Association of body mass index with the development of metabolic acidosis in patients with chronic kidney disease
Bibliographic record
Abstract
Abstract Aims Higher body mass index (BMI) is associated with higher bone mass and bone serves as a buffer during the development of metabolic acidosis. The authors sought to examine the relationship between BMI and metabolic acidosis in patients with chronic kidney disease (CKD). Materials and Methods The study utilized a large US longitudinal data repository including over 103 million patients from healthcare provider organizations to evaluate the relationship between the exposure variable (BMI) and the prevalence and incidence of metabolic acidosis among patients with estimated glomerular filtration rate <60 ml/min/1.73 m2. Incident metabolic acidosis was identified at the first of two consecutive post‐index serum bicarbonate values, 10–365 days apart, between 12 and <22 mEq/L in patients with normal index serum bicarbonate. Cox proportional hazard models were adjusted for multiple variables including demographics, comorbidities, income, education, and kidney function. Results 103,766 patients qualified for this study; 6472 (6.2%) had metabolic acidosis at index. An inverse association between BMI category and metabolic acidosis was observed for both baseline (prevalence) and new‐onset (incidence) metabolic acidosis. Compared to BMI category of 18.5 to <25 kg/m2, each category of incrementally higher BMI was associated with a decreasing risk of incident metabolic acidosis; the adjusted hazard ratios (95% confidence interval) were 0.866 (0.824–0.911), 0.770 (0.729–0.813), 0.664 (0.622–0.709), and 0.612 (0.571–0.655) for BMI 25 to <30, 30 to <35, 35 to <40, and 40+ kg/m2, respectively. Conclusions Among patients with CKD, an incremental increase in BMI was inversely associated with both the prevalence and incidence of metabolic acidosis. These associations suggest that increased body weight may protect against the development of metabolic acidosis, a risk factor for progressive loss of kidney function.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".