dc.contributor.author | Dickerman, Barbra A. | |
dc.contributor.author | Ebot, Ericka M. | |
dc.contributor.author | Healy, Brian C. | |
dc.contributor.author | Wilson, Kathryn M. | |
dc.contributor.author | Eliassen, A. Heather | |
dc.contributor.author | Ascherio, Alberto | |
dc.contributor.author | Pernar, Claire H. | |
dc.contributor.author | Zeleznik, Oana A. | |
dc.contributor.author | Vander Heiden, Matthew G. | |
dc.contributor.author | Clish, Clary B. | |
dc.contributor.author | Giovannucci, Edward | |
dc.contributor.author | Mucci, Lorelei A. | |
dc.date.accessioned | 2020-05-20T17:30:45Z | |
dc.date.available | 2020-05-20T17:30:45Z | |
dc.date.issued | 2020-03 | |
dc.date.submitted | 2020-02 | |
dc.identifier.issn | 2218-1989 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/125348 | |
dc.description.abstract | Obesity is associated with a higher risk of advanced prostate cancer, but men with the same body mass index (BMI) may differ in their underlying metabolic health. Using metabolomics data from nested case-control studies in the Health Professionals Follow-Up Study, we calculated Pearson correlations between 165 circulating metabolites and three adiposity measures (BMI, waist circumference, and derived fat mass from a validated prediction equation) to identify adiposity-associated metabolites. We used Lasso to further select metabolites for prediction models of adiposity measures, which we used to calculate metabolic scores representing metabolic obesity. In an independent set of 212 advanced prostate cancer cases (T3b/T4/N1/M1 or lethal during follow-up) and 212 controls, we used logistic regression to evaluate the associations between adiposity measures and metabolic scores with risk of advanced disease. All adiposity measures were associated with higher blood levels of carnitines (Pearson <i>r</i> range, 0.16 to 0.18) and lower levels of glutamine (<i>r</i> = −0.19) and glycine (<i>r,</i> −0.29 to −0.20), in addition to alterations in various lipids. No adiposity measure or metabolic score was associated with risk of advanced prostate cancer (e.g., odds ratio for a 5 kg/m<sup>2</sup> increase in BMI 0.96 (95% CI: 0.73, 1.27) and BMI metabolic score 1.18 (95% CI: 0.57, 2.48)). BMI, waist circumference, and derived fat mass were associated with a broad range of metabolic alterations. Neither adiposity nor metabolic scores were associated with risk of advanced prostate cancer. | en_US |
dc.publisher | Multidisciplinary Digital Publishing Institute | en_US |
dc.relation.isversionof | http://dx.doi.org/10.3390/metabo10030099 | en_US |
dc.rights | Creative Commons Attribution | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Multidisciplinary Digital Publishing Institute | en_US |
dc.title | A Metabolomics Analysis of Adiposity and Advanced Prostate Cancer Risk in the Health Professionals Follow-Up Study | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Dickerman, Barbra A. et al. "A Metabolomics Analysis of Adiposity and Advanced Prostate Cancer Risk in the Health Professionals Follow-Up Study." Metabolites 10, 3 (March 2020): 99 © 2020 The Author(s) | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Biology | en_US |
dc.contributor.department | Koch Institute for Integrative Cancer Research at MIT | en_US |
dc.relation.journal | Metabolites | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2020-03-13T13:09:47Z | |
dspace.date.submission | 2020-03-13T13:09:47Z | |
mit.journal.volume | 10 | en_US |
mit.journal.issue | 3 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Complete | |