Aging-related processes such as cellular senescence are believed to underlie the accumulation of diseases in time, causing (co-)morbidity, including (pancreatic) cancer and (ischemic) stroke. Intervening into these processes may delay, stop or reverse morbidity. To study the link between (co-)morbidity and aging, by exploring biomarkers and molecular mechanisms of disease-triggered deterioration, we will recruit 100 patients with pancreatic ductal adenocarcinoma and ischemic stroke, and 50 controls, at the clinics of hematology/oncology and neurology of the Rostock University Medical Center. We will subject them to blood routine, clinical scores and patient-reported outcomes at up to 9 time points, including an in-depth transcriptomics & proteomics characterization at two early time points. The definition of outcomes aims for clinically relevant predictive biomarkers of morbidity (quality of life, probable sarcopenia, cognitive decline) and comorbidity (vascular event, cancer). The primary outcome is a composite of probable sarcopenia, clinical performance score and quality of life. The data analysis is comprehensive in that it includes biostatistics & machine learning, both following canonical role models & explorative approaches. Predictive biomarkers for “seno-interventions” may become available if the biomarkers that we find are predominantly related to aging / cellular senescence. Similarly, diagnostic biomarkers will be explored for their relationship to aging / cellular senescence. Our findings will require validation in independent studies, and our dataset shall be useful to validate the findings of other studies. In some of the explorative analyses, we shall include insights from systems-biology modeling as well as insights from animal models.