A Potential Participant in Type 2 Diabetes Bone Fragility: TIMP-1 at Sites of Osteocyte Lacunar-Canalicular System

Bone strength is reduced in a neonatal androgenized rat model

Background

Whether polycystic ovary syndrome (PCOS) affects bone health during a woman's lifespan remains controversial. An androgenized rodent model replicated many metabolic and reproductive features of women with PCOS, and we aimed to use it to investigate the impact of androgens on microarchitecture (by micro-CT), bone mechanical strength, bone formation and resorption markers in rats with intact ovaries (SHAM) who underwent oophorectomy.

Methods

Wistar rats (Rattus norvegicus albinus) were employed for the experiments in this study. The protocol of androgenization consisted of the application of 1.25 mg s.c. testosterone propionate beteween days 2-5 of life, while the controls received the same amount of corn oil s.c. as previously established. Androgenized SHAM rats exhibited chronic anovulation identified by vaginal cytology and a reduction in the proportion of corpus luteum in the ovary in comparison to control SHAM rats. The realization of the ovariectomy or SHAM procedure occurred on Day 100 of life. All groups (n = 8) were followed-up for 180 days to address the study endpoints.

Results

Micro-CT from androgenized female rats (SHAM) showed a divergence between the trabecular and cortical bone profiles. Compared to SHAM controls, these rats had an increase in trabecular bone mass with a diminution in bone resorption C-terminal telopeptide of type 1 collagen (CTX) (p < 0.05), a concomitant decrease in cortical area and thickness in the femur, and a reduction in the strength of the femur on the mechanical test (p < 0.01).

Conclusions

Our results suggest that a reduction in the cortical thickness and cortical area observed in PCOS model rats was associated with a reduced strength of the femur, despite increased trabecular formation. Ovariectomy in the androgenized OVX group limited the progression rate of cortical bone loss, resulting in bone resistance and cortical thickness comparable to those observed in the control OVX group.

Utility and Limitations of TALLYHO/JngJ as a Model for Type 2 Diabetes-Induced Bone Disease

Type 2 diabetes mellitus (T2DM) increases risk of fractures due to bone microstructural and material deficits, though the mechanisms remain unclear. Preclinical models mimicking diabetic bone disease are required to further understand its pathogenesis. The TALLYHO/JngJ (TH) mouse is a polygenic model recapitulating adolescent-onset T2DM in humans. Due to incomplete penetrance of the phenotype ~25% of male TH mice never develop hyperglycemia, providing a strain-matched nondiabetic control. We performed a comprehensive characterization of the metabolic and skeletal phenotype of diabetic TH mice and compared them to either their nondiabetic TH controls or the recommended SWR/J controls to evaluate their suitability to study diabetic bone disease in humans. Compared to both controls, male TH mice with T2DM exhibited higher blood glucose levels, weight along with impaired glucose tolerance and insulin sensitivity. TH mice with/without T2DM displayed higher cortical bone parameters and lower trabecular bone parameters in the femurs and vertebrae compared to SWR/J. The mechanical properties remained unchanged for all three groups except for a low-energy failure in TH mice with T2DM only compared to SWR/J. Histomorphometry analyses only revealed higher number of osteoclasts and osteocytes for SWR/J compared to both groups of TH. Bone turnover markers procollagen type 1 N-terminal propeptide (P1NP) and tartrate-resistant acid phosphatase (TRAP) were low for both groups of TH mice compared to SWR/J. Silver nitrate staining of the femurs revealed low number of osteocyte lacunar and dendrites in TH mice with T2DM. Three-dimensional assessment showed reduced lacunar parameters in trabecular and cortical bone. Notably, osteocyte morphology changed in TH mice with T2DM compared to SWR/J. In summary, our study highlights the utility of the TH mouse to study T2DM, but not necessarily T2DM-induced bone disease, as there were no differences in bone strength and bone cell parameters between diabetic and non-diabetic TH mice. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Decoding Diabetes Biomarkers and Related Molecular Mechanisms by Using Machine Learning, Text Mining, and Gene Expression Analysis

The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes by using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top 10 DEGs. These genes were enriched in aerobic respiration, T-cell antigen receptor pathway, tricarboxylic acid metabolic process, vitamin D receptor pathway, toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for machine learning (ML) analysis. The decision tree, extra-tree regressor and random forest algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. Machine learning algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.