Objective: Lumbar disc degeneration (LDD) is an important cause of low back pain, which is a common and costly problem. LDD is characterised by disc space narrowing and osteophyte growth at the circumference of the disc. To date, the agnostic search of the genome by genome-wide association (GWA) to identify common variants associated with LDD has not been fruitful. This study is the first GWA meta-analysis of LDD.
Methods: We have developed a continuous trait based on disc space narrowing and osteophytes growth which is measurable on all forms of imaging (plain radiograph, CT scan and MRI) and performed a meta-analysis of five cohorts of Northern European extraction each having GWA data imputed to HapMap V.2.
Results: This study of 4600 individuals identified four single nucleotide polymorphisms with p<5×10(-8), the threshold set for genome-wide significance. We identified a variant in the PARK2 gene (p=2.8×10(-8)) associated with LDD. Differential methylation at one CpG island of the PARK2 promoter was observed in a small subset of subjects (β=8.74×10(-4), p=0.006).
Conclusions: LDD accounts for a considerable proportion of low back pain and the pathogenesis of LDD is poorly understood. This work provides evidence of association of the PARK2 gene and suggests that methylation of the PARK2 promoter may influence degeneration of the intervertebral disc. This gene has not previously been considered a candidate in LDD and further functional work is needed on this hitherto unsuspected pathway.
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http://dx.doi.org/10.1136/annrheumdis-2012-201551 | DOI Listing |
FEBS J
January 2025
Greg Marzolf Jr. Muscular Dystrophy Center and Department of Neurology, University of Minnesota Medical School, Minneapolis, MN, USA.
Pathogenic variants in HMGCR were recently linked to a limb-girdle muscular dystrophy (LGMD) phenotype. The protein product HMG CoA reductase (HMGCR) catalyzes a key component of the cholesterol synthesis pathway. The two other muscle diseases associated with HMGCR, statin-associated myopathy (SAM) and autoimmune anti-HMGCR myopathy, are not inherited in a Mendelian pattern.
View Article and Find Full Text PDFAntimicrob Steward Healthc Epidemiol
August 2024
Department of Medicine, Hurley Medical Center, Flint, MI, USA.
Background: This cross-sectional study aims to determine the mortality trends in patients with SARS-CoV-2 infection during the pandemic in Flint, MI.
Methods: Records from 1,663 consecutive adult patients (≥18 years of age) with confirmed SARS-CoV-2 infection, admitted and discharged from our facility from 03/2020 through 02/2022, were abstracted and analyzed. Multivariable logistic regression analysis was performed to examine the association between study explanatory variables (ie, sex, age, co-morbidities, etc.
Transl Pediatr
December 2024
Department of Pediatric Intensive Care Unit, National Regional Medical Center, Guizhou Branch of Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Guizhou Provincial People's Hospital, Guiyang, China.
Background: Metabolic cardiomyopathy is characterized by structural and functional changes to the heart and interstitial fibrosis without coronary artery disease or hypertension. Inborn metabolic defects are a common cause of cardiomyopathy in children. There are more than 40 kinds of inborn metabolic defects that cause cardiomyopathy.
View Article and Find Full Text PDFLearn Health Syst
January 2025
Department of Biomedical Informatics University of Arkansas for Medical Sciences, College of Medicine Little Rock Arkansas USA.
Objective: This project demonstrates the feasibility of connecting medical imaging data and features, SARS-CoV-2 genome variants, with clinical data in the National Clinical Cohort Collaborative (N3C) repository to accelerate integrative research on detection, diagnosis, and treatment of COVID-19-related morbidities. The N3C curated a rich collection of aggregated and de-identified electronic health records (EHR) data of over 18 million patients, including 7.5 million COVID-positive patients, seen at hospitals across the United States.
View Article and Find Full Text PDFAlzheimers Dement (N Y)
January 2025
Indiana Alzheimer Disease Research Center and Center for Neuroimaging, Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis Indiana USA.
Introduction: The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.
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