Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent Versus Infrequent Gout Flares

Ming Wang¹, Rui Li², Han Qi¹, Lei Pang¹, Lingling Cui³, Zhen Liu³, Jie Lu³, Rong Wang¹, Shuhui Hu¹, Ningning Liang⁴, Yongzhen Tao⁵, Nicola Dalbeth⁶, Tony R Merriman⁷, Robert Terkeltaub⁸, Huiyong Yin⁹, Changgui Li³

Affiliations

¹ Department of Endocrinology and Metabolism, the Affiliated Hospital of Qingdao University, Qingdao, China and Shandong Provincial Clinical Research Center for Immune Diseases and Gout, Qingdao, China.
² School of Life Science and Technology, ShanghaiTech University, Shanghai, China and Chinese Academy of Sciences (CAS) Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, CAS, Shanghai, China and Innovation Center for Intervention of Chronic Disease and Promotion of Health, Shanghai, China.
³ Department of Endocrinology and Metabolism, the Affiliated Hospital of Qingdao University, Qingdao, China and Shandong Provincial Key Laboratory of Metabolic Diseases, the Affiliated Hospital of Qingdao University, Qingdao, China.
⁴ CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, CAS, Shanghai, China and Innovation Center for Intervention of Chronic Disease and Promotion of Health, Shanghai, China and University of Chinese Academy of Sciences, CAS, Beijing, China.
⁵ CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, CAS, Shanghai, China and Innovation Center for Intervention of Chronic Disease and Promotion of Health, Shanghai, China.
⁶ Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
⁷ Department of Biochemistry, University of Otago, Dunedin, New Zealand and Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham.
⁸ VA San Diego Healthcare System, San Diego, California and University of California San Diego, La Jolla, California.
⁹ School of Life Science and Technology, ShanghaiTech University, Shanghai, China and CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, CAS, Shanghai, China and Innovation Center for Intervention of Chronic Disease and Promotion of Health, Shanghai, China and Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China.

PMID: 37390372
DOI: 10.1002/art.42635

Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent Versus Infrequent Gout Flares

Ming Wang et al. Arthritis Rheumatol. 2023 Dec.

. 2023 Dec;75(12):2252-2264.

doi: 10.1002/art.42635. Epub 2023 Nov 9.

Authors

Affiliations

¹ Department of Endocrinology and Metabolism, the Affiliated Hospital of Qingdao University, Qingdao, China and Shandong Provincial Clinical Research Center for Immune Diseases and Gout, Qingdao, China.
² School of Life Science and Technology, ShanghaiTech University, Shanghai, China and Chinese Academy of Sciences (CAS) Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, CAS, Shanghai, China and Innovation Center for Intervention of Chronic Disease and Promotion of Health, Shanghai, China.
³ Department of Endocrinology and Metabolism, the Affiliated Hospital of Qingdao University, Qingdao, China and Shandong Provincial Key Laboratory of Metabolic Diseases, the Affiliated Hospital of Qingdao University, Qingdao, China.
⁴ CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, CAS, Shanghai, China and Innovation Center for Intervention of Chronic Disease and Promotion of Health, Shanghai, China and University of Chinese Academy of Sciences, CAS, Beijing, China.
⁵ CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, CAS, Shanghai, China and Innovation Center for Intervention of Chronic Disease and Promotion of Health, Shanghai, China.
⁶ Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
⁷ Department of Biochemistry, University of Otago, Dunedin, New Zealand and Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham.
⁸ VA San Diego Healthcare System, San Diego, California and University of California San Diego, La Jolla, California.
⁹ School of Life Science and Technology, ShanghaiTech University, Shanghai, China and CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, CAS, Shanghai, China and Innovation Center for Intervention of Chronic Disease and Promotion of Health, Shanghai, China and Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China.

PMID: 37390372
DOI: 10.1002/art.42635

Abstract

Objective: The objective of this study was to discover differential metabolites and pathways underlying infrequent gout flares (InGF) and frequent gout flares (FrGF) using metabolomics and to establish a predictive model by machine learning (ML) algorithms.

Methods: Serum samples from a discovery cohort of 163 patients with InGF and 239 patients with FrGF were analyzed by mass spectrometry-based untargeted metabolomics to profile differential metabolites and explore dysregulated metabolic pathways using pathway enrichment analysis and network propagation-based algorithms. ML algorithms were performed to establish a predictive model based on selected metabolites, which was further optimized by a quantitative targeted metabolomics method and validated in an independent validation cohort with 97 participants with InGF and 139 participants with FrGF.

Results: A total of 439 differential metabolites between InGF and FrGF groups were identified. Top dysregulated pathways included carbohydrates, amino acids, bile acids, and nucleotide metabolism. Subnetworks with maximum disturbances in the global metabolic networks featured cross-talk between purine metabolism and caffeine metabolism, as well as interactions among pathways involving primary bile acid biosynthesis, taurine and hypotaurine metabolism, alanine, aspartate, and glutamate metabolism, suggesting epigenetic modifications and gut microbiome in metabolic alterations underlying InGF and FrGF. Potential metabolite biomarkers were identified using ML-based multivariable selection and further validated by targeted metabolomics. Area under receiver operating characteristics curve for differentiating InGF and FrGF achieved 0.88 and 0.67 for the discovery and validation cohorts, respectively.

Conclusion: Systematic metabolic alterations underlie InGF and FrGF, and distinct profiles are associated with differences in gout flare frequencies. Predictive modeling based on selected metabolites from metabolomics can differentiate InGF and FrGF.

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References

REFERENCES

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Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent Versus Infrequent Gout Flares

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Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent Versus Infrequent Gout Flares

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