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Record W3000516097 · doi:10.1161/circgen.119.002605

Identification of Circulating Proteins Associated With Blood Pressure Using Mendelian Randomization

2020· article· en· W3000516097 on OpenAlexafffund
Sébastien Thériault, Jennifer Sjaarda, Michael Chong, Sibylle Hess, Hertzel C. Gerstein, Guillaume Paré

Bibliographic record

VenueCirculation Genomic and Precision Medicine · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversité LavalMcMaster UniversityPopulation Health Research Institute
FundersMedical Research CouncilCanadian Institutes of Health Research
KeywordsMendelian randomizationBlood pressureMedicineInternal medicinePulse pressureCardiologyPlasminogen activatorRisk factorEndocrinologyBiologyGeneticsGenotype

Abstract

fetched live from OpenAlex

Background: Hypertension is a common modifiable risk factor for cardiovascular disease and mortality. Pathophysiological mechanisms leading to hypertension remain incompletely understood. Mendelian randomization (MR) allows the evaluation of the causal role of markers by minimizing the risk of biases such as reverse causation and confounding. We aimed to identify novel circulating proteins associated with blood pressure through a comprehensive screen of 227 blood biomarkers using MR. Methods: Genetic determinants of 227 biomarkers were identified in ORIGIN (Outcome Reduction With Initial Glargine Intervention; URL: http://www.clinicaltrials.gov . Unique identifier: NCT00069784) participants (N=4147) and combined with genetic effects on systolic blood pressure, diastolic blood pressure, mean arterial pressure, and pulse pressure from the International Consortium for Blood Pressure (74 064 individuals) using MR. Results were replicated in the UK Biobank (up to 319 103 individuals) and using another biomarker dataset (N=3301). MR analyses with cardiovascular risk factors and outcomes as well as other biomarkers were performed to further evaluate the mechanisms involved. Results: Six biomarkers were associated with blood pressure using MR after adjustment for multiple hypothesis testing. Relationships between NT-proBNP (N-terminal Pro-B-type natriuretic peptide), systolic blood pressure, and diastolic blood pressure confirmed previous reports. Novel circulating proteins associated with blood pressure were also identified. uPA (urokinase-type plasminogen activator) was related to systolic blood pressure; ADM (adrenomedullin) was related to systolic blood pressure and pulse pressure; IL (interleukin) 16 was related to diastolic blood pressure; cFn (cellular fibronectin) and IGFBP3 (insulin-like growth factor-binding protein 3) were related to pulse pressure. With the exception of IL16 and diastolic blood pressure ( P =0.58), these relationships were validated in the UK Biobank ( P <0.0001). Further MR analyses with cardiovascular risk factors and outcomes showed relationships between NT-proBNP and large-artery atherosclerotic stroke, IGFBP3 and diabetes mellitus as well as cFn and body mass index. Conclusions: We identified novel biomarkers associated with blood pressure using MR. These markers could prove useful for risk assessment and as potential therapeutic targets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.263
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2020
Admission routes2
Has abstractyes

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