Researchers have discovered a gene that determines how quickly a person metabolizes nicotine. Some variations of the gene cause a cytochrome called CYP2A6 to work more efficiently in nicotine metabolism. By testing patients for these gene markers, doctors may be able to predict the person’s risk of developing lung cancer. The details are in a paper just published in the journal Cancer Research.
Some people seem to metabolize nicotine faster than others, though the exact cause has been previously unknown. Individuals with fast nicotine metabolism tend to smoke more cigarettes to satisfy cravings. This increased dosage is linked with an increased risk for lung cancer development.
Researchers from the University of Hawai’i Cancer Center studied the genomes of 2,239 smokers belonging to a variety of ethnic groups. They found 248 variants of CYP2A6, a gene that encodes for a cytochrome of the same name. The cytochrome CYP2A6 helps the body metabolize nicotine by oxidizing the chemical. The research team found that different CYP2A6 variants had differing levels of activity, resulting in some patients metabolizing nicotine faster than other individuals. The team also found that increased CYP2A6 activity was correlated with a higher risk of lung cancer. The researchers speculate that the increased risk is due to individuals smoking more cigarettes to make up for fast nicotine metabolism.
The team was able to identify genetic markers that indicate the rate of nicotine metabolism. Increased rates of nicotine metabolism, due to extra CYP2A6 activity, also increase the risk of the patient developing lung cancer. The exact cause is unknown but it’s believed that patients with higher nicotine metabolism will smoke more cigarettes, leading to extra exposure to cancer-causing chemicals. The research team hopes that their research will help doctors predict which patients are most at risk for nicotine addiction and lung cancer.
M. Patel et al. Novel Association of Genetic Markers Affecting CYP2A6 activity and Lung Cancer Risk. Cancer Research (2016).