| 1. |
- Naudin, Sabine, et al.
(författare)
-
Lifetime and baseline alcohol intakes and risk of pancreatic cancer in the European Prospective Investigation into Cancer and Nutrition study
- 2018
-
Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 143:4, s. 801-812
-
Tidskriftsartikel (refereegranskat)abstract
- Recent evidence suggested a weak relationship between alcohol consumption and pancreatic cancer (PC) risk. In our study, the association between lifetime and baseline alcohol intakes and the risk of PC was evaluated, including the type of alcoholic beverages and potential interaction with smoking. Within the European Prospective Investigation into Cancer and Nutrition (EPIC) study, 1,283 incident PC (57% women) were diagnosed from 476,106 cancer-free participants, followed up for 14 years. Amounts of lifetime and baseline alcohol were estimated through lifestyle and dietary questionnaires, respectively. Cox proportional hazard models with age as primary time variable were used to estimate PC hazard ratios (HR) and their 95% confidence interval (CI). Alcohol intake was positively associated with PC risk in men. Associations were mainly driven by extreme alcohol levels, with HRs comparing heavy drinkers (>60 g/day) to the reference category (0.1-4.9 g/day) equal to 1.77 (95% CI: 1.06, 2.95) and 1.63 (95% CI: 1.16, 2.29) for lifetime and baseline alcohol, respectively. Baseline alcohol intakes from beer (>40 g/day) and spirits/liquors (>10 g/day) showed HRs equal to 1.58 (95% CI: 1.07, 2.34) and 1.41 (95% CI: 1.03, 1.94), respectively, compared to the reference category (0.1-2.9 g/day). In women, HR estimates did not reach statistically significance. The alcohol and PC risk association was not modified by smoking status. Findings from a large prospective study suggest that baseline and lifetime alcohol intakes were positively associated with PC risk, with more apparent risk estimates for beer and spirits/liquors than wine intake.
|
|
| 2. |
- Li, Kuanrong, et al.
(författare)
-
Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts
- 2018
-
Ingår i: Breast Cancer Research. - : BioMed Central. - 1465-5411 .- 1465-542X. ; 20
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction.METHODS: We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention.RESULTS: Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail.CONCLUSIONS: Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.
|
|
