Evaluation of the Age-Dependence of Conventional and Novel Photoplethysmography Parameters for the Identification of New Cardiovascular Ageing Indicators

doi:10.21203/rs.3.rs-5368476/v1

Background:Cardiovascular (CV) mortality increases with age partly due to physiological ageing of the CV system. Early vascular ageing raises CV risks. Personalizing CV risk assessment by defining CV age could reduce CV events. Photoplethysmography (PPG), analyzing the peripheral arterial pulse wave may be an effective method for estimating CV age. Ageing index and some other PPG parameters were proven to have age correlation; however, the age dependence of many other pulse wave parameters remains unclear. We aimed to identify age correlations of PPG indices and pulse rate variability (PRV) parameters including few novel parameters which were calculated to further investigate the various aspects of CV ageing.

Our study included 118 healthy (M/F:53/65, mean age:31.8±11.8SD) volunteers for PPG parameter calculation and 106 (M/F:44/62, mean age:32.6±12.2SD) for PRV parameters (age:19-74). 2-minute pulse wave recording was obtained using a pulse oximeter. An automated, proprietary software evaluated PPG and PRV parameter values, which were compared with age (Pearson’s correlation).

Results: PPG parameters describing cardiac ejection time positively correlated with age, while those indicating arterial elasticity showed negative correlation. Composite PPG parameters proposed as indicators of CV health and fitness had negative correlation. (p<0.001, IrI>0.4) Most PRV parameters exhibited negative correlation, indicating reduced adaptive capacity due to ageing. (p<0.05, IrI>0.3)

Conclusions: PPG-based pulse waveform analysis provides a wide range of age-related parameters, making it a promising method for estimating cardiovascular age. Future studies will include subjects with vascular ageing conditions beyond physiological values (e.g., hypertension, heart failure, coronary artery disease).

photoplethysmography

vascular ageing

pulsewave analysis

pulse rate variability

Cardiovascular diseases, including atherosclerosis and stroke are major public health challenges, consistently ranking among the leading causes of death worldwide in recent decades, especially in the elderly population.(1, 2) Age-related phenotypic alterations in the cardiovascular system, and more importantly their accelerated development brought about by cardiovascular risk factors, are among the most relevant (patho)physiological changes that drive these diseases.(3, 4) Therefore, identifying new, affordable biomarkers that reflect cardiovascular aging is critical for improving treatments and preventive strategies.

Peripheral pulse wave analysis may offer a valuable method for monitoring cardiovascular health and predicting disease progression.(5, 6) Calculating heart rate from continuous pulse wave recordings may have relevance in diagnostics, as pulse rate variability (PRV) is an important indicator of various diseases.(7–9) Beyond PRV, the morphological characteristics of pulse waves have yielded significant attention, with numerous studies suggesting that these parameters may be associated with CV disease states such as atherosclerosis and heart failure.(10–12)

Photoplethysmography (PPG) is a simple, easily accessible, and highly repeatable method for real-time monitoring of pulse waves.(13) This non-invasive technique involves illuminating the skin and tissues below, typically the finger, with an LED and measuring the intensity of the reflected or transmitted light, which corresponds to pressure changes in the vascular system. Importantly, PPG has no known adverse effects.(14)

The promising results from previous studies suggest that PPG-based pulse wave analysis could gain traction in CV diagnostics and home monitoring in the near future.(15) While it also shows potential as a tool for assessing cardiovascular aging, its broader application is limited by the fact that, for most PPG-derived parameters, the relationship with age has not been thoroughly assessed. Although age-related changes in certain parameters have been described, the majority remain unexplored.(6, 16, 17)

The primary goal of our research was to identify age-dependent changes in a large set of pulse wave parameters, including PRV parameters, pulse morphology parameters and newly developed composite score parameters, aiming to establish the utility of PPG-based pulse wave analysis as a tool to assess CV aging. For this purpose, we utilized an efficient, automated software that enables accurate, rapid, and reproducible evaluation of large datasets; and a comprehensive database of pulse wave data from a healthy adult population was established.

Methods

Our study included 118 healthy (M/F:53/65, mean age:31.8 ± 11.8SD) volunteers for PPG parameter calculation and 106 (M/F:44/62, mean age:32.6 ± 12.2SD) for PRV parameters (age:19–74).

Participants were required to meet specific inclusion criteria, including self-reported good physical and mental health, absence of cardiovascular disease, no use of cardiovascular medications, non-pregnancy, a BMI between 18 and 26 kg/m², non-smoking, limited alcohol consumption, and no chronic or cancerous diseases.

Subjects were primarily recruited from among the healthy employees, relatives of employees, and students of Semmelweis University. Recruitment was facilitated by the University's Occupational Health Service and social networking platforms. All tests were conducted in the laboratory facilities of Semmelweis University. IRB approval number: 120/2018

Participants provided informed consent and completed a health questionnaire, which collected personal and health-related data, including medical history, lifestyle, and medication use. Blood pressure was measured three times using an automatic sphygmomanometer. Subjects with systolic blood pressure higher than 140, and/or diastolic blood pressure exceeding 90 mmHg were excluded from the study. All data were recorded anonymously.

Pulse wave recordings were obtained using a Berry BM 1000B pulse oximeter placed on the right index finger. This non-invasive device, certified by the manufacturer, recorded pulse waves for 140 seconds while the participant remained seated and still. The pulse oximeter transmitted data via Bluetooth to a mobile application (SCN4ALL/HeartReader), developed by E-Med4All Europe Ltd., which uploaded the recordings to a secure online database. The studies for the repeatability and reliability of the measurements with the system are already published. (18, 19)

The proprietary software used for analysis identified fiducial points on the pulse wave, allowing for the calculation of both classical and novel pulse wave parameters (PPG parameters), including pulse rate variability (PRV parameters) metrics. Table 1 shows the parameters and their descriptions.

Table 1

PPG and PRV parameter names and descriptions
PPG/PRV parameters	Abbreviations/ Parameter names	Descriptions
Stiffness index	Si	Si = h/ PTT (m/s); h is the height of the person in meters. PTT is the pulse transit time in seconds. (6)
b/a	b/a	The ratio of the first two inflection points of the second derivative of the pulse wave. Correlates with the elasticity of the large arteries and the contractility of the left ventricle.(6)
d/a	d/a	The ratio of the first and fourth inflection points of the second derivative of the pulse wave. Independent cardiovascular risk factor. (6)
Ageing-index	AGEi	The value derived from the second derivative of the pulse wave. AGEi = b-c-d-e/a (6)
Reflection index	Ri	The ratio of the amplitude of the diastolic peak to the amplitude of the systolic peak.(6)
Systolic slope inclination	SysAlpha	The angle between the maximal inclination of systolic upstroke and the horizontal axis.(20)
c-d point detection ratio *	c-d incidence *	c-d point detection ratio specifies the percentage of those pulse cycles in the recording in which c and d points of the second derivative of the pulse wave curve are successfully identified by the algorithm over all identified heart cycles.
Left ventricular ejection time index	LVETi	Left ventricular ejection time indexed for heart rate (LVETi) was calculated from sex-specific resting regression equations LVETi(male) = 1,7 × heart rate + ET, LVETi(female) = 1,6 × heart rate + ET. (21)
Dicrotic notch index *	DNi *	Describes the relative position of the diastolic peak to the dicrotic notch (the valley induced by the aortic valve closure before the diastolic peak).
Early left ventricular ejection time 1 and Early left ventricular ejection time 2	eLVET1 *	The early left ventricular ejection time 1 and 2 are the two-time components of "Crest Time”. eLVET1 is measured from the start of the period to the first peak of the first derivative of the pulse, whereas eLVET2 is defined as the time duration from the first peak of the first derivative PTG to the peak of the systolic wave.
	eLVET2 *
Crest Time	Crest Time	The time elapsed between the beginning of the period (foot) and the maximum systolic amplitude (peak).(6)
Left ventricular ejection time	ET(PPG)	The ejection time is the time elapsed between the beginning of the pulse period and the aortic valve closure (dicrotic notch/e-point).(22)
Score parameters: Scores were calculated based on the 30 + parameters derived from the proprietary analysis of the 2 minutes pulse-wave recording. The max. value is 100. Values below 70 might indicate that there is reason to evaluate details and to consult professionals for a more thorough health check.
Scores *	Total Score *	Calculated on the basis of the all 30 + cardiovascular and pulserate variability parameters derived from the proprietary analysis of the 2 minutes pulse-wave recording. The Total Score is the master of all the other sub-scores, which can help to identify stronger and weaker aspects of the subject's CV status/health.
	CV Health Score *	Obtained from the parameters that correspond with the function of the heart and the condition and aging of the arteries.
	Heart Fitness Score *	Certain pulse wave parameters are influenced by the athletic lifestyle and athletic capabilities of the subject, so these aspects are marked by this score. This score provides information mostly about the health level of the HEART.
PRV Time-domain parameters
correctedMNN	cMRR	PRV Time-domain parameter. The mean normal-to-normal interbeat interval. (23) Corrected: see comment at “cTotalPower”
corrected SDNN	cSDRR	PRV Time-domain parameter. The standard deviation of the interbeat intervals (ms). Its value is influenced by all cyclic components affecting heart rate variability and can be considered a quasi-summative autonomic nervous system index. Corrected: see comment at “cTotalPower”.(23)
correctedrMSSD	crMSSD	PRV Time-domain parameter. The square root of the mean squared differences of successive interbeat intervals. Its value provides information primarily about the parasympathetic regulation of the heart and used to estimate the vagally mediated changes. The value of rMSSD could refer to the quality of electrical stability of the heart. (23)Corrected: see comment at “cTotalPower”
corrected pNN50	cpNN50	PRV Time-domain parameter. The proportion of differences of successive IBIs exceeding 50 ms. It characterizes parasympathetic activity. (23)Corrected: see comment at “cTotalPower”
PRV Frequency-domain parameters
corrected Total Power (ms2)	cTotalPower	PRV Frequency-domain parameter. It specifies the area under the complete frequency-domain analysis curve. This reflects the activity of the entire autonomic nervous system. Corrected: automatic detection of irregular cycle lengths and application of cubic spline interpolation applied. (23)
corrected HF Power	cHFpow	PRV Frequency-domain parameter. Absolute power of the high-frequency band (0.15–0.4 Hz). HF power is the marker of parasympathetic activity (23) Corrected: see comment at “cTotalPower”
corrected LF Power	cLFpow	PRV Frequency-domain parameter. Absolute power of the low-frequency band (0.04–0.15 Hz). LF power is a marker of both sympathetic and parasympathetic activity. The LF band mainly reflects fluctuations in baroreceptor activity during resting conditions. LF power value correlates with the progression of atherosclerosis.(24) Corrected: see comment at “cTotalPower”
PRV Non-linear-domain parameters
corrected SD1	cSD1	PRV Non-linear parameter. Standard deviation 1 of the Poincaré plot representing the length of the ellipse fitted to the plot.(23) Corrected: see comment at “cTotalPower”
corrected SD2	cSD2	PRV Non-linear parameter. Standard deviation 2 of the Poincaré plot representing the width of the ellipse fitted to the plot. (23)Corrected: see comment at “cTotalPower”
* These parameters are developed by our research group. Most of them are not yet validated in clinical studies.

The parameter values obtained from pulse waveform analysis were compared (using Pearson correlation) with the age (in years) of the volunteers (JASP 0.19.1 software, JASP Team (2024)).

During the preparation of this work the author(s) used ChatGPT and Grammarly to improve the readability and find shorter expressions to fit word limit. After using these tools/services, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Age-dependence of pulse wave morphology parameters

Tables 2 summarizes the Pearson correlation results between PPG parameters and age. All proprietary score parameters showed significant correlation with age (Total Score: r=-0.301, CV Health Score: r=-0.45, Heart fitness score: r=-0.493; in all cases p < 0.001). Among the conventional parameters that had significant correlation with age, d/a (r=-0.376), AGEi (r = 0.485) (Fig. 1A) and SysAlpha (r=-0. 418) exhibited the strongest age-dependence Time parameters of the PPG curve that characterize time durations of ejection-related ventricular activity, such as LVETi (r = 0.539), Crest Time (r = 0.570), and ET(PPG) (r = 0.589)), and the proprietary eLVET1* (r = 0.548) and eLVET2* (r = 0.450) also showed relevant age-dependence (Table 2 and Fig. 2). Moreover, new could also detect age correlation in case of other novel parameters including c-d incidence* (r = 0.419) and DNi* (r=-0. 517) (Fig. 1B).

Table 2

**Results of correlation analysis of PPG parameters and age**
Correlation values of PPG parameters with Age
PPG parameters	Pearson's r	p
Total Score*	-0.301	< .001
CV Health Score*	-0.450	< .001
Heart Fitness Score*	-0.493	< .001
Si	0.181	0.050
b/a	0.207	0.025
d/a	-0.376	< .001
AGEi	0.485	< .001
Ri	-0.159	0.085
SysAlpha	-0.418	< .001
c-d incidence*	0.419	< .001
LVETi	0.539	< .001
DNi*	-0.517	< .001
eLVET1*	0.548	< .001
eLVET2*	0.450	< .001
Crest Time	0.570	< .001
ET(PPG)	0.589	< .001

* These parameters are developed by our research group. Most of them are not yet validated in clinical studies.

Figure 1A shows the scatter plot of the correlation analysis between age and AGEi, and Fig. 1B shows the scatter plot of the correlation analysis between age and DNi* parameters

Figure 2A shows the scatter plots of the correlation analysis between LVETi, Fig. 2B eLVET1, Fig. 2C Crest Time and Fig. 2D ET(PPG) and age.

Age-dependence of pulse rate variability parameters

Several of the PRV parameters exhibited a negative correlation with age (Table 3 and Fig. 3). The cTotalPower (r=-0.325) (Fig. 3A) and cSDRR (r=-0.401) parameters (Fig. 3B) exhibited the strongest age dependence (p < 0.001) among frequency-domain and time-domain measures, respectively. The age-correlation of non-linear PRV parameters proved to be weaker, except for cSD2 (r=-0.428).

Table 3

*Results of correlation analysis of PRV parameters and age*
Correlation values of PRV parameters with Age
	Pearson's r	p
Time domain parameters
cMRR	0.082	0.404
cSDRR	-0.401	< .001
crMSSD	-0.266	0.006
cpNN50	-0.225	0.021
Frequency domain parameters
cTotalPower	-0.325	< .001
cHFpow	-0.299	0.002
cLFpow	-0.277	0.004
Non-linear parameter
cSD1	-0.266	0.006
cSD2	-0.428	< .001

Figure 3A shows the scatter plot of the correlation analysis between age and cTotalPower, and Fig. 3B shows the scatter plot of the correlation analysis between age and cSDRR parameters

From a public health perspective, addressing the assessment and monitoring of cardiovascular ageing is crucial, as cardiovascular diseases continue to be a leading cause of mortality, particularly in older populations. As the global population ages, the demand for reliable, non-invasive methods to meet this need is increasing. Photoplethysmography (PPG) appears to be a promising tool in this regard, as it offers a simple but effective way to monitor cardiovascular function by pulse wave analysis. Although the age correlation of some PPG parameters has been investigated, the full scope of age-related changes in pulse wave characteristics is not yet fully understood.(25–27) This study, by examining both conventional and novel PPG parameters, as well as PRV characteristics, provides a more comprehensive understanding of the effects of cardiovascular ageing on the pulse waveform morphology. Our study identified a diverse set of PPG parameters including ones that are related to cardiac ejection time, arterial elasticity and loss of pulse rate variability, which can serve as reliable indicators for CV aging. These findings strongly support the use of PPG analysis for monitoring cardiovascular aging and assessing cardiovascular health across different age groups. In addition, a major strength of this study lies in the use of a proprietary, automated software system capable of analyzing large datasets with high efficiency that enhances reliability, ensures the reproducibility of study's results. Among the 16 PPG morphology parameters, ejection time, including its subcomponent eLVET1, as described by our research group, and LVETi, as described by Weber et al., demonstrated the strongest correlations with age, indicating a gradual decline in cardiovascular efficiency as individuals age.(21, 25, 28)

Of the parameters that are considered to characterize arterial stiffness, the dicrotic notch index, a proposed marker of aortic distensibility and coronary flow pressure gradient, also showed high correlation values. Our results also confirmed the findings of previous studies that described the age-related changes of ageing index, a parameter derived from the second derivative of the pulse contour wave, and widely recognised for its correlation with age and its reflection of arterial stiffness (as noted by Takazawa et al.(16)). However, our analysis revealed that ejection time and the dicrotic notch index may offer more precise insights into the ageing cardiovascular system, suggesting that these parameters could be even more effective than aging index in monitoring the progressive decline in both heart function and arterial compliance. Interestingly, stiffness index, a PPG parameter proposed in the literature also to characterize arterial distensibility showed poor correlation with age in our study.(29)

In addition to these individual parameters, multiple-PPG-parameter “composite scores „such as the CV Health Score also demonstrated significant correlations with age. This supports the unpublished observations of the manufacturer that suggested strong age-dependence of these parameters in a large inhomogeneous patient population.(30) These composite scores may offer a broader and more comprehensive assessment of cardiovascular health. By capturing a wide range of age-related changes, these scores hold promise for improving the accuracy and personalization of cardiovascular risk evaluations in clinical practice.

Some PRV parameters, such as total power (cTotalPower) and SDNN (cSDRR), showed a significant correlation with age. Both parameters showed a decrease with increasing age; this could indicate a less sensitive autonomic nervous system, which may contribute to the reduced cardiovascular adaptive capacity observed in the elderly. This finding is consistent with the existing literature, which suggests that decreased heart rate variability reflects reduced autonomic control of the cardiovascular system and highlights the importance of monitoring autonomic function through PRV parameters as part of a comprehensive cardiovascular health assessment.(7, 31–33)

The results of the study offer significant potential for early detection of cardiovascular risk using non-invasive methods. The age-related changes identified in various PPG and PRV parameters may provide a pathway for the development of more personalised cardiovascular age assessment tools. Moreover, the combination of different PPG parameters may enhance the predictive power for identifying cardiovascular events or conditions. By exploiting these correlates, healthcare providers could better identify individuals at higher risk for cardiovascular events earlier in the ageing process, allowing for timely intervention and more effective management of cardiovascular health. Building on these insights, further studies are needed to determine whether individuals above or below the correlation trend line represent different cardiovascular ageing phenotypes, such as early vascular ageing and supernormal vascular ageing.(34, 35) Identification of these potential phenotypes could significantly improve the accuracy of cardiovascular risk assessments, allowing more accurate prediction of cardiovascular events and tailoring of interventions to individual needs.

It is a limitation of our study that age distribution in the sample population is not entirely even and may not fully represent the general population. Therefore, future research should be extended to a broader more diverse cohort to validate these findings. Moreover, further studies involving patients with established cardiovascular conditions are relevant to further specify the interpretation of the identified age-related indicators, as different PPG parameters might be more relevant in specific pathological contexts, such as hypertension or heart failure.

This study has successfully identified significant age-related correlations across both conventional and novel PPG parameters, highlighting their potential as valuable indicators of cardiovascular ageing. The findings demonstrate that parameters related to cardiac ejection time, arterial elasticity, and pulse rate variability, among others, consistently correlate with age, offering a comprehensive view of how the cardiovascular system evolves over time. The introduction of novel composite PPG score parameters, which also showed remarkable age correlations, provides additional layers of insight, potentially revealing subtle aspects of cardiovascular ageing that traditional metrics might overlook. The clinical relevance of these findings is that they draw attention to the potential of pulse wave analysis to monitor cardiovascular ageing non-invasively and position PPG as a promising tool in both clinical and preventive cardiology.

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki. All tests were conducted in the laboratory facilities of Semmelweis University. IRB approval number: 120/2018. Written informed consent was obtained from all participants involved in the study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Competing interests

F.A., D.K. and S.K. had financial relationships with E-Med4All Europe Ltd. (D.K. and S.K. as co-owners; F.A. is a former employee who worked during the data collection period). Z.M. is the PhD supervisor of D.K. and F.A. Z.M. did not receive any compensation for their contributions.

Funding

No specific funding was received for this study.

Authors' contributions

F.A. and D.K. designed the study and conducted the data collection, furthermore they drafted the first version of the manuscript. S.K. contributed to the analysis and interpretation of the results. Z.M. provided critical revisions to the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We would like to acknowledge all our participants for their time and assistance during the study.

Authors' information

F.A and D.K. are PhD candidates at Semmelweis University. S.K. is the CEO and co-owner of E-Med4All Europe Ltd. F.A. is a former employee of E-Med4All Europe Ltd. Z.M. is the head of the Translational Medicine Department at the National Korányi Pulmonology Institute.

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Competing interest reported. F.A., D.K. and S.K. had financial relationships with E-Med4All Europe Ltd. (D.K. and S.K. as co-owners; F.A. is a former employee who worked during the data collection period). Z.M. is the PhD supervisor of D.K. and F.A. Z.M. did not receive any compensation for their contributions.

Evaluation of the Age-Dependence of Conventional and Novel Photoplethysmography Parameters for the Identification of New Cardiovascular Ageing Indicators

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Version 1

Abstract

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Introduction

Results

Discussion

Conclusion

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