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000301323 245__ $$aExploring metabolomics for colorectal cancer risk prediction: evidence from the UK Biobank and ESTHER cohorts.
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000301323 520__ $$aWhile metabolic pathway alterations are linked to colorectal cancer (CRC), the predictive value of pre-diagnostic metabolomic profiling in CRC risk assessment remains to be clarified. This study evaluated the predictive performance of a metabolomics risk panel (MRP) both independently and in combination with established risk factors.We derived, internally validated (IV), and externally validated (EV) a metabolomics risk panel (MRP) for CRC from data of the UK Biobank (UKB) and the German ESTHER cohort. Baseline blood samples were assessed for 249 metabolites using nuclear magnetic resonance spectroscopy analysis. We applied LASSO Cox proportional hazards regression to identify metabolites for inclusion in the MRP and evaluated the model performance using the concordance index (C-index). We compared the performance of the MRP to an environmental risk panel (ERP; sex, age, body mass index, smoking status, and alcohol consumption) and a genetic risk panel (GRP; polygenic risk score).The study included 154,892 participants of the UKB cohort (mean age at baseline 54.5 years; 55.5% female) with 1879 incident CRC and 3242 participants of the ESTHER cohort (mean age 61.5 years; 52.2% female) with 103 CRC cases. Twenty-three metabolites, primarily amino acid and lipid-related metabolites, were selected for the MRP, showing moderate predictive performance (C-index 0.60 [IV] and 0.54 [EV]). The ERP and GRP showed superior performance, with C-index values of 0.73 (IV) and 0.69 (EV). Adding the MRP to these risk models did not change the C-indices in both cohorts.Genetic and environmental risk information provided strong predictive accuracy for CRC risk, with no improvements from adding metabolomics data. These findings suggest that metabolomics data may have limited impact on enhancing established CRC risk models in clinical practice.
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000301323 650_7 $$2Other$$aBiomarkers
000301323 650_7 $$2Other$$aColorectal cancer
000301323 650_7 $$2Other$$aMetabolomics
000301323 650_7 $$2Other$$aRisk stratification
000301323 650_2 $$2MeSH$$aHumans
000301323 650_2 $$2MeSH$$aColorectal Neoplasms: metabolism
000301323 650_2 $$2MeSH$$aColorectal Neoplasms: epidemiology
000301323 650_2 $$2MeSH$$aColorectal Neoplasms: diagnosis
000301323 650_2 $$2MeSH$$aColorectal Neoplasms: genetics
000301323 650_2 $$2MeSH$$aMale
000301323 650_2 $$2MeSH$$aFemale
000301323 650_2 $$2MeSH$$aMiddle Aged
000301323 650_2 $$2MeSH$$aMetabolomics: methods
000301323 650_2 $$2MeSH$$aUnited Kingdom: epidemiology
000301323 650_2 $$2MeSH$$aRisk Assessment: methods
000301323 650_2 $$2MeSH$$aBiological Specimen Banks
000301323 650_2 $$2MeSH$$aAged
000301323 650_2 $$2MeSH$$aRisk Factors
000301323 650_2 $$2MeSH$$aCohort Studies
000301323 650_2 $$2MeSH$$aUK Biobank
000301323 7001_ $$0P:(DE-HGF)0$$aCardoso, Rafael$$b1
000301323 7001_ $$0P:(DE-He78)9976da2c4ac21202b44584c21d8404e7$$aStevenson-Hoare, Joshua$$b2$$udkfz
000301323 7001_ $$0P:(DE-HGF)0$$aHolleczek, Bernd$$b3
000301323 7001_ $$0P:(DE-He78)c67a12496b8aac150c0eef888d808d46$$aSchöttker, Ben$$b4$$udkfz
000301323 7001_ $$0P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f$$aHoffmeister, Michael$$b5$$udkfz
000301323 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b6$$eLast author$$udkfz
000301323 773__ $$0PERI:(DE-600)2131669-7$$a10.1186/s12916-025-04107-w$$gVol. 23, no. 1, p. 283$$n1$$p283$$tBMC medicine$$v23$$x1741-7015$$y2025
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