Metabolomics of Lean/Overweight Insulin-Resistant Females Reveals Alterations in Steroids and Fatty Acids
Author(s) -
Ilhame Diboun,
Layla Al-Mansoori,
Hend Al-Jaber,
Omar Albagha,
Mohamed A. Elrayess
Publication year - 2020
Publication title -
the journal of clinical endocrinology and metabolism
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.206
H-Index - 353
eISSN - 1945-7197
pISSN - 0021-972X
DOI - 10.1210/clinem/dgaa732
Subject(s) - overweight , insulin resistance , metabolomics , metabolic syndrome , endocrinology , medicine , diabetes mellitus , obesity , receiver operating characteristic , physiology , biology , chemistry , bioinformatics
Background The global diabetes epidemic is largely attributed to obesity-triggered metabolic syndrome. However, the impact of insulin resistance (IR) prior to obesity on the high prevalence of diabetes and the molecular mediators remain largely unknown. This study aims to compare the metabolic profiling of apparently healthy lean/overweight participants with IR and insulin sensitivity (IS), and identify the metabolic pathways underlying IR. Methods In this cross-sectional study, clinical and metabolic data for 200 seemingly healthy young female participants (100 IR and 100 IS) was collected from Qatar Biobank. Orthogonal partial least square analysis was performed to assess the extent of separation between individuals from the 2 groups based on measured metabolites. Classical linear models were used to identify the metabolic signature of IR, followed by elastic-net-regularized generalized linear model (GLMNET) and receiver operating characteristic (ROC) analysis to determine top metabolites associated with IR. Results Compared to lean/overweight participants with IS, those with IR showed increased androgenic steroids, including androsterone glucuronide, in addition to various microbiota byproducts, such as the phenylalanine derivative carboxyethylphenylalanine. On the other hand, participants with IS had elevated levels of long-chain fatty acids. A ROC analysis suggested better discriminatory performance using 20 metabolites selected by GLMNET in comparison to the classical clinical traits (area under curve: 0.93 vs 0.73, respectively). Conclusion Our data confirm the multifactorial mechanism of IR with a diverse spectrum of emerging potential biomarkers, including steroids, long-chain fatty acids, and microbiota metabolites. Further studies are warranted to validate these markers for diagnostic and therapeutic applications.
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