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A new study suggests that permanent standard time may reduce obesity and stroke

Large-scale evidence suggests cannabis, cocaine, and amphetamines may directly raise stroke risk, including in younger adults. As recreational drug use becomes increasingly common, researchers are unc...

Any level of leisure-time physical activity — even below the recommended amount — reduced the risk for stroke in adults, a study published in the Journal of Neurology, Neurosurgery & P...

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A new study reveals that adults whose parents divorced during their childhood have a significantly higher risk of experiencing a stroke later in life. This research, which did not consider childhood abuse,...

Diabetes, if not timely controlled and managed, can severely affect the health of your eyes and brain, said experts on Thursday.

A new study shows that even low levels of air pollution and traffic noise increase stroke risk, especially when combined.

A study by Karolinska Institutet shows high glucose levels and inflammation can speed up brain aging, increasing dementia risk. Healthy lifestyles promote brain health.

A new international study shows that even mild COVID-19 infections can age blood vessels by up to five years, increasing the risk of heart disease, stroke, and dementia in the long term.

A research team from the University of Minnesota Medical School has identified a strong predictor of ischemic stroke and dementia, a discovery that could lead to improved patient outcomes.

A large-scale analysis suggests that GLP-1 receptor agonists may do more than manage weight or blood sugar. New research suggests that GLP-1 weight loss drugs may provide lasting protection against he...

Stroke was the fourth leading cause of death in the U.S. in 2023, according to the Centers for Disease Control and Prevention, and more than half a million Americans have a stroke every year.

Background: Cardiovascular magnetic resonance (CMR) is widely used across various cardiac conditions and systematically assesses cardiac anatomical structures and functional dynamics. Machine learning (ML) can accurately predict outcomes and understand the inherent features of clinical data. Objective: This study aimed to derive CMR phenotypes related to cardiovascular aging, investigate the relationship between these phenotypes and stroke risk, and relearn these phenotypes using supervised ML. Methods: We enrolled 36,467 participants without stroke and extracted CMR parameters from the UK Biobank, with follow-up data extending until September 30, 2023. Using the generative topographic mapping technique, we identified latent grid nodes among participants and then derived phenotypes through agglomerative hierarchical clustering. We used supervised ML models to predict cardiac function phenotypes and used Cox proportional hazards models to assess the association between these phenotypes and long-term stroke risk. Results: We enrolled 36,467 participants in the study. The mean age was 54.9 (SD 7.5) years, with 17,442 (47.8%) male participants. During a mean follow-up time of 14.7 (SD 1.1) years, 500 (1.4%) participants developed stroke and 664 (1.8%) participants died, respectively. After generative topographic mapping modeling, we identified 2 distinct phenotypes: phenotype 1, characterized by adverse cardiac function and an accumulation of cardiovascular risk factors, reflecting cardiovascular aging; and phenotype 2, associated with a lower risk of stroke (hazard ratio 0.695, 95% CI 0.559-0.864; =.001), which remained significant after accounting for competing mortality (hazard ratio 0.578, 95% CI 0.484-0.691;