Stratification of the Type 2 Diabetes Mellitus Based on Heart Rate Variability Parameters in Elderly Women at Rest

Abstract

Cardiac autonomic neuropathy in type 2 diabetes mellitus (T2DM) patients is frequent and associated with high cardiovascular mortality. The purpose of the present study was to stratify the T2DM using a logistic model based on parameters derived from heart rate variability (HRV). This study was designed as a cross-sectional study of consisted of thirty elderly women subjects 60 to 70 yrs of age with diagnosed with T2DM (N = 15) and healthy (N = 15). All subjects were instructed to lie in the supine position for 5 min at rest while breathing normally with a heart rate monitor Polar RS810 working at a sampling rate of 1000 Hz was used to record RR intervals (RRi). The HRV analysis in the time domain was performed to obtain the classical parameters pNN50, SDNN, RMSSD and MeanRRi and, subsequently, re-sampling procedure to bootstrapping based on 1000 samples. The model for predicting T2DM was obtained by backward stepwise multivariate logistic regression assuming as independent variable MeanRRi. This model presented 0.80 positive predictive value, 0.73 negative predictive value and 0.76 total accuracy. In conclusion, the use of the proposed MeanRRi parameter measured at rest seems to be able to stratify the T2DM in elderly women. The benefits of HRV monitoring the severity of T2DM should be potential as a reliable and non-invasive.