Raija Lithovius1,2,3, Iiro Toppila1,2,3, Valma Harjutsalo1,2,3,4, Carol Forsblom1,2,3, Per-Henrik Groop5,6,7,8, Ville-Petteri Mäkinen9,10,11. 1. Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, 00014, Helsinki, Finland. 2. Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. 3. Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland. 4. National Institute for Health and Welfare, Chronic Disease Prevention Unit, Helsinki, Finland. 5. Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, 00014, Helsinki, Finland. per-henrik.groop@helsinki.fi. 6. Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. per-henrik.groop@helsinki.fi. 7. Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland. per-henrik.groop@helsinki.fi. 8. The Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia. per-henrik.groop@helsinki.fi. 9. South Australian Health and Medical Research Institute, SAHMRI North Terrace, PO Box 11060, Adelaide, SA, 5001, Australia. ville-petteri.makinen@sahmri.com. 10. School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia. ville-petteri.makinen@sahmri.com. 11. Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter, Oulu, Finland. ville-petteri.makinen@sahmri.com.
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 (1994-2007) 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.
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 (1994-2007) 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.
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