Unraveling the causal nexus between serum lactate levels and cancer risk: A Mendelian randomization study
Bibliographic record
Abstract
Lactate, traditionally regarded as a metabolic byproduct, has emerged as a potential signaling molecule involved in tumorigenesis. Although numerous observational studies have linked serum lactate levels to various tumors, establishing a direct causal relationship remains challenging. We conducted a 2-sample Mendelian randomization (MR) analysis using genetic instrumental variables to assess the causal effects of serum lactate levels on the risk of various cancer types. The primary analytical method used in this investigation was the random inverse-variance weighted (IVW) method, supported by auxiliary methods such as MR-Egger, weighted median, simple mode, and weighted mode, with the IVW method enabling the meta-analysis of their combined effects. To obtain exposure data, we extracted genome-wide association studies (GWAS) data on metabolite levels from the Canadian Longitudinal Study on Aging and the UK Biobank cohorts. Concurrently, GWAS data for 17 types of cancer were obtained from the IEU Open GWAS project and the GWAS Catalog project. Sensitivity analyses were performed using the Cochran Q test, MR-Egger intercept test, MR-PRESSO, and the leave-one-out method. Our MR analysis identified a causal relationship between serum lactate and endometrial cancer (odds ratio [OR]IVW = 1.1217, 95% confidence interval [CI] = 1.0264-1.2258, P = .0112), melanoma (ORIVW = 1.0015, 95% CI = 1.0006-1.0024, P = .0010), and prostate cancer (ORIVW = 0.9578, 95% CI = 0.9319-0.9844, P = .0020). Notably, elevated lactate levels were identified as a risk factor for endometrial cancer and melanoma, while having a protective effect against prostate cancer. However, this observed relationship was not replicated in other cancer types. Our study, using GWAS data, establishes a causal link between circulating lactate and the risk of endometrial cancer, melanoma, and prostate cancer. The identification of these associations suggests the potential utility of lactate as a biomarker for these cancers or as a target for cancer prevention strategies.
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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 | 0.038 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".