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Abstract 4137457: Cross-sectional Investigation of Proteomic Signatures of Blood Pressure Traits.

2024· article· en· W4404322783 on OpenAlexaff
Mohit Aggarwal, Tianxiao Huan, Paul Courchesne, Roby Joehanes, Josée Dupuis, George O'connor, Daniel Levy

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineCross-sectional studyBlood pressureInternal medicinePathology

Abstract

fetched live from OpenAlex

Background: Hypertension is a major risk factor for cardiovascular disease affecting one third of the adult population worldwide. Over 90% cases of hypertension are without a distinct etiology. Identification of protein biomarkers of hypertension may provide insight into disease pathophysiology and highlight novel therapeutic targets. Methods: We conducted association analyses between 2922 plasma proteins (exposure) and blood pressure (BP) traits (outcome) in 45,926 UK Biobank participants (aged 57 ± 8 years; 54% women). We employed linear mixed models to study protein associations with systolic and diastolic BP and logistic mixed models to test abundance of proteins in hypertensive vs. normal individuals. The associations were adjusted for age, sex, body mass index, estimated glomerular filtration rate, smoking and alcohol drinking status, and batch effects. Hypertension was defined as systolic BP ≥140 mmHg or diastolic BP ≥90 mmHg) or current use of anti-hypertensive medication. Sensitivity analysis excluded individuals taking BP medication (n=10,229). Functional enrichment analysis was performed to elucidate biological functions and tissue specificity of significant protein signatures ( P < 0.00017). We further performed Mendelian randomization (MR) to infer causal relations of protein biomarkers and BP. Results: We identified 1469 proteins associated with systolic BP and 1638 with diastolic BP. The corresponding numbers were 1410 and 1566 after excluding treated individuals. Plasma levels of 1215 proteins (1105 upregulated, 110 downregulated) differed in individuals with hypertension (N=24,724) compared to normal (N= 21,202). Most of these proteins were specific to liver, adipose tissue, renal cortex and coronary artery. Protein signatures of hypertension showed enrichment in pathways involved in inflammatory response, cholesterol homeostasis, complement and coagulation cascades, and hemostasis. Putatively causal associations were observed for 141 proteins with SBP and 142 with DBP. Discussion: This study elucidates protein signatures associated with BP and hypertension. Systemic inflammation and cholesterol homeostasis play important roles in the pathophysiology of hypertension.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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 teacher head, not a consensus.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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