online ISSN 2415-3176
print ISSN 1609-6371
logoExperimental and Clinical Physiology and Biochemistry
  • 8 of 10
ECPB 2020, 90(2): 57–63
Research articles

Study of the effect of body fat content, measured by different methods, on the indicators of central hemodynamics


OBJECTIVES Investigate whether the effect of body fat on central hemodynamics is different in the body mass index (BMI) and fat distribution model using a bioimpedance method. BACKGROUND Central hemodynamic indicators are independent predictors of the risk of cardiovascular disease (CVD) development and progression. Obesity is a well-known risk factor for metabolic disorders and one of the first triggers of the mechanism of atherosclerosis. Excess weight leads to maladaptive modification of myocardium. However, another scientific works point on positive effect of overweight on prognosis and risk of development of CVD, so called “paradox of obesity”. For example, patients with hypertension and coronary heart disease (CHD) who were overweight had a lower mortality rate than the normal weight group. Another study also reported overall mortality from both CVD and noncardiovascular disease, which was higher in the group with the lowest BMI. Whereas another large study indicated a significantly increased risk of coronary heart disease and stroke in groups with a BMI> 25 compared with the normal weight group. In our study, we wanted to investigate whether the influence of body fat distribution, measured by various methods, on hemodynamic parameters differs. METHODS The survey was attended by 174 women who had their body composition indicators measured using a TANITA BC-601 weight analyzer and a BMI calculation. A total of 151 healthy women, aged 26–53 years, were selected for the survey (exclusion criteria were acute and chronic diseases, menopause, lactation or pregnancy) who were investigated for the parameters of central hemodynamics using the ReoCom (KhAI-Medica) rheographic complex. We made two models for statistical analysis. In the first model we divided women on groups depending on their BMI. In the second model we used bioimpedance method for grouping (depending on total fat content in percent, according to their age). Statistical analysis were processed using ANOVA analysis of variance and using the hypothesis of equality of the two means using Student’s t-test in the program STATISTICA 10.0 (StatSoftInc, USA). RESULTS Dispersion analysis showed that in the BMI fat grouping model, fat had an effect on more parameters of central hemodynamics than in the total fat% grouping model In the first model fat had statistically significantly effect on the following parameters: stroke volume, total blood volume, total peripheral vascular resistance, left ventricle work, index of left ventricle work, left ventricle capacity . In the BMI model, most indicators differed statistically significantly in all three groups (obesity, overweight and normal fat content), whereas in the other model, the hemodynamic indicators had a significant difference between the obese and normal fat groups. However, the mean of parameters of central hemodynamics in the respective groups were not significantly different in the two models. CONCLUSIONS The data obtained may indicate better use of a bioimpedance method for measuring body fat content compared to BMI measurement, for possible avoidance of the “obesity paradox”. However, for the statistical division into fat content groups, both methods are equivalent.

Added: 13.05.2020

Keywords: BMI, fat percentage, obesity, central hemodynamics, obesity paradox

Full text: PDF (Ukr) 392K

  1. 1. Jokinen E. Obesity and cardiovascular disease. Minerva Pediatr. 2015;67(1):25-32.
  2. 2. Marinou K, Tousoulis D, Antonopoulos AS, Stefanadi E, Stefanadis C. Obesity and car- diovascular disease: from pathophysiology to risk stratification. Int J Cardiol. 2010;138(1):3-8.
  3. 3. Uretsky S, Messerli FH, Bangalore S, Champion A, Cooper-Dehoff Rh. M, Zhou Q et al. Obesity paradox in patients with hypertension and coronary artery disease. Am J Med. 2007;120:863-70.
  4. 4. Tuomilehto J. Body mass index and prognosis in elderly hypertensive patients: a report from the European Working Party on High Blood Pressure in the Elderly. Am J Med. 1991;90:34S-41S.
  5. 5. Lu Y, Hajifathalian K, Ezzati M, Woodward M, Rimm EB, Danaei G. Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: A pooled analysis of 97 prospective cohorts with 1.8 million participants. Lancet. 2014;383:970-83.
  6. 6. Alexander JK, Dennis EW, Smith WG, Amad KH, Duncan WC, Austin RC. Blood vol- ume, cardiac output and distribution of systemic blood flow in extreme obesity. Cardiovasc Res Center Bull. 1962;1:39-44.
  7. 7. De Divitiis O, Fazio S, Petitto M, Maddalena G, Contaldo F, Mancini M. Obesity and cardiac function. Circulation. 1981;64:477-82.
  8. 8. Alexander JK. Obesity and cardiac performance. Am I Cardiol. 1964;14:860-5.
  9. 9. Alpert MA, Omran J, Mehra A, Ardhanari S. Impact of Obesity and Weight Loss on Cardiac Performance and Morphology in Adults. Progress in Cardiovascular Diseases. 2014;56(4):391-400.
  10. 10. 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(1):71-82.
  11. 11. Wells JC. Commentary: the paradox of body mass index in obesity assessment: not a good index of adiposity, but not a bad index of cardio-metabolic risk. Int J Epidemiol. 2014;43(3):672-4. 12. Ortega FB, Sui X, Lavie CJ, Blair SN. Body Mass Index, the Most Widely Used But Also Widely Criticized Index: Would a Criterion Standard Measure of Total Body Fat Be a Better Predictor of Cardiovascular Disease Mortality? Mayo Clin Proc. 2016;91:443-55.

Програмування -