Chronic noncommunicable diseases (CNCD), including cardiovascular diseases (CVD), nutritional metabolic diseases, cancers, mental and psychological disorders, chronic respiratory diseases and connective tissue, and rheumatic diseases, account for 74% of global deaths, constituting a major public health challenge. An estimated 17 million people below 70!years of age die annually due to CNCD, and 86% of the premature deaths are happening in the middle- and lower-income countries. Therefore, a more comprehensive assessment of the association between the influencing factors and CNCD have significant public health implications. Our study aim to specifically assess the interplay and association between the influencing factors such as genetic factors, age, gender, lifestyle, environment, society, mental health and CNCD, and provide a more effective and comprehensive strategy to prevent CNCD. In our research, multivariable linear regression, logistic regression and cox proportional hazards models will be used to explore the associations between behavioral, mental or social factors and the outcome and mortality of CNCD. Moreover, the potential nonlinear relationships between measurable determinants and CNCD outcomes and mortality will be examined using restricted cubic spline regression. For multiple exposure analysis of environmental determinants, we will use the least absolute shrinkage and selection operator regression, weighted quantile sum regression and Bayesian Kernel Machine Regression. The effects of genetic variants on the above diseases will be investigated by Mendelian randomization. This research can help governments, enterprises, and medical institutions to better understand the factors influencing CNCD, and effectively control the growth of medical expenses to reduce the financial burden on the government, the work burden on hospitals, and the disease burden on patients. It has beneficial effects on the primary and secondary prevention of multiple CNCD.
