Metabolomics of Lean/Overweight Insulin-Resistant Females Reveals Alterations in Steroids and Fatty Acids

Abstract

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 females (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 two 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, participant with IS had elevated levels of long chain fatty acids. A ROC analysis suggested a 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 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.

Comparing Levels of Metabolic Predictors of Coronary Heart Disease between Healthy Lean and Overweight Females

“…Of the 17 available biomarkers, only five were statistically different between the BMI groups. The raw metabolomics data were previously published [ 12 ]. Among profiled metabolites, 17 metabolites previously shown to be associated with CHD risk were compared between the two studied groups, including nucleotides (uridine), carbohydrates (mannose), amino acids (dimethylglycine, asparagine, N-acetylalanine, indole-lactate, N-acetylthreonine, p -cresol sulfate, 2-methylbutyrylcarnitine, N-acetyl-1-methylhistidine), xenobiotics (theophylline, erythritol, 4-vinylphenol sulfate, O-sulfo-L-tyrosine) and lipids (linolenate alpha or gamma (18:3n3 or 6), 1-arachidonoyl-glycerylphosphorylcholine (GPC) (20:4n6), 13-hydroxyoctadecadienoic acid (HODE) and 9-HODE).…”

“…Metabolomics is one of the most recent disciplines that describe the association of metabolites to diseases [ 7 ]. The application of metabolomics can provide an added value in various therapeutic and preventative measures for many chronic diseases such as heart diseases, cancer and diabetes [ 8 , 9 , 10 , 11 , 12 ]. Metabolic profiling of blood and body secretions could provide powerful diagnostic and potentially therapeutic tools [ 13 ].…”

A distinct metabolic profile associated with a fatal outcome in COVID-19 patients during early epidemic in Italy

“…Six predicted biomarkers (out of the 10 previously identified with p<0.025, unadjusted) were highly correlated and were present in the network and belong to the lipid pathways. These metabolites are also known to be associated with peroxisomal fatty acid oxidation disorders (3-hydroxysebacate) [13] or insuline resistance (5-dodecenate (12:1n7), tetradecadienoate (14:2)* and myristoleate (14:1n5)) [14].…”

“…Six predicted biomarkers (out of the 10 previously identified with p <0.025, unadjusted) were highly correlated and were present in the network and belong to the lipid pathways. These metabolites are also known to be associated with peroxisomal fatty acid oxidation disorders (3-hydroxysebacate) [13] or insuline resistance (5-dodecenate (12:1n7), tetradecadienoate (14:2)* and myristoleate (14:1n5)) [14]. Finally, to further discriminate the subset of metabolites significantly associated with a fatal clinical outcome, we selected all the biomarkers and their first neighbors in the network analysis previously described (238 metabolites).…”

Distinct Metabolic Profile Associated with a Fatal Outcome in COVID-19 Patients during the Early Epidemic in Italy

“…1F ). These metabolites are also known to be associated with peroxisomal fatty acid oxidation disorders (3-hydroxysebacate) ( 18 ) or insulin resistance [5-dodecenate (12:1n7), tetradecadienoate (14:2), and myristoleate (14:1n5)] ( 19 ).…”