Investigating epigenetic remodeling in monocytes and hepatocytes is a powerful approach to understanding the mechanisms underlying metabolic disorders such as type 2 diabetes (T2D), metabolic dysfunction–associated steatotic liver disease (MASLD), and cardiovascular disease (CVD). These conditions are closely linked through shared features including chronic inflammation, insulin resistance, and lipid dysregulation. Epigenetic processes-heritable yet reversible modifications that regulate gene expression without altering the DNA sequence-provide a key interface between environmental exposures and persistent cellular dysfunction. Monocytes and hepatocytes are central to this investigation. Monocytes contribute to systemic inflammation and can infiltrate tissues, differentiating into macrophages that exacerbate metabolic disease. Hepatocytes, as the main functional cells of the liver, regulate glucose and lipid metabolism and play a critical role in MASLD progression. Epigenetic remodeling in these cells can drive pathological transcriptional programs, such as pro-inflammatory signaling in monocytes and lipid accumulation or fibrotic responses in hepatocytes. To study these mechanisms, we primarily employ high-throughput sequencing-based approaches. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used to map histone modifications and transcription factor binding across the genome, enabling identification of regulatory elements involved in disease-associated gene expression. RNA sequencing (RNA-seq), both bulk and single-cell, provides complementary insights into transcriptional changes: bulk RNA-seq captures global expression patterns, while single-cell RNA-seq reveals cellular heterogeneity and distinct subpopulations. In addition, Cleavage Under Targets and Tagmentation (CUT&Tag) offers a sensitive and efficient alternative to ChIP-seq, particularly advantageous for limited or rare samples. Our studies integrate both animal models and human samples, allowing us to combine mechanistic insights with clinical relevance. Animal models enable controlled investigation of causal pathways, whereas human samples capture real-world variability and disease complexity. A critical aspect of this work is the analysis of large-scale sequencing datasets, which requires substantial computational resources. We perform most data processing and analysis using UPPMAX, a high-performance computing infrastructure that supports efficient handling of genome-wide data, including quality control, alignment, peak calling, and downstream integrative analyses. For human-derived datasets that involve sensitive personal or clinical information, analyses are conducted within Bianca, a secure platform designed to meet strict ethical and legal standards. Bianca provides controlled access and secure data storage, ensuring compliance with data protection regulations while enabling advanced bioinformatic workflows. By combining advanced epigenomic technologies with secure and scalable computational environments, this research aims to generate a comprehensive understanding of how epigenetic remodeling in monocytes and hepatocytes contributes to T2D, MASLD, and CVD, ultimately supporting the discovery of novel biomarkers and therapeutic targets.