Abstract 291: Health Effects of Northern Contaminants and Alcohol Consumption in a Rodent Model of Human Metabolic and Cardiovascular Diseases
Bibliographic record
Abstract
For the last few decades, Northern populations have been exposed to elevated levels of Northern contaminants (NC) such as polychlorinated biphenyls and heavy metals, mainly through consumption of contaminated fish and marine mammals. Along with this, there has also been an increased prevalence of obesity, hypertension, diabetes, and cardiovascular disease. Although changes in lifestyle and diets have been associated with this prevalence of chronic diseases, it remains to be clarified if exposure to NC also plays a part, and if lifestyles choices such as consumption of alcohol may modulate the effects of NC. This study explored these questions using a rodent model of human metabolic and cardiovascular diseases. Obese male JCR rats at 8 weeks of age were acclimatized on a purified AIN93G diet. Animals were treated with 10% alcohol in drinking water or drinking water only for 6 weeks. From the third week of alcohol treatment, animals were orally dosed with a mixture of 23 NC (NCM) at 0 (corn oil), 1.6, or 16 mg/kg BW/day for 4 weeks. During the whole study, body weight and water/alcohol consumption were measured daily. Food consumption was measured weekly. Blood and organs were collected and weighed at the end of the study. Tissue samples were analyzed for hematology and serum NCMs, glucose, insulin, and ethanol levels, as well as clinical biochemistry markers. Serum NCM levels increased with NCM dose, and were mostly in the range of NCM levels found in Inuit blood. Serum NCM levels were generally lower in the rats treated with alcohol than those without. NCM at 16 mg/kg BW slightly decreased body weight gain and water and food consumption especially in the rats given no alcohol. Regardless of alcohol, this dose of NCM significantly increased absolute and relative liver weight, relative kidney weight, and serum C-reactive protein and ethanol levels. NCM also decreased serum paraoxonase-1, insulin, cholesterol, triglyceride, HDL, and LDL levels. Our data suggested that NCM at levels relevant to human exposure may alter lipid metabolism and/or transport in the liver, insulin secretion from the pancreas, and circulating cardiovascular health markers. This research was supported by Canadian Institute of Health Research, Postdoctoral Fellowship No 200910-JHF-214255.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".