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Background/Objectives: Vascular function measurements, including central parameters [pulse wave velocity (PWV) and augmentation index (AI)], as well as peripheral measures [finger photoplethysmography fitness index (PPGF)], have been introduced to detect early vascular damage associated with coronary artery disease (CAD) risk factors. This study aimed to compare peripheral and central vascular function marker levels among subjects with hypertension (HPT), dyslipidemia, and obesity. We also aimed to determine the relationship between these markers and CAD risk factors among these groups. Methods: A total of 320 subjects including healthy individuals and those with CAD risk factors were recruited. Peripheral vascular function was assessed using the PPGF, whereas central vascular markers included measurements of PWV and AI. The Framingham risk score (FRS) was calculated using an online calculator. Results: The mean age of the subjects was 33.73 ± 7.29 years. PWV and AI were significantly higher in HPT subjects (8.03 ± 1.40 m/s and 21.90% ± 10.57%) than the control. PPGF levels showed no significant differences between the groups. PWV was associated with FRS in the HPT and dyslipidemia groups, whereas AI was associated with FRS in the obese group. PPGF showed associations with PWV and AI in the dyslipidemia group. Conclusions: PWV and AI serve as robust macrovascular markers indicating arterial stiffness and systemic vascular resistance linked to CAD risk, while PPGF, as a microvascular marker, offers valuable insights into early endothelial dysfunction and microcirculatory anomalies, especially in dyslipidemia subjects.

Arterial stiffness, a significant cardiovascular risk marker, is particularly important in patients with diabetes mellitus (DM). Pulse wave velocity (PWV), a non-invasive measure of arterial stiffness, has emerged as an independent predictor of cardiovascular morbidity and mortality. However, its precise prognostic value in DM patients for cardiovascular risk stratification remains unclear. To address this, a systematic review was conducted. A thorough search of Ovid and Scopus databases was performed for cohort studies on PWV measurements for cardiovascular risk stratification in DM patients. Nine studies were included, examining the relationship between PWV and cardiovascular events or composite endpoints in DM patients asymptomatic of cardiovascular diseases (CVD). The review revealed that optimal PWV cutoffs to predict composite cardiovascular events ranged from 10 to 12.16 m/s (aortic PWV) and 14 to 16.72 m/s (brachial-ankle PWV). In addition, meta-analysis yielded a HR of 1.15 (95 % CI 1.07–1.24, p < 0.001, I2 = 70 %) for aortic PWV in predicting cardiovascular events. The assessment of arterial stiffness via PWV shows promise as an early diagnostic marker for CVD in DM patients, aiding in improved disease management. This underscores the potential of PWV in enhancing cardiovascular risk assessment and guiding clinical decisions in this high-risk population, without invasive procedures or radiation exposure. •Increased arterial stiffness is associated with cardiovascular risk and diabetes.•Pulse wave velocity (PWV) is the surrogate marker for arterial stiffness.•Cardiovascular diseases (CVD) often remain asymptomatic in diabetic patients.•PWV prognostic values in diabetic patients for CVD risk stratification is unclear.•PWV measurement is useful for diabetic patients in early detection of CVD.

Cardiovascular disease (CVD) remains a leading cause of mortality globally, underscoring the need for robust predictive biomarkers to enhance risk stratification. Soluble lectin-like oxidized low-density lipoprotein receptor-1 (sLOX-1) has emerged as a promising biomarker linked to oxidative stress and endothelial dysfunction, both critical mechanisms in atherogenesis and cardiovascular events. Objectives: This study aimed to evaluate the prognostic value of sLOX-1 in predicting major adverse cardiovascular and cerebrovascular events (MACCEs), myocardial infarction (MI), heart failure (HF), and stroke outcomes through a systematic review and meta-analysis. Methods: A systematic literature search was conducted across PubMed, Scopus, Web of Science, and Ovid databases for studies published between 2014 and October 2024. Eligible studies assessed the association between sLOX-1 levels and future CVD outcomes in adult populations. Meta-analysis pooled hazard ratios (HRs) were assessed using random- and fixed-effects models. Heterogeneity was evaluated using the I2 statistic, and study quality was assessed using the Newcastle–Ottawa Scale. Results: Fourteen studies were included, encompassing diverse populations with coronary artery disease (CAD), acute coronary syndrome (ACS), or stroke, with follow-up durations ranging from 30 days to 19.5 years. The meta-analysis of three studies on CAD patients demonstrated a significant association between elevated sLOX-1 levels and increased MACCE risk (HR: 2.3, 95% CI: 0.99–5.33, p = 0.05), albeit with high heterogeneity (I2 = 83%). The fixed-effects analysis yielded a more consistent HR of 1.47 (95% CI: 1.19–1.81, p < 0.01). Conclusions: sLOX-1 shows promising potential as a prognostic biomarker for CVD and is associated with an increased risk of MACCEs in CAD patients. However, the high heterogeneity among the included studies highlights the need for standardized protocols and larger, well-designed prospective studies to validate its clinical utility. The integration of sLOX-1 into risk prediction models could improve CVD management by identifying high-risk individuals for targeted interventions.

Coronary artery disease (CAD) is the leading cause of death globally and is a heart condition involving insufficient blood supply to the heart muscle due to atherosclerotic plaque formation. Atherosclerosis is a chronic disease in which plaques, made up of fat, cholesterol, calcium, and other substances, build up on the inner walls of arteries. Recently, there has been growing interest in finding reliable biomarkers to understand the pathogenesis and progression of atherosclerosis. Tissue Inhibitors of Metalloproteinases (TIMPs) have emerged as potential candidates for monitoring atherosclerotic development. TIMPs are a family of endogenous proteins that regulate matrix metalloproteinases (MMPs), enzymes involved in remodeling the extracellular matrix. A systematic search using Prisma guidelines was conducted and eleven studies were selected from four different databases: Web of Science (WOS), Scopus, Ovid, and PubMed. The Newcastle–Ottawa Scale (NOS) score was used to assess the risk of bias for each study. A meta-analysis was performed, and the hazard ratio (HR) and its 95% confidence interval (CI) were determined. Among the eleven studies, six reported a positive association between higher levels of TIMPs and an increased risk of atherosclerosis. Conversely, four studies support low TIMPs with high CAD risk and one study showed no significant association between TIMP-2 G-418C polymorphism and CAD. This divergence in findings underscores the complexity of the relationship between TIMPs, atherosclerosis, and CAD. In addition, a meta-analysis from two studies yielded a HR (95% CI) of 1.42 (1.16–1.74; p < 0.001; I2 = 0%) for TIMP-2 in predicting major adverse cardiovascular events (MACEs). In conclusion, the existing evidence supports the notion that TIMPs can serve as biomarkers for predicting the severity of atherosclerosis, myocardial damage, and future MACEs among CAD patients. However, further exploration is warranted through larger-scale human studies, coupled with in vitro and in vivo investigations.

Finger photoplethysmography (PPG) waveform is blood volume change of finger microcirculation that reflects vascular function. Reflection index (RI), stiffness index (SI) and second derivative of photoplethysmogram (SDPPG) are derived from PPG waveforms proposed as cardiovascular disease (CVD) markers. Heart rate (HR) is a known factor that affects vascular function. Individual resting HR variation may affect RI, SI and SDPPG. This review aims to identify studies about the relationship between HR with RI, SI and SDPPG among humans. A literature search was conducted in Medline via the Ebscohost and Scopus databases to find relevant articles published within 11 years. The main inclusion criteria were articles in the English language that discuss the relationship between HR with RI, SI and SDPPG using PPG among humans. The search found 1960 relevant articles but only six articles that met the inclusion criteria. SI and RI showed an association with HR. SDPPG (SDPPG-b/SDPPG-a ratio, SDPPG-d/SDPPG-a ratio, aging index (AGI) and revised aging index (RAGI)) also had an association with HR. Only RI had a considerable association with HR, the association between SI and HR was non-considerable and the association between HR and SDPPG was inconclusive. Further interventional studies should be conducted to investigate this issue, as a variation in resting HR may challenge the validity of PPG-based CVD markers.

The application of artificial intelligence (AI) to electrocardiograms (ECGs) and photoplethysmograph (PPG) for diagnosing significant coronary artery disease (CAD) is not well established. This study aimed to determine whether the combination of ECG and PPG signals could accurately identify the location of blocked coronary arteries in CAD patients. Simultaneous measurement of ECG and PPG signal data were collected from a Malaysian university hospital, including patients with confirmed significant CAD based on invasive coronary angiography. ECG and PPG datasets were concatenated to form a single dataset, thereby enhancing the information available for the training process. Experimental results demonstrate that the Convolutional Neural Networks (CNN) + Long Short-Term Memory (LSTM) + Attention (ATTN) mechanisms model significantly outperforms standalone CNN and CNN + LSTM models, achieving an accuracy of 98.12% and perfect Area Under the Curve (AUC) scores of 1.00 for the detection of blockages in the left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary artery (RCA). The integration of LSTM layers captures temporal dependencies in the sequential data, while the attention mechanism selectively highlights the most relevant signal features. This study demonstrates that AI-enhanced models can effectively analyze simultaneous measurement of standard single-lead ECGs and PPG to predict the location of coronary artery blockages and could be a valuable screening tool for detecting coronary artery obstructions, potentially enabling their use in routine health checks and in identifying patients at high risk for future coronary events.

Coronary artery stenosis (CAS) is a critical cardiovascular condition that demands accurate localization for effective treatment and improved patient outcomes. This study addresses the challenge of enhancing CAS localization through a comparative analysis of deep learning techniques applied to electrocardiogram (ECG), photoplethysmograph (PPG), and their combined signals. The primary research question centers on whether the fusion of ECG and PPG signals, analyzed through advanced deep learning architectures, can surpass the accuracy of individual modalities in localizing stenosis in the left anterior descending (LAD), left circumflex (LCX), and right coronary arteries (RCA). Using a dataset of 7,165 recordings from CAS patients, three models—CNN, CNN-LSTM, and CNN-LSTM-ATTN—were evaluated. The CNN-LSTM-ATTN model achieved the highest localization accuracy (98.12%) and perfect AUC scores (1.00) across all arteries, demonstrating the efficacy of multimodal signal integration and attention mechanisms. This research highlights the potential of combining ECG and PPG signals for non-invasive CAS diagnostics, offering a significant advancement in real-time clinical applications. However, limitations include the relatively small dataset size and the focus on single-lead ECG and PPG signals, which may affect the generalizability to broader populations. Future studies should explore larger datasets and multi-lead signal integration to further validate the findings.

The finger photoplethysmography fitness index (PPGF), a marker of peripheral vascular function, has been linked to heart rate (HR) variability. However, the influence of acute HR changes on resting PPGF, a purported indicator of local blood flow, remains unclear. This study aimed to determine the influence of acute HR changes on resting PPGF. A total of 22 pacemaker recipients (mean age: 52.27 ± 10.43 years) underwent a controlled study. Baseline assessments included demographics, blood pressure (BP), blood analysis, PPGF, and vascular functions. HR was progressively increased from 70 bpm to 90 bpm in 10 bpm increments with 20 min resting periods at baseline and between pacing intervals. HR, PPGF, and BP were recorded at each pacing level. Systolic and diastolic BP increased with rising HR. Conversely, PPGF remained stable across different HR levels (70 bpm: 51.02 ± 11.52%, 80 bpm: 51.15 ± 11.82%, 90 bpm: 49.73 ± 11.55%; p > 0.05), suggesting that resting PPGF is independent of acute HR fluctuations. Our findings demonstrate that PPGF accurately reflects local blood flow, unaffected by short-term HR variations. This study supports the use of PPGF as a reliable marker for vascular health and age assessment, even in individuals with fluctuating HR, such as older adults with multiple comorbidities. Further research is warranted to establish the applicability of PPGF in younger, healthier populations.