Mendelian Randomization Identifies Circulating Plasma Proteins as Biomarkers for Steroid-Sensitive Nephrotic Syndrome
Bibliographic record
Abstract
Background: Steroid-sensitive nephrotic syndrome (SSNS) is the most common glomerular disease in children worldwide. The pathogenesis of SSNS is unknown, limiting us to non-specific treatments that have a heavy burden of side effects. Genome-wide association studies (GWAS) have identified several immunogenetic loci associated with disease, but have not identified causal variants or etiologic pathways, which limits our ability to provide targeted treatment to patients. Novel validated targets for the development of non-toxic treatments of SSNS are needed. One source of such targets is circulating plasma proteins. We aimed to identify plasma proteins associated with SSNS in European individuals using a Mendelian randomization (MR) approach. Methods: Using eight large proteomic GWAS from 119252 European individuals, we selected cis genetic determinants of 3833 plasma proteins in adults and 1216 proteins in children. We screened these proteins for causal associations with SSNS using two-sample MR in 422 European pediatric SSNS cases and 5642 control subjects. We then colocalized significantly associated proteins using HLAcoloc. Results: We tested 1628 unique proteins in adults and 210 proteins in children, and we found four plasma proteins significantly associated with SSNS (HLA-E: p=2.95e-7, Odds Ratio [OR]=8.17, Confidence Intervals [CI] 3.66-18.25; C4A: p=1.63e-6, [OR]=0.12, [CI] 0.05-0.29; APOM: p=1.37e-5, [OR]=0.40, [CI] 0.27-0.61 ; TNXB: p=2.95e-4, [OR]=0.49, [CI] 0.33-0.72). Two of these proteins successully colocalized using HLAcoloc (TNXB, 100% probability at HLA-C; APOM, 97% probability at HLA-DRB1). Conclusion: We identified four novel plasma proteins associated with SSNS, two of which colocalized. Our findings support a potential utility of these proteins as targets for development or repositioning of drugs to treat SSNS.
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 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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".