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Genome-wide characterization of circulating metabolic biomarkers.

ABSTRACT: Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism^1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases^8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

SUBMITTER: Karjalainen MK

PROVIDER: S-EPMC10990933 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Genome-wide characterization of circulating metabolic biomarkers.

Karjalainen Minna K MK Karthikeyan Savita S Oliver-Williams Clare C Sliz Eeva E Allara Elias E Fung Wing Tung WT Surendran Praveen P Zhang Weihua W Jousilahti Pekka P Kristiansson Kati K Salomaa Veikko V Goodwin Matt M Hughes David A DA Boehnke Michael M Fernandes Silva Lilian L Yin Xianyong X Mahajan Anubha A Neville Matt J MJ van Zuydam Natalie R NR de Mutsert Renée R Li-Gao Ruifang R Mook-Kanamori Dennis O DO Demirkan Ayse A Liu Jun J Noordam Raymond R Trompet Stella S Chen Zhengming Z Kartsonaki Christiana C Li Liming L Lin Kuang K Hagenbeek Fiona A FA Hottenga Jouke Jan JJ Pool René R Ikram M Arfan MA van Meurs Joyce J Haller Toomas T Milaneschi Yuri Y Kähönen Mika M Mishra Pashupati P PP Joshi Peter K PK Macdonald-Dunlop Erin E Mangino Massimo M Zierer Jonas J Acar Ilhan E IE Hoyng Carel B CB Lechanteur Yara T E YTE Franke Lude L Kurilshikov Alexander A Zhernakova Alexandra A Beekman Marian M van den Akker Erik B EB Kolcic Ivana I Polasek Ozren O Rudan Igor I Gieger Christian C Waldenberger Melanie M Asselbergs Folkert W FW Hayward Caroline C Fu Jingyuan J den Hollander Anneke I AI Menni Cristina C Spector Tim D TD Wilson James F JF Lehtimäki Terho T Raitakari Olli T OT Penninx Brenda W J H BWJH Esko Tonu T Walters Robin G RG Jukema J Wouter JW Sattar Naveed N Ghanbari Mohsen M Willems van Dijk Ko K Karpe Fredrik F McCarthy Mark I MI Laakso Markku M Järvelin Marjo-Riitta MR Timpson Nicholas J NJ Perola Markus M Kooner Jaspal S JS Chambers John C JC van Duijn Cornelia C Slagboom P Eline PE Boomsma Dorret I DI Danesh John J Ala-Korpela Mika M Butterworth Adam S AS Kettunen Johannes J

Nature 20240306 8006

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism<sup>1-7</sup>. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases<sup>8-11</sup>. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up t ...[more]

PMID: 38448586

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Project description:Inflammatory Bowel Disease--comprised of Crohn's Disease and Ulcerative Colitis (UC)--is a complex, multi-factorial inflammatory disorder of the gastrointestinal tract. In this study we have explored the utility of naturally occurring circulating miRNAs as potential blood-based biomarkers for non-invasive prediction of UC incidences. Whole genome maps of circulating miRNAs in micro-vesicles, Peripheral Blood Mononuclear Cells and platelets have been constructed from a cohort of 20 UC patients and 20 normal individuals. Through Significance Analysis of Microarrays, a signature of 31 differentially expressed platelet-derived miRNAs has been identified and biomarker performance estimated through a non-probabilistic binary linear classification using Support Vector Machines. Through this approach, classifier measurements reveal a predictive score of 92.8% accuracy, 96.2% specificity and 89.5% sensitivity in distinguishing UC patients from normal individuals. Additionally, the platelet-derived biomarker signature can be validated at 88% accuracy through qPCR assays, and a majority of the miRNAs in this panel can be demonstrated to sub-stratify into 4 highly correlated intensity based clusters. Analysis of predicted targets of these biomarkers reveal an enrichment of pathways associated with cytoskeleton assembly, transport, membrane permeability and regulation of transcription factors engaged in a variety of regulatory cascades that are consistent with a cell-mediated immune response model of intestinal inflammation. Interestingly, comparison of the miRNA biomarker panel and genetic loci implicated in IBD through genome-wide association studies identifies a physical linkage between hsa-miR-941 and a UC susceptibility loci located on Chr 20. Taken together, analysis of these expression maps outlines a promising catalog of novel platelet-derived miRNA biomarkers of clinical utility and provides insight into the potential biological function of these candidates in disease pathogenesis.

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Genome-wide characterization of circulating metabolic biomarkers.

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Genome-wide characterization of circulating metabolic biomarkers.

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