Glucose Measurements at Various Time Points During the OGTT and Their Role in Capturing Glucose Response Patterns

Hulmán, Ádám; Wagner, Róbert; Vistisen, Dorte; Færch, Kristine; Balkau, Beverley; Manco, Melania; Golay, Alain; Häring, Hans‐Ulrich; Heni, Martin; Fritsche, Andreas; Witte, Daniel R.

doi:10.2337/dc18-2397

Cited by 10 publications

(7 citation statements)

References 5 publications

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“…Moreover, the fifth group probably includes those who F I G U R E 1 Four glucose curve subgroups following an oral glucose tolerance test depicting estimated mean trajectories with 95% CIs identified by the latent class trajectory analysis progressed to diabetes and we excluded those with diabetes at baseline, possibly explaining why we had four subgroups at baseline instead of five. Another study, 26 like the present study, showed that heterogeneous glucose response curve patterns can be estimated with fewer time points during the OGTT.…”

Section: Subgroupssupporting

confidence: 60%

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

et al. 2020

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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.

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Section: Subgroupssupporting

confidence: 60%

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

et al. 2020

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“…Further, although this modeling is most robust when utilizing five glucose time-points, reliable results can still be achieved with only three glucose time-points [171]. The integrated glucose response classifier model is available online for public use at https://steno.shinyapps.io/grc2h/.…”

Section: Modeling Of the Glucose Curvementioning

confidence: 99%

Review of methods for detecting glycemic disorders

Bergman

Abdul‐Ghani

Manco

et al. 2020

Diabetes Research and Clinical Practice

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116

101

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Highlights A 1-hour plasma glucose (1-h PG) threshold >155 mg/dl (8.6 mmol/L) during an oral glucose tolerance test (OGTT) may be a suitable biomarker for identifying normal glucose tolerant (NGT) individuals at risk for future type 2 diabetes (T2D). A one-hour, non-fasting, 50g Glucose Challenge Test (GCT) performed during a routine health care visit has potential for practical screening of glucose disorders. The shape of the glucose curve reflects the cumulative effect of insulin sensitivity and response on glucose concentrations with prospective studies warranted to evaluate its prognostic utility. The continuous glucose monitor (CGM) has facilitated insight into the pathophysiology of prediabetes and phenotypes of T2D and holds promise for detecting glycemic disorders. Metabolomic profiling including amino acids, lipids, carbohydrates and other metabolites may be useful for early diagnosis of glycemic disorders. Non-classical markers for assessing glycemic disorders including fructosamine, glycated albumin, and 1,5-anhydroglucitol that evaluate shorter periods of glucose exposure than HbA1c have potential use as adjunctive tools.

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“…The area under the curve (AUC) is derived from the OGTT data to calculate the total rise in blood glucose during the OGTT using the trapezoidal rule[ 24 ]. It has been applied in scientific reports to show the variations in increased blood glucose during the OGTT.…”

Section: Ogtt Through Quantitative Analysismentioning

confidence: 99%

“…Therefore, the incremental AUC (iAUC) was developed to minimize this difference[ 25 ]. However, the iAUC obtained by subtracting the baseline value of fasting plasma glucose has been challenged as being problematic[ 24 ]. Then, the positive incremental AUC (pAUC) was further suggested, and only the values above the baseline value were considered; those below the baseline were ignored in studies[ 25 ].…”

Section: Ogtt Through Quantitative Analysismentioning

confidence: 99%

Oral glucose tolerance test in diabetes, the old method revisited

Kuo¹,

Cheng²,

Li³

2021

WJD

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The oral glucose tolerance test (OGTT) has been widely used both in clinics and in basic research for a long time. It is applied to diagnose impaired glucose tolerance and/or type 2 diabetes mellitus in individuals. Additionally, it has been employed in research to investigate glucose utilization and insulin sensitivity in animals. The main aim of each was quite different, and the details are also somewhat varied. However, the time or duration of the OGTT was the same, using the 2-h post-glucose load glycemia in both, following the suggestions of the American Diabetes Association. Recently, the use of 30-min or 1-h post-glucose load glycemia in clinical practice has been recommended by several studies. In this review article, we describe this new view and suggest perspectives for the OGTT. Additionally, quantification of the glucose curve in basic research is also discussed. Unlike in clinical practice, the incremental area under the curve is not suitable for use in the studies involving animals receiving repeated treatments or chronic treatment. We discuss the potential mechanisms in detail. Moreover, variations between bench and bedside in the application of the OGTT are introduced. Finally, the newly identified method for the OGTT must achieve a recommendation from the American Diabetes Association or another official unit soon. In conclusion, we summarize the recent reports regarding the OGTT and add some of our own perspectives, including machine learning and others.

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Glucose Measurements at Various Time Points During the OGTT and Their Role in Capturing Glucose Response Patterns

Cited by 10 publications

References 5 publications

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

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

Review of methods for detecting glycemic disorders

Oral glucose tolerance test in diabetes, the old method revisited

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