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Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time: An IMI-DIRECT study

Lookup NU author(s): Neil Walker

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

© 2020 Diabetes UK. Aim: To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration. Methods: We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months. Results: At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1–4. Participants in Subgroups 2–4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (β = 0.36, 95% CI 0.13–0.58), Subgroup 3 (β = 0.30; 95% CI 0.10–0.50) and Subgroup 2 (β = 0.18; 95% CI 0.04–0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months. Conclusions: Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.


Publication metadata

Author(s): Obura M, Beulens JWJ, Slieker R, Koopman ADM, Hoekstra T, Nijpels G, Elders P, Dekker JM, Koivula RW, Kurbasic A, Laakso M, Hansen TH, Ridderstrale M, Hansen T, Pavo I, Forgie I, Jablonka B, Ruetten H, Mari A, McCarthy MI, Walker M, McDonald TJ, Perry MH, Pearson ER, Franks PW, 't Hart LM, Rutters F, IMI-DIRECT Consortium

Publication type: Article

Publication status: Published

Journal: Diabetic Medicine

Year: 2021

Volume: 38

Issue: 2

Print publication date: 01/02/2021

Online publication date: 17/10/2020

Acceptance date: 14/10/2020

Date deposited: 04/02/2021

ISSN (print): 0742-3071

ISSN (electronic): 1464-5491

Publisher: John Wiley & Sons Ltd

URL: https://doi.org/10.1111/dme.14428

DOI: 10.1111/dme.14428

PubMed id: 33067862


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Funding

Funder referenceFunder name
Innovative Medicines Initiative. Grant Number: 115317

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