Hemogram-based decision tree for predicting the metabolic syndrome and cardiovascular diseases in the elderly.

BACKGROUND: This study aimed to build a hemogram-based decision tree to evaluate the association between current probability of metabolic syndrome (MetS) and prediction of future hypertension, type 2 diabetes and cardiovascular diseases (CVD) risk. METHODS: A total of 40 395 elder participants (≥60 years) were enrolled in a standard health examination program in Taiwan from January 1999 to December 2014. A decision tree classification of the presence or absence of MetS at baseline, using age, sex and hemogram (white blood cell, hemoglobin and platelet) as independent variables, was conducted for the randomly assigned training (70%) and validation (30%) groups. Participants without MetS at baseline (n = 25 643) were followed up to observe whether they developed MetS, hypertension, type 2 diabetes or CVD in the future. RESULTS: Modest accuracy of the decision tree in the training and validation groups with area under the curves of 0.653 and 0.652, respectively, indicated an acceptable generalizability of results. The predicted probability of baseline MetS was obtained from decision tree analysis. Participants without MetS at baseline were categorized into three equally sized groups according to the predicted probability. Participants in the third tertile had significantly higher risks of future MetS (hazard ratio 1.40, 95% confidence interval 1.25-1.58); type 2 diabetes (1.46, 1.17-1.83); hypertension (1.14, 1.01-1.28); and CVD (1.21, 1.01-1.44), compared with those in the first tertile. CONCLUSIONS: Execution of hemogram-based decision tree analysis can assist in early identification and prompt management of elderly patients at a high risk of future hypertension, type 2 diabetes and CVD.

Improvement of glycemic control in streptozotocin-induced diabetic rats by Atlantic salmon skin gelatin hydrolysate as the dipeptidyl-peptidase IV inhibitor.

In our previous study, Atlantic salmon skin gelatin hydrolysed with flavourzyme possessed 42.5% dipeptidyl-peptidase (DPP)-IV inhibitory activity at a concentration of 5 mg mL(-1). The oral administration of the hydrolysate (FSGH) at a single dose of 300 mg per day in streptozotocin (STZ)-induced diabetic rats for 5 weeks was evaluated for its antidiabetic effect. During the 5-week experiment, body weight increased, and the food and water intake was reduced by FSGH in diabetic rats. The daily administration of FSGH for 5 weeks was effective for lowering the blood glucose levels of diabetic rats during an oral glucose tolerance test (OGTT). After the 5-week treatment, plasma DPP-IV activity was inhibited; the plasma activity of glucagon-like peptide-1 (GLP-1), insulin, and the insulin-to-glucagon ratio were increased by FSGH in diabetic rats. The results indicate that FSGH has the function of inhibiting GLP-1 degradation by DPP-IV, resulting in the enhancement of insulin secretion and improvement of glycemic control in STZ-induced diabetic rats.

Fast search algorithms for vector quantization of images using multiple triangle inequalities and wavelet transform.

The encoding of vector quantization (VQ) needs expensive computation for searching the closest codevector to the input vector. This paper presents several fast encoding algorithms based on multiple triangle inequalities and wavelet transform to overcome this problem. The multiple triangle inequalities confine a search range using the intersection of search areas generated from several control vectors. A systematic way for designing the control vectors is also presented. The wavelet transform combined with the partial distance elimination is used to reduce the computational complexity of the distance calculation of vectors. The proposed algorithms provide the same coding quality as the full search method. The experimental results indicate that the new algorithms perform more efficiently than existing algorithms.