BACKGROUND: Epidemiological studies have identified potentially modifiable risks for colorectal cancer, including alcohol intake, diet and a sedentary lifestyle. Modelling these environmental factors alongside genetic risk is critical in obtaining accurate estimates of disease risk and improving our understanding of behavioural modifications. METHODS: 14 independent single nucleotide polymorphisms identified though GWAS studies and reported on by the international consortium COGENT were used to model genetic disease risk at a population level. Six well validated environmental risks were selected for modelling together with the genetic risk factors (alcohol intake; smoking; exercise levels; BMI; fibre intake and consumption of red and processed meat). Through a simulation study using risk modelling software, we assessed the potential impact of behavioural modifications on disease risk. RESULTS: Modelling the genetic data alone leads to 24% of the population being classified as reduced risk; 60% average risk; 10% elevated risk and 6% high risk for colorectal cancer. Adding alcohol consumption to the model reduced the elevated and high risk categories to 9% and 5% respectively. The simulation study suggests that a substantial proportion of individuals could reduce their disease risk profile by altering their behaviour, including reclassification of over 62% of heavy drinkers. CONCLUSION: Modelling lifestyle factors alongside genetic risk can provide useful strategies to select individuals for screening for colorectal cancer risk. IMPACT: Quantifying the impact of moderating behaviour, particularly related to alcohol intake and obesity levels, is beneficial for informing health campaigns and tailoring prevention strategies.
BACKGROUND: Epidemiological studies have identified potentially modifiable risks for colorectal cancer, including alcohol intake, diet and a sedentary lifestyle. Modelling these environmental factors alongside genetic risk is critical in obtaining accurate estimates of disease risk and improving our understanding of behavioural modifications. METHODS: 14 independent single nucleotide polymorphisms identified though GWAS studies and reported on by the international consortium COGENT were used to model genetic disease risk at a population level. Six well validated environmental risks were selected for modelling together with the genetic risk factors (alcohol intake; smoking; exercise levels; BMI; fibre intake and consumption of red and processed meat). Through a simulation study using risk modelling software, we assessed the potential impact of behavioural modifications on disease risk. RESULTS: Modelling the genetic data alone leads to 24% of the population being classified as reduced risk; 60% average risk; 10% elevated risk and 6% high risk for colorectal cancer. Adding alcohol consumption to the model reduced the elevated and high risk categories to 9% and 5% respectively. The simulation study suggests that a substantial proportion of individuals could reduce their disease risk profile by altering their behaviour, including reclassification of over 62% of heavy drinkers. CONCLUSION: Modelling lifestyle factors alongside genetic risk can provide useful strategies to select individuals for screening for colorectal cancer risk. IMPACT: Quantifying the impact of moderating behaviour, particularly related to alcohol intake and obesity levels, is beneficial for informing health campaigns and tailoring prevention strategies.
Authors: Catherine L Saunders; Britt Kilian; Deborah J Thompson; Luke J McGeoch; Simon J Griffin; Antonis C Antoniou; Jon D Emery; Fiona M Walter; Joe Dennis; Xin Yang; Juliet A Usher-Smith Journal: Cancer Prev Res (Phila) Date: 2020-02-18
Authors: Keum Ji Jung; Daeyoun Won; Christina Jeon; Soriul Kim; Tae Il Kim; Sun Ha Jee; Terri H Beaty Journal: BMC Genet Date: 2015-05-09 Impact factor: 2.797
Authors: Jan Pander; Lieke van Huis-Tanja; Stefan Böhringer; Tahar van der Straaten; Hans Gelderblom; Cornelis Punt; Henk-Jan Guchelaar Journal: PLoS One Date: 2015-07-29 Impact factor: 3.240