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Vascular aging (VA) involves structural and functional changes in blood vessels that contribute to cardiovascular disease. Several noninvasive pulse wave (PW) indices have been proposed to assess the arterial stiffness component of VA in the clinic and daily life. This study investigated 19 of these indices, identified in recent review articles on VA, by using a database comprising 3,837 virtual healthy subjects aged 25-75 yr, each with unique PW signals simulated under various levels of artificial noise to mimic real measurement errors. For each subject, VA indices were calculated from filtered PW signals and compared with the precise theoretical value of aortic Young's modulus ( E Ao ). In silico PW indices showed age-related changes that align with in vivo population studies. The cardio-ankle vascular index (CAVI) and all pulse wave velocity (PWV) indices showed strong linear correlations with E Ao (Pearson's r p > 0.95). Carotid distensibility showed a strong negative nonlinear correlation (Spearman's r s < -0.99). CAVI and distensibility exhibited greater resilience to noise compared with PWV indices. Blood pressure-related indices and photoplethysmography (PPG)-based indices showed weaker correlations with E Ao ( r p and r s < 0.89, | r p | and | r s | < 0.84, respectively). Overall, blood pressure-related indices were confounded by more cardiovascular properties (heart rate, stroke volume, duration of systole, large artery diameter, and/or peripheral vascular resistance) compared with other studied indices, and PPG-based indices were most affected by noise. In conclusion, carotid-femoral PWV, CAVI and carotid distensibility emerged as the superior clinical VA indicators, with a strong E Ao correlation and noise resilience. PPG-based indices showed potential for daily VA monitoring under minimized noise disturbances. NEW & NOTEWORTHY For the first time, 19 noninvasive pulse wave indices for assessing vascular aging were examined together in a single database of nearly 4,000 subjects aged 25-75 yr. The dataset contained precise values of the aortic Young's modulus and other hemodynamic measures for each subject, which enabled us to test each index's ability to measure changes in aortic stiffness while accounting for confounding factors and measurement errors. The study provides freely available tools for analyzing these and additional indices.

The arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health. It is widely measured by both consumer and clinical devices. However, the physical determinants of the PW are not yet fully understood, and the development of PW analysis algorithms is limited by a lack of PW data sets containing reference CV measurements. Our aim was to create a database of PWs simulated by a computer to span a range of CV conditions, representative of a sample of healthy adults. The typical CV properties of 25-75 yr olds were identified through a literature review. These were used as inputs to a computational model to simulate PWs for subjects of each age decade. Pressure, flow velocity, luminal area, and photoplethysmographic PWs were simulated at common measurement sites, and PW indexes were extracted. The database, containing PWs from 4,374 virtual subjects, was verified by comparing the simulated PWs and derived indexes with corresponding in vivo data. Good agreement was observed, with well-reproduced age-related changes in hemodynamic parameters and PW morphology. The utility of the database was demonstrated through case studies providing novel hemodynamic insights, in silico assessment of PW algorithms, and pilot data to inform the design of clinical PW algorithm assessments. In conclusion, the publicly available PW database is a valuable resource for understanding CV determinants of PWs and for the development and preclinical assessment of PW analysis algorithms. It is particularly useful because the exact CV properties that generated each PW are known. NEW & NOTEWORTHY First, a comprehensive literature review of changes in cardiovascular properties with age was performed. Second, an approach for simulating pulse waves (PWs) at different ages was designed and verified against in vivo data. Third, a PW database was created, and its utility was illustrated through three case studies investigating the determinants of PW indexes. Fourth, the database and tools for creating the database, analyzing PWs, and replicating the case studies are freely available.

Objective: A recent numerical study investigated the potential utility of peripheral PWV measurements for assessing aortic stiffness by simulating pulse wave propagation through the arterial tree., Approach: In this Comment we provide additional analysis of the simulations in which arterial compliances were changed., Main Results: The analysis indicates that relationships between aortic and peripheral pulse transit times (PTTs) may not be constant when compliances change., Significance: Peripheral PWV measurements may have greatest utility in particular clinical settings in which either an assumption can be made about possible changes in compliance, allowing aortic PTT to be estimated from peripheral PTT; or, one wishes to assess changes in peripheral PWV over time.

Vascular aging (VA) involves structural and functional changes in blood vessels that contribute to cardiovascular disease. Several noninvasive pulse wave (PW) indices have been proposed to assess the arterial stiffness component of VA in the clinic and daily life. This study investigated 19 of these indices, identified in recent review articles on VA, by using a database comprising 3,837 virtual healthy subjects aged 25-75 yr, each with unique PW signals simulated under various levels of artificial noise to mimic real measurement errors. For each subject, VA indices were calculated from filtered PW signals and compared with the precise theoretical value of aortic Young's modulus (EAo). In silico PW indices showed age-related changes that align with in vivo population studies. The cardio-ankle vascular index (CAVI) and all pulse wave velocity (PWV) indices showed strong linear correlations with EAo (Pearson's rp > 0.95). Carotid distensibility showed a strong negative nonlinear correlation (Spearman's rs < -0.99). CAVI and distensibility exhibited greater resilience to noise compared withPWVindices.Blood pressure-related indices and photoplethysmography (PPG)-based indices showed weaker correlations with EAo (rp and rs < 0.89, ΙrpΙ and ΙrsΙ < 0.84, respectively). Overall, blood pressure-related indices were confounded by more cardiovascular properties (heart rate, stroke volume, duration of systole, large artery diameter, and/or peripheral vascular resistance) compared with other studied indices, and PPG-based indices were most affected by noise. In conclusion, carotid- femoral PWV, CAVI and carotid distensibility emerged as the superior clinical VA indicators, with a strong EAo correlation and noise resilience. PPG-based indices showed potential for dailyVAmonitoring underminimized noise disturbances. [ABSTRACT FROM AUTHOR]

• A method was developed to simulate arterial pulse waves (PWs) across ages, including different circle of Willis (CoW) variations. • A PW database comprising over 30,000 virtual subjects was created and verified using in vivo data. • Seven machine learning models were trained and tested to classify CoW topology based on carotid and vertebral flow velocity PWs. • Both the database and machine learning models are freely available. The topology of the circle of Willis (CoW) is crucial in cerebral circulation and significantly impacts patient management. Incomplete CoW structures increase stroke risk and post-stroke damage. Current detection methods using computed tomography and magnetic resonance scans are often invasive, time-consuming, and costly. This study investigated the use of machine learning (ML) to classify CoW topology through arterial blood flow velocity pulse waves (PWs), which can be noninvasively measured with Doppler ultrasound. A database of in silico PWs from 30,618 virtual subjects, aged 25 to 75 years, with complete and incomplete CoW topologies was created and validated against in vivo data. Seven ML architectures were trained and tested using 45 combinations of carotid, vertebral and brachial artery PWs, with varying levels of artificial noise to mimic real-world measurement errors. SHapley Additive exPlanations (SHAP) were used to interpret the predictions made by the artificial neural network (ANN) models. A convolutional neural network achieved the highest accuracy (98%) for CoW topology classification using a combination of one vertebral and one common carotid velocity PW without noise. Under a 20% noise-to-signal ratio, a multi-layer perceptron model had the highest prediction rate (79%). All ML models performed best for topologies lacking posterior communication arteries. Mean and peak systolic velocities were identified as key features influencing ANN predictions. ML-based PW analysis shows significant potential for efficient, noninvasive CoW topology detection via Doppler ultrasound. The dataset, post-processing tools, and ML code, are freely available to support further research. [ABSTRACT FROM AUTHOR]

The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age. [ABSTRACT FROM AUTHOR]