Relationship Between Metabolic Acidosis and CKD Progression Is Evident Across US Racial and Ethnic Groups
Bibliographic record
Abstract
Background: Metabolic acidosis is a known risk factor for CKD progression, but little is known about the impact of race and ethnicity on this relationship. We used a large electronic medical record (EMR) database of >100 million patients from all 50 states and insurance types to evaluate the relationship between metabolic acidosis and adverse renal outcomes and death by race and ethnicity. Methods: De-identified electronic medical records (Optum® EHR), 2007-2019 were queried to identify patients with non-dialysis CKD Stages 3-5, ≥2 years of post-index data or death within 2 years, and grouped by baseline metabolic acidosis (12 to < 22 mEq/L) vs normal serum bicarbonate (22 to < 30 mEq/L). Patients (N = 136,067) were classified as Asian (N=1,328), Black (N=15,248), Hispanic (N=4,137), White (N=111,953) or Other (N=3,401). The primary endpoint was the composite outcome of death, kidney dialysis or transplant, or 40% decline in eGFR from baseline (DD40). Cox proportional hazards models examined the impact of serum bicarbonate on DD40 within each racial/ethnic group, adjusted for age, sex, eGFR, log albumin-to-creatinine ratio, diabetes, hypertension, heart failure, Charlson Comorbidity Score. Results: Overall, 47,032 patients (34.6%) experienced DD40 events within 2 years: Asian, 35%; Black, 44%; Hispanic, 48%; White, 32%; Other, 48%. Serum bicarbonate independently predicted DD40 in all racial/ethnic groups. Adjusted Hazard Ratios for DD40 per 1 mEq/L increase in serum bicarbonate (median 4.2 yrs, max 11.5 yrs follow-up) were as follows: Asian, 0.942 (CI: 0.917- 0.968); Black, 0.976 (CI: 0.969-0.983); Hispanic, 0.970 (CI: 0.956-0.984); White, 0.960 (CI: 0.957-0.963); P< 0.0001 for all groups. Conclusions: In a large community-dwelling US population, serum bicarbonate was independently associated with adverse kidney outcomes and death in Asians, Blacks, Hispanics and Whites with CKD. Since race and ethnicity are associated with other sociodemographic factors that affect health, further exploration of the potential reasons for the observed range of hazard ratios across these groups is warranted. Funding: Commercial Support - Tricida, Inc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".