Mendelian Randomization Identifies Circulating Proteins APOM and TNXB as Biomarkers for Steroid Sensitive Nephrotic Syndrome
Bibliographic record
Abstract
ABSTRACT Introduction Steroid-sensitive nephrotic syndrome (SSNS) is the most common glomerular disease in children worldwide. Current treatments are not targeted and lead to serious adverse effects. We sought to identify circulating proteins associated with SSNS in European children using an unbiased two-sample Mendelian randomization (MR) and colocalization approach to inform novel drug targets for disease. Methods We conducted a large-scale MR study using cis genetic determinants (protein quantitative trait loci, pQTL) of 1,540 circulating proteins from eight large genome-wide association studies to screen for causal association of these proteins with SSNS risk in 422 children with SSNS and 5642 control subjects. We then performed genetic colocalization to further investigate loci identified by MR. Results We found four proteins causally linked to SSNS by MR, two of which colocalized. We found that genetically predicted increases in apolipoprotein M (APOM) level and Tenascin XB (TNXB) level were associated with decreased risk of SSNS [p = 1.37×10 −5 ; MR Odds Ratio (OR) 0.40, 95% CI 0.27-0.61 for APOM, and p = 2.95×10 −4 ; OR 0.49, 95% CI 0.33-0.72 for TNXB]. Colocalization with SSNS occurred at HLA-DRB1 (98%) and HLA-DQA1 (79%) for APOM and TNXB, respectively. Follow-up binding affinity and gene expression analysis showed that APOM and TNXB peptides have high binding affinity for their respective HLA-pQTLs, and that APOM has a biologically plausible causal relationship with SSNS. Conclusions We identified two novel blood proteins associated with steroid sensitive nephrotic syndrome in children using an MR and colocalization approach. These biomarkers are promising targets for development of drugs and/or screening tools for early prediction of disease.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".