Population insulin sensitivity from sparsely sampled oral glucose tolerance tests
Copyright © 2020 Elsevier Inc. All rights reserved..
OBJECTIVE: This work aimed to estimate population-level insulin sensitivity (SI) from 2-hour oral glucose tolerance tests (OGTT) with less than 7 samples.
RESEARCH DESIGN AND METHODS: The current methodology combines the OGTT mathematical model developed by Dalla Man et al., with nonlinear multilevel (NLML) statistical model to estimate population-level insulin sensitivity (SI) from sparsely sampled datasets (3 or 4 samples per subject obtained in 120 min). To validate our novel methodology of population SI estimation, we simulated 50 virtual subjects. We simulated 10 observations per subject over 240 minutes. After estimating their SI using the OGTT model, the virtual subjects were split into two groups, subjects with SI above the average and ones with below average. Subsequently, the simulated data were analyzed using statistical software and employing a t-test. The mean estimates of population SI for the two groups of virtual subjects and their respective 95% CI were compared to the estimates obtained with our novel NLML group SI estimates obtained using the 3 and 4 time points per subject. To further validate the performance of the novel NLML model, a set of 34 prediabetic and 30 diabetic subjects with T2D was used. As outlined above for the in-silico subjects, differences between the prediabetic and T2D subjects in regard to SI was assessed using the classical two-stage approach (individual SI estimation followed by statistical comparison of the two groups). The average estimates obtained with the classical two-stage approach were compared to the group estimated obtained with the NLML approach using 3 (0, 60, and 120 minutes) points per subject obtained in 120 minutes.
RESULTS: Unique and identifiable individual estimates of SI were obtained for all virtual subjects. In comparison to the subjects with above average SI (n=25), the subjects with simulated below average SI (n=25) exhibited significantly lower insulin sensitivity (P<0.001). Our novel NLML population model confirmed these findings (4-point OGTT: P<0.001; 3-point OGTT: P<0.001). In a similar fashion to the one outlined for the virtual subjects, the median insulin sensitivities estimated with the classical two-stage approach were different between the prediabetic (n=34) and T2D subjects (n=32, P=0.004). Using 3 points per subject, our novel NLML model confirmed these findings (P<0.001).
CONCLUSIONS: The population estimates of SI from OGTT data is an effective tool to assess population insulin sensitivity and assess differences that may not be possible when calculating individual SI or when less than 7 samples are available.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:110 |
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| Enthalten in: |
Metabolism: clinical and experimental - 110(2020) vom: 05. Sept., Seite 154298 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Stefanovski, Darko [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 08.10.2020 Date Revised 03.09.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.metabol.2020.154298 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM311482392 |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 Elsevier Inc. All rights reserved. | ||
| 520 | |a OBJECTIVE: This work aimed to estimate population-level insulin sensitivity (SI) from 2-hour oral glucose tolerance tests (OGTT) with less than 7 samples | ||
| 520 | |a RESEARCH DESIGN AND METHODS: The current methodology combines the OGTT mathematical model developed by Dalla Man et al., with nonlinear multilevel (NLML) statistical model to estimate population-level insulin sensitivity (SI) from sparsely sampled datasets (3 or 4 samples per subject obtained in 120 min). To validate our novel methodology of population SI estimation, we simulated 50 virtual subjects. We simulated 10 observations per subject over 240 minutes. After estimating their SI using the OGTT model, the virtual subjects were split into two groups, subjects with SI above the average and ones with below average. Subsequently, the simulated data were analyzed using statistical software and employing a t-test. The mean estimates of population SI for the two groups of virtual subjects and their respective 95% CI were compared to the estimates obtained with our novel NLML group SI estimates obtained using the 3 and 4 time points per subject. To further validate the performance of the novel NLML model, a set of 34 prediabetic and 30 diabetic subjects with T2D was used. As outlined above for the in-silico subjects, differences between the prediabetic and T2D subjects in regard to SI was assessed using the classical two-stage approach (individual SI estimation followed by statistical comparison of the two groups). The average estimates obtained with the classical two-stage approach were compared to the group estimated obtained with the NLML approach using 3 (0, 60, and 120 minutes) points per subject obtained in 120 minutes | ||
| 520 | |a RESULTS: Unique and identifiable individual estimates of SI were obtained for all virtual subjects. In comparison to the subjects with above average SI (n=25), the subjects with simulated below average SI (n=25) exhibited significantly lower insulin sensitivity (P<0.001). Our novel NLML population model confirmed these findings (4-point OGTT: P<0.001; 3-point OGTT: P<0.001). In a similar fashion to the one outlined for the virtual subjects, the median insulin sensitivities estimated with the classical two-stage approach were different between the prediabetic (n=34) and T2D subjects (n=32, P=0.004). Using 3 points per subject, our novel NLML model confirmed these findings (P<0.001) | ||
| 520 | |a CONCLUSIONS: The population estimates of SI from OGTT data is an effective tool to assess population insulin sensitivity and assess differences that may not be possible when calculating individual SI or when less than 7 samples are available | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 700 | 1 | |a Vellanki, Priyathama |e verfasserin |4 aut | |
| 700 | 1 | |a Smiley-Byrd, Dawn D |e verfasserin |4 aut | |
| 700 | 1 | |a Umpierrez, Guillermo E |e verfasserin |4 aut | |
| 700 | 1 | |a Boston, Raymond C |e verfasserin |4 aut | |
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