Serum anion gap, bicarbonate and biomarkers of inflammation in healthy individuals in a national survey
Bibliographic record
Abstract
Background: In vitro data suggest that lower extracellular pH activates the immune system. We conducted a population-based study of the relation between serum acid–base status and inflammation. Methods: We examined the serum anion gap and serum levels of bicarbonate and inflammatory biomarkers in 4525 healthy adults who participated in the National Health and Nutrition Examination Survey during 1999–2006. We excluded participants who had chronic disease, recent infection and an estimated glomerular filtration rate of less than 60 mL/min per 1.73 m 2. Results: The mean values of serum anion gap, bicarbonate level, leukocyte count and C-reactive protein level were all within normal limits. After adjustment for age, sex, ethnic background, body mass index, serum albumin level and other factors, we found that a higher anion gap and lower bicarbonate level were associated with a higher leukocyte count and higher C-reactive protein level. Compared with participants in the lowest quartile of anion gap, those in the highest quartile had a leukocyte count that was 1.0 × 10 9 /L higher and a C-reactive protein level that was 10.9 nmol/L higher (p < 0.01). Compared with participants in the highest quartile of bicarbonate level, those in the lowest quartile had a leukocyte count that was 0.7 × 10 9 /L higher and a C-reactive protein level that was 4.0 nmol/L higher (p ≤ 0.02). A higher anion gap and lower bicarbonate level were also associated with a higher platelet count, a larger mean platelet volume and a higher ferritin level. Interpretation: A higher serum anion gap and lower bicarbonate level were associated with higher levels of inflammatory biomarkers in a healthy sample of the general population. Further studies are needed to elucidate the relation between acid–base status and inflammation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".