Cardiac autonomic modulation in response to a glucose stimulus.

This paper focuses on the effect of a sudden increase of plasma glucose concentration in the cardiac autonomic modulation using time-domain and frequency-domain heart rate variability (HRV) measures. Plasma glucose and insulin levels, measured each 30 min during an oral glucose tolerance test, and [Formula: see text] (mean of the RR interval), SDNN (standard deviation of normal-to-normal heartbeats), rMSSD (root-mean-square of successive differences between normal heartbeats), TP (total spectral power), LF and HF (power of the low- and high-frequency bands), LF norm and HF norm (LF and HF in normalized units), and LF/HF ratio of the HRV signal, obtained from 5-min-long ECG recordings during each phase of the test, were analyzed for subjects with the metabolic syndrome, marathon runners, and a control group. Results show that, after the glucose load, subjects with the metabolic syndrome experienced an increased sympathetic and decreased parasympathetic tone, which suggests an imbalance in cardiac autonomic modulation as a consequence of hyperglycemia and hyperinsulinemia. The significance of this study lies in the use of the ECG to assess the effects of a sudden increase in plasma glucose concentration on the cardiac autonomic modulation in subjects with different cardiovascular and metabolic conditions. Graphical Abstract Time-domain and frequency-domain heart rate variability measures are altered in subjects with different cardiovascular and metabolic conditions during an oral glucose tolerance test.

Comparing glucose and insulin data from the two-hour oral glucose tolerance test in metabolic syndrome subjects and marathon runners.

Glucose is the main energy source of the body's cells and is essential for normal metabolism. Two pancreatic hormones, insulin and glucagon, are involved in glucose home-ostasis. Alteration in the plasma glucose and insulin concentrations could lead to distinct symptoms and diseases, ranging from mental function impairment to coma and even death. Type 2 diabetes, insulin resistance and metabolic syndrome are typical examples of abnormal glucose metabolism that increase the risk for cardiovascular disease and mortality. The oral glucose tolerance test (OGTT) is a medical test used to screen for prediabetes, type 2 diabetes and insulin resistance. In the 5-sample 2-hour OGTT, plasma glucose and insulin concentrations are measured after a fast and then after oral intake of glucose, at intervals of 30 minutes. In this work, a statistical analysis is carried out to find significant differences between the five stages of the OGTT for plasma glucose and insulin data. In addition, the behavior of the glucose and insulin data is compared between subjects with the metabolic syndrome and marathon runners. Results show that marathon runners have plasma glucose and insulin levels significantly lower (p <; 0.05) than people with the metabolic syndrome in all the stages of the OGTT. Insulin secretion decreases in marathon runners due to a significant reduction in plasma glucose concentration, but insulin secretion does not decrease in metabolic syndrome subjects due to insulin resistance, consequently plasma glucose concentration does not achieve normal levels.

A new on-line electrocardiographic records database and computer routines for data analysis.

Gathering experimental data to test computer methods developed during a research is a hard work. Nowadays, some databases have been stored online that can be freely downloaded, however there is not a wide range of databases yet and not all pathologies are covered. Researchers with low resources are in need of more data they can consult for free. To cope with this we present an on-line portal containing a compilation of ECG databases recorded over the last two decades for research purposes. The first version of this portal contains four databases of ECG records: ischemic cardiopathy (72 patients, 3-lead ECG each), ischemic preconditioning (20 patients, 3-lead ECG each), diabetes (51 patients, 8-lead ECG each) and metabolic syndrome (25 subjects, 12-lead ECG each). In addition, one computer program and three routines are provided in order to correctly read the signals, and two digital filters along with two ECG waves detectors are provided for further processing. This portal will be constantly growing, other ECG databases and signal processing software will be uploaded. With this project, we give the scientific community a resource to avoid hours of data collection and to develop free software.