Primary hyperparathyroidism (PHPT) is an endocrine disorder in which one or more parathyroid glands oversecrete parathyroid hormone, leading to hypercalcemia and potential complications such as kidney stones, bone fractures, and other systemic issues. Machine learning (ML) is extensively applied to diagnose medical diseases due to its high accuracy and ease of implementation. The microarray expression dataset (GSE83421) was selected from the Gene Expression Omnibus Database (GEO) for analysis. Differential expression analysis was performed with false discovery rate (FDR) correction. Weighted gene co-expression network analysis (WGCNA) identified correlated gene modules. Consensus feature selection using six algorithms (LASSO, SVM-RFE, random forest, Boruta, XGBoost, and decision tree) was applied to identify robust biomarkers and mitigate overfitting. A penalized logistic regression model was developed and evaluated using leave-one-out cross-validation (LOOCV). Ten candidate genes were identified by intersecting 24 DEGs with the WGCNA red module. Consensus ML selected five hub genes (LOC51092, KIAA1479, PRO2214, TBX4, and ZNF361). T. The LOOCV-validated penalized logistic regression model effectively distinguished PHPT from normal controls. Our analysis identified a 5-gene signature (LOC51092, KIAA1479, PRO2214, TBX4, and ZNF361) with diagnostic potential for PHPT. The model was internally validated using LOOCV. However, without nested feature selection or external validation, the reported performance likely represents an optimistic estimate. Given the limited sample size, these findings represent preliminary hypotheses for PHPT diagnosis, requiring validation in independent cohorts.
There is an extensive pipeline of therapies for diabetes mellitus. It has been known for the past three decades that obesity is a key pathophysiological player, involving inflammation, insulin resistance and metabolic dysfunction leading to type 2 diabetes and to significant cardiovascular, renal and hepatic complications, all central to the metabolic syndrome. On the basis of this knowledge and the disposition of drugs with multiple effects on different disease components, the diabetes therapeutic paradigm has shifted from merely glucocentric to multitargeted against these interconnected pathways. Much more recently, incretin-based multi-agonists have been developed that deliver unprecedented glycemic and weight benefits as well as weekly insulins and oral GLP-1 agonists that are able to reduce the burden of injectable therapies. An antibody-peptide conjugate that acts as a dual GLP-1/GIP receptor modulator has also shown promising results. Today, novel insulin sensitizers are modulating metabolic pathways towards higher energy expenditure with fewer side effects. Adjunctive agents for type 1 diabetes are improving glucose control while reducing hypoglycemia risk. Finally, drugs designed to restrain the inevitable muscle mass loss caused by antiobesity treatments are being tested. The diabetes pipeline is rich and there is hope that in the next few years, the therapeutic landscape will be further transformed with even more efficacious and individualized treatments.
Adults with osteopenia or osteoporosis have increased risks of adverse outcomes; however, mortality risk varies substantially and may be influenced by body composition. Evidence for the joint impact of low muscle mass and percentage-based adiposity in this population remains limited. To examine the independent and combined associations of low muscle mass and obesity with all-cause mortality among U.S. adults with osteopenia or osteoporosis and to characterize the dose-response relationship between total fat mass and mortality. We performed a retrospective cohort analysis of NHANES cycles with concurrent whole-body DXA (appendicular lean mass and total fat mass) and site-specific BMD assessments (1999–2006, 2013–2014, and 2017–2018). Eligible participants with osteopenia or osteoporosis and mortality linkage were included (unweighted n = 3,918). Low muscle mass (LMM) was defined by DXA-derived appendicular lean mass index (ALMI; men < 7.0 kg/m², women < 5.5 kg/m²), while obesity was operationally defined by DXA body fat percentage (FM
Ultra-processed foods (UPFs) have been increasingly linked to adverse metabolic outcomes. The aim of this study was to assess the association between UPF consumption and thyroid hormone levels in a nationally representative sample of U.S. adults. A cross-sectional analysis was conducted using data from 5,961 adults aged ≥ 18 years participating in NHANES 2007–2012. Dietary intake was assessed using two 24-hour dietary recall and categorized using the NOVA classification system. UPF intake was expressed as a percentage of total daily energy consumption and divided into tertiles. Serum levels of thyroid-stimulating hormone (TSH), free and total triiodothyronine (FT3, TT3), and thyroxine (FT4, TT4) were measured using standardized immunoassays. Weighted linear regression, adjusting for covariates, was used to evaluate associations. Participants had a mean age of 46.8 years and 51.9
Early identification of individuals at risk for osteoporosis is essential for timely intervention and prevention of fragility fractures. This study aimed to assess the diagnostic value of dental radiographic indices obtained from panoramic radiography and cone beam computed tomography (CBCT) for the early detection of osteoporosis, using dual-energy X-ray absorptiometry (DXA) as the reference standard. Seventy (n = 70) postmenopausal women underwent panoramic radiography, CBCT, and DXA examinations. Images were evaluated by an oral surgeon certified in oral radiology. The Mandibular Cortical Index (MCI), Mental Index (MI), Panoramic Mandibular Index (PMI), and Molar Index (M index) were evaluated using panoramic images. Corresponding computed tomography (CT)-based indices, specifically the Computed Tomography Mandibular Cortical Index (CTMCI), Computed Tomography Mental Index (CTMI), Computed Tomography Panoramic Mandibular Index (CTPMI), and Computed Tomography Molar Index (CTM index), were derived from CBCT measurements. PMI, CTPMI, and the M index demonstrated the highest diagnostic performance for osteoporosis detection. Specifically, the highest areas under the curve (AUC) for distinguishing normal individuals from those with osteoporosis were observed for PMI (AUC = 0.89, 95
