Abstract C018: Urinary Arsenic and Other Heavy Metals in Gallbladder Cancer Patients: A Pilot Study Correlating Exposure with Drinking Water and Assessing Carcinogenic Risk in Northeast India
Bibliographic record
Abstract
Abstract Introduction: Gallbladder cancer (GBC) incidence is disproportionately high in Northeast India, and the second most affected epidemiological region in the world, where the contribution of environmental factors like heavy metals remains understudied. Aims: To investigate the association between exposure to arsenic and other heavy metals in drinking water and urine with the risk of gallbladder cancer. Methods: This cross-sectional study, with institutional ethical approval, included 201 histopathologically confirmed GBC patients from a tertiary cancer hospital in Northeast India. Spot urine samples (N=201) and paired drinking water samples (N=100) were analyzed for twelve metals using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The analytical method was validated for precision (%RSD < 5%) and accuracy (85–109%). Statistical analysis involved non-parametric tests (Mann-Whitney U, Kruskal-Wallis) and Spearman's correlation, with health risks assessed using the US EPA model. Results: Urinary analysis of 201 patients revealed high mean concentrations of selenium (83.51 µg/L), strontium (84.40 µg/L), and arsenic (20.97 µg/L). In 100 drinking water samples, arsenic levels reached a maximum of 476.38 µg/L (48 times the WHO limit), while lead and nickel exceeded safety thresholds in 22% and 18% of samples, respectively. The total carcinogenic risk was 400-fold higher than EPA safety limits (4.0 × 10-3), and the non-carcinogenic Hazard Index (HI) from urine was 3.51, primarily driven by arsenic (HQ=2.44). Significant positive correlations were observed between urinary arsenic-selenium (r=0.504, p<0.01) and cadmium-selenium (r=0.552, p<0.01). Females exhibited significantly higher urinary concentrations of vanadium, chromium, and nickel (p<0.05). Fish consumption and smoking were found to significantly influence metal accumulation. Conclusions: This study provides novel evidence directly linking multi-metal exposure, particularly from contaminated drinking water to the risk of GBC. The findings underscore an urgent need for public health interventions focused on improving water quality, specifically targeting arsenic, and for further longitudinal research to confirm a causal relationship between heavy metal exposure and GBC. Citation Format: Christy Thomas, Gaurav Das, Munlima Hazarika, Nilotpal Saharia, Krishna Undela. Urinary Arsenic and Other Heavy Metals in Gallbladder Cancer Patients: A Pilot Study Correlating Exposure with Drinking Water and Assessing Carcinogenic Risk in Northeast India [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr C018.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".