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The detrimental impact of heavy metals on cardiovascular well-being is a global concern, and engaging in suitable physical activity has been shown to confer cardiovascular advantage. Nevertheless, the potential of exercise to mitigate the deleterious effects of heavy metals on stroke remains uncertain. We conducted a cross-sectional survey to assess the influence of blood cadmium and blood lead on stroke occurrence, while also examining the role of physical activity. Weighted multivariate regression analysis was employed to examine the potential correlation, while subgroup and interaction analyses were used to investigate the sensitivity and robustness of the results. After controlling risk factors, it revealed a positive correlation between blood cadmium and lead levels and the occurrence of stroke. Specifically, a 50% increase in blood cadmium was associated with a 28% increase in stroke incidence, while a 50% increase in blood lead was associated with a 47% increase in stroke incidence. To estimate the non-linear relationship, we employed restricted cubic models. The results demonstrate a gradual decrease in the slope of the model curve as the intensity of physical activity increases, implying that engaging in physical activity may contribute to a reduction in the occurrence of stroke caused by blood cadmium and lead. Our findings suggest that blood cadmium and lead could be considered an autonomous risk factor for stroke within the general population of the United States. Moreover, engaging in physical activity has the potential to mitigate the potential detrimental consequences associated with exposure to heavy metals. • Blood cadmium and lead are positively associated with stroke. • There exists a threshold effect between heavy metals and stroke. • Physical activity can reduce the risk of cadmium and lead on stroke. • Intense physical activity effectively mitigates the stroke risk posed by heavy metals. • Women have a higher vulnerability to stroke when exposed to heavy metals. [ABSTRACT FROM AUTHOR]

Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)