Genome-wide association study of longitudinal urinary albumin excretion in patients with type 1 diabetes
Bibliographic record
Abstract
Abstract Identifying genetic determinants for longitudinal changes in albumin excretion in individuals with type 1 diabetes may help identify those that are predisposed to renal, retinal and cardiovascular complications. Most studies have focussed on genetic predisposition to diabetic kidney disease and used cross-sectional measurements of urinary albumin excretion, but with limited success. Here, we utilise the wealth of longitudinal data and bio-samples collected from cohorts of childhood-onset type 1 diabetes followed over the last 30 years to describe a novel trajectory phenotype quantifying urinary albumin excretion changes during childhood and adolescence. We conducted a genome-wide association study and fine-mapping analysis for albumin excretion in 1584 individuals, finding one signal for cross-sectional albumin excretion close to GALNTL6 (rs150766792), which validated in a previous independent study, and a novel genome-wide significant signal for albumin excretion trajectory in the CDH18 gene region (rs145715205). Our trajectory phenotype quantifies albumin progression and offers a complementary measure to an albumin excretion phenotype based on a single measurement (i.e. most recent data collection) or an average of repeated measurements in longitudinal studies. It can be used to identify genetic or other risk factors which predict better or worse prognosis, thus facilitating the development of new preventive and therapeutic approaches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".