Shift workers are commonly associated with circadian misalignment due to irregular working hours, which often leads to poor sleep quality and is associated with HPA axis misalignment and changes in cardiovascular outcome. Background: This systematic review aimed to investigate the association between cortisol production and cardiovascular health with sleep quality in healthcare shift workers. Methods: A comprehensive search of PubMed, Web of Science, and Scopus was conducted for studies published between 2010 and 2025, according to PRISMA guidelines. Fourteen studies met the inclusion criteria. Results: Among the included studies, eight studies focused on the relationship between sleep quality and cortisol regulation, five studies investigated the link between sleep quality and cardiovascular health, and one study examined sleep quality, cortisol regulation, and cardiovascular outcome. A significant relationship between cortisol and sleep quality was observed, as lower cortisol levels upon awakening were associated with low sleep quality. Several studies reported that sleep disturbances were associated with adverse cardiovascular outcomes, including reduced heart rate variability (HRV) and increased risk of metabolic syndrome. Conclusions: This review highlights existing literature on the critical role of sleep quality as a key factor in cortisol level and cardiovascular health in shift workers, along with the factors influencing circadian rhythm.

Background: Cardiovascular disease (CVD) is a significant public health challenge in the Western Pacific region, including Malaysia. Objective: This study aimed to develop and validate machine learning (ML) models to predict 10-year CVD risk in a Malaysian cohort, which could serve as a model for other Asian populations with similar genetic and environmental backgrounds. Methods: Utilizing data from the REDISCOVER Registry (5,688 participants from 2007 to 2017), 30 clinically relevant features were selected, and several ML algorithms were trained: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Neural Network (NN) and Naive Bayes (NB). Ensemble model were also created using three commonly used meta learners, including RF, Generalized Linear Model (GLM), and Gradient Boosting Model (GBM). The dataset was split into a 70:30 train-test ratio, with 5-fold cross-validation to ensure robust performance. Model evaluation was primarily based on the Area Under the Curve (AUC), with additional metrics such as sensitivity, specificity, and the Net Reclassification Index (NRI) to compare the ML models against traditional risk scores like the Framingham Risk Score (FRS) and Revised Pooled Cohort Equations (RPCE). Results: The LR model achieved the highest AUC of 0.77, outperforming the FRS (AUC = 0.72) and RPCE (AUC = 0.74). The ensemble model provided robust performance, though it did not significantly exceed the best individual model. SHAP (SHapley Additive exPlanations) analysis identified key predictors such as systolic blood pressure, weight and waist circumference. The study showed a significant NRI improvement of 13.15% compared to the FRS and 7.00% compared to the RPCE, highlighting the potential of ML approaches to enhance CVD risk prediction in Malaysia. The best-performing model was deployed on a web platform for real-time use, ensuring ongoing validation and clinical applicability. Conclusions: These findings underscore the effectiveness of ML models in improving CVD risk stratification and decision-making in Malaysia and beyond.

Air pollution is a growing cardiovascular risk in Southeast Asia, particularly in the Western Pacific region where transboundary haze and urban emissions are prevalent. Despite its relevance, traditional risk scores for Acute Coronary Syndrome (ACS) often overlook environmental factors. This study aims to assess the predictive value of air pollution exposure on ACS mortality using machine learning (ML) techniques, thereby addressing this clinical-environmental data gap. We combined clinical data from the National Cardiovascular Disease Database (NCVD) of Malaysia and daily air quality data (NOx, SO₂, O₃, PM₁₀) from the Department of Environment (2006–2017). ML algorithms including logistic regression, random forest (RF), XGBoost, and ensemble learning were developed to predict in-hospital mortality. SHapley Additive exPlanations (SHAP) were applied to enhance model interpretability. Model performance was compared against the conventional TIMI risk scores for STEMI and NSTEMI patients. From 14,145 ACS cases, the RF model achieved the highest AUC (0.843), outperforming TIMI scores (0.791 for STEMI, 0.565 for NSTEMI). Net Reclassification Index improvements were 8.71% (STEMI) and 86.94% (NSTEMI), both statistically significant (p < 0.001). SHAP analysis identified NOx and O₃, along with clinical factors like Killip class and fasting blood glucose, as top contributors to mortality prediction. Our results highlight the feasibility of combining environmental and clinical features to improve ACS mortality prediction using ML models. While the model shows strong potential for Malaysia, external validation in other Western Pacific populations is necessary before broader generalization. This framework can inform future region-specific public health interventions targeting pollution-related cardiovascular risk.

Background Occupational diseases are one of the most important health problems related to employment However, in Malaysia, there are few epidemiological studies discussing these issues, especially among workers in the industry. For that, this study aimed to screen workers from high-risk industrial sectors, identify hazards in the workplace and recommend improvement measures in the workplace to prevent occupational diseases. Methods and analysis This is a 3-year project in which a survey of 100 000 workers from all 13 states in Malaysia will be conducted using a web-based screening tool that is comprised of two parts: occupational disease screening tool and hazard identification, risk assessment and risk control method. Data will be collected using a multistage stratified sampling method from 500 companies, including seven critical industrial sectors. The independent variables will be sociodemographic characteristics, comorbidities, previous medical history, high-risk behaviour and workplace profile. The dependent variable will be the types of occupational diseases (noise-induced hearing loss, respiratory, musculoskeletal, neurotoxic, skin and mental disorders). Subsequently, suggestions of referral for medium and high-risk workers to occupational health clinics will be attained. The approved occupational health service clinics/providers will make a confirmatory diagnosis of each case as deemed necessary. Subsequently, a walk-through survey to identify workplace hazards and recommend workplace improvement measures to prevent these occupational diseases will be achieved. Both descriptive and inferential statistics will be used in this study. Simple and adjusted binary regression will be used to find the determinants of occupational diseases. Ethics and dissemination This study has been approved by the MARA University of Technology Research Ethics Board. Informed, written consent will be obtained from all study participants. Findings will be disseminated to the Department of Occupational Health and Safety, involved industries, and through peer-reviewed publications.

Introduction: High-Intensity Interval Training (HIIT) and Moderate-Intensity Continuous Training (MICT) both provide notable benefits for endothelial function, metabolic health, and cardiovascular fitness in adults with non-communicable diseases who are overweight or obese. HIIT, due to its efficacy in enhancing VO₂ max and vascular function, may offer significant benefits in mitigating cardiovascular risk. This study underscores the need of structured exercise training in the management of vascular health, considering the growing prevalence of non-communicable diseases (NCDs). Objective: To identify the effect of exercise on endothelial function among NCDs adults with overweight or obese. Methods: A literature search was conducted using the internet database PubMed to compare HIIT and MICT studies focused on endothelial function, specifically assessed by FMD, in adults with NCDs with overweight or obese. Results: HIIT led to a notable enhancement in FMD (0.48%; p=0.007) with a 41.14% increase, in contrast to 8.33% in MICT. VO₂ max showed a greater enhancement in HIIT (1.00%; p=0.003), with increases of 17.68 ml/kg/min (HIIT) compared to 13.17 ml/kg/min (MICT). HDL levels showed a modest increase in HIIT (5.61 mmol/L) compared to MICT (4.63 mmol/L) (p=0.022). Glucose results favor in HIIT than MICT. In contrast, LDL cholesterol exhibited a marginally greater decrease in MICT (-0.06; p=0.12). However, results suggest no clear advantages on neither HIIT nor MICT in reducing triglycerides. The results indicate that high-intensity interval training is more effective for improving vascular health and aerobic capacity, whereas moderate-intensity continuous training may be more beneficial for reducing LDL cholesterol levels. Conclusion: HIIT demonstrates greater effectiveness for improving FMD and VO₂ max, HDL, and glucose whereas MICT is more effective for reducing LDL. A 12-week HIIT program (4×4 min at 85–95% HR max, 3 times/week) has the potential to greatly improve vascular health and overall fitness levels.