Related Experiment Video For Bayesian meta-analysis
Updated: Dec 26, 2025

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy
Published on: April 29, 2014
Components of one-carbon metabolism and renal cell carcinoma: a systematic review and meta-analysis
Joanna L Clasen1, Alicia K Heath2, Ghislaine Scelo3
1Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. j.clasen18@imperial.ac.uk.
Purpose:
Little is known about the aetiology of renal cell carcinoma (RCC). Components of one-carbon (1C) metabolism, which are required for nucleotide synthesis and methylation reactions, may be related to risk of RCC but existing evidence is inconclusive. We conducted a systematic review and independent exposure-specific meta-analyses of dietary intake and circulating biomarkers of 1C metabolites and RCC risk.
Methods:
Medline and Embase databases were searched for observational studies investigating RCC or kidney cancer incidence or mortality in relation to components of 1C metabolism and 12 eligible articles were included in the meta-analyses. We used Bayesian meta-analyses to estimate summary relative risks (RRs) and 95% credible intervals (CrIs) comparing the highest versus lowest categories as well as the between-study heterogeneity.
Results:
We did not find convincing evidence of an association between any exposure (riboflavin, vitamin B6, folate, vitamin B12, methionine, homocysteine, choline, or betaine) and RCC risk. However, vitamin B6 biomarker status did have a protective (RR = 0.62) but imprecise (95% CrI 0.39-1.14) effect estimate and folate intake had a notable association as well (RR = 0.85, 95% CrI 0.71-1.01).
Conclusion:
There was a lack of precision due largely to the low number of studies. Further investigation is warranted, especially for folate and vitamin B6, which had consistent suggestive evidence of a protective effect for both dietary intake and biomarker status. A unique strength of this review is the use of Bayesian meta-analyses which allowed for robust estimation of between-study heterogeneity.
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