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Record W4403520235 · doi:10.2215/cjn.0000000000000567

Albuminuria and Rapid Kidney Function Decline as Selection Criteria for Kidney Clinical Trials in Type 1 Diabetes Mellitus

2024· article· en· W4403520235 on OpenAlexafffund
Youngshin Keum, Maria Luiza Caramori, David Z.I. Cherney, Jill P. Crandall, Ian H. de Boer, Ildiko Lingvay, Janet B. McGill, Sarit Polsky, Rodica Pop‐Busui, Peter Rossing, Ronald J. Sigal, Michael Mauer, Alessandro Doria

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of CalgaryUniversity of TorontoUniversity Health Network
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesJuvenile Diabetes Research Foundation United States of AmericaCenter for Big Data Analytics, University of Texas at AustinNational Institute on AgingNational Institutes of HealthUniversity of Toronto
KeywordsMedicineAlbuminuriaRenal functionType 2 Diabetes MellitusDiabetes mellitusClinical trialInternal medicineKidneySelection (genetic algorithm)Kidney diseaseType 2 diabetesIntensive care medicineEndocrinology

Abstract

fetched live from OpenAlex

Key Points Severely increased urinary albumin excretion rate is an effective criterion to select persons with type 1 diabetes at high risk of GFR decline for enrollment in clinical trials. A history of rapid GFR decline is less effective but can be used to extend clinical trials to person with normoalbuminuric diabetic kidney disease. These findings have immediate implications for the design of clinical trials of novel renoprotective interventions in type 1 diabetes. Background The optimal criteria to select individuals with type 1 diabetes mellitus and albuminuric or normoalbuminuric diabetic kidney disease, who are at risk of rapid kidney function decline, for clinical trials are unclear. Methods This study analyzed data from the Preventing Early Renal Loss in Diabetes clinical trial, which investigated whether allopurinol slowed kidney function decline in persons with type 1 diabetes mellitus and early-to-moderate diabetic kidney disease. Rates of iohexol GFR (iGFR) and eGFR decline during the 3-year study were compared by linear mixed effect regression between participants enrolled based on a history of moderately or severely increased albuminuria ( n =394) and those enrolled based on a recent history of rapid kidney function decline (≥3 ml/min per 1.73 m 2 per year) in the absence of a history of albuminuria ( n =124). The association between baseline albuminuria and iGFR/eGFR decline during the trial was also evaluated. Results Rates of eGFR decline during the trial were higher in participants with a history of albuminuria than in those with a history of rapid kidney function decline (−3.56 [95% confidence intervals (CIs), −3.17 to −3.95] versus −2.35 [95% CI, −1.86 to −2.84] ml/min per 1.73 m 2 per year, P = 0.001). The results were similar for iGFR decline, although the difference was not significant ( P = 0.07). Within the history of albuminuria group, the rate of eGFR decline was −5.30 (95% CI, −4.52 to −6.08) ml/min per 1.73 m 2 per year in participants with severely increased albuminuria as compared with −2.97 (95% CI, 2.44 to −3.50) and −2.32 (95% CI, −1.61 to −3.03) ml/min per 1.73 m 2 per year in those with moderately increased or normal/mildly increased albuminuria at baseline ( P < 0.001). Conclusions Severely increased albuminuria at screening is a powerful criterion for selecting persons with type 1 diabetes mellitus at high risk of kidney function decline. A history of rapid eGFR decline without a history of albuminuria is less effective for this purpose, but it can still identify individuals with type 1 diabetes mellitus who will lose kidney function more rapidly than expected from physiological aging. Clinical Trial registry name and registration number: NCT02017171.

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)0.303
metaresearch head score (Gemma)0.379
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.860

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3030.379
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0030.005
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.088
GPT teacher head0.438
Teacher spread0.350 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
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

Citations3
Published2024
Admission routes2
Has abstractyes

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