Assessment of Corrected QT Interval and QT Dispersion in Patients with Uncomplicated Metabolic Syndrome

Aim

Metabolic syndrome (MS) itself has become a new entity being a constellation of physiological, biochemical, metabolic, and clinical factors that have been related to an increased risk of cardiovascular diseases, type 2 diabetes mellitus (T2DM), and morbidity and mortality. The burden of MS is increasing all over the world with the current prevalence being 30%. The QT interval and QT dispersion (QTd) have been long associated with ventricular arrhythmia and sudden cardiac deaths. The association between QT and diabetes and healthy subjects has been clearly studied, but the association between QT and uncomplicated MS has not been very well defined.

Methods

A total of 400 patients visiting the medicine clinics were assessed for blood pressure, anthropometric measurements, fasting serum lipid profile, FBS, and electrocardiogram (ECG). The patients diagnosed with uncomplicated MS as per the International Diabetic Federation (IDF) criteria were included in the study. QT interval in the ECG was recorded, and the relationship between various parameters of an uncomplicated MetS and the QT interval was recorded.

Results

Our study revealed more females with uncomplicated MS as compared to males and increasing burden as age progressed. Body mass index (BMI), waist circumference (WC), hip circumference (HC), WHP, systolic blood pressure (SBP), Diastolic blood pressure (DBP), Fasting blood sugar (FBS), serum cholesterol, triglyceride (TG), and low-density lipoprotein (LDL) levels were positively correlated with QT values.

Conclusion

QT interval, a strong predictor of malignant ventricular arrhythmias and sudden cardiac deaths, is associated with uncomplicated MS.

Comparison of Heart Rate Variability, QTc, and JT Interval Between Diabetic Patients and Healthy Controls: Role of Gender and Phases of Menstrual Cycle

Background and objectives

Type 2 Diabetes mellitus (T2DM) is a heterogeneous group of metabolic disorders with variable degrees of insulin resistance and altered glucose metabolism. Increased attention in studying the role of gonadal hormones in diabetes is not only due to their relation to insulin sensitivity, and glucose tolerance but also to the gender-specific nature of the prevalence of various diabetic complications. The cyclical change in the hormone level in females will make it necessary to consider the menstrual cycle while analyzing the risk factors for diabetes. Hence, the role of gender and menstrual cycle in T2DM are analyzed here using the simple non-invasive cardiovascular risk indices like heart rate variability (HRV), QT interval corrected for heart rate (QTc), and JT interval.

Materials and methods

In this analytical study, T2DM patients in the age group of 18-45 years with less than five years duration from diagnosis and taking not more than two anti-hyperglycemic drugs were included. Time and frequency domains of HRV analysis, QTc, and JT intervals were compared with age and BMI matched control group. The comparison of these parameters was also made between two genders in the diabetic group and they were analyzed across different phases of the menstrual cycle in female diabetic patients when physiological variation in the gonadal hormones occurred as a natural phenomenon.

Results

HRV parameters were reduced and the QTc and JT intervals were prolonged in diabetic patients of both genders. Reduction in low-frequency (LF) band power and high-frequency (HF) band power of HRV analysis in diabetic females were statistically significant in the luteal phase of the menstrual cycle in comparison with age and BMI-matched healthy controls. There was no significant difference in the HRV parameters, QTc, and JT interval between the male and female diabetic groups. HF band power is significantly reduced in the menstrual phase and relatively higher in the follicular phase when compared to the luteal phase among female diabetic patients.

Conclusion

The reduced sympathetic and parasympathetic activity were observed in diabetic patients of both genders and they were significant in the luteal phase of diabetic females compared to the healthy control group. Vagal activity is relatively higher in the follicular phase of the menstrual cycle in female diabetic patients.

Increased serum gamma-glutamyltransferase levels are associated with ventricular instability in type 2 diabetes

The purpose of our study is to examine the association between serum GGT levels and ventricular instability in Chinese patients with T2DM. We conducted a cross-sectional, community-based study in Nanjing, China from June to November 2011. Among 10,050 patients aged 40-79 years, we enrolled 2444 with pre-diabetes, 2496 with T2DM, and 4521 without diabetes (non-diabetes). Electrocardiograms were performed to measure the QT interval corrected for heart rate (QTc) and QT interval dispersion (QTd). Serum GGT levels, metabolic parameters, body mass index, and blood pressure were also measured. We found that there were no significant associations of increased QTc/QTd with serum GGT levels in participants with pre-existing T2DM and non-diabetes, after adjusting for age, duration of diabetes, and metabolic parameters. Even after adjustment, higher risks of QTc ≥ 440 ms/√s and QTd ≥ 58 ms were found in participants with serum GGT levels ≥49 U/L compared with those with <15 U/L in the pre-diabetes (QTc: OR 1.96, 95 % CI 1.23-2.47; QTd: OR 1.34, 95 % CI 1.07-1.94) and newly diagnosed T2DM (QTc: OR 2.01, 95 % CI 1.39-2.51; QTd: OR 1.53, 95 % CI 1.03-1.99) groups. We conclude that Increased serum GGT levels are associated with some markers of ventricular repolarization abnormalities in the early stage of T2DM.

NON-INVASIVE ESTIMATION OF DIABETES RELATED FEATURES FROM ECG USING GRAPHICAL PROGRAMAMING LANGUAGE AND MATLAB

Electrocardiography deals with the electrical activity of the heart. The condition of cardiac health is given by the electrocardiogram (ECG). ECG analysis is one of the most important aspects of research in the field of biomedical sciences and healthcare. The precision in the identification of various parameters in the ECG is of great importance for the reliability of an automated ECG analyzing system and diagnosis of cardiac diseases. Many algorithms have been developed in the last few years, each with their own advantages and limitations. In this work, we have developed an algorithm for 12-lead ECG parameter detection which works in three steps. Initially, the signal is denoised by the wavelet transform approach using a graphical programming language called LabVIEW (Laboratory Virtual Instrument Engineering Workbench). Next, primary features are detected from the denoised ECG signal using Matlab, and lastly, the secondary features related to diabetes are estimated from the detected primary features. Diabetes mellitus (DM), which is characterized by raised blood glucose levels in an individual, affects an estimated 2–4% of the world's population, making it one of the major chronic illnesses prevailing today. Recently, there has been increasing interest in the study of relationship between diabetes and cardiac health. Thus, in this work, we estimate diabetic-related secondary ECG features like corrected QT interval (QTc), QT dispersion (QTd), P wave dispersion (PD), and ST depression (STd). Our software performance is evaluated using CSE DS-3 multi-lead data base and the data acquired at SGGS IE & T, Nanded, MS, which contains 5000 samples recorded at a sampling frequency of 500 HZ. The proposed algorithm gives a sensitivity of 99.75% and a specificity of 99.83%.

A method for characterizing circadian changes in QT intervals of diabetic patients

This paper proposes a method to characterize circadian changes in QT intervals for diabetic studies. Although properties of QT intervals for diabetic patients are extensively studied, their circadian changes are not fully understood. Recently, the traditional cosinor method has been utilized for a study examining the relationship between QT circadian changes and the insulin resistance of the diabetic patients. For better characterization of the circadian change in QT intervals of this kind, spline smoothing technique applied to a decimated data set of QT intervals is proposed. New indices named QT circadian transition time (QTCT) and QT circadian transition amplitude (QTCA) associated with the subjects' awakening process are defined to characterize diabetic patients' condition. The method is applied to ten normal and fifteen type 2 diabetic patients. The proposed indices showed significantly lower values for type 2 diabetic patients compared to the control subjects indicating their effectiveness for the characterization.