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Abstract MP012: Plasma Galectin-3 Levels and Subsequent Risk of Incident Chronic Kidney Disease

2017· article· en· W2956382139 on OpenAlexaff
Casey M. Rebholz, Elizabeth Selvin, Menglu Liang, Christie M. Ballantyne, Ron C. Hoogeveen, Morgan E. Grams, Josef Coresh

Bibliographic record

VenueCirculation · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsChristie (Canada)
Fundersnot available
KeywordsMedicineKidney diseaseRenal functionInternal medicineProportional hazards modelQuartileRisk factorKidneyCreatinineBiomarkerProspective cohort studyGastroenterologyConfidence interval

Abstract

fetched live from OpenAlex

Introduction: Galectin-3 is a 35 kDa β-galactoside-binding lectin which has been proposed as a novel biomarker of heart failure primarily due to its involvement in myocardial fibrosis. Elevated levels of galectin-3 may be associated with fibrosis of other organs, such as the kidney, and increase the risk of developing kidney disease. Methods: Using Cox proportional hazards regression, we prospectively analyzed Atherosclerosis Risk in Communities (ARIC) study participants with measurements of plasma galectin-3 levels at baseline (visit 4, 1996-98) and without prevalent kidney disease or heart failure (N=9,647). Incident chronic kidney disease was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m 2 accompanied by 25% eGFR decline, chronic kidney disease-related hospitalization or death, or end-stage renal disease between baseline and December 31, 2013. Results: 2,105 participants (22%) developed incident chronic kidney disease over a median follow-up of 16 years. The mean (standard deviation) plasma level of galectin-3 was 14.7 (4.4) ng/mL. At baseline, galectin-3 was cross-sectionally associated with eGFR (r = -0.31) and urine albumin-to-creatinine ratio (UACR) (r = 0.19). After adjusting for demographics and kidney disease risk factors, there was a significant, graded, and positive association between galectin-3 and incident chronic kidney disease (quartile 4 vs. 1 HR: 1.84, 95% CI: 1.62, 2.09, p for trend <0.001). The association between galectin-3 and incident chronic kidney disease was attenuated but remained significant after accounting for eGFR and UACR (quartile 4 vs. 1 HR: 1.58, 95% CI: 1.39, 1.80, p for trend <0.001). The association was similar by diabetes status (p for interaction = 0.33) and stronger among those with hypertension (p for interaction = 0.004). Conclusion: In this community-based population, higher plasma galectin-3 levels were associated with elevated risk of developing incident chronic kidney disease, particularly among those with hypertension.

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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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.023
GPT teacher head0.266
Teacher spread0.243 · 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 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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