Salicylic acid and risk of colorectal cancer: a two sample Mendelian randomization study
Bibliographic record
Abstract
Abstract Background Salicylic acid (SA) is a metabolite that can be obtained from the diet via fruit and vegetable ingestion, of which increased consumption has observationally been shown to decrease risk of colorectal cancer (CRC). Whilst primary prevention trials of SA and CRC risk are lacking, there is strong evidence from clinical trials and prospective cohort studies that aspirin (acetylsalicylic acid) is an effective primary and secondary chemopreventative agent. Since aspirin is rapidly deacetylated to form SA, it follows that SA may have a central role for aspirin chemoprevention. Through a Mendelian randomization (MR) approach, we aimed to address whether levels of SA affected CRC risk, and whether aspirin intake as a proxy for increased SA levels was required to identify an effect. Methods and Findings A two sample MR analysis was carried out using genome-wide association study summary statistics of SA from INTERVAL and EPIC-Norfolk (N= 14,149) and CRC from Colon Cancer Family Registry (CCFR), Colorectal Cancer Transdisciplinary Study (CORECT), Genetics and Epidemiology of Colorectal Cancer (GECCO) consortia and UK Biobank (55,168 cases and 65,160 controls). The Darmkrebs: Chancen der Verhütung durch Screening (DACHS) study (4,410 cases and 3,441 controls) was used for replication and stratification of aspirin-users and non-users. Single nucleotide polymorphisms (SNPs) for SA were selected via three methods: (1) Functional SNPs that influence aspirin and SA metabolising enzymes’ activity; (2) Pathway SNPs, those that are present in the coding regions of genes involved in aspirin and SA metabolism; and (3) genome-wide significant SNPs associated with levels of circulating SA. No association was found between the functional SNPs and SA levels, therefore they were not taken forward in an MR analysis. We identified 2 pathway SNPs (explaining 0.03% of the variance in SA levels and with an F statistic of 1.74) and 1 genome-wide independent SNP (explaining 0.05% of the variance and with an F statistic of 7.44) to proxy for SA levels. Using the pathway SNPs, an inverse variance weighted approach found no association between an SD increase in SA and CRC risk (GECCO OR:1.03, 95% CI: 0.84-1.27 and DACHS OR:1.10, 95% CI:0.58-2.07) and no association was found upon stratification between aspirin users and non-users in the DACHS study (OR:0.93, 95% CI:0.23-3.73 and OR:1.24, 95% CI:0.57-2.69, respectively). Wald ratio results using the genome-wide SNP also showed no association between an SD increase in SA and CRC risk (GECCO OR: 1.08, 95% CI:0.86-1.34 and DACHS OR: 1.01, 95% CI:0.44-2.31) and no effect was observed upon stratification by aspirin use (users OR:0.66, 95% CI: 0.11-4.12 and non-users OR: 1.12, 95% CI: 0.42-2.97). Conclusions We found no evidence to suggest that an SD increase in genetically predicted SA protects against CRC risk in the general population and upon stratification by aspirin use. However, based on the calculated variance explained by the SNPs and the F statistic, we acknowledge the possibility of weak instrument bias and the need to find better instruments for SA levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".