Evidence map›Paper›PMID 33253307›Full record

ArticlePloS one2020

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes: An IMI-DIRECT study.

Morgan Obura, Joline W J Beulens, Roderick Slieker, Anitra D M Koopman, Trynke Hoekstra, Giel Nijpels, Petra Elders, Robert W Koivula, Azra Kurbasic, Markku Laakso and 29 more

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Review
  5. Article
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

39 authors.

Morgan OburaEpidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, The Netherlands.
Joline W J BeulensEpidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, The Netherlands.ORCID 0000-0002-6788-5232
Roderick SliekerEpidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, The Netherlands.
Anitra D M KoopmanEpidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, The Netherlands.
Trynke HoekstraEpidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, The Netherlands.
Giel NijpelsDepartment of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, The Netherlands.
Petra EldersDepartment of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, The Netherlands.
Robert W KoivulaDepartment of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden.ORCID 0000-0002-1646-4163
Azra KurbasicDepartment of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden.
Markku LaaksoDepartment of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.
Tue H HansenThe Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Martin RidderstråleThe Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Torben HansenThe Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Imre PavoEli Lilly Regional Operations GmbH, Vienna, Austria.ORCID 0000-0002-2016-7049
Ian ForgieDivision of Cardiovascular & Diabetes Medicine, Medical Research Institute, University of Dundee, Dundee, United Kingdom.
Bernd JablonkaSanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany.
Hartmut RuettenSanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany.
Andrea MariInstitute of Biomedical Engineering, National Research Council, Padova, Italy.
Mark I McCarthyOxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Oxford, United Kingdom.
Mark WalkerInstitute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, United Kingdom.
Alison HeggieInstitute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, United Kingdom.
Timothy J McDonaldNIHR Exeter Clinical Research Facility, University of Exeter Medical School and Royal Devon and Exeter NHS Foundation Trust, Exeter, United Kingdom.
Mandy H PerryDepartment of Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, United Kingdom.
Federico De MasiDepartment of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.
Søren BrunakDepartment of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.
Anubha MahajanWellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
Giuseppe N GiordanoDepartment of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden.
Tarja KokkolaDepartment of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.
Emmanouil DermitzakisDepartment of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
Ana ViñuelaDepartment of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.ORCID 0000-0003-3771-8537
Oluf PedersenThe Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Jochen M SchwenkAffinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Solna, Sweden.ORCID 0000-0001-8141-8449
Jurek AdamskiResearch Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany.
Harriet J A TeareHeLEX, Nuffield Department of Population Health, University of Oxford, Headington, Oxford, United Kingdom.
Ewan R PearsonDivision of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
Paul W FranksDepartment of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden.
Leen M 't HartEpidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, The Netherlands.
Femke RuttersEpidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Location VU University Medical Center, Amsterdam, The Netherlands.
IMI-DIRECT Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimSubclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D.

methodsThe study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders.

resultsAt baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose.

conclusionsDifferent glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk.

Indexed as

AgedBlood GlucoseDiabetes Mellitus, Type 2FastingFemaleFollow-Up StudiesGlycated HemoglobinHumansMaleMiddle AgedTriglyceridesBlood GlucoseGlycated Hemoglobinhemoglobin A1c protein, humanTriglycerides

Identifiers

PMID33253307
PMCPMC7703960

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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.