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Record W4396989307 · doi:10.1681/asn.20223311s1754c

Genetic Glomerular Disorders Are Associated With Worse Outcomes in CureGN

2022· article· en· W4396989307 on OpenAlexaff
Mark Elliott, Natalie Vena, Enrico Cocchi, Maddalena Marasà, Hila Milo Rasouly, Matthias Kretzler, Krzysztof Kiryluk, Ali G. Gharavi

Bibliographic record

VenueJournal of the American Society of Nephrology · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineInternal medicine

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.250
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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