Genetic Glomerular Disorders Are Associated With Worse Outcomes in CureGN
Bibliographic record
Abstract
Background: The true pevalence of monogenic glomerular disease is not known. Prognosis and treatment response may differ between genetic and sporadic forms of disease thus identifying a genetic etiology can have clinical significance in this patient population. Methods: 2018 individuals enrolled in the international, multicenter Cure Glomerulonephropathy Network (CureGN) underwent genome sequencing. 513 with focal segmental glomerulosclerosis (FSGS), 465 with minimal change disease (MCD), 476 with membranous nephropathy (MN), and 564 with IgA nephropathy. Cases with a strong suspicion of a genetic diagnosis and those with kidney failure were excluded prior to enrolment. Variants in 180 genes with glomerular phenotypes were classified per ACMG/AMP guidelines. Pathogenic and likely pathogenic variants consistent with the inheritance pattern and patient phenotype were considered diagnostic. APOL1 high risk genotypes were evaluated. The risk of immunosuppression resistance and kidney failure, defined by chronic dialysis or transplantation, was determined over a median of 4.3 years of follow-up, adjusted for demographic, clinical and biopsy characteristics. Results: 14 different monogenic glomerular disorders were detected in 42 individuals (2% diagnostic rate): 28 with FSGS (5.4% diagnostic rate), 8 with MCD (1.7% diagnostic rate), 6 with IgAN (1.1% diagnostic rate), and 0 in MN. Over half were due to variants in NPHS2 (16 variants in 10 individuals) and Alport spectrum disorder genes (18 variants in 18 individuals). 124 individuals have high-risk APOL1 genotypes, including 3 with Mendelian diagnostic variants. On logistic regression, individuals with monogenic glomerular disease and those with high-risk APOL1 genotypes were more likely to have immunosuppression resistant disease (OR=4.46, P=7x10-4; OR=1.83, P=0.02, respectively), particularly resistance to two or more therapies (OR=3.40, P=0.002; OR=2.19, P=0.003, respectively), and were also at increased risk of kidney failure (Cox proportional hazards OR=2.03, P=0.02; OR=1.88, P=0.002, respectively). Conclusions: Monogenic glomerular diseases were identified in 42 subjects, with the highest diagnostic rate among FSGS cases. Individuals with monogenic disorders and those with high risk APOL1 genotypes had an increased risk of multidrug resistant disease and kidney failure. Funding: NIDDK Support
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".