Objective: To identify single-nucleotide polymorphisms (SNPs) associated with risk and age at onset of Alzheimer disease (AD) in a genomewide association study of 469 438 SNPs.
Design: Case-control study with replication.
Setting: Memory referral clinics in Canada and the United Kingdom.
Participants: The hypothesis-generating data set consisted of 753 individuals with AD by National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association criteria recruited from 9 memory referral clinics in Canada and 736 ethnically matched control subjects; control subjects were recruited from nonbiological relatives, friends, or spouses of the patients and did not exhibit cognitive impairment by history or cognitive testing. The follow-up data set consisted of 418 AD cases and 249 nondemented control cases from the United Kingdom Medical Research Council Genetic Resource for Late-Onset AD recruited from clinics at Cardiff University, Cardiff, Wales, and King's College London, London, England.
Main Outcome Measures: Odds ratios and 95% confidence intervals for association of SNPs with AD by logistic regression adjusted for age, sex, education, study site, and French Canadian ancestry (for the Canadian data set). Hazard ratios and 95% confidence intervals from Cox proportional hazards regression for age at onset with similar covariate adjustments.
Results: Unadjusted, SNP RS4420638 within APOC1 was strongly associated with AD due entirely to linkage disequilibrium with APOE. In the multivariable adjusted analyses, 3 SNPs within the top 120 by P value in the logistic analysis and 1 in the Cox analysis of the Canadian data set provided additional evidence for association at P< .05 within the United Kingdom Medical Research Council data set: RS7019241 (GOLPH2), RS10868366 (GOLPH2), RS9886784 (chromosome 9), and RS10519262 (intergenic between ATP8B4 and SLC27A2).
Conclusions: Our genomewide association analysis again identified the APOE linkage disequilibrium region as the strongest genetic risk factor for AD. This could be a consequence of the coevolution of more than 1 susceptibility allele, such as APOC1, in this region. We also provide new evidence for additional candidate genetic risk factors for AD that can be tested in further studies.
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http://dx.doi.org/10.1001/archneurol.2007.3 | DOI Listing |
BMC Public Health
January 2025
Al-Barkaat Institute of Management Studies, Aligarh 202122, Dr. A. P. J. Abdul Kalam Technical University, Lucknow 226010, India.
Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and its incidence and prevalence are increasing in many countries. Modeling of CVD plays a crucial role in understanding the trend of CVD death cases, evaluating the effectiveness of interventions, and predicting future disease trends. This study aims to investigate the modeling and forecasting of CVD mortality, specifically in the Sindh province of Pakistan.
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December 2024
Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, Guangdong Pharmaceutical University, Guangzhou 510006, PR China. Electronic address:
In the present study, we uncovered and validated potential biomarkers related to gout, characterized by the accumulation of sodium urate crystals in different joint and non-joint structures. The data set GSE160170 was obtained from the GEO database. We conducted differential gene expression analysis, GO enrichment assessment, and KEGG pathway analysis to understand the underlying processes.
View Article and Find Full Text PDFAnal Chem
January 2025
Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium.
Addressing the global challenge of ensuring access to safe drinking water, especially in developing countries, demands cost-effective, eco-friendly, and readily available technologies. The persistence, toxicity, and bioaccumulation potential of organic pollutants arising from various human activities pose substantial hurdles. While high-performance liquid chromatography coupled with high-resolution mass spectrometry (HPLC-HRMS) is a widely utilized technique for identifying pollutants in water, the multitude of structures for a single elemental composition complicates structural identification.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Martensstraße 7, 91058 Erlangen, Germany.
Emerging photovoltaics for outer space applications are one of the many examples where radiation hard molecular semiconductors are essential. However, due to a lack of general design principles, their resilience against extra-terrestrial high-energy radiation can currently not be predicted. In this work, the discovery of radiation hard materials is accelerated by combining the strengths of high-throughput, lab automation and machine learning.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Shenyang 110819, China.
Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet.
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