Chen Chen1, G A Nagana Gowda2, Jiangjiang Zhu3, Lingli Deng4, Haiwei Gu2, E Gabriela Chiorean5,6,7, Mohammad Abu Zaid5, Marietta Harrison8, Dabao Zhang1, Min Zhang1,9,10, Daniel Raftery2,7,11. 1. Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. 2. Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA. 3. Department of Chemistry & Biochemistry, Miami University, Oxford, OH 45056, USA. 4. Department of Electronic Science and Communication Engineering, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, Fujian, China. 5. Indiana University Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, USA. 6. Department of Medicine, University of Washington, 825 Eastlake Ave East, Seattle, WA 98109, USA. 7. Fred Hutchinson Cancer Research Center, 1100 Fairview Ave North, Seattle, WA 98109, USA. 8. Department of Medicinal Chemistry, Purdue University, West Lafayette, IN 47907, USA. 9. Bioinformatics Center, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China. 10. Beijing Institute for Brain Disorders, Capital Medical University, Beijing 100069, China. 11. Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA.
Abstract
Introduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.
Introduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRCpatients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyppatients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.
Authors: Nicola Tempest; C J Hill; A Whelan; A De Silva; A J Drakeley; M M Phelan; D K Hapangama Journal: Reprod Sci Date: 2021-09-15 Impact factor: 3.060