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American Diabetes Association, Diabetes Care, 11(41), p. 2396-2403, 2018

DOI: 10.2337/dc18-1032

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First Genome-Wide Association Study of Latent Autoimmune Diabetes in Adults Reveals Novel Insights Linking Immune and Metabolic Diabetes

Journal article published in 2018 by Diana L. Cousminer, Emma Ahlqvist, Rajashree Mishra, Mette K. Andersen, Alessandra Chesi, Mikael Åkerlund, Mohammad I. Hawa, Kaixin Zhou, Asa Davis, Kenyaita M. Hodge, Jonathan P. Bradfield, Mette Wod, Vanessa C. Guy, Lars G. Fritsche, M. Akerlund and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

OBJECTIVE Latent autoimmune diabetes in adults (LADA) shares clinical features with both type 1 and type 2 diabetes; however, there is ongoing debate regarding the precise definition of LADA. Understanding its genetic basis is one potential strategy to gain insight into appropriate classification of this diabetes subtype. RESEARCH DESIGN AND METHODS We performed the first genome-wide association study of LADA in case subjects of European ancestry versus population control subjects (n = 2,634 vs. 5,947) and compared against both case subjects with type 1 diabetes (n = 2,454 vs. 968) and type 2 diabetes (n = 2,779 vs. 10,396). RESULTS The leading genetic signals were principally shared with type 1 diabetes, although we observed positive genetic correlations genome-wide with both type 1 and type 2 diabetes. Additionally, we observed a novel independent signal at the known type 1 diabetes locus harboring PFKFB3, encoding a regulator of glycolysis and insulin signaling in type 2 diabetes and inflammation and autophagy in autoimmune disease, as well as an attenuation of key type 1–associated HLA haplotype frequencies in LADA, suggesting that these are factors that distinguish childhood-onset type 1 diabetes from adult autoimmune diabetes. CONCLUSIONS Our results support the need for further investigations of the genetic factors that distinguish forms of autoimmune diabetes as well as more precise classification strategies.