[Studying the interaction effects of gene polymorphisms on low level of HDL over time using transition logic regression: Tehran lipid and glucose study]

As High-density lipoprotein [HDL] is directly associated with cardiovascular disease, the factors affecting the levels of this fat can be effective in reducing heart diseases. In addition to biochemical and environmental factors, genetic interactions also affect HDL level. Since polymorphism effects can be time-dependent, study of genetic interactions on HDL over time is important. In this study, we proposed Transition Logic Regression to analyze interactions in binary longitudinal data and used it to investigate polymorphism interactions related to low HDL over time. Data of 329 subjects who participated in three phases of TLGS was analyzed using the proposed model. Results showed that subjects with high triglyceride levels and increased waist circumference have an odds ratio of 2.29 [CI 95%: 1.51, 3.48] of having low HDL. Also, being in phase 2 and being a carrier of the minor allele of ApoA1M1 or being homozygous for the common allele of ApoCIII, were associated with an increased odds of having low HDL [OR= 2.30, CI 95%: 1.77, 2.99]. The odds ratio for having low HDL in male subjects with high blood pressure or being homozygous for the minor allele of SRB1 is 0.38 [CI 95%: 0.25,0.59]. Considering the identification of gene interactions in genetic studies and their importance over time, Transition Logic Regression was introduced and used to find gene interactions influencing low HDL over time and the most important models for gene interactions were identified

[ possibility of applying less tracer gas in ASHRAE-110-95 method of hood performance test]

A considerable amount of sulfur hexafluoride is applied to evaluate the performance of each laboratory hood according to ASHRAE-110-95 method.SF [6] is extremely hostile to environment and expensive. In present work, the possibility of conducting this method of hood performance test with less volume of SF [6] was investigated. The performance of a laboratory hood was evaluated using ASHRAE110-95 standard method at three different ventilation capacity as well as three different volumetric flow rates of injected SF 6 while a mannequin was located at the front of hood. Face velocity was measured 180 times using a thermal anemometer TA-2 model.Air flow was visualized through injecting low and high volume of smokes at 18 tests. Sulfur hexafluoride was injected at three different volumetric flow rates of 2, 3 and 4 lit / min.The occupational exposure of a hypothetic hood operator was determined 27 times through direct reading. The average and standard deviation of face velocity at hood inlet were 0.42 +/- 0.04, 0.6 +/- 0.07, 0.7 +/- 0.11 m / s respectively, ranging from 0.36-1.1 m / s. the studied hood did not have an acceptable performance when tested with high volumes of smoke, but it did have an acceptable performance while it was tested with low volumes of smoke. The application of ASHRAE 110-95 hood performance test with smaller volume of tracer sulfur hexafluoride gas is not recommended.

[Logic regression analysis for finding interaction effects of genes polymorphisms and other risk factors on low HDL: Tehran lipid and glucose study]

Logic regression is a generalized regression method that can identify complex Boolean interactions of binary variables. This method has been successfully used for analyzing single-nucleotide polymorphism data, because in SNP association studies interactions are important. The aim of this study is to investigate the associations between some candidate gene polymorphisms and HDL concentration using Logic Regression. Subjects for this cross sectional study, 436 subjects [172 men and 264 women] aged >/= 20 with some polymorphisms, were randomly selected from among participants of the Tehran Lipid and Glucose Study [TLGS]. Logic regression analysis was used to identify combinations of main genetic effects and interactions associated with HDL. Cross validation and randomization test were done to avoid over fitting of the models. Cross validation test suggested that the Logic model with four Boolean combinations and four predictors was the best logic model, which after fitting, showed that individuals who carry Apoe SNP Reversed Ze 3 or have high TG have an odds ratio of 2.35 [CI 95%:1.3-4.25] for having low HDL compared to other subjects. Also subjects with high TG have odds ratio 2.73 [CI 95%: 1.65,4.53] for having low HDL. Results of this study shows that Logic Regression is a powerful method to determine the interaction effect between high TG and ApoE SNP for having low HDL

Prediction of diabetes using logic regression

Detection of population at risk of type II diabetes, as a multi-factorial disease, is an important issue because of its individual and social impacts. To date, several studies have been conducted to predict the incidence of diabetes, using different statistical methods. However, despite its clinical importance, it is highly difficult to consider all interactions among risk factors, in ordinary statistical models. This study aimed to extract appropriate logic combination of type 2 diabetes risk factors employing the recently introduced method, Logic regression. The study population was selected from a cohort of the Tehran Lipid and Glucose Study [TLGS]. Data for 3523 participants, aged 20 years and over [57.8% female and 42.2% male] were entered into analysis, for which logistic logic regression method was used. The model parameters were estimated using the Annealing algorithm. To avoid overestimation, the optimal number of logic combinations was determined by the cross-validation method. Deviance, sensitivity and specificity measures were computed to evaluate the logic model and its comparison to ordinary logistic regression; the latter accommodated only the main effects. The prediction power of the two models was compared by Area under ROC curve. R software version 2.8.1 was employed for analyses. Logistic logic regression with the 4 Boolean combination including 5 variables was fitted using the Annealing algorithm and resulted in in deviance of 1203.30. This model had better fit compared to other logic models and also ordinary logistic regression with forward procedure [deviance=1206.88]. The Boolean combination of the above model included impaired fasting glucose [OR=5.53, 95%CI: 4.03-7.59], IGT [OR=5.54, 95%CI: 3.96-7.49], family history of diabetes [OR=1.89, 95%CI: 1.38-2.63], and interaction of high triglycerides or abnormal waist circumference [OR=2.4, 95%CI: 1.73-3.32]; all p-values <0.001. The area under ROC curve for the model was 0.843 [95%CI: 0.813-0.874]. This study showed that the logic regression as a newly introduced method has the ability of recognizing and modelling the interactions between different risk factors. Therefore, it is recommended as an appropriate tool for screening of the multi-factorial diseases such as diabetes