TY - JOUR
T1 - Circulating Metabolic Biomarkers of Screen-Detected Prostate Cancer in the ProtecT Study
AU - Adams, Charleen D
AU - Richmond, Rebecca
AU - Ferreira, Diana L Santos
AU - Spiller, Wes
AU - Tan, Vanessa
AU - Zheng, Jie
AU - Würtz, Peter
AU - Donovan, Jenny
AU - Hamdy, Freddie
AU - Neal, David
AU - Lane, J Athene
AU - Smith, George Davey
AU - Relton, Caroline
AU - Eeles, Rosalind A
AU - Haiman, Christopher A
AU - Kote-Jarai, ZSofia
AU - Schumacher, Fredrick R
AU - Olama, Ali Amin Al
AU - Benlloch, Sara
AU - Muir, Kenneth
AU - Berndt, Sonja I
AU - Conti, David V
AU - Wiklund, Fredrik
AU - Chanock, Stephen J
AU - Gapstur, Susan
AU - Stevens, Victoria L
AU - Tangen, Catherine M
AU - Batra, Jyotsna
AU - Clements, Judith A
AU - Gronberg, Henrik
AU - Pashayan, Nora
AU - Schleutker, Johanna
AU - Albanes, Demetrius
AU - Wolk, Alicja
AU - West, Catharine M L
AU - Mucci, Lorelei A
AU - Cancel-Tassin, Géraldine
AU - Koutros, Stella
AU - Sorensen, Karina Dalsgaard
AU - Maehle, Lovise
AU - Travis, Ruth C
AU - Hamilton, Robert J
AU - Ingles, Sue Ann
AU - Rosenstein, Barry S
AU - Lu, Yong-Jie
AU - Giles, Graham G
AU - Kibel, Adam S
AU - Vega, Ana
AU - Kogevinas, Manolis
AU - Nordestgaard, Børge G
AU - PRACTICAL consortium
N1 - ©2018 American Association for Cancer Research.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Background: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer–metabolite associations using two-sample Mendelian randomization (MR). Methods: The case–control portion of the study was conducted in nine UK centers with men ages 50–69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Results: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. Conclusions: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. Impact: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.
AB - Background: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer–metabolite associations using two-sample Mendelian randomization (MR). Methods: The case–control portion of the study was conducted in nine UK centers with men ages 50–69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Results: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. Conclusions: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. Impact: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.
UR - http://www.scopus.com/inward/record.url?scp=85059928972&partnerID=8YFLogxK
U2 - 10.1158/1055-9965.EPI-18-0079
DO - 10.1158/1055-9965.EPI-18-0079
M3 - Journal article
C2 - 30352818
SN - 1055-9965
VL - 28
SP - 208
EP - 216
JO - Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
JF - Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
IS - 1
ER -