Background: Dietary glycemic load (GL) and glycemic index (GI) in relation to cardiovascular disease have been investigated in a few prospective studies with inconsistent results, particularly in men. The present EPICOR study investigated the association of GI and GL with coronary heart disease (CHD) in a large and heterogeneous cohort of Italian men and women originally recruited to the European Prospective Investigation into Cancer and Nutrition study.
Methods: We studied 47 749 volunteers (15 171 men and 32 578 women) who completed a dietary questionnaire. Multivariate Cox proportional hazards modeling estimated adjusted relative risks (RRs) of CHD and 95% confidence intervals (CIs).
Results: During a median of 7.9 years of follow-up, 463 CHD cases (158 women and 305 men) were identified. Women in the highest carbohydrate intake quartile had a significantly greater risk of CHD than did those in the lowest quartile (RR, 2.00; 95% CI, 1.16-3.43), with no association found in men (P = .04 for interaction). Increasing carbohydrate intake from high-GI foods was also significantly associated with greater risk of CHD in women (RR, 1.68; 95% CI, 1.02-2.75), whereas increasing the intake of low-GI carbohydrates was not. Women in the highest GL quartile had a significantly greater risk of CHD than did those in the lowest quartile (RR, 2.24; 95% CI, 1.26-3.98), with no significant association in men (P = .03 for interaction).
Conclusion: In this Italian cohort, high dietary GL and carbohydrate intake from high-GI foods increase the overall risk of CHD in women but not men.
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http://dx.doi.org/10.1001/archinternmed.2010.15 | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFFront Cardiovasc Med
January 2025
Department of Nursing, School of Medical and Health Engineering, Changzhou University, Changzhou, Jiangsu, China.
Background: Coronary atherosclerotic heart disease (coronary heart disease; CHD) is the leading cause of death in women worldwide, and the number of patients and deaths is increasing each year. Approximately 3.8 million women die from CHD every year globally.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of Emergency, Wuhan Fourth Hospital, Wuhan, Hubei, China.
Background: At present, the relationship among inflammatory markers [monocytes/HDL-c (MHR), neutrophils/HDL-c (NHR) and lymphocytes/HDL-c (LHR)] and long-term prognosis of coronary heart disease (CHD) is still unclear. Therefore, this study explores the relationship between inflammatory indicators and the risk of long-term major adverse cardiovascular events (MACE) in elderly patients with CHD.
Methods: A retrospective analysis was conducted on 208 elderly patients who underwent coronary angiography at Wuhan Fourth Hospital from August 2022 to August 2023.
BMC Med Genomics
January 2025
Department of Cardiovascular Surgery, Gansu Provincial Hospital, No. 204, Donggang West Road, Lanzhou City, Gansu Province, 730000, China.
Background: We did this study to better clarify the correlations of methylenetetrahydrofolate dehydrogenase 1 (MTHFD1)-G1958A (rs2236225) gene polymorphism with the risk of congenital heart diseases (CHD) and its subgroups.
Methods: Relevant articles were searched in PubMed, Web of Science, Cochrane Library, Embase, CNKI, VIP database and Wanfang DATA until October 2023. We will use odds ratios (ORs) and 95% confidence intervals (CIs) to examine the potential associations of MTHFD1- G1958A gene polymorphism with CHD and its subgroups.
Med Phys
January 2025
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Background: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments.
Purpose: This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT.
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