Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning.

Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.

Profiling the genome and proteome of metabolic dysfunction-associated steatotic liver disease identifies potential therapeutic targets.

Background & Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects over 25% of the population and currently has no effective treatments. Plasma proteins with causal evidence may represent promising drug targets. We aimed to identify plasma proteins in the causal pathway of MASLD and explore their interaction with obesity.

Evaluation of long acting GLP1R/GCGR agonist in a DIO and biopsy-confirmed mouse model of NASH suggest a beneficial role of GLP-1/glucagon agonism in NASH patients.

Objective: The metabolic benefits of GLP-1 receptor (GLP-1R) agonists on glycemic and weight control are well established as therapy for type 2 diabetes and obesity. Glucagon's ability to increase energy expenditure is well described, and the combination of these mechanisms-of-actions has the potential to further lower hepatic steatosis in metabolic disorders and could therefore be attractive for the treatment for non-alcoholic steatohepatitis (NASH). Here, we have investigated the effects of a dual GLP-1/glucagon receptor agonist NN1177 on hepatic steatosis, fibrosis, and inflammation in a preclinical mouse model of NASH.

MLX plays a key role in lipid and glucose metabolism in humans: Evidence from in vitro and in vivo studies.

Background And Aim: Enhanced hepatic de novo lipogenesis (DNL) has been proposed as an underlying mechanism for the development of NAFLD and insulin resistance. Max-like protein factor X (MLX) acts as a heterodimer binding partner for glucose sensing transcription factors and inhibition of MLX or downstream targets has been shown to alleviate intrahepatic triglyceride (IHTG) accumulation in mice. However, its effect on insulin sensitivity remains unclear.

Central androgen action reverses hypothalamic astrogliosis and atherogenic risk factors induced by orchiectomy and high-fat diet feeding in male mice.

Hypogonadism in males confers elevated cardiovascular disease (CVD) risk by unknown mechanisms. Recent radiological evidence suggests that low testosterone (T) is associated with mediobasal hypothalamic (MBH) gliosis, a central nervous system (CNS) cellular response linked to metabolic dysfunction. To address mechanisms linking CNS androgen action to CVD risk, we generated a hypogonadal, hyperlipidemic mouse model with orchiectomy (ORX) combined with hepatic PCSK9 overexpression.

NMDA Receptor Antagonists Increase the Release of GLP-1 From Gut Endocrine Cells.

Type 2 diabetes mellitus (T2DM) remains one of the most pressing health issues facing modern society. Several antidiabetic drugs are currently in clinical use to treat hyperglycaemia, but there is a need for new treatments that effectively restore pancreatic islet function in patients. Recent studies reported that both murine and human pancreatic islets exhibit enhanced insulin release and β-cell viability in response to N-methyl-D-aspartate (NMDA) receptor antagonists.

Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome.

The 22q11.2 deletion syndrome (22q11DS) is associated with a 20-25% risk of schizophrenia. In a cohort of 962 individuals with 22q11DS, we examined the shared genetic basis between schizophrenia and schizophrenia-related early trajectory phenotypes: sub-threshold symptoms of psychosis, low baseline intellectual functioning and cognitive decline.

Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks.

Background: Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states.

Results: In this study, we present SCPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks that detects active modules in cells of different transcriptional states.