SEARCHCASE-CONTROLendocrinology, internal medicine

case-control·endocrinology, internal medicine, epidemiology, biomedical research·PMC10053148

An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case–control study

Journal of Translational Medicine · 11 authors, 7 centres

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TL;DR

This case-control study analyzed multi-omic data from whole blood to identify molecular differences between obese individuals with and without metabolic syndrome. It found eight distinct metabolic pathways that can differentiate the two groups.

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This case-control study used an integrative bioinformatics pipeline on whole blood samples from 39 human participants to compare molecular signatures between obesity with metabolic syndrome (OBM) and uncomplicated obesity (OBO). The analysis included 754 microRNAs, 704 metabolites, and 25,682 transcripts. The researchers identified eight significantly enriched metabolic pathways containing 8 metabolites, 25 protein-coding genes, and 9 microRNAs that were differentially expressed between groups. Unsupervised hierarchical clustering on these pathway enrichment data could approximately segregate the OBO and OBM groups. The findings suggest these identified pathways and associated elements have potential as biomarkers for differentiating metabolic complications in obesity.

STUDY TYPE

case-control

SPECIALTY

endocrinology

SUMMARISED BY

AI pipeline

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100% · A

ORIGINAL PAPER

PMC10053148

An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case–control study

Journal of Translational Medicine

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ABSTRACT

Abstract Objectives To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. Methods We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications. Results We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome. Conclusions The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-023-04074-x.

CITATION EXPORT

Fayaz Ahmad Mir, Raghvendra Mall, Ehsan Ullah et al.. An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case–control study. Journal of Translational Medicine. (2023). https://doi.org/10.1186/s12967-023-04074-x

AUTHORS

11 · 7 CENTRES

Fayaz Ahmad Mir, Raghvendra Mall, Ehsan Ullah, Ahmad Iskandarani, Farhan Cyprian, Tareq A Samra, Meis Alkasem, Ibrahem Abdalhakam, Faisal Farooq, Shahrad Taheri, Abdul-Badi Abou-Samra

1Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
2Qatar Computational Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
3Department of Immunology, St. Jude Children’s Research Hospital, Memphis, USA
4College of Medicine, QU Health, Qatar University, Doha, Qatar
5National Obesity Treatment Center, Hamad Medical Corporation, Doha, Qatar
6Weil Cornell Medicine – Qatar, Doha, Qatar
7Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates