab. 104(8), 3303–3310. https://doi.org/10.1210/jc.2018-02539 (2019).
Liu, X. Z., Fan, J. & Pan, S. J. METS-IR, a novel simple insulin resistance indexes, is associated with hypertension in normal-weight Chinese adults. J. Clin. Hypertens. 21(8), 1075–1081. https://doi.org/10.1111/jch.13591 (2019).
Fan, J. et al. Association of three simple insulin resistance indexes with prehypertension in normoglycemic subjects. Metab. Syndr. Relat. D. 17(7), 374–379. https://doi.org/10.1089/met.2019.0029 (2019).
Li, Y. et al. Insulin resistance surrogates predict hypertension plus hyperuricemia. J. Diabetes Investig. 12(11), 2046–2053. https://doi.org/10.1111/jdi.13573 (2021).
Zhang, M. et al. Association of metabolic score for insulin resistance and its 6-year change with incident type 2 diabetes mellitus. J. Diabetes 13(9), 725–734. https://doi.org/10.1111/1753-0407.13161 (2021).
Xu, C. et al. Association of METS-IR with incident hypertension in non-overweight adults based on a cohort study in Northeastern China. Eur. J. Public Health https://doi.org/10.1093/eurpub/ckac140 (2022).
Acknowledgements
We would like to thank all the patients and their families who participated in the trial and all the medical staff in the Department of traumatic orthopaedics and Endocrinology of the first affiliated Hospital of Guangzhou University of Traditional Chinese Medicine for their support and help.
Funding
This work was supported by the High-Level Hospital Construction Project of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (211010010729) to cover the consultation fees of data statistical analysis. Xiaohui Zheng received scientific funding from the High-Level Hospital Construction Project of the First Affiliated Hospital of Guangzhou University of Chinese Medicine, and the grant number is 211010010729.
Author information
Authors and Affiliations
Contributions
P.G., B.P. and Q.X. designed the study, wrote, reviewed and edited the manuscript. D.Y., L.L.L., J.S.T. and M.C. analyzed data. H.S.L., M.H.H., X.R.H. and X.H.Z. reviewed and edited the manuscript. All authors approved the final version of the manuscript to be published. Z.P.Z. is the guarantor of this work.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Gu, P., Pu, B., Xin, Q. et al. The metabolic score of insulin resistance is positively correlated with bone mineral density in postmenopausal patients with type 2 diabetes mellitus. Sci Rep 13, 8796 (2023). https://doi.org/10.1038/s41598-023-32931-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-023-32931-8
- Springer Nature Limited
This article is cited by
-
Association between triglyceride glucose index and total bone mineral density: a cross-sectional study from NHANES 2011–2018
Scientific Reports (2024)
-
Metabolic dysfunction–associated fatty liver disease and osteoporosis: the mechanisms and roles of adiposity
Osteoporosis International (2024)