ArticleNature communications2018
A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic and prognostic value of serum S100B in sepsis-associated encephalopathy: A systematic review and meta-analysis.Frontiers in immunology · 2023Pooled it
- Association of Thyroid Function Genetic Predictors With Atrial Fibrillation: A Phenome-Wide Association Study and Inverse-Variance Weighted Average Meta-analysis.JAMA cardiology · 2019Pooled it
- How quantum computing can enhance biomarker discovery.Patterns (New York, N.Y.) · 2025Review
- Article
- Knowledgebase strategies to aid interpretation of clinical correlation research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Development and Evaluation of a Peptide Heterodimeric Tracer Targeting CXCR4 and Integrin αPharmaceutics · 2022Article
- Analysis of risk factors progression of preterm delivery using electronic health records.BioData mining · 2022Article
- A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Generalizability of Polygenic Risk Scores for Breast Cancer Among Women With European, African, and Latinx Ancestry.JAMA network open · 2021Article
- The genetic architecture of plasma kynurenine includes cardiometabolic disease mechanisms associated with the SH2B3 gene.Scientific reports · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
34 authors.
Funding
Abstract
Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.