Diabetes-Specific Kidney Function Loci Identified in Genome-Wide Association Study of eGFR in 52,000 Individuals with Diabetes
Bibliographic record
Abstract
Background: Diabetic kidney disease (DKD) is a distinct life-altering pathological condition that is caused by both environmental and genetic factors. Previous genome-wide association studies (GWAS) have identified several loci associated with kidney disease and function both in the general population and in diabetes. While type 2 diabetes (T2D) is more common than type 1 diabetes (T1D), individuals with T2D have higher rates of kidney-damaging co-morbidities, and thus many develop non-specific kidney disease. Therefore, a comprehensive approach which incorporates T1D and T2D to maximize sample size and also integrates diabetes subtype, duration, and co-morbidities is key to improving the success of large-scale genomic discovery for DKD. Methods: As part of the GENIE consortium, we leveraged widely available eGFR as a powerful quantitative trait to conduct the largest GWAS of eGFR in diabetes, including 17 T1D cohorts, UK Biobank, and SUMMIT Consortium T2D data with a total of 17K individuals with T1D and 36K individuals with T2D. To identify genetic loci most likely impacting kidney function via hyperglycemic pathways, we analyzed eGFR in a variety of settings that considered DKD disease status, diabetes subtype and duration, BMI, HbA1c, and the relationship between eGFR and albuminuria. We integrated multi-omics data to nominate candidate genes and elucidate mechanism. Results: GWAS identified 13 loci associated with eGFR (P<5x10-8); five were not associated or were in opposite directions with eGFR in the general population. rs11032245 near HIPK3 had opposite effects on eGFR in DKD cases versus diabetes controls, and single-cell RNA sequencing revealed a large difference in HIPK3 expression in podocytes between youth-onset T2D and healthy controls. rs76300256 near LPP had opposite effects in diabetes versus no diabetes and is a methylation QTL in kidney tissue for 3 CpGs in a kidney enhancer. Conclusion: Together, our multi-faceted approach enabled us to identify candidate genes with diabetes-specific impact on kidney function, a critical step towards elucidating DKD pathophysiology and the development of novel more personalized therapies. Funding: NIDDK Support
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".