Approved projects

Published

December 13, 2024

Completed

Non-alcoholic fatty liver disease (NAFLD) may be considered the hepatic manifestation of metabolic syndrome. NAFLD-related primary liver cancer is a growing concern, and preventive interventions are critical. We will statistically model replacement of red meat consumption with legumes in relation to incident primary liver cancer and assess whether the association is mediated through NAFLD.

Ongoing

The overall aim is to understand the genetics behind beta-cell function (BCF). We will use an in-house BCF-GWAS to apply various analyses and data to unravel the dynamics influencing the insulin secretory function.

Socioeconomic status (SES) is a key upstream determinant of multimorbidity. Accumulating evidence, including findings from our recent study, has demonstrated an inverse association between SES and the risk of developing multiple chronic conditions. However, the underlying mechanisms remain poorly understood. There have been increasing calls to explore pathways that can help reduce social inequalities in morbidity and mortality. SES influences various health-related behaviors; for example, individuals from lower socioeconomic backgrounds are more likely to engage in unhealthy lifestyle choices, such as smoking, poor diet, and physical inactivity. These behaviors are further linked to detrimental metabolic changes through various biological pathways. Lifestyle factors and biomarkers may thus mediate the causal relationship between SES and multimorbidity. A systematic review has reported that lifestyle factors account for approximately 20%-30% of the socioeconomic disparity in health outcomes.However, few studies have quantified the proportion of the association between SES and multimorbidity mediated by metabolic biomarkers, particularly in distinguishing biomarkers from different metabolic pathways. Understanding the extent to which these biomarkers mediate the total effect is crucial for elucidating treatment mechanisms and evaluating their potential as intervention targets. Traditional mediation analyses are limited in their ability to handle high-dimensional mediators, and intermediate confounding presents a further challenge by complicating the estimation of path-specific effects. In this study, we aim to provide a comprehensive investigation of the mediating role of lifestyle factors (i.e., smoking, unhealthy diet, physical inactivity, alcohol consumption, and sleep deprivation) and biomarkers (including blood lipids, HbA1c, glucose, IGF, CRP, Cystatin C, blood pressure, and other plasma metabolites). Specifically, we will quantify the extent to which the association between SES and multimorbidity is mediated through different pathways. The findings will contribute to understanding the metabolic mechanisms underlying the link between SES disadvantage, unhealthy lifestyle, and multimorbidity risk, providing evidence for potential disease prevention strategies. Causal machine learning methods, which can handle high-dimensional mediators and intermediate confounders will be applied.

The proposed project seeks to explore how deviations from an individual’s genetically-determined BMI, as calculated through familial relationships, influence the risk of premature mortality. By constructing a family-based BMI Homeostatic Index (BHI), we aim to capture the genetically-determined BMI for each individual using data from multiple generations within families. This index will serve as a measure of how much an individual’s BMI deviates from what would be expected based on their genetic background.The project involves calculating the BHI by analyzing BMI data across family members, taking into account the degree of relatedness between individuals. By using a pedigree-based approach, the BHI will quantify the divergence of an individual’s BMI from their family’s average, adjusted for factors such as age and sex. This approach allows us to assess whether these deviations are associated with increased mortality risk, offering insights into the genetic basis of obesity and its health consequences.

The aim of this project aims to link deviations from homeostatic BMI with fat mass, lean mass, and mortality risk. Further, it intends to examine the connection between genetically predicted BMI and observed BMI and how it relates to mortality risk. We will assess deviations from homeostatic BMI using a combination of phenotypic and genetic data to explore their potential impact on health indicators. Genetic risk scores for adult and childhood BMI will help us understand the implications of genetically predicted BMI versus observed BMI on mortality risk.

The proposed project aims to investigate the impact of deviations from genetically-determined body mass index (BMI) homeostasis on premature mortality, by leveraging large-scale population studies.This project involves utilizing childhood Polygenic Risk Scores (PRS) as a proxy for genetic homeostatic BMI. Given that secular changes in the environment promoting weight gain have likely influenced adult BMI PRS, the childhood PRS offers a more stable and unbiased estimate of an individual’s genetic predisposition to obesity. This will allow us to assess the long-term health risks associated with deviations from genetically-determined BMI across the lifespan. The findings from this research could lead to the development of personalized health interventions, helping to identify those at higher risk of mortality due to weight-related factors.

The aim of the project is to investigate the associations between the following domains and the incidence of heart failure. 1) Body composition based on DXA-derived measurements. 2) Daily physical activity based on accelerometry metrics. 3) Physical capacity based on DXA data, handgrip and gait speed. 4) Malnutrition based on weight loss, DXA data and CRP. Furthermore, the project aims at investigating the impact of the four domains on survival in heart failure patients.

Reducing red and processed meat intake or adopting a plant-based diet may lower the risk of colorectal cancer. However, the effects of decreasing the consumption of one type of food while concomitantly increasing the intake of another on colorectal cancer risk are not well understood. Thus, this project aims to investigate the association of statistically modelled replacement of animal-based foods with a variety of plant-based foods and the risk of developing colorectal cancer.

The aim is to investigate the association between adherence to the DASH diet and risk of heart failure and subtypes of heart failure. We will use multi-state models to investigate transitions of cardiovascular diseases.

With the growing number of people suffering from diabetes-multimorbidity, and its heterogeneity in presentation and outcome, there is an urgent need to gain a deeper understanding of the mechanisms that determine different courses of progression towards diabetes-multimorbidity. The overall aim of the project is to describe the disease transition processes that lead to diabetes-multimorbidity and to determine drivers of multimorbidity trajectories. We will apply advanced epidemiological methods to three large populations using a multi-dimensional and multi-stage approach. There are two specific aims for the project. 1) To map the heterogeneity in trajectories of diabetes-multimorbidity and to identify risk factors contributing to pronounced diabetes-multimorbidity. This will be achieved by using a machine learning approach for risk prediction, which involves combining a genetic algorithm with a random forest classifier to enhance the results. 2) To understand mortality risk at different stages of the diabetes-multimorbidity trajectory, by comparing mortality as people transition from a healthy condition to single disease, and diabetes-multimorbidity using multi-state models. To develop a web application that helps clinicians visualize the potential impact of an intervention or achieving tighter therapeutic targets on the future risk of all components of diabetes-multimorbidity. This step will be conducted by integrating results from aims 1 and 2 using simulation approaches.

Increased consumption of legumes and pulses are recommended globally to mitigate non-communicable disease risk and negative environmental impacts of human diets. Research has found that high consumption of legumes may alter the risk of liver and gallbladder disease (hepatobiliary diseases), the underlying pathways are however understudied in humans. This project aims to investigate the association between consuming legumes and changes in hepatobiliary biomarker levels over time.

Higher legume consumption is recommended as a meat substitute to increase healthiness and minimize the climate impact from humans diets. Legumes has in animal studies and a few human studies shown to increase gallstone formation through altering the lipid metabolism in the liver. A high consumption of legumes may thus be associated with an increased risk of gallstones, yet there exists very few studies investigating this in human populations, particularly due to low consumption of legumes in many countries. Aim: To investigate the association between substituting meat, poultry, and fish for legumes and the risk of developing gallstone diseases in a cohort with moderate to high legume consumption.

The primary objective of this study is to examine the potential connection between legume consumption and the risk of various hepato-biliary disorders, including non-alcoholic steatohepatitis (NASH), non-alcoholic fatty liver disease (NAFLD), cholelithiasis, cholecystitis, cholecystectomy, pancreatitis, and pancreatectomy. The investigation takes into account possible factors that could influence this association.The study focuses on a population comprising individuals enrolled in the UK Biobank (UKB) who did not exhibit hepato-biliary diseases at the baseline assessment. Eligible participants are those who have completed one or more 24-hour dietary recall surveys. The intervention or exposure of interest is the daily consumption of legumes in grams, specifically looking at scenarios where legumes replace meat in the diet. In essence, the study seeks to explore whether legume consumption, when considered against potential confounding variables, is associated with the occurrence of hepato-biliary disorders, offering insights into dietary factors that may impact liver and gallbladder health.

Aim: To map out likely causal pathways using metabolites to link dietary patterns to changes in adiposity and metabolic health markersPopulation: UK Biobank will be used as a validation cohort for cross-sectional analysis conducted in the Fenland Study. We excluded those with diabetes at baseline. Intervention/exposure: Adherence to the Mediterranean diet, Alternate Healthy Eating Index and the EAT-Lancet diet scoreComparison: Those with low adherence to the dietary patterns.Mediators: 249 plasma metabolites including routine lipids, lipoprotein subclass profiling with lipid concentrations within 14 subclasses, fatty acid composition, amino acids, ketone bodies and glycolysis metabolites.Outcome: BMI, waist circumference, blood pressure, blood lipids, blood glucose, insulin, HOMA-IR

Abandoned

There are many reasons a project might be abandoned, for instance, to split the work into smaller projects or because the lead researcher moved on.

The study aims to examine the association between kidney function and cardiovascular diseases (CVD) and mortality in the general population, and examine whether this potential association is causal by employing the Mendelian Randomization (MR) approach. Firstly, we will investigate the association between kidney function (eGFR-based on plasma creatinine CKD-EPI, and possibly also cystatin C) and risk of MI, heart failure, stroke and all-cause mortality in 500,000 individuals from the UK Biobank. Then we will assess the causal direction between kidney function and CVD risk using a bidirectional MR analysis.