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Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling
PURPOSE: Abnormal lipoprotein and amino acid profiles are associated with insulin resistance and may help to identify this condition. The aim of this study was to create models estimating skeletal muscle and whole-body insulin sensitivity using fasting metabolite profiles and common clinical and lab...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093091/ https://www.ncbi.nlm.nih.gov/pubmed/32232183 http://dx.doi.org/10.1210/jendso/bvaa026 |
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author | Klén, Riku Honka, Miikka-Juhani Hannukainen, Jarna C Huovinen, Ville Bucci, Marco Latva-Rasku, Aino Venäläinen, Mikko S Kalliokoski, Kari K Virtanen, Kirsi A Lautamäki, Riikka Iozzo, Patricia Elo, Laura L Nuutila, Pirjo |
author_facet | Klén, Riku Honka, Miikka-Juhani Hannukainen, Jarna C Huovinen, Ville Bucci, Marco Latva-Rasku, Aino Venäläinen, Mikko S Kalliokoski, Kari K Virtanen, Kirsi A Lautamäki, Riikka Iozzo, Patricia Elo, Laura L Nuutila, Pirjo |
author_sort | Klén, Riku |
collection | PubMed |
description | PURPOSE: Abnormal lipoprotein and amino acid profiles are associated with insulin resistance and may help to identify this condition. The aim of this study was to create models estimating skeletal muscle and whole-body insulin sensitivity using fasting metabolite profiles and common clinical and laboratory measures. MATERIAL AND METHODS: The cross-sectional study population included 259 subjects with normal or impaired fasting glucose or type 2 diabetes in whom skeletal muscle and whole-body insulin sensitivity (M-value) were measured during euglycemic hyperinsulinemic clamp. Muscle glucose uptake (GU) was measured directly using [(18)F]FDG-PET. Serum metabolites were measured using nuclear magnetic resonance (NMR) spectroscopy. We used linear regression to build the models for the muscle GU (Muscle-insulin sensitivity index [ISI]) and M-value (whole-body [WB]-ISI). The models were created and tested using randomly selected training (n = 173) and test groups (n = 86). The models were compared to common fasting indices of insulin sensitivity, homeostatic model assessment—insulin resistance (HOMA-IR) and the revised quantitative insulin sensitivity check index (QUICKI). RESULTS: WB-ISI had higher correlation with actual M-value than HOMA-IR or revised QUICKI (ρ = 0.83 vs −0.67 and 0.66; P < 0.05 for both comparisons), whereas the correlation of Muscle-ISI with the actual skeletal muscle GU was not significantly stronger than HOMA-IR’s or revised QUICKI’s (ρ = 0.67 vs −0.58 and 0.59; both nonsignificant) in the test dataset. CONCLUSION: Muscle-ISI and WB-ISI based on NMR-metabolomics and common laboratory measurements from fasting serum samples and basic anthropometrics are promising rapid and inexpensive tools for determining insulin sensitivity in at-risk individuals. |
format | Online Article Text |
id | pubmed-7093091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-70930912020-03-30 Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling Klén, Riku Honka, Miikka-Juhani Hannukainen, Jarna C Huovinen, Ville Bucci, Marco Latva-Rasku, Aino Venäläinen, Mikko S Kalliokoski, Kari K Virtanen, Kirsi A Lautamäki, Riikka Iozzo, Patricia Elo, Laura L Nuutila, Pirjo J Endocr Soc Clinical Research Article PURPOSE: Abnormal lipoprotein and amino acid profiles are associated with insulin resistance and may help to identify this condition. The aim of this study was to create models estimating skeletal muscle and whole-body insulin sensitivity using fasting metabolite profiles and common clinical and laboratory measures. MATERIAL AND METHODS: The cross-sectional study population included 259 subjects with normal or impaired fasting glucose or type 2 diabetes in whom skeletal muscle and whole-body insulin sensitivity (M-value) were measured during euglycemic hyperinsulinemic clamp. Muscle glucose uptake (GU) was measured directly using [(18)F]FDG-PET. Serum metabolites were measured using nuclear magnetic resonance (NMR) spectroscopy. We used linear regression to build the models for the muscle GU (Muscle-insulin sensitivity index [ISI]) and M-value (whole-body [WB]-ISI). The models were created and tested using randomly selected training (n = 173) and test groups (n = 86). The models were compared to common fasting indices of insulin sensitivity, homeostatic model assessment—insulin resistance (HOMA-IR) and the revised quantitative insulin sensitivity check index (QUICKI). RESULTS: WB-ISI had higher correlation with actual M-value than HOMA-IR or revised QUICKI (ρ = 0.83 vs −0.67 and 0.66; P < 0.05 for both comparisons), whereas the correlation of Muscle-ISI with the actual skeletal muscle GU was not significantly stronger than HOMA-IR’s or revised QUICKI’s (ρ = 0.67 vs −0.58 and 0.59; both nonsignificant) in the test dataset. CONCLUSION: Muscle-ISI and WB-ISI based on NMR-metabolomics and common laboratory measurements from fasting serum samples and basic anthropometrics are promising rapid and inexpensive tools for determining insulin sensitivity in at-risk individuals. Oxford University Press 2020-03-11 /pmc/articles/PMC7093091/ /pubmed/32232183 http://dx.doi.org/10.1210/jendso/bvaa026 Text en © Endocrine Society 2020. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Clinical Research Article Klén, Riku Honka, Miikka-Juhani Hannukainen, Jarna C Huovinen, Ville Bucci, Marco Latva-Rasku, Aino Venäläinen, Mikko S Kalliokoski, Kari K Virtanen, Kirsi A Lautamäki, Riikka Iozzo, Patricia Elo, Laura L Nuutila, Pirjo Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling |
title | Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling |
title_full | Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling |
title_fullStr | Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling |
title_full_unstemmed | Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling |
title_short | Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling |
title_sort | predicting skeletal muscle and whole-body insulin sensitivity using nmr-metabolomic profiling |
topic | Clinical Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093091/ https://www.ncbi.nlm.nih.gov/pubmed/32232183 http://dx.doi.org/10.1210/jendso/bvaa026 |
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