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Background: Cardiovascular risk factors are strongly associated with adverse clinical outcomes, including acute coronary syndrome (ACS). Although individual risk factors have been related to specific plaque phenotypes, the relationship between the cumulative number of risk factors and plaque vulnerability has not been systematically explored.
Objectives: The purpose of this study was to investigate the association between the number of cardiovascular risk factors and plaque vulnerability defined by optical coherence tomography.
Methods: Patients with ACS were divided into 5 groups based on their number of traditional risk factors (diabetes, hypertension, hyperlipidemia, smoking) or into 2 groups (0-1 vs ≥2 risk factors). Features of vulnerability in both culprit and nonculprit lesions were analyzed.
Results: Of 2,187 plaques analyzed, 1,581 were culprit and 606 nonculprit plaques. In culprit plaques, the prevalence of lipid-rich plaques (P trend = 0.027), thin-cap fibroatheromas (P trend = 0.006), macrophages (P trend <0.001), microvessels (P trend <0.001), and cholesterol crystals (P trend = 0.032) increased as the number of risk factors increased. The presence of ≥2 risk factors was independently associated with all vulnerable features except lipid-rich plaques. Plaque rupture showed an increasing prevalence as the number of risk factors increased (P trend = 0.015), whereas plaque erosion showed a decreasing trend (P trend <0.001). In nonculprit plaques, only macrophages, cholesterol crystals, and the cumulative number of vulnerable features in each plaque exhibited a significant positive association with the number of risk factors.
Conclusions: In patients with ACS, an increasing number of cardiovascular risk factors were strongly associated with greater plaque vulnerability, especially for culprit lesions. These findings may explain the relationship between traditional risk factors and adverse clinical outcomes.
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http://dx.doi.org/10.1016/j.jacc.2025.04.070 | DOI Listing |
World J Pediatr Congenit Heart Surg
September 2025
Postgraduate Program in Health Sciences, Medical School, Federal University of Amazonas (UFAM), Manaus, Amazonas, Brazil.
To analyze in-hospital mortality in children undergoing congenital heart interventions in the only public referral center in Amazonas, North Brazil, between 2014 and 2022. This retrospective cohort study included 1041 patients undergoing cardiac interventions for congenital heart disease, of whom 135 died during hospitalization. Records were reviewed to obtain demographic, clinical, and surgical data.
View Article and Find Full Text PDFJAMA Netw Open
September 2025
Social and Behavioral Sciences Branch, Division of Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland.
Importance: Higher intellectual abilities have been associated with lower mortality risk in several longitudinal cohort studies. However, these studies did not fully account for early life contextual factors or test whether the beneficial associations between higher neurocognitive functioning and mortality extend to children exposed to early adversity.
Objective: To explore how the associations of child neurocognition with mortality changed according to the patterns of adversity children experienced.
Int J Surg
September 2025
Department of Gynecology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China.
Background: Ovarian cancer remains the most lethal gynecological cancer, with fewer than 50% of patients surviving more than five years after diagnosis. This study aimed to analyze the global epidemiological trends of ovarian cancer from 1990 to 2021 and also project its prevalence to 2050, providing insights into these evolving patterns and helping health policymakers use healthcare resources more effectively.
Methods: This study comprehensively analyzes the original data related to ovarian cancer from the GBD 2021 database, employing a variety of methods including descriptive analysis, correlation analysis, age-period-cohort (APC) analysis, decomposition analysis, predictive analysis, frontier analysis, and health inequality analysis.