Data-driven metabolic subtypes predict future adverse events in individuals with type 1 diabetes
- 457 Downloads
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.
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.
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.
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.
KeywordsAll-cause mortality Cardiovascular mortality Data-driven model Diabetic kidney disease Ischaemic heart disease Metabolic subtypes Self-organising map Sex difference
End-stage renal disease
Finnish Diabetic Nephropathy Study
Standardised mortality ratio
The authors acknowledge the contributions by physicians and nurses at each of the FinnDiane study centres (see ESM). Some of the data were presented as an abstract at the 52nd EASD Annual Meeting, Munich 2016, at the ADS-ADEA (Australian Diabetes Society and Australian Diabetes Educators Association) Annual Scientific Meeting, Cold Coast, 2016, at the 76th ADA Scientific Sessions, New Orleans, 2016 and at the 29th EDNSG (European Diabetic Nephropathy Study Group) Annual Meeting, Pisa, 2016.
No data are available. The ethical statement and the informed consent do not allow for free data availability.
This research was supported by grants from the Folkhälsan Research Foundation, the Wilhelm and Else Stockmann Foundation, the Päivikki and Sakari Sohlberg Foundation, the Novo Nordisk Foundation (NNF14SA0003) and the Academy of Finland (134379).
Duality of interest
P-HG has received lecture honorary honoraria from Boehringer Ingelheim, Genzyme, Novartis, Novo Nordisk, MSD, Eli Lilly and Medscape. P-HG is an advisory board member for Boehringer Ingelheim, Eli Lilly, Novartis, Abbott and AbbVie. P-HG has received investigator-initiated study grants from Eli Lilly and Roche. The funding sources were not involved in the design or conduct of the study. The authors declare that there is no duality of interest associated with this manuscript.
RL, IT and V-PM designed and carried out the data analyses, interpreted the results and drafted the manuscript. CF, VH and P-HG contributed to the acquisition of data and revised the manuscript critically for important intellectual content. V-PM and P-HG are the guarantors of this work and, as such, had full access to all the data in the study and take full responsibility for the integrity of the data and the accuracy of the data analysis. All authors gave their final approval of this version of the manuscript.
- 1.Jacobson AM, Braffett BH, Cleary PA, Gubitosi-Klug RA, Larkin ME, DCCT/EDIC Research Group (2013) The long-term effects of type 1 diabetes treatment and complications on health-related quality of life: a 23-year follow-up of the diabetes control and complications/epidemiology of diabetes interventions and complications cohort. Diabetes Care 36:3131–3138CrossRefPubMedPubMedCentralGoogle Scholar
- 10.Alyass A, Turcotte M, Meyre D (2015) From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genet 8:33Google Scholar
- 13.R Development Core Team (2011) R: a language and environment for statistical computing. Vienna, Austria: the R foundation for statistical computing. ISBN 3-900051-07-0. Available online at http://www.R-project.org/
- 23.Pambianco G, Costacou T, Orchard TJ (2007) The prediction of major outcomes of type 1 diabetes: a 12-year prospective evaluation of three separate definitions of the metabolic syndrome and their components and estimated glucose disposal rate: the Pittsburgh epidemiology of diabetes complications study experience. Diabetes Care 30:1248–1254CrossRefPubMedGoogle Scholar