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Physiology and Biochemistry

Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome

International Journal of Obesity volume 44, pages 1596–1606 (2020)Cite this article

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Abstract

Background

Obesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals.

Methods

We expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS.

Results

We identified ten known metabolites that are more likely to be causes (e.g., alpha-hydroxybutyrate) or effects (e.g., valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g., glycine). Importantly, we also identified about five times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms.

Conclusions

These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.

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Fig. 1: Overview for identifying and characterizing causal connections in the obesity metabolome.
Fig. 2: Joint Manhattan plots summarizing GWAS of BMI-associated metabolites in OE and MCDS.
Fig. 3: Classifying BMI-associated metabolites using IV effect estimate p values for GM (metabolite-to-BMI direction, x-axis) and GB (BMI-to-metabolite direction, y-axis).
Fig. 4: Clustered heat map of cause and effect metabolites’ memberships in metabolite sets prioritized by pathway analysis.

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References

  1. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384:766–81.

    Article  Google Scholar 

  2. Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444:840–6.

    Article  CAS  Google Scholar 

  3. Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, Pi-Sunyer FX, et al. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss. Arter Thromb Vasc Biol. 2006;26:968–76.

    Article  CAS  Google Scholar 

  4. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 2008;371:569–78.

    Article  Google Scholar 

  5. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309:71–82.

    Article  CAS  Google Scholar 

  6. 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.

    Article  CAS  Google Scholar 

  7. 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.

    Article  Google Scholar 

  8. Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation. 2012;125:2222–31.

    Article  CAS  Google Scholar 

  9. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17:448–53.

    Article  Google Scholar 

  10. Cirulli ET, Guo L, Leon Swisher C, Shah N, Huang L, Napier LA, et al. Profound perturbation of the metabolome in obesity is associated with health risk. Cell Metab. 2019. https://doi.org/10.1016/j.cmet.2018.09.022.

  11. Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol. 2004;33:30–42.

    Article  Google Scholar 

  12. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89–98.

    Article  CAS  Google Scholar 

  13. Fall T, Hagg S, Magi R, Ploner A, Fischer K, Horikoshi M, et al. The role of adiposity in cardiometabolic traits: a Mendelian randomization analysis. PLoS Med. 2013;10:e1001474.

    Article  Google Scholar 

  14. Holmes MV, Lange LA, Palmer T, Lanktree MB, North KE, Almoguera B, et al. Causal effects of body mass index on cardiometabolic traits and events: a Mendelian randomization analysis. Am J Hum Genet. 2014;94:198–208.

    Article  CAS  Google Scholar 

  15. Wurtz P, Wang Q, Kangas AJ, Richmond RC, Skarp J, Tiainen M, et al. Metabolic signatures of adiposity in young adults: mendelian randomization analysis and effects of weight change. PLoS Med. 2014;11:e1001765.

    Article  Google Scholar 

  16. Liu J, van Klinken JB, Semiz S, van Dijk KW, Verhoeven A, Hankemeier T, et al. A Mendelian randomization study of metabolite profiles, fasting glucose, and type 2 diabetes. Diabetes. 2017;66:2915–26.

    Article  CAS  Google Scholar 

  17. Haase CL, Tybjaerg-Hansen A, Qayyum AA, Schou J, Nordestgaard BG, Frikke-Schmidt R. LCAT, HDL cholesterol and ischemic cardiovascular disease: a Mendelian randomization study of HDL cholesterol in 54,500 individuals. J Clin Endocrinol Metab. 2012;97:E248–56.

    Article  CAS  Google Scholar 

  18. Patti GJ, Tautenhahn R, Siuzdak G. Meta-analysis of untargeted metabolomic data from multiple profiling experiments. Nat Protoc. 2012;7:508–16.

    Article  CAS  Google Scholar 

  19. Hsu Y-HH, Churchhouse C, Pers TH, Mercader JM, Metspalu A, Fischer K, et al. PAIRUP-MS: pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data. PLoS Comput Biol. 2019;15:1–26.

    Article  Google Scholar 

  20. Leitsalu L, Haller T, Esko T, Tammesoo ML, Alavere H, Snieder H, et al. Cohort profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int J Epidemiol. 2015;44:1137–47.

    Article  Google Scholar 

  21. Williams Amy AL, Jacobs Suzanne SBR, Moreno-Macías H, Huerta-Chagoya A, Churchhouse C, Márquez-Luna C, et al. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature. 2014;506:97–101.

    Article  CAS  Google Scholar 

  22. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42:348–54.

    Article  CAS  Google Scholar 

  23. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.

    Article  CAS  Google Scholar 

  24. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.

    Article  Google Scholar 

  25. Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, Chakravarti A, Clark AG, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65.

    Article  Google Scholar 

  26. Rhee EP, Ho JE, Chen MH, Shen D, Cheng S, Larson MG, et al. A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab. 2013;18:130–43.

    Article  CAS  Google Scholar 

  27. Draisma HHM, Pool R, Kobl M, Jansen R, Petersen AK, Vaarhorst AAM, et al. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels. Nat Commun. 2015;6:7208.

    Article  CAS  Google Scholar 

  28. Shin S-YY, Fauman EB, Petersen A-KK, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46:543–50.

    Article  CAS  Google Scholar 

  29. Long T, Hicks M, Yu HC, Biggs WH, Kirkness EF, Menni C, et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet. 2017;49:568–78.

    Article  CAS  Google Scholar 

  30. Burkhardt R, Kirsten H, Beutner F, Holdt LM, Gross A, Teren A, et al. Integration of genome-wide SNP data and gene-expression profiles reveals six novel loci and regulatory mechanisms for amino acids and acylcarnitines in whole blood. PLoS Genet. 2015;11:e1005510.

    Article  Google Scholar 

  31. Kettunen J, Demirkan A, Wurtz P, Draisma HH, Haller T, Rawal R, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016;7:11122.

    Article  CAS  Google Scholar 

  32. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.

    Article  CAS  Google Scholar 

  33. Loh PR, Tucker G, Bulik-Sullivan BK, Vilhjalmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet. 2015;47:284–90.

    Article  CAS  Google Scholar 

  34. Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017;26:2333–55.

    Article  Google Scholar 

  35. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.

    Article  CAS  Google Scholar 

  36. Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vazquez-Fresno R, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018;46:D608–D617.

    Article  CAS  Google Scholar 

  37. Gall WE, Beebe K, Lawton KA, Adam KP, Mitchell MW, Nakhle PJ, et al. Alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE. 2010;5:e10883.

    Article  Google Scholar 

  38. Thompson Legault J, Strittmatter L, Tardif J, Sharma R, Tremblay-Vaillancourt V, Aubut C, et al. A metabolic signature of mitochondrial dysfunction revealed through a monogenic form of leigh syndrome. Cell Rep. 2015;13:981–9.

    Article  CAS  Google Scholar 

  39. Burgess S, Harshfield E. Mendelian randomization to assess causal effects of blood lipids on coronary heart disease: Lessons from the past and applications to the future. Curr Opin Endocrinol Diabetes Obes. 2016. https://doi.org/10.1097/MED.0000000000000230.

  40. Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 2012. https://doi.org/10.1016/S0140-6736(12)60312-2.

  41. Lotta LA, Scott RA, Sharp SJ, Burgess S, Luan J, Tillin T, et al. Genetic Predisposition to an Impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a mendelian randomisation analysis. PLoS Med. 2016. https://doi.org/10.1371/journal.pmed. 1002179.

  42. Van Der weele T, Vansteelandt S. Mediation analysis with multiple mediators. Epidemiol Method. 2013. https://doi.org/10.1515/em-2012-0010.

  43. Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181:251–60.

    Article  Google Scholar 

  44. Wittemans LBL, Lotta LA, Oliver-Williams C, Stewart ID, Surendran P, Karthikeyan S, et al. Assessing the causal association of glycine with risk of cardio-metabolic diseases. Nat Commun. 2019;10:1–13.

    Article  CAS  Google Scholar 

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Acknowledgements

We thank the Broad Metabolomics Platform and SIGMA T2D Consortium for sharing data resources. This research has been conducted using the UK Biobank Resource under Application Number 11898. This work was supported by the National Heart, Lung, and Blood Institute grant F31HL126581 (YHH), National Institute of Diabetes and Digestive and Kidney Diseases grants T32DK110919 (YHH), K12DK094721 (CMA), and R01DK075787 (JNH), Endocrine Scholars Award (CMA), Doris Duke Charitable Foundation grant 215205 (JNH), Estonian Research Council grants IUT20-60 (AM), PUT1665 (KF), and PUT1660 (TE), European Union through Horizon 2020 grant 692145 (AM), and European Union through the European Regional Development Fund Project No. 2014-2020.4.01.15-0012 (AM). The funding sources had no role in the design, analysis, and interpretation of this research.

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Author notes

  1. These authors contributed equally: Yu-Han H. Hsu, Christina M. Astley

Authors and Affiliations

  1. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Yu-Han H. Hsu & Joel N. Hirschhorn

  2. Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA, USA

    Yu-Han H. Hsu, Christina M. Astley, Joanne B. Cole, Sailaja Vedantam & Joel N. Hirschhorn

  3. Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Yu-Han H. Hsu, Christina M. Astley, Joanne B. Cole, Sailaja Vedantam, Josep M. Mercader, Tonu Esko & Joel N. Hirschhorn

  4. Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Joanne B. Cole & Josep M. Mercader

  5. Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia

    Andres Metspalu, Krista Fischer & Tonu Esko

  6. Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia

    Krista Fischer

  7. BioAge Labs, Richmond, CA, USA

    Kristen Fortney & Eric K. Morgen

  8. Instituto Nacional de Salud Publica, Cuernavaca, Morelos, Mexico

    Clicerio Gonzalez & Maria E. Gonzalez

  9. Centro de Estudios en Diabetes, Mexico City, Mexico

    Clicerio Gonzalez & Maria E. Gonzalez

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Correspondence to Joel N. Hirschhorn.

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K Fortney and EKM are affiliated with BioAge Labs, Inc.; JNH serves on the Scientific Advisory Board of Camp4 Therapeutics.

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Hsu, YH.H., Astley, C.M., Cole, J.B. et al. Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome. Int J Obes 44, 1596–1606 (2020). https://doi.org/10.1038/s41366-020-0603-x

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