Background: This study investigated whether expanding waist circumference (WC) is causally associated with an elevated risk of coronary heart disease (CHD), using a two-sample Mendelian randomization (MR) study through integrating summarized data from genome-wide association study. Methods: The data included in this analysis were mainly from the Genetic Investigation of ANthropometric Traits (GIANT), Consortium and Coronary ArteryDisease Genome wide Replication, and Meta-analysis plus the Coronary Artery Disease (C4D) Genetics (CARDIoGRAMplusC4D) Consortium. Three statistical approaches, inverse-variance weighted (IVW), weighted median, and MR-Egger regression method were conducted to assess the casual relationship. The exposure was WC, measured by 46 single-nucleotide polymorphisms from GIANT and the outcome was the risk of CHD. Then, we used the genetic data from Neale Lab and TAG to infer whether WC causally affected the established risk factors of CHD. Results: The IVW method presented that genetically predicted WC was positively casually associated with CHD (odds ratio [OR]: 1.57, 95% CI = 1.33-1.84; p = 4.81e-08), which was consistent with the result of weighted median and MR-Egger regression. MR-Egger regression indicated that there was no directional horizontal pleiotropy to violate the MR assumption. Additionally, expanded WC was also associated with higher risk of hypertension and diabetes, higher cholesterol, more smoking intensity, and decreased frequency of physical activity. Conclusion: Our analysis provided strong evidence to indicate a causal relationship between WC and increased risk of CHD. K E Y W O R D Scoronary heart disease, Mendelian randomization, waist circumference 2 of 11 | CHEN Et al.

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1130 radiomic characteristics were extracted from each patient using an open source software package. Furthermore, image pre-processing was performed with open source software. We used feature-selection methods, such as LASSO, Recursive Feature Elimination, forward selection, backward selection, Boruta, and Multivariate Adaptive Regression Spline (MARS), and evaluated their reduction efficiency and consistencies in feature selection. The MARS method provided the best results by identifying nine consistent features, which were used to build survival models. The accuracy of all models was estimated using 50-fold cross-validation. Results: The Cox regression model was predictive with a C-index of 0.6552. The decision tree method exhibited the following: