Heart rate variation and human body burdens of environmental mixtures in the Cree First Nation communities of Eeyou Istchee, Canada
Bibliographic record
Abstract
INTRODUCTION: Heart rate variability (HRV) is a measure of cardiac autonomic regulation that examines the variation in beat-to-beat fluctuations in heart rate. While many exposure-based studies have examined the effects of single or similar groups of contaminants on HRV parameters, none have examined the association between complex environmental mixtures, including organic and elemental contaminants, and HRV. METHODS: Using data collected from the Multi-Community Environment-and-Health Study in Eeyou Istchee (Quebec, Canada), we assessed HRV in two time domain measures: root mean square of successive differences (RMSSD) and standard deviation of the N-N (RR) intervals (SDNN); and in three frequency domains: high frequency (HF), low-frequency (LF), and very-low frequency (VLF) in 443 participants. We first examined mixture effects of nineteen organic and metal contaminants in blood using principal component analysis (PCA) and a multivariable general linear regression on HRV responses, adjusting for age, sex, body mass index, smoking status, and kidney disease covariates. We subsequently assessed HRV outcome response variables using Bayesian kernel machine regression (BKMR) to further examine individual contaminant contribution and overall mixture effects. RESULTS: In the PCA, a significant positive association was observed between RMSSD and principal component (PC) axis 2, which was highly positively-loaded for nickel and moderately negatively-loaded for mercury. A negative association between SDNN and PC-1, which was highly positively-loaded for all PCBs (polychlorinated biphenyls) and organochlorines and moderately positively-loaded for mercury, was observed. Additionally, a significant and positive association was observed between PC-2 and SDNN and a significant and negative association between PC-3 (negatively loaded for cadmium) and LF. Associations with contaminants were not observed for HF or VLF. BKMR results suggest that trans-nonachlor and cis-nonachlor are primarily responsible for reductions in HRV; however, their contributions to HRV deficits was non-significant when examined as a complete mixture. CONCLUSIONS: While PCA results suggested that organochlorines, mercury, nickel, and cadmium may all play a role in altering various HRV, subsequent BKMR analysis illustrated the individual components driving these effects may be trans- and cis-nonachlor and nickel and likely not mercury or cadmium. However, other contaminants appear to temper this effect when the entire mixture of chemicals is assessed as a whole. This study expands our knowledge of the effects of environmental contaminant mixtures on HRV, which is especially important in exposed populations such as those in the Eeyou Istchee territory, whilst utilizing novel statistical methods such as PCA and BKMR to examine co-exposures concurrently.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".