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Endometrial cancer risk prediction including serum-based biomarkers: Results from the EPIC cohort

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Title: Endometrial cancer risk prediction including serum-based biomarkers: Results from the EPIC cohort
Authors: Fortner, RT
Husing, A
Kuhn, T
Konar, M
Overvad, K
Tjonneland, A
Hansen, L
Boutron-Ruault, MC
Severi, G
Fournier, A
Boeing, H
Trichopoulou, A
Benetou, V
Orfanos, P
Masala, G
Agnoli, C
Mattiello, A
Tumino, R
Sacerdote, C
Bueno-de-Mesquita, B
Peeters, PHM
Weiderpass E
Gram, IT
Gavrilyuk, O
Quiros, JR
Huerta, JM
Ardanaz, E
Larranaga, N
Lujan-Barroso, L
Sanchez-Cantalejo, E
Tuna Butt, S
Borgquist, S
Idahl, A
Lundin, E
Khaw, KT
Allen, NE
Rinaldi, S
Dossus, L
Gunter, M
Merritt, MA
Tzoulaki, I
Riboli, E
Kaaks, R
Item Type: Journal Article
Abstract: Endometrial cancer risk prediction models including lifestyle, anthropometric, and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated 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 selection 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 selected 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 etiologic markers on independent pathways and genetic markers may further improve discrimination.
Issue Date: 27-Jan-2017
Date of Acceptance: 17-Nov-2016
URI: http://hdl.handle.net/10044/1/43237
DOI: https://dx.doi.org/10.1002/ijc.30560
ISSN: 1097-0215
Publisher: Wiley
Start Page: 1317
End Page: 1323
Journal / Book Title: International Journal of Cancer
Volume: 140
Issue: 6
Copyright Statement: © 2016 UICC. This is the accepted version of the following article, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/ijc.30560/abstract
Sponsor/Funder: University Medical Center Utrecht
Imperial College Trust
Funder's Grant Number: N/A
P47328
Keywords: Science & Technology
Life Sciences & Biomedicine
Oncology
endometrial cancer
risk prediction
prospective cohort
sex steroids
cytokines
adipokines
inflammatory markers
lipids
growth factors
metabolic markers
POSTMENOPAUSAL WOMEN
NUTRITION
HORMONES
MARKERS
EUROPE
Adult
Aged
Biomarkers, Tumor
Blood Glucose
Blood Proteins
Case-Control Studies
Comorbidity
Cytokines
Endometrial Neoplasms
Europe
Female
Follow-Up Studies
Hormones
Humans
Incidence
Inflammation
Lipids
Metabolic Syndrome X
Middle Aged
Risk
Risk Assessment
Single-Blind Method
Surveys and Questionnaires
Oncology & Carcinogenesis
1112 Oncology And Carcinogenesis
Publication Status: Published
Appears in Collections:Faculty of Medicine
Epidemiology, Public Health and Primary Care



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