Dataset Information

Improved quantification of muscle insulin sensitivity using oral glucose tolerance test data: the MISI Calculator.

ABSTRACT: The Muscle Insulin Sensitivity Index (MISI) has been developed to estimate muscle-specific insulin sensitivity based on oral glucose tolerance test (OGTT) data. To date, the score has been implemented with considerable variation in literature and initial positive evaluations were not reproduced in subsequent studies. In this study, we investigate the computation of MISI on oral OGTT data with differing sampling schedules and aim to standardise and improve its calculation. Seven time point OGTT data for 2631 individuals from the Maastricht Study and seven time point OGTT data combined with a hyperinsulinemic-euglycaemic clamp for 71 individuals from the PRESERVE Study were used to evaluate the performance of MISI. MISI was computed on subsets of OGTT data representing four and five time point sampling schedules to determine minimal requirements for accurate computation of the score. A modified MISI computed on cubic splines of the measured data, resulting in improved identification of glucose peak and nadir, was compared with the original method yielding an increased correlation (ρ = 0.576) with the clamp measurement of peripheral insulin sensitivity as compared to the original method (ρ = 0.513). Finally, a standalone MISI calculator was developed allowing for a standardised method of calculation using both the original and improved methods.

SUBMITTER: O'Donovan SD

PROVIDER: S-EPMC6598992 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improved quantification of muscle insulin sensitivity using oral glucose tolerance test data: the MISI Calculator.

O'Donovan Shauna D SD Lenz Michael M Goossens Gijs H GH van der Kallen Carla J H CJH Eussen Simone J M P SJMP Stehouwer Coen D A CDA van Greevenbroek Marleen M MM Schram Miranda T MT Sep Simone J SJ Peeters Ralf L M RLM Blaak Ellen E EE van Riel Natal A W NAW de Kok Theo M C M TMCM Arts Ilja C W ICW

Scientific reports 20190628 1

The Muscle Insulin Sensitivity Index (MISI) has been developed to estimate muscle-specific insulin sensitivity based on oral glucose tolerance test (OGTT) data. To date, the score has been implemented with considerable variation in literature and initial positive evaluations were not reproduced in subsequent studies. In this study, we investigate the computation of MISI on oral OGTT data with differing sampling schedules and aim to standardise and improve its calculation. Seven time point OGTT d ...[more]

PMID: 31253846

Similar Datasets

Project description:IntroductionImpaired glucose tolerance (IGT) is diagnosed by a standardized oral glucose tolerance test (OGTT). However, the OGTT is laborious, and when not performed, glucose tolerance cannot be determined from fasting samples retrospectively. We tested if glucose tolerance status is reasonably predictable from a combination of demographic, anthropometric, and laboratory data assessed at one time point in a fasting state.MethodsGiven a set of 22 variables selected upon clinical feasibility such as sex, age, height, weight, waist circumference, blood pressure, fasting glucose, HbA1c, hemoglobin, mean corpuscular volume, serum potassium, fasting levels of insulin, C-peptide, triglyceride, non-esterified fatty acids (NEFA), proinsulin, prolactin, cholesterol, low-density lipoprotein, HDL, uric acid, liver transaminases, and ferritin, we used supervised machine learning to estimate glucose tolerance status in 2,337 participants of the TUEF study who were recruited before 2012. We tested the performance of 10 different machine learning classifiers on data from 929 participants in the test set who were recruited after 2012. In addition, reproducibility of IGT was analyzed in 78 participants who had 2 repeated OGTTs within 1 year.ResultsThe most accurate prediction of IGT was reached with the recursive partitioning method (accuracy = 0.78). For all classifiers, mean accuracy was 0.73 ± 0.04. The most important model variable was fasting glucose in all models. Using mean variable importance across all models, fasting glucose was followed by NEFA, triglycerides, HbA1c, and C-peptide. The accuracy of predicting IGT from a previous OGTT was 0.77.ConclusionMachine learning methods yield moderate accuracy in predicting glucose tolerance from a wide set of clinical and laboratory variables. A substitution of OGTT does not currently seem to be feasible. An important constraint could be the limited reproducibility of glucose tolerance status during a subsequent OGTT.

Project description:ContextThe associations of body mass index (BMI) and liver fat (LF) with circulating prandial metabolomic markers are incompletely understood.ObjectiveWe aimed to characterize circulating metabolite excursions during an oral glucose tolerance test (OGTT) and evaluate whether the metabolomic signatures of BMI discordance coassociate with LF content.Design, setting, and participantsWe measured 80 metabolite parameters by nuclear magnetic resonance, together with glucose and insulin, during a 2-hour OGTT in 64 monozygotic (MZ) and 73 dizygotic (DZ) twin pairs (aged 22.8 to 36.2 years). Metabolite excursions during the OGTT were compared within BMI-discordant (intrapair difference, BMI ≥ 3 kg/m2) cotwins separately within MZ and DZ pairs. Insulin-based indices were calculated from the OGTT. LF was measured by magnetic resonance spectroscopy in 25 BMI-discordant MZ pairs. Metabolite profiles were compared with respect to LF discordance (ΔLF% ≥ 2%).ResultsWe replicated many previously reported OGTT-induced metabolite excursions in all 274 individuals and report novel lipoprotein excursions. The associations between some metabolite excursions and BMI differed in MZ and DZ twins. In BMI-discordant MZ pairs (mean ΔBMI = 4.9 kg/m2) who were concordant for LF (Δ0.2%), few metabolites differed between the cotwins: very-low-density lipoprotein (VLDL) cholesterol and apolipoprotein B were elevated, and high-density lipoprotein size and concentration were decreased in the cotwins with higher BMI. In contrast, in BMI-discordant MZ pairs (ΔBMI = 6.1 kg/m2) who were discordant for LF (Δ6.8%), cotwins with higher BMI exhibited lower insulin sensitivity and widespread metabolomic differences: elevations in small VLDL and low-density lipoprotein particles, fatty acids (FAs), and isoleucine. Within all 64 MZ twin pairs, lower insulin sensitivity associated with higher levels of VLDLs, triglycerides, FAs, and isoleucine.ConclusionsBMI-discordant MZ twin pairs who also are discordant for LF have more pronounced within-pair differences in metabolomics profiles during an OGTT than BMI-discordant pairs without LF discordance.

Dataset Information

Improved quantification of muscle insulin sensitivity using oral glucose tolerance test data: the MISI Calculator.

Publications

Improved quantification of muscle insulin sensitivity using oral glucose tolerance test data: the MISI Calculator.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets