A marked increase in the population aged 60 years and over is evident; the proportion of the older adult population will rise 18.6% in 2025. On the other hand, obesity, metabolic syndrome (MS), diabetes and insulin resistance (or low insulin sensitivity-IS) are diseases related to lifestyle, they have become a social and public health problem. IS is the ability of cells to react due to insulin´s presence; when this ability is diminished, low insulin sensitivity or insulin resistance (IR) is considered. Studies show that IS decreases with age, though no one knows exactly if it is directly due to aging or changes in muscle mass. IS can be determined using direct or indirect methods. This paper aims to propose an insulin sensitivity method design from anthropometries and lipid measures. The methodology consist in a simple correspondence analysis for determine the variables, and a parametrical optimization using Avignon method as optimal function. The database used is composed by 120 Ecuadorian older adults with and without MS. The results show that the proposed optimized method got a better correlation with Avignon compared to non-optimized method. The proposed method could discriminate between subjects with and without IR and with and without MS. This is an important contribution since other methods like HOMA-IR, which is the most used in clinical practice, cannot find these differences, this means that HOMA-IR is not sensitive for IS estimation in elderly people. Future works will focus in the determination of cutoffs for insulin resistance diagnosis in the proposed method.
Insulin sensitivity (IS) is the ability of cells to react due to insulin´s presence; when this ability is diminished, low insulin sensitivity or insulin resistance (IR) is considered. IR had been related to other metabolic disorders as metabolic syndrome (MS), obesity, dyslipidemia and diabetes. IS can be determined using direct or indirect methods. The indirect methods are less accurate and invasive than direct and they use glucose and insulin values from oral glucose tolerance test (OGTT). The accuracy is established by comparison using spearman rank correlation coefficient between direct and indirect method. This paper aims to propose a lipid-anthropometric index which offers acceptable correlation to insulin sensitivity index for different populations (DB1=MS subjects, DB2=sedentary without MS subjects and DB3=marathoners subjects) without to use OGTT glucose and insulin values. The proposed method is parametrically optimized through a random cross-validation, using the spearman rank correlation as comparator with CAUMO method. CAUMO is an indirect method designed from a simplification of the minimal model intravenous glucose tolerance test direct method (MINMOD-IGTT) and with acceptable correlation (0.89). The results show that the proposed optimized method got a better correlation with CAUMO in all populations compared to non-optimized. On the other hand, it was observed that the optimized method has better correlation with CAUMO in DB2 and DB3 groups than HOMA-IR method, which is the most widely used for diagnosing insulin resistance. The optimized propose method could detect incipient insulin resistance, when classify as insulin resistant subjects that present impaired postprandial insulin and glucose values.
Heart diseases are the main cause of death worldwide. The first step in the diagnose of these diseases is the analysis of the electrocardiographic (ECG) signal. In turn, the ECG analysis begins with the detection of the QRS complex, which is the one with the most energy in the cardiac cycle. Numerous methods have been proposed in the bibliography for QRS complex detection, but few authors have analyzed the possibility of taking advantage of the information redundancy present in multiple ECG leads (simultaneously acquired) to produce accurate QRS detection. In our previous work we presented such an approach, proposing various data fusion techniques to combine the detections made by an algorithm on multiple ECG leads. In this paper we present further studies that show the advantages of this multi-lead detection approach, analyzing how many leads are necessary in order to observe an improvement in the detection performance. A well known QRS detection algorithm was used to test the fusion techniques on the St. Petersburg Institute of Cardiological Technics database. Results show improvement in the detection performance with as little as three leads, but the reliability of these results becomes interesting only after using seven or more leads. Results were evaluated using the detection error rate (DER). The multi-lead detection approach allows an improvement from DER = 3:04% to DER = 1:88%. Further works are to be made in order to improve the detection performance by implementing further fusion steps.