Back

Mendelian Randomization Identifies Circulating Proteins APOM and TNXB as Biomarkers for Steroid Sensitive Nephrotic Syndrome

Heydari, D.; Langlois, S.; Norouzi, M.; Myette, R. L.; Samuel, S.; Zhou, S.; Takano, T.; Butler-Laporte, G.; Downie, M. L.

2025-10-05 nephrology
10.1101/2025.10.03.25337251 medRxiv
Show abstract

IntroductionSteroid-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. MethodsWe 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. ResultsWe 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 = 1.37x10-5; MR Odds Ratio (OR) 0.40, 95% CI 0.27-0.61 for APOM, and p = 2.95x10-4; 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. ConclusionsWe 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.

Published in Pediatric Nephrology · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.