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Stratified Probabilistic Bias Analysis for Body Mass Index-related Exposure Misclassification in Postmenopausal Women.

ABSTRACT: BACKGROUND:There is widespread concern about the use of body mass index (BMI) to define obesity status in postmenopausal women because it may not accurately represent an individual's true obesity status. The objective of the present study is to examine and adjust for exposure misclassification bias from using an indirect measure of obesity (BMI) compared with a direct measure of obesity (percent body fat). METHODS:We used data from postmenopausal non-Hispanic black and non-Hispanic white women in the Women's Health Initiative (n=126,459). Within the Women's Health Initiative, a sample of 11,018 women were invited to participate in a sub-study involving dual-energy x-ray absorptiometry scans. We examined indices of validity comparing BMI-defined obesity (?30?kg/m), with obesity defined by percent body fat. We then used probabilistic bias analysis models stratified by age and race to explore the effect of exposure misclassification on the obesity-mortality relationship. RESULTS:Validation analyses highlight that using a BMI cutpoint of 30?kg/m to define obesity in postmenopausal women is associated with poor validity. There were notable differences in sensitivity by age and race. Results from the stratified bias analysis demonstrated that failing to adjust for exposure misclassification bias results in attenuated estimates of the obesity-mortality relationship. For example, in non-Hispanic white women 50-59 years of age, the conventional risk difference was 0.017 (95% confidence interval = 0.01, 0.023) and the bias-adjusted risk difference was 0.035 (95% simulation interval = 0.028, 0.043). CONCLUSIONS:These results demonstrate the importance of using quantitative bias analysis techniques to account for nondifferential exposure misclassification of BMI-defined obesity. See video abstract at, http://links.lww.com/EDE/B385.

SUBMITTER: Banack HR

PROVIDER: S-EPMC6481627 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Stratified Probabilistic Bias Analysis for Body Mass Index-related Exposure Misclassification in Postmenopausal Women.

Banack Hailey R HR Stokes Andrew A Fox Matthew P MP Hovey Kathleen M KM Cespedes Feliciano Elizabeth M EM LeBlanc Erin S ES Bird Chloe C Caan Bette J BJ Kroenke Candyce H CH Allison Matthew A MA Going Scott B SB Snetselaar Linda L Cheng Ting-Yuan David TD Chlebowski Rowan T RT Stefanick Marcia L ML LaMonte Michael J MJ Wactawski-Wende Jean J

Epidemiology (Cambridge, Mass.) 20180901 5

<h4>Background</h4>There is widespread concern about the use of body mass index (BMI) to define obesity status in postmenopausal women because it may not accurately represent an individual's true obesity status. The objective of the present study is to examine and adjust for exposure misclassification bias from using an indirect measure of obesity (BMI) compared with a direct measure of obesity (percent body fat).<h4>Methods</h4>We used data from postmenopausal non-Hispanic black and non-Hispani ...[more]

PMID: 29864084

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Project description:Background:Elevated body mass index (BMI) is associated with increased risk of postmenopausal breast cancer. The underlying mechanisms, however, remain elusive. Methods:In a nested case-control study of 621 postmenopausal breast cancer case participants and 621 matched control participants, we measured 617 metabolites in prediagnostic serum. We calculated partial Pearson correlations between metabolites and BMI, and then evaluated BMI-associated metabolites (Bonferroni-corrected ? level for 617 statistical tests = P < 8.10?×?10-5) in relation to invasive breast cancer. Odds ratios (ORs) of breast cancer comparing the 90th vs 10th percentile (modeled on a continuous basis) were estimated using conditional logistic regression while controlling for breast cancer risk factors, including BMI. Metabolites with the lowest P values (false discovery rate < 0.2) were mutually adjusted for one another to determine those independently associated with breast cancer risk. Results:Of 67 BMI-associated metabolites, two were independently associated with invasive breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR?=?1.65, 95% confidence interval [CI] = 1.22 to 2.22) and 3-methylglutarylcarnitine (OR?=?1.67, 95% CI?=?1.21 to 2.30). Four metabolites were independently associated with estrogen receptor-positive (ER+) breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR?=?1.84, 95% CI?=?1.27 to 2.67), 3-methylglutarylcarnitine (OR?=?1.91, 95% CI?=?1.23 to 2.96), allo-isoleucine (OR?=?1.76, 95% CI?=?1.23 to 2.51), and 2-methylbutyrylcarnitine (OR?=?1.89, 95% CI?=?1.22 to 2.91). In a model without metabolites, each 5 kg/m2 increase in BMI was associated with a 14% higher risk of breast cancer (OR?=?1.14, 95% CI?=?1.01 to 1.28), but adding 16a-hydroxy-DHEA-3-sulfate and 3-methylglutarylcarnitine weakened this association (OR?=?1.06, 95% CI?=?0.93 to 1.20), with the logOR attenuating by 57.6% (95% CI?=?21.8% to 100.0+%). Conclusion:These four metabolites may signal metabolic pathways that contribute to breast carcinogenesis and that underlie the association of BMI with increased postmenopausal breast cancer risk. These findings warrant further replication efforts.

Dataset Information

Stratified Probabilistic Bias Analysis for Body Mass Index-related Exposure Misclassification in Postmenopausal Women.

Publications

Stratified Probabilistic Bias Analysis for Body Mass Index-related Exposure Misclassification in Postmenopausal Women.

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