Elevated blood pressure in adolescent girls: correlation to body size and composition

Ashley L Devonshire, Erin R Hager, Maureen M Black, Marie Diener-West, Nicholas Tilton, Soren Snitker, Ashley L Devonshire, Erin R Hager, Maureen M Black, Marie Diener-West, Nicholas Tilton, Soren Snitker

Abstract

Background: To improve understanding of the pathophysiology of hypertension in adolescents and pave the way for risk stratification, studies have sought to determine the correlates of blood pressure (BP). Inconsistencies in dependent and independent variables have resulted in an elusive consensus. The aim of this report is to examine an inclusive array of correlates of BP, as a continuous (systolic and diastolic BP) and a dichotomous variable.

Methods: Subjects were a school-based sample of 730 urban, mostly African American, non-referred 6th and 7th grade girls. To find independent correlates of SBP/DBP, we used a stepwise model selection method based on the Schwarz Bayesian Information Criterion, enabling selection of a parsimonious model among highly correlated covariates. Candidate variables were: age, stature, heart rate, pubertal development, BMI, BMI z-score, waist circumference, waist-to-height ratio (WHtR), body surface area, fat mass (by bioelectrical impedance analysis), fat-free mass (FFM), percentage of body fat, and presence of overweight/obesity.

Results: The best-fitting models for DBP and SBP (considered separately) included fat-free mass, heart rate and, in the case of SBP, stature. The best-fitting model for high-normal/elevated blood pressure (H-N/EBP) included WHtR with no independent relation of any other variable. The prevalence of H-N/EBP tripled between a WHtR of 0.5 and 0.7.

Conclusions: The easily obtained and calculated WHtR is the strongest correlate of elevated blood pressure among available variables and is a prime candidate for longitudinal studies of predictors of the development of hypertension.

Trial registration: ClinicalTrials.gov Identifier, NCT00746083.

Figures

Fig. 1
Fig. 1
The curve is the prevalence estimate of H-N/EBP (high-normal/elevated blood pressure) in 730 adolescent girls as a function of waist-to-height ratio according to the generalized estimation equation (p <0.0001). Columns are observed prevalence stratified according to waist-to-height ratio (N = 257, 284, 141, and 48 respectively)

References

    1. Gillum RF, Prineas RJ, Horibe H. Maturation vs age: Assessing blood pressure by height. J Natl Med Assoc. 1982;74(1):43–6.
    1. Berenson GS, Cresanta JL, Webber LS. High blood pressure in the young. Annu Rev Med. 1984;35:535–60. doi: 10.1146/annurev.me.35.020184.002535.
    1. Kahn HS, Imperatore G, Cheng YJ. A population-based comparison of BMI percentiles and waist-to-height ratio for identifying cardiovascular risk in youth. J Pediatr. 2005;146:482–8. doi: 10.1016/j.jpeds.2004.12.028.
    1. Freedman DS, Kahn HS, Mei Z, Grummer-Strawn LM, Dietz WH, Srinivasan SR, et al. Relation of body mass index and waist-to-height ratio to cardiovascular disease risk factors in children and adolescents: the Bogalusa Heart Study. Am J Clin Nutr. 2007;86:33–40.
    1. Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444(7121):881–7. doi: 10.1038/nature05488.
    1. Roemmich JN, Clark PA, Lusk M, Friel A, Weltman A, Epstein LH, et al. Pubertal alterations in growth and body composition. VI. pubertal insulin resistance: Relation to adiposity, body fat distribution and hormone release. Int J Obes Relat Metab Disord. 2002;26:701–9. doi: 10.1038/sj.ijo.0801975.
    1. Schwarz G. Estimating the dimension of a model. Ann Statistics. 1978;6:461–4. doi: 10.1214/aos/1176344136.
    1. Cohen R. Introducing the GLMSELECT Procedure for Model Selection, Proceedings of the Thirty-First Annual SAS Users Group International Conference, 2006. Available at , accessed on 22 January 2016.
    1. Ogden CL, Lamb MM, Carroll MD, Flegal KM. Obesity and socioeconomic status in children and adolescents: United States, 2005-2008. NCHS Data Brief. 2010;51:1–8.
    1. Centers for Disease Control and Prevention. Growth Charts. Available at . Accessed on 22 January 2016.
    1. Du Bois D, Du Bois EF. A formula to estimate the approximate surface area if height and weight be known. Arch Intern Med. 1916;17:863–71. doi: 10.1001/archinte.1916.00080130010002.
    1. Taylor SJ, Whincup PH, Hindmarsh PC, Lampe F, Odoki K, Cook DG. Performance of a new pubertal self-assessment questionnaire: A preliminary study. Paediatr Perinat Epidemiol. 2001;15(1):88–94. doi: 10.1046/j.1365-3016.2001.00317.x.
    1. National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics. 2004;114(2 Suppl 4th Report):555–76. doi: 10.1542/peds.114.2.S2.555.
    1. Daniels SR, Kimball TR, Khoury P, Witt S, Morrison JA. Correlates of the hemodynamic determinants of blood pressure. Hypertension. 1996;28:37–41. doi: 10.1161/01.HYP.28.1.37.
    1. Syme C, Abrahamowicz M, Leonard GT, Perron M, Richer L, Veillette S, et al. Sex differences in blood pressure and its relationship to body composition and metabolism in adolescence. Arch Pediatr Adolesc Med. 2009;163:818–25. doi: 10.1001/archpediatrics.2009.92.
    1. Higgins M, Kannel W, Garrison R, Pinsky J, Stokes J., 3rd Hazards of obesity--the Framingham experience. Acta Med Scand Suppl. 1988;723:23–36.
    1. Vlasova M, Purhonen AK, Jarvelin MR, Rodilla E, Pascual J, Herzig KH. Role of adipokines in obesity-associated hypertension. Acta Physiol (Oxf) 2010;200(2):107–27. doi: 10.1111/j.1748-1716.2010.02171.x.
    1. Manolopoulos KN, Karpe F, Frayn KN. Gluteofemoral body fat as a determinant of metabolic health. Int J Obes (Lond) 2010;34(6):949–59. doi: 10.1038/ijo.2009.286.
    1. Savgan-Gurol E, Bredella M, Russell M, Mendes N, Klibanski A, Misra M. Waist to hip ratio and trunk to extremity fat (DXA) are better surrogates for IMCL and for visceral fat respectively than for subcutaneous fat in adolescent girls. Nutr Metab (Lond) 2010;7:86. doi: 10.1186/1743-7075-7-86.
    1. Savva SC, Tornaritis M, Savva ME, Kourides Y, Panagi A, Silikiotou N, et al. Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. Int J Obes Relat Metab Disord. 2000;24:1453–8. doi: 10.1038/sj.ijo.0801401.
    1. Hara M, Saitou E, Iwata F, Okada T, Harada K. Waist-to-height ratio is the best predictor of cardiovascular disease risk factors in Japanese schoolchildren. J Atheroscler Thromb. 2002;9:127–32. doi: 10.5551/jat.9.127.
    1. Meyer AA, Kundt G, Lenschow U, Schuff-Werner P, Kienast W. Improvement of early vascular changes and cardiovascular risk factors in obese children after a six-month exercise program. J Am Coll Cardiol. 2006;48:1865–70. doi: 10.1016/j.jacc.2006.07.035.
    1. Farpour-Lambert NJ, Aggoun Y, Marchand LM, Martin XE, Herrmann FR, Beghetti M. Physical activity reduces systemic blood pressure and improves early markers of atherosclerosis in pre-pubertal obese children. J Am Coll Cardiol. 2009;54:2396–406. doi: 10.1016/j.jacc.2009.08.030.
    1. Maggio AB, Aggoun Y, Martin XE, Marchand LM, Beghetti M, Farpour-Lambert NJ. Long-term follow-up of cardiovascular risk factors after exercise training in obese children. Int J Pediatr Obes. 2011;6:e603–10. doi: 10.3109/17477166.2010.530665.
    1. Butani L, Morgenstern BZ. Are pitfalls of oscillometric blood pressure measurements preventable in children? Pediatr Nephrol. 2003;18(4):313–8.

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