UCLA

Epigenetics and proteomics have emerged as powerful fields with the potential to transform our understanding of the relationship between lifestyle factors and health outcomes. This paper utilizes data from an exploratory, cross-sectional study conducted by Prosper DNA Inc. to identify proteomic and epigenetic biomarkers that can predict clinical lifestyle traits related to individual health. The study collected data from two groups, one representing healthy individuals and the other of unhealthy individuals with specific lifestyle characteristics. Imputation, Principal Component Analysis (PCA), and correlation analysis were performed on the proteomic and methylation data, which resulted in 10 principal components (PCs) each and 3 correlation matrices. Linear regression models were developed using proteomic or methylation PCs as predictors for each lifestyle trait. The models were evaluated using Leave-One-Out-Cross-Validation and Pearson correlation coefficient (r) to determine significance and confirm the accuracy of the models. 19 significant proteomic models and 28 significant methylation models were identified. Notably, 15 models across the proteomic and methylation models were directly associated with the original selection criteria, such as body mass index (BMI), fitness, and dietary habits. Through a correlation analysis, PC3, PC9, and PC9 of the proteomic PCs and MPC1, MPC3, and MPC4 of the methylation PCs were selected for PC loading analysis based on the strength and significance of the correlation between a given PC and the traits associated with the biomarkers. The most influential proteins and CpG sites were extracted from the loading and passed through STRING and Cistrome, respectively, to gather protein network information and transcription factor information. For the proteomic PCs, the proteins were involved in networks relating to platelet activation/coagulation, inflammatory response, and the regulation of proteolytic activity. For the methylation PCs, the transcription factors that bind to the CpG sites showed some relation to adipogenesis and tumor suppression. Future steps should include deeper analyses of the individual proteins, CpG sites, and transcription factors to provide concrete validation for the trends observed in this study.