Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses.

chronic kidney disease epidemiology human cohort studies kidney disease etiologies metabolomics study design nephrology

Journal

Metabolites
ISSN: 2218-1989
Titre abrégé: Metabolites
Pays: Switzerland
ID NLM: 101578790

Informations de publication

Date de publication:
16 Jul 2021
Historique:
received: 15 06 2021
revised: 08 07 2021
accepted: 12 07 2021
entrez: 6 8 2021
pubmed: 7 8 2021
medline: 7 8 2021
Statut: epublish

Résumé

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

Identifiants

pubmed: 34357354
pii: metabo11070460
doi: 10.3390/metabo11070460
pmc: PMC8304377
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Subventions

Organisme : Bundesministerium für Bildung, Wissenschaft und Forschung
ID : 01ZX1912A
Organisme : Bundesministerium für Bildung, Wissenschaft und Forschung
ID : 01ZX1912B
Organisme : Bundesministerium für Bildung, Wissenschaft und Forschung
ID : 01ZX1912C

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Auteurs

Ulla T Schultheiss (UT)

Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany.
Department of Medicine IV-Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany.

Robin Kosch (R)

Computational Biology, University of Hohenheim, 70599 Stuttgart, Germany.

Fruzsina Kotsis (F)

Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany.
Department of Medicine IV-Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany.

Michael Altenbuchinger (M)

Institute of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany.

Helena U Zacharias (HU)

Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany.
Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany.

Classifications MeSH