Genetic susceptibility to adverse arsenic-related cardiometabolic outcomes: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Millions of people worldwide are chronically exposed to environmental arsenic through drinking water, increasing their risk of various adverse cardiometabolic outcomes. To understand the inter-individual variation in arsenic susceptibility, this systematic review explores all epidemiological evidence on interactions between single nucleotide polymorphisms (SNPs) and arsenic exposure in relation to cardiometabolic health. Five electronic databases were searched until April 2023. From 42,202 retrieved publications, 18 candidate gene-environment (cGxE) studies were included, and no genome-wide association studies were found. Of 676 SNPs in 148 genes tested, 40 SNPs in 24 genes, 4 haplotypes and combined SNPs in MCP-1/APOE , were reported to statistically significantly interact with arsenic exposure. These genes were involved in arsenic metabolism , oxidative stress or defence, DNA damage repair, endothelial (dys) function, inflammation or immune function, tumour suppressor activity, or were previously implicated in cardiometabolic disease pathways. Most studies did not explore the same SNPs (or strong proxies), and none of the identified SNP-arsenic interactions were replicated for the same arsenic species and cardiometabolic outcome. Whilst some SNPs are suggestive of influencing susceptibility to arsenic for various cardiometabolic outcomes, further research is needed to understand the interplay between arsenic and genetic variants, identify at-risk populations, and improve risk assessment.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.018 |
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 it