Energy metabolism dyshomeostasis is associated with multiple health problems. For example, abundant epidemiological data show that obesity and overweight increase the risk of cardiometabolic diseases and early mortality. Type 2 diabetes (T2D), characterized by chronically elevated blood glucose, is also associated with debilitating complications, high healthcare costs and mortality, with cardiovascular complications accounting for more than half of T2D-related deaths. Prediabetes, which is defined as elevated blood glucose below the diagnostic threshold for T2D, affects approximately 350M people worldwide, with about 35-50% developing T2D within 5 years. Further, non-alcoholic fatty liver disease, a form of ectopic fat deposition as a result of energy imbalance, is associated with increased risk of T2D, CVD and hepatocellular carcinoma.
Determination of causal relationships between phenotypes related to positive energy balance and disease outcomes, as well as elucidation of the nature of these relationships, may help inform public health intervention policies. In addition, utilizing big data and machine learning (ML) approaches can improve prediction of outcomes related to excess adiposity both for research purposes and eventual validation and clinical translation.
In paper 1, I set out to summarize observational evidence and further determine the causal relationships between prediabetes and common vascular complications associated with T2D i.e., coronary artery disease (CAD), stroke and renal disease. In paper 2, I studied the association between LRIG1 genetic variants and BMI, T2D and lipid biomarkers. In paper 3, we used ML to identify novel molecular features associated with non-alcoholic fatty liver disease (NAFLD). In paper 4, I elucidate the nature of causal relationships between BMI and cardiometabolic traits and investigate sex differences within the causal framework.
Prediabetes was associated with CAD and stroke but not renal disease in observational analyses, whilst in the causal inference analyses, prediabetes was only associated with CAD. Common LRIG1 variant (rs4856886) was associated with increased BMI and lipid hyperplasia but a decreased risk of T2D. In paper 3, models using common clinical variables showed strong NAFLD prediction ability (ROCAUC = 0.73, p < 0.001); addition of hepatic and glycemic biomarkers and omics data to these models strengthened predictive power (ROCAUC = 0.84, p < 0.001). Finally, there was evidence of non-linearity in the causal effect of BMI on T2D and CAD, biomarkers and blood pressure. The causal effects BMI on CAD were different in men and women, though this difference did no hold after Bonferroni correction.
We show that derangements in energy homeostasis are causally associated with increased risk of cardiometabolic outcomes and that early intervention on perturbed glucose control and excess adiposity may help prevent these adverse health outcomes. In addition, effects of novel LRIG1 genetic variants on BMI and T2D might enrich our understanding of lipid metabolism and T2D and thus warrant further investigations. Finally, application of ML to multidimensional data improves prediction of NAFLD; similar approaches could be used in other disease research.
- Institutionen för kliniska vetenskaper, Malmö
- Giordano, Nick, handledare
- Franks, Paul, Biträdande handledare
|Tilldelningsdatum||2022 juni 16|
|Status||Published - 2022|
Place: Aulan, CRC, Jan Waldenströms gata 35, Skånes Universitetssjukhus i Malmö. Join by Zoom: https://lu-se.zoom.us/j/69556433172?pwd=WUVoS29LZHE2cUVGS3VnbDQrVkp5UT09
Name: Tyrell, Jess
Affiliation: University of Exeter, UK
- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi