Research question and Objective
Gynecological and obstetrical diseases (such as ovarian cancer, preeclampsia, gestational diabetes, etc.) are major public health challenges. The increasing severity of environmental pollution and population aging further exacerbate the disease burden. Although genetic and lifestyle factors have been partially elucidated, the interplay between environmental exposures, metabolic changes, and molecular mechanisms remains unclear. This study aims to utilize the UK Biobank’s multi-omics data , environmental records, and lifestyle metrics to achieve the following goals:
Identify new risk factors/biomarkers through integrated omics-environment analysis
Elucidate the causal relationships between environmental exposures, molecular changes, and disease progression
Develop machine learning models for early risk prediction and personalized intervention
Scientific Rationale
Specific proteins and metabolites circulating in the blood can not only serve as potential biomarkers for gynecological and obstetrical diseases but also provide deep insights into disease onset and progression. Studies have shown that long-term exposure to environmental stressors and unhealthy dietary habits may increase the risk of gynecological and obstetrical diseases by disrupting oxidative stress balance, inflammatory responses, and cellular metabolic pathways. However, existing studies are often limited to single-omics data or lack precise environmental exposure assessments.
By combining multi-dimensional omics analyses, detailed environmental exposure monitoring data, and objectively measured physical activity levels obtained through accelerometers, we can uncover new biological pathways, identify key risk factors, and provide a scientific basis for the prevention and treatment of gynecological and obstetrical diseases. We will apply advanced statistical methods and machine learning techniques to ensure that the developed predictive models achieve accuracy.
