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Enregistrement W7047681679

Human Biomonitoring: Levels, Determinants and Sources of Polychlorinated Biphenyl Exposure in the Northwest Territories and Yukon

2023· dissertation· en· W7047681679 sur OpenAlexaboutno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2023
Typedissertation
Langueen
DomainePhysics and Astronomy
ThématiqueSuperconducting and THz Device Technology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBiomonitoringHuman healthSubarctic climateIndigenousExposure assessmentRisk assessment
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background: Human biomonitoring is a useful tool to assess contaminant exposure levels in human populations. Several human biomonitoring projects have been established in Canada to measure and report analyte exposure levels. However, these projects often do not include participants from Arctic and subarctic Indigenous communities in the Northwest Territories and Yukon. Thus, to fill a data gap in Canada, biomonitoring projects in the Dehcho and Sahtú regions of the Northwest Territories as well as Old Crow, Yukon were established between 2016 and 2019 to assess contaminant and nutrient exposure status as well as report information related to traditional food diets. Data on traditional food diets is also useful to collect as these diets provide several benefits related to health and well-being. Importantly, identifying exposure levels is the first step in identifying potential sources of exposure, which often include diet for many classes of contaminants. Polychlorinated biphenyl’s (PCB) were among the dozens of analytes measured in these projects. These man-made chemicals are an important chemical to monitor due to their persistent nature and their potential for adverse human health effects. Although production of PCBs has been banned in North America since 1979 and worldwide since 2001, levels of PCBs are still detected in human biological samples. This research aims to report levels of PCB exposure in participating regions of the biomonitoring project described above as well as identify potential determinants and sources of these chemicals. \nMethods Biomonitoring clinics were set up in communities within the Dehcho and Sahtú regions of the Northwest Territories and in the community of Old Crow, Yukon. At these clinics, select biological samples were collected, including plasma for POP analysis. Surveys and questionnaires were also administered to collect demographic and dietary data. Using the analytical and survey data collected from these biomonitoring projects, statistical analyses were completed to determine if PCB exposure levels were within a normal range, what potential determinants were associated with exposure levels, and if traditional food consumption was a potential source of PCB concentration level. Plasma samples (n=328) collected from these biomonitoring projects were used to develop simple linear regression models to identify potential determinants of PCBs between log-normalized PCB exposure levels and BMI, omega-3 fatty acid level and age. Independent samples T-tests were used to determine associations between PCB concentration level and binary variables including sex and smoking status. ANOVA multiple comparisons were used to assess if levels appeared elevated between the study regions. To determine if levels were within a normal range, comparisons were completed between exposure levels reported in the First Nation Biomonitoring Initiative (FNBI) and the Canadian Health Measures Survey (CHMS). Food frequency questionnaire (FFQ) data were used to report consumption patterns in participating regions. To identify if traditional food was associated with PCB biomarkers, multiple linear regression models were developed using FFQ data and PCB exposure levels, while adjusting for age and sex. \nResults: Among those participants below the age of 60 in all the study regions, all PCB biomarkers were within the normal range observed in Canada (i.e., below or similar to the CHMS or FNBI levels). As well, in Old Crow, those participants over 60 also had all PCB levels within the normal range. In contrast, for participants over 60 years in the Dehcho and Sahtú, levels began to appear outside of the normal range. For example, in the Dehcho, PCB187 and PCB201 appeared elevated compared to CHMS levels for those over 60 years. In the Sahtú, Arochlor 1260, PCB146, PCB153, PCB163, PCB170, PCB178, PCB180, PCB183, PCB187, PCB194, PCB201 and PCB203 appeared elevated compared to CHMS levels for those over 60 years. As well in the Sahtú, Arochlor 1260 was observed at an elevated level compared to the FNBI. When results were stratified by sex, PCB levels were largely observed within the normal range. The main exception was for male participants in the Sahtú, for whom PCB187 was elevated compared to the CHMS but not the FNBI. \nBiomarkers of PCBs were all strongly correlated (p<0.05) with one another. As well, age and PCB exposure level were strongly correlated (p<0.05) in all PCB congeners and regions. Omega-3 fatty acids were also correlated (p<0.05) with PCB congeners, but less so compared to age. Sahtú PCB levels appeared highest compared to the Dehcho and Old Crow regions, with geometric mean differences ranging between 1.5 (PCB163) to 2.9-fold (PCB153) among the study regions. In the Dehcho, men had higher PCB levels (up to 1.98-fold) compared to women for Arochlor 1260, PCB138, PCB153, PCB170, PCB180 and ∑PCB138,153,180. Some associations (p<0.05) were found between BMI and higher PCB exposure levels in Old Crow and Sahtú, but not for the Dehcho region. In the Dehcho and Sahtú regions, those who reported smoking in the last 24 hours had lower GM PCB levels compared to those who had not reported smoking. In contrast, Old Crow participants who reported smoking had generally higher PCB levels than those that did not. When PCB biomarkers were pooled across regions, some traditional foods were associated with higher PCB exposure levels. For example, among the traditional foods that were included in the multiple linear regression models, controlling for age and sex, lake trout (=0.152), fish eating waterfowl including black duck, fish duck, long-tailed duck, pintail, swan, canvasback, white-winged scoter, and mallard (=0.136), as well as game birds including spruce grouse, sharp-tailed grouse, ptarmigan, black duck, mallard, fish duck, oldsquaw (long-tailed duck), wigeon, speckle belly goose, canvasback, Canada goose, snow goose, pintail, white-winged scoter, and swan (=0.159) were associated (p<0.05) with increased PCB exposure levels of ∑PCB138,153,180. \n \nConclusions: These results provide baseline PCB exposure levels in the Dehcho and Sahtú regions of the Northwest Territories and Old Crow, Yukon. PCB levels were similar if not lower to the CHMS and FNBI in younger participants but, for older participants, especially in the Sahtú region, PCB levels appeared elevated to the CHMS. As well, some demographic (e.g., age, sex, region), lifestyle (e.g., smoking status) and dietary variables (e.g., lake trout and bird consumption) were identified as determinants and potential sources of PCBs; but the strength of those links varied by region. On a broad scale, these results are important in identifying temporal trends of contaminants, as several initiatives were established to protect environmental and human health from several contaminants. On a small scale, these results offer guidance for further localized monitoring efforts related to environmental exposure sources or pathways including traditional diet, for these communities. Further work is required to draw more conclusions about traditional food consumption and PCB exposure levels.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,234
Score d'incertitude au seuil0,471

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,018
Tête enseignante GPT0,239
Écart entre enseignants0,220 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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