Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach

Abstract The study of metabolomics has improved our knowledge of the biology behind type 2 diabetes and its related metabolic physiology. We aimed to investigate markers of adipose tissue morphology, as well as insulin and glucose metabolism in 53 non-obese male individuals. The participants underwe...

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Autores principales:	Josefin Henninger, Björn Eliasson, Ulf Smith, Aidin Rawshani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2794c4cfd08e401fadee7681757b962b
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spelling	oai:doaj.org-article:2794c4cfd08e401fadee7681757b962b2021-12-02T19:02:39ZIdentification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach10.1038/s41598-021-95688-y2045-2322https://doaj.org/article/2794c4cfd08e401fadee7681757b962b2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95688-yhttps://doaj.org/toc/2045-2322Abstract The study of metabolomics has improved our knowledge of the biology behind type 2 diabetes and its related metabolic physiology. We aimed to investigate markers of adipose tissue morphology, as well as insulin and glucose metabolism in 53 non-obese male individuals. The participants underwent extensive clinical, biochemical and magnetic resonance imaging phenotyping, and we also investigated non-targeted serum metabolites. We used a multi-modal machine learning approach to evaluate which serum metabolomic compounds predicted markers of glucose and insulin metabolism, adipose tissue morphology and distribution. Fasting glucose was associated with metabolites of intracellular insulin action and beta-cell dysfunction, namely cysteine-s-sulphate and n-acetylgarginine, whereas fasting insulin was predicted by myristoleoylcarnitine, propionylcarnitine and other metabolites of beta-oxidation of fatty acids. OGTT-glucose levels at 30 min were predicted by 7-Hoca, a microbiota derived metabolite, as well as eugenol, a fatty acid. Both insulin clamp and HOMA-IR were predicted by metabolites involved in beta-oxidation of fatty acids and biodegradation of triacylglycerol, namely tartrate and 3-phosphoglycerate, as well as pyruvate, xanthine and liver fat. OGTT glucose area under curve (AUC) and OGTT insulin AUC, was associated with bile acid metabolites, subcutaneous adipocyte cell size, liver fat and fatty chain acids and derivates, such as isovalerylcarnitine. Finally, subcutaneous adipocyte size was associated with long chain fatty acids, markers of sphingolipid metabolism, increasing liver fat and dopamine-sulfate 1. Ectopic liver fat was predicted by methylmalonate, adipocyte cell size, glutathione derived metabolites and fatty chain acids. Ectopic heart fat was predicted visceral fat, gamma-glutamyl tyrosine and 2-acetamidophenol sulfate. Adipocyte cell size, age, alpha-tocopherol and blood pressure were associated with visceral fat. We identified several biomarkers associated with adipose tissue pathophysiology and insulin and glucose metabolism using a multi-modal machine learning approach. Our approach demonstrated the relative importance of serum metabolites and they outperformed traditional clinical and biochemical variables for most endpoints.Josefin HenningerBjörn EliassonUlf SmithAidin RawshaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Josefin Henninger Björn Eliasson Ulf Smith Aidin Rawshani Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach
description	Abstract The study of metabolomics has improved our knowledge of the biology behind type 2 diabetes and its related metabolic physiology. We aimed to investigate markers of adipose tissue morphology, as well as insulin and glucose metabolism in 53 non-obese male individuals. The participants underwent extensive clinical, biochemical and magnetic resonance imaging phenotyping, and we also investigated non-targeted serum metabolites. We used a multi-modal machine learning approach to evaluate which serum metabolomic compounds predicted markers of glucose and insulin metabolism, adipose tissue morphology and distribution. Fasting glucose was associated with metabolites of intracellular insulin action and beta-cell dysfunction, namely cysteine-s-sulphate and n-acetylgarginine, whereas fasting insulin was predicted by myristoleoylcarnitine, propionylcarnitine and other metabolites of beta-oxidation of fatty acids. OGTT-glucose levels at 30 min were predicted by 7-Hoca, a microbiota derived metabolite, as well as eugenol, a fatty acid. Both insulin clamp and HOMA-IR were predicted by metabolites involved in beta-oxidation of fatty acids and biodegradation of triacylglycerol, namely tartrate and 3-phosphoglycerate, as well as pyruvate, xanthine and liver fat. OGTT glucose area under curve (AUC) and OGTT insulin AUC, was associated with bile acid metabolites, subcutaneous adipocyte cell size, liver fat and fatty chain acids and derivates, such as isovalerylcarnitine. Finally, subcutaneous adipocyte size was associated with long chain fatty acids, markers of sphingolipid metabolism, increasing liver fat and dopamine-sulfate 1. Ectopic liver fat was predicted by methylmalonate, adipocyte cell size, glutathione derived metabolites and fatty chain acids. Ectopic heart fat was predicted visceral fat, gamma-glutamyl tyrosine and 2-acetamidophenol sulfate. Adipocyte cell size, age, alpha-tocopherol and blood pressure were associated with visceral fat. We identified several biomarkers associated with adipose tissue pathophysiology and insulin and glucose metabolism using a multi-modal machine learning approach. Our approach demonstrated the relative importance of serum metabolites and they outperformed traditional clinical and biochemical variables for most endpoints.
format	article
author	Josefin Henninger Björn Eliasson Ulf Smith Aidin Rawshani
author_facet	Josefin Henninger Björn Eliasson Ulf Smith Aidin Rawshani
author_sort	Josefin Henninger
title	Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach
title_short	Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach
title_full	Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach
title_fullStr	Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach
title_full_unstemmed	Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach
title_sort	identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2794c4cfd08e401fadee7681757b962b
work_keys_str_mv	AT josefinhenninger identificationofmarkersthatdistinguishadiposetissueandglucoseandinsulinmetabolismusingamultimodalmachinelearningapproach AT bjorneliasson identificationofmarkersthatdistinguishadiposetissueandglucoseandinsulinmetabolismusingamultimodalmachinelearningapproach AT ulfsmith identificationofmarkersthatdistinguishadiposetissueandglucoseandinsulinmetabolismusingamultimodalmachinelearningapproach AT aidinrawshani identificationofmarkersthatdistinguishadiposetissueandglucoseandinsulinmetabolismusingamultimodalmachinelearningapproach
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Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach

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