{"paper_id":"0ccb1cff-669f-4096-af92-a6e3efd14396","body_text":"Predictive Value of Decreased Pulse Wave Amplitude Index for Cardiovascular | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive Value of Decreased Pulse Wave Amplitude Index for Cardiovascular Siai Chen, Min Li, Huizhong Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7925111/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Obstructive sleep apnea (OSA) elevates cardiovascular risk, but existing biomarkers fail to capture sleep-specific hemodynamic changes. This study evaluates the pulse wave amplitude decrease (PWAD) index, a photoplethysmography-derived measure of vascular/autonomic reactivity during respiratory events, in 1,034 OSA patients (AHI ≥ 15). Each 1-unit PWAD increase reduced major adverse cardiovascular events (MACE) risk by 2.6% (HR 0.974, 95%CI 0.953–0.995), with PWAD > 23.5 (median) showing 50.8% risk reduction (HR 0.508, 95%CI 0.291–0.885). The combination of low PWAD (≤ 23.5) and high AHI synergistically increased MACE risk by 116% (HR 2.161, 95%CI 1.049–4.454, P < 0.001) versus double-negative group. Low PWAD correlated with older age, higher LDL, and elevated cardiovascular risk (all P < 0.05), suggesting endothelial dysfunction and autonomic dysregulation as mechanisms. The PWAD index independently predicts cardiovascular risk in OSA and can be automatically measured via standard pulse oximetry, offering a practical tool for identifying high-risk patients needing intensive treatment.​ sleep apnea syndrome cardiovascular disease biomarker photoplethysmography autonomic nervous system Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Obstructive sleep apnea (OSA) is characterized by recurrent episodes of complete and partial upper airway obstruction, leading to intermittent hypoxemia, autonomic fluctuations, and fragmented sleep 1 . It represents a major global health challenge with profound implications for the cardiovascular system. It is estimated that approximately 1 billion people aged 30 to 69 among the world's 7.3 billion population suffer from this disease. The prevalence of obstructive sleep apnea is rising and affects all countries, imposing a heavy burden on healthcare systems 2 . Its pathophysiological mechanisms primarily involve intermittent hypoxemia and sleep fragmentation caused by impaired gas exchange. This triggers systemic inflammatory responses and oxidative stress, leading to endothelial dysfunction and changes in hemodynamic and cardiac workload, as well as autonomic nervous system dysfunction.These are closely associated with multiple cardiovascular diseases such as hypertension, This triggers systemic inflammatory responses and oxidative stress, endothelial dysfunction leading to hemodynamic and cardiac workload changes, and autonomic nervous system dysfunction. heart failure, coronary artery disease, pulmonary hypertension, atrial fibrillation, and stroke 3 , 4 . Notably, moderate-to-severe OSA, particularly when accompanied by severe nocturnal hypoxia, significantly elevates cardiovascular and all-cause mortality risks, underscoring the urgency of developing effective risk stratification tools. Although current diagnostic methods primarily focus on respiratory parameters and PSG, 5 reliable biomarkers specifically predictive of OSA cardiovascular risk remain extremely limited. Existing vascular assessment tools are often compromised by confounding factors and perform poorly in OSA populations, further underscoring the necessity for developing sleep-specific biomarkers capable of capturing unique hemodynamic changes during sleep. Pulse Wave Amplitude Drop (PWAD) or pulse wave dynamics metrics have been identified in multiple recent studies as a potential biomarker for assessing cardiovascular risk in OSA patients 5 . This is achieved by quantifying the magnitude and frequency of pulse amplitude reduction in finger pulse oximetry signals during sleep, particularly during apnea/hypopnea events. Grote and colleagues showed that pulse wave amplitude drops (PWADs) reflect transient vasoconstriction followed by a vasodilation that occurs in response to surges in sympathetic activity, which are then followed by a compensatory parasympathetic response. 6 These indicators sensitively reflect the vascular system's response to acute hypoxic events and chronic sympathetic-parasympathetic dysfunction. The hemodynamic instability they capture is highly correlated with core pathophysiological mechanisms of OSA, such as intermittent hypoxia, recurrent micro-awakenings, and excessive sympathetic activation. Early cohort studies and signal analysis indicate that PWAD-related parameters not only show significant associations with traditional vascular dysfunction indicators (e.g., nocturnal oxygen desaturation index, altered heart rate variability) but also reveal unique vascular stress patterns specific to OSA patients, offering new insights into the mechanisms underlying OSA-related cardiovascular complications. This study aims to evaluate the clinical value of the PWAD index as a cardiovascular risk biomarker in OSA patients. By analyzing the association between PWAD and cardiovascular events in this cohort, we expect to provide key evidence for the precise identification and management of high-risk OSA patients, ultimately guiding optimized clinical decision-making and improving patient outcomes. Methods Study Design and Subjects This retrospective cohort study analyzed clinical data from 1,034 patients, including 950 without cardiovascular events and 84 with cardiovascular events. Participants were obstructive sleep apnea (OSA) patients undergoing sleep monitoring. OSA diagnosis followed the 2012 American Academy of Sleep Medicine (AASM) sleep event scoring criteria, 7 with an apnea-hypopnea index (AHI) ≥15 events/hour as the diagnostic threshold. Data Collection and Variable Definition Baseline Data Collection The following baseline data were collected. Demographic characteristics: age, gender; Clinical indicators: Body Mass Index (BMI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP); Laboratory tests: Low-density lipoprotein (LDL), blood glucose (GLU), creatinine; Sleep parameters: AHI, mean SpO₂, NBPF index, mean nocturnal systolic pressure elevation; Cardiovascular risk factors: history of hypertension, history of coronary heart disease, history of heart failure, history of diabetes mellitus (DM), smoking history, lipid-lowering medication use; Cardiac function indicators: Ejection fraction (EF), nocturnal ePWV PWAD Index Measurement and Definition The PWAD (Pulse Wave Amplitude Decrease) index is defined as the total number of pulse wave amplitude decrease events occurring per hour of sleep time. A PWAD event is defined as a decrease in pulse wave amplitude exceeding 30% from baseline levels, persisting for more than four consecutive cardiac cycles. 8 PWAD event identification employs a validated automated algorithm previously applied in sleep-related cardiovascular monitoring studies using photoplethysmography (PPG) signals . The total number of PWAD events throughout the night is averaged per hour of sleep to yield the PWAD count (number of 30% PWA decreases per hour). Figure 1 Illustration of PWAD Definition and Evaluation of Pulse Wave Amplitude (PWA) Pulse wave amplitude (PWA) is extracted from photoplethysmography (PPG) signals recorded during nocturnal sleep. A valid PWA decrease event is defined as a ≥30% reduction in pulse wave amplitude relative to baseline, persisting for more than four heartbeat cycles. Event detection is based on the PPG signal processing algorithm developed by Kwon et al. (2021), validated in the Multi-Ethnic Study of Atherosclerosis cohort. The PWA Decrease Index is calculated by dividing the total number of PWA decrease events meeting the above criteria throughout the night by the total sleep duration, yielding the number of PWA decrease events per hour. This methodology aligns with previous approaches used in sleep-disordered breathing studies. Primary Endpoint The primary endpoint was a composite cardiovascular event, including fatal cardiovascular events: death due to myocardial infarction or stroke; and non-fatal cardiovascular events: myocardial infarction, stroke, transient ischemic attack, and coronary heart disease. 9 Follow-up began on the dte of the sleep study and continued until the occurrence of a cardiovascular event (including cardiovascular death), other death, or the last follow-up date. 10 Follow-up duration ranged from 12.4 to 64.5 months, with a mean follow-up of 39.3 months and a median follow-up of 39.6 months. Statistical Analysis Methods To compare baseline characteristics between groups, use an independent samples t-test for continuous variables (if data is normal) and a Chi-square test for categorical variables. For non-normal continuous data, use the Mann-Whitney U test, and for small categorical samples, use Fisher's exact test. Risk Factor Analysis Multivariate Cox proportional hazards regression models and multivariable logistic regression models were employed to evaluate the relationship between PWAD indices and cardiovascular events. Subgroup analysis Patients were divided into high-PWAD and low-PWAD groups based on a median PWAD index of 23.5, and the difference in cardiovascular event incidence between the two groups was compared. Quality control All sleep monitoring data were collected by experienced technicians following standardized protocols. The PWAD event identification algorithm was validated in a preliminary study. Statistical analysis employed two-sided tests, with P < 0.05 considered statistically significant. Ethics approval and consent to participate The studies involving humans were approved by the Institutional Review Board (IRB) of Fujian Medical University Union Hospital (Approval Number: 2024KJCX004). The studies were conducted in accordance with the local legislation and institutional requirements. All procedures adopted in this study followed the Declaration of Helsinki. Informed consent was obtained from all individual participants included for study participation. Results 1.Patient characteristics A total of 1,127 patients with suspected OSA who underwent sleep apnea monitoring at Fujian Medical University Union Hospital from January 2020 to July 2024 were enrolled. After excluding 58 patients with incomplete data and 35 lost to follow-up, 1,034 patients were ultimately included. Follow-up duration ranged from 12.4 to 64.5 months, with a mean follow-up of 39.38 months and a median follow-up of 39.6 months. Among these, 84 patients experienced MACE events, while 950 patients did not. (Figure 1) Figure 1. Flow chart of study patient enrollment. 2.Baseline characteristics of the MACE(+) and MACE(-) groups are shown in Table 1. Significant differences existed between groups in AHI stratification, PWAD index stratification, EF values, LDL levels, creatinine levels, and nocturnal ePWV. Patients with MACE events were significantly older. The proportions of patients with a history of coronary heart disease, heart failure, lipid-lowering medication use, diabetes, and smoking were significantly higher in the MACE group. Table 1 Baseline clinical characteristics of patients Variable No MACE events With MACE events P N=950 N=81 AHI 0.013 (-) 551 (57.6%) 37 (44.0%) (+) 399 (42.0%) 47 (56.0%) PWAD Index <0.001 (+) 458 (48.2%) 60 (74.1%) (-) 492 (51.8%) 24 (29.6%) Age 50.89 ±15.78 61.64 ±12.50 0.017 Gender (Male) 675 (71.1%) 59 (72.8%) 0.863 BMI 26.66±5.53 26.16±4.56 0.355 Mean SpO2 95.16 ±2.48 95.02 ± 2.38 0.961 NBPF Index 32.44 ± 25.03 35.51 ± 19.93 0.151 Mean Elevated Blood Pressure (mmHg) 14.65±1.62 14.65±1.26 0.982 EF 65.52 ± 7.68 63.61±10.80 <0.001 LDL 2.95±0.78 2.71±1.11 <0.001 GLU 7.03±21.15 6.26±2.35 0.755 Creatinine 80.61±49.08 97.27±92.38 0.015 Nocturnal ePWV 9.09 ± 2.20 10.20 ± 2.30 <0.001 History of hypertension 596 (62.7%) 59 (70.2%) 0.171 History of coronary heart disease 125 (13.2%) 43 (51.2%) <0.001 History of heart failure 62 (6.5%) 15 (17.9%) <0.001 History of lipid-lowering medication use 354 (37.3%) 53 (63.1%) <0.001 History of DM 153 (16.1%) 30 (35.7%) <0.001 History of smoking 214 (22.5%) 30 (37.0%) 0.006 Note: AHI < 15 constitutes Group 1; AHI > 15 constitutes Group 2. PWAD index < 23.5 constitutes Group 1 (+); PWAD index > 23.5 constitutes Group 2 (-).The value following the ± symbol represents the standard deviation (s.d.), expressed as: \"Mean ± standard deviation\" All abbreviations in the table require a full name listing. Abbreviation: PWAD : Pulse Wave Amplitude decrease Index ; SpO2:Peripheral Oxygen Saturation;BMI：Body Mass Index; EF:Ejection Fraction; ePWV: Estimated pulse wave velocity;LDL:Low-Density Lipoprotein;DM:Diabetes Mellitus;GLU:Glucose Nocturnal blood pressure fluctuations (NBPF) refer to nocturnal blood pressure elevation events. NBPF Definition: A sustained blood pressure increase exceeding 12 mmHg over 3–30 seconds. Min and Max values computed as the mean of 8 readings. 3. Treat PWA and AHI as continuous variables. The PWAD mean is lower in the MACE event group, P < 0.05 (statistically significant difference). Although AHI is higher in the MACE event group, the difference is not statistically significant (Table 2). Table 2 Distribution of AHI and PWAD in Different MACE Subgroups Variable No MACE events With MACE events P AHI 18.161 (19.1883) 20.028(17.5246) 0.363 PWAD Index 25.471(15.13826) 19.88(11.39884) ＜0.001 PWAD : Pulse Wave Amplitude decrease Index 4. As shown in Table 3 Model 3, for MACE events as the outcome variable, each 1-unit increase in PWAD reduces MACE risk by 2.6%, with an HR of 0.974 (0.953-0.995). Additionally, using individuals with PWAD ≤ 23.5 as the reference group, the MACE risk associated with individuals with PWAD > 23.5 is reduced by 50.8%, with an HR of 0.508 (0.291-0.885). (Table 3) Table 3 Association between PWAD and MACE Variables Model 1 Model 2 Model 3 HR (95%CI) P HR (95%CI) P HR (95%CI) P PWAD 0.963 (0.946-0.980) <0.001 0.979 (0.960-0.997) 0.025 0.974 (0.953-0.995) 0.016 PWAD ≤ 23.5 Ref Ref Ref > 23.5 0.331 (0.205-0.534) <0.001 0.495 (0.294-0.832) 0.008 0.508 (0.291-0.885) 0.017 Abbreviation: PWAD: Pulse Wave Amplitude Index; CI: Confidence interval; HR: Hazard ratio; Ref: Reference. Note: Model 1: No adjustments made. Model 2: Adjusted for age, BMI, and sex. Model 3: Adjusted for age, BMI, and sex. AHI, mean SPO2, history of hypertension, history of diabetes, history of smoking, history of coronary heart disease, history of heart failure, lipid-lowering medication, LDL, EF, NBPF index The Log-rank test was used to examine survival differences between the two groups, revealing significant differences. The PWAD ≤ 23.5 group had a higher incidence of MACE events than the PWAD > 23.5 group. (Note: The PWAD index was categorized as (+) for median < 23.5 and (-) for PWAD index > 23.5 (Figure.2). Figure 2 Kaplan–Meier survival curves for patients with different degrees of PWAD 5. Further analysis of the interaction between AHI and PWAD As shown in Table 4 Model 3, for MACE as the outcome variable, compared with the PWAD(-)AHI(-) group (reference), the MACE risk in the PWAD(+)AHI(+) group increased by 116%, with a hazard ratio (HR) of 2.161 (1.049–4.454). (Table 4) Table 4 Association between PWAD+AHI and MACE Variables Model 1 Model 2 Model 3 HR (95%CI) P HR (95%CI) P HR (95%CI) P PWAD(-)AHI(-) Ref Ref Ref PWAD(+)AHI(-) 0.793(0.347-1.814) 0.584 0.868(0.376-2.002) 0.740 0.974(0.394-2.404) 0.954 PWAD(-)AHI(+) 1.909(1.017-3.582) 0.044 1.424(0.732-2.767) 0.298 1.437(0.687-3.008) 0.336 PWAD(+)AHI(+) 3.467(1.875-6.411) <0.001 2.184(1.131-4.218) 0.020 2.161(1.049-4.454) 0.037 Abbreviation: ePWV: Estimated pulse wave velocity; CI: Confidence interval; HR: Hazard ratio; Ref: Reference. Note: Model 1: No adjustments made. Model 2: Adjusted for age, BMI, and sex. Model 3: Adjusted for age, BMI, and sex. AHI, mean SPO₂, history of hypertension, history of diabetes, history of smoking, history of coronary heart disease, history of heart failure, lipid-lowering medication, LDL, EF, NBPF index The log-rank test examined MACE differences between the two AHI groups, revealing no significant difference between them (Figure 3). Further analysis combining PWAD showed that within the OSA group (AHI > 15), the lowest PWAD group had the highest MACE risk (i.e., PWAD(+) AHI(+) group), with a statistically significant difference (P < 0.001) (Figure 4). Figure 3 Kaplan–Meier survival curves for patients with different degrees of AHI Figure 4 Kaplan–Meier survival curves for patients with different degrees of PWAD+AHI Discussion This study represents the first systematic evaluation of the PWAD index's predictive value in cardiovascular risk stratification for OSA patients, providing crucial evidence for the clinical application of this emerging biomarker. Our core findings indicate that a PWAD index <23.5 (median) independently predicts the risk of cardiovascular events, a finding that remains statistically significant after adjusting for traditional risk factors such as age, sex, BMI, hypertension, and diabetes. This result holds significant clinical importance, offering a novel assessment tool for cardiovascular risk management in OSA patients. The observed association between lower PWAD values and increased cardiovascular risk supports hypotheses from prior studies suggesting impaired vascular reactivity and blunted autonomic responses during sleep may represent key pathways linking OSA to cardiovascular disease. 8,10 Our findings align with Kwon et al., who demonstrated that attenuated PAT (pulse transit time) responses following respiratory events correlate with subclinical cardiovascular disease markers .Similarly, Salari Shahrbabaki et al. reported that reduced nocturnal PWA attenuation predicts adverse cardiovascular outcomes, particularly in male and African American populations. 11 The consistency of these independent findings strengthens the biological plausibility of PWAD as a marker of vascular dysfunction in OSA. The pathophysiological significance of our findings is multifaceted. First, the negative correlation between PWAD index and cardiovascular risk suggests that patients with diminished peripheral vasomotor responses may exhibit more severe endothelial dysfunction (ECD)—a \"prerequisite\" for the formation and progression of atherosclerotic lesions, which is a hallmark of atherosclerotic advancement 1 . This interpretation aligns with emerging evidence that intermittent hypoxia and autonomic dysfunction induced by OSA trigger oxidative stress (increased ROS) and systemic inflammation, leading to endothelial injury 3 . Second, blunted PWAD responses may reflect autonomic dysfunction, where chronic sympathetic hyperactivity in OSA patients causes receptor downregulation and impaired vascular reactivity . 8,10 This mechanism is supported by experimental studies showing that acute blood pressure surges following obstructive events manifest as shortened PAT, while chronic exposure leads to vascular remodeling. Current clinical diagnosis of OSA primarily relies on polysomnography (PSG) and the apnea-hypopnea index (AHI). However, AHI inadequately reflects disease complexity and shows poor correlation with clinical outcomes such as cardiovascular disease and cognitive impairment. 10 Our findings indicate that te PWAD index provides incremental prognostic value beyond traditional risk factors and AHI, addressing a key limitation in current OSA management. The weak correlation observed by Kwon et al. between PAT response and AHI (r=0.1), along with AHI's minimal explanatory power in predicting cardiovascular outcomes, underscores the need for alternative measures like PWAD to better reflect the physiological consequences of respiratory events 1 . This is particularly important given the growing recognition that AHI alone fails to capture crucial aspects of OSA pathophysiology. The clinical implications of our findings are substantial. First, PWAD reflects autonomic nervous system activity and vascular reactivity, which are closely associated with cardiovascular event risk in OSA patients, offering a novel assessment tool for cardiovascular risk management in this population. Second, it aids in identifying high-risk OSA patients who may benefit from more aggressive treatment. The automated nature of PWAD measurement facilitates its implementation in clinical practice. CPAP (continuous positive airway pressure) remains the recommended treatment for OSA, with the strongest evidence of efficacy for reducing AHI, symptoms, and comorbidities . 12 PWAD may serve as a predictor of CPAP treatment response. Several limitations warrant consideration. First, as a retrospective study, we cannot establish causality between PWAD and cardiovascular outcomes. Second, the single-center design may limit generalizability, although our findings align with multicenter studies. Third, despite adjusting for numerous confounders, residual confounding remains possible. Fourth, optimal PWAD thresholds may vary across populations and require validation in independent cohorts. Finally, the underlying mechanisms linking PWAD to cardiovascular risk warrant further elucidation through physiological studies. Future research directions should include: 1) prospectively validating PWAD thresholds across diverse populations; 2) investigating PWAD dynamics in response to OSA treatment; 3) developing integrated risk scores combining PWAD with other biomarkers; 4) conducting mechanistic studies exploring molecular pathways linking PWAD to cardiovascular disease. The potential for longitudinal PWAD monitoring via wearable technology offers exciting opportunities for personalized risk assessment and management. In summary, this study establishes the PWAD index as a novel biomarker for cardiovascular risk assessment in OSA patients. Our findings not only enrich the pathophysiological understanding of OSA-related cardiovascular risk but also provide a practical risk assessment tool for clinical practice. Future research should focus on validating these findings and further exploring the potential value of PWAD in guiding OSA treatment decisions. Declarations Author Contributions Huizhong Lin: Conceptualization, Study design, Writing – Review & Editing, Supervision, Project administration. Siai Chen: Methodology, Writing – Original Draft. Min Li: Investigation, Data Curation, Writing – Original Draft. Siai Chen and Min Li have contributed equally to this work. Conflict of Interest Statement The authors declare that there is no conflict of interest. Data Availability Statement The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Consent for publication Not applicable. Funding We acknowledge the financial support for this research provided by the Joint Funds： for the Innovation of Science and Technology, Fujian Province (No. 2024Y9246). Ethics approval and consent to participate The studies involving humans were approved by the Institutional Review Board (IRB) of Fujian Medical University Union Hospital (Approval Number: 2024KJCX004). The studies were conducted in accordance with the local legislation and institutional requirements. All procedures adopted in this study followed the Declaration of Helsinki. Informed consent was obtained from all individual participants included for study participation. Clinical Trial Number: 2024KJCX004 References Gimbrone MA. García-Cardeña, G. Endothelial Cell Dysfunction and the Pathobiology of Atherosclerosis. Circ Res. 2016;118:620–36. Lyons MM, Bhatt NY, Pack AI, Magalang UJ. Global burden of sleep-disordered breathing and its implications. Respirology. 2020;25:690–702. May AM, Van Wagoner DR, Mehra R. OSA and Cardiac Arrhythmogenesis. Chest. 2017;151:225–41. Meszaros M, Bikov A. Obstructive Sleep Apnoea and Lipid Metabolism: The Summary of Evidence and Future Perspectives in the Pathophysiology of OSA-Associated Dyslipidaemia. Biomedicines. 2022;10:2754. Kapur VK, et al. Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline. J Clin Sleep Med. 2017;13:479–504. Grote L, Zou D, Kraiczi H, Hedner J. Finger plethysmography–a method for monitoring finger blood flow during sleep disordered breathing. Respir Physiol Neurobiol. 2003;136:141–52. Berry RB, et al. Rules for Scoring Respiratory Events in Sleep: Update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. J Clin Sleep Med. 2012;8:597–619. Kwon Y, et al. Pulse arrival time, a novel sleep cardiovascular marker: the multi-ethnic study of atherosclerosis. Thorax. 2021;76:1124–30. Beuret H, et al. Comparison of Swiss and European risk algorithms for cardiovascular prevention in Switzerland. Eur J Prev Cardiol. 2021;28:204–10. Solelhac G, et al. Pulse Wave Amplitude Drops Index: A Biomarker of Cardiovascular Risk in Obstructive Sleep Apnea. Am J Respir Crit Care Med. 2023;207:1620–32. Shahrbabaki SS, Linz D, Baumert M. Nocturnal pulse wave amplitude attenuations are associated with long-term cardiovascular events. Int J Cardiol. 2023;385:55–61. Gambino F, Zammuto MM, Virzì A, Conti G, Bonsignore MR. Treatment options in obstructive sleep apnea. Intern Emerg Med. 2022;17:971–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 10 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviews received at journal 24 Dec, 2025 Reviewers agreed at journal 07 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviews received at journal 03 Dec, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 20 Nov, 2025 Editor invited by journal 30 Oct, 2025 Editor assigned by journal 30 Oct, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 22 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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07:00:20\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":63344,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIllustration of PWAD\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7925111/v1/05ef00eb60c42edf7f4033c8.png\"},{\"id\":97141224,\"identity\":\"f77177f2-058d-4358-b1ef-77690d36b8fd\",\"added_by\":\"auto\",\"created_at\":\"2025-12-01 10:06:26\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":182030,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlow chart of study patient enrollment.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7925111/v1/f53335e3a276d761794fef76.jpeg\"},{\"id\":97114620,\"identity\":\"bb1e1715-d615-41f2-937d-2f84eecbbd01\",\"added_by\":\"auto\",\"created_at\":\"2025-12-01 07:00:20\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":14650,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKaplan–Meier survival curves for patients with different degrees of PWAD\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7925111/v1/023a07f1d21bf82bd4ee48f9.png\"},{\"id\":97114621,\"identity\":\"6201d9f7-c5af-49ca-a8b2-e64186ccf672\",\"added_by\":\"auto\",\"created_at\":\"2025-12-01 07:00:20\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":13522,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKaplan–Meier survival curves for patients with different degrees of AHI\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7925111/v1/ee4ec67869bb7e8c6c74b755.png\"},{\"id\":97114623,\"identity\":\"c0d5fbbb-4630-4156-8765-bda5d70c6eea\",\"added_by\":\"auto\",\"created_at\":\"2025-12-01 07:00:20\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":18309,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKaplan–Meier survival curves for patients with different degrees of PWAD+AHI\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7925111/v1/f94ff064d136b0a0f284b0cf.png\"},{\"id\":97248527,\"identity\":\"825b6fac-8829-4ba3-b2e0-061d559a536f\",\"added_by\":\"auto\",\"created_at\":\"2025-12-02 13:02:35\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":929823,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7925111/v1/25d0a92b-6a3b-47db-9c5e-d04e3b57328b.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Predictive Value of Decreased Pulse Wave Amplitude Index for Cardiovascular\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eObstructive sleep apnea (OSA) is characterized by recurrent episodes of complete and partial upper airway obstruction, leading to intermittent hypoxemia, autonomic fluctuations, and fragmented sleep\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e. It represents a major global health challenge with profound implications for the cardiovascular system. It is estimated that approximately 1\\u0026nbsp;billion people aged 30 to 69 among the world's 7.3\\u0026nbsp;billion population suffer from this disease. The prevalence of obstructive sleep apnea is rising and affects all countries, imposing a heavy burden on healthcare systems\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e. Its pathophysiological mechanisms primarily involve intermittent hypoxemia and sleep fragmentation caused by impaired gas exchange. This triggers systemic inflammatory responses and oxidative stress, leading to endothelial dysfunction and changes in hemodynamic and cardiac workload, as well as autonomic nervous system dysfunction.These are closely associated with multiple cardiovascular diseases such as hypertension, This triggers systemic inflammatory responses and oxidative stress, endothelial dysfunction leading to hemodynamic and cardiac workload changes, and autonomic nervous system dysfunction. heart failure, coronary artery disease, pulmonary hypertension, atrial fibrillation, and stroke\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e. Notably, moderate-to-severe OSA, particularly when accompanied by severe nocturnal hypoxia, significantly elevates cardiovascular and all-cause mortality risks, underscoring the urgency of developing effective risk stratification tools.\\u003c/p\\u003e\\u003cp\\u003eAlthough current diagnostic methods primarily focus on respiratory parameters and PSG, \\u003csup\\u003e5\\u003c/sup\\u003ereliable biomarkers specifically predictive of OSA cardiovascular risk remain extremely limited. Existing vascular assessment tools are often compromised by confounding factors and perform poorly in OSA populations, further underscoring the necessity for developing sleep-specific biomarkers capable of capturing unique hemodynamic changes during sleep. Pulse Wave Amplitude Drop (PWAD) or pulse wave dynamics metrics have been identified in multiple recent studies as a potential biomarker for assessing cardiovascular risk in OSA patients\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e. This is achieved by quantifying the magnitude and frequency of pulse amplitude reduction in finger pulse oximetry signals during sleep, particularly during apnea/hypopnea events. Grote and colleagues showed that pulse wave amplitude drops (PWADs) reflect transient vasoconstriction followed by a vasodilation that occurs in response to surges in\\u003c/p\\u003e\\u003cp\\u003esympathetic activity, which are then followed by a compensatory parasympathetic response.\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003eThese indicators sensitively reflect the vascular system's response to acute hypoxic events and chronic sympathetic-parasympathetic dysfunction. The hemodynamic instability they capture is highly correlated with core pathophysiological mechanisms of OSA, such as intermittent hypoxia, recurrent micro-awakenings, and excessive sympathetic activation. Early cohort studies and signal analysis indicate that PWAD-related parameters not only show significant associations with traditional vascular dysfunction indicators (e.g., nocturnal oxygen desaturation index, altered heart rate variability) but also reveal unique vascular stress patterns specific to OSA patients, offering new insights into the mechanisms underlying OSA-related cardiovascular complications.\\u003c/p\\u003e\\u003cp\\u003eThis study aims to evaluate the clinical value of the PWAD index as a cardiovascular risk biomarker in OSA patients. By analyzing the association between PWAD and cardiovascular events in this cohort, we expect to provide key evidence for the precise identification and management of high-risk OSA patients, ultimately guiding optimized clinical decision-making and improving patient outcomes.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy Design and Subjects\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;This retrospective cohort study analyzed clinical data from 1,034 patients, including 950 without cardiovascular events and 84 with cardiovascular events. Participants were obstructive sleep apnea (OSA) patients undergoing sleep monitoring. OSA diagnosis followed the 2012 American Academy of Sleep Medicine (AASM) sleep event scoring criteria,\\u003csup\\u003e7\\u003c/sup\\u003e with an apnea-hypopnea index (AHI) \\u0026ge;15 events/hour as the diagnostic threshold.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Collection and Variable Definition\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBaseline Data Collection\\u003c/p\\u003e\\n\\u003cp\\u003eThe following baseline data were collected.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eDemographic characteristics: age, gender;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eClinical indicators: Body Mass Index (BMI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP);\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Laboratory tests: Low-density lipoprotein (LDL), blood glucose (GLU), creatinine; Sleep parameters: AHI, mean SpO₂, NBPF index, mean nocturnal systolic pressure elevation;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eCardiovascular risk factors: history of hypertension, history of coronary heart disease, history of heart failure, history of diabetes mellitus (DM), smoking history, lipid-lowering medication use;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eCardiac function indicators: Ejection fraction (EF), nocturnal ePWV\\u003c/p\\u003e\\n\\u003cp\\u003ePWAD Index Measurement and Definition\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The PWAD (Pulse Wave Amplitude Decrease) index is defined as the total number of pulse wave amplitude decrease events occurring per hour of sleep time. A PWAD event is defined as a decrease in pulse wave amplitude exceeding 30% from baseline levels, persisting for more than four consecutive cardiac cycles. \\u003csup\\u003e8\\u003c/sup\\u003ePWAD event identification employs a validated automated algorithm previously applied in sleep-related cardiovascular monitoring studies using photoplethysmography (PPG) signals . The total number of PWAD events throughout the night is averaged per hour of \\u0026nbsp;sleep to yield the PWAD count (number of 30% PWA decreases per hour).\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 1 \\u0026nbsp;Illustration of PWAD\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDefinition and Evaluation of Pulse Wave Amplitude (PWA)\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Pulse wave amplitude (PWA) is extracted from photoplethysmography (PPG) signals recorded during nocturnal sleep. A valid PWA decrease event is defined as a \\u0026ge;30% reduction in pulse wave amplitude relative to baseline, persisting for more than four heartbeat cycles. Event detection is based on the PPG signal processing algorithm developed by Kwon et al. (2021), validated in the Multi-Ethnic Study of Atherosclerosis cohort. The PWA Decrease Index is calculated by dividing the total number of PWA decrease events meeting the above criteria throughout the night by the total sleep duration, yielding the number of PWA decrease events per hour. This methodology aligns with previous approaches used in sleep-disordered breathing studies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePrimary Endpoint\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The primary endpoint was a composite cardiovascular event, including fatal cardiovascular events: death due to myocardial infarction or stroke; and non-fatal cardiovascular events: myocardial infarction, stroke, transient ischemic attack, and coronary heart disease.\\u003csup\\u003e9\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Follow-up began on the dte of the sleep study and continued until the occurrence of a cardiovascular event (including cardiovascular death), other death, or the last follow-up date.\\u003csup\\u003e10\\u003c/sup\\u003e Follow-up duration ranged from 12.4 to 64.5 months, with a mean follow-up of 39.3 months and a median follow-up of 39.6 months.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Statistical Analysis Methods\\u003c/p\\u003e\\n\\u003cp\\u003eTo compare baseline characteristics between groups, use an independent samples t-test for continuous variables (if data is normal) and a Chi-square test for categorical variables. For non-normal continuous data, use the Mann-Whitney U test, and for small categorical samples, use Fisher\\u0026apos;s exact test.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Risk Factor Analysis\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Multivariate Cox proportional hazards regression models and multivariable logistic regression models were employed to evaluate the relationship between PWAD indices and cardiovascular events.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Subgroup analysis\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Patients were divided into high-PWAD and low-PWAD groups based on a median PWAD index of 23.5, and the difference in cardiovascular event incidence between the two groups was compared.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Quality control\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;All sleep monitoring data were collected by experienced technicians following standardized protocols.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The PWAD event identification algorithm was validated in a preliminary study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Statistical analysis employed two-sided tests, with P \\u0026lt; 0.05 considered statistically significant.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe studies involving humans were approved by the Institutional Review Board (IRB) of Fujian Medical University Union Hospital (Approval Number: 2024KJCX004). The studies were conducted in accordance with the local legislation and institutional requirements. All procedures adopted in this study followed the Declaration of Helsinki. Informed consent was obtained from all individual participants included for study participation.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003ch2\\u003e1.Patient characteristics\\u003c/h2\\u003e\\n\\u003cp\\u003e\\u0026nbsp; \\u0026nbsp; A total of 1,127 patients with suspected OSA who underwent sleep apnea monitoring at Fujian Medical University Union Hospital from January 2020 to July 2024 were enrolled. After excluding 58 patients with incomplete data and 35 lost to follow-up, 1,034 patients were ultimately included. Follow-up duration ranged from 12.4 to 64.5 months, with a mean follow-up of 39.38 months and a median follow-up of 39.6 months. Among these, 84 patients experienced MACE events, while 950 patients did not. (Figure 1)\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 1. Flow chart of study patient enrollment.\\u003c/p\\u003e\\n\\u003cp\\u003e2.Baseline characteristics of the MACE(+) and MACE(-) groups are shown in Table 1. Significant differences existed between groups in AHI stratification, PWAD index stratification, EF values, LDL levels, creatinine levels, and nocturnal ePWV. Patients with MACE events were significantly older. The proportions of patients with a history of coronary heart disease, heart failure, lipid-lowering medication use, diabetes, and smoking were significantly higher in the MACE group.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;Table 1 \\u0026nbsp; \\u0026nbsp;Baseline clinical characteristics of patients\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;Variable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;No MACE events\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;With MACE events\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;P\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;N=950\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;N=81\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;AHI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.013\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(-)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;551 (57.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;37 (44.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(+)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;399 (42.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;47 (56.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;PWAD Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(+)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e458 (48.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;60 (74.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(-)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;492 (51.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;24 (29.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Age\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;50.89 \\u0026plusmn;15.78\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;61.64 \\u0026plusmn;12.50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.017\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Gender (Male)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;675 (71.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;59 (72.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.863\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;BMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;26.66\\u0026plusmn;5.53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;26.16\\u0026plusmn;4.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.355\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Mean SpO2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;95.16 \\u0026plusmn;2.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;95.02 \\u0026plusmn; 2.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.961\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;NBPF Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;32.44 \\u0026plusmn; 25.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;35.51 \\u0026plusmn; 19.93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.151\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Mean Elevated Blood Pressure (mmHg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;14.65\\u0026plusmn;1.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;14.65\\u0026plusmn;1.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.982\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;EF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;65.52 \\u0026plusmn; 7.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;63.61\\u0026plusmn;10.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;LDL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;2.95\\u0026plusmn;0.78\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;2.71\\u0026plusmn;1.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;GLU\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;7.03\\u0026plusmn;21.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;6.26\\u0026plusmn;2.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.755\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Creatinine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;80.61\\u0026plusmn;49.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;97.27\\u0026plusmn;92.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.015\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Nocturnal ePWV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;9.09 \\u0026plusmn; 2.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;10.20 \\u0026plusmn; 2.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;History of hypertension\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;596 (62.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;59 (70.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.171\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;History of coronary heart disease\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;125 (13.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;43 (51.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;History of heart failure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;62 (6.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;15 (17.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;History of lipid-lowering medication use\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;354 (37.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e53 (63.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;History of DM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;153 (16.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;30 (35.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;History of smoking\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;214 (22.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;30 (37.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eNote: AHI \\u0026lt; 15 constitutes Group 1; AHI \\u0026gt; 15 constitutes Group 2. PWAD index \\u0026lt; 23.5 constitutes Group 1 (+); PWAD index \\u0026gt; 23.5 constitutes Group 2 (-).The value following the \\u0026plusmn; symbol represents the standard deviation (s.d.), expressed as:\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026quot;Mean \\u0026plusmn; standard deviation\\u0026quot;\\u003c/p\\u003e\\n\\u003cp\\u003eAll abbreviations in the table require a full name listing.\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviation: PWAD : Pulse Wave Amplitude decrease Index ; SpO2:Peripheral Oxygen Saturation;BMI：Body Mass Index; EF:Ejection Fraction; ePWV: Estimated pulse wave velocity;LDL:Low-Density Lipoprotein;DM:Diabetes Mellitus;GLU:Glucose\\u003c/p\\u003e\\n\\u003cp\\u003eNocturnal blood pressure fluctuations (NBPF) refer to nocturnal blood pressure elevation events.\\u003c/p\\u003e\\n\\u003cp\\u003eNBPF Definition: A sustained blood pressure increase exceeding 12 mmHg over 3\\u0026ndash;30 seconds. Min and Max values computed as the mean of 8 readings.\\u003c/p\\u003e\\n\\u003cp\\u003e3. Treat PWA and AHI as continuous variables. The PWAD mean is lower in the MACE event group, P \\u0026lt; 0.05 (statistically significant difference). Although AHI is higher in the MACE event group, the difference is not statistically significant (Table 2).\\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;Table 2 \\u0026nbsp; Distribution of AHI and PWAD in Different MACE Subgroups\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" align=\\\"left\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 112px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;Variable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 131px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;No MACE\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eevents\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 119px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;With MACE events\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;P\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 112px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;AHI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 131px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;18.161 (19.1883)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 119px;\\\"\\u003e\\n \\u003cp\\u003e20.028(17.5246)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;0.363\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 112px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;PWAD Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 131px;\\\"\\u003e\\n \\u003cp\\u003e25.471(15.13826)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 119px;\\\"\\u003e\\n \\u003cp\\u003e19.88(11.39884)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e＜0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003ePWAD : Pulse Wave Amplitude decrease Index\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;4. As shown in Table 3 \\u0026nbsp;Model 3, for MACE events as the outcome variable, each 1-unit increase in PWAD reduces MACE risk by 2.6%, with an HR of 0.974 (0.953-0.995). Additionally, using individuals with PWAD \\u0026le; 23.5 as the reference group,\\u0026nbsp;the MACE risk associated with individuals with PWAD \\u0026gt; 23.5\\u0026nbsp;is reduced by 50.8%, with an HR of 0.508 (0.291-0.885). (Table 3)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;Table 3 \\u0026nbsp;Association between PWAD and MACE\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"643\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariables\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel 1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 170px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel 2\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel 3\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 121px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR (95%CI)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR (95%CI)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR (95%CI)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003ePWAD\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 121px;\\\"\\u003e\\n \\u003cp\\u003e0.963 (0.946-0.980)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.979 (0.960-0.997)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e0.025\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e0.974 (0.953-0.995)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.016\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003ePWAD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 121px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026le; 23.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 121px;\\\"\\u003e\\n \\u003cp\\u003eRef\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003eRef\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003eRef\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt; 23.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 121px;\\\"\\u003e\\n \\u003cp\\u003e0.331 (0.205-0.534)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.495 (0.294-0.832)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e0.008\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e0.508 (0.291-0.885)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.017\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Abbreviation: PWAD: Pulse Wave Amplitude Index; CI: Confidence interval; HR: Hazard ratio; Ref: Reference.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Note: Model 1: No adjustments made.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Model 2: Adjusted for age, BMI, and sex.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Model 3: Adjusted for age, BMI, and sex. AHI, mean SPO2, history of hypertension, history of diabetes, history of smoking, history of coronary heart disease, history of heart failure, lipid-lowering medication, LDL, EF, NBPF index\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The Log-rank test was used to examine survival differences between the two groups, revealing significant differences. The PWAD \\u0026le; 23.5 group had a higher incidence of MACE events than the PWAD \\u0026gt; 23.5 group. (Note: The PWAD index was categorized as (+) for median \\u0026lt; 23.5 and (-) for PWAD index \\u0026gt; 23.5 (Figure.2).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Figure 2 \\u0026nbsp; \\u0026nbsp;Kaplan\\u0026ndash;Meier survival curves for patients with different degrees of PWAD\\u003c/p\\u003e\\n\\u003cp\\u003e5. Further analysis of the interaction between AHI and PWAD\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;As shown in Table 4 \\u0026nbsp;Model 3, for MACE as the outcome variable, compared with the PWAD(-)AHI(-) group (reference), the MACE risk in the PWAD(+)AHI(+) group increased by 116%, with a hazard ratio (HR) of 2.161 (1.049\\u0026ndash;4.454). (Table 4)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;Table 4 \\u0026nbsp;Association between PWAD+AHI and MACE\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"643\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariables\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 178px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel 1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 172px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel 2\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 179px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModel 3\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR (95%CI)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 55px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR (95%CI)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHR (95%CI)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003ePWAD(-)AHI(-)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003eRef\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 55px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003eRef\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003eRef\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003ePWAD(+)AHI(-)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e0.793(0.347-1.814)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 55px;\\\"\\u003e\\n \\u003cp\\u003e0.584\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e0.868(0.376-2.002)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.740\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e0.974(0.394-2.404)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e0.954\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003ePWAD(-)AHI(+)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e1.909(1.017-3.582)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 55px;\\\"\\u003e\\n \\u003cp\\u003e0.044\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e1.424(0.732-2.767)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.298\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e1.437(0.687-3.008)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e0.336\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003ePWAD(+)AHI(+)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e3.467(1.875-6.411)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 55px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e2.184(1.131-4.218)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.020\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e2.161(1.049-4.454)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e0.037\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Abbreviation: ePWV: Estimated pulse wave velocity; CI: Confidence interval; HR: Hazard ratio; Ref: Reference.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Note: Model 1: No adjustments made.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Model 2: Adjusted for age, BMI, and sex.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Model 3: Adjusted for age, BMI, and sex. AHI, mean SPO₂, history of hypertension, history of diabetes, history of smoking, history of coronary heart disease, history of heart failure, lipid-lowering medication, LDL, EF, NBPF index\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The log-rank test examined MACE differences between the two AHI groups, revealing no significant difference between them (Figure 3). Further analysis combining PWAD showed that within the OSA group (AHI \\u0026gt; 15), the lowest PWAD group had the highest MACE risk (i.e., PWAD(+) AHI(+) group), with a statistically significant difference (P \\u0026lt; 0.001) (Figure 4).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Figure 3 \\u0026nbsp; Kaplan\\u0026ndash;Meier survival curves for patients with different degrees of AHI\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Figure 4 Kaplan\\u0026ndash;Meier survival curves for patients with different degrees of PWAD+AHI\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study represents the first systematic evaluation of the PWAD index's predictive value in cardiovascular risk stratification for OSA patients, providing crucial evidence for the clinical application of this emerging biomarker. Our core findings indicate that a PWAD index \\u0026lt;23.5 (median) independently predicts the risk of cardiovascular events, a finding that remains statistically significant after adjusting for traditional risk factors such as age, sex, BMI, hypertension, and diabetes. This result holds significant clinical importance, offering a novel assessment tool for cardiovascular risk management in OSA patients.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The observed association between lower PWAD values and increased cardiovascular risk supports hypotheses from prior studies suggesting impaired vascular reactivity and blunted autonomic responses during sleep may represent key pathways linking OSA to cardiovascular disease.\\u003csup\\u003e8,10\\u003c/sup\\u003e Our findings align with Kwon et al., who demonstrated that attenuated PAT (pulse transit time) responses following respiratory events correlate with subclinical cardiovascular disease markers .Similarly, Salari Shahrbabaki et al. reported that reduced nocturnal PWA attenuation predicts adverse cardiovascular outcomes, particularly in male and African American populations. \\u003csup\\u003e11\\u003c/sup\\u003eThe consistency of these independent findings strengthens the biological plausibility of PWAD as a marker of vascular dysfunction in OSA.\\u003c/p\\u003e\\n\\u003cp\\u003eThe pathophysiological significance of our findings is multifaceted. First, the negative correlation between PWAD index and cardiovascular risk suggests that patients with diminished peripheral vasomotor responses may exhibit more severe endothelial dysfunction (ECD)—a \\\"prerequisite\\\" for the formation and progression of atherosclerotic lesions, which is a hallmark of atherosclerotic advancement\\u003csup\\u003e1\\u003c/sup\\u003e . This interpretation aligns with emerging evidence that intermittent hypoxia and autonomic dysfunction induced by OSA trigger oxidative stress (increased ROS) and systemic inflammation, leading to endothelial injury\\u003csup\\u003e3\\u003c/sup\\u003e . Second, blunted PWAD responses may reflect autonomic dysfunction, where chronic sympathetic hyperactivity in OSA patients causes receptor downregulation and impaired vascular reactivity . \\u003csup\\u003e8,10\\u003c/sup\\u003eThis mechanism is supported by experimental studies showing that acute blood pressure surges following obstructive events manifest as shortened PAT, while chronic exposure leads to vascular remodeling.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Current clinical diagnosis of OSA primarily relies on polysomnography (PSG) and the apnea-hypopnea index (AHI). However, AHI inadequately reflects disease complexity and shows poor correlation with clinical outcomes such as cardiovascular disease and cognitive impairment.\\u0026nbsp;\\u003csup\\u003e10\\u003c/sup\\u003eOur findings indicate that te PWAD index provides incremental prognostic value beyond traditional risk factors and AHI, addressing a key limitation in current OSA management. The weak correlation observed by Kwon et al. between PAT response and AHI (r=0.1), along with AHI's minimal explanatory power in predicting cardiovascular outcomes, underscores the need for alternative measures like PWAD to better reflect the physiological consequences of respiratory events \\u003csup\\u003e1\\u003c/sup\\u003e. This is particularly important given the growing recognition that AHI alone fails to capture crucial aspects of OSA pathophysiology.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The clinical implications of our findings are substantial. First, PWAD reflects autonomic nervous system activity and vascular reactivity, which are closely associated with cardiovascular event risk in OSA patients, offering a novel assessment tool for cardiovascular risk management in this population. Second, it aids in identifying high-risk OSA patients who may benefit from more aggressive treatment. The automated nature of PWAD measurement facilitates its implementation in clinical practice. CPAP (continuous positive airway pressure) remains the recommended treatment for OSA, with the strongest evidence of efficacy for reducing AHI, symptoms, and comorbidities .\\u003csup\\u003e12\\u003c/sup\\u003e PWAD may serve as a predictor of CPAP treatment response.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Several limitations warrant consideration. First, as a retrospective study, we cannot establish causality between PWAD and cardiovascular outcomes. Second, the single-center design may limit generalizability, although our findings align with multicenter studies. Third, despite adjusting for numerous confounders, residual confounding remains possible. Fourth, optimal PWAD thresholds may vary across populations and require validation in independent cohorts. Finally, the underlying mechanisms linking PWAD to cardiovascular risk warrant further elucidation through physiological studies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Future research directions should include: 1) prospectively validating PWAD thresholds across diverse populations; 2) investigating PWAD dynamics in response to OSA treatment; 3) developing integrated risk scores combining PWAD with other biomarkers; 4) conducting mechanistic studies exploring molecular pathways linking PWAD to cardiovascular disease. The potential for longitudinal PWAD monitoring via wearable technology offers exciting opportunities for personalized risk assessment and management.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;In summary, this study establishes the PWAD index as a novel biomarker for cardiovascular risk assessment in OSA patients. Our findings not only enrich the pathophysiological understanding of OSA-related cardiovascular risk but also provide a practical risk assessment tool for clinical practice. Future research should focus on validating these findings and further exploring the potential value of PWAD in guiding OSA treatment decisions.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eAuthor Contributions\\u003c/p\\u003e\\n\\u003cp\\u003eHuizhong Lin:\\u0026nbsp;Conceptualization, Study design, Writing \\u0026ndash; Review \\u0026amp; Editing, Supervision, Project administration.\\u003c/p\\u003e\\n\\u003cp\\u003eSiai Chen:\\u0026nbsp;Methodology, Writing \\u0026ndash; Original Draft.\\u003c/p\\u003e\\n\\u003cp\\u003eMin Li:\\u0026nbsp;Investigation, Data Curation, Writing \\u0026ndash; Original Draft. Siai Chen and Min Li have contributed equally to this work.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of Interest Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that there is no conflict of interest.\\u003c/p\\u003e\\n\\u003cp\\u003eData Availability Statement\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eWe acknowledge the financial support for this research provided by the Joint Funds：\\u0026nbsp;for the Innovation of Science and Technology, Fujian Province (No. 2024Y9246).\\u003c/p\\u003e\\n\\u003cp\\u003eEthics approval and consent to participate\\u003c/p\\u003e\\n\\u003cp\\u003eThe studies involving humans were approved by the Institutional Review Board (IRB) of Fujian Medical University Union Hospital (Approval Number: 2024KJCX004). The studies were conducted in accordance with the local legislation and institutional requirements. All procedures adopted in this study followed the Declaration of Helsinki. Informed consent was obtained from all individual participants included for study participation.\\u003c/p\\u003e\\n\\u003cp\\u003eClinical Trial Number: 2024KJCX004\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eGimbrone MA. Garc\\u0026iacute;a-Carde\\u0026ntilde;a, G. Endothelial Cell Dysfunction and the Pathobiology of Atherosclerosis. Circ Res. 2016;118:620\\u0026ndash;36.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLyons MM, Bhatt NY, Pack AI, Magalang UJ. Global burden of sleep-disordered breathing and its implications. Respirology. 2020;25:690\\u0026ndash;702.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMay AM, Van Wagoner DR, Mehra R. OSA and Cardiac Arrhythmogenesis. Chest. 2017;151:225\\u0026ndash;41.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMeszaros M, Bikov A. Obstructive Sleep Apnoea and Lipid Metabolism: The Summary of Evidence and Future Perspectives in the Pathophysiology of OSA-Associated Dyslipidaemia. Biomedicines. 2022;10:2754.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKapur VK, et al. Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline. J Clin Sleep Med. 2017;13:479\\u0026ndash;504.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGrote L, Zou D, Kraiczi H, Hedner J. Finger plethysmography\\u0026ndash;a method for monitoring finger blood flow during sleep disordered breathing. Respir Physiol Neurobiol. 2003;136:141\\u0026ndash;52.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBerry RB, et al. Rules for Scoring Respiratory Events in Sleep: Update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. J Clin Sleep Med. 2012;8:597\\u0026ndash;619.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKwon Y, et al. Pulse arrival time, a novel sleep cardiovascular marker: the multi-ethnic study of atherosclerosis. Thorax. 2021;76:1124\\u0026ndash;30.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBeuret H, et al. Comparison of Swiss and European risk algorithms for cardiovascular prevention in Switzerland. Eur J Prev Cardiol. 2021;28:204\\u0026ndash;10.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSolelhac G, et al. Pulse Wave Amplitude Drops Index: A Biomarker of Cardiovascular Risk in Obstructive Sleep Apnea. Am J Respir Crit Care Med. 2023;207:1620\\u0026ndash;32.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eShahrbabaki SS, Linz D, Baumert M. Nocturnal pulse wave amplitude attenuations are associated with long-term cardiovascular events. Int J Cardiol. 2023;385:55\\u0026ndash;61.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGambino F, Zammuto MM, Virz\\u0026igrave; A, Conti G, Bonsignore MR. Treatment options in obstructive sleep apnea. Intern Emerg Med. 2022;17:971\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-cardiovascular-disorders\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bcar\",\"sideBox\":\"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bcar/default.aspx\",\"title\":\"BMC Cardiovascular Disorders\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"sleep apnea syndrome, cardiovascular disease, biomarker, photoplethysmography, autonomic nervous system\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7925111/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7925111/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eObstructive sleep apnea (OSA) elevates cardiovascular risk, but existing biomarkers fail to capture sleep-specific hemodynamic changes. This study evaluates the pulse wave amplitude decrease (PWAD) index, a photoplethysmography-derived measure of vascular/autonomic reactivity during respiratory events, in 1,034 OSA patients (AHI\\u0026thinsp;\\u0026ge;\\u0026thinsp;15). Each 1-unit PWAD increase reduced major adverse cardiovascular events (MACE) risk by 2.6% (HR 0.974, 95%CI 0.953\\u0026ndash;0.995), with PWAD\\u0026thinsp;\\u0026gt;\\u0026thinsp;23.5 (median) showing 50.8% risk reduction (HR 0.508, 95%CI 0.291\\u0026ndash;0.885). The combination of low PWAD (\\u0026le;\\u0026thinsp;23.5) and high AHI synergistically increased MACE risk by 116% (HR 2.161, 95%CI 1.049\\u0026ndash;4.454, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) versus double-negative group. Low PWAD correlated with older age, higher LDL, and elevated cardiovascular risk (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), suggesting endothelial dysfunction and autonomic dysregulation as mechanisms. The PWAD index independently predicts cardiovascular risk in OSA and can be automatically measured via standard pulse oximetry, offering a practical tool for identifying high-risk patients needing intensive treatment.​\\u003c/p\\u003e\",\"manuscriptTitle\":\"Predictive Value of Decreased Pulse Wave Amplitude Index for Cardiovascular\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-01 07:00:15\",\"doi\":\"10.21203/rs.3.rs-7925111/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-03-10T05:43:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-04T17:29:48+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-02T22:16:47+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"129706074485617597796212596849978399219\",\"date\":\"2026-02-18T13:59:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"332106435261737636281072529078554665843\",\"date\":\"2026-02-16T13:42:10+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-24T10:00:24+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"321115956974831600951076152277268933974\",\"date\":\"2025-12-07T10:12:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-06T14:17:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-03T21:24:24+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"140736404327774603600130931851855286348\",\"date\":\"2025-11-27T16:44:42+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"193730789287179278734175610655496229834\",\"date\":\"2025-11-25T17:43:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-11-20T15:33:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-10-30T14:00:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-10-30T13:35:37+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-10-30T13:34:09+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Cardiovascular Disorders\",\"date\":\"2025-10-22T15:23:38+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-cardiovascular-disorders\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bcar\",\"sideBox\":\"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bcar/default.aspx\",\"title\":\"BMC Cardiovascular Disorders\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"7301a48c-e559-4252-9774-bace47a5470e\",\"owner\":[],\"postedDate\":\"December 1st, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"in-revision\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-10T05:54:19+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-01 07:00:15\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7925111\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7925111\",\"identity\":\"rs-7925111\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}