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Body mass index modifies genetic susceptibility to high systolic blood pressure in adolescents and young adults: results from an 18-year longitudinal study

Journal of Human Hypertension volume 39, pages 334–342 (2025)Cite this article

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Abstract

Genome-wide association studies (GWAS) in adults have identified single nucleotide polymorphisms (SNPs) associated with systolic blood pressure (SBP), but it is unclear whether the findings apply in youth. Further, the role of body mass index (BMI) in these associations is understudied. Our objective was to determine whether BMI modifies genetic susceptibility to high SBP in young people. The sample comprised 714 participants of European ancestry recruited in 1999–2000 from 10 Montreal-area high schools for a longitudinal study. SBP was measured at ages 12, 15, 17, 24, and 30. Blood and saliva samples were collected at ages 14, 20, and 25. Two evidence-based genetic risk scores (GRS) were constructed based on GWAS results in adults: GRS22 used 22 SNPs and GRS182 added 160 additional SNPs to GRS22. Sex-specific associations between each GRS and repeated measures of SBP were estimated using linear mixed models including BMI and a GRS*BMI product term. GRS182 explained a greater proportion of SBP variance than GRS22, and a greater proportion in females than males. The associations increased monotonically with BMI values between 22 kg/m2 and 35 kg/m2. Results indicate that BMI modifies the association between a GRS and SBP levels from adolescence to adulthood.

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Fig. 1: Distribution of systolic blood pressure and body mass index by sex over time.
Fig. 2: Association between GRS22 and systolic blood pressure conditional on body mass index.
Fig. 3: Association between GRS182 and systolic blood pressure conditional on body mass index.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. Access to NDIT data is open to any university-appointed or affiliated investigator upon successful completion of the application process. Masters, doctoral and postdoctoral students may apply through their primary supervisor. To gain access, applicants must complete a data access form available on our website (www.celphie.ca) and return it to the principal investigator (jennifer.oloughlin@umontreal.ca). The procedure to obtain access to NDIT data is described in O’Loughlin, J., Dugas, E. N., Brunet, J., DiFranza, J., Engert, J. C., Gervais, A., Gray-Donald, K., Karp, I., Low, N. C., Sabiston, C., Sylvestre, M. P., Tyndale, R. F., Auger, N., Belanger, M., Barnett, T., Chaiton, M., Chenoweth, M. J., Constantin, E., Contreras, G., Kakinami, L., Labbe, A., Maximova, K., McMillan, E., O’Loughlin, E. K., Pabayo, R., Roy-Gagnon, M. H., Tremblay, M., Wellman, R. J., Hulst, A., Paradis, G., 2015. Cohort Profile: The Nicotine Dependence in Teens (NDIT) Study. Int J Epidemiol. 44(5), 1537–1546. doi: 10.1093/ije/dyu135.

References

  1. Forouzanfar MH, Liu P, Roth GA, Ng M, Biryukov S, Marczak L, et al. Global burden of hypertension and systolic blood pressure of at least 110 to 115 mm Hg, 1990-2015. JAMA. 2017;317:165–82.

    Article  PubMed  Google Scholar 

  2. Yang L, Magnussen CG, Yang L, Bovet P, Xi B. Elevated blood pressure in childhood or adolescence and cardiovascular outcomes in adulthood: a systematic review. Hypertension. 2020;75:948–55.

    Article  CAS  PubMed  Google Scholar 

  3. Flynn JT, Kaelber DC, Baker-Smith CM, Blowey D, Carroll AE, Daniels SR, et al. Clinical practice guideline for screening and management of high blood pressure in children and adolescents. Pediatrics. 2017;140:e20171904.

    Article  PubMed  Google Scholar 

  4. Chen X, Wang Y. Tracking of blood pressure from childhood to adulthood: a systematic review and meta-regression analysis. Circulation. 2008;117:3171–80.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Shi Y, de Groh M, Morrison H. Increasing blood pressure and its associated factors in Canadian children and adolescents from the Canadian Health Measures Survey. BMC Public Health. 2012;12:388.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Statistics Canada. Canadian Health Measures Survey, 2015–2019. https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=5071. (2019).

  7. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478:103–9.

    Article  CAS  PubMed  Google Scholar 

  8. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50:1412–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kurniansyah N, Goodman MO, Khan AT, Wang J, Feofanova E, Bis JC, et al. Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups. Nat Commun. 2023;14:3202.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Juhola J, Oikonen M, Magnussen CG, Mikkila V, Siitonen N, Jokinen E, et al. Childhood physical, environmental, and genetic predictors of adult hypertension: the cardiovascular risk in young Finns study. Circulation. 2012;126:402–9.

    Article  PubMed  Google Scholar 

  11. Petkeviciene J, Klumbiene J, Simonyte S, Ceponiene I, Jureniene K, Kriaucioniene V, et al. Physical, behavioural and genetic predictors of adult hypertension: the findings of the Kaunas Cardiovascular Risk Cohort study. PLoS ONE. 2014;9:e109974.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kauko A, Aittokallio J, Vaura F, Ji H, Ebinger JE, Niiranen T, et al. Sex differences in genetic risk for hypertension. Hypertension. 2021;78:1153–5.

    Article  CAS  PubMed  Google Scholar 

  13. Xi B, Shen Y, Zhao X, Chandak GR, Cheng H, Hou D, et al. Association of common variants in/near six genes (ATP2B1, CSK, MTHFR, CYP17A1, STK39 and FGF5) with blood pressure/hypertension risk in Chinese children. J Hum Hypertens. 2014;28:32–36.

    Article  CAS  PubMed  Google Scholar 

  14. Xie T, Wang B, Nolte IM, van der Most PJ, Oldehinkel AJ, Hartman CA, et al. Genetic risk scores for complex disease traits in youth. Circ Genom Precis Med. 2020;13:e002775.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Oikonen M, Tikkanen E, Juhola J, Tuovinen T, Seppala I, Juonala M, et al. Genetic variants and blood pressure in a population-based cohort: the Cardiovascular Risk in Young Finns study. Hypertension. 2011;58:1079–85.

    Article  CAS  PubMed  Google Scholar 

  16. Howe LD, Parmar PG, Paternoster L, Warrington NM, Kemp JP, Briollais L, et al. Genetic influences on trajectories of systolic blood pressure across childhood and adolescence. Circ Cardiovasc Genet. 2013;6:608–14.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Harbin MM, Hultgren NE, Kelly AS, Dengel DR, Evanoff NG, Ryder JR. Measurement of central aortic blood pressure in youth: role of obesity and sex. Am J Hypertens. 2018;31:1286–92.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Rao D, Kropac E, Do M, Roberts K, Jayaraman G. Childhood overweight and obesity trends in Canada. Health Promot Chronic Dis Prev Can. 2016;36:194.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wang B, Wu T, Neale MC, Verweij R, Liu G, Su S, et al. Genetic and environmental influences on blood pressure and body mass index in the national academy of sciences-national research council world war II veteran twin registry. Hypertension. 2020;76:1428–34.

    Article  CAS  PubMed  Google Scholar 

  20. Sarzynski MA, Rankinen T, Sternfeld B, Fornage M, Sidney S, Bouchard C. SNP-by-fitness and SNP-by-BMI interactions from seven candidate genes and incident hypertension after 20 years of follow-up: the CARDIA Fitness Study. J Hum Hypertens. 2011;25:509–18.

    Article  CAS  PubMed  Google Scholar 

  21. Taylor JY, Sun YV, Hunt SC, Kardia SLR. Gene-environment interaction for hypertension among African American women across generations. Biol Res Nurs. 2010;12:149–55.

    Article  PubMed  Google Scholar 

  22. Wu T, Snieder H, Li L, Cao W, Zhan S, Lv J, et al. Genetic and environmental influences on blood pressure and body mass index in Han Chinese: a twin study. Hypertens Res. 2011;34:173–9.

    Article  PubMed  Google Scholar 

  23. Xi B, Zhao X, Chandak GR, Shen Y, Cheng H, Hou D, et al. Influence of obesity on association between genetic variants identified by genome-wide association studies and hypertension risk in Chinese children. Am J Hypertens. 2013;26:990–6.

    Article  CAS  PubMed  Google Scholar 

  24. O’Loughlin J, Dugas EN, Brunet J, DiFranza J, Engert JC, Gervais A, et al. Cohort profile: the nicotine dependence in teens (NDIT) study. Int J Epidemiol. 2015;44:1537–46.

    Article  PubMed  Google Scholar 

  25. O’Connell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S, Cocca M, et al. A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet. 2014;10:e1004234.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Clarke L, Zheng-Bradley X, Smith R, Kulesha E, Xiao C, Toneva I, et al. The 1000 Genomes Project: data management and community access. Nat Methods. 2012;9:459–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Das S, Forer L, Schonherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics. 2015;31:3555–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Solt F, Hu Y. interplot: Plot the effects of variables in interaction terms. CRAN R package, v.0.2.3. https://cran.rproject.org/web/packages/interplot/ (2021).

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

    Article  Google Scholar 

  31. Lever J, Krzywinski M, Altman N. Model selection and overfitting. Nat Methods. 2016;13:703–4.

    Article  CAS  Google Scholar 

  32. Øvretveit K, Ingeström EML, Spitieris M, Tragante V, Wade KH, Thomas LF, et al. Polygenic risk scores associate with blood pressure traits across the lifespan. Eur J Prev Cardiol. 2024;31:644–54.

    Article  PubMed  Google Scholar 

  33. Simino J, Shi G, Weder A, Boerwinkle E, Hunt SC, Rao DC. Body mass index modulates blood pressure heritability: the family blood pressure program. Am J Hypertens. 2014;27:610–9.

    Article  PubMed  Google Scholar 

  34. Lim JE, Kim HO, Rhee SY, Kim MK, Kim Y-J, Oh B. Gene–environment interactions related to blood pressure traits in two community-based Korean cohorts. Genet Epidemiol. 2019;43:402–13.

    Article  PubMed  Google Scholar 

  35. Lewis CM, Vassos E. Polygenic risk scores: from research tools to clinical instruments. Genome Med. 2020;12:44.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Igo RP Jr, Kinzy TG, Cooke Bailey JN. Genetic risk scores. Curr Protoc Hum Genet. 2019;104:e95.

    Article  PubMed  PubMed Central  Google Scholar 

  37. What will it take to make precision health a global reality. Nat Med. 2024;30:1793–4.

  38. Pearson Thomas A, Vitalis D, Pratt C, Campo R, Armoundas Antonis A, Au D, et al. The science of precision prevention. JACC: Advances. 2024;3:100759.

    PubMed  Google Scholar 

  39. Hekler E, Tiro JA, Hunter CM, Nebeker C. Precision health: the role of the social and behavioral sciences in advancing the vision. Ann Behav Med. 2020;54:805–26.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wray NR, Lin T, Austin J, McGrath JJ, Hickie IB, Murray GK, et al. From basic science to clinical application of polygenic risk scores: a primer. JAMA Psychiatry. 2021;78:101–9.

    Article  PubMed  Google Scholar 

  41. Bloss CS, Schork NJ, Topol EJ. Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med. 2011;364:524–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wang X, Snieder H. Assessing genetic risk of hypertension at an early age: future research directions. Expert Rev Cardiovasc Ther. 2017;15:809–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Flegal KM, Shepherd JA, Looker AC, Graubard BI, Borrud LG, Ogden CL, et al. Comparisons of percentage body fat, body mass index, waist circumference, and waist-stature ratio in adults. Am J Clin Nutr. 2009;89:500–8.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The NDIT study was supported by the Canadian Cancer Society (grant numbers 010271, 017435, 704031) and the Canadian Institutes of Health Research (grant number 364471). MPS is supported by aa salary award from the Fonds de recherche du Québec-Santé. JOL held a Tier 1 Canada Research Chair from 2004–2021. TR is supported by the Fonds de recherche du Québec-Santé and Bureau de la Coopération Interuniversitaire doctoral scholarships. The authors thank the NDIT participants, their parents, and the schools that participated in the NDIT study.

Author information

Author notes

  1. These authors contributed equally: Teodora Riglea, Tatiana Dessy.

Authors and Affiliations

  1. Centre de recherche du Centre hospitalier de l’Université de Montréal (CrCHUM), Montréal, QC, Canada

    Teodora Riglea, Jodi Kalubi, Jennifer O’Loughlin & Marie-Pierre Sylvestre

  2. Department of Social and Preventive Medicine, Université de Montréal, Montréal, QC, Canada

    Teodora Riglea, Tatiana Dessy, Jodi Kalubi, Fiston Ikwa Ndol Mbutiwi, Jennifer O’Loughlin & Marie-Pierre Sylvestre

  3. Montreal Heart Institute, Montréal, QC, Canada

    Tatiana Dessy

  4. Centre de Pharmacogénomique Beaulieu-Saucier de l’Université de Montréal, Montréal, QC, Canada

    Tatiana Dessy

  5. Centre de recherche en santé publique (CReSP), Université de Montréal & CIUSSS du Centre-Sud-de-l’Île-de-Montréal, Montreal, QC, Canada

    Jodi Kalubi

  6. School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada

    Danick Goulet

  7. Faculty of Medicine, University of Kikwit, Kikwit, Democratic Republic of the Congo

    Fiston Ikwa Ndol Mbutiwi

  8. Case Western Reserve University School of Medicine Department of Population and Quantitative Health Sciences, Cleveland, OH, USA

    Scott M. Williams

  9. Cleveland Institute for Computational Biology, Cleveland, OH, USA

    Scott M. Williams

  10. McGill University Department of Medicine, Montréal, QC, Canada

    James C. Engert & Hao Yu Chen

  11. McGill University Department of Human Genetics, Montréal, QC, Canada

    James C. Engert

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Contributions

TR and TD are co-first authors and prepared the first draft of the manuscript under the supervision of MPS. TR conducted the literature review. TD performed the statistical analysis. JOL acquired the funding for the NDIT study, and JOL, MPS, SMW and JCE acquired the funding for the genetic data collection and analysis. TR, TD, JK, DG, FINM, SMW, JCE, HYC, JOL, and MPS contributed to the interpretation of the results and critically reviewed the manuscript. All authors have read and approved the submission of the manuscript.

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Correspondence to Marie-Pierre Sylvestre.

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Riglea, T., Dessy, T., Kalubi, J. et al. Body mass index modifies genetic susceptibility to high systolic blood pressure in adolescents and young adults: results from an 18-year longitudinal study. J Hum Hypertens 39, 334–342 (2025). https://doi.org/10.1038/s41371-025-01003-x

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