Genome-Wide Association Study of Quantitative Kidney Function in 52,531 Individuals with Diabetes Identifies Five Diabetes-Specific Loci
Bibliographic record
Abstract
Key Points Comprehensive genome-wide association study of eGFR in diabetes, accounting for diabetes duration, kidney disease, and known modifiers, identified novel genetic effects. Incorporation of various kidney multi-omics data provides supporting evidence for the role of novel genome-wide association study loci in diabetic kidney disease. Background Diabetic kidney disease (DKD) is a serious diabetes complication caused by both environmental and genetic risk factors. Previous genome-wide association studies (GWAS) have identified several loci associated with kidney function and kidney disease in the general population and, to a lesser extent, in diabetes. Methods To uncover the genetic factors driving diabetes-induced kidney function, we conducted a series of GWAS meta-analyses of eGFR in 17,267 individuals with type 1 diabetes and 35,264 with type 2 diabetes (52,531 total), using multiple well-characterized cohorts of type 1 diabetes DKD and data from the UK Biobank and SUrrogate markers for Micro- and Macrovascular hard end points for Innovative diabetes Tools (SUMMIT) consortium. We further accounted for DKD case/control status, diabetes duration and subtype, body mass index, glycated hemoglobin levels, and the relationship between eGFR and albuminuria. Results GWAS identified 13 loci associated with eGFR ( P < 5×10 −8 ), with five loci (candidate genes: HIPK3 , TRIM5 , RORA , ERBB4 , and BCL6 / LPP ) not associated with or were in opposite directions as compared with eGFR in the general population. Four candidate genes ( HIPK3 , BCL6, LPP , and RORA ) demonstrated evidence of differential expression in kidney compartments and cells among subgroups with DKD or diabetes versus controls. Lead single-nucleotide polymorphisms rs8027829 ( RORA ) and rs76300256 ( BCL6/LPP ) were methylation quantitative trait loci in whole blood and kidney tissue, respectively, and rs76300256 and its related CpGs all cluster in a kidney enhancer. Conclusions Our integrated approach identified candidate genes with diabetes-specific effects on kidney function.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".