Diabetes and Osteoporosis: Part II, Clinical Management

Correlation between bone mineral density and type 2 diabetes mellitus in elderly men and postmenopausal women

The relationship between bone mineral density and type 2 diabetes is still controversial. The aim of this study is to investigate the relationship between type 2 diabetes mellitus (T2DM) and bone mineral density (BMD) in elderly men and postmenopausal women. The participants in this study included 692 postmenopausal women and older men aged ≥ 50 years, who were divided into the T2DM group and non-T2DM control group according to whether or not they had T2DM. The data of participants in the two groups were collected from the inpatient medical record system and physical examination center systems, respectively, of the Tertiary Class A Hospital. All data analysis is performed in SPSS Software. Compared with all T2DM group, the BMD and T scores of lumbar spines 1-4 (L1-L4), left femoral neck (LFN) and all left hip joints (LHJ) in the non-T2DM group were significantly lower than those in the T2DM group (P < 0.05), and the probability of major osteoporotic fracture in the next 10 years (PMOF) was significantly higher than that in T2DM group (P < 0.001). However, with the prolongation of the course of T2DM, the BMD significantly decreased, while fracture risk and the prevalence of osteoporosis significantly increased (P < 0.05). We also found that the BMD of L1-4, LFN and LHJ were negatively correlated with homeostatic model assessment-insulin resistance (HOMA-IR) (P = 0.028, P = 0.01 and P = 0.047, respectively). The results also showed that the BMD of LHJ was positively correlated with indirect bilirubin (IBIL) (P = 0.018). Although the BMD was lower in the non-T2DM group than in the T2DM group, the prolongation of the course of T2DM associated with the lower BMD. And the higher prevalence of osteoporosis and fracture risk significantly associated with the prolongation of the course of T2DM. In addition, BMD was significantly associated with insulin resistance (IR) and bilirubin levels in T2DM patients.Registration number: China Clinical Trials Registry: MR-51-23-051741; https://www.medicalresearch.org.cn/search/research/researchView?id=c0e5f868-eca9-4c68-af58-d73460c34028 .

Biochanin A abrogates osteoclastogenesis in type 2 diabetic osteoporosis via regulating ROS/MAPK signaling pathway based on integrating molecular docking and experimental validation

BACKGROUND

There are accumulating type 2 diabetes patients who have osteoporosis simultaneously. More effective therapeutic strategies should be discovered. Biochanin A (BCA) has been indicated that can play a role in improving metabolic disorders of type 2 diabetes and preventing osteoporosis. But whether BCA can treat type 2 diabetic osteoporosis has not been studied.

PURPOSE

To investigate if the BCA can protect against type 2 diabetic osteoporosis and clarify the mechanism.

METHODS

Micro-CT and histology assays were performed to detect the trabecular bone and analyze the bone histomorphology effect of BCA. CCK-8 assay was performed to detect the toxicity of BCA. TRAcP staining, immunofluorescence and hydroxyapatite resorption assay were used to observe osteoclasts differentiation and resorptive activity. Molecular docking provided evidence about BCA regulating the MAPK axis via prediction by the algorithm. QRT-PCR and Western Blotting were utilized to detect the expression of osteoclastogenesis-related markers and MAPK signaling pathway.

RESULTS

Accumulation of bone volume after BCA treatment could be found based on the 3D reconstruction. Besides, there were fewer osteoclasts in db/db mice treated with BCA than db/db mice treated with saline. In vitro, we found that BCA hadn't toxicity in osteoclasts precursor, but also inhibited differentiation of osteoclasts. Further, we found that BCA suppresses osteoclastogenesis via ROS/MAPK signaling pathway.

CONCLUSION

BCA can prevent type 2 diabetic osteoporosis by restricting osteoclast differentiation via ROS/MAPK signaling pathway.

Osteoporosis and diabetes - possible links and diagnostic difficulties

Objectives

In this review, the authors aimed to clarify the relationship between the occurrence of osteoporosis and diabetes, analyze the differences between the pathogenesis of osteoporosis in different types of diabetes and propose the most effective diagnostic strategy and fracture risk assessment in diabetic patients.

Material and methods

A analysis of publications in MEDLINE, COCHRANE and SCOPUS databases was performed, searching for reports on the diagnostics, fracture risk assessment, prevention, and treatment of osteoporosis in patients with diabetes mellitus (DM) published in the years 2016-2022. The key words for the search were: diabetes, osteoporosis, and low-energy fracture.

Results

Bone complications of T1DM are more severe than T2DM, because of the lack of anabolic effect of insulin on bones. In T2DM the risk of fractures is elevated; however, identifying the mechanisms underlying the increased risk of fractures in T2DM is not clear. The FRAX tool is not appropriate for assessing the fracture risk in young patients with T1DM. It is quite useful in older patients with T2DM, but in these patients the calculated fracture risk may be underestimated. In T2DM the fracture risk often does not correspond to BMD value as measured by dual-energy X-ray absorptiometry (DXA). Diagnostic tools such as the trabecular bone score may play a significant role in this group of patients. Conclusions: Optimal strategies to identify and treat high risk individuals require further research and proper definition. The diagnostic criteria for osteoporosis should be clearly defined as well as fracture risk assessment and choice of anti-osteoporotic medication. In all cases of secondary osteoporosis, treatment of the underlying disease is the most important. The relationship between high risk of fractures and diabetes is inseparable, and its full understanding seems to be the key to effective management.

Identification of ferroptosis-related molecular clusters and genes for diabetic osteoporosis based on the machine learning

Background

Diabetic osteoporosis exhibits heterogeneity at the molecular level. Ferroptosis, a controlled form of cell death brought on by a buildup of lipid peroxidation, contributes to the onset and development of several illnesses. The aim was to explore the molecular subtypes associated with ferroptosis in diabetic osteoporosis at the molecular level and to further elucidate the potential molecular mechanisms.

Methods

Integrating the CTD, GeneCards, FerrDb databases, and the microarray data of GSE35958, we identified ferroptosis-related genes (FRGs) associated with diabetic osteoporosis. We applied unsupervised cluster analysis to divide the 42 osteoporosis samples from the GSE56814 microarray data into different subclusters based on FRGs. Subsequently, FRGs associated with two ferroptosis subclusters were obtained by combining database genes, module-related genes of WGCNA, and differentially expressed genes (DEGs). Eventually, the key genes from FRGs associated with diabetic osteoporosis were identified using the least absolute shrinkage and selection operator (LASSO), Boruta, support vector machine recursive feature elimination (SVM - RFE), and extreme gradient boosting (XGBoost) machine learning algorithms. Based on ROC curves of external datasets (GSE56815), the model's efficiency was examined.

Results

We identified 15 differentially expressed FRGs associated with diabetic osteoporosis. In osteoporosis, two distinct molecular clusters related to ferroptosis were found. The expression results and GSVA analysis indicated that 15 FRGs exhibited significantly different biological functions and pathway activities in the two ferroptosis subclusters. Therefore, we further identified 17 FRGs associated with diabetic osteoporosis between the two subclusters. The results of the comprehensive analysis of 17 FRGs demonstrated that these genes were heterogeneous and had a specific interaction between the two subclusters. Ultimately, the prediction model had a strong foundation and excellent AUC values (0.84 for LASSO, 0.84 for SVM - RFE, 0.82 for Boruta, and 0.81 for XGBoost). IDH1 is a common gene to all four algorithms thus being identified as a key gene with a high AUC value (AUC = 0.698).

Conclusions

As a ferroptosis regulator, IDH1 is able to distinguish between distinct molecular subtypes of diabetic osteoporosis, which may offer fresh perspectives on the pathogenesis of the disease's clinical symptoms and prognostic heterogeneity.

Therapeutic approach of natural products that treat osteoporosis by targeting epigenetic modulation

Osteoporosis (OP) is a metabolic disease that affects bone, resulting in a progressive decrease in bone mass, quality, and micro-architectural degeneration. Natural products have become popular for managing OP in recent years due to their minimal adverse side effects and suitability for prolonged use compared to chemically synthesized products. These natural products are known to modulate multiple OP-related gene expressions, making epigenetics an important tool for optimal therapeutic development. In this study, we investigated the role of epigenetics in OP and reviewed existing research on using natural products for OP management. Our analysis identified around twenty natural products involved in epigenetics-based OP modulation, and we discussed potential mechanisms. These findings highlight the clinical significance of natural products and their potential as novel anti-OP therapeutics.

Prediabetes and osteoporotic fracture risk: A meta-analysis of prospective cohort studies

BACKGROUND

Diabetes confers an increased risk of fracture. However, whether prediabetes is also a risk factor of osteoporotic fracture has not been comprehensively examined. We performed a meta-analysis to evaluate the relationship between prediabetes and osteoporotic fracture risk.

METHODS

This meta-analysis included relevant prospective cohort studies from Medline, Embase, and Web of Science databases. A random-effect model after incorporation of the intra-study heterogeneity was selected to pool the results. Subgroup analyses were applied to evaluate the influences of study characteristics on relationship between prediabetes and osteoporotic fracture risk.

RESULTS

Eight studies including 33,136 community dwelling adult patients were included, and 7429 (22.4%) patients were prediabetic. Prediabetes was not independently associated with a higher risk of osteoporotic fracture compared with normoglycemia (adjusted risk ratio: 1.03, 95% confidence interval: 0.88-1.21, P = 0.69, I²  = 42%). Sensitivity limited to the elderly population showed consistent results (RR: 1.10, 95% CI: 0.91-1.24, P = 0.15, I²  = 0%). Subgroup analysis suggested that prediabetes defined by HbA1c (approximately 5.7%-6.4%) was associated with a higher risk of osteoporotic fracture (RR: 1.24, 95% CI: 1.01-1.53, P = 0.04), but not that defined by impaired fasting glucose or impaired glucose tolerance (P = 0.60). Sex, follow-up duration, and adjustment of bone mineral density did not significantly affect the outcome.

CONCLUSIONS

Current evidence does not support that prediabetes is independently associated with osteoporotic fracture risk. Different definitions of prediabetes may affect the association between prediabetes and osteoporotic fracture risk.

Update of the fracture risk prediction tool FRAX: a systematic review of potential cohorts and analysis plan

We describe the collection of cohorts together with the analysis plan for an update of the fracture risk prediction tool FRAX with respect to current and novel risk factors. The resource comprises 2,138,428 participants with a follow-up of approximately 20 million person-years and 116,117 documented incident major osteoporotic fractures.

INTRODUCTION

The availability of the fracture risk assessment tool FRAX® has substantially enhanced the targeting of treatment to those at high risk of fracture with FRAX now incorporated into more than 100 clinical osteoporosis guidelines worldwide. The aim of this study is to determine whether the current algorithms can be further optimised with respect to current and novel risk factors.

METHODS

A computerised literature search was performed in PubMed from inception until May 17, 2019, to identify eligible cohorts for updating the FRAX coefficients. Additionally, we searched the abstracts of conference proceedings of the American Society for Bone and Mineral Research, European Calcified Tissue Society and World Congress of Osteoporosis. Prospective cohort studies with data on baseline clinical risk factors and incident fractures were eligible.

RESULTS

Of the 836 records retrieved, 53 were selected for full-text assessment after screening on title and abstract. Twelve cohorts were deemed eligible and of these, 4 novel cohorts were identified. These cohorts, together with 60 previously identified cohorts, will provide the resource for constructing an updated version of FRAX comprising 2,138,428 participants with a follow-up of approximately 20 million person-years and 116,117 documented incident major osteoporotic fractures. For each known and candidate risk factor, multivariate hazard functions for hip fracture, major osteoporotic fracture and death will be tested using extended Poisson regression. Sex- and/or ethnicity-specific differences in the weights of the risk factors will be investigated. After meta-analyses of the cohort-specific beta coefficients for each risk factor, models comprising 10-year probability of hip and major osteoporotic fracture, with or without femoral neck bone mineral density, will be computed.

CONCLUSIONS

These assembled cohorts and described models will provide the framework for an updated FRAX tool enabling enhanced assessment of fracture risk (PROSPERO (CRD42021227266)).

Upregulated miR-9-5p inhibits osteogenic differentiation of bone marrow mesenchymal stem cells under high glucose treatment

INTRODUCTION

Diabetic osteoporosis (DOP) is a chronic diabetic complication, which is attributed to high glucose (HG)-induced dysfunction of bone marrow mesenchymal stem cells (BMSCs). Studies have revealed that microRNAs (miRNAs) play critical roles in osteogenic differentiation of BMSCs in DOP. Here, the role of miR-9-5p in DOP progression was explored.

MATERIALS AND METHODS

The rat model of DOP was established by intraperitoneal injection of streptozotocin (STZ). BMSCs were treated with high glucose (HG) to establish in vitro models. Gene expression in BMSCs and bone tissues of rats was tested by RT-qPCR. The degree of osteogenic differentiation of BMSCs was examined by Alizarin Red staining and ALP activity analysis. The protein levels of collagen-I (COL1), osteocalcin (OCN), osteopontin (OPN), runt-related transcription factor-2 (RUNX2), and DEAD-Box Helicase 17 (DDX17) in BMSCs were evaluated by western blotting. The interaction between miR-9-5p and DDX17 was identified by luciferase reporter assay. H&E staining was used to test morphological structure of femurs of rats with STZ treatment.

RESULTS

MiR-9-5p was overexpressed in HG-treated BMSCs, while DDX17 was downregulated. Functionally, miR-9-5p knockdown promoted BMSCs osteogenic differentiation under HG condition. Mechanically, miR-9-5p targeted DDX17. DDX17 knockdown reversed the effect of miR-9-5p silencing on osteogenic differentiation of HG-treated BMSCs. In in vivo studies, miR-9-5p downregulation ameliorated the DOP condition of rats and miR-9-5p expression was negatively correlated with DDX17 expression in bone tissues of rats with STZ treatment.

CONCLUSION

MiR-9-5p knockdown promotes HG-induced osteogenic differentiation BMSCs in vitro and mitigates the DOP condition of rats in vivo by targeting DDX17.