Racial difference in BMI and lung cancer diagnosis: analysis of the National Lung Screening Trial.
- Sidney Kimmel Medical College, Thomas Jefferson University, 1101 Locust Street, Philadelphia, PA, USA.
- Division of Pulmonary and Critical Care Medicine, Jane and Leonard Korman Respiratory Institute, Thomas Jefferson University, 834 Walnut Street, Philadelphia, PA, USA.
- Jefferson College of Population Health, Thomas Jefferson University, 901 Walnut Street, Philadelphia, PA, USA.
- Division of Population Science, Department of Medical Oncology, Thomas Jefferson University, 834 Chestnut Street, Philadelphia, PA, USA.
- Division of Population Science, Department of Medical Oncology, Thomas Jefferson University, 834 Chestnut Street, Philadelphia, PA, USA. hee-soon.juon@jefferson.edu.
BACKGROUND: The inverse relationship between BMI and lung cancer diagnosis is well defined. However, few studies have examined the racial differences in these relationships. The purpose of this paper is to explore the relationships amongst race, BMI, and lung cancer diagnosis using the National Lung Screening Trial (NLST) data.
METHODS: Multivariate regression analysis was used to analyze the BMI, race, and lung cancer diagnosis relationships.
RESULTS: Among 53,452 participants in the NLST cohort, 3.9% were diagnosed with lung cancer, 43% were overweight, and 28% were obese. BMI was inversely related to lung cancer diagnosis among Whites: those overweight (aOR = .83, 95%CI = .75-.93), obese (aOR = .64, 95%CI = .56-.73) were less likely to develop lung cancer, compared to those with normal weight. These relationships were not found among African-Americans.
CONCLUSION: Our findings indicate that the inverse relationship of BMI and lung cancer risk among Whites is consistent, whereas this relationship is not significant for African-Americans. In consideration of higher lung cancer incidence among African Americans, we need to explore other unknown mechanisms explaining this racial difference.
- NLST-361: Assessing Racial Disparities of Lung Cancer Risk Prediction Model (Hee-Soon Juon - 2017)