{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T22:24:28Z","timestamp":1783981468109,"version":"3.55.0"},"reference-count":29,"publisher":"American Diabetes Association","issue":"11","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.diabetesjournals.org\/journals\/pages\/license"}],"funder":[{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["75N92019D00027"],"award-info":[{"award-number":["75N92019D00027"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["75N92019D00028"],"award-info":[{"award-number":["75N92019D00028"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["75N92019D00029"],"award-info":[{"award-number":["75N92019D00029"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["75N92019D00030"],"award-info":[{"award-number":["75N92019D00030"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["R01HL109282"],"award-info":[{"award-number":["R01HL109282"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["R01HL109284"],"award-info":[{"award-number":["R01HL109284"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["R01HL109301"],"award-info":[{"award-number":["R01HL109301"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["R01HL109315"],"award-info":[{"award-number":["R01HL109315"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["R01HL109319"],"award-info":[{"award-number":["R01HL109319"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["U01HL41642"],"award-info":[{"award-number":["U01HL41642"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["U01HL41652"],"award-info":[{"award-number":["U01HL41652"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["U01HL41654"],"award-info":[{"award-number":["U01HL41654"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["U01HL65520"],"award-info":[{"award-number":["U01HL65520"]}]},{"name":"the NHLBI, NIH, Department of Health and Human Services","award":["U01HL65521"],"award-info":[{"award-number":["U01HL65521"]}]},{"DOI":"10.13039\/100014653","name":"NIHR Manchester Biomedical Research Centre","doi-asserted-by":"crossref","award":["21754"],"award-info":[{"award-number":["21754"]}],"id":[{"id":"10.13039\/100014653","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100014653","name":"NIHR Manchester Biomedical Research Centre","doi-asserted-by":"crossref","award":["NIHR203308"],"award-info":[{"award-number":["NIHR203308"]}],"id":[{"id":"10.13039\/100014653","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100005239","name":"Indian Health Service","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100005239","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Institute of Health","award":["R01DK117445"],"award-info":[{"award-number":["R01DK117445"]}]},{"name":"National Institute of Health","award":["R01HL163972"],"award-info":[{"award-number":["R01HL163972"]}]},{"name":"the National Institute on Minority Health and Health Disparities, NIH","award":["R01MD012765"],"award-info":[{"award-number":["R01MD012765"]}]}],"content-domain":{"domain":["diabetesjournals.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:sec>\n                    <jats:title\/>\n                    <jats:p>Diabetes has a large medical and public health impact in American Indians. Studies have used genetic data to distinguish type 1 diabetes (T1D) and type 2 diabetes (T2D) and uncover biologic mechanisms underlying T2D clinical heterogeneity. We applied a T1D polygenic score (PS) to 3,084 American Indians (mean age 56 years, 58% women, 39% diabetes). We also calculated partitioned PS for eight clusters of T2D-associated variants and evaluated their association with 20 cardiometabolic traits and five clinical outcomes. The profile of T1D PS for individuals with diabetes was consistent with T2D. A total T2D PS was significantly associated with early age of T2D onset (P = 3.5\u2009\u00d7\u200910\u221211). Partitioned PS for T2D clusters were significantly associated with cardiometabolic traits for the obesity cluster (increased measures of body fat and total triglycerides but lower HDL cholesterol), while the lipodystrophy cluster was associated with increased fasting insulin, waist-to-hip ratio, triglycerides, and blood pressure, and lower body fat percentage and HDL cholesterol. T2D clusters were not associated with cardiovascular and kidney outcomes. Our findings support a relationship of cluster-specific T2D partitioned PS with cardiometabolic traits described in other populations, but there are opportunities for developing improved clustering methods using genetic variation from American Indians.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Article Highlights<\/jats:title>\n                    <jats:p>Diabetes is highly prevalent in American Indians and a main cause of morbidity and mortality, and its clinical heterogeneity can be uncovered using genetic data. We are interested in identifying type 1 versus type 2 diabetes in American Indians with diabetes and identifying biological mechanisms for type 2 diabetes that are related to clinical outcomes using genetic data. Using genetic data, we found a high probability of participants having type 2 diabetes. We identified similar associations of type 2 diabetes genetic clusters, that are related to biological mechanisms, with cardiometabolic traits as previously described in other populations, but no associations of genetic clusters with cardiovascular and kidney outcomes. Our findings support type 2 diabetes as the main cause of diabetes in our American Indian cohort and provide insights into improvements using genetic data to uncover type 2 biological mechanisms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2337\/db25-0322","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T17:47:33Z","timestamp":1755539253000},"page":"2132-2139","update-policy":"https:\/\/doi.org\/10.2337\/ada-journal-policies","source":"Crossref","is-referenced-by-count":1,"title":["Diabetes Genetic Clusters and Clinical Outcomes in American Indians"],"prefix":"10.2337","volume":"74","author":[{"given":"Kaylia M.","family":"Reynolds","sequence":"first","affiliation":[{"name":"Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina 1 , Chapel Hill, NC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of North Carolina 2 , Chapel Hill, NC"},{"name":"Center for Computational and Genomic Medicine, Children\u2019s Hospital of Philadelphia 3 , Philadelphia, PA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"The University of Oklahoma Health Sciences 4 , Oklahoma City, OK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Umans","sequence":"additional","affiliation":[{"name":"MedStar Institute 5 , Washington, DC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shelley A.","family":"Cole","sequence":"additional","affiliation":[{"name":"Texas Biomedical Research Institute 6 , San Antonio, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6805-6014","authenticated-orcid":false,"given":"Andrew P.","family":"Morris","sequence":"additional","affiliation":[{"name":"Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester 7 , Manchester, U.K."},{"name":"NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust 8 , Manchester, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9755-2175","authenticated-orcid":false,"given":"Nora","family":"Franceschini","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina 1 , Chapel Hill, NC"},{"name":"Department of Genetics, School of Medicine, University of North Carolina 9 , Chapel Hill, NC"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1167","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"2026061115074394400_B1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s11892-021-01406-3","article-title":"Epidemiology of type 2 diabetes in Indigenous communities in the United States","volume":"21","author":"Wedekind","year":"2021","journal-title":"Curr Diab Rep"},{"key":"2026061115074394400_B2","unstructured":"National Institute of Diabetes and Digestive and Kidney Diseases. Diabetes Statistics.\n          \n          2024. Accessed 1 December 2024. Available from https:\/\/www.niddk.nih.gov\/health-information\/health-statistics\/diabetes-statistics#factsstatsh"},{"key":"2026061115074394400_B3","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1210\/jc.2015-4309","article-title":"Metabolic risk factors and type 2 diabetes incidence in American Indian children","volume":"101","author":"Wheelock","year":"2016","journal-title":"J Clin Endocrinol Metab"},{"key":"2026061115074394400_B4","doi-asserted-by":"crossref","first-page":"161","DOI":"10.15585\/mmwr.mm6906a3","article-title":"Trends in incidence of type 1 and type 2 diabetes among youths - selected counties and Indian reservations, United States, 2002-2015","volume":"69","author":"Divers","year":"2020","journal-title":"MMWR Morb Mortal Wkly Rep"},{"key":"2026061115074394400_B5","doi-asserted-by":"crossref","first-page":"337","DOI":"10.2337\/dc15-1111","article-title":"A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults","volume":"39","author":"Oram","year":"2016","journal-title":"Diabetes Care"},{"key":"2026061115074394400_B6","doi-asserted-by":"crossref","first-page":"200","DOI":"10.2337\/dc18-1785","article-title":"Development and standardization of an improved type 1 diabetes genetic risk score for use in newborn screening and incident diagnosis","volume":"42","author":"Sharp","year":"2019","journal-title":"Diabetes Care"},{"key":"2026061115074394400_B7","doi-asserted-by":"crossref","first-page":"S20","DOI":"10.2337\/dc24-S002","article-title":"2. Diagnosis and classification of diabetes: Standards of Care in Diabetes\u20142024","volume":"47","author":"American Diabetes Association Professional Practice Committee.","year":"2024","journal-title":"Diabetes Care"},{"key":"2026061115074394400_B8","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1038\/s41591-023-02502-5","article-title":"Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine","volume":"29","author":"Tobias","year":"2023","journal-title":"Nat Med"},{"key":"2026061115074394400_B9","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S2213-8587(18)30051-2","article-title":"Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables","volume":"6","author":"Ahlqvist","year":"2018","journal-title":"Lancet Diabetes Endocrinol"},{"key":"2026061115074394400_B10","doi-asserted-by":"crossref","first-page":"e1002654","DOI":"10.1371\/journal.pmed.1002654","article-title":"Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis","volume":"15","author":"Udler","year":"2018","journal-title":"PLoS Med"},{"key":"2026061115074394400_B11","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.3390\/jcm9072083","article-title":"Factors associated with risk of diabetic complications in novel cluster-based diabetes subgroups: a Japanese retrospective cohort study","volume":"9","author":"Tanabe","year":"2020","journal-title":"J Clin Med"},{"key":"2026061115074394400_B12","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1038\/s41586-024-07019-6","article-title":"Genetic drivers of heterogeneity in type 2 diabetes pathophysiology","volume":"627","author":"Suzuki","year":"2024","journal-title":"Nature"},{"key":"2026061115074394400_B13","first-page":"2023.09.28.23296294","article-title":"Multi-ancestry polygenic mechanisms of type 2 diabetes elucidate disease processes and clinical heterogeneity","author":"Smith","year":"2023","journal-title":"medRxiv"},{"key":"2026061115074394400_B14","doi-asserted-by":"crossref","first-page":"617628","DOI":"10.3389\/fendo.2021.617628","article-title":"Clinical characteristics and risk of diabetic complications in data-driven clusters among type 2 diabetes","volume":"12","author":"Xing","year":"2021","journal-title":"Front Endocrinol (Lausanne)"},{"key":"2026061115074394400_B15","doi-asserted-by":"crossref","first-page":"2194","DOI":"10.1007\/s00125-013-2988-8","article-title":"Epidemiology and genetic determinants of progressive deterioration of glycaemia in American Indians: the Strong Heart Family Study","volume":"56","author":"Franceschini","year":"2013","journal-title":"Diabetologia"},{"key":"2026061115074394400_B16","doi-asserted-by":"crossref","first-page":"9317","DOI":"10.1038\/s41598-022-12866-2","article-title":"Association of protein function-altering variants with cardiometabolic traits: the Strong Heart Study","volume":"12","author":"Shan","year":"2022","journal-title":"Sci Rep"},{"key":"2026061115074394400_B17","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1093\/oxfordjournals.aje.a115757","article-title":"The Strong Heart Study. 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