Differentiation of Overweight from Normal Weight Young Adults by Postprandial Heart Rate Variability and Systolic Blood Pressure

Abstract

Introduction: Obesity and cardiovascular disease are inextricably linked and the health community's response to the current epidemic of adolescent obesity may be improved by the ability to target adolescents at highest risk for developing cardiovascular disease in the future. Overweight manifests early as autonomic dysregulation and current methods do not permit differentiation of overweight adolescents or young adults at highest risk for developing cardiovascular disease.

Aim: This study was designed to test the hypothesis that scaling exponents motivated by nonlinear fractal analyses of Heart Rate Variability (HRV) differentiate overweight, otherwise healthy adolescent/young adult subjects at risk for developing prehypertension, the primary forerunner of cardiovascular disease.

Materials and methods: The subjects were 18-20year old males with Body Mass Index (BMI) 20.1-42.5kg/m(2). Electrocardiographic inter-beat (RR) intervals were measured during 3h periods of bed rest after overnight fasting and ingestion of 900Cal high-carbohydrate and high-fat test beverages on separate days. Detrended Fluctuation Analysis (DFA), k-means cluster and ANOVA analyses of scaling coefficients α, α(1), and α(2), showed dependencies on hourly measurements of systolic blood pressure and on premeasured BMI.

Results: It was observed that α value increased during the caloric challenge, appears to represent metabolically-induced changes in HRV across the participants. An ancillary analysis was performed to determine the dependency on BMI without BMI as a parameter. Cluster analysis of the high-carbohydrate test beverage treatment and the high-fat treatment produced grouping with very little overlap. ANOVA on both clusters demonstrated significance at p<0.001. We were able to demonstrate increased sympathetic modulation of our study group during ingestion and metabolism of isocaloric high-carbohydrate and high-fat test beverages.

Conclusion: These findings demonstrate significantly different clustering of α, α1, and α2 and Systolic Blood Pressure (SBP) with respect to normal, overweight and obese BMI.

Keywords: Body mass index; Detrended fluctuation analysis; Energy substrate metabolism; K-means cluster analysis; Obesity.

[Table/Fig-3]:

Fractal coefficients, application to metabolic challenges. α₁ is representative of the study intervention and α₂ of the overall health of the subject. α₁> α₂ indicates crossover and α₁< α₂ reverse crossover. The plots are shifted vertically for viewing purposes.

[Table/Fig-5]:

Fractal coefficients α, α₁, α₂, and body mass index, related k-means clusters for all time intervals and test beverage treatments. The legend on the right displays the cluster number and corresponding cluster marker.

[Table/Fig-9]:

K-means cluster analysis of the high-carbohydrate test beverage treatment based on the fractal coefficient parameter α and on the α₁, α₂ ratio. The legend on the right displays the cluster number and corresponding cluster marker. No significant differences for both systolic blood pressure and body mass index were found between any of the clusters.

[Table/Fig-10]:

K-means cluster analysis of the high-fat test beverage treatment based on the fractal coefficient parameter and on the α₁, α₂ ratio. The legend on the right displays the cluster number and corresponding cluster marker. Significant differences for both systolic blood pressure and for body mass index were found between clusters 1 and 5, 1 and 6, 1 and 7 and 1 and 8.