Objective: To investigate the association of cardiac disease with amnestic and nonamnestic mild cognitive impairment (aMCI and naMCI, respectively). Nonamnestic mild cognitive impairment, a putative precursor of vascular and other non-Alzheimer dementias, is hypothesized to have a vascular etiology.
Design: A prospective, population-based, cohort study with a median 4.0 years of follow-up.
Setting: Olmsted County, Minnesota.
Participants: A total of 2719 participants were evaluated at baseline and every 15 months using the Clinical Dementia Rating scale, a neurological evaluation, and neuropsychological testing. A diagnosis of normal cognition, MCI, or dementia was made by consensus. Cardiac disease at baseline was assessed from the participant's medical records.
Main Outcome Measures: Incident MCI, aMCI, or naMCI.
Results: Of 1450 participants without MCI or dementia at baseline, 366 developed MCI. Cardiac disease was associated with an increased risk of naMCI (hazard ratio, 1.77 [95% CI, 1.16-2.72]). However, the association varied by sex (P = .02 for interaction). Cardiac disease was associated with an increased risk of naMCI (hazard ratio, 3.07 [95% CI, 1.58-5.99]) for women but not for men (hazard ratio, 1.16 [95% CI, 0.68-1.99]). Cardiac disease was not associated with any type of MCI or with aMCI.
Conclusions: Cardiac disease is an independent risk factor for naMCI; within-sex comparisons showed a stronger association for women. Prevention and management of cardiac disease and vascular risk factors may reduce the risk of naMCI.
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http://dx.doi.org/10.1001/jamaneurol.2013.607 | DOI Listing |
J Mol Med (Berl)
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Cardiovascular Surgery Department of The First Affiliated Hospital of Harbin Medical University, and Pharmacology Department of Pharmacy College of Harbin Medical University, Harbin, 150081, China.
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Division of Cardiac Critical Care, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Neonates with congenital heart disease (CHD) who undergo cardiopulmonary bypass (CPB) are at high-risk for unfavorable neurodevelopmental (ND) outcomes and are recommended for ND evaluation (NDE); however, poor rates have been reported. We aimed to identify risk factors associated with lack of NDE. This single-center retrospective observational study included neonates < 30 days old who underwent CPB and survived to discharge between 2012 and 2018.
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January 2025
Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, People's Republic of China.
Previous observational studies have reported inconsistent associations between nut consumption and cardiovascular diseases (CVD). This study aims to identify the causal relationship between different types of nuts consumption and CVD, and to quantify the potential mediating effects of cardiometabolic factors. We utilized Genome-Wide Association Study (GWAS) data to assess the causal effects of nut consumption on CVD using two-sample Mendelian randomization (MR) and a two-step MR analysis.
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January 2025
Genetic Epidemiology Group, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Experiencing a traumatic event may lead to Posttraumatic Stress Disorder (PTSD), including symptoms such as flashbacks and hyperarousal. Individuals suffering from PTSD are at increased risk of cardiovascular disease (CVD), but it is unclear why. This study assesses shared genetic liability and potential causal pathways between PTSD and CVD.
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Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs & Fisheries college, Jimei University, Xiamen, Fujian, People's Republic of China.
Deep phenotyping can enhance the power of genetic analysis, including genome-wide association studies (GWAS), but the occurrence of missing phenotypes compromises the potential of such resources. Although many phenotypic imputation methods have been developed, the accurate imputation of millions of individuals remains challenging. In the present study, we have developed a multi-phenotype imputation method based on mixed fast random forest (PIXANT) by leveraging efficient machine learning (ML)-based algorithms.
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