Background

Waterpipe use has increased in many regions, including Saudi Arabia. Evidence shows that waterpipe smoke delivers nicotine, carbon monoxide, and carcinogens at levels comparable to or higher than cigarette smoke, yet molecular research on waterpipe exposure remains limited. Epigenetic alterations, especially DNA methylation changes, are sensitive indicators of tobacco exposure and may help explain cancer risk and addiction biology. Prior work from Lebanon reported methylation signatures that distinguish waterpipe from cigarette smoking using population-based data. No study has yet examined these biomarkers in Saudi Arabia, despite the increasing prevalence of waterpipe use among young adults. This study will generate the first Saudi molecular dataset on tobacco-related epigenetic alterations, integrating epidemiology with high-throughput methylome profiling. Findings may support early detection strategies and guide public health policies on waterpipe control.

Objectives

Identify DNA methylation biomarkers associated with waterpipe and cigarette smoking among Saudi adults.
Compare biomarker patterns with international reference datasets.
Introduce standardized epigenetic laboratory and digital protocols in collaboration with IARC.
Provide early molecular evidence relevant to tobacco control policy.

Secondary objectives explore additional smoking-related biomarkers and potential disease links using exploratory pathway analysis.

Methods

This cross-sectional study uses blood samples from the Saudi Biobank and newly recruited participants from MNGHA clinics. Adults aged 18 years or older will be allocated into three groups, waterpipe smokers, cigarette smokers, and non-smoker controls. DNA will be isolated using Qiagen kits and processed with the Infinium MethylationEPIC v2.0 array to generate genome-wide methylation profiles. Data will undergo functional normalization and quality control using established bioinformatics pipelines. Differential methylation analysis will compare groups, supported by surrogate variable analysis and dimension reduction tools. Machine learning models will be used to evaluate the ability of methylation markers to classify tobacco exposure type. Significant findings will be prioritized for validation through pyrosequencing and compared with ongoing international studies.

Results
Data collection and molecular processing are scheduled to begin begining of  2026. Preliminary work has focused on protocol standardization and harmonization with IARC workflows. Early expectations are to generate high-quality methylome data for all exposure groups, enabling biomarker discovery. Anticipated analytic outputs include differentially methylated CpGs and regions associated with waterpipe or cigarette exposure, classification model performance metrics, and pathway enrichment results. Validation and replication stages are planned but not yet initiated.

Conclusions and Implications

This study will create the first molecular dataset describing tobacco-related epigenetic biomarkers in Saudi Arabia. The work is expected to improve understanding of how waterpipe and cigarette smoking shape the methylome and to provide a scientific foundation for future cancer prevention efforts. The introduction of standardized IARC protocols and machine-learning-based analysis may support future regional research capacity. Findings may contribute to early detection strategies, inform cessation programs, and support national and international tobacco control policy discussions.