Genome-wide studies to identify risk factors for kidney disease with a focus on patients with diabetes
Bibliographic record
Abstract
Chronic kidney disease (CKD) affects 10-13% of the general population and diabetic nephropathy (DN) is the leading cause of end-stage renal disease (ESRD). In addition to known demographic, biochemical and lifestyle risk factors, genetics is also contributing to CKD risk. In recent years, genome-wide association studies (GWAS) have provided a hypothesis-free approach to identify common genetic variants that could account for the genetic risk component of common diseases such as CKD. The identification of these variants might reveal the biological processes underlying renal impairment and could aid in improving risk estimates for CKD. This review aims to describe the methods as well as strengths and limitations of GWAS in CKD and to summarize the findings of recent GWAS in DN. Several loci and SNPs have been found to be associated with distinct CKD traits such as eGFR and albuminuria. For diabetic kidney disease, several loci were identified in different populations. Subsequent functional studies provided insights into the mechanism of action of some of these variants, such as UMOD or CERS2. However, overall, the results were ambiguous, and a few of the variants were not consistently replicated. In addition, the slow progression from albuminuria to ESRD could limit the power of longitudinal studies. The typically small effect size associated with genetic variants as well as the small portion of the variability of the phenotype explained by these variants limits the utility of genetic variants in improving risk prediction. Nevertheless, identifying these variants could give a deeper understanding of the molecular pathways underlying CKD, which in turn, could potentially lead to the development of new diagnostic and therapeutic tools.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".