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Pulse wave velocity (PWV) has been established as a promising biomarker in cardiovascular diagnostics, providing deep insights into vascular health and cardiovascular risk. Defined as the velocity at which the mechanical wave propagates along the arterial wall, PWV represents a useful surrogate marker for arterial vessel stiffness. PWV has garnered clinical attention, particularly in monitoring patients suffering from vascular diseases such as hypertension and diabetes mellitus. Its utility extends to preventive cardiology, aiding in identifying and stratifying cardiovascular risk. Despite the development of various measurement techniques, direct or indirect tonometry, Doppler ultrasound, oscillometric analysis, and magnetic resonance imaging (MRI), methodological variability and lack of standardization lead to inconsistencies in PWV assessment. In addition, PWV can be estimated through surrogate parameters, such as pulse arrival or pulse transit times, although this heterogeneity limits standardization and, therefore, its clinical use. Furthermore, confounding factors, such as variations in sympathetic tone, strongly influence PWV readings, thereby necessitating careful control during assessments. The bidirectional relationship between heart rate variability (HRV) and PWV underscores the interplay between cardiac autonomic function and vascular health, suggesting that alterations in one could directly influence the other. Future research should prioritize the standardization and increase comparability of PWV measurement techniques and explore the complex physiological variables influencing PWV. Integrating multiple physiological parameters such as PWV and HRV into algorithms based on artificial intelligence holds immense promise for advancing personalized vascular health assessments and cardiovascular care.

Pulse wave velocity (PWV) is the most widely used measurement of arterial stiffness in clinical practice. This study aimed to evaluate and compare the relationships between carotid‐femoral pulse wave velocity (cfPWV) and brachial‐ankle PWV (baPWV) and the presence of carotid plaque. This study was designed cross‐sectionally and included 6027 participants from a community‐based cohort in Beijing. Logistic regression analyses were performed to evaluate and compare the associations of cfPWV and baPWV with the presence of carotid plaque. The mean (SD) cfPWV and baPWV were 8.55 ± 1.83 and 16.79 ± 3.36, respectively. The prevalence of carotid plaque was 45.26% (n = 2728). Both cfPWV (per 1 m/s increase: OR = 1.11, 95% CI: 1.07–1.16) and baPWV (OR = 1.04, 95% CI: 1.02–1.06) were independently associated with carotid plaque after adjusting for various confounders. Compared with bottom quartile (cfPWV ≤7.31 m/s and baPWV ≤14.44 m/s), the top quartile of cfPWV and baPWV had a significantly higher prevalence of carotid plaque (for cfPWV: OR = 1.59, 95% CI: 1.32–1.92; for baPWV: OR = 1.53, 95% CI: 1.26–1.86). However, the relationship of baPWV and carotid plaque was nonlinear, with a positive trend only when baPWV < 16.85 m/s. When comparing relationships between PWV indices and carotid plaque in one model, both cfPWV and baPWV were significantly associated with carotid plaque in participants with baPWV < 16.85 m/s; however, only cfPWV was independently associated with carotid plaque in participants with baPWV ≥16.85 m/s. Both cfPWV and baPWV were significantly associated with carotid plaque in the Chinese community‐based population. Furthermore, cfPWV was more strongly correlated with carotid plaque than baPWV in participants with baseline baPWV ≥16.85 m/s.

Accumulated evidence has shown that carotid‐femoral and brachial‐ankle PWV well predict cardiovascular events but it is still unclear if the predictability is same or not. In this cross‐sectional study based on a community atherosclerosis cohort in Beijing, China, a total of 5282 participants without previous coronary heart disease and stroke were enrolled from a community atherosclerosis cohort in Beijing, China. The 10‐year atherosclerotic cardiovascular disease (ASCVD) risk were calculated by the China‐PAR model, and < 5%, 5%–10% and > 10% were defined as low, intermediate, and high risk, respectively. The average baPWV and cfPWV values were 16.63 ± 3.35 m/s and 8.45 ± 1.78 m/s, respectively. The mean 10‐year ASCVD risk was 6.98% (interquartile range: 3.90%–12.01%). The patients with low, intermediate, and high 10‐year ASCVD risk accounted for 34.84% (1840), 31.94% (1687),, and 33.23% (1755) respectively. Multivariate analysis showed that for every 1 m/s increase in baPWV and cfPWV, the 10‐year ASCVD risk increased by 0.60% (95% confidence interval: 0.56%–0.65%, p < .001) and 1.17% (95% confidence interval: 1.09%–1.25%, p < .001), respectively. The diagnostic ability of the baPWV was comparable to the cfPWV (area under the curve: 0.870 [0.860–0.879] vs. 0.871 [0.861–0.881], p = .497). In conclusion, baPWV and cfPWV are positively associated with the 10‐year risk of ASCVD in the Chinese community‐based population, with a nearly identical association with a high 10‐year risk of ASCVD.

Carotid‐femoral pulse wave velocity (cfPWV) and brachial‐ankle pulse wave velocity (baPWV) act as two most frequently applied indicators to evaluate arterial stiffness. Limited studies have systematically compared the relationships between cfPWV/baPWV and increased carotid intima‐media thickness (cIMT). This study aimed to investigate the associations of the two PWV indices with cIMT in a Chinese community‐based population. A total of 6026 Chinese participants from an atherosclerosis cohort were included in our analysis. Increased cIMT was defined as the maximum of cIMT > 0.9 mm in end‐systolic period of carotid artery. Mean (SD) cfPWV and baPWV were 8.55±1.83  and 16.79±3.35 m/s, respectively. The prevalence of increased cIMT was 59.58%. In multivariable logistic regression, both PWVs were independently associated with increased cIMT after adjustment for various confounders (for 1 m/s increase of cfPWV: OR = 1.07, 95% CI: 1.02‐1.11; for 1 m/s increase of baPWV: OR = 1.03, 95% CI: 1.00‐1.05). The highest cfPWV and baPWV quartile groups had higher prevalence of increased cIMT when compared with the lowest quartile groups (for cfPWV: OR = 1.28, 95% CI: 1.06‐1.55; for baPWV: OR = 1.23, 95% CI: 1.00‐1.50). However, when both PWVs were added into multivariable model simultaneously, only cfPWV was associated with odds of increased cIMT. Subgroup analyses further showed cfPWV was more strongly associated with increased cIMT than baPWV in males, participants aged ≥65 years, and those with other cardiovascular risk factors. In conclusion, both cfPWV and baPWV are associated with increased cIMT in a Chinese community‐based population. Furthermore, cfPWV is more strongly correlated with increased cIMT compared to baPWV.

Background: Evidence from prospective studies on the relationship of the dietary vitamin intake and the progression of central and peripheral arterial stiffness remains limited. Objective: To evaluate the association between dietary vitamin intake with the changes in central and peripheral arterial stiffness over a five-year follow-up in adults without previous cardiovascular disease. Methods: This five-year longitudinal study included 466 participants from the EVA study who were evaluated at baseline and follow-up (mean age 55.96 ± 14.15 years; 51.1% women). Central arterial stiffness was assessed using carotid–femoral pulse wave velocity (cfPWV), and peripheral arterial stiffness was measured using brachial–ankle pulse wave velocity (baPWV). Dietary vitamin intake was estimated using the EVIDENT smartphone application, developed and validated by CGB and the Salamanca Primary Care (APISAL; registration number 00/2014/2207). Results: In multivariable linear regression analyses adjusted for age, sex, lifestyle factors, and cardiovascular risk factors, greater increases in cfPWV were inversely associated with vitamin B9 (folate) intake (β = −0.233; 95% CI: −0.390 to −0.075) and vitamin C intake (β = −0.291; 95% CI: −0.507 to −0.075). Similarly, increases in baPWV were inversely associated with vitamin B9 intake (β = −0.156; 95% CI: −0.287 to −0.025) and vitamin C intake (β = −0.223; 95%CI: −0.402 to −0.044). Conclusions: The progression of central and peripheral arterial stiffness over five years was greater in individuals with lower dietary intakes of vitamin B9 and vitamin C. These findings provide novel evidence supporting the possible role of dietary vitamin intake in the progression of arterial stiffness with aging.

With the increasing use of wearable devices equipped with various sensors, information on human activities, biometrics, and surrounding environments can be obtained via sensor data at any time and place. When such devices are attached to arbitrary body parts and multiple devices are used to capture body-wide movements, it is important to estimate where the devices are attached. In this study, we propose a method that estimates the load positions of wearable devices without requiring the user to perform specific actions. The proposed method estimates the time difference between a heartbeat obtained by an ECG sensor and a pulse wave obtained by a pulse sensor, and it classifies the pulse sensor position from the estimated time difference. Data were collected at 12 body parts from four male subjects and one female subject, and the proposed method was evaluated in both user-dependent and user-independent environments. The average F-value was 1.0 when the number of target body parts was from two to five.

The aortic-femoral arterial stiffness gradient, defined as the ratio of femoral-ankle pulse wave velocity to carotid-femoral pulse-wave velocity, demonstrates good between-day reliability in young healthy adults.

Even though as a gold standard for noninvasive measurement of arterial stiffness, carotid‐femoral pulse wave velocity (cfPWV) is not widely used in primary healthcare institutions due to time‐consuming and unavailable equipment. The aim of this study was to develop a convenient and low‐cost nomogram model for arterial stiffness screening. A cross‐sectional study was undertaken in the department of general practice, the First Affiliated Hospital of Fujian Medical University. Arterial stiffness was defined as cfPWV ≥ 10 m/s. A total of 2717 participants were recruited to construct the nomogram using the least absolute shrinkage and selection operator and logistic regressions. Receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis, clinical impact curve were used to evaluate the performance of the model. The model was validated internally and externally (399 participants) by bootstrap method. Arterial stiffness was identified in 913 participants (33.60%). Age, sex, waist to hip ratio, systolic blood pressure, duration of diabetes, heart rate were selected to construct the nomogram model. Good discrimination and accuracy were exhibited with area under curve of 0.820 (95% CI 0.803–0.837) in ROC curve and mean absolute error = 0.005 in calibration curve. A positive net benefit was shown in decision curve analysis and clinical impact curve. A satisfactory agreement was displayed in internal validation and external validation. The low cost and user‐friendly nomogram is suitable for arterial stiffness screening in primary healthcare institutions.

Aims As a potential surrogate of carotid‐femoral pulse wave velocity, estimated pulse wave velocity (ePWV) has been confirmed to independently predict the cardiovascular events, but the association between ePWV and heart failure has not been well confirmed. Therefore, we performed this cohort study to evaluate the association between ePWV and risk of new‐onset heart failure. Methods and results A total of 98 269 employees (mean age: 51.77 ± 12.56 years, male accounted for 79.9%) without prior heart failure who participated in the 2006–2007 health examination were selected as the observation cohort, with an average follow‐up of 13.85 ± 1.40 years. Area under the receiver operator characteristic curve (AUC) of ePWV was calculated in prediction of heart failure. The adjusted Cox proportional hazard models were used to estimate hazard ratios and 95% confidence intervals. The category‐free net reclassification index (NRI) was calculated to evaluate the reclassification performance of cardiovascular risk models after adding ePWV. The AUC of ePWV was 0.74 in prediction of heart failure. After adjusting for the traditional cardiovascular risk factors except for age and blood pressure, the risk of new‐onset heart failure increased by 35% [hazard ratio (HR): 1.35, 95% confidence interval (CI): 1.33–1.37] for each 1 m/s increase in ePWV. Subgroup analysis showed that ePWV was significantly associated with incident heart failure regardless of THE presence (HR: 1.33, 95% CI: 1.31–1.36, P < 0.01) or absence (HR: 1.59, 95% CI: 1.46–1.73, P < 0.01) of cardiovascular risk factors, male (HR: 1.33, 95% CI: 1.31–1.36, P < 0.01) or female (HR: 1.44, 95% CI: 1.38–1.51, P < 0.01), young and middle‐aged (<52 years) (HR: 1.50, 95% CI: 1.41–1.58, P < 0.01), or middle‐aged and elderly (≥52 years) (HR: 1.23, 95% CI: 1.21–1.26, P < 0.01). The addition of ePWV to the traditional cardiovascular risk model including age and mean arterial pressure could significantly improve the reclassification ability by 31.1% (category‐free NRI = 0.311, P < 0.01). Conclusions ePWV was an independent predictor for new‐onset heart failure.

The local pulse wave velocity (PWV) from large elastic arteries and its pressure-dependent changes within a cardiac cycle are potential biomarkers for cardiovascular risk stratification. However, pulse wave reflections can impair the accuracy of local PWV measurements. We propose a method to measure pressure-dependent variations in local PWV while minimizing the influence of pulse wave reflections. The PWV is computed from the pulse transit time between two forward-traveling pulse waveforms obtained across known path length, after measured/modelled flow-based wave separation analysis (WSA). An in-vivo study of 60 participants (24 female), was conducted to compare inter- and intra-cycle variations in PWV obtained from measured and forward pulse waves. For this, proximal and distal diameter waveforms from the carotid artery, along with carotid tonometry, were recorded using a custom bi-modal arterial probe. The carotid blood flow for WSA was captured with an ultrasound imaging system. The reference PWV was derived from the Bramwell-Hill equation. After WSA, the reliability of PWV measurement improved with coefficient of variation reducing from 25% to 10% near the peak of the pulse waves and matched the reference PWV with no statistically significant difference. The average PWV at foot of the pulse wave before and after WSA were comparable to the reference PWV with no statistically significant difference. The coherence of carotid pulse pressure obtained from the mean values of PWV within a cardiac cycle after WSA with that of the carotid pulse pressure from tonometry, substantiates the results obtained for reflection-free PWV. The reliability of measuring local PWV and its pressure dependent variations within a cardiac cycle is improved by combining transit-time approach with WSA.