Evidence map›Paper›PMID 28439641›Full record

ArticleDiabetologia2017

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, FinnDiane Study Group

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Diabetologia, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
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  3. Observational
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  6. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Raija LithoviusFolkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, 00014, Helsinki, Finland.
Iiro ToppilaFolkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, 00014, Helsinki, Finland.
Valma HarjutsaloFolkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, 00014, Helsinki, Finland.
Carol ForsblomFolkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, 00014, Helsinki, Finland.
Per-Henrik GroopFolkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, 00014, Helsinki, Finland. per-henrik.groop@helsinki.fi.
Ville-Petteri MäkinenSouth Australian Health and Medical Research Institute, SAHMRI North Terrace, PO Box 11060, Adelaide, SA, 5001, Australia. ville-petteri.makinen@sahmri.com.
FinnDiane Study Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aims/hypothesisPreviously, 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.

methodsFinnish 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.

resultsThe 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/

interpretationData-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.

Indexed as

AdultBiomarkersCardiovascular DiseasesCholesterol, HDLCohort StudiesDiabetes Mellitus, Type 1Diabetic AngiopathiesDiabetic NephropathiesDisease ProgressionFemaleFinlandFollow-Up StudiesHumansMaleMiddle AgedMultivariate AnalysisBiomarkersCholesterol, HDLAll-cause mortalityCardiovascular mortalityData-driven modelDiabetic kidney diseaseIschaemic heart diseaseMetabolic subtypesSelf-organising mapSex difference

Identifiers

PMID28439641

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.