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Untargeted metabolomics and novel data analysis strategies to identify biomarkers of diet and type 2 diabetes

Shi, Lin (2017). Untargeted metabolomics and novel data analysis strategies to identify biomarkers of diet and type 2 diabetes. Diss. (sammanfattning/summary) Uppsala : Sveriges lantbruksuniv., Acta Universitatis Agriculturae Sueciae, 1652-6880 ; 2017:108
ISBN 978-91-7760-106-7
eISBN 978-91-7760-107-4
[Doctoral thesis]

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Type 2 diabetes (T2D) is a major global health problem and prevention could be improved by identifying individuals at risk at an early stage, followed by preventive strategies, e.g., dietary modifications. Untargeted LC-MS metabolomics offers the possibility to identify predictive biomarkers that may improve risk prediction and dietary biomarkers that may facilitate investigation of diet-T2D relationships. However, untargeted metabolomics generates large-scale data, resulting in demanding data processing and statistical analyses preceding meaningful biological interpretation.

The work presented in this thesis sought to develop bioinformatics tools for dealing with large-scale data generated from untargeted LC-MS metabolomics, to apply such tools to identify predictive metabolites of T2D and metabolites related to predefined healthy Nordic dietary indices, and to investigate whether such metabolites are associated with T2D risk in a Swedish population.

Two novel R programming based packages were developed: ‘batchCorr’, a data-processing strategy to correct for within- and between-batch variability in LC-MS experiments, and ‘MUVR’, a statistical framework for multivariate analysis with unbiased variable selection. These tools were applied on untargeted LC-MS metabolomics data obtained from plasma samples from a nested case-control study. Overall, 46 predictive metabolites of T2D were identified. Several metabolites showed good long-term reproducibility among healthy participants, reinforcing their potential as predictive biomarkers, while some changed in the disease-associated direction among cases, reflecting disease progression. In total, 38 metabolites were found to be associated with two predefined healthy Nordic dietary indices. No evidence was found to support association between indices and T2D risk. Instead, metabolites related to unhealthy foods not captured in indices were associated with increased risk.

In conclusion, the novel bioinformatics tools developed here can overcome vital data-analytical challenges inherent in large-scale untargeted metabolomics studies. Predictive metabolites have great potential to provide information related to T2D pathophysiology and monitoring of disease progression, though only a limited improvement in disease prediction was achieved when adding them to models based on optimally selected traditional risk factors. Moreover, no evidence was found of an association between healthy Nordic dietary indices and T2D risk. Future studies should investigate how diet/lifestyle risk factors affect pathological pathways of T2D and prevent disease development by integration of multi-omics techniques and traditional methods.

Authors/Creators:Shi, Lin
Title:Untargeted metabolomics and novel data analysis strategies to identify biomarkers of diet and type 2 diabetes
Series Name/Journal:Acta Universitatis Agriculturae Sueciae
Year of publishing :20 November 2017
Depositing date:20 November 2017
Number of Pages:99
IBrunius C.*, Shi L., Landberg R.(2016). Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction. Metabolomics 12(173),1-13.
IIShi L.*, Rosén J., Westerhuis J.A., Landberg R., Brunius C. (2017).Unbiased variable selection and validation in multivariate modelling(Submitted).
IIIShi L.*, Brunius C., Lehtonen M., Auriola S., Bergdahl I.A., Rolandsson O., Hanhineva K., Landberg R.(2017). Plasma metabolites associated with type 2 diabetes in a Swedish population –A case-control study nested in a prospective cohort. Diabetologia(In press).
IVShi L.*, Brunius C., Johansson J., Bergdahl J.A. Lindahl B., Hanhineva K., Landberg R. (2017) Plasma metabolites associated with healthy Nordic dietary indices and risk of type 2 diabetes–A nested case control study in a Swedish population(Submitted).
Place of Publication:Uppsala
Publisher:Department of Molecular Sciences, Swedish University of Agricultural Sciences
ISBN for printed version:978-91-7760-106-7
ISBN for electronic version:978-91-7760-107-4
Publication Type:Doctoral thesis
Full Text Status:Public
Agris subject categories.:X Agricola extesions > X30 Life sciences
X Agricola extesions > X38 Human medicine, health, and safety
X Agricola extesions > X50 Chemistry
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 102 Computer and Information Science > 10203 Bioinformatics (Computational Biology) (applications to be 10610)
(A) Swedish standard research categories 2011 > 1 Natural sciences > 104 Chemical Sciences > Analytical Chemistry
(A) Swedish standard research categories 2011 > 1 Natural sciences > 106 Biological Sciences (Medical to be 3 and Agricultural to be 4) > Bioinformatics and Systems Biology (methods development to be 10203)
(A) Swedish standard research categories 2011 > 3 Medical and Health Sciences > 303 Health Sciences > Public Health, Global Health, Social Medicine and Epidemiology
Agrovoc terms:bioinformatics, data analysis, multivariate analysis, metabolism, metabolic disorders, diabetes, diet, risk assessment
Keywords:Biomarkers, bioinformatics, healthy Nordic dietary index, multivariate analysis, nested case-control study, risk prediction, type 2 diabetes, untargeted LC-MS metabolomics
Permanent URL:
ID Code:14740
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Department of Molecular Sciences
Deposited By: LIN LIN SHI
Deposited On:21 Nov 2017 16:04
Metadata Last Modified:09 Sep 2020 14:17

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