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J Womens Health (Larchmt). 2015 Mar;24(3):218-27. doi: 10.1089/jwh.2014.4977. Epub 2015 Jan 14.

Optimal cutoffs of obesity measures in relation to cancer risk in postmenopausal women in the Women's Health Initiative Study.

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1 Department of Epidemiology and Population Health, Albert Einstein College of Medicine , Bronx, New York.



Obesity is a risk factor for several cancers in postmenopausal women. We attempted to determine cutoffs of adiposity measures in relation to risk of obesity-related cancers among postmenopausal women and to examine the effects of hormone therapy (HT) use on the cutoffs, neither of which has been broadly studied.


We used data from the Women's Health Initiative cohort (n=144,701) and applied Cox-proportional hazards regressions to each combination of 17 cancer types and 6 anthropometric measures (weight, body mass index [BMI], weight to height ratio, waist circumference, waist to hip ratio [WHR], and waist to height ratio). Interactions between the anthropometric measures and HT use were also examined. Cutoffs were determined by applying a grid search followed by a two-fold cross validation method. Survival ROC analysis of 5- and 10-year incidence followed.


Breast, colorectal, colon, endometrium, kidney, and all cancers combined were significantly positively associated with all six anthropometric measures, whereas lung cancer among ever smokers was significantly inversely associated with all measures except WHR. The derived cutoffs of each obesity measure varied across cancers (e.g., BMI cutoffs for breast and endometrium cancers were 30 kg/m(2) and 34 kg/m(2), respectively), and also depended on HT use. The Youden indices of the cutoffs for predicting 5- and 10-year cancer incidence were higher among HT never users.


Using a panel of different anthropometric measures, we derived optimal cut-offs categorizing populations into high- and low-risk groups, which differed by cancer type and HT use. Although the discrimination abilities of these risk categories were generally poor, the results of this study could serve as a starting point from which to determine adiposity cutoffs for inclusion in risk prediction models for specific cancer types.

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