Genetic susceptibility to adverse arsenic-related cardiometabolic outcomes: a systematic review
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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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".