Article | Published:

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

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    Hanzu FA, Vinaixa M, Papageorgiou A, Parrizas M, Correig X, Delgado S, et al. Obesity rather than regional fat depots marks the metabolomic pattern of adipose tissue: an untargeted metabolomic approach. Obesity. 2014;22:698–704.

  2. 2.

    Jennings A, MacGregor A, Pallister T, Spector T, Cassidy A. Associations between branched chain amino acid intake and biomarkers of adiposity and cardiometabolic health independent of genetic factors: a twin study. Int J Cardiol. 2016;223:992–8.

  3. 3.

    Kraus WE, Pieper CF, Huffman KM, Thompson DK, Kraus VB, Morey MC, et al. Association of plasma small-molecule intermediate metabolites with age and body mass index across six diverse study populations. J Gerontol A Biol Sci Med Sci. 2016;71:1507–13.

  4. 4.

    Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9:311–26.

  5. 5.

    McKillop AM, Flatt PR. Emerging applications of metabolomic and genomic profiling in diabetic clinical medicine. Diabetes Care. 2011;34:2624–30.

  6. 6.

    Zhang A, Sun H, Wang X. Serum metabolomics as a novel diagnostic approach for disease: a systematic review. Anal Bioanal Chem. 2012;404:1239–45.

  7. 7.

    Segal KR, Dunaif A, Gutin B, Albu J, Nyman A, Pi-Sunyer FX. Body composition, not body weight, is related to cardiovascular disease risk factors and sex hormone levels in men. J Clin Invest. 1987;80:1050–5.

  8. 8.

    Lahmann PH, Lissner L, Gullberg B, Berglund G. A prospective study of adiposity and all-cause mortality: the Malmö diet and cancer study. Obes Res. 2002;10:361–9.

  9. 9.

    Carroll JF, Chiapa AL, Rodriquez M, Phelps DR, Cardarelli KM, Vishwanatha JK, et al. Visceral fat, waist circumference, and BMI: impact of race/ethnicity. Obesity. 2008;16:600–7.

  10. 10.

    Bosy-Westphal A, Geisler C, Onur S, Korth O, Selberg O, Schrezenmeir J, et al. Value of body fat mass vs anthropometric obesity indices in the assessment of metabolic risk factors. Int J Obes. 2006;30:475–83.

  11. 11.

    Lee K, Song YM, Sung J. Which obesity indicators are better predictors of metabolic risk?: healthy twin study. Obesity. 2008;16:834–40.

  12. 12.

    Paniagua L, Lohsoonthorn V, Lertmaharit S, Jiamjarasrangsi W, Williams MA. Comparison of waist circumference, body mass index, percent body fat and other measure of adiposity in identifying cardiovascular disease risks among Thai adults. Obes Res Clin Pract. 2008;2:215–23.

  13. 13.

    Ranasinghe C, Gamage P, Katulanda P, Andraweera N, Thilakarathne S, Tharanga P. Relationship between body mass index (BMI) and body fat percentage, estimated by bioelectrical impedance, in a group of Sri Lankan adults: a cross sectional study. BMC Public Health. 2013;13:797.

  14. 14.

    Camhi SM, Bray GA, Bouchard C, Greenway FL, Johnson WD, Newton RL, et al. The relationship of waist circumference and BMI to visceral, subcutaneous, and total body fat: sex and race differences. Obesity. 2011;19:402–8.

  15. 15.

    Schwimmer JB, Celedon MA, Lavine JE, Salem R, Campbell N, Schork NJ, et al. Heritability of nonalcoholic fatty liver disease. Gastroenterology. 2009;136:1585–92.

  16. 16.

    Frankenfield DC, Rowe WA, Cooney RN, Smith JS, Becker D. Limits of body mass index to detect obesity and predict body composition. Nutrition. 2001;17:26–30.

  17. 17.

    Gao X, Zhang W, Wang Y, Pedram P, Cahill F, Zhai G, et al. Serum metabolic biomarkers distinguish metabolically healthy peripherally obese from unhealthy centrally obese individuals. Nutr Metab. 2016;13:33.

  18. 18.

    Schlecht I, Gronwald W, Behrens G, Baumeister SE, Hertel J, Hochrein J, et al. Visceral adipose tissue but not subcutaneous adipose tissue is associated with urine and serum metabolites. PLoS ONE. 2017;12:e0175133.

  19. 19.

    Rämö JT, Kaye SM, Jukarainen S, Bogl LH, Hakkarainen A, Lundbom J, et al. Liver fat and insulin sensitivity define metabolite profiles during a glucose tolerance test in young adult twins. J Clin Endocrinol Metab. 2016;102:220-23.

  20. 20.

    Boulet MM, Chevrier G, Grenier-Larouche T, Pelletier M, Nadeau M, Scarpa J, et al. Alterations of plasma metabolite profiles related to adipose tissue distribution and cardiometabolic risk. Am J Physiol Endocrinol Metab. 2015;309:E736–46.

  21. 21.

    Bogl LH, Kaye SM, Ramo JT, Kangas AJ, Soininen P, Hakkarainen A, et al. Abdominal obesity and circulating metabolites: a twin study approach. Metab Clin Exp. 2016;65:111–21.

  22. 22.

    Pascot A, Lemieux I, Prud’homme D, Tremblay A, Nadeau A, Couillard C, et al. Reduced HDL particle size as an additional feature of the atherogenic dyslipidemia of abdominal obesity. J Lipid Res. 2001;42:2007–14.

  23. 23.

    van Dongen J, Slagboom PE, Draisma HHM, Martin NG, Boomsma DI. The continuing value of twin studies in the omics era. Nat Rev Genet. 2012;13:640–653.

  24. 24.

    Hong Y, Rice T, Gagnon J, Després J-P, Nadeau A, Pérusse L, et al. Familial clustering of insulin and abdominal visceral fat: the HERITAGE Family Study 1. J Clin Endocrinol Metab. 1998;83:4239–45.

  25. 25.

    Pérusse L, Després JP, Lemieux S, Rice T, Rao DC, Bouchard C, et al. Familial aggregation of abdominal visceral fat level: results from the Quebec family study. Metabolism. 1996;45:378–82.

  26. 26.

    Kettunen J, Tukiainen T, Sarin AP, Ortega-Alonso A, Tikkanen E, Lyytikainen LP, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet. 2012;44:269–76.

  27. 27.

    Elder SJ, Lichtenstein AH, Pittas AG, Roberts SB, Fuss PJ, Greenberg AS, et al. Genetic and environmental influences on factors associated with cardiovascular disease and the metabolic syndrome. J Lipid Res. 2009;50:1917–26.

  28. 28.

    Teucher B, Skinner J, Skidmore PM, Cassidy A, Fairweather-Tait SJ, Hooper L, et al. Dietary patterns and heritability of food choice in a UK female twin cohort. Twin Res Hum Genet. 2007;10:734–48.

  29. 29.

    Gomez-Ambrosi J, Silva C, Galofre JC, Escalada J, Santos S, Millan D, et al. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes. 2012;36:286–94.

  30. 30.

    Kaprio J. Twin studies in Finland 2006. Twin Res Human Genet. 2006;9:772–7.

  31. 31.

    Muniandy M, Heinonen S, Yki-Jarvinen H, Hakkarainen A, Lundbom J, Lundbom N, et al. Gene expression profile of subcutaneous adipose tissue in BMI-discordant monozygotic twin pairs unravels molecular and clinical changes associated with sub-types of obesity. Int J Obes. 2017;41:1176-1184.

  32. 32.

    Pietrobelli A, Formica C, Wang Z, Heymsfield SB. Dual-energy X-ray absorptiometry body composition model: review of physical concepts. Am J Physiol. 1996;271:941.

  33. 33.

    Lundbom J, Hakkarainen A, Söderlund S, Westerbacka J, Lundbom N, Taskinen M-R, et al. Long-TE 1H MRS suggests that liver fat is more saturated than subcutaneous and visceral fat. NMR Biomed. 2011;24:238–45.

  34. 34.

    Roman-Garcia P, Quiros-Gonzalez I, Mottram L, Lieben L, Sharan K, Wangwiwatsin A, et al. Vitamin B12–dependent taurine synthesis regulates growth and bone mass. J Clin Invest. 2014;124:2988–3002.

  35. 35.

    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.

  36. 36.

    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289–300.

  37. 37.

    Wold S, Ruhe A, Wold H, Dunn I WJ. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J Sci Stat Comput. 1984;5:735–43.

  38. 38.

    Smith SR, Lovejoy JC, Greenway F, Ryan D, deJonge L, de la Bretonne J, et al. Contributions of total body fat, abdominal subcutaneous adipose tissue compartments, and visceral adipose tissue to the metabolic complications of obesity. Metab Clin Exp. 2001;50:425–35.

  39. 39.

    Ho JE, Larson MG, Ghorbani A, Cheng S, Chen MH, Keyes M, et al. Metabolomic profiles of body mass index in the Framingham heart study reveal distinct cardiometabolic phenotypes. PLoS ONE. 2016;11:e0148361.

  40. 40.

    Kim JY, Park JY, Kim OY, Ham BM, Kim H-J, Kwon DY, et al. Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). J Proteome Res. 2010;9:4368–75.

  41. 41.

    Assmann G, Schulte H, von Eckardstein A, Huang Y. High-density lipoprotein cholesterol as a predictor of coronary heart disease risk. The PROCAM experience and pathophysiological implications for reverse cholesterol transport. Atherosclerosis. 1996;124:S11–20.

  42. 42.

    Després JP, Moorjani S, Ferland M, Tremblay A, Lupien PJ, Nadeau A, et al. Adipose tissue distribution and plasma lipoprotein levels in obese women. Importance of intra-abdominal fat. Arterioscler Thromb Vasc Biol. 1989;9:203–10.

  43. 43.

    Nieves DJ, Cnop M, Retzlaff B, Walden CE, Brunzell JD, Knopp RH, et al. The atherogenic lipoprotein profile associated with obesity and insulin resistance is largely attributable to intra-abdominal fat. Diabetes. 2003;52:172–9.

  44. 44.

    Smith SC. Multiple risk factors for cardiovascular disease and diabetes mellitus. Am J Med. 2007;120:S3–11.

  45. 45.

    Ngo S, Li X, O’Neill R, Bhoothpur C, Gluckman P, Sheppard A, et al. Elevated S-adenosylhomocysteine alters adipocyte functionality with corresponding changes in gene expression and associated epigenetic marks. Diabetes. 2014;63:2273–83.

  46. 46.

    Newgard CB. Interplay between lipids and branched-chain amino acids in development of insulin resistance. Cell Metab. 2012;15:606–14.

  47. 47.

    Pallares-Mendez R, Aguilar-Salinas CA, Cruz-Bautista I, Del Bosque-Plata L. Metabolomics in diabetes, a review. Ann Med. 2016;48:89–102.

  48. 48.

    Pietilainen KH, Naukkarinen J, Rissanen A, Saharinen J, Ellonen P, Keranen H, et al. Global transcript profiles of fat in monozygotic twins discordant for BMI: pathways behind acquired obesity. PLoS Med. 2008;5:e51.

  49. 49.

    Leskinen T, Rinnankoski-Tuikka R, Rintala M, Seppanen-Laakso T, Pollanen E, Alen M, et al. Differences in muscle and adipose tissue gene expression and cardio-metabolic risk factors in the members of physical activity discordant twin pairs. PLoS ONE. 2010;5. pii: e12609.

  50. 50.

    She P, Van Horn C, Reid T, Hutson SM, Cooney RN, Lynch CJ, et al. Obesity-related elevations in plasma leucine are associated with alterations in enzymes involved in branched-chain amino acid metabolism. Am J Physiol Endocrinol Metab. 2007;293:1552.

  51. 51.

    Brass EP, Beyerinck RA. Effects of propionate and carnitine on the hepatic oxidation of short- and medium-chain-length fatty acids. Biochem J. 1988;250:819–25.

  52. 52.

    Sullivan Lucas B, Gui Dan Y, Hosios Aaron M, Bush Lauren N, Freinkman E, Vander Heiden Matthew G. Supporting aspartate biosynthesis is an essential function of respiration in proliferating cells. Cell. 2015;162:552–63.

  53. 53.

    Heinonen S, Buzkova J, Muniandy M, Kaksonen R, Ollikainen M, Ismail K, et al. Impaired mitochondrial biogenesis in adipose tissue in acquired obesity. Diabetes. 2015;64:3135–45.

  54. 54.

    Radu CG, Shu CJ, Nair-Gill E, Shelly SM, Barrio JR, Satyamurthy N, et al. Molecular imaging of lymphoid organs and immune activation using positron emission tomography with a new 18F-labeled 2′-deoxycytidine analog. Nat Med. 2008;14:783.

  55. 55.

    Mai M, Tonjes A, Kovacs P, Stumvoll M, Fiedler GM, Leichtle AB, et al. Serum levels of acylcarnitines are altered in prediabetic conditions. PLoS ONE. 2013;8:e82459.

  56. 56.

    Adams SH, Hoppel CL, Lok KH, Zhao L, Wong SW, Minkler PE, et al. Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid beta-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women. J Nutr. 2009;139:1073–81.

  57. 57.

    Mills GW, Avery PJ, McCarthy MI, Hattersley AT, Levy JC, Hitman GA, et al. Heritability estimates for beta cell function and features of the insulin resistance syndrome in UK families with an increased susceptibility to type 2 diabetes. Diabetologia. 2004;47:732–8.

  58. 58.

    Sharrett AR, Ballantyne CM, Coady SA, Heiss G, Sorlie PD, Catellier D, et al. Coronary heart disease prediction from lipoprotein cholesterol levels, triglycerides, lipoprotein(a), apolipoproteins A-I and B, and HDL density subfractions. The Atherosclerosis Risk in Communities (ARIC) Study. Circulation. 2001;104:1108–13.

  59. 59.

    Brass EP. Supplemental carnitine and exercise. Am J Clin Nutr. 2000;72:618s–23s.

  60. 60.

    Martin N, Boomsma D, Machin G. A twin-pronged attack on complex traits. Nat Genet. 1997;17:387–92.

Download references

Acknowledgements

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.

Funding

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.

Author information

Author notes

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

Affiliations

  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

Authors

  1. Search for Maheswary Muniandy in:

  2. Search for Vidya Velagapudi in:

  3. Search for Antti Hakkarainen in:

  4. Search for Jesper Lundbom in:

  5. Search for Nina Lundbom in:

  6. Search for Aila Rissanen in:

  7. Search for Jaakko Kaprio in:

  8. Search for Kirsi H. Pietiläinen in:

  9. Search for Miina Ollikainen in:

Conflict of interest

The authors declare that they have no conflict of interest.

Corresponding author

Correspondence to Miina Ollikainen.

Electronic supplementary material

About this article

Publication history

Received

Revised

Accepted

Published

DOI

https://doi.org/10.1038/s41366-018-0132-z