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Int J Obes (Lond). 2019 Mar;43(3):487-502. doi: 10.1038/s41366-018-0132-z. Epub 2018 Jun 15.

Plasma metabolites reveal distinct profiles associating with different metabolic risk factors in monozygotic twin pairs.

Author information

1
Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
2
HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
3
Obesity Research Unit, Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland.
4
Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland.
5
Finnish Twin Cohort Study, Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland.
6
Endocrinology, Abdominal Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
7
Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland. miina.ollikainen@helsinki.fi.
8
Finnish Twin Cohort Study, Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland. miina.ollikainen@helsinki.fi.

Abstract

BACKGROUND:

Obesity is related to a myriad of cardiometabolic outcomes, each of which may have a specific metabolomic signature and a genetic basis. We identified plasma metabolites associating with different cardiometabolic risk factors (adiposity, cholesterol, insulin resistance, and inflammation) in monozygotic (MZ) twins. Additionally, we assessed if metabolite profiling can identify subgroups differing by cardiometabolic risk factors.

METHODS:

We quantified 111 plasma metabolites (Acquity UPLC-triple quadrupole mass spectrometry), and measured blood lipids, HOMA index, CRP, and adiposity (BMI, %bodyfat by DEXA, fat distribution by MRI) in 40 MZ twin pairs (mean BMI 27.9 kg/m2, age 30.7). We determined associations among individuals (via linear regression) between metabolites and clinical phenotypes, and assessed, with within-twin pair analysis, if these associations were free from genetic confounding. We also performed cluster analysis to identify distinct subgroups based on subjects' metabolite profiles.

RESULTS:

We identified 42 metabolite-phenotype associations (FDR < 0.05), 19 remained significant after controlling for shared factors within the twin pairs. Aspartate, propionylcarnitine, tyrosine hexanoylcarnitine, and deoxycytidine associated positively with two or more adiposity measures. HDL cholesterol (HDL-C) associated negatively and BMI positively with the most numbers of metabolites; 12 were unique for HDL-C and 3 for BMI. Metabolites associating with HDL-C had the strongest effect size. Metabolite profiling revealed two distinct subgroups of individuals, differing by 32 metabolites (p < 0.05), and by total and LDL cholesterol (LDL-C). Forty-two metabolites predicted subgroup membership in correlation with total cholesterol and 45 metabolites predicted subgroup membership in correlation with LDL-C.

CONCLUSIONS:

Different fat depots share metabolites associating with general adiposity. BMI and HDL-C associated with the most pronounced and specific metabolomic signature. Metabolomics profiling can be used to identify distinct subgroups of individuals that differ by cholesterol measures. Most of the observed metabolite-phenotype associations are free of confounding by genetics and environmental factors shared by the co-twins.

PMID:
29907843
DOI:
10.1038/s41366-018-0132-z

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