Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Components of one-carbon metabolism and renal cell carcinoma: a systematic review and meta-analysis
Ist Teil von
European journal of nutrition, 2020-12, Vol.59 (8), p.3801-3813
Ort / Verlag
Berlin/Heidelberg: Springer Berlin Heidelberg
Erscheinungsjahr
2020
Quelle
MEDLINE
Beschreibungen/Notizen
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 B
6
, folate, vitamin B
12
, methionine, homocysteine, choline, or betaine) and RCC risk. However, vitamin B
6
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 B
6
, 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.