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Li T, Ihanus A, Ohukainen P, Järvelin MR, Kähönen M, Kettunen J, Raitakari OT, Lehtimäki T, Mäkinen VP, Tynkkynen T, Ala-Korpela M. Clinical and biochemical associations of urinary metabolites: quantitative epidemiological approach on renal-cardiometabolic biomarkers. Int J Epidemiol 2024; 53:dyad162. [PMID: 38030573 PMCID: PMC10859141 DOI: 10.1093/ije/dyad162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 11/17/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Urinary metabolomics has demonstrated considerable potential to assess kidney function and its metabolic corollaries in health and disease. However, applications in epidemiology remain sparse due to technical challenges. METHODS We added 17 metabolites to an open-access urinary nuclear magnetic resonance metabolomics platform, extending the panel to 61 metabolites (n = 994). We also introduced automated quantification for 11 metabolites, extending the panel to 12 metabolites (+creatinine). Epidemiological associations between these 12 metabolites and 49 clinical measures were studied in three independent cohorts (up to 5989 participants). Detailed regression analyses with various confounding factors are presented for body mass index (BMI) and smoking. RESULTS Sex-specific population reference concentrations and distributions are provided for 61 urinary metabolites (419 men and 575 women), together with methodological intra-assay metabolite variations as well as the biological intra-individual and epidemiological population variations. For the 12 metabolites, 362 associations were found. These are mostly novel and reflect potential molecular proxies to estimate kidney function, as the associations cannot be simply explained by estimated glomerular filtration rate. Unspecific renal excretion results in leakage of amino acids (and glucose) to urine in all individuals. Seven urinary metabolites associated with smoking, providing questionnaire-independent proxy measures of smoking status in epidemiological studies. Common confounders did not affect metabolite associations with smoking, but insulin had a clear effect on most associations with BMI, including strong effects on 2-hydroxyisobutyrate, valine, alanine, trigonelline and hippurate. CONCLUSIONS Urinary metabolomics provides new insight on kidney function and related biomarkers on the renal-cardiometabolic system, supporting large-scale applications in epidemiology.
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Affiliation(s)
- Tianqi Li
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Andrei Ihanus
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Pauli Ohukainen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Marjo-Riitta Järvelin
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere, Finland
| | - Johannes Kettunen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere, Finland
| | - Ville-Petteri Mäkinen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Tuulia Tynkkynen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
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