Clustering of immunological, metabolic and genetic features in latent autoimmune diabetes in adults: evidence from principal component analysis View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-06

AUTHORS

Giovanni Mario Pes, Alessandro Palmerio Delitala, Alessandra Errigo, Giuseppe Delitala, Maria Pina Dore

ABSTRACT

Latent autoimmune diabetes in adults (LADA) which accounts for more than 10 % of all cases of diabetes is characterized by onset after age 30, absence of ketoacidosis, insulin independence for at least 6 months, and presence of circulating islet-cell antibodies. Its marked heterogeneity in clinical features and immunological markers suggests the existence of multiple mechanisms underlying its pathogenesis. The principal component (PC) analysis is a statistical approach used for finding patterns in data of high dimension. In this study the PC analysis was applied to a set of variables from a cohort of Sardinian LADA patients to identify a smaller number of latent patterns. A list of 11 variables including clinical (gender, BMI, lipid profile, systolic and diastolic blood pressure and insulin-free time period), immunological (anti-GAD65, anti-IA-2 and anti-TPO antibody titers) and genetic features (predisposing gene variants previously identified as risk factors for autoimmune diabetes) retrieved from clinical records of 238 LADA patients referred to the Internal Medicine Unit of University of Sassari, Italy, were analyzed by PC analysis. The predictive value of each PC on the further development of insulin dependence was evaluated using Kaplan-Meier curves. Overall 4 clusters were identified by PC analysis. In component PC-1, the dominant variables were: BMI, triglycerides, systolic and diastolic blood pressure and duration of insulin-free time period; in PC-2: genetic variables such as Class II HLA, CTLA-4 as well as anti-GAD65, anti-IA-2 and anti-TPO antibody titers, and the insulin-free time period predominated; in PC-3: gender and triglycerides; and in PC-4: total cholesterol. These components explained 18, 15, 12, and 12 %, respectively, of the total variance in the LADA cohort. The predictive power of insulin dependence of the four components was different. PC-2 (characterized mostly by high antibody titers and presence of predisposing genetic markers) showed a faster beta-cells failure and PC-3 (characterized mostly by gender and high triglycerides) and PC-4 (high cholesterol) showed a slower beta-cells failure. PC-1 (including dislipidemia and other metabolic dysfunctions), showed a mild beta-cells failure. In conclusion variable clustering might be consistent with different pathogenic pathways and/or distinct immune mechanisms in LADA and could potentially help physicians improve the clinical management of these patients. More... »

PAGES

561-567

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11739-015-1352-z

DOI

http://dx.doi.org/10.1007/s11739-015-1352-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049081553

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26612761


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49 schema:description Latent autoimmune diabetes in adults (LADA) which accounts for more than 10 % of all cases of diabetes is characterized by onset after age 30, absence of ketoacidosis, insulin independence for at least 6 months, and presence of circulating islet-cell antibodies. Its marked heterogeneity in clinical features and immunological markers suggests the existence of multiple mechanisms underlying its pathogenesis. The principal component (PC) analysis is a statistical approach used for finding patterns in data of high dimension. In this study the PC analysis was applied to a set of variables from a cohort of Sardinian LADA patients to identify a smaller number of latent patterns. A list of 11 variables including clinical (gender, BMI, lipid profile, systolic and diastolic blood pressure and insulin-free time period), immunological (anti-GAD65, anti-IA-2 and anti-TPO antibody titers) and genetic features (predisposing gene variants previously identified as risk factors for autoimmune diabetes) retrieved from clinical records of 238 LADA patients referred to the Internal Medicine Unit of University of Sassari, Italy, were analyzed by PC analysis. The predictive value of each PC on the further development of insulin dependence was evaluated using Kaplan-Meier curves. Overall 4 clusters were identified by PC analysis. In component PC-1, the dominant variables were: BMI, triglycerides, systolic and diastolic blood pressure and duration of insulin-free time period; in PC-2: genetic variables such as Class II HLA, CTLA-4 as well as anti-GAD65, anti-IA-2 and anti-TPO antibody titers, and the insulin-free time period predominated; in PC-3: gender and triglycerides; and in PC-4: total cholesterol. These components explained 18, 15, 12, and 12 %, respectively, of the total variance in the LADA cohort. The predictive power of insulin dependence of the four components was different. PC-2 (characterized mostly by high antibody titers and presence of predisposing genetic markers) showed a faster beta-cells failure and PC-3 (characterized mostly by gender and high triglycerides) and PC-4 (high cholesterol) showed a slower beta-cells failure. PC-1 (including dislipidemia and other metabolic dysfunctions), showed a mild beta-cells failure. In conclusion variable clustering might be consistent with different pathogenic pathways and/or distinct immune mechanisms in LADA and could potentially help physicians improve the clinical management of these patients.
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