Background Metabolic syndrome (MS) has grown in recognition to contribute to the pathogenesis of osteoarthritis (OA), which is the most prevalent arthritis characterized by joint dysfunction. However, the specific mechanism between OA and MS remains unclear. Methods The gene expression profiles and clinical information data of OA and MS were retrieved from the Gene Expression Omnibus (GEO) database. The genes in the key module of MS were identified by weighted gene co-expression network analysis (WGCNA), which intersected with the differentially expressed genes (DEGs) between control and MS samples to obtain hub genes for MS. The potential functions and pathways of hub genes were detected through the Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses. The genes involved in the different KEGG pathways between the control and OA samples overlapped with the DEGs between the two groups via the Venn analysis to gain the hub genes for OA affected by MS (MOHGs). Additionally, the least absolute shrinkage and selection operator (LASSO) was performed on the MOHGs to establish a diagnostic model for each disease. Results A total of 61 hub genes for MS were identified that significantly enriched in platelet activation, complement and coagulation cascades, and hematopoietic cell lineage. Besides, 4 candidate genes (ELOVL7, F2RL3, GP9, and ITGA2B) were screened among the 6 MOHGs to construct a diagnostic model, showing good performance for distinguishing controls from patients with MS and OA. GSEA suggested that these diagnostic genes were closely associated with immune response, adipocytokine signaling, fatty acid metabolism, cell cycle, and platelet activation. Conclusion Taken together, we identified 4 potential gene biomarkers for diagnosing MS and OA patients, providing a theoretical basis and reference for the diagnostics and treatment targets of MS and OA.

BackgroundFractures of pelvis and/or Acetabulum are leading risks of death worldwide. However, the capability of in-hospital mortality prediction by conventional system is so far limited. Here, we hypothesis that the use of machine learning (ML) algorithms could provide better performance of prediction than the traditional scoring system Simple Acute Physiologic Score (SAPS) II for patients with pelvic and acetabular trauma in intensive care unit (ICU).MethodsWe developed customized mortality prediction models with ML techniques based on MIMIC-III, an open access de-defined database consisting of data from more than 25,000 patients who were admitted to the Beth Israel Deaconess Medical Center (BIDMC). 307 patients were enrolled with an ICD-9 diagnosis of pelvic, acetabular or combined pelvic and acetabular fractures and who had an ICU stay more than 72 hours. ML models including decision tree, logistic regression and random forest were established by using the SAPS II features from the first 72 hours after ICU admission and the traditional first-24-hours features were used to build respective control models. We evaluated and made a comparison of each model’s performance through the area under the receiver-operating characteristic curve (AUROC). Feature importance method was used to visualize top risk factors for disease mortality.ResultsAll the ML models outperformed the traditional scoring system SAPS II (AUROC=0.73), among which the best fitted random forest model had the supreme performance (AUROC of 0.90). With the use of evolution of physiological features over time rather than 24-hours snapshots, all the ML models performed better than respective controls. Age remained the top of feature importance for all classifiers. Age, BUN (minimum value on day 2), and BUN (maximum value on day 3) were the top 3 predictor variables in the optimal random forest experiment model. In the best decision tree model, the top 3 risk factors, in decreasing order of contribution, were age, the lowest systolic blood pressure on day 1 and the same value on day 3.ConclusionThe results suggested that mortality modeling with ML techniques could aid in better performance of prediction for models in the context of pelvic and acetabular trauma and potentially support decision-making for orthopedics and ICU practitioners.

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