Regression calibration in nutritional epidemiology: Example of fat density and total energy in relationship to postmenopausal breast cancer

Ross L. Prentice, Mary Pettinger, Lesley F. Tinker, Ying Huang, Cynthia A. Thomson, Karen C. Johnson, Jeannette Beasley, Garnet Anderson, James M. Shikany, Rowan T. Chlebowski, Marian L. Neuhouser

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Regression calibration using biomarkers provides an attractive approach to strengthening nutritional epidemiology. We consider this approach to assessing the relationship of fat and total energy consumption with postmenopausal breast cancer. In analyses that included fat density data, biomarker-calibrated total energy was positively associated with postmenopausal breast cancer incidence in cohorts of the US Women's Health Initiative from 1994-2010. The estimated hazard ratio for a 20% increment in calibrated food frequency questionnaire (FFQ) energy was 1.22 (95% confidence interval (CI): 1.15, 1.30). This association was not evident without biomarker calibration, and it ceased to be apparent following control for body mass index (weight (kg)/height (m)2), suggesting that the association is mediated by body fat deposition over time. The hazard ratio for a corresponding 40% increment in FFQ fat density was 1.05 (95% CI: 1.00, 1.09). A stronger fat density association, with a hazard ratio of 1.19 (95% CI: 1.00, 1.41), emerged from analyses that used 4-day food records for dietary assessment. FFQ-based analyses were also carried out by using a second dietary assessment in place of the biomarker for calibration. This type of calibration did not correct for systematic bias in energy assessment, but may be able to accommodate the "noise" component of dietary measurement error. Implications for epidemiologic applications more generally are described.

Original languageEnglish (US)
Pages (from-to)1663-1672
Number of pages10
JournalAmerican Journal of Epidemiology
Volume178
Issue number11
DOIs
StatePublished - Dec 1 2013

Fingerprint

Calibration
Epidemiology
Biomarkers
Fats
Breast Neoplasms
Food
Confidence Intervals
Diet Records
Women's Health
Noise
Adipose Tissue
Body Mass Index
Weights and Measures
Incidence
Surveys and Questionnaires

Keywords

  • Bias
  • Biological markers
  • Breast cancer
  • Dietary assessment
  • Dietary energy
  • Dietary fat
  • Postmenopausal women

ASJC Scopus subject areas

  • Epidemiology

Cite this

Prentice, R. L., Pettinger, M., Tinker, L. F., Huang, Y., Thomson, C. A., Johnson, K. C., ... Neuhouser, M. L. (2013). Regression calibration in nutritional epidemiology: Example of fat density and total energy in relationship to postmenopausal breast cancer. American Journal of Epidemiology, 178(11), 1663-1672. https://doi.org/10.1093/aje/kwt198

Regression calibration in nutritional epidemiology : Example of fat density and total energy in relationship to postmenopausal breast cancer. / Prentice, Ross L.; Pettinger, Mary; Tinker, Lesley F.; Huang, Ying; Thomson, Cynthia A.; Johnson, Karen C.; Beasley, Jeannette; Anderson, Garnet; Shikany, James M.; Chlebowski, Rowan T.; Neuhouser, Marian L.

In: American Journal of Epidemiology, Vol. 178, No. 11, 01.12.2013, p. 1663-1672.

Research output: Contribution to journalArticle

Prentice, RL, Pettinger, M, Tinker, LF, Huang, Y, Thomson, CA, Johnson, KC, Beasley, J, Anderson, G, Shikany, JM, Chlebowski, RT & Neuhouser, ML 2013, 'Regression calibration in nutritional epidemiology: Example of fat density and total energy in relationship to postmenopausal breast cancer', American Journal of Epidemiology, vol. 178, no. 11, pp. 1663-1672. https://doi.org/10.1093/aje/kwt198
Prentice, Ross L. ; Pettinger, Mary ; Tinker, Lesley F. ; Huang, Ying ; Thomson, Cynthia A. ; Johnson, Karen C. ; Beasley, Jeannette ; Anderson, Garnet ; Shikany, James M. ; Chlebowski, Rowan T. ; Neuhouser, Marian L. / Regression calibration in nutritional epidemiology : Example of fat density and total energy in relationship to postmenopausal breast cancer. In: American Journal of Epidemiology. 2013 ; Vol. 178, No. 11. pp. 1663-1672.
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