Daily Endocrinology Research Analysis

UNLABELLED: Insulin secretion varies widely in preclinical type 1 diabetes. To understand the pathogenesis of this metabolic heterogeneity, we asked whether genetic predisposition to type 2 diabetes, quantified by a type 2 diabetes genetic risk score (T2D-GRS), modulates β-cell function and disease progression in individuals at risk of type 1 diabetes. We analyzed 4,324 islet autoantibody-positive TrialNet Pathway to Prevention participants with genome-wide genotyping and oral glucose tolerance testing. Both T2D-GRS and the type 1 diabetes genetic risk score 2 (T1D-GRS2) differed significantly across five previously described groups defined by C-peptide area under the curve (AUC; a measure of insulin secretion). The highest C-peptide AUC group, compared with the lowest, had significantly higher T2D-GRS, lower T1D-GRS2, higher BMI z-score, greater insulin resistance, older age, and lower prevalence of male participants; multiple islet autoantibody positivity; and IA-2 or insulin autoantibody positivity. Progression to clinical (stage 3) type 1 diabetes was significantly associated with T1D-GRS2 across all groups and with T2D-GRS in all but the lowest C-peptide AUC group. In conclusion, type 2 diabetes genetic burden shapes metabolic heterogeneity and accelerates progression in preclinical type 1 diabetes. These results support the evaluation of type 2 diabetes-related mechanisms as targets to improve the prediction and prevention of type 1 diabetes.

BACKGROUND: Nonfunctioning pituitary neuroendocrine tumors (NFPitNETs) account for ∼30-35% of PitNETs; ∼75% arise from the SF1 lineage. Recurrence remains common despite resection (∼30% in 10 years), and routine histopathology/IHC has limited value in predicting recurrence risk. This study evaluated whether DNA methylation profiling improves recurrence risk stratification. MATERIAL AND METHODS: Genome-wide tissue methylation (Illumina EPIC v1, 850K) was analyzed in 117 retrospective NFPitNETs with clinical and imaging follow-up. Unsupervised consensus clustering defined methylation-based subgroups, followed by supervised differential methylation analysis to identify cluster-specific differentially methylated probes (DMPs). A classifier was trained using these signatures, with predicted subgroup memberships correlated with regrowth and progression-free survival (PFS). To ensure reliable estimations, longitudinal mixed-effects models were restricted to the interval of model stability (∼9 years), reflecting cohort follow-up. External validation was performed in three independent cohorts. RESULTS: Five clusters (k1-k5) emerged: four SF1 positive-predominant (k1, k2, k3, k5) and one TPIT/PIT1-enriched NFPitNETs (k4). Among the 562 differentially methylated probes, many mapped to genes regulating cell-cycle and immune pathways. Compared with k1-k2, k3, k4, and k5 possessed significantly higher recurrence risk. Within SF1-lineage tumors, k3 exhibited postoperative tumor-volume expansion beginning at ∼6 years. The methylation-based classifier achieved ∼97% accuracy in assigning clusters and maintained prognostic separation across independent cohorts. CONCLUSIONS: DNA methylation profiling identifies biologically and clinically distinct NFPitNET subgroups, particularly within the SF1 lineage, and may enhance prediction of recurrence risk. Prospective validation and demonstration of clinical utility are warranted to support integration into precision management workflows.

BACKGROUND AND OBJECTIVES: Individuals with type 2 diabetes mellitus (T2DM) are at an increased risk of developing epilepsy, particularly in later life. While preclinical studies suggest neuroprotective properties of glucagon-like peptide-1 receptor agonists (GLP-1 RAs), real-world comparative effectiveness data remain limited. We aimed to evaluate whether GLP-1 RA use is associated with a lower risk of incident epilepsy compared with dipeptidyl peptidase-4 inhibitor (DPP-4i) use in adults with T2DM. METHODS: We conducted a retrospective cohort study using the TriNetX network from 2015 to 2023, including adults aged 18 years or older with T2DM who were new users of either GLP-1 RAs or DPP-4is. Patients with a previous diagnosis of epilepsy or seizure, or those using antiepileptic drugs, were excluded. The primary outcome was incident epilepsy, identified using ICD-10-CM codes. Propensity score matching (1:1) was performed based on demographics, socioeconomic status, body mass index, comorbidities, and baseline medications. Cox proportional hazard models estimated hazard ratios (HRs) with 95% CIs. We also conducted prespecified subgroup and sensitivity analyses to assess the robustness of the findings. RESULTS: After matching, 452,766 patients were included (226,383 in each group; mean age 60.5 years; 47.1% female). During follow-up, 1,670 individuals in the GLP-1 RA group and 1,886 in the DPP-4i group developed epilepsy, corresponding to cumulative incidences of 2.35% vs 2.41%. GLP-1 RA use was associated with a significantly lower risk of epilepsy (HR 0.84, 95% CI 0.78-0.90), with protective associations evident at 1 year (HR 0.71, 95% CI 0.62-0.80), 3 years (HR 0.81, 95% CI 0.74-0.88), and 5 years (HR 0.82, 95% CI 0.76-0.88). Among individual agents, semaglutide showed the strongest association (HR 0.68, 95% CI 0.60-0.77). The results were consistent across major subgroups, including both age and sex. Sensitivity analyses excluding patients with overlapping or switching exposure yielded similar findings (HR 0.71, 95% CI 0.64-0.78). DISCUSSION: GLP-1 RA therapy was associated with a significantly lower epilepsy risk compared with DPP-4i use in adults with T2DM. These results support the hypothesis that GLP-1 RAs may exert neurologic benefits beyond glycemic control. Limitations include the observational design and potential residual confounding. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that the use of GLP-1 RAs in people with T2DM results in a lower risk of developing epilepsy compared with those treated with DPP-4i.