The effect of diabetes and the diabetogenic <i>TBC1D4</i> p.Arg684ter variant on kidney function in Inuit in Greenland
Bibliographic record
Abstract
The aim of this study was to examine the effect of diabetes and the diabetogenic TBC1D4 variant on kidney function in Greenland in a population-based setting. Health survey data and TBC1D4 genotypes from 5,336 Greenlanders were used to estimate odds ratios (ORs) of albuminuria (>30 mg/g creatinine) and chronic kidney disease (CKD, eGFR <60 ml/min/1.73m2), comparing individuals with and without diabetes, including the effect of TBC1D4 variant. Of the 3,909 participants with complete data, 9.3% had diabetes. Albuminuria was found in 27.6% and 9.5% and CKD was found in 10.8% and 6.3% among those with and without diabetes, respectively. Diabetes was cross-sectionally associated with an increased risk of albuminuria (OR (95% CI) = 2.37 (1.69,3.33); p < 0.001) and the TBC1D4 variant protected against albuminuria (OR (95% CI) = 0.44 (0.22,0.90); p = 0.02) in a multivariable model. Neither diabetes nor the TBC1D4 variant significantly associated with CKD. The presence/absence of diabetes did not predict changes in eGFR and UACR in longitudinal analyses. Diabetes conferred an increased risk of albuminuria, and the TBC1D4 variant was associated with a decreased risk of albuminuria, but neither was associated with CKD. The potential renoprotective association of the TBC1D4 variant on albuminuria calls for further studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".