Osteoporosis and Osteopenia Among Patients With Type 2 Diabetes Aged ≥50: Role of Sex and Clinical Characteristics

Significance of non-motor symptoms and development of a screening tool for osteoporosis in Parkinson's disease

BACKGROUND AND PURPOSE

Parkinson's disease (PD) patients have a higher likelihood of having osteoporosis compared to controls, therefore deserving special attention. This study was to 1) investigate the association of non-motor symptoms with osteoporosis amongst PD patients, and 2) develop screening tools for osteoporosis.

MATERIALS AND METHODS

PD Patients were included (n = 109). The factors/variables were obtained from clinical records due to the retrospective nature of this study. The bone mineral density (BMD) of the lumbar spine and femoral neck was examined using a dual-energy X-ray absorptiometry machine, according to which they were categorized as either having (T-score ≤ -2.5) or not having osteoporosis (T-score>-2.5) at the two sites. The non-motor symptoms were assessed using clinical scales, including non-motor experiences of daily living, depression, anxiety, cognitive function, and autonomic function. The potential covariates included demographic and clinical factors/variables, such as age and sex. Logistic regression was used to investigate the associations and establish the screening tools.

RESULTS

Patients with autonomic dysfunction had significantly (p = 0.011) higher odds of having femoral neck osteoporosis compared to those with no/minimal dysfunction after adjusting for sex, disease duration, and body mass index, demonstrating a strong association (odds ratio=12.81). Based on the four factors/variables, a screening tool with a good accuracy was established (C-statistic = 0.85).

CONCLUSION

PD patients with autonomic dysfunction had greater odds of having femoral neck osteoporosis compared to those with no/minimal dysfunction. The screening tool may lay a foundation for developing screening models with higher accuracy to identify which PD patients may require a BMD test.

Effect of milk fat globules on growth and metabolism in rats fed an unbalanced diet

We assessed the effects of supplementing milk fat globules (MFG) on the growth and development of the skeleton in rats fed a Western unbalanced diet (UBD). The UBD is high in sugar and fat, low in protein, fiber, and micronutrients, and negatively impacts health. The MFG-a complex lipid-protein assembly secreted into milk-has a unique structure and composition, which differs significantly from isolated and processed dietary ingredients. Rats consuming the UBD exhibited growth retardation and disrupted bone structural and mechanical parameters; these were improved by supplementation with small MFG. The addition of small MFG increased the efficiency of protein utilization for growth, and improved trabecular and cortical bone parameters. Furthermore, consumption of UBD led to a decreased concentration of saturated fatty acids and increased levels of polyunsaturated fatty acids (PUFA), particularly omega-6 PUFA, in the serum, liver, and adipose tissue. The addition of small MFG restored PUFA concentration and the ratio of omega-6 to omega-3 PUFA in bone marrow and adipose tissue. Finally, large but not small MFG supplementation affected the cecal microbiome in rats. Overall, our results suggest that natural structure MFG supplementation can improve metabolism and bone development in rats fed an UBD, with the effects depending on MFG size. Moreover, the benefits of small MFG to bone development and metabolism were not mediated by the microbiome, as the detrimental effects of an UBD on the microbiome were not mitigated by MFG supplementation.

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.

Associations of estrogen therapy and non-estrogen anti-resorptive therapy with diabetes mellitus risk: A classical and Bayesian meta-analysis

Anti-resorptive therapy (AT) increases insulin resistance and decrease insulin secretion through reduced undercarboxylated osteocalcin in mice. However, there are inconsistent findings regarding the impact of AT use on the risk of diabetes mellitus in humans. We examined the association between AT and incident diabetes mellitus using classical and Bayesian meta-analysis. We searched Pubmed, Medline, Embase, Web of Science, Cochrane, and Google Scholar for studies listed from database inception to 25 February 2022. Randomized controlled trials (RCTs) and cohort studies reporting associations of estrogen therapy (ET) and non-estrogen anti-resorptive therapy (NEAT) with incident diabetes mellitus were included. Two reviewers independently extracted research data such as ET and NEAT, diabetes mellitus, risk ratios (RRs), and 95 % confidence intervals (CIs) for incident diabetes mellitus associated with ET and NEAT from individual studies. This meta-analysis included data from nineteen original studies, consisting of fourteen ET and five NEAT studies. ET was associated with reduced risk of diabetes mellitus in the classical meta-analysis (RR: 0.90; 95 % CI: 0.81-0.99). Slightly stronger results were found in the meta-analysis of RCTs (RR: 0.83; 95 % CI: 0.77-0.89). The probability that RR < 1.0 was 95 % in the overall analysis and 99 % in RCTs under weakly informative prior. Although NEAT was associated with reduced risk of diabetes mellitus overall (RR: 0.80; 95 % CI: 0.67-0.97), this was not found in the RCT meta-analysis (RR: 0.90; 95 % CI: 0.75-1.10). Under weakly informative prior, the probabilities that NEAT reduces diabetes mellitus by >0 % were 99 %, and 73 % in the overall and RCT meta-analysis, respectively. In conclusion, meta-analysis provided consistent evidence against the hypothesis that AT increases diabetes risk. ET may reduce the risk of diabetes mellitus. Whether NEAT reduces the risk of diabetes mellitus is uncertain and requires additional evidence from RCTs.

Higher BMI and lower femoral neck strength in males with type 2 diabetes mellitus and normal bone mineral density

BACKGROUND

Type 2 diabetes mellitus (T2DM) and osteoporosis are two age-associated diseases. Body mass index (BMI) is positively associated with osteoporosis or osteopenia in T2DM population. Bone mineral density does not necessarily reflect the alterations in bone microarchitecture. Our aims were to investigate the relationship between BMI and femoral neck strength in males with T2DM and normal range of bone mineral density (BMD).

METHODS

This study enrolled 115 males (median age 53.3 years) with T2DM and normal BMD. Femoral neck strength indexes, including compression strength index (CSI), bending strength index (BSI), impact strength index (ISI), were calculated by parameters generated from Dual-energy X-ray absorptiometry software. Pearson correlation analysis was performed to evaluate the relationships between BMI and femoral neck strength variables.

RESULTS

Compared with T2DM-normal weight group, T2DM-overweight group and T2DM-obesity group had a higher femur neck and total femur BMDs. Cross sectional moment of inertia (CSMI), cross sectional area (CSA), section modulus (SM) were significantly higher (all p<0.05), and buckling ratio (BR) (6.35±2.08 vs 7.18±1.71) was lower in T2DM-obesity group than in T2DM-normal weight group. Compared with T2DM-normal weight group, CSI (all p<0.001), BSI (all p<0.001), ISI (all p<0.001) were significantly reduced in T2DM-obesity and T2DM-overweight groups. Pearson correlation analysis indicated that BMI was negatively correlated with CSI (r= - 0.457, p<0.001), BSI(r = -0.397, p<0.001), ISI (r = - 0.414, p<0.001).

CONCLUSIONS

Higher BMI is associated with lower femoral neck strength in males with T2DM and normal BMD. It implies that femoral neck fracture risk increases in obese and diabetic males, despite their high bone density.

Bone Metabolism Discrepancy in Type 2 Diabetes Mellitus Patients With and Without Non-Alcoholic Fatty Liver Disease

To explore the distribution of several bone metabolic indicators in type 2 diabetes patients (T2DM) with and without non-alcoholic fatty liver disease (NAFLD) and to preliminarily evaluate the relationship of bone metabolism with NAFLD in patients with T2DM. The hospitalized patients with T2DM were divided into the group of T2DM complicated with NAFLD and the group of T2DM alone according to the results of ultrasonic diagnosis. The general information and laboratory test data such as bone metabolism indexes of these patients were collected and the differences of the indexes between the 2 groups were compared. Furthermore, the independent influencing factors of NAFLD in patients with T2DM were analyzed. A total of 186 patients were included in the study. Compared with patients with T2DM only, patients with T2DM combined with NAFLD were characterized with younger age (p &lt; 0.001), higher BMI (p = 0.016), ALT (p = 0.001), TG (p = 0.005), HOMA-IR (p = 0.005), and lower HDL-C (p = 0.031). Significant discrepancy of age (OR 1.052, p = 0.001), ALT (OR 0.964, p = 0.047), HOMA-IR (OR 0.801, p = 0.005), and T-PINP (OR 1.022, p = 0.008) was found using multivariate logistic regression model. Significant discrepancy of T-PINP was found in T2DM patients with and without NAFLD. Further studies are needed to explore whether T-PINP could be used as a predictor of fatty liver disease, osteoporosis, and other related complications in patients with T2DM.

Does Serum Osteocalcin Level Affect Carotid Atherosclerosis in Post-Menopausal Diabetic Females? A Case-Control Study

Purpose

To determine the association between serum osteocalcin and carotid intima media thickness (CIMT) in a group of post-menopausal females with type 2 diabetes (T2DM).

Patients and Methods

This cross-sectional study enrolled 75 postmenopausal women with T2DM and 40 age matched postmenopausal healthy females. Age, body mass index, blood pressure were recorded for all subjects. Laboratory tests including fasting blood glucose (FBG), glycated hemoglobin (HbA1c) and lipid profile were measured. Serum osteocalcin was measured using ELISA. Bone mineral density (BMD) was measured by DEXA scan. CIMT was assessed with B-mode ultrasonography.

Results

Patients with T2DM had significantly lower serum osteocalcin compared to control (63.73±27.20 vs 136.16±21.96 pg/mL, P<0.001). Patients with osteoporosis had significantly lower osteocalcin level compared to those with normal BMD. Patients with T2DM had a significant negative correlation between serum osteocalcin and CIMT (r= -0.332; P=0.003), FBG (r= -0.732; P< 0.001), HbA1c (r=-0.672; P< 0.001), and HOMA-IR (r= -0.672; P< 0.001). However, multiple linear regression analysis revealed that CIMT in patients with diabetes was only significantly associated with age (P= 0.001), duration of diabetes (P< 0.001), SBP (P< 0.001), HOMA-IR (P=0.033), LDL (P=0.005), and HDL (P< 0.001). Furthermore, serum insulin (β= -0.183, P=0.033), FBG (β= -0.604, P< 0.001) and LDL (β= -0.195, P= 0.02) were independently negatively correlated with serum osteocalcin.

Conclusion

In this study, Postmenopausal women with diabetes had significantly lower osteocalcin compared to non-diabetic women. Although serum osteocalcin was negatively correlated with CIMT, multivariate regression analysis revealed that osteocalcin level was only independently related to worse glycemic parameters in postmenopausal women with T2DM.

Increased Glycemic Variability Evaluated by Continuous Glucose Monitoring is Associated with Osteoporosis in Type 2 Diabetic Patients

BACKGROUND

Subjects with type 2 diabetes mellitus (T2DM) are susceptible to osteoporosis. This study was conducted to evaluate the association between glycemic variability evaluated by continuous glucose monitoring (CGM) and osteoporosis in type 2 diabetic patient.

METHODS

A total of 362 type 2 diabetic subjects who underwent bone mineral density (BMD) measurement and were monitored by a CGM system from Jan 2019 to May 2020 were enrolled in this cross-sectional study. Glycemic variability was calculated with the Easy GV software, including 24-hour mean blood glucose (24-h MBG), the standard deviation of 24-h MBG (SDBG), coefficient of variation (CV), mean amplitude of glycemic excursions (MAGE), and time in range between 3.9 and 10.0 mmol/L (TIR). Other potential influence factors for osteoporosis were also examined.

RESULTS

Based on the T-scores of BMD measurement, there were 190 patients with normal bone mass, 132 patients with osteopenia and 40 patients with osteoporosis. T2DM patients with osteoporosis showed a higher 24-h MBG, SDBG, CV, and MAGE, but a lower TIR (all p < 0.05). Multivariate logistic regression analysis revealed that age, female gender, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), serum uric acid (SUA) and MAGE independently contribute to osteoporosis, and corresponding odds ratio [95% confidence interval (CI)] was 1.129 (1.072-1.190), 4.215 (1.613-11.012), 0.801 (0.712-0.901), 2.743 (1.385-5.431), 0.993 (0.988-0.999), and 1.380 (1.026-1.857), respectively. Further receiver operating characteristic analysis with Youden index indicated that the area under the curve and its 95% CI were 0.673 and 0.604-0.742, with the optimal cut-off value of MAGE predicting osteoporosis being 4.31 mmol/L.

CONCLUSION

In addition to conventional influence factors including age, female gender, BMI, LDL-C and SUA, increased glycemic variability assessed by MAGE is associated with osteoporosis in type 2 diabetic patients.

Gut Microbiota in Bone Health and Diabetes

PURPOSE OF REVIEW

Patients with diabetes mellitus (DM) are at increased risk of developing osteopathogenesis and skeletal fragility. The role of the gut microbiota in both DM and osteopathy is not fully explored and may be involved in the pathology of both diseases.

RECENT FINDINGS

Gut microbiota alterations have been observed in DM and osteopathogenic disorders as compared with healthy controls, such as significantly lower abundance of Prevotella and higher abundance of Lactobacillus, with a diminished bacterial diversity. Other overlapping gastro-intestinal features include the loss of intestinal barrier function with translocation of bacterial metabolites to the blood stream, induction of immunological deficits and changes in hormonal and endocrinal signalling, which may lead to the development of diabetic osteopathy. Signalling pathways involved in both DM and osteopathy are affected by gut bacteria and their metabolites. Future studies should focus on gut microbiota involvement in both diseases.

Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network

Background

Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults.

Methods

We conduct a cross-sectional survey with convenience sampling in this study. We use a questionnaire From January 2019 to December 2019 to collect data on disease history and adults’ living habits who got dual-energy X-ray absorptiometry. We established the prediction models of osteoporosis in three steps. Firstly, we performed feature selection to identify risk factors related to osteoporosis. Secondly, the qualified participants were randomly divided into a training set and a test set in the ratio of 7:3. Then the prediction models of osteoporosis were established based on Artificial Neural Network (ANN), Deep Belief Network (DBN), Support Vector Machine (SVM) and combinatorial heuristic method (Genetic Algorithm - Decision Tree (GA-DT)). Finally, we compared the prediction models’ performance through accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to select the optimal prediction model.

Results

The univariate logistic model found that taking calcium tablet (odds ratio [OR] = 0.431), SBP (OR = 1.010), fracture (OR = 1.796), coronary heart disease (OR = 4.299), drinking alcohol (OR = 1.835), physical exercise (OR = 0.747) and other factors were related to the risk of osteoporosis. The AUCs of the training set and test set of the prediction models based on ANN, DBN, SVM and GA-DT were 0.901, 0.762; 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. After evaluating four prediction models’ performance, we selected a three-layer back propagation neural network (BPNN) with 18, 4, and 1 neuron in the input layer, hidden and output layers respectively, as the optimal prediction model. When the probability was greater than 0.330, osteoporosis would occur.

Conclusions

Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11002-5.

Follicle-stimulating hormone and estradiol are associated with bone mineral density and risk of fractures in men with type 2 diabetes mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) is associated with a higher fracture risk. Sex hormones are important for maintaining skeletal health. It is not clear which sex hormone(s) contribute(s) to bone mineral density (BMD) and fracture risk in males with T2DM. This study investigated the relationships of these parameters in males with T2DM.

METHODS

This study involved 482 men with T2DM. BMDs at the lumbar spine (L2-4), femoral neck (FN), and total hip (TH) were measured by dual-energy X-ray absorptiometry (DXA). The 10-year probability of fractures was assessed using the modified Fracture Risk Algorithm (FRAX) tool. Serum levels of sex hormones were measured.

RESULTS

Follicle-stimulating hormone (FSH) and estradiol (E2) were associated with BMDs at L2-4 (FSH, β = -.162, P < .05; E2, β = .176, P < .001), and E2 was associated with BMD at FN (β = .137, P < .05) and TH (β = .140, P < .05). FSH was associated with major osteoporotic fractures (β = .288, P < .001) and hip fractures (β = .235, P < .001). Higher FSH was a risk factor for osteoporosis/osteopenia (odds ratios [OR] = 2.92, 95% CI = 1.66-5.14, P < .001), whereas higher E2 was a protective factor (OR = 0.37, 95% CI = 0.22-0.60, P < .001). Patients in the higher tertile of FSH and lower tertile of E2 had an increased risk of osteoporosis/osteopenia (OR = 5.05, 95% CI = 1.37-18.65, P < .05).

CONCLUSIONS

For males with T2DM, FSH and E2 are significantly associated with BMD, osteoporosis/osteopenia, and fracture risk.

[Effects of advanced glycation end products on osteoclasts at different stages of differentiation]

OBJECTIVE

To explore the effect of advanced glycation end products (AGEs) on osteoclasts at different stages of differentiation.

METHODS

Raw264.7 cells cultured in vitro were induced for osteoclastogenesis using RANKL, and the stages of differentiation of the osteoclasts were determined with TRAP staining. The cells were then randomly divided into control group, early-stage AGEs intervention group and late-stage AGEs intervention group. The viability of the cells after AGEs treatment was assessed using CCK-8 method. The cells were examined after the induction for osteoclastogenesis using TRAP staining, and the expression levels of RANK, NFATC-1, TRAF-6, TRAP and CTSK mRNAs were tested with RT-PCR; the expressions of CTSK and RANK proteins were detected using Western boltting.

RESULTS

We defined the initial 3 days of induction as the early stage of differentiation and the time beyond 3 days as the late stage of differentiation of Raw264.7 cells. Intervention with AGEs at 100 mg/L produced no significant effects on the viability of the cells, but AGEs suppressed the cell proliferation at a concentration exceeding 100 mg/L. The number of osteolasts in the early- and late-stage intervention groups was greater than that in the control group, but the cell count differed significantly only between the early-stage intervention group and control group (P < 0.05). The gene expressions of RANK, NFATC-1, TRAF-6, TRAP and CTSK all increased after the application of AGEs in both the early and late stages of differentiation, but the changes were significant only in the early-stage intervention group (P < 0.05). The changes in CTSK and RANK protein expressions were consistent with their mRNA expressions.

CONCLUSIONS

AGEs can affect the differentiation of osteoclasts differently when applied at different stages, and intervention with AGEs at the early stage produces stronger effect to promote osteoclast differentiation than its application at a late stage.