Daily Endocrinology Research Analysis

BACKGROUND: Gestational diabetes mellitus (GDM) remains a prevalent and heterogeneous pregnancy complication with limited strategies for early identification. We aimed to investigate efficient approaches for early prediction of GDM with clinical and genetic risk factors. METHODS: A previously developed machine-learning model based on clinical characteristics achieved an area under the curve (AUC) of 0.77. To improve predictive accuracy, we further collected non-invasive prenatal testing (NIPT) results from 595 pregnant women (295 with GDM, 300 without). A cumulative polygenic risk score (PRS) was calculated using 1,170 selected single nucleotide variants (SNVs). Logistic regression, support vector machines, random forest, decision tree, linear model and naïve Bayes machine learning models were employed. External validation was performed with an additional 2,350 blood samples independently collected from two other centers. RESULTS: Logistic regression analysis showed that the PRS alone achieved an AUC of 0.75 for GDM discrimination. From cell-free DNA (cfDNA) sequencing performed during NIPT, we identified 357 gene transcripts with differential coverage at transcription start sites. A cfDNA-based linear model achieved an AUC of 0.83 using a subset of 166 signature genes, which reached 0.85 when combined with clinical features. Integration of clinical features, cfDNA, and SNVs yielded the highest performance using a random forest model (AUC = 0.89, specificity = 0.74, sensitivity = 0.89). For external validation, a clinically practical model incorporating clinical features and cfDNA achieved an AUC of 0.83 using linear approach. CONCLUSIONS: Our GDM prediction model has reached high accuracy fully using accessible clinical and genetic data routinely generated from current antenatal testing, enabling early screening and interventions for women at risk.

Nicotinamide adenine dinucleotide (NAD) is a classical coenzyme regulating cellular energy metabolism. Emerging evidence demonstrates the causal relationship between defective NAD metabolism and various age-associated diseases. The major purpose of the present study was to investigate the role of adipocyte mitochondrial NAD biology in age-associated metabolic diseases. To this end, we focused on solute carrier family 25 member 51 (SLC25A51), a recently identified mitochondrial NAD transporter. We found that aging was associated with decreased adipose tissue SLC25A51 expression in both humans and mice. We next generated and analyzed novel knockout and overexpression models, which we have named adipocyte-specific Slc25a51 knockout (ASKO) and Slc25a51 overexpressing (ASLO) mice. ASKO mice had a marked decrease in adipose tissue mitochondrial NAD levels and exhibited age-associated systemic metabolic complications, such as obesity, glucose intolerance, insulin resistance, hyperinsulinemia, metabolic inflexibility, dyslipidemia, and hepatosteatosis. Mechanistically, loss of Slc25a51 reduced mitochondrial respiratory function, fatty acid oxidation capacity, and adiponectin production in adipose tissue, likely contributing to the development of systemic metabolic complications. Conversely, ASLO mice were protected from obesity and insulin resistance caused by aging. In conclusion, our results provide novel mechanistic and therapeutic insights into understanding the critical role of adipocyte mitochondrial NAD transporter SLC25A51 in the pathophysiology of age-associated metabolic diseases, particularly obesity and insulin resistance.

BACKGROUND: The incidence of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) is increasing, but population registries seldom capture detailed clinical data. The Italian Association for Neuroendocrine Tumours (Itanet) established a nationwide prospective database to describe presentation, diagnostic pathways, management, and outcomes of newly diagnosed GEP-NENs in Italy. METHODS: This multicentre prospective observational study enrolled 2138 consecutive patients with newly diagnosed GEP-NENs across 38 Italian centres (2019-2024). Clinical, pathological, imaging, and treatment data were prospectively collected and centrally validated. Descriptive and survival analyses were performed; Ki-67 was modelled as a continuous variable. FINDINGS: Median age was 60.6 years, and 55.9% (1195/2138) were male. Tumours were well-differentiated NETs in 90.8% of patients (1942/2138), mainly of pancreatic (41.4%, 886/2138) or ileal (19.7%, 422/2138) origin. Median Ki-67 was 2%. An incidental diagnosis occurred in 58.6% (1254/2138) of cases. Among symptomatic patients, the mean diagnostic delay was 197 days (224 for pancreatic vs 184 for small bowel; p = 0.039). INTERPRETATION: This nationwide prospective study delineates real-world diagnostic and therapeutic patterns of GEP-NENs in Italy, confirms Ki-67 as a continuous prognostic biomarker, and identifies a 15% threshold associated with worse survival, providing a benchmark for outcome assessment and future clinical research. FUNDING: None.