Urinary albumin/creatinine ratio tertiles predict risk of diabetic retinopathy progression: a natural history study from the Adolescent Cardio-Renal Intervention Trial (AdDIT) observational cohort
Bibliographic record
Abstract
Abstract Aims/hypothesis We hypothesised that adolescents with type 1 diabetes with a urinary albumin/creatinine ratio (ACR) in the upper tertile of the normal range (high ACR) are at greater risk of three-step diabetic retinopathy progression (3DR) independent of glycaemic control. Methods This was a prospective observational study in 710 normoalbuminuric adolescents with type 1 diabetes from the non-intervention cohorts of the Adolescent Cardio-Renal Intervention Trial (AdDIT). Participants were classified as ‘high ACR’ or ‘low ACR’ (lowest and middle ACR tertiles) using baseline standardised log 10 ACR. The primary outcome, 3DR, was determined from centrally graded, standardised two-field retinal photographs. 3DR risk was determined using multivariable Cox regression for the effect of high ACR, with HbA 1c , BP, LDL-cholesterol and BMI as covariates; diabetes duration was the time-dependent variable. Results At baseline mean ± SD age was 14.3 ± 1.6 years and mean ± SD diabetes duration was 7.2 ± 3.3 years. After a median of 3.2 years, 83/710 (12%) had developed 3DR. In multivariable analysis, high ACR (HR 2.1 [1.3, 3.3], p =0.001), higher mean IFCC HbA 1c (HR 1.03 [1.01, 1.04], p= 0.001) and higher baseline diastolic BP SD score (HR 1.43 [1.08, 1.89], p =0.01) were independently associated with 3DR risk. Conclusions/interpretation High ACR is associated with greater risk of 3DR in adolescents, providing a target for future intervention studies. Trial registration isrctn.org ISRCTN91419926. Graphical abstract
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".