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Träfflista för sökning "WFRF:(Mattiello Amalia) ;srt2:(2015-2019);srt2:(2017);pers:(Quirós J. Ramón)"

Sökning: WFRF:(Mattiello Amalia) > (2015-2019) > (2017) > Quirós J. Ramón

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1.
  • Fortner, Renee T., et al. (författare)
  • Correlates of circulating ovarian cancer early detection markers and their contribution to discrimination of early detection models : results from the EPIC cohort
  • 2017
  • Ingår i: Journal of Ovarian Research. - : Springer Science and Business Media LLC. - 1757-2215. ; 10
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Ovarian cancer early detection markers CA125, CA15.3, HE4, and CA72.4 vary between healthy women, limiting their utility for screening.Methods: We evaluated cross-sectional relationships between lifestyle and reproductive factors and these markers among controls (n = 1910) from a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC). Improvements in discrimination of prediction models adjusting for correlates of the markers were evaluated among postmenopausal women in the nested case-control study (n = 590 cases). Generalized linear models were used to calculate geometric means of CA125, CA15.3, and HE4. CA72.4 above vs. below limit of detection was evaluated using logistic regression. Early detection prediction was modeled using conditional logistic regression.Results: CA125 concentrations were lower, and CA15.3 higher, in post- vs. premenopausal women (p ≤ 0.02). Among postmenopausal women, CA125 was higher among women with higher parity and older age at menopause (ptrend ≤ 0.02), but lower among women reporting oophorectomy, hysterectomy, ever use of estrogen-only hormone therapy, or current smoking (p < 0.01). CA15.3 concentrations were higher among heavier women and in former smokers (p ≤ 0.03). HE4 was higher with older age at blood collection and in current smokers, and inversely associated with OC use duration, parity, and older age at menopause (≤ 0.02). No associations were observed with CA72.4. Adjusting for correlates of the markers in prediction models did not improve the discrimination.Conclusions: This study provides insights into sources of variation in ovarian cancer early detection markers in healthy women and informs about the utility of individualizing marker cutpoints based on epidemiologic factors.
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2.
  • Fortner, Renee T., et al. (författare)
  • Endometrial cancer risk prediction including serum-based biomarkers : results from the EPIC cohort
  • 2017
  • Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 140:6, s. 1317-1323
  • Tidskriftsartikel (refereegranskat)abstract
    • Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimina-tion. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigat-ed for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selec-tion process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were select-ed into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including eti-ologic markers on independent pathways and genetic markers may further improve discrimination.
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