Examining Relationships between Pb and Nutrient Intake and Sources of Pb in the Sahtú, NWT
Bibliographic record
Abstract
Background: \nThe Sahtú is a subarctic region located in the Northwest Territories, Canada. It is comprised of five remote communities, with a total population of approximately 2600 people. Over 75% of the total population is Indigenous, with the largest group being Dene First Nation. Previous human biomonitoring work in the region had indicated lead (Pb) levels in both blood and urine were higher when compared to both the neighbouring Dehcho region, and the general Canadian population. \nPb is an element that is toxic to humans in high concentrations. Pb is commonly absorbed through inhalation, ingestion, and dermal exposure, and is distributed in bone and blood. Negative health implications related to chronic exposure include neurological, cardiovascular, and renal impacts. Pb is still widespread in environmental media including soil, air, and water, from anthropogenic releases into the environment, as well as use in industry and hobbies. Specific to the Northern Canada, diet, and hunting practices, such as the use of Pb ammunition, are key routes of exposure. \n \nMethods: \nUsing data collected from the biomonitoring project, and supplemental analysis on biobanked blood samples, statistical analysis to determine the correlations and associations between nutrient biomarkers and Pb was conducted. Nonparametric and parametric correlations were conducted between age, BMI, sex, smoking status, alcohol consumption status, zinc, iron, vitamin D with Pb, followed by multivariate linear regression. Nutritional levels of zinc, iron, and vitamin D were assessed and compared to national averages and deficiency thresholds. \n \nAn exposure survey adapted for northern Canadian Indigenous populations was developed through supplementing and adapting questions collated from existing tools and information from a literature review. The survey was piloted, and feedback was gathered from community members after interviews with research team members. The survey was finalized and ready for future implementation. \n \nResults: \n128 biobanked blood samples were used for analysis. Geometric mean values for blood Pb in the Sahtú were 2.3 ug/dL, which were more than twice higher than those in the Dehcho (1.1 ug/dL) and almost twice higher than the Canadian population (1.2 ug/dL). Non-parametric and parametric testing indicated positive correlations existed between age, vitamin D, and zinc, with Pb, and mean differences existed across sex. A linear regression model was created using age, sex, smoking status, and zinc levels as explanatory variables. Comparison of Akaike information criterion (AIC) and R2 values indicated it was the best model at exploring Pb values. \n \nAverages for all nutritional biomarkers, zinc, iron, and vitamin D, were lower in the Sahtú population compared to the Canadian Health Measures Survey. Based on nutritional thresholds, 10% of adults and 27% of children had probable zinc deficiency, 19% adults and 18% children had probable iron deficiency, and 69% of adults and 100% of children had probable vitamin D deficiency. \n \nThe development process of the survey resulted in a draft survey with 62 total questions across five sections: socio-demographic, harvesting practices and food preparation, other Pb exposure determinants, and symptoms of Pb poisoning and a supplementary 12 questions in an option hunter only sections. A total of six participants from the community assisted in the feedback gathering through remote interviews indicating change was necessary to 43.5% of questions. During the finalization process, 66/74 existing questions were changed, and or adapted, and the survey was finalized to a total of 63 + 12 questions in five sections. \n \nConclusions: \nPositive correlations between Pb and nutritional biomarkers were contrary to what was found in literature. These results may point towards a shared exposure and intake pathway. A possible pathway by which this occurs is the consumption of traditional food which is hunted with Pb ammunition. The development of an adapted and specific exposure survey to Pb in a Northern Indigenous context was vital towards the future implementation of the survey, and towards assessing Pb exposure within the region. Future work into assessing other routes of exposure to Pb in the community is recommended.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".