Associations of APOL1 Biallelic and Monoallelic Kidney Disease Variants with CKDs in West Africans: H3Africa KDRN Study
Bibliographic record
Abstract
Background: Background: Apolipoprotein L1 gene (APOL1) variants are risk factors for chronic kidney disease (CKD) among African Americans (AA). Data are sparse on the genetic epidemiology and clinical association of APOL1 variants with CKD in West Africans, a major group among the AA population. Methods: The Human Health and Heredity in Africa (H3Africa) Kidney Disease Research Network studied 8,355 participants from Ghana and Nigeria: 4,712 participants with CKD stages 2-5, 866 participants with biopsy proven glomerular diseases, and 2777 controls (eGFR ≥90 ml/min/1.73m2 and no proteinuria). The association of CKD with high-risk carriers (two APOL1 alleles) and low-risk carriers (<2 APOL1 alleles) was determined by fitting logistic regression models controlling for covariates, including clinical site, age, and sex. Results: Monoallelic and biallelic APOL1 variant prevalence were 43.0% and 29.7%, respectively. Compared with low-risk carriers, the adjusted odds of CKD and focal segmental glomerulosclerosis (FSGS) among high-risk carriers were 1.25 (95%CI: 1.11-1.40) and 1.84 (95%CI 1.30-2.61), respectively. Compared with those with no APOL1 variant (G0/G0), persons with one APOL1 variant (G0/G1, G0/G2) had higher odds of CKD (OR 1.18, 95% CI 1.04-1.33) and FSGS (adjusted OR 1.61; 95% CI 1.04-2.48). Covariates did not modify the association of 1-2 APOL1 variants with CKD or FSGS. Conclusion: Both monoallelic (G1/G0, G2/G0) and biallelic (G1/G1, G2/G2, G1/G2) risk variants have 18% and 25% higher odds of CKD, and 61% and 84% higher odds of FSGS, respectively. Individuals with monoallelic APOL1 variant should be classified as being at high risk for CKD and FSGS. Funding: NIDDK Support - NIDDK Support, 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".