AIRCARE (Air Pollution and Cancer Research Ecosystem): Center for Advanced Research on Environmental Health and Lung Cancer Risk

1. To investigate the relationship between PM 2.5 and lung cancer risk, focusing on individual and cumulative effects along with the multiplicative interaction of air pollution with other risk factors for lung cancer. Methodology - Study Design A combination of a prospective cohort study and case-control study will be employed. The prospective cohort will establish the temporal relationship between air pollution exposure and lung cancer incidence. The case-control study will facilitate detailed exposure assessment and biomarker analysis within a subset of the cohort. The case-control design will also be utilized to further explore gene-environment interactions and detailed exposure assessments.

Prospective and retrospective data from existing patient records, environmental monitoring stations, and the cohort will be used. The clinical workup of the enrolled participants will be done according to a standardised proforma (attached in additional documents) which will contain items relating to participant demographics, work and exposure history, medical history, tobacco history, alcohol history, family history, allergy history, prior history of chronic medical conditions and history pertaining to the nonsurgical treatment of chronic medical conditions. In a longitudinal cohort, participants will be followed over 3 years, with repeated exposure assessments and health outcome monitoring. Lung cancer cases diagnosed within the cohort will be matched to controls based on age, sex, and residential area. Study area The study will be conducted at AIIMS, Delhi, and areas in Delhi and NCR with varying levels of PM 2.5 exposure and will encompass regions with diverse socio-economic profiles and industrial activities to capture the heterogeneity of exposure and risk. The study will be conducted within the same geographical areas as the overall AIRCARE cohort study, encompassing diverse urban and peri-urban regions. The study area will have varying levels of air pollution (PM2.5, VOCs, NOx) and a diverse distribution of other risk factors (smoking, occupational exposures, genetic susceptibility) in Delhi and NCR with established air quality monitoring infrastructure, and different socioeconomic profiles. Sample size estimation and sampling strategy Power calculations will be performed to determine the required sample size to detect a clinically significant increase in lung cancer risk associated with air pollution exposure.

Assuming an average prevalence of lung cancer of 1.5% in India and odds of at least 2 times for high PM-2.5 exposure above the normal range (>37 μg/m³,) and an alpha level of 0.05 with 80% power, we estimate enrolling approximately 3230 participants (1615 lung cancer cases and 1615 controls). We will use the diagnosed lung cancer cases from outpatient department and Delhi cancer registry (DCR) and control will be recruited from the family members of lung cancer patients to get the matched population in terms of PM 2.5 exposure. Project Implementation Plan Cases will include patients with lung cancer (smokers and non-smokers), taken from OPD and from DCR data set. Control will be family members of patients with lung cancer (smokers and non-smokers). The primary outcome will be measured in terms of histologically confirmed incidence of lung cancer. The interaction effects between air pollution (PM2.5) and other risk factors (smoking, Alcohol consumption, occupational exposures, genetic susceptibility) on lung cancer incidence and cumulative risk of lung cancer associated with combined exposure to air pollution and other risk factors will be evaluated. Secondary Outcomes will be measured with the impact of interaction effects on lung cancer histological subtypes, modification of lung cancer risk by genetic variants in the presence of air pollution exposure, changes in biomarker profiles associated with combined exposures, and synergistic effects of PM 2.5 and other risk factors. Design of statistical analysis Ambient air pollution exposure will be quantified using a combination of satellite data, ground-based monitoring, and individual exposure measurements with a measure of PM2.5 concentration. For the cohort study, cox proportional hazards models will be used to estimate hazard ratios (HRs) for lung cancer incidence and mortality associated with air pollution exposure. Time- dependent covariates will be included to account for changes in exposure over time. Survival analysis (Kaplan-Meier method) will be used to examine survival outcomes. For the case-control study, logistic regression will be used to estimate odds ratios (ORs) for lung cancer associated with detailed exposure assessments and biomarker levels. Analysis of the interaction between pollutants will be performed using interaction terms in the regression models. Sensitivity analyses will be conducted to assess the robustness of the findings to potential confounding factors and exposure misclassification. Dose-response relationships between PM 2.5 exposure and lung cancer risk will be examined using categorical and continuous exposure variables. Subgroup analyses will be conducted to examine the effect of air pollution on lung cancer risk in different demographic subgroups (e.g., age, sex, smoking status). 2. To develop and validate a risk-based stratification model based on PM-2.5 exposure for lung cancer screening. Study Design This objective will involve development and validation phases. In the development Phase, we will utilize a retrospective cohort or a prospective cohort dataset, to develop a risk prediction model. This process will use existing data from the ongoing cohort study, or historical data from cancer registries and air quality monitoring stations. The developed model will be externally validated using an independent dataset from a geographically distinct population.

This will have a new cohort or a separate dataset, that will be enrolled for prospective validation. Study area This will be conducted at AIIMS, Delhi, and areas with a diverse range of PM2.5 exposure levels in Delhi and NCR. We will require a geographically distinct region, within India, with independent data on PM2.5 exposure and lung cancer incidence for validation purposes. This region will have demographic and environmental characteristics that differ from the area being studied for lung cancer development, to ensure external validity. Sample size estimation and sampling strategy For model development, the sample size will be determined based on the number of lung cancer cases and controls available in the retrospective/prospective datasets. Assuming an average prevalence of lung cancer of 1.5% in India and odds of at least 2 times for high PM- 2.5 exposure above the normal range (>37 µg/m³,) and an alpha level of 0.05 with 80% power, we estimate enrolling approximately 3230 participants (1615 lung cancer cases and 1615 controls).

Sampling Strategy: