Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide and are driven by the interplay between oxidative stress and chronic inflammation. Although these processes have been extensively investigated, their integration into a unified mechanistic framework and their translational relevance for biomarker-guided precision medicine remain insufficiently addressed. This narrative review integrates mechanistic, biomarker, and translational perspectives on the redox–inflammation axis in atherosclerosis and acute ischemic stroke, critically evaluating oxidative and inflammatory biomarkers while highlighting current challenges and emerging therapeutic opportunities. Reactive oxygen and nitrogen species (ROS/RNS) promote endothelial dysfunction, lipid oxidation, and activation of redox-sensitive pathways, including NF-κB, Nrf2/Keap1, JAK/STAT, and the NLRP3 inflammasome, resulting in vascular inflammation and injury. Biomarkers such as hs-CRP, IL-6, MPO, MDA, F2-isoprostanes, oxLDL, and antioxidant enzyme activity have demonstrated associations with cardiovascular risk, disease severity, and clinical outcomes, and therefore show diagnostic and prognostic potential. However, with the exception of selected inflammatory biomarkers, most oxidative stress biomarkers have not yet been sufficiently validated for routine clinical use because of biological variability, limited specificity, and insufficient analytical standardization. Disappointing outcomes of antioxidant supplementation further reflect the complexity of redox biology and the dual physiological and pathological roles of ROS. Here, we highlight that future progress in cardiovascular medicine will require a shift from non-specific antioxidant supplementation to precision redox modulation, integrating multimarker profiling, system biology, multi-omics, and artificial intelligence and machine-learning approaches to improve cardiovascular risk stratification and personalized therapy.

Long-term survival after solid organ transplantation remains limited by chronic remodeling, fibrosis, vascular complications, and malignancy despite advances in immunosuppressive therapy. Current monitoring strategies primarily rely on functional and immunological parameters that often identify complications only after irreversible injury has occurred. There is a critical need for earlier, mechanistically informative biomarkers that can predict survival outcomes. Many platelet-associated growth factors (PDGF, TGF-β, VEGF, EGF, and IGF-1) are stored in platelet α-granules but can also originate from immune, endothelial, and stromal cells, regulate angiogenesis, extracellular matrix deposition, immune modulation, and tissue repair—processes central to graft adaptation and chronic injury. In this review, we propose the growth factor signaling network as a conceptual framework that potentially links platelet biology, ischemia-reperfusion injury, alloimmune responses, and chronic immunosuppression to sustained growth factor signaling and maladaptive graft remodeling. This framework should be interpreted as a biologically plausible integrative model rather than a fully validated mechanistic pathway in transplant recipients. Importantly, direct clinical evidence linking platelet activation markers (e.g., P-selectin, PF4, β-thromboglobulin) with circulating growth factor levels and long-term transplant outcomes remains limited, highlighting a critical gap in current biomarker research. Emerging clinical evidence suggests their potential prognostic relevance in transplant outcomes. Elevated TGF-β levels have been associated with increased risk of opportunistic infections, while early postoperative IGF-1 concentrations predict short-term survival. Increased VEGF-A levels correlate with primary graft dysfunction and cardiac allograft vasculopathy, while PDGF isoforms contribute to fibrotic and vascular progression across transplanted organs. However, their clinical applicability is limited by methodological variability and lack of large-scale validation. Rather than serving solely as markers of rejection, platelet-associated growth factors may reflect dynamic processes involved in transplant remodeling and mortality risk. Incorporating growth factor profiling into multiparametric survival prediction models may improve early risk stratification and support precision post-transplant management strategies.