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Genetics and Epigenetics

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

International Journal of Obesity (2018) | Download Citation



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.


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.


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.


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.

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We thank the twins for their invaluable contribution to the study. The Obesity Research Unit team and the staff at the Finnish Twin Cohort Study and FIMM Metabolomics Unit are acknowledged for their help in the collection of the data.


This study was supported by The Academy of Finland (grant number 266286 to KHP, and 251316 and 297908 to MO, and 141054, 265240, 263278, and 264146 to JK); Centre of Excellence in Research on Mitochondria, Metabolism and Disease (FinMIT) to KHP (grant 272376); Helsinki University Central Hospital (NL, AH, AR, KHP); The University of Helsinki Research Funds (MO, KHP); grants from following Foundations: Novo Nordisk (KHP), Signe and Ane Gyllenberg (KHP), Jalmari and Rauha Ahokas (KHP), The Sigrid Juselius Foundation (MO and JK), Finnish Diabetes Research Foundation (KHP), Finnish Foundation for Cardiovascular Research (KHP). Data collection in FinnTwin16 and FinnTwin12 was supported by the National Institute of Alcohol Abuse and Alcoholism (grants AA-12502 and AA-09203 to RJ Rose).

Author contributions

M.O. and K.H.P. conceived and designed the study, supervised the work, and participated in the discussion and interpretation of the results. M.M. analyzed all the data and wrote the manuscript. K.H.P., J.K., and A.R. collected the study material. A.H., J.L., and N.L. participated in the imaging of the twins. V.V. supervised the metabolomics sample analyses. J.K. was responsible for the base cohorts from which the pairs were sampled. All authors read and approved the manuscript. M.O. and K.H.P. are the guarantors of this work and as such had full access to the data and take responsibility for the integrity of the data and the accuracy of the data analysis.

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Author notes

  1. These authors contributed equally: Kirsi H. Pietiläinen, Miina Ollikainen.


  1. Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland

    • Maheswary Muniandy
    • , Vidya Velagapudi
    • , Jaakko Kaprio
    •  & Miina Ollikainen
  2. HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland

    • Antti Hakkarainen
    • , Jesper Lundbom
    •  & Nina Lundbom
  3. Obesity Research Unit, Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland

    • Aila Rissanen
    •  & Kirsi H. Pietiläinen
  4. Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland

    • Aila Rissanen
  5. Finnish Twin Cohort Study, Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland

    • Jaakko Kaprio
    •  & Miina Ollikainen
  6. Endocrinology, Abdominal Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland

    • Kirsi H. Pietiläinen


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The authors declare that they have no conflict of interest.

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Correspondence to Miina Ollikainen.

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