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Record W4381377561 · doi:10.2337/db23-413-p

413-P: Intensive Insulin Therapy Delays the “Breakpoint” to Progressive Kidney Function Decline—New Findings and Implications of eGFR Trajectory Analysis in DCCT/EDIC Data

2023· article· en· W4381377561 on OpenAlexaboutno aff
Katsuhito Ihara, Jan Skupień, Eiichiro Satake, BRUCE A. PERKINS, Andrzej S. Królewski

Bibliographic record

VenueDiabetes · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBreakpointMedicineInternal medicineRenal functionCohortKidney diseaseAlbuminuriaEndocrinologyBiologyGeneticsChromosomal translocationGene

Abstract

fetched live from OpenAlex

Patterns of kidney function decline vary among those with T1D. End-stage kidney disease (ESKD) develops after progressive kidney function decline that begins when kidney function is normal, regardless of albuminuria. We aimed to determine if these findings were observed in the DCCT/EDIC cohort, which enrolled 1,441 individuals and randomly assigned them to intensive insulin therapy (n=711) or conventional insulin therapy (n=730). Baseline mean eGFR and AER were 125 ml/min/1.73m2 and 12 mg/24 hours, respectively. During 27 years of follow-up 129 individuals reached reduced eGFR <60ml/min/1.73m2 or ESKD. For these individuals we applied latent class trajectory analysis minimizing sum of squares of linear spline models with varying knot placement to identify patterns of eGFR decline. We identified two patterns: i) slow decline without eGFR breakpoint (n=56) and ii) fast decline with clear breakpoint (n=73). Slow decliners reached reduced eGFR but rarely ESKD (2%), while many fast decliners progressed to ESKD (35%) within 5-15 years after the breakpoint. Fast decline was less frequent in intensive compared to conventional insulin therapy [26 (3.7%) vs 47 (6.4%), respectively, p=0.022] and had longer median time from enrollment to breakpoint (18 vs 13 years, respectively, p=0.0004). However, the slope of annual loss of eGFR after breakpoint did not differ. In conclusion, we identified a unique phenotype - the “breakpoint” to fast decline in eGFR - as the main predictor of ESKD in T1D. This breakpoint started while eGFR was normal, subsequent decline was linear, and the onset of the breakpoint was substantially delayed by intensive insulin therapy rather than the slope of decline itself. Research on biomarkers that predict the onset of the eGFR ‘breakpoint’ is needed. Disclosure K.Ihara: None. J.K.Skupien: None. E.Satake: None. B.A.Perkins: Advisory Panel; Dexcom, Inc., Insulet Corporation, Novo Nordisk, Sanofi, Vertex Pharmaceuticals Incorporated, Other Relationship; Abbott, Medtronic, Sanofi, Research Support; Novo Nordisk, Bank of Montreal (BMO). A.Krolewski: None.

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)0.006
metaresearch head score (Gemma)0.015
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 score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.304
Teacher spread0.267 · 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
Published2023
Admission routes1
Has abstractyes

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