The authors describe their aim as to assess predictors. However, the presented conclusions from the assessment are presented in terms of risk. It is thus unclear if it is the authors' ambition to develop a statistical model for individual prediction or a model for evaluating average risk factors. The former approach should have been based on an evaluation of sensitivity and specificity and include validation to avoid overfitting, see Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal 2014;35:1925–1931. The latter approach would need to be based on parameter estimation and include adjustment for potential confounding factors, see e.g. Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol 2008;8:70 and Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol 2013;177:292-298. A rationale for the adjustment, in terms of cause-effect relationships, would be expected. In both cases, I recommend complying with developed checklists, the TRIPOD Statement for prediction and the STROBE Statement for risk factor estimatimation (https://equator-network.org/).
I recommend avoiding the term "predictor" as this refers to individual prediction and not to the average effects that are estimated by the autors.