Regional Variation in Hemoglobin Distribution Among Individuals With CKD: the ISN International Network of CKD Cohorts
Bibliographic record
Abstract
Introduction Despite recognized geographic and sex-based differences in hemoglobin in the general population, these factors are typically ignored in patients with chronic kidney disease (CKD) in whom a single therapeutic range for hemoglobin is recommended. We sought to compare the distribution of hemoglobin across international nondialysis CKD populations and evaluate predictors of hemoglobin. Methods In this cross-sectional study, hemoglobin distribution was evaluated in each cohort overall and stratified by sex and estimated glomerular filtration rate (eGFR). Relationships between candidate predictors and hemoglobin were assessed from linear regression models in each cohort. Estimates were subsequently pooled in a random effects model. Results A total of 58,613 participants from 21 adult cohorts (median eGFR range of 17–49 ml/min) and 3 pediatric cohorts (median eGFR range of 26–45 ml/min) were included with broad geographic representation. Hemoglobin values varied substantially among the cohorts, overall and within eGFR categories, with particularly low mean hemoglobin observed in women from Asian and African cohorts. Across the eGFR range, women had a lower hemoglobin compared to men, even at an eGFR of 15 ml/min (mean difference 5.3 g/l, 95% confidence interval [CI] 3.7–6.9). Lower eGFR, female sex, older age, lower body mass index, and diabetic kidney disease were all independent predictors of a lower hemoglobin value; however, this only explained a minority of variance (R 2 7%–44% across cohorts). Conclusion There are substantial regional differences in hemoglobin distribution among individuals with CKD, and the majority of variance is unexplained by demographics, eGFR, or comorbidities. These findings call for a renewed interest in improving our understanding of hemoglobin determinants in specific CKD populations.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".