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

Abstract

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.

Post-Load Insulin Secretion Patterns are Associated with Glycemic Status and Diabetic Complications in Patients with Type 2 Diabetes Mellitus

“…However, we found that the classification based on the duration of diabetes alone cannot demonstrate a significant difference in either glycemic variability or prevalence of complications, although different β-cell function was observed. In previous studies, post-load glycemic patterns predicted the risk of type 2 diabetes 28 and adverse cardiovascular events 29 in non-diabetic patients, and some biochemical parameters differed significantly among non-diabetic patients 20 and newly diagnosed diabetic patients 30 . However, in this study, although the post-load glucose pattern was able to partially differentiate the glycemic status of patients, it was unable to detect differences in the prevalence of DR and atherosclerosis.…”

Profiles of patients’ self-reported health after acute stroke

“…To this end, a systematic assessment of patient reported outcomes measures (PROMs) has been shown to deliver information beyond routine examinations by physicians and to identify disabling deficits in every-day life after stroke [17,18]. A possible strategy towards understanding the development of different outcomes and towards risk stratification is classifying patients by the results of their outcomes and analyzing class characteristics, as shown for diabetes [24] and heart transplantation [20]. In mild to moderately affected stroke patients from an outpatient setting, a retrospective analysis showed promising results building distinct profiles or classes using the Patient-reported Outcomes Measurement Information System (PROMIS) [16].…”