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Record W4404674237 · doi:10.1073/pnas.2404848121

Genetic risk factors for Mesoamerican nephropathy

2024· article· en· W4404674237 on OpenAlexaff
David J. Friedman, Dominick Leone, Juan José Amador, Joseph Kupferman, Lauren J. Francey, Damaris López-Pilarte, Jorge Luis Lau, Iris Delgado, W. Katherine Yih, Alejandro José Salinas, Minxian Wang, Giulio Genovese, Shrijal S. Shah, Calum F. Tattersfield, Nathan H. Raines, Leny Dias, Achilleas Pitsillides, Oriana Ramírez‐Rubio, Marissa Cortopassi, Katie M. Applebaum, Seth L. Alper, Alex S. Banks, Michael D. McClean, Jessica H. Leibler, Madeleine K. Scammell, Josée Dupuis, Daniel R. Brooks

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDigestive system and related health
Canadian institutionsMcGill University
FundersNational Institutes of HealthMinisterio de SaludSatellite HealthcareNational Institute of Diabetes and Digestive and Kidney DiseasesDoris Duke Charitable Foundation
KeywordsNephropathyMedicineEvolutionary biologyBiologyDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

Mesoamerican nephropathy (MeN) is a progressive kidney disease found on the Pacific coast of Central America primarily in young male agricultural workers without typical kidney disease risk factors. While it is generally accepted that environmental exposures contribute to MeN, we hypothesized that there was also an important genetic component. We performed a genome-wide association study comparing individuals with MeN versus individuals with normal kidney function. We found that Native American ancestry was strongly associated with increased risk of MeN. We also identified candidate variants in the OPCML gene, which encodes a protein that binds opioid receptors, that were associated with ~sixfold reduced odds of MeN (allele frequency 0.029 in controls and 0.005 in cases, OR = 0.16; P = 4 × 10 −8 ). Sugarcane workers with the protective OPCML variants had markedly increased urine osmolality suggesting greater ability to defend against hypovolemia. Experiments with Opcml knock-out mice revealed roles for OPCML in fluid balance and temperature regulation consistent with our findings in humans. Our data suggest that heritable differential sensitivity to heat stress and dehydration contributes to high rates of kidney disease in Central America.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.147

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.313
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations14
Published2024
Admission routes1
Has abstractyes

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