Introduction: Lipid metabolism is one of the main concerns of cardiovascular disease and atherosclerosis. Little is known about the association between dietary patterns and dyslipidemia. Therefore, the present study aimed to determine such association among Iranian adults. Methods: This cross-sectional study was conducted on 1433 Iranian adults in Isfahan Healthy Heart Program (IHHP). Usual dietary intakes were assessed with the use of a 48 items food frequency questionnaire (FFQ). Factor analysis was used to identify dietary patterns. Three major dietary patterns were identified: western, semi healthy and healthy fat patterns. Results: After adjustment, subjects in the upper quartiles of western dietary pattern were more likely to have high total cholesterol concentrations than those in the first quartile (odds ratio [OR]: 2.07; 95% CI: 1.25-3.42). Individuals with greater adherence to western dietary pattern had greater odds of having high low-density lipoprotein-cholesterol (LDL-C) levels compared with those in the lowest quartiles (2.53; 1.45-4.40). Conclusion: Semi healthy dietary pattern was not associated with cardiovascular disease (CVD) risk factors. Same trend was observed for healthy fat dietary pattern. Significant association was found between western dietary pattern and dyslipidemia among Iranian adults.
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.