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Plasma metabolite biomarkers associated with different meat consumption and risk of type 2 diabetes: A case-control study nested within a northern Swedish cohort
University of Skövde, School of Bioscience.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Abstract [en]

Numerous epidemiological studies show that processed meat consumption is associated to type 2 diabetes (T2D), but the results are less consistent for unprocessed meat and poultry intake. However, in the studies the meat consumption has been estimated through self-reporting using questionnaires. This method is known to suffer from measurement errors, which may lead to dubious observed diet-disease associations. Dietary biomarkers may be used as more objective instruments of specific meat intake. Therefore, the aim in this study was to (1) identify plasma metabolites associated to total, processed, unprocessed meat and poultry intake, and (2) to investigate their associations with the risk of developing T2D. Furthermore, a correlation analysis was performed to elucidate dietary patterns related to the reported meat intakes and their metabolites. Through multivariate modelling, panels of 22, 22, 15 and 19 metabolites where selected, that mirrors total, processed, unprocessed meat and poultry intake, respectively. Ridge predictive modelling was performed generating metabolite scores reflective of the diverse meat consumptions. These scores and the reported meat intakes were used in conditional logistic regression for the assessment of their association with risk of developing T2D. Both the processed metabolite scores and reported intakes were associated with higher risk of developing T2D, whereas the unprocessed meat, poultry scores and associated intakes were not. In conclusion, assessments of plasma metabolite scores appears promising as objective measurements of dietary meat intakes and could be used as a complement to the questionnaires. Results need to be validated in independent cohorts.

Place, publisher, year, edition, pages
2019. , p. 61
Keywords [en]
Feature selection, Variable selection, MUVR, Random forest, Bianca, UPPMAX, Biomarker, Meat biomarker, Meat intake, Meat consumption, Processed meat, Unprocessed meat, Total meat, Poultry, Ridge regression, Conditional logistic regression, Logistic regression, Partial spearman correlation, Diabetes, Type 2 diabetes, Type 2 diabetes risk, Västerbotten intervention program, VIP, Healthy diet, Dietary intake
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:his:diva-17429OAI: oai:DiVA.org:his-17429DiVA, id: diva2:1336798
External cooperation
Rikard Landberg, Food and Nutrition Science, Chalmers University of Technology, Sweden
Subject / course
Systems Biology
Educational program
Biomarkers in Molecular Medicine - Master's Programme 120 ECTS
Supervisors
Examiners
Available from: 2019-07-11 Created: 2019-07-10 Last updated: 2019-07-11Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf