Novel molecular plasma signatures on cardiovascular disease can stratify patients throughout life.

Several models are available to calculate the risk of developing cardiovascular complications in mid-life. The estimation of lifetime risk in the long-term remains an unmet clinical need. We previously identified new molecular plasma signatures for cardiovascular risk stratification in a young population (30-50-years old). The aim of the present study was to determine if the specific signature found in young population changes with age. Proteomic analysis was performed in plasma samples obtained from different age groups, middle-age (50-70-years old, n = 63) and elderly (>70-years old, n = 61), which, in turn were classified into 3 subgroups according to cardiovascular risk. Our previous results in a young population clearly showed two different proteomic signatures. Building on these findings, targeted-mass spectrometry and turbidimetry analyses were used to test these signatures in middle-age and elderly populations. This strategy identified three common proteomic signatures between young and adult patients related to cardiovascular stratification, organ damage and risk prediction. Furthermore, receiver operating characteristic analysis revealed the potential value of these novel markers for lifetime risk stratification. Our results provide new insight into altered molecular mechanisms in the pathogenesis of cardiovascular disease and, more importantly, identify novel protein panels that can stratify patients throughout life. SIGNIFICANCE: Our results revealed three common proteomic signatures between young and adult patients related to cardiovascular stratification, organ damage and risk prediction. The results obtained provide a deeper insight into the pathogenesis of CV diseases and allow the identification of novel protein panels to stratify patients according to CV risk throughout life. While current estimators calculate the risk of having a CV event considering age as the most important factor to CV disease, our results represent an alternative to traditional CV risk factors, allowing the stratification of CV risk regardless of the age. Using a combination of traditional markers and established algorithms with these findings as a future preventive strategy, could facilitate an adequate assessment of CV risk.