Dysregulation of lipid metabolism contributes to a range of diseases including hyperlipidemia, cardiovascular disease, type 2 diabetes, non-alcoholic fatty liver disease (NAFLD). Early detection of metabolic disorders remains challenging as pathological processes often begin years before clinical symptoms manifest. In this study, we aims to identify novel risk factors and biomarkers associated with lipid metabolism dysregulation by analyzing genetic, dietary, lifestyle and environmental factors. Specifically, we aim to screen and identify plasma protein biomarkers of lipid metabolism and obesity-related diseases and develop machine learning-based disease models for early diagnosis and progression prediction.
Over the expected duration of 12 to 36 months, our project will systematically collect and preprocess necessary datasets, which include comprehensive plasma proteomics outputs. Specifically, we will gather data from the UK Biobank, focusing on genetic, dietary, lifestyle, and environmental factors associated with lipid metabolism dysregulation. In terms of model development, we plan to employ and compare a variety of advanced machine learning algorithms tailored to address research questions, including Random Forest (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVMs), and even Deep Neural Networks (DNNs), to enhance ur capability to predict disease progression and response to interventions. Once these models are constructed and trained using our datasets, they will undergo rigorous evaluation. Performance metrics such as accuracy, precision, recall, F1-score, and area under the curve (AUC) will be used to assess the efficacy of each model, followed by cross-validation and sensitivity analysis. The proposed study’s outcomes are expected to significantly advance our understanding of lipid metabolism-related and obesity-related diseases, leading to improved patient care and public health strategies.
