Association of telomere length with diabetes mellitus and idiopathic dilated cardiomyopathy in a South Indian population: A pilot study.

Telomere shortening has been associated with ageing and with many age-related diseases including cancer, coronary artery disease, heart failure and diabetes. We sought to investigate the link between telomere shortening and age-related diseases like type 2 diabetes mellitus (DM) (without any complications: DM; with neuropathic complication: DN) and idiopathic dilated cardiomyopathy (IDCM) in south Indian population. We compared telomere lengths of blood lymphocytes taken from patients with associated age-related diseases, namely DM (n = 47), DN (n = 52) and IDCM (n = 34) and controls (n = 46). In addition, we evaluated the relationship between echocardiographic left ventricular ejection fraction (LVEF), left ventricular end diastolic and systolic diameters (LVEDd and LVESd) and telomere length in IDCM patients. Telomere length negatively correlated with age in the cohorts with diabetes and IDCM, and in controls. Average telomere length in diabetes and IDCM patients was significantly shorter than that of controls either before or after adjustments for age and sex. Duration of diabetes in patients with type 2 diabetes did not correlate with telomere length. No correlation was found between the length of telomeres and echocardiography parameters like LVEF, LVEDd and LVESd in IDCM patients. Though echocardiographic characteristics of IDCM did not correlate with telomere length, telomere shortening was found to be accelerated in diabetes (both DM and DN) and IDCM in a south Indian population. Neuropathic complication in diabetes had no effect on telomere shortening. While telomere shortening is a cause or a consequence of diabetic and cardiac pathology remains further investigation, the current study substantiates the usefulness of telomere length measurements as a marker in conjunction with other biochemical markers of age-related diseases.

Classification of PCR-SSCP bands in T2DM by probabilistic neural network: a reliable tool.

A Probabilistic Neural Network (PNN) is a statistical algorithm and consists of a grouping of multi-class data. The conventional method of detection of DNA mutations by the human eye may not detect the minute variations in PCR-SSCP bands, which may lead to false positive or false negative results. The detection by photographic images may contain a blare (noise) caused during the time of photography; therefore, image processing techniques were used to reduce image noise. PCR-SSCP gels of T2DM patients (n = 100) and controls (n = 100) were initially photographed with equal ratio of pixels and later subjected to a two-stage analysis: feature extraction and PNN. The evaluation of the results was done by quality training and the accuracy was up to 95%, and the human eye analysis showed 80% mutation detection rate. This study proves to be very reliable and gives accurate and fast detection for mutation analysis in diabetes. This method could be extended for analysis in other human diseases.