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Association between within-visit systolic blood pressure variability and development of pre-diabetes and diabetes among overweight/obese individuals

Journal of Human Hypertension volume 32, pages 26–33 (2018)Cite this article

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Abstract

Short-term blood pressure variability is associated with pre-diabetes/diabetes cross-sectionally, but there are no longitudinal studies evaluating this association. The objective of this study is to evaluate the association between within-visit systolic and diastolic blood pressure variability and development of pre-diabetes/diabetes longitudinally. The study was conducted among eligible participants from the San Juan Overweight Adults Longitudinal Study (SOALS), who completed the 3-year follow-up exam. Participants were Hispanics, 40–65 years of age, and free of diabetes at baseline. Within-visit systolic and diastolic blood pressure variability was defined as the maximum difference between three measures, taken a few minutes apart, of systolic and diastolic blood pressure, respectively. Diabetes progression was defined as development of pre-diabetes/diabetes over the follow-up period. We computed multivariate incidence rate ratios adjusting for baseline age, gender, smoking, physical activity, waist circumference, and hypertension status. Participants with systolic blood pressure variability ≥10 mmHg compared to those with <10 mmHg, showed higher progression to pre-diabetes/diabetes (RR = 1.77, 95% CI: 1.30–2.42). The association persisted among never smokers. Diastolic blood pressure variability ≥10 mmHg (compared to <10 mmHg) did not show an association with diabetes status progression (RR = 1.20, 95% CI: 0.71–2.01). Additional adjustment of baseline glycemia, C-reactive protein, and lipids (reported dyslipidemia or baseline HDL or triglycerides) did not change the estimates. Systolic blood pressure variability may be a novel independent risk factor and an early predictor for diabetes, which can be easily incorporated into a single routine outpatient visit at none to minimal additional cost.

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References

  1. Conway J, Boon N, Davies C, Jones JV, Sleight P. Neural and humoral mechanisms involved in blood pressure variability. J Hypertens. 1984;2:203–8.

    Article  CAS  Google Scholar 

  2. Faramawi MF, Delongchamp R, Said Q, Jadhav S, Abouelenien S. Metabolic syndrome is associated with visit-to-visit systolic blood pressure variability in the US adults. Hypertens Res. 2014;37:875–9.

    Article  Google Scholar 

  3. Li Y, Liu J, Wang W, Zhao D. The association between within-visit blood pressure variability and carotid artery atherosclerosis in general population. PLoS ONE. 2014;9:e97760.

    Article  Google Scholar 

  4. Mancia G, Grassi G. Mechanisms and clinical implications of blood pressure variability. J Cardiovasc Pharmacol. 2000;35(7Suppl 4):S15–9.

    Article  CAS  Google Scholar 

  5. Mokhtar RH, Ayob A, Mohd Noor N. Blood pressure variability in patients with diabetes mellitus. Asian Cardiovasc Thorac Ann. 2010;18:344–8.

    Article  Google Scholar 

  6. Ozawa M, Tamura K, Iwatsubo K, Matsushita K, Sakai M, Tsurumi-Ikeya Y, et al. Ambulatory blood pressure variability is increased in diabetic hypertensives. Clin Exp Hypertens. 2008;30:213–24.

    Article  Google Scholar 

  7. Schillaci G, Bilo G, Pucci G, Laurent S, Macquin-Mavier I, Boutouyrie P, et al. Relationship between short-term blood pressure variability and large-artery stiffness in human hypertension: findings from 2 large databases. Hypertension. 2012;60:369–77.

    Article  CAS  Google Scholar 

  8. Shin JH, Shin J, Kim BK, Lim YH, Park HC, Choi SI, et al. Within-visit blood pressure variability: relevant factors in the general population. J Hum Hypertens. 2013;27:328–34.

    Article  CAS  Google Scholar 

  9. Rossignol P, Kessler M, Zannad F. Visit-to-visit blood pressure variability and risk for progression of cardiovascular and renal diseases. Curr Opin Nephrol Hypertens. 2013;22:59–64.

    Article  CAS  Google Scholar 

  10. Nagai M, Hoshide S, Ishikawa J, Shimada K, Kario K. Visit-to-visit blood pressure variations: new independent determinants for carotid artery measures in the elderly at high risk of cardiovascular disease. J Am Soc Hypertens. 2011;5:184–92.

    Article  Google Scholar 

  11. Yano Y, Fujimoto S, Kramer H, Sato Y, Konta T, Iseki K, et al. Long-term blood pressure variability, new-onset diabetes mellitus, and new-onset chronic kidney disease in the Japanese general population. Hypertension. 2015;66:30–6.

    Article  CAS  Google Scholar 

  12. McKinlay S, Foster C, Clark A, Clark S, Kemp F, Denver E, et al. Increased blood pressure variability during 24h blood pressure monitoring as an early sign of autonomic dysfunction in non-insulin-dependent diabetics. J Hum Hypertens. 1994;8:887–90.

    CAS  PubMed  Google Scholar 

  13. Oktay AA, Akturk HK, Jahangir E. Diabetes mellitus and hypertension: a dual threat. Curr Opin Cardiol. 2016;31:402–9.

    Article  Google Scholar 

  14. Emdin CA, A S, Woodward M, Rahimi K. Usual blood pressure and risk of new-onset diabetes evidence from 4.1 million adults and a meta-analysis of prospective studies. J Am Coll Cardiol. 2015;66:1552–62.

    Article  Google Scholar 

  15. Okada R, Yasuda Y, Tsushita K, Wakai K, Hamajima N, Matsuo S. Within-visit blood pressure variability is associated with prediabetes and diabetes. Sci Rep. 2015;5:7964.

    Article  CAS  Google Scholar 

  16. Perloff D, Grim C, Flack J, Frohlich ED, Hill M, McDonald M, et al. Human blood pressure determination by sphygmomanometry. Circulation. 1993;88(5 Pt 1):2460–70.

    Article  CAS  Google Scholar 

  17. Curb JD, Labarthe DR, Cooper SP, Cutter GR, Hawkins CM. Training and certification of blood pressure observers. Hypertension. 1983;5:610–4.

    Article  CAS  Google Scholar 

  18. Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, et al. Recommendations for blood pressure measurement in humans and experimental animals: part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation. 2005;111:697–716.

    Article  Google Scholar 

  19. King GE. Influence of rate of cuff inflation and deflation on observed blood pressure by sphygmomanometry. Am Heart J. 1963;65:303–6.

    Article  CAS  Google Scholar 

  20. Global Recommendations on Physical Activity for Health. WHO Guidelines approved by the Guidelines Review Committee. Geneva, 2010.

  21. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22:719–48.

    CAS  PubMed  Google Scholar 

  22. Rothman KJ, Boice, John D. Epidemiologic analysis with a programmable calculator. Bethesda: U.S. Dept. of Health, Education, and Welfare, Public Health Service for sale by the Supt. of Docs.; 1979. p. 142.

  23. Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159:702–6.

    Article  Google Scholar 

  24. Levi-Marpillat N, Macquin-Mavier I, Tropeano AI, Parati G, Maison P. Antihypertensive drug classes have different effects on short-term blood pressure variability in essential hypertension. Hypertens Res. 2014;37:585–90.

    Article  CAS  Google Scholar 

  25. James GD, Toledano T, Datz G, Pickering TG. Factors influencing the awake-sleep difference in ambulatory blood pressure: main effects and sex differences. J Hum Hypertens. 1995;9:821–6.

    CAS  PubMed  Google Scholar 

  26. Kario K, Schwartz JE, Pickering TG. Ambulatory physical activity as a determinant of diurnal blood pressure variation. Hypertension. 1999;34(4 Pt 1):685–91.

    Article  CAS  Google Scholar 

  27. Loredo JS, Ancoli-Israel S, Dimsdale JE. Sleep quality and blood pressure dipping in obstructive sleep apnea. Am J Hypertens. 2001;14(9 Pt 1):887–92.

    Article  CAS  Google Scholar 

  28. Kario K, Schwartz JE, Gerin W, Robayo N, Maceo E, Pickering TG. Psychological and physical stress-induced cardiovascular reactivity and diurnal blood pressure variation in women with different work shifts. Hypertens Res. 2002;25:543–51.

    Article  Google Scholar 

  29. Chenniappan M. Blood pressure variability: assessment, prognostic significance and management. J Assoc Phys India. 2015;63:47–53.

    CAS  Google Scholar 

  30. de Vente W, van Amsterdam JG, Olff M, Kamphuis JH, Emmelkamp PM. Burnout is associated with reduced parasympathetic activity and reduced HPA axis responsiveness, predominantly in males. Biomed Res Int. 2015;2015:431725.

    Article  Google Scholar 

  31. Schulte W, Neus H, Thones M, von Eiff AW. Basal blood pressure variability and reactivity of blood pressure to emotional stress in essential hypertension. Basic Res Cardiol. 1984;79:9–16.

    Article  CAS  Google Scholar 

  32. Filipovsky J, Ducimetiere P, Eschwege E, Richard JL, Rosselin G, Claude JR. The relationship of blood pressure with glucose, insulin, heart rate, free fatty acids and plasma cortisol levels according to degree of obesity in middle-aged men. J Hypertens. 1996;14:229–35.

    Article  CAS  Google Scholar 

  33. Parati G, Ochoa JE, Salvi P, Lombardi C, Bilo G. Prognostic value of blood pressure variability and average blood pressure levels in patients with hypertension and diabetes. Diabetes Care. 2013;36(Suppl 2):S312–24.

    Article  Google Scholar 

  34. Kiba T. Relationships between the autonomic nervous system and the pancreas including regulation of regeneration and apoptosis: recent developments. Pancreas. 2004;29:e51–8.

    Article  Google Scholar 

  35. Ortiz-Alonso FJ, Herman WH, Zobel DL, Perry TJ, Smith MJ, Halter JB. Effect of epinephrine on pancreatic beta-cell and alpha-cell function in patients with NIDDM. Diabetes. 1991;40:1194–202.

    Article  CAS  Google Scholar 

  36. McEwen BS. Stress, adaptation, and disease. Allostasis and allostatic load. Ann N Y Acad Sci. 1998;840:33–44.

    Article  CAS  Google Scholar 

  37. Diaz KM, Veerabhadrappa P, Kashem MA, Thakkar SR, Feairheller DL, Sturgeon KM, et al. Visit-to-visit and 24-h blood pressure variability: association with endothelial and smooth muscle function in African Americans. J Hum Hypertens. 2013;27:671–7.

    Article  CAS  Google Scholar 

  38. Liu YP, Gu YM, Thijs L, Asayama K, Jin Y, Jacobs L, et al. Do level and variability of systolic blood pressure predict arterial properties or vice versa? J Hum Hypertens. 2014;28:316–22.

    Article  CAS  Google Scholar 

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Acknowledgements

We acknowledge the SOALS team (Dr. O.M. Andriankaja, PhD, DDS; Ms. T. Ginebra, RN; Ms. C. León, MPH; Ms. Y. Maldonado, BS; Dr. S. Martinez, PhD; Ms. X. O’Farrill; Ms. S. Ordaz, BS; Dr. C. Perez, PhD; Ms. E. Rodriguez, MT; Ms. R. Roman, MT; Mr. R. Ruiz, BS; Ms. Y. Santaella, RN; Ms. G. Velez, BS; Mr. J. Vergara, BS; Ms. L. Wah, MPH; Mr. J. Fernández) and PRCTRC laboratory personnel (Ms. A. Arroyo, MS, HIM, MT and Ms. N. Gonzalez, MS, MT) who contributed to the conduct/oversight/planning of data collection, and Ms. K. Giovannetti, MPH, MS for conducting a program review, for their help with the study. Research reported in this publication was supported by the National Institute of Dental and Craniofacial Research Grant R01DE020111 and the National Institute on Minority Health and Health Disparities Grant 2U54MD007587 of the National Institutes of Health.

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Authors and Affiliations

  1. Center for Clinical Research and Health Promotion, University of Puerto Rico Medical Sciences Campus, School of Dental Medicine, San Juan, PR, USA

    Kaumudi J. Joshipura, Francisco J. Muñoz-Torres, Maribel Campos & Alba D. Rivera-Díaz

  2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Kaumudi J. Joshipura

  3. Department of Medical and Population Sciences Research, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA

    Juan C. Zevallos

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  1. Kaumudi J. Joshipura
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Correspondence to Kaumudi J. Joshipura.

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Joshipura, K.J., Muñoz-Torres, F.J., Campos, M. et al. Association between within-visit systolic blood pressure variability and development of pre-diabetes and diabetes among overweight/obese individuals. J Hum Hypertens 32, 26–33 (2018). https://doi.org/10.1038/s41371-017-0009-y

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