Optimizing Exposome-wide Assessments in Cardiometabolic Risk

Research output: ThesisDoctoral Thesis (compilation)

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Abstract

This thesis is focused on cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D), two concomitant conditions that appear with growing concern. In our work, we aim to improve the identification of individuals at-risk of cardiometabolic disease through the characterization of complex environmental exposures (i.e. diet, physical activity), that temporally vary, and the health effects on cardiometabolic traits and disease. Our projects were based upon the Västerbotten Health Survey (VHU) and the Malmö Diet and Cancer (MDCS) studies, which included extensive data on lifestyle, biological intermediates, and clinical outcomes. In Paper I, we utilized the so-called environmental-wide association approach (EWAS), using longitudinal data from > 31,000 adults in VHU study. Under generalized linear models, from ~ 300 candidate exposures, 11 modifiable variables were associated with most of the cardiometabolic traits; the prioritised variables belonged to smoking, coffee intake, physical activity, alcohol intake, and context-specific lifestyle domains. In Paper II, we implemented a machine learning-based model to identify individuals with variable susceptibility to lifestyle risk factors for T2D and CVD. Individuals with sensitivity to blood lipids, and blood pressure associated predictors were at higher risk to develop cardiometabolic disease. Furthermore, when pooling across sensitive groups from the two cohorts, the findings suggest a particular vulnerable subpopulation with different risk profile. In Paper III, a series of causal-inference experiments from VHU and publicly available genome-wide association study (GWAS) summary statistics were used to triangulate evidence of the direct and mediated effects by adiposity and physical activity, of macronutrient intake (fat, carbohydrates, protein and sugar) and cardiometabolic disease. Using structural equation modelling, the mediation analyses enhanced with Mendelian randomization analysis, showed a likely causal putative association between carbohydrate intake and T2D. In addition, the integrative genomic analyses suggested a candidate causal variant localized to the established T2D gene TCF7L2. In Paper IV, we conducted a systematic review and metanalysis of observational studies, complemented by Mendelian randomization analysis using GWAS summary statistics, investigating causal associations of individuals with high, yet normal, glycaemia associated with cardiovascular complications. Prediabetes was likely causally associated with coronary heart disease; suggesting higher, but not diabetic levels of blood glucose confer a risk, thus, effective preventive strategies may prove successful in prediabetes.
Original languageEnglish
QualificationDoctor
Awarding Institution
  • Department of Clinical Sciences, Malmö
Supervisors/Advisors
  • Franks, Paul, Supervisor
  • Giordano, Giuseppe, Assistant supervisor
  • Rosengren, Anders, Assistant supervisor
Award date2022 Oct 6
Place of PublicationLund
Publisher
ISBN (Print)978-91-8021-292-2
Publication statusPublished - 2022

Bibliographical note

Defence details
Date: 2022-10-06
Time: 13:00
Place: Agardh föreläsningssal, CRC, Jan Waldenströms gata 35, Skånes Universitetssjukhus i Malmö
External reviewer(s)
Name: Landberg, Rikard
Title: Professor
Affiliation: Head of Division of Food and Nutrition Science, Chalmers University of Technology

Subject classification (UKÄ)

  • Nutrition and Dietetics

Keywords

  • Lifestyle
  • Nutritional epidemiology
  • Cardiometabolic risk
  • Prediabetes
  • cardiovascular disease (CVD)
  • Type 2 diabetes (T2D)
  • Machine learning
  • causal inference
  • Mediation analysis
  • Mendelian randomization

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