| 11. |
- Allen, Naomi E., et al.
(author)
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Macronutrient intake and risk of urothelial cell carcinoma in the European prospective investigation into cancer and nutrition
- 2013
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In: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 132:3, s. 635-644
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Journal article (peer-reviewed)abstract
- Previous studies have suggested that dietary factors may be important in the development of bladder cancer. We examined macronutrient intake in relation to risk of urothelial cell carcinoma among 469,339 men and women in the European Prospective Investigation into Cancer and Nutrition. Associations were examined using Cox regression, stratified by sex, age at recruitment and centre and further adjusted for smoking status and duration, body mass index and total energy intake. After an average of 11.3 years of follow-up, 1,416 new cases of urothelial cell carcinoma were identified. After allowing for measurement error, a 3% increase in the consumption of energy intake from animal protein was associated with a 15% higher risk (95% confidence interval [CI]: 330%; ptrend = 0.01) and a 2% increase in energy from plant protein intake was associated with a 23% lower risk (95% CI: 367%, ptrend = 0.006). Dietary intake of fat, carbohydrate, fibre or calcium was not associated with risk. These findings suggest that animal and/or plant protein may affect the risk of urothelial cell carcinoma, and examination of these associations in other studies is needed.
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| 12. |
- Fedirko, Veronika, et al.
(author)
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Alcohol drinking and endometrial cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study
- 2013
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In: Annals of Epidemiology. - : Elsevier BV. - 1047-2797 .- 1873-2585. ; 23:2, s. 93-98
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Journal article (peer-reviewed)abstract
- Purpose: Alcohol intake may adversely affect the concentrations of endogenous sex hormones, and thus increase the risk of endometrial cancer. However, epidemiologic studies have provided conflicting results. Therefore, we investigated the association between alcohol intake and endometrial cancer risk a large, multicenter, prospective study. Methods: From 1992 through 2010, 301,051 women in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort were followed for incident endometrial cancer (n = 1382). Baseline alcohol consumption was assessed by country-specific, validated dietary questionnaires. Information on past alcohol consumption was collected by lifestyle questionnaires. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated from Cox proportional hazard models. Results: The multivariable HRs (and 95% CIs) compared with light drinkers (0.1-6 g/d) were 1.03(0.88-1.20) for 0 g of alcohol per day at baseline, 1.01 (0.86-1.17) for 6.1-12 g/d, 1.03 (0.87-1.22) for 12.1-24 g/d, 1.07(0.87-1.38) for 241-36 g/d, and 0.85(0.61-1.18) for more than 36 g/d (p(trend) = 0.77). No association was observed among former drinkers (OR, 1.28; 95% CI, 0.98-1.68 compared with light drinkers). Null associations were also found between alcohol consumption at age 20 years, lifetime pattern of alcohol drinking, and baseline alcohol intake from specific alcoholic beverages and endometrial cancer risk. Conclusions: Our findings suggest no association between alcohol intake and endometrial cancer risk.
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| 13. |
- Hoggart, Clive, et al.
(author)
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A Risk Model for Lung Cancer Incidence
- 2012
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In: Cancer Prevention Research. - Philadelphia : American Association for Cancer Research. - 1940-6207 .- 1940-6215. ; 5:6, s. 834-846
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Journal article (peer-reviewed)abstract
- Risk models for lung cancer incidence would be useful for prioritising individuals for screening and participation in clinical trials of chemoprevention. We present a risk model for lung cancer built using prospective cohort data from a general population which predicts individual incidence in a given time period.We build separate risk models for current and former smokers utilising 169,035 ever smokers from the multicentre European Prospective Investigation into Cancer and Nutrition (EPIC) and considered a model for never smokers. The data set was split into independent training and test sets. Lung cancer incidence was modelled using survival analysis, stratifying by age started smoking, and for former smokers, also smoking duration. Other risk factors considered were smoking intensity, ten occupational/environmental exposures previously implicated with lung cancer, and SNPs at two loci identified by genome-wide association studies of lung cancer. Individual risk in the test set was measured by the predicted probability of lung cancer incidence in the year preceding last follow-up time, predictive accuracy was measured by the area under the receiver operator characteristic curve (AUC).Utilising smoking information alone gave good predictive accuracy: the AUC and 95% confidence interval in ever smokers was 0.843 (0.810, 0.875), the Bach model applied to the same data gave an AUC of 0.775 (0.737, 0.813). Other risk factors had negligible effect on the AUC, including never smokers for whom prediction was poor.Our model is generalisable and straightforward to implement. Its accuracy can be attributed to its modelling of lifetime exposure to smoking.
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