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EndocrinologyDec 18, 20256 min read

Endocrinology trends (Dec 7–14, 2025)

Words by Yuriy Poteshkin, MD, phD

  • Recent PubMed endocrinology papers highlight next-generation metabolic therapy (once-weekly basal insulin; dual GCGR/GLP-1R agonism), growing outcome comparisons of GLP-1RAs vs bariatric surgery in obesity/HFpEF, and intensified safety surveillance.

  • In parallel, interpretable ML is used for prescribing patterns and thyroid/NTIS risk prediction, alongside renewed focus on 1-h glucose metrics and sex-steroid trajectories

Latest endocrinology trend topics (PubMed; posted Dec 7–14, 2025)

  • Diabetes/obesity drugs are evolving from “daily, single-target” to “weekly, multi-target” (and being judged on real outcomes, not just glucose/weight). (1-4)
    What this means in normal language: companies are trying to make treatments easier to take (once weekly) and more powerful by hitting more than one hormone pathway at a time.

    • Once-weekly basal insulin: studies are testing whether weekly basal insulin can match daily basal insulin for glucose control and hypoglycemia risk (how often sugars go too low). (1)

    • Dual-agonist biology (GCGR/GLP-1R): mechanistic work is trying to explain why some dual agonists produce greater weight/metabolic effects and which organ targets matter (e.g., liver glucagon receptor signaling). (2)

    • Comparative effectiveness: GLP-1 receptor agonists are being compared against bariatric surgery for outcomes in obesity + HFpEF (heart failure with preserved EF), which is a big “real-world outcomes” question. (3)

    • Real-world prescribing: machine learning analyses show how clinicians actually choose GLP-1RA/SGLT2i in practice (sometimes influenced more by BMI/age than by cardiorenal risk). (4)

  • GLP-1RA safety and “benefit–risk” surveillance is heating up (especially around long-term risks). (3,5)
    These drugs are expanding fast, so papers that look for rare harms get a lot of attention.

    • On the benefit side, there’s focus on clinical outcomes in high-risk groups (e.g., obesity + HFpEF). (3)

    • On the risk side, there are observational studies asking whether GLP-1RA exposure is linked with specific cancers (example: esophageal cancer signal work). This doesn’t automatically prove cause, but it triggers closer monitoring and follow-up studies. (5)

  • AI/ML is becoming “clinic-adjacent” in endocrinology: prediction, risk stratification, and treatment allocation. (4,6-8)
    The trend isn’t “AI replacing clinicians.” It’s more like: AI is being used to help handle complex risk patterns and large datasets.

    • Treatment allocation & patterns: ML is used to analyze who receives which drugs and what factors drive those choices. (4)

    • Thyroid cancer decision support: interpretable ML aims to predict things like occult lymph node metastasis to guide surgery planning. (8)

    • Critical illness thyroid dysfunction: ML models try to predict non-thyroidal illness syndrome (NTIS) and outcomes in sepsis. (7)

    • Reproductive endocrinology: reviews summarize where AI is already being applied across ART workflows and where validation gaps remain. (6)

  • Earlier and more precise metabolic-risk detection (beyond fasting glucose and HbA1c). (9-11)
    The “big idea” is: some people have significant metabolic risk that standard tests miss.

    • 1-hour plasma glucose is being argued as a stronger diagnostic/stratification tool than fasting glucose, 2-hour glucose, or HbA1c in some contexts—this could reshape screening strategies if adopted. (9)

    • Mechanistic small-RNA work (miRNA): animal studies exploring specific miRNAs that influence glucose metabolism (basic science today → potential targets tomorrow). (10)

    • Endocrine biomarkers (FGF21): studies link biomarker changes with behavior change (like smoking cessation), which matters because clinicians increasingly want biomarkers that reflect metabolic stress and recovery. (11)

  • Sex-hormone endocrinology: renewed attention to long-term trajectories and what big cohorts really taught us. (12,13)
    This is partly about improving risk counseling for aging-related metabolic and cardiovascular outcomes.

    • Large cohorts track how sex hormones shift with aging in men, which influences interpretation of “low T” and associated risks. (12)

    • Updated syntheses revisit Women’s Health Initiative (WHI) lessons—timing, outcomes interpretation, and what “then vs now” means for contemporary care. (13)

  • Other “watchlist” signals that popped this week (narrower topics but clinically relevant): (14-16)

    • PPGL perioperative management: questions around presurgical beta-blockade and surgical outcomes. (14)

    • Gut somatostatin receptors in T2D: mapping somatostatin/receptor distribution may connect GI physiology with glucose regulation and therapy concepts. (15)

    • Bone health + social determinants: socioeconomic gradients showing up in trabecular bone health measures. (16)

References (AMA)

  1. Cheng AYY, Anil G, Canani L, Vinther S, Wang N, Mathieu C. Efficacy and hypoglycaemia outcomes of once-weekly insulin icodec versus once-daily basal insulin comparators across baseline HbA1c, BMI and duration of type 2 diabetes subgroups: a post hoc analysis of ONWARDS 1-5. Diabetes Obes Metab. Published online December 12, 2025. doi:10.1111/dom.70358. PMID: 41387317. PubMed: https://pubmed.ncbi.nlm.nih.gov/41387317/

  2. Long F, Challa TD, Efthymiou V, et al. Hepatic GCGR is required for the superior weight loss and metabolic effects of a structurally related analogue of the dual GCGR/GLP-1R agonist survodutide in mice. Diabetes Obes Metab. Published online December 12, 2025. doi:10.1111/dom.70359. PMID: 41388343. PubMed: https://pubmed.ncbi.nlm.nih.gov/41388343/

  3. Ibrahim R, Han W, Wang W, et al. Glucagon-like peptide-1 receptor agonists versus bariatric surgery in patients with obesity and heart failure with preserved ejection fraction. J Am Heart Assoc. Published online December 11, 2025. doi:10.1161/JAHA.125.044577. PMID: 41378487. PubMed: https://pubmed.ncbi.nlm.nih.gov/41378487/

  4. Tuccinardi D, Zilich R, Ponzani P, et al; AMD Annals study group. Real-world prescriptions of GLP-1RAs and SGLT2is in type 2 diabetes prioritise BMI and age over cardiorenal risk: a machine learning-based large cohort analysis. Cardiovasc Diabetol Endocrinol Rep. 2025;11(1):38. doi:10.1186/s40842-025-00251-7. PMID: 41366491. PubMed: https://pubmed.ncbi.nlm.nih.gov/41366491/

  5. Hardvik Åkerström J, Santoni G, von Euler-Chelpin M, et al. Glucagon-like peptide-1 receptor agonist treatment and risk of esophageal cancer. Am J Gastroenterol. Published online December 12, 2025. doi:10.14309/ajg.0000000000003885. PMID: 41384845. PubMed: https://pubmed.ncbi.nlm.nih.gov/41384845/

  6. Khorshid A, Jiang VS, Pavlovic ZJ, Hariton E. Current applications of artificial intelligence in assisted reproductive technologies. Minerva Obstet Gynecol. Published online December 11, 2025. doi:10.23736/S2724-606X.25.05754-9. PMID: 41378913. PubMed: https://pubmed.ncbi.nlm.nih.gov/41378913/

  7. Li Y, Li Y, He J, et al. Machine learning predicts non-thyroidal illness syndrome (NTIS) occurrence and mortality in sepsis patients. Eur J Med Res. Published online December 10, 2025. doi:10.1186/s40001-025-03629-6. PMID: 41373009. PubMed: https://pubmed.ncbi.nlm.nih.gov/41373009/

  8. Wang Y, Ram J, Bianchi C, et al. An interpretable machine learning model for predicting occult central lymph node metastasis in papillary thyroid cancer. J Clin Endocrinol Metab. Published online December 11, 2025. doi:10.1210/clinem/dgaf636. PMID: 41378767. PubMed: https://pubmed.ncbi.nlm.nih.gov/41378767/

  9. Wang Y, Ram J, Bianchi C, et al. Superiority of 1 h plasma glucose vs fasting plasma glucose, 2 h plasma glucose and HbA1c for the diagnosis of type 2 diabetes. Diabetologia. Published online December 12, 2025. doi:10.1007/s00125-025-06632-y. PMID: 41388091. PubMed: https://pubmed.ncbi.nlm.nih.gov/41388091/

  10. Sugawara L, Morino K, Iwasaki H, et al. miR-494 deletion improves glucose metabolism independently of obesity in mice. Diabetes. Published online December 12, 2025. doi:10.2337/db25-0355. PMID: 41384877. PubMed: https://pubmed.ncbi.nlm.nih.gov/41384877/

  11. Sailer CO, Vogel CF, Monnerat S, et al. Changes in fibroblast growth factor 21 levels associated with alcohol consumption and smoking cessation. Endocr Connect. Published online December 12, 2025. doi:10.1530/EC-25-0713. PMID: 41384608. PubMed: https://pubmed.ncbi.nlm.nih.gov/41384608/

  12. Hills ST, Mansfield TA, Bhasin S, Orwoll ES. Longitudinal changes in sex hormones in a population of older community dwelling men. J Clin Endocrinol Metab. Published online December 11, 2025. doi:10.1210/clinem/dgaf652. PMID: 41379806. PubMed: https://pubmed.ncbi.nlm.nih.gov/41379806/

  13. Lambrinoudaki I, Armeni E, Milli N, Anagnostis P. Then and now: what we have learned from the WHI. J Clin Endocrinol Metab. Published online December 11, 2025. doi:10.1210/clinem/dgaf638. PMID: 41379766. PubMed: https://pubmed.ncbi.nlm.nih.gov/41379766/

  14. Araujo-Castro M, Donato S, García Sanz I, et al. Impact of presurgical beta-blockade on surgical outcomes in patients with PPGLs. Endocr Relat Cancer. 2025;32(12):e250308. doi:10.1530/ERC-25-0308. PMID: 41384864. PubMed: https://pubmed.ncbi.nlm.nih.gov/41384864/

  15. Nielsen SW, Gilliam-Vigh H, Jorsal T, et al. Distribution of somatostatin and its receptors in the intestinal tract in healthy and in patients with type 2 diabetes. J Clin Endocrinol Metab. Published online December 11, 2025. doi:10.1210/clinem/dgaf648. PMID: 41379811. PubMed: https://pubmed.ncbi.nlm.nih.gov/41379811/

  16. Zhang Y, Zhao T, Li Y, Chen K, Sun Q. Unveiling the wealth-bone connection: how socioeconomic status influences trabecular bone health. PLoS One. 2025;20(12):e0338378. doi:10.1371/journal.pone.0338378. PMID: 41385530. PubMed: https://pubmed.ncbi.nlm.nih.gov/41385530/