Abstract
Introduction. This literature review focuses on modern methods for assessing cardiovascular risk using photoplethysmography and heart rate variability. Non-invasive approaches to assessing vascular wall health, arterial stiffness, heart rate, and microcirculation are discussed, along with their relationships with cardiovascular risk factors. Objective: To analyze the potential of photoplethysmography as a non-invasive method for early detection of cardiovascular risk and to justify the need for its more widespread use in clinical practice. Materials and methods: A search of international and domestic sources on methods for assessing heart rate variability and photoplethysmography was conducted across PubMed, Google Scholar, the Cochrane Library, Scopus Preview, eLibrary, and Cyberleninka databases. The search period was 10 years. Results and discussion. The review revealed the potential of photoplethysmography and heart rate variability as informative digital markers that can reflect subclinical vascular changes and predict cardiovascular events. However, existing limitations are highlighted: the need for standardized signal recording protocols, variability in data quality, limited data on long-term clinical outcomes, and limited external validation of the models. The presented data support the need for further prospective studies, improved signal quality, the development of interpretable AI models, and the integration of photoplethysmography/heart rate variability with other biomarkers for a comprehensive assessment of cardiovascular health. Conclusions. The literature review demonstrates that photoplethysmography is a promising, accessible, non-invasive method for early assessment of cardiovascular health. Photoplethysmography signal analysis allows for the detection of arterial stiffness, vascular tone, and other early subclinical changes associated with сardiovascular diseases risk. However, despite its high potential, the clinical application of this method is limited by the lack of standardized protocols, device heterogeneity, variability in data quality, insufficient clinical validation, and the predominance of cross-sectional studies without long-term outcomes.