Association of genetic risk score and chronic kidney disease in a Japanese population
Bibliographic record
Abstract
ABSTRACT Chronic kidney disease (CKD) is a public health problem worldwide including Japan. Recent genome‐wide association studies have discovered CKD susceptibility variants. We developed a genetic risk score (GRS) based on CKD‐associated variants and assessed a possibility that the GRS can improve the discrimination capability for the prevalence of CKD in a Japanese population. The present study consists of 11 283 participants randomly selected from 12 Japan Multi‐Institutional Collaborative Cohort Study sites. Individual GRS was constructed combining 18 single‐nucleotide polymorphisms identified in a Japanese population. Participants with eGFR <60 mL/min per 1.73 m2 was defined as case (stage 3 CKD or higher) in this study. Logistic regression analysis was used to examine the association between the GRS and CKD risk with adjustment for sex, age, hypertension and type 2 diabetes mellitus. The frequency of individuals with CKD was 8.3%, which was relatively low compared with those previously reported in a Japanese population. The odds ratio of having CKD was 1.120 (95% confidence interval: 1.042–1.203) per 10 GRS increment in the fully adjusted model (P = 0.002). The C‐statistic was significantly increased in the model with the GRS, comparing with the model without the GRS (0.720 vs 0.719, Pdifference = 0.008). Increment of the GRS was associated with increased risk of CKD. Additionally, the GRS significantly improved the discriminatory ability of CKD prevalence in a Japanese population; however, the improvement of discriminatory ability brought about by the GRS seemed to be small compared with that of non‐genetic CKD risk factors.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".