Phenome-wide Mendelian randomization identifying circulating proteins for cardiovascular traits in populations of African ancestry
Bibliographic record
Abstract
Abstract Background Circulating proteins represent robust drug targets with therapeutic potential. Many discoveries have focused on European-ancestry populations, disregarding minuscule yet substantial proteomic differences that may contribute to disease and/or alter drug generalizability in other ancestry groups. Methods Using two-sample Mendelian randomization and colocalization, we analyzed the effects of 1,556 circulating proteins on 145 cardiometabolic centric outcomes to identify robust protein-phenotype associations in African-ancestry populations and reveal African-ancestry associations with heterogenous effects. We further replicated these findings using the proteomic data available from the UK Biobank Pharma Proteomics Project (UKB-PPP), and tested the effect of protein quantity in association with select phenotypes. Population branch statistics (PBS) were also constructed to examine whether protein-genetic instruments under natural selection could lead to significant protein-outcome associations specific to the African ancestry. Results We identified 115 robust protein-phenotype associations in African-ancestry populations. Among these, 52 demonstrated heterogenous effects between African- and European-ancestry populations. We further replicated four cross-platform African-ancestry associations in the UKB-PPP and also revealed four significant, direct associations between protein levels and phenotypes. Ultimately, based on our prioritization criteria, we found that CD36, APOC1, GSTA1, and FOLH1, were shown to influence lipids and heart diseases and were uniquely represented in African-ancestry populations. In addition, using PBS, we showed that 47.5% of the 115 significant protein-outcome associations were possibly driven by cis -acting-protein quantitative trait loci under natural selection. Conclusions Multiple lines of evidence were used to interrogate proteomic determinants of cardiometabolic diseases and traits in African-ancestry populations. We highlighted actionable circulating protein targets that could represent potential drug targets for cardiovascular diseases specific to populations with African ancestry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".