Associations between blood volatile organic compounds, and changes in hematologic and biochemical profiles, in a population-based study
Bibliographic record
Abstract
OBJECTIVE: To investigate the influence of volatile organic compound (VOC) levels in blood, on hematological and serum biochemical parameters in the Canadian population. METHODS: We tested the association between seven selected VOCs and hematological profiles and serum tests reflecting liver and kidney function and glucose metabolism using a cross-sectional study design in 3950 participants of the Canadian Health Measures Survey from 2012 to 2015. We used generalized linear mixed models adjusting for age, sex, smoking, alcohol consumption, BMI, education and household income. RESULTS: An increase in blood concentration equivalent to the geometric mean for benzene, ethylbenzene, toluene, m-, p-xylenes, styrene, and total xylenes was associated with 0.68% (95% CI 0.36, 1.0) to 0.91% (95% CI 0.52, 1.3) increase in hemoglobin, and a 1.79% (95% CI 0.96, 2.62) to 4.11% (95% CI 3.11, 5.11) increase in total white blood cell count. Ethylbenzene, toluene, m-, p-xylenes and styrene were positively associated with increased platelet counts. A geometric mean increase for all VOCs was associated with decreases in creatinine. m- and p-xylenes were associated with a significant change in every measured blood cell count and liver function parameter, and in creatinine. Ethylbenzene was also positively associated with an increase in every measured hematologic parameter, two of the three liver function tests, and creatinine. Results were similar when stratified by age, but differed by smoking status and sex. CONCLUSIONS: This study provides evidence that VOCs in blood, at levels found in the Canadian population, may influence blood cell counts and indicators of liver and kidney function, including an inverse association between serum VOC and creatinine. This novel finding merits further investigation to understand the impact of VOCs on human physiology and population health.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".