Clinical Characteristics and Controllable Risk Factors of Osteoporosis in Elderly Men with Diabetes Mellitus

Based on HbA1c Analysis: Bone Mineral Density and Osteoporosis Risk in Postmenopausal Female with T2DM

INTRODUCTION

This study aims to investigate association between glycosylated hemoglobin (HbA1c) with bone mineral density (BMD) and osteoporosis-risk in postmenopausal female with type 2 diabetes mellitus (T2DM).

METHODOLOGY

HbA1c values, BMD of L3 vertebra and basic clinical data of 152 postmenopausal females with T2DM and 326 postmenopausal females without T2DM were retrospectively analyzed. The propensity score matching was used to match the T2DM and the non-T2DM group at a ratio of 1:1. Restricted cubic spline (RCS) analysis and piecewise linear regression were used to evaluate the relationship between HbA1c and BMD. Univariable and multivariable logistic regression were utilized to evaluate the effect of HbA1c on the risk of osteoporosis in matched diabetes population.

RESULTS

After matching, the BMD (66.60 (46.58, 93.23) vs. 63.50 (36.70, 83.33), P < 0.05), HbA1c value (7.50 (6.72, 8.80) vs 5.30 (5.14, 5.50), P  <  0.05) in the T2DM group were significantly higher than that of non-T2DM group. We found a nonlinear relation between HbA1c value and BMD, which showing a U-shaped curve with the cutoff value around 7.5 % (Poverall < 0.001, Pnonliearity < 0.05). The prevalence of osteoporosis in T2DM group was similar to that in controls (64.9 % vs 73.6 %, P = 0.102). Age-adjusted HbA1c value was not risk factor of osteoporosis in postmenopausal females with T2DM.

CONCLUSION

In postmenopausal females with T2DM, high BMD and similar risk of osteoporosis were confirmed; HbA1c was a contributing factor to BMD when values exceed 7.5 %. However, HbA1c does not seem to be associated with osteoporosis risk.

Development and validation of a risk prediction model for osteoporosis in elderly patients with type 2 diabetes mellitus: a retrospective and multicenter study

BACKGROUND

This study aimed to construct a risk prediction model to estimate the odds of osteoporosis (OP) in elderly patients with type 2 diabetes mellitus (T2DM) and evaluate its prediction efficiency.

METHODS

This study included 21,070 elderly patients with T2DM who were hospitalized at six tertiary hospitals in Southwest China between 2012 and 2022. Univariate logistic regression analysis was used to screen for potential influencing factors of OP and least absolute shrinkage. Further, selection operator regression (LASSO) and multivariate logistic regression analyses were performed to select variables for developing a novel predictive model. The area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the performance and clinical utility of the model.

RESULTS

The incidence of OP in elderly patients with T2DM was 7.01% (1,476/21,070). Age, sex, hypertension, coronary heart disease, cerebral infarction, hyperlipidemia, and surgical history were the influencing factors. The seven-variable model displayed an AUROC of 0.713 (95% confidence interval [CI]:0.697-0.730) in the training set, 0.716 (95% CI: 0.691-0.740) in the internal validation set, and 0.694 (95% CI: 0.653-0.735) in the external validation set. The optimal decision probability cut-off value was 0.075. The calibration curve (bootstrap = 1,000) showed good calibration. In addition, the DCA and CIC demonstrated good clinical practicality. An operating interface on a webpage ( https://juntaotan.shinyapps.io/osteoporosis/ ) was developed to provide convenient access for users.

CONCLUSIONS

This study constructed a highly accurate model to predict OP in elderly patients with T2DM. This model incorporates demographic characteristics and clinical risk factors and may be easily used to facilitate individualized prediction.

Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus-A Preliminary Study

Purpose

Diagnosing osteoporosis in T2DM based on bone mineral density (BMD) remains challenging. We sought to develop prediction models employing machine learning algorithms for use as screening instruments for osteoporosis in T2DM patients.

Patients and Methods

Data were collected from 433 participants and analyzed using nine categorical machine learning algorithms to select features based on demographic and clinical variables. Multiple classification models were compared using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, specificity, the average precision (AP), precision, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA) to determine the best model. In addition, 5-fold cross-validation was utilized to optimize the model, followed by an evaluation of feature significance using Shapley Additive exPlanations (SHAP). Using latent class analysis (LCA), distinct subpopulations were identified by constructing several discrete clusters.

Results

In this study, nine feature variables were identified to construct predictive models for osteoporosis in individuals with T2DM. The machine learning algorithms achieved an AP range of 0.444-1.000. The XGBoost model was selected as the final prediction model with an AUROC of 0.940 in the training set, 0.772 in the validation set for 5-fold cross-validation, and 0.872 in the test set. Using SHAP methodology, 25(OH)D was identified as the most important risk factor. Additionally, a 3-Class model was constructed using LCA, which categorized individuals into high, medium, and low-risk groups.

Conclusion

Our study developed a predictive model with high accuracy and clinical validity for predicting osteoporosis in type 2 diabetes patients. We also identified three subpopulations with varying osteoporosis risk using clustering. However, limited sample size warrants cautious interpretation of results, and validation in larger cohorts is needed.

Effect of Diabetes Mellitus on Implant Osseointegration of Titanium Screws: An Animal Experimental Study

Objective

To explore the effect of diabetes mellitus (DM) on implant osseointegration of titanium screws.

Methods

Sixty rats were randomly divided into a DM group and a control group (each group, n = 30). DM group rats were injected with 1% Streptozotocin solution at 65 mg/kg to establish a DM model. Titanium screws were implanted into the rats' distal femurs in both groups. The rats were sacrificed for micro‐CT scanning, micro‐indentation, biomechanical detection, confocal Raman microspectroscopy, and histological and histomorphometric analysis at 4, 8, and 12 weeks post‐implantation, respectively. Messenger RNA (mRNA) expression and protein expression of the related growth factors around the implant were analyzed using real‐time polymerase chain reaction and Western blots.

Results

At 4, 8 and 12 weeks, micro‐CT scanning, hematoxylin‐eosin (HE) staining, Gieson's acid‐magenta staining, and fluorescent labeled staining showed disorder in the bone tissue arrangement, a lack of new bone tissue, poor maturity and continuity, and poor trabecular bone parameters around the implant in the DM group. At 4, 8, and 12 weeks, the interfacial bone binding rate in the DM group was significantly lower (16.2% ± 4.8%, 25.7% ± 5.7%, 42.5% ± 5.8%, respectively) than that in the control group (23.6% ± 5.2%, 40.8% ± 6.3%, 64.2% ± 7.3%, respectively; P < 0.05). At 8 and 12 weeks, the elastic modulus (17.0 ± 1.8 and 15.1 ± 1.5 GPa, respectively) and trabecular bone hardness (571 ± 39 and 401 ± 37 MPa, respectively) in the DM group were significantly lower than the elastic modulus (23.4 ± 2.3 and 23.8 ± 1.8 GPa, respectively) and trabecular bone hardness (711 ± 45 and 719 ± 46 MPa, respectively) in the control group (P < 0.05). The maximum load required for the prosthesis pull‐out experiment in the DM group at 4, 8, and 12 weeks (55.14 ± 6.74 N, 73.34 ± 8.43 N, and 83.45 ± 8.32 N, respectively) was significantly lower than that in the control group (77.45 ± 7.48 N, 93.28 ± 8.29 N, and 123.62 ± 9.43 N, respectively, P < 0.05). At 8 and 12 weeks, the mineral‐to‐collagen ratio in the DM group (6.56 % ± 1.35% and 4.45%± 1.25%, respectively) was significantly higher than that in the control group (5.31% ± 1.42% and 3.62% ± 1.33%, respectively, P < 0.05). At 12 weeks, mRNA and protein expression levels of bone morphogenetic protein 2, transforming growth factor‐β1, vascular endothelial growth factor, osteopontin, osteocalcin, and runt‐related transcription factor 2 in the DM group were significantly lower than that in the control group.

Conclusions

DM can negatively affect bone osseointegration, manifesting as disorder in bone tissue arrangement around the implant, a lack of new bone tissue, poor maturity and continuity, poor trabecular bone parameters and lower expression of the related growth factors.

Effects of testosterone and exercise training on bone microstructure of rats

Background and Aim:

Male hypogonadism results from failure to produce physiological levels of testosterone. Testosterone in men is essential in masculine development, sperm production, and adult man’s health. Osteoporosis is one of the consequences of hypogonadism. Regular physical exercise and exogenous testosterone administration are frequently used to prevent or treat this condition. This study aimed to understand the effects of lifelong exercise training and testosterone levels (isolated and together) in the main bone structure parameters.

Materials and Methods:

A total of 24 rats were used and randomly divided into four groups: Control group (CG; n=6), exercised group (EG, n=6), testosterone group (TG, n=6), and testosterone EG (TEG, n=6). A micro-computed tomography equipment was used to evaluate 15 bone parameters.

Results:

Both factors (exercise training and testosterone) seem to improve the bone resistance and microstructure, although in different bone characteristics. Testosterone influenced trabecular structure parameters, namely, connectivity density, trabecular number, and trabecular space. The exercise promoted alterations in bone structure as well, although, in most cases, in different bone structure parameters as bone mineral density and medullar mineral density.

Conclusion:

Overall, exercise and testosterone therapy seems to have a synergistic contribution to the general bone structure and resistance. Further studies are warranted, comparing different individual factors, as gender, lifestyle, or testosterone protocols, to constantly improve the medical management of hypogonadism (and osteoporosis).

Research progress of coumarins and their derivatives in the treatment of diabetes

Diabetes is a group of metabolic diseases characterised by chronic hyperglycaemia caused by multiple causes, which is caused by insulin secretion and/or utilisation defects. It is characterised by increased fasting and postprandial blood glucose levels due to insulin deficiency or insulin resistance. It is reported that the harm of diabetes mainly comes from its complications, and the cardiovascular disease caused by diabetes is the primary cause of its harm. China has the largest number of diabetic patients in the world, and the prevention and control of diabetes are facing great challenges. In recent years, many kinds of literature have been published abroad, which have proved that coumarin and its derivatives are effective in the treatment of diabetic complications such as nephropathy and cardiovascular disease. In this paper, the types of antidiabetic drugs and the anti-diabetic mechanism of coumarins were reviewed.

Increased Risk of Temporomandibular Joint Disorder in Osteoporosis Patients: A Longitudinal Study

BACKGROUND

The objective of this study was to investigate the risk of temporomandibular joint disorder (TMD) in patients with osteoporosis.

METHODS

Data from the Korean National Health Insurance Service-Health Screening Cohort from 2002 to 2015 were collected. Participants ≥ 40 years old were enrolled, and the history of osteoporosis was evaluated. The 62,328 osteoporosis patients were matched for age, sex, income, and region of residence with 62,328 control participants. The occurrence of TMD was assessed in both the osteoporosis and control groups during the follow-up period. Stratified Cox proportional hazard analyses for TMD were conducted for the osteoporosis and control groups. The hazard ratios (HRs) of osteoporosis for TMD were further analyzed by age and sex subgroups.

RESULTS

A total of 1.2% (725/61,320) of the osteoporosis patients and 0.6% (339/61,320) of the control participants had TMD (P<0.001). Osteoporosis was associated with an elevated HR of TMD (adjusted HR=1.96, 95% CI=1.72-2.23, P<0.001). Among the age and sex subgroups, the < 60-year-old mal\e group demonstrated an adjusted HR of osteoporosis for TMD as high as 4.47 (95% CI=1.17-17.12, P=0.029). Other age and sex subgroups also showed a higher HR for TMD associated with osteoporosis (adjusted HR=2.30, 95% CI=1.90-2.78, P<0.001 for the ≥ 60-year-old female group).

CONCLUSION

Osteoporosis was related to a higher risk of TMD in the adult population. A prominent association of osteoporosis with TMD was noted in middle-aged men and older women.

Novel nomogram for predicting the 3-year incidence risk of osteoporosis in a Chinese male population

OBJECTIVE

To establish a rapid, cost-effective, accurate, and acceptable osteoporosis (OP) screening model for the Chinese male population (age ≥ 40 years) based on data mining technology.

MATERIALS AND METHODS

This was a 3-year retrospective cohort study, which belonged to the sub-cohort of the Chinese Reaction Study. The research period was from March 2011 to December 2014. A total of 1834 subjects who did not have OP at the baseline and completed a 3-year follow-up were included in this study. All subjects underwent quantitative ultrasound examinations for calcaneus at the baseline and follow-ups that lasted for 3 years. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select feature variables. The characteristic variables selected in the LASSO regression were analyzed by multivariable logistic regression (MLR) to construct the predictive model. This predictive model was displayed through a nomogram. We used the receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance and the bootstrapping validation to internally validate the model.

RESULTS

The predictive factors included in the prediction model were age, neck circumference, waist-to-height ratio, BMI, triglyceride, impaired fasting glucose, dyslipidemia, osteopenia, smoking history, and strenuous exercise. The area under the ROC (AUC) curve of the risk nomogram was 0.882 (95% CI, 0.858-0.907), exhibiting good predictive ability and performance. The C-index for the risk nomogram was 0.882 in the prediction model, which presented good refinement. In addition, the nomogram calibration curve indicated that the prediction model was consistent. The DCA showed that when the threshold probability was between 1 and 100%, the nomogram had a good clinical application value. More importantly, the internally verified C-index of the nomogram was still very high, at 0.870.

CONCLUSIONS

This novel nomogram can effectively predict the 3-year incidence risk of OP in the male population. It also helps clinicians to identify groups at high risk of OP early and formulate personalized intervention measures.