, Volume 60, Issue 7, pp 1234–1243 | Cite as

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

  • Raija Lithovius
  • Iiro Toppila
  • Valma Harjutsalo
  • Carol Forsblom
  • Per-Henrik Groop
  • Ville-Petteri Mäkinen
  • on behalf of the FinnDiane Study Group



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.


All-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


Interquartile range


Standardised mortality ratio


Self-organising map



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.

Data availability

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.

Author contributions

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.

Supplementary material

125_2017_4273_MOESM1_ESM.pdf (742 kb)
ESM (PDF 742 kb)


  1. 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
  2. 2.
    Ray JA, Valentine WJ, Secnik K et al (2005) Review of the cost of diabetes complications in Australia, Canada, France, Germany, Italy and Spain. Curr Med Res Opin 21:1617–1629CrossRefPubMedGoogle Scholar
  3. 3.
    Groop PH, Thomas MC, Moran JL et al (2009) The presence and severity of chronic kidney disease predicts all-cause mortality in type 1 diabetes. Diabetes 58:1651–1658CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Gross JL, de Azevedo MJ, Silveiro SP, Canani LH, Caramori ML, Zelmanovitz T (2005) Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care 28:164–176CrossRefPubMedGoogle Scholar
  5. 5.
    D’agostino RBS, Grundy S, Sullivan LM, Wilson P, CHD Risk Prediction Group (2001) Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 286:180–187CrossRefPubMedGoogle Scholar
  6. 6.
    Zgibor JC, Piatt GA, Ruppert K, Orchard TJ, Roberts MS (2006) Deficiencies of cardiovascular risk prediction models for type 1 diabetes. Diabetes Care 29:1860–1865CrossRefPubMedGoogle Scholar
  7. 7.
    Mäkinen VP, Kangas AJ, Soininen P, Wurtz P, Groop PH, Ala-Korpela M (2013) Metabolic phenotyping of diabetic nephropathy. Clin Pharmacol Ther 94:566–569CrossRefPubMedGoogle Scholar
  8. 8.
    Mäkinen VP, Soininen P, Forsblom C et al (2008) 1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death. Mol Syst Biol 4:167CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Richardson A, Signor BM, Lidbury BA, Badrick T (2016) Clinical chemistry in higher dimensions: machine-learning and enhanced prediction from routine clinical chemistry data. Clin Biochem 49:1213–1220CrossRefPubMedGoogle Scholar
  10. 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
  11. 11.
    Mäkinen VP, Forsblom C, Thorn LM et al (2008) Metabolic phenotypes, vascular complications, and premature deaths in a population of 4,197 patients with type 1 diabetes. Diabetes 57:2480–2487CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Thorn LM, Forsblom C, Fagerudd J et al (2005) Metabolic syndrome in type 1 diabetes: association with diabetic nephropathy and glycemic control (the FinnDiane study). Diabetes Care 28:2019–2024CrossRefPubMedGoogle Scholar
  13. 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/
  14. 14.
    Jørgensen ME, Almdal TP, Carstensen B (2013) Time trends in mortality rates in type 1 diabetes from 2002 to 2011. Diabetologia 56:2401–2404CrossRefPubMedGoogle Scholar
  15. 15.
    Petrie D, Lung TW, Rawshani A et al (2016) Recent trends in life expectancy for people with type 1 diabetes in Sweden. Diabetologia 59:1167–1176CrossRefPubMedGoogle Scholar
  16. 16.
    Harjutsalo V, Forsblom C, Groop PH (2011) Time trends in mortality in patients with type 1 diabetes: nationwide population based cohort study. BMJ 343:d5364CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Gagnum V, Stene LC, Sandvik L et al (2015) All-cause mortality in a nationwide cohort of childhood-onset diabetes in Norway 1973-2013. Diabetologia 58:1779–1786CrossRefPubMedGoogle Scholar
  18. 18.
    Lung TW, Hayes AJ, Herman WH, Si L, Palmer AJ, Clarke PM (2014) A meta-analysis of the relative risk of mortality for type 1 diabetes patients compared to the general population: exploring temporal changes in relative mortality. PLoS One 9:e113635CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Miller RG, Secrest AM, Sharma RK, Songer TJ, Orchard TJ (2012) Improvements in the life expectancy of type 1 diabetes. The Pittsburgh epidemiology of diabetes complications cohort study. Diabetes 61:2987–2992CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Harding JL, Shaw JE, Peeters A, Davidson S, Magliano DJ (2016) Age-specific trends from 2000-2011 in all-cause and cause-specific mortality in type 1 and type 2 diabetes: a cohort study of more than one million people. Diabetes Care 39:1018–1026CrossRefPubMedGoogle Scholar
  21. 21.
    Van der Zee S, Baber U, Elmariah S, Winston J, Fuster V (2009) Cardiovascular risk factors in patients with chronic kidney disease. Nat Rev Cardiol 6:580–589CrossRefPubMedGoogle Scholar
  22. 22.
    Thorn LM, Forsblom C, Waden J et al (2009) Metabolic syndrome as a risk factor for cardiovascular disease, mortality, and progression of diabetic nephropathy in type 1 diabetes. Diabetes Care 32:950–952CrossRefPubMedPubMedCentralGoogle Scholar
  23. 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
  24. 24.
    Kilpatrick ES, Rigby AS, Atkin SL (2007) Insulin resistance, the metabolic syndrome, and complication risk in type 1 diabetes: "double diabetes" in the diabetes control and complications trial. Diabetes Care 30:707–712CrossRefPubMedGoogle Scholar
  25. 25.
    Orchard TJ, Nathan DM, Zinman B et al (2015) Association between seven years of intensive treatment of type 1 diabetes and long term mortality. JAMA 313:45–53CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Mendelsohn ME, Karas RH (2005) Molecular and cellular basis of cardiovascular gender differences. Science 308:1583–1587CrossRefPubMedGoogle Scholar
  27. 27.
    Huxley RR, Peters SA, Mishra GD, Woodward M (2015) Risk of all-cause mortality and vascular events in women versus men with type 1 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol 3:198–206CrossRefPubMedGoogle Scholar
  28. 28.
    Lloyd CE, Kuller LH, Ellis D, Becker DJ, Wing RR, Orchard TJ (1996) Coronary artery disease in IDDM. Gender difference in risk factors but not risk. Aterioscler Thromb Vasc Biol 16:720–726CrossRefGoogle Scholar
  29. 29.
    Brown SA, Jiang B, McElwee-Malloy M, Wakeman C, Breton MD (2015) Fluctuations of hyperglycemia and insulin sensitivity are linked to menstrual cycle phases in women with T1D. J Diabetes Sci Technol 9:1192–1199CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Tolonen N, Forsblom C, Thorn L et al (2009) Lipid abnormalities predict progression of renal disease in patients with type 1 diabetes. Diabetologia 52:2522–2530CrossRefPubMedGoogle Scholar
  31. 31.
    Saraheimo M, Teppo AM, Forsblom C, Fagerudd J, Groop PH (2003) Diabetic nephropathy is associated with low-grade inflammation in type 1 diabetic patients. Diabetologia 6:1402–1407CrossRefGoogle Scholar
  32. 32.
    Holmes MV, Asselbergs FW, Palmer TM et al (2015) Mendelian randomization of blood lipids for coronary heart disease. Eur Heart J 36:539–550CrossRefPubMedGoogle Scholar
  33. 33.
    Voight B, Peloso G, Orho-Melander M et al (2012) Plasma HDL cholesterol and risk of myocardial infarction: a Mendelian randomisation study. Lancet 380:572–580CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Costacou T, Evans RW, Orchard TJ (2011) High density lipoprotein cholesterol in diabetes: is high always better? J Clin Lipidol 5:387–394CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Raija Lithovius
    • 1
    • 2
    • 3
  • Iiro Toppila
    • 1
    • 2
    • 3
  • Valma Harjutsalo
    • 1
    • 2
    • 3
    • 4
  • Carol Forsblom
    • 1
    • 2
    • 3
  • Per-Henrik Groop
    • 1
    • 2
    • 3
    • 5
  • Ville-Petteri Mäkinen
    • 6
    • 7
    • 8
  • on behalf of the FinnDiane Study Group
  1. 1.Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum HelsinkiUniversity of HelsinkiHelsinkiFinland
  2. 2.Abdominal Center NephrologyUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
  3. 3.Research Programs Unit, Diabetes and ObesityUniversity of HelsinkiHelsinkiFinland
  4. 4.National Institute for Health and Welfare, Chronic Disease Prevention UnitHelsinkiFinland
  5. 5.The Baker IDI Heart and Diabetes InstituteMelbourneAustralia
  6. 6.South Australian Health and Medical Research Institute, SAHMRI North TerraceAdelaideAustralia
  7. 7.School of Biological SciencesUniversity of AdelaideAdelaideAustralia
  8. 8.Computational Medicine, Faculty of MedicineUniversity of Oulu and BiocenterOuluFinland

Personalised recommendations