Prognosis and personalized in-silico prediction of treatment efficacy in cardiovascular and chronic kidney disease: a proof-of-concept study

(1) Backgroundkidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for onset and progression of these diseases and should hold information about optimal means for treatment and prevention. (2) Methodswe investigated prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2 and CAD160 in a cohort of 5585 subjects in a retrospective study. (3) Resultswe demonstrate highly significant prediction of events with HR of 2.59, 1.71, and 4.12 for HF, CAD and CKD respectively. We applied in silico treatment, implementing on each patient urinary profile, changes onto the classifiers corresponding to exactly defined peptide abundance changes following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil and exercise), as defined in previous studies. Applying the proteomic classifiers after in silico treatment indicated individual benefits of specific interventions on a personalized level. (4) Conclusionsthe in-silico evaluation may provide information on the future impact of specific drugs and intervention on endpoints, opening the door to a precision medicine approach. Investigation of the extent of the benefit of this approach in a prospective clinical trial is warranted.