Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain


Autoři: Nina Geidenstam aff001;  Yu-Han H. Hsu aff002;  Christina M. Astley aff003;  Josep M. Mercader aff004;  Martin Ridderstråle aff001;  Maria E. Gonzalez aff006;  Clicerio Gonzalez aff006;  Joel N. Hirschhorn aff002;  Rany M. Salem aff008
Působiště autorů: Clinical Obesity, Institution of Clinical Sciences, Malmö, Lund University, Sweden aff001;  Department of Genetics, Harvard Medical School, Boston, MA, United States of America aff002;  Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA, United States of America aff003;  Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, United States of America aff004;  Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States of America aff005;  Instituto Nacional de Salud Publica, Cuernavaca, Morelos, Mexico aff006;  Centro de Estudios en Diabetes, Mexico City, Mexico aff007;  Department of Family Medicine and Public Health, UC San Diego, San Diego, CA, United States of America aff008
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222445

Souhrn

Background

Excess weight gain throughout adulthood can lead to adverse clinical outcomes and are influenced by complex factors that are difficult to measure in free-living individuals. Metabolite profiling offers an opportunity to systematically discover new predictors for weight gain that are relatively easy to measure compared to traditional approaches.

Methods and results

Using baseline metabolite profiling data of middle-aged individuals from the Framingham Heart Study (FHS; n = 1,508), we identified 42 metabolites associated (p < 0.05) with longitudinal change in body mass index (BMI). We performed stepwise linear regression to select 8 of these metabolites to build a metabolite risk score (MRS) for predicting future weight gain. We replicated the MRS using data from the Mexico City Diabetes Study (MCDS; n = 768), in which one standard deviation increase in the MRS corresponded to ~0.03 increase in BMI (kg/m2) per year (i.e. ~0.09 kg/year for a 1.7 m adult). We observed that none of the available anthropometric, lifestyle, and glycemic variables fully account for the MRS prediction of weight gain. Surprisingly, we found the MRS to be strongly correlated with baseline insulin sensitivity in both cohorts and to be negatively predictive of T2D in MCDS. Genome-wide association study of the MRS identified 2 genome-wide (p < 5 × 10−8) and 5 suggestively (p < 1 × 10−6) significant loci, several of which have been previously linked to obesity-related phenotypes.

Conclusions

We have constructed and validated a generalizable MRS for future weight gain that is an independent predictor distinct from several other known risk factors. The MRS captures a composite biological picture of weight gain, perhaps hinting at the anabolic effects of preserved insulin sensitivity. Future investigation is required to assess the relationships between MRS-predicted weight gain and other obesity-related diseases.

Klíčová slova:

Anthropometry – Body Mass Index – Genetic loci – Genome-wide association studies – Insulin – Metabolites – Obesity – Weight gain


Zdroje

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: 766–781. doi: 10.1016/S0140-6736(14)60460-8 24880830

2. Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444: 840–846. doi: 10.1038/nature05482 17167471

3. Mandviwala T, Khalid U, Deswal A. Obesity and Cardiovascular Disease: a Risk Factor or a Risk Marker? Curr Atheroscler Rep. 2016;18: 21. doi: 10.1007/s11883-016-0575-4 26973130

4. Fabbrini E, Sullivan S, Klein S. Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications. Hepatology. 2010;51: 679–689. doi: 10.1002/hep.23280 20041406

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. doi: 10.1001/jama.2012.113905 23280227

6. Williamson DF. Descriptive epidemiology of body weight and weight change in U.S. adults. Ann Intern Med. 1993;119: 646–649. Available: https://www.ncbi.nlm.nih.gov/pubmed/8363190 doi: 10.7326/0003-4819-119-7_part_2-199310011-00004 8363190

7. Lewis CE, Jacobs Jr. DR, McCreath H, Kiefe CI, Schreiner PJ, Smith DE, et al. Weight gain continues in the 1990s: 10-year trends in weight and overweight from the CARDIA study. Coronary Artery Risk Development in Young Adults. Am J Epidemiol. 2000;151: 1172–1181. Available: https://www.ncbi.nlm.nih.gov/pubmed/10905529 doi: 10.1093/oxfordjournals.aje.a010167 10905529

8. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med. 2011;364: 2392–2404. doi: 10.1056/NEJMoa1014296 21696306

9. Hall KD, Heymsfield SB, Kemnitz JW, Klein S, Schoeller DA, Speakman JR. Energy balance and its components: implications for body weight regulation. Am J Clin Nutr. 2012;95: 989–994. doi: 10.3945/ajcn.112.036350 22434603

10. Kimokoti RW, Newby PK, Gona P, Zhu L, Jasuja GK, Pencina MJ, et al. Diet quality, physical activity, smoking status, and weight fluctuation are associated with weight change in women and men. J Nutr. 2010;140: 1287–1293. doi: 10.3945/jn.109.120808 20484553

11. Chaput JP, Tremblay A, Rimm EB, Bouchard C, Ludwig DS. A novel interaction between dietary composition and insulin secretion: effects on weight gain in the Quebec Family Study. Am J Clin Nutr. 2008;87: 303–309. Available: https://www.ncbi.nlm.nih.gov/pubmed/18258618 doi: 10.1093/ajcn/87.2.303 18258618

12. Astley CM, Todd JN, Salem RM, Vedantam S, Ebbeling CB, Huang PL, et al. Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity. Clin Chem. 2018;64: 192–200. doi: 10.1373/clinchem.2017.280727 29295838

13. 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. doi: 10.1038/nature14177 25673413

14. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ~700 000 individuals of European ancestry. Hum Mol Genet. 2018; doi: 10.1093/hmg/ddy271 30124842

15. Rukh G, Ahmad S, Ericson U, Hindy G, Stocks T, Renstrom F, et al. Inverse relationship between a genetic risk score of 31 BMI loci and weight change before and after reaching middle age. Int J Obes. 2016;40: 252–259. doi: 10.1038/ijo.2015.180 26374450

16. Steffen A, Sørensen TIA, Knüppel S, Travier N, Sánchez MJ, Huerta JM, et al. Development and Validation of a Risk Score Predicting Substantial Weight Gain over 5 Years in Middle-Aged European Men and Women. PLoS One. 2013; doi: 10.1371/journal.pone.0067429 23874419

17. Bachlechner U, Boeing H, Haftenberger M, Schienkiewitz A, Scheidt-Nave C, Vogt S, et al. Predicting risk of substantial weight gain in German adults-A multi-center cohort approach. Eur J Public Health. 2017; doi: 10.1093/eurpub/ckw216 28013243

18. Douketis JD, Macie C, Thabane L, Williamson DF. Systematic review of long-term weight loss studies in obese adults: clinical significance and applicability to clinical practice. Int J Obes. 2005;29: 1153–1167. doi: 10.1038/sj.ijo.0802982 15997250

19. Dombrowski SU, Knittle K, Avenell A, Araujo-Soares V, Sniehotta FF. Long term maintenance of weight loss with non-surgical interventions in obese adults: systematic review and meta-analyses of randomised controlled trials. BMJ. 2014;348: g2646. doi: 10.1136/bmj.g2646 25134100

20. 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: 311–326. doi: 10.1016/j.cmet.2009.02.002 19356713

21. 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. doi: 10.1371/journal.pmed.1001765 25490400

22. 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. doi: 10.1371/journal.pone.0148361 26863521

23. 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: 2222–2231. doi: 10.1161/CIRCULATIONAHA.111.067827 22496159

24. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.; 2011;17: 448–453. doi: 10.1038/nm.2307 21423183

25. Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121: 1402–1411. doi: 10.1172/JCI44442 21403394

26. Wurtz P, Soininen P, Kangas AJ, Ronnemaa T, Lehtimaki T, Kahonen M, et al. Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care. 2013;36: 648–655. doi: 10.2337/dc12-0895 23129134

27. Geidenstam N, Al-Majdoub M, Ekman M, Spégel P, Ridderstråle M. Metabolite profiling of obese individuals before and after a one year weight loss program. Int J Obes. Nature Publishing Group; 2017;41: 1369–1378. doi: 10.1038/ijo.2017.124 28529327

28. Wahl S, Vogt S, Stuckler F, Krumsiek J, Bartel J, Kacprowski T, et al. Multi-omic signature of body weight change: results from a population-based cohort study. BMC Med. 2015;13: 48. doi: 10.1186/s12916-015-0282-y 25857605

29. Menni C, Migaud M, Kastenmuller G, Pallister T, Zierer J, Peters A, et al. Metabolomic Profiling of Long-Term Weight Change: Role of Oxidative Stress and Urate Levels in Weight Gain. Obes (Silver Spring). 2017;25: 1618–1624. doi: 10.1002/oby.21922 28758372

30. Zhao H, Shen J, Djukovic D, Daniel-MacDougall C, Gu H, Wu X, et al. Metabolomics-identified metabolites associated with body mass index and prospective weight gain among Mexican American women. Obes Sci Pr. 2016;2: 309–317. doi: 10.1002/osp4.63 27708848

31. Kannel WB, Feinleib M, McNamara PM, Garrison RJ, Castelli WP. An investigation of coronary heart disease in families. The Framingham offspring study. Am J Epidemiol. 1979;110: 281–290. Available: http://www.ncbi.nlm.nih.gov/pubmed/474565 doi: 10.1093/oxfordjournals.aje.a112813 474565

32. 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. Elsevier Inc.; 2013;18: 130–143. doi: 10.1016/j.cmet.2013.06.013 23823483

33. 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. doi: 10.1038/nature12828 24390345

34. Hsu YHH, 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; doi: 10.1371/journal.pcbi.1006734 30640898

35. van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. J Stat Softw. 2011;45: 1–67. doi: 10.18637/jss.v045.i03

36. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28: 412–419. Available: https://www.ncbi.nlm.nih.gov/pubmed/3899825 doi: 10.1007/bf00280883 3899825

37. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999;22: 1462–1470. Available: https://www.ncbi.nlm.nih.gov/pubmed/10480510 doi: 10.2337/diacare.22.9.1462 10480510

38. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab. 2000;85: 2402–2410. doi: 10.1210/jcem.85.7.6661 10902785

39. Kang HM. EPACTS (Efficient and Parallelizable Association Container Toolbox). Available: http://genome.sph.umich.edu/wiki/EPACTS

40. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26: 2190–2191. doi: 10.1093/bioinformatics/btq340 20616382

41. 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. doi: 10.1186/s13742-015-0047-8 25722852

42. Genomes Project C, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012/11/07. 2012;491: 56–65. doi: 10.1038/nature11632 23128226

43. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17: 122. doi: 10.1186/s13059-016-0974-4 27268795

44. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 2017;45: D896–D901. doi: 10.1093/nar/gkw1133 27899670

45. Forstmeier W, Schielzeth H. Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner’s curse. Behav Ecol Sociobiol. 2011;65: 47–55. doi: 10.1007/s00265-010-1038-5 21297852

46. Marquardt A, Stohr H, White K, Weber B+H. cDNA cloning, genomic structure, and chromosomal localization of three members of the human fatty acid desaturase family. Genomics. 2000;66: 175–183. doi: 10.1006/geno.2000.6196 10860662

47. Teslovich TM, Musunuru K, Smith A V, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010/08/06. 2010;466: 707–713. doi: 10.1038/nature09270 20686565

48. Mozaffarian D, Kabagambe EK, Johnson CO, Lemaitre RN, Manichaikul A, Sun Q, et al. Genetic loci associated with circulating phospholipid trans fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. Am J Clin Nutr. 2015;101: 398–406. doi: 10.3945/ajcn.114.094557 25646338

49. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42: 105–116. doi: 10.1038/ng.520 20081858

50. Chambers JC, Zhang W, Sehmi J, Li X, Wass MN, Van der Harst P, et al. Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat Genet. 2011;43: 1131–1138. doi: 10.1038/ng.970 22001757

51. den Hoed M, Eijgelsheim M, Esko T, Brundel BJ, Peal DS, Evans DM, et al. Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders. Nat Genet. 2013;45: 621–631. doi: 10.1038/ng.2610 23583979

52. Arking DE, Pulit SL, Crotti L, van der Harst P, Munroe PB, Koopmann TT, et al. Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization. Nat Genet. 2014;46: 826–836. doi: 10.1038/ng.3014 24952745

53. Verweij N, Mateo Leach I, van den Boogaard M, van Veldhuisen DJ, Christoffels VM, LifeLines Cohort S, et al. Genetic determinants of P wave duration and PR segment. Circ Cardiovasc Genet. 2014;7: 475–481. doi: 10.1161/CIRCGENETICS.113.000373 24850809

54. Lauffart B, Gangisetty O, Still IH. Molecular cloning, genomic structure and interactions of the putative breast tumor suppressor TACC2. Genomics. 2003; doi: 10.1016/S0888-7543(02)00039-3

55. Albrecht E, Waldenberger M, Krumsiek J, Evans AM, Jeratsch U, Breier M, et al. Metabolite profiling reveals new insights into the regulation of serum urate in humans. Metabolomics. 2014; doi: 10.1007/s11306-013-0565-2 24482632

56. Kraus D, Yang Q, Kong D, Banks AS, Zhang L, Rodgers JT, et al. Nicotinamide N-methyltransferase knockdown protects against diet-induced obesity. Nature. 2014;508: 258–262. doi: 10.1038/nature13198 24717514

57. Yang SJ, Choi JM, Kim L, Park SE, Rhee EJ, Lee WY, et al. Nicotinamide improves glucose metabolism and affects the hepatic NAD-sirtuin pathway in a rodent model of obesity and type 2 diabetes. J Nutr Biochem. 2014;25: 66–72. doi: 10.1016/j.jnutbio.2013.09.004 24314867

58. Yamakado M, Nagao K, Imaizumi A, Tani M, Toda A, Tanaka T, et al. Plasma Free Amino Acid Profiles Predict Four-Year Risk of Developing Diabetes, Metabolic Syndrome, Dyslipidemia, and Hypertension in Japanese Population. Sci Rep. 2015;5: 11918. doi: 10.1038/srep11918 26156880

59. Jitrapakdee S, Wutthisathapornchai A, Wallace JC, MacDonald MJ. Regulation of insulin secretion: role of mitochondrial signalling. Diabetologia. 2010;53: 1019–1032. doi: 10.1007/s00125-010-1685-0 20225132

60. Jensen M V, Joseph JW, Ronnebaum SM, Burgess SC, Sherry AD, Newgard CB. Metabolic cycling in control of glucose-stimulated insulin secretion. Am J Physiol Endocrinol Metab. 2008;295: E1287–97. doi: 10.1152/ajpendo.90604.2008 18728221

61. Swinburn BA, Nyomba BL, Saad MF, Zurlo F, Raz I, Knowler WC, et al. Insulin resistance associated with lower rates of weight gain in Pima Indians. J Clin Invest. 1991;88: 168–173. doi: 10.1172/JCI115274 2056116

62. Wedick NM, Snijder MB, Dekker JM, Heine RJ, Stehouwer CD, Nijpels G, et al. Prospective investigation of metabolic characteristics in relation to weight gain in older adults: the Hoorn Study. Obes (Silver Spring). 2009;17: 1609–1614. doi: 10.1038/oby.2008.666 19197256


Článek vyšel v časopise

PLOS One


2019 Číslo 9
Nejčtenější tento týden