Chronic pain (CP) is the leading cause of disability, and is strongly associated with fatigue, anxiety and depression ─ also major contributors to disability, and with cardiovascular disease (CVD) and mortality. Twin studies indicate that these associations are a consequence of common causal mechanisms. The main objective of PainFACT is to identify these mechanisms. Using hypothesis-free genomic, proteomic, transcriptomic and brain-imaging discovery in available human studies and in a large cohort of outbred mice with multiple comorbidities, we aim to identify biomarkers that are associated across conditions. Predictive algorithms will be developed through machine learning techniques and tested in prospective analysis. Mendelian randomization approaches will be applied to test for causality. Mechanistic studies will be carried out in validated behavioral and atherosclerotic mouse models. Predictive markers will be tested as possible mediators of effects of lifestyle and obesity. Unique features of this program of research is the strong emphasis on experimental pain models and brain imaging techniques, facilitating translation of findings between mice and humans, and exploitation of the largest study of experimental pain worldwide and of multiple clinical datasets ranging in size from tens of thousands to 1.1 million. A custom protein panel will be developed together with sex and age stratified algorithms, with expected impact for the prediction and monitoring of disease and comorbidity, and for tracking effects of life-style changes. It is also expected that PainFACT results will have major impact on the diagnostic criteria and classification of affective disorders and CP. The identification of novel causal biomarkers will provide new targets for development of medicines and yield new insight into the causes of comorbidity.