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@ARTICLE{AlmanzaAguilera:295915,
author = {E. Almanza-Aguilera and M. Martínez-Huélamo and Y.
López-Hernández and D. Guiñón-Fort and A. Guadall and M.
Cruz and A. Perez-Cornago and A. L. Rostgaard-Hansen and A.
Tjønneland and C. C. Dahm and V. Katzke$^*$ and M. B.
Schulze and G. Masala and C. Agnoli and R. Tumino and F.
Ricceri and C. Lasheras and M. Crous-Bou and M.-J. Sánchez
and A. Aizpurua-Atxega and M. Guevara and K. K. Tsilidis and
A. C. Chatziioannou and E. Weiderpass and R. C. Travis and
D. S. Wishart and C. Andrés-Lacueva and R. Zamora-Ros},
title = {{P}rediagnostic {P}lasma {N}utrimetabolomics and {P}rostate
{C}ancer {R}isk: {A} {N}ested {C}ase-{C}ontrol {A}nalysis
{W}ithin the {EPIC} {S}tudy.},
journal = {Cancers},
volume = {16},
number = {23},
issn = {2072-6694},
address = {Basel},
publisher = {MDPI},
reportid = {DKFZ-2024-02728},
pages = {4116},
year = {2024},
abstract = {Background and Objective: Nutrimetabolomics may reveal
novel insights into early metabolic alterations and the role
of dietary exposures on prostate cancer (PCa) risk. We aimed
to prospectively investigate the associations between plasma
metabolite concentrations and PCa risk, including clinically
relevant tumor subtypes. Methods: We used a targeted and
large-scale metabolomics approach to analyze plasma samples
of 851 matched PCa case-control pairs from the European
Prospective Investigation into Cancer and Nutrition (EPIC)
cohort. Associations between metabolite concentrations and
PCa risk were estimated by multivariate conditional logistic
regression analysis. False discovery rate (FDR) was used to
control for multiple testing correction. Results: Thirty-one
metabolites (predominately derivatives of food intake and
microbial metabolism) were associated with overall PCa risk
and its clinical subtypes (p < 0.05), but none of the
associations exceeded the FDR threshold. The strongest
positive and negative associations were for dimethylglycine
(OR = 2.13; $95\%$ CI 1.16-3.91) with advanced PCa risk (n =
157) and indole-3-lactic acid (OR = 0.28; $95\%$ CI
0.09-0.87) with fatal PCa risk (n = 57), respectively;
however, these associations did not survive correction for
multiple testing. Conclusions: The results from the current
nutrimetabolomics study suggest that apart from early
metabolic deregulations, some biomarkers of food intake
might be related to PCa risk, especially advanced and fatal
PCa. Further independent and larger studies are needed to
validate our results.},
keywords = {EPIC (Other) / nested case–control (Other) /
nutrimetabolomics (Other) / prostate cancer (Other)},
cin = {C020},
ddc = {610},
cid = {I:(DE-He78)C020-20160331},
pnm = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
pid = {G:(DE-HGF)POF4-313},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:39682302},
doi = {10.3390/cancers16234116},
url = {https://inrepo02.dkfz.de/record/295915},
}