MiR-1908/EXO1 and MiR-203a/FOS, regulated by scd1, are associated with fracture risk and bone health in postmenopausal diabetic women.

BACKGROUND: Stearoyl-coenzyme A desaturase-1 (SCD1) can inhibit the development of diabetic bone disease by promoting osteogenesis. In this study, we examined whether this regulation by SCD1 is achieved by regulating the expression of related miRNAs. METHODS: SCD1 expression levels were observed in human bone-marrow mesenchymal stem cells (BM-MSCs) of patients with type 2 diabetes mellitus (T2DM), and the effect of SCD1 on osteogenesis was observed in human adipose-derived MSCs transfected with the SCD1 lentiviral system. We designed a bioinformatics prediction model to select important differentially expressed miRNAs, and established protein-protein interaction and miRNA-mRNA networks. miRNAs and mRNAs were extracted and their differential expression was detected. The SCD1-miRNA-mRNA network was validated. FINDINGS: SCD1 expression in bone marrow was downregulated in patients with T2DM and low-energy fracture, and SCD1 expression promotes BM-MSC osteogenic differentiation. The predictors in the nomogram were seven microRNAs, including hsa-miR-1908 and hsa-miR-203a. SCD1 inhibited the expression of CDKN1A and FOS, but promoted the expression of EXO1 and PLS1. miR-1908 was a regulator of EXO1 expression, and miR-203a was a regulator of FOS expression. INTERPRETATION: The regulation of BM-MSCs by SCD1 is a necessary condition for osteogenesis through the miR-203a/FOS and miR-1908/EXO1 regulatory pathways.

Predicting the risk of sarcopenia in elderly patients with patellar fracture: development and assessment of a new predictive nomogram.

PURPOSE: To develop a risk prediction model for postoperative sarcopenia in elderly patients with patellar fractures in China. PATIENTS AND METHODS: We conducted a community survey of patients aged ≥55 years who underwent surgery for patellar fractures between January 2013 and October 2018, through telephone interviews, community visits, and outpatient follow-up. We established a predictive model for assessing the risk of sarcopenia after patellar fractures. We developed the prediction model by combining multivariate logistic regression analysis with the least absolute shrinkage model and selection operator regression (lasso analysis) as well as the Support Vector Machine (SVM) algorithm. The predictive quality and clinical utility of the predictive model were determined using C-index, calibration plots, and decision curve analysis. We also conducted internal sampling methods for qualitative assessment. RESULT: We recruited 137 participants (53 male; mean age, 65.7 years). Various risk factors were assessed, and low body mass index and advanced age were identified as the most important risk factor (P < 0.05). The prediction rate of the model was good (C-index: 0.88; 95% CI [0.80552-0.95448]), with a satisfactory correction effect. The C index is 0.97 in the validation queue and 0.894 in the entire cohort. Decision curve analysis suggested good clinical practicability. CONCLUSION: Our prediction model shows promise as a cost-effective tool for predicting the risk of postoperative sarcopenia in elderly patients based on the following: advanced age, low body mass index, diabetes, less outdoor exercise, no postoperative rehabilitation, different surgical methods, diabetes, open fracture, and removal of internal fixation.