The Lancet Diabetes & Endocrinology最新文献

{"title":"Analogue insulin, GLP-1, and the WHO Model Essential Medicines List - Authors reply.","authors":"David Beran, Amanda Adler, Naomi Levitt, Janeth Tenorio-Mucha, Stephen Colagiuri","doi":"10.1016/S2213-8587(26)00066-5","DOIUrl":"10.1016/S2213-8587(26)00066-5","url":null,"abstract":"","PeriodicalId":48790,"journal":{"name":"The Lancet Diabetes & Endocrinology","volume":" ","pages":"377-378"},"PeriodicalIF":41.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analogue insulin, GLP-1, and the WHO Model Essential Medicines List.","authors":"Jennifer Manne-Goehler, Giulia Segafredo, Jing Luo, Nomathemba Chandiwana, Willem D F Venter","doi":"10.1016/S2213-8587(26)00068-9","DOIUrl":"10.1016/S2213-8587(26)00068-9","url":null,"abstract":"","PeriodicalId":48790,"journal":{"name":"The Lancet Diabetes & Endocrinology","volume":" ","pages":"377"},"PeriodicalIF":41.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for interpreting diabetes data using clinician-curated benchmarks.","authors":"David C Klonoff, Elizabeth Healey, Kenneth D Mandl, David Kerr","doi":"10.1016/S2213-8587(26)00044-6","DOIUrl":"10.1016/S2213-8587(26)00044-6","url":null,"abstract":"","PeriodicalId":48790,"journal":{"name":"The Lancet Diabetes & Endocrinology","volume":" ","pages":"375"},"PeriodicalIF":41.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes-related complications: an evolving spectrum.","authors":"The Lancet Diabetes Endocrinology","doi":"10.1016/S2213-8587(26)00103-8","DOIUrl":"https://doi.org/10.1016/S2213-8587(26)00103-8","url":null,"abstract":"","PeriodicalId":48790,"journal":{"name":"The Lancet Diabetes & Endocrinology","volume":" ","pages":""},"PeriodicalIF":41.8,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What is an optimal serum thyroid-stimulating hormone value in older adults?","authors":"Elizabeth N Pearce","doi":"10.1016/S2213-8587(26)00031-8","DOIUrl":"https://doi.org/10.1016/S2213-8587(26)00031-8","url":null,"abstract":"","PeriodicalId":48790,"journal":{"name":"The Lancet Diabetes & Endocrinology","volume":" ","pages":""},"PeriodicalIF":41.8,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural history of thyroid function in ageing: an individual participant data analysis of 137 488 participants from 31 prospective cohort studies.","authors":"Yanning Xu, Ola Hysaj, Xiaoyi Qi, Martin Feller, Alessandro Pingitore, Suzanne J Brown, Till Ittermann, Massimo Iacoviello, Stella Trompet, Ji Won Han, Börge Schmidt, José A Sgarbi, Sergio Valdés, Axel Muendlein, Johannes Riis, Stig Andersen, Heinz Drexel, Alexander Teumer, Marcus Dörr, Mark P J Vanderpump, Nick Wareham, Bert Vaes, W Edward Visser, J Wouter Jukema, Misa Imaizumi, Robin P F Dullaart, Marco Medici, Howard A Fink, Graziano Ceresini, Luigi Ferrucci, M Arfan Ikram, Graeme J Hankey, Giorgio Iervasi, Richard Eastell, Douglas C Bauer, Graham R Williams, Kristien Boelaert, Bruce M Psaty, Dagmar Führer-Sakel, Stephan J L Bakker, Lambertus A L M Kiemeney, Niels P Riksen, Ki Woong Kim, Fereidoun Azizi, Henry Völzke, Bu B Yeap, Salman Razvi, Jacobijn Gussekloo, John P Walsh, Isabela M Bensenor, Jennifer Mammen, Nicolas Rodondi, Anne R Cappola, Robin P Peeters, Layal Chaker","doi":"10.1016/S2213-8587(26)00009-4","DOIUrl":"https://doi.org/10.1016/S2213-8587(26)00009-4","url":null,"abstract":"<p><strong>Background: </strong>Evidence regarding thyroid function changes with ageing remains inconsistent and the implications of potential changes are unclear. We aimed to investigate ageing-related thyroid function changes and their associations with mortality.</p><p><strong>Methods: </strong>In this individual participant data (IPD) analysis, prospective population-based cohorts were eligible for inclusion when data on thyroid function measurements and mortality were available in individuals aged 18 years and older. Eligible datasets were identified through a systematic search of PubMed. We excluded cohorts of participants with only thyroid disease or thyroid-altering medications, or pregnant individuals. We requested data from all eligible cohorts that agreed to participate in the study. Linear mixed models were used to investigate associations between age and thyroid function, stratified for sex and regional iodine status. Annual changes in thyroid-stimulating hormone (TSH) and free thyroxine (FT<sub>4</sub>) were estimated per individual and categorised into quintiles, with the highest and lowest quintiles defined as increasing and decreasing, respectively, and the rest as stable. Patterns of thyroid function change were identified based on combined TSH and FT<sub>4</sub> evolution. We used cohort-stratified Cox models to assess associations between changing patterns and all-cause mortality. This study is registered with PROSPERO, CRD42023408086.</p><p><strong>Findings: </strong>In this IPD analysis, we analysed data collected between Jan 1, 2011, and Oct 13, 2022, from 31 cohorts across Europe (n=19), the USA (n=5), Asia (n=3), Brazil (n=2), and Australia (n=2; 137 488 participants; 68 322 [49·7%] were female and 69 166 [50·3%] were male; median age 60 years [range 18-106]). Cross-sectionally, older age was associated with higher TSH in iodine-sufficient regions and with lower TSH in iodine-insufficient regions. Longitudinal analyses showed that TSH increased with increasing age regardless of iodine status. The overall increase in TSH from age 18 years to 100 years was 0·61 mIU/L (0·52 SD) for female participants and 0·99 mIU/L (0·76) for male participants from iodine-sufficient regions. Greater variability in population distribution and longitudinal TSH changes was observed in adults aged 65 years or older. Higher FT<sub>4</sub> with older age was suggested cross-sectionally, but longitudinally FT<sub>4</sub> increased in iodine-sufficient regions and decreased in iodine-insufficient regions. Compared with stable thyroid function, all changing patterns were associated with increased all-cause mortality: hazard ratios of 1·80 (95% CI 1·57-2·06) for increasing TSH with stable or decreasing FT<sub>4</sub>; 2·45 (2·01-2·97) for increasing TSH and increasing FT<sub>4</sub>; 2·45 (1·99-3·01) for decreasing TSH with decreasing FT<sub>4</sub>; and 1·94 (1·68-2·24) for decreasing TSH with stable or increasing FT<sub>4</sub>.</p><p><strong>Inter","PeriodicalId":48790,"journal":{"name":"The Lancet Diabetes & Endocrinology","volume":" ","pages":""},"PeriodicalIF":41.8,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries.","authors":"Alicia Huerta-Chagoya, Joohyun Kim, Ravi Mandla, Yingchang Lu, Ken Suzuki, Lauren E Petty, Hong Kiat Ng, Jaewon Choi, Simon Lee, Madhusmita Rout, Kuang Lin, Katherine Taylor, Carlos A Aguilar-Salinas, Lourdes García-García, Clicerio González-Villalpando, Christopher A Haiman, Young Jin Kim, Soo Heon Kwak, Aaron Leong, Ruth J F Loos, Andres Moreno-Estrada, Andrew P Morris, Lorena Orozco, Jerome I Rotter, Dharambir Sanghera, Teresa Tusie-Luna, Benjamin F Voight, Marijana Vujkovic, Robin G Walters, Tian Ge, Alisa K Manning, Marie Loh, Jennifer E Below, Xueling Sim, Josep M Mercader, Maggie C Y Ng","doi":"10.1016/S2213-8587(25)00405-X","DOIUrl":"10.1016/S2213-8587(25)00405-X","url":null,"abstract":"<p><strong>Background: </strong>Polygenic risk scores (PRSs) improve prediction of the development of type 2 diabetes over the use of clinical risk factors alone; however, they perform poorly in populations of non-European ancestry, limiting their global clinical utility. We aimed to deliver comprehensive and rigorously tested multi-ancestry PRSs for prediction in type 2 diabetes.</p><p><strong>Methods: </strong>We conducted meta-analyses using data from type 2 diabetes genome-wide association studies (GWAS) across cohorts from five major global ancestries: European, African or African American, Admixed American, South Asian, and East Asian. We used summary statistics from the GWAS to construct single-ancestry PRSs (using the continuous-shrinkage PRS-CS method) and multi-ancestry PRSs (using the PRS-CSx method), and constructed ancestry-specific linkage disequilibrium panels to model pairwise correlations between single-nucleotide polymorphisms in GWAS during PRS construction. Models were validated for association with type 2 diabetes in at least four independent cohorts per ancestry. The effect sizes of PRSs were estimated as the odds ratio (OR) per SD of the PRS, and ORs for individuals at the 90th, 95th, and 97·5th PRS percentiles were compared with the IQR as a reference. We also tested our PRS models for prediction of diabetes incidence with or without additional clinical factors, as well as microvascular complications and comorbidities.</p><p><strong>Findings: </strong>Our analysis used data from 409 959 individuals with type 2 diabetes and 1 983 345 controls: respectively, 359 819 and 1 825 729 indivduals were included in the GWAS dataset, with 10 992 and 31 792 individuals in the training dataset and 39 148 and 125 824 individuals in the validation dataset. The best predictive performance for the single-ancestry PRSs was in European (incremental AUC 0·07-0·14) and East Asian (0·02-0·16) ancestries, whereas prediction was poorer for African or African American (0·02-0·03), Admixed American (0·02-0·04), and South Asian (0·02-0·04) ancestries, correlating with sample sizes in the GWAS. Compared with single-ancestry PRSs, our multi-ancestry PRSs showed higher effect sizes and smaller 95% CIs across all ancestries: OR per SD 1·73 (95% CI 1·67-1·80) in African or African American, 2·82 (2·67-2·97) in Admixed American, 2·45 (2·36-2·54) in East Asian, 2·36 (2·32-2·41) in European, and 2·23 (2·05-2·42) in South Asian ancestries. Individuals in the 97·5th PRS percentile had a 3-7 times increased risk of type 2 diabetes compared with those in the IQR (OR 3·43 [95% CI 2·80-4·21] in African or African American, 7·47 [5·64-9·89] in Admixed American, 6·62 [5·58-7·85] in East Asian, 6·25 [5·72-6·82] in European, and 4·50 [2·70-7·53] in South Asian ancestries). These PRSs were also associated with earlier onset of type 2 diabetes, higher risk of developing microvascular complications, and provide additional predictive value beyond clinical factors. In ind","PeriodicalId":48790,"journal":{"name":"The Lancet Diabetes & Endocrinology","volume":" ","pages":""},"PeriodicalIF":41.8,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Burosumab treatment for children younger than 12 months with X-linked hypophosphataemia","authors":"Salvatore Minisola, Jessica Pepe, Cristiana Cipriani","doi":"10.1016/s2213-8587(26)00073-2","DOIUrl":"https://doi.org/10.1016/s2213-8587(26)00073-2","url":null,"abstract":"No Abstract","PeriodicalId":48790,"journal":{"name":"The Lancet Diabetes & Endocrinology","volume":"28 1","pages":""},"PeriodicalIF":44.5,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147744076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes in sub-Saharan Africa: aligning biology, culture, and health systems for improved outcomes.","authors":"Louise M Goff, Anxious J Niwaha","doi":"10.1016/S2213-8587(26)00099-9","DOIUrl":"https://doi.org/10.1016/S2213-8587(26)00099-9","url":null,"abstract":"","PeriodicalId":48790,"journal":{"name":"The Lancet Diabetes & Endocrinology","volume":" ","pages":""},"PeriodicalIF":41.8,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Burden and determinants of diabetes in sub-Saharan Africa.","authors":"Charles Agyemang, John Tetteh, Maïmouna Ndour Mbaye, Roberta Lamptey, Samuel Seidu, Kamlesh Khunti, Andre-Pascal Kengne","doi":"10.1016/S2213-8587(26)00065-3","DOIUrl":"https://doi.org/10.1016/S2213-8587(26)00065-3","url":null,"abstract":"<p><p>The prevalence of type 2 diabetes is rising rapidly across sub-Saharan Africa; however, its epidemiology, clinical phenotypes, and underlying mechanisms remain insufficiently characterised. This first paper in a Series on diabetes in sub-Saharan Africa synthesises current evidence on the burden, distribution, and determinants of diabetes, including emerging phenotypes and the roles of early life adversity, psychosocial stress, and interactions with infectious disease. We also identify major gaps in surveillance systems, research capacity, prevention, and clinical management across the region. Sub-Saharan Africa is experiencing one of the fastest global increases in diabetes, with the highest proportion of undiagnosed cases and a projected steep rise in intermediate hyperglycaemia and diabetes by 2050. Urbanisation, ageing, obesity, and lifestyle transitions are major contributors; however, a substantial proportion of type 2 diabetes occurs in lean individuals (BMI <25 kg/m²), particularly in rural settings, suggesting distinct metabolic and developmental pathways not captured by models derived from high-income countries. Bidirectional interactions between diabetes and malaria, tuberculosis, HIV, or COVID-19 make disease trajectories complex. Persistent gaps in surveillance, a reliance on modelled estimates, low genomic representation, and constrained access to modern diabetes medications hinder progress. Strengthening health system capacity, improving data infrastructure, and investing in regionally driven research are essential to develop effective, context-specific interventions and advance precision medicine tailored to sub-Saharan African populations.</p>","PeriodicalId":48790,"journal":{"name":"The Lancet Diabetes & Endocrinology","volume":" ","pages":""},"PeriodicalIF":41.8,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}