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  • 1.
    Ahonen, Linda
    et al.
    Steno Diabetes Center Copenhagen, Gentofte, Denmark.
    Jäntti, Sirkku
    Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland.
    Suvitaival, Tommi
    Steno Diabetes Center Copenhagen, Gentofte, Denmark.
    Theilade, Simone
    Steno Diabetes Center Copenhagen, Gentofte, Denmark.
    Risz, Claudia
    Steno Diabetes Center Copenhagen, Gentofte, Denmark.
    Kostiainen, Risto
    Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland.
    Rossing, Peter
    Steno Diabetes Center Copenhagen, Gentofte, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
    Oresic, Matej
    Örebro universitet, Institutionen för medicinska vetenskaper. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.
    Hyötyläinen, Tuulia
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Targeted Clinical Metabolite Profiling Platform for the Stratification of Diabetic Patients2019Ingår i: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 9, nr 9, artikel-id E184Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Several small molecule biomarkers have been reported in the literature for prediction and diagnosis of (pre)diabetes, its co-morbidities, and complications. Here, we report the development and validation of a novel, quantitative method for the determination of a selected panel of 34 metabolite biomarkers from human plasma. We selected a panel of metabolites indicative of various clinically-relevant pathogenic stages of diabetes. We combined these candidate biomarkers into a single ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method and optimized it, prioritizing simplicity of sample preparation and time needed for analysis, enabling high-throughput analysis in clinical laboratory settings. We validated the method in terms of limits of detection (LOD) and quantitation (LOQ), linearity (R2), and intra- and inter-day repeatability of each metabolite. The method's performance was demonstrated in the analysis of selected samples from a diabetes cohort study. Metabolite levels were associated with clinical measurements and kidney complications in type 1 diabetes (T1D) patients. Specifically, both amino acids and amino acid-related analytes, as well as specific bile acids, were associated with macro-albuminuria. Additionally, specific bile acids were associated with glycemic control, anti-hypertensive medication, statin medication, and clinical lipid measurements. The developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.

  • 2.
    Playdon, Mary C.
    et al.
    Department of Nutrition and Integrative Physiology, College of Health, University of Utah, Salt Lake City, USA; Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT, USA.
    Joshi, Amit D.
    Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA; Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
    Tabung, Fred K.
    Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH, USA; The Ohio State University Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Columbus, OH, USA; Division of Epidemiology, The Ohio State University College of Public Health, Columbus, OH, USA.
    Cheng, Susan
    Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
    Henglin, Mir
    Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA.
    Kim, Andy
    Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA.
    Lin, Tengda
    Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT, USA; Department of Population Health Sciences, School of Medicine, University of Utah, Salt Lake City, UT, USA.
    van Roekel, Eline H.
    Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, MD Maastricht, The Netherlands.
    Huang, Jiaqi
    Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA.
    Krumsiek, Jan
    Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
    Wang, Ying
    Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA.
    Mathé, Ewy
    College of Medicine, Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
    Temprosa, Marinella
    Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC, USA.
    Moore, Steven
    Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA.
    Chawes, Bo
    COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
    Eliassen, A Heather
    Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
    Gsur, Andrea
    Institute of Cancer Research, Department of Medicine, Medical University of Vienna, Vienna, Austria.
    Gunter, Marc J.
    Section of Nutrition and Metabolism, International Agency for Research on Cancer, World Health Organization, Lyon, France.
    Harada, Sei
    Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan.
    Langenberg, Claudia
    MRC Epidemiology Unit, Public Health, University of Cambridge, Cambridge, UK; The Francis Crick Institute, London, UK.
    Oresic, Matej
    Örebro universitet, Institutionen för medicinska vetenskaper. Turku Centre for Biotechnology, University of Turku, Turku, Finland.
    Perng, Wei
    Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA; Life course epidemiology of adiposity and diabetes (LEAD) Center, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA.
    Seow, Wei Jie
    Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore.
    Zeleznik, Oana A.
    Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
    Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)2019Ingår i: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 9, nr 7, artikel-id E145Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.

  • 3.
    Sen, Partho
    et al.
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland; .
    Oresic, Matej
    Örebro universitet, Institutionen för medicinska vetenskaper. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.
    Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview2019Ingår i: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 9, nr 2, artikel-id E22Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.

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