A novel multi-ancestry proteome-wide Mendelian randomization study implicates extracellular proteins, tubular cells, and fibroblasts in estimated glomerular filtration rate regulation
Bibliographic record
Abstract
Estimated glomerular filtration rate (eGFR) impacts the concentration of plasma biomarkers confounding biomarker association studies of eGFR with reverse causation. To identify biomarkers causally associated with eGFR, we performed a proteome-wide Mendelian randomization study. Genetic variants nearby biomarker coding genes were tested for association with plasma concentration of 1,161 biomarkers in a multi-ancestry sample of 12,066 participants from the Prospective Urban and Rural Epidemiological (PURE) study. Using two-sample Mendelian randomization, individual variants' effects on biomarker concentration were correlated with their effects on eGFR and kidney traits from published genome-wide association studies (GWAS). Genetically altered concentrations of 22 biomarkers were associated with eGFR above a Bonferroni-corrected significance threshold. Five biomarkers were previously identified by GWAS (UMOD, FGF5, LGALS7, NINJ1, COL18A1). Nine biomarkers were within 1 Mb of the lead GWAS variant but the gene for the biomarker was unidentified as the candidate for the GWAS signal (INHBC, TNFRSF11A, TCN2, PXN1, PRTN3, PSMD9, TFPI, ITGB6, CA3). Single-cell transcriptomic data indicated the 22 biomarkers are expressed in kidney tubules, collecting duct, fibroblasts, and immune cells. Pathway analysis showed significant enrichment of identified biomarkers in the extracellular kidney parenchyma. Thus, using genetic regulators of biomarker concentration via proteome-wide Mendelian randomization, we identified 22 biomarkers that appear to causally impact eGFR in either a beneficial or adverse manner. The current study provides rationale for novel therapeutic targets for eGFR and emphasized a role for extracellular proteins produced by tubular cells and fibroblasts for impacting eGFR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".