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- Cramer, Daniel W., et al.
(författare)
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Anti-CA15.3 and Anti-CA125 Antibodies and ovarian cancer risk : Results from the EPIC cohort
- 2018
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Ingår i: Cancer Epidemiology Biomarkers and Prevention. - : American Association for Cancer Research. - 1055-9965 .- 1538-7755. ; 27:7, s. 790-804
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Tidskriftsartikel (refereegranskat)abstract
- Background: Neoplastic and non-neoplastic events may raise levels of mucins, CA15.3, and CA125, and generate antibodies against them, but their impact on epithelial ovarian cancer (EOC) risk has not been fully defined. Methods: CA15.3, CA125, and IgG1 antibodies against them were measured in 806 women who developed EOC and 1,927 matched controls from the European Prospective Investigation of Nutrition and Cancer. Associations between epidemiologic factors and anti-mucin antibodies were evaluated using generalized linear models; EOC risks associated with anti-mucin antibodies, by themselves or in combination with respective antigens, were evaluated using conditional logistic regression. Results: In controls, lower antibodies against both mucins were associated with current smoking; and, in postmenopausal women, higher levels with longer oral contraceptive use and later-age-at and shorter-interval-since last birth. Lower anti-CA15.3 antibodies were associated with higher body mass and, in premenopausal women, more ovulatory cycles. Higher anti-CA15.3 and anti-CA125 antibodies were associated with higher risk for mucinous EOC occurring ≥ 3 years from enrollment. Long-term risk for serous EOC was reduced in women with low CA125 and high anti-CA125 antibodies relative to women with low concentrations of both. Conclusions: We found general support for the hypothesis that anti-mucin antibody levels correlate with risk factors for EOC. Antibodies alone or in combinations with their antigen may predict longer term risk of specific EOC types. Impact: Anti-CA125 and anti-CA15.3 antibodies alone or in perspective of antigens may be informative in the pathogenesis of EOC subtypes, but less useful for informing risk for all EOC.
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| 2. |
- Freisling, Heinz, et al.
(författare)
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Nut intake and 5-year changes in body weight and obesity risk in adults: results from the EPIC-PANACEA study
- 2018
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Ingår i: European Journal of Nutrition. - : Springer Science and Business Media LLC. - 1436-6207 .- 1436-6215. ; 57:7, s. 2399-2408
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Tidskriftsartikel (refereegranskat)abstract
- © 2017 Springer-Verlag GmbH Germany Purpose: There is inconsistent evidence regarding the relationship between higher intake of nuts, being an energy-dense food, and weight gain. We investigated the relationship between nut intake and changes in weight over 5 years. Methods: This study includes 373,293 men and women, 25–70 years old, recruited between 1992 and 2000 from 10 European countries in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Habitual intake of nuts including peanuts, together defined as nut intake, was estimated from country-specific validated dietary questionnaires. Body weight was measured at recruitment and self-reported 5 years later. The association between nut intake and body weight change was estimated using multilevel mixed linear regression models with center/country as random effect and nut intake and relevant confounders as fixed effects. The relative risk (RR) of becoming overweight or obese after 5 years was investigated using multivariate Poisson regressions stratified according to baseline body mass index (BMI). Results: On average, study participants gained 2.1 kg (SD 5.0 kg) over 5 years. Compared to non-consumers, subjects in the highest quartile of nut intake had less weight gain over 5 years (−0.07 kg; 95% CI −0.12 to −0.02) (P trend = 0.025) and had 5% lower risk of becoming overweight (RR 0.95; 95% CI 0.92–0.98) or obese (RR 0.95; 95% CI 0.90–0.99) (both P trend < 0.008). Conclusions: Higher intake of nuts is associated with reduced weight gain and a lower risk of becoming overweight or obese.
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| 3. |
- Li, Kuanrong, et al.
(författare)
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Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts
- 2018
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Ingår i: Breast Cancer Research. - : BioMed Central. - 1465-5411 .- 1465-542X. ; 20
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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.
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