Data-driven metabolic subtypes predict future adverse events in individuals with type 1 diabetes

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Lithovius , R , Toppila , I , Harjutsalo , V , Forsblom , C , Groop , P-H , Makinen , V-P & FinnDiane Study Grp 2017 , ' Data-driven metabolic subtypes predict future adverse events in individuals with type 1 diabetes ' , Diabetologia , vol. 60 , no. 7 , pp. 1234-1243 . https://doi.org/10.1007/s00125-017-4273-8

Title: Data-driven metabolic subtypes predict future adverse events in individuals with type 1 diabetes
Author: Lithovius, Raija; Toppila, Iiro; Harjutsalo, Valma; Forsblom, Carol; Groop, Per-Henrik; Makinen, Ville-Petteri; FinnDiane Study Grp
Contributor: University of Helsinki, Department of Medicine
University of Helsinki, Diabetes and Obesity Research Program
University of Helsinki, Clinicum
University of Helsinki, Clinicum
University of Helsinki, Clinicum
Date: 2017-07
Language: eng
Number of pages: 10
Belongs to series: Diabetologia
ISSN: 0012-186X
URI: http://hdl.handle.net/10138/237001
Abstract: Aims/hypothesis Previously, we proposed that data-driven metabolic subtypes predict mortality in type 1 diabetes. Here, we analysed new clinical endpoints and revisited the subtypes after 7 years of additional follow-up. Methods Finnish individuals with type 1 diabetes (2059 men and 1924 women, insulin treatment before 35 years of age) were recruited by the national multicentre FinnDiane Study Group. The participants were assigned one of six metabolic subtypes according to a previously published self-organising map from 2008. Subtype-specific all-cause and cardiovascular mortality rates in the FinnDiane cohort were compared with registry data from the entire Finnish population. The rates of incident diabetic kidney disease and cardiovascular endpoints were estimated based on hospital records. Results The advanced kidney disease subtype was associated with the highest incidence of kidney disease progression (67.5% per decade, p <0.001), ischaemic heart disease (26.4% per decade, p <0.001) and all-cause mortality (41.5% per decade, p <0.001). Across all subtypes, mortality rates were lower in women compared with men, but standardised mortality ratios (SMRs) were higher in women. SMRs were indistinguishable between the original study period (19942007) and the new period (2008-2014). The metabolic syndrome subtype predicted cardiovascular deaths (SMR 11.0 for men, SMR 23.4 for women, p <0.001), and women with the high HDL-cholesterol subtype were also at high cardiovascular risk (SMR 16.3, p <0.001). Men with the low-cholesterol or good glycaemic control subtype showed no excess mortality. Conclusions/interpretation Data-driven multivariable metabolic subtypes predicted the divergence of complication burden across multiple clinical endpoints simultaneously. In particular, men with the metabolic syndrome and women with high HDL-cholesterol should be recognised as important subgroups in interventional studies and public health guidelines on type 1 diabetes.
Subject: All-cause mortality
Cardiovascular mortality
Data-driven model
Diabetic kidney disease
Ischaemic heart disease
Metabolic subtypes
Self-organising map
Sex difference
ALL-CAUSE MORTALITY
CHRONIC KIDNEY-DISEASE
PITTSBURGH EPIDEMIOLOGY
GENDER-DIFFERENCES
LIFE EXPECTANCY
RISK-FACTORS
COMPLICATIONS
COHORT
NEPHROPATHY
TRENDS
3121 General medicine, internal medicine and other clinical medicine
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