Jinnie J Rhee1, Eunyoung Cho2, Walter C Willett1. 1. 1 Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA. 2. 3 Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Abstract
OBJECTIVE: Adjustment for body weight and physical activity has been suggested as an alternative to adjusting for reported energy intake in nutritional epidemiology. We examined which of these approaches would yield stronger correlations between nutrients and their biomarkers. DESIGN: A cross-sectional study in which dietary fatty acids, carotenoids and retinol were adjusted for reported energy intake and, separately, for weight and physical activity using the residual method. Correlations between adjusted nutrients and their biomarkers were examined. SETTING: USA. SUBJECTS: Cases and controls from a nested case-control study of erythrocyte fatty acids and CHD (n 442) and of plasma carotenoids and retinol and breast cancer (n 1254). RESULTS: Correlations between intakes and plasma levels of trans-fatty acids were 0·30 (energy-adjusted) and 0·16 (weight- and activity-adjusted); for erythrocyte levels, the corresponding correlations were 0·37 and 0·25. Energy-adjusted intakes of linoleic acid and α-linolenic acid were more strongly correlated with their respective biomarkers than weight- and activity-adjusted intakes, but the differences were not significant except for linoleic acid (erythrocyte). Weight- and activity-adjusted DHA intake was slightly more strongly correlated with its plasma biomarker than energy-adjusted intake (0·37 v. 0·34). Neither method made a difference for DHA (erythrocyte), carotenoids and retinol. CONCLUSIONS: The effect of energy adjustment depends on the nutrient under investigation, and adjustment for energy calculated from the same questionnaire used to estimate nutrient intakes improves the correlation of some nutrients with their biomarkers appreciably. For the nutrients examined, adjustment using weight and physical activity had at most a small effect on these correlations.
OBJECTIVE: Adjustment for body weight and physical activity has been suggested as an alternative to adjusting for reported energy intake in nutritional epidemiology. We examined which of these approaches would yield stronger correlations between nutrients and their biomarkers. DESIGN: A cross-sectional study in which dietary fatty acids, carotenoids and retinol were adjusted for reported energy intake and, separately, for weight and physical activity using the residual method. Correlations between adjusted nutrients and their biomarkers were examined. SETTING: USA. SUBJECTS: Cases and controls from a nested case-control study of erythrocyte fatty acids and CHD (n 442) and of plasma carotenoids and retinol and breast cancer (n 1254). RESULTS: Correlations between intakes and plasma levels of trans-fatty acids were 0·30 (energy-adjusted) and 0·16 (weight- and activity-adjusted); for erythrocyte levels, the corresponding correlations were 0·37 and 0·25. Energy-adjusted intakes of linoleic acid and α-linolenic acid were more strongly correlated with their respective biomarkers than weight- and activity-adjusted intakes, but the differences were not significant except for linoleic acid (erythrocyte). Weight- and activity-adjusted DHA intake was slightly more strongly correlated with its plasma biomarker than energy-adjusted intake (0·37 v. 0·34). Neither method made a difference for DHA (erythrocyte), carotenoids and retinol. CONCLUSIONS: The effect of energy adjustment depends on the nutrient under investigation, and adjustment for energy calculated from the same questionnaire used to estimate nutrient intakes improves the correlation of some nutrients with their biomarkers appreciably. For the nutrients examined, adjustment using weight and physical activity had at most a small effect on these correlations.
Authors: B E Ainsworth; W L Haskell; A S Leon; D R Jacobs; H J Montoye; J F Sallis; R S Paffenbarger Journal: Med Sci Sports Exerc Date: 1993-01 Impact factor: 5.411
Authors: Rulla M Tamimi; Susan E Hankinson; Hannia Campos; Donna Spiegelman; Shumin Zhang; Graham A Colditz; Walter C Willett; David J Hunter Journal: Am J Epidemiol Date: 2005-01-15 Impact factor: 4.897
Authors: Rupert W Jakes; Nicholas E Day; Robert Luben; Ailsa Welch; Sheila Bingham; Jo Mitchell; Susie Hennings; Kirsten Rennie; Nicholas J Wareham Journal: Int J Epidemiol Date: 2004-08-27 Impact factor: 7.196
Authors: W C Willett; L Sampson; M J Stampfer; B Rosner; C Bain; J Witschi; C H Hennekens; F E Speizer Journal: Am J Epidemiol Date: 1985-07 Impact factor: 4.897
Authors: A M Wolf; D J Hunter; G A Colditz; J E Manson; M J Stampfer; K A Corsano; B Rosner; A Kriska; W C Willett Journal: Int J Epidemiol Date: 1994-10 Impact factor: 7.196
Authors: Miranda R Jones; Maria Tellez-Plaza; Dhananjay Vaidya; Maria Grau; Kevin A Francesconi; Walter Goessler; Eliseo Guallar; Wendy S Post; Joel D Kaufman; Ana Navas-Acien Journal: Am J Epidemiol Date: 2016-10-04 Impact factor: 4.897
Authors: Miranda Jones Spratlen; Mary V Gamble; Maria Grau-Perez; Chin-Chi Kuo; Lyle G Best; Joseph Yracheta; Kevin Francesconi; Walter Goessler; Yasmin Mossavar-Rahmani; Meghan Hall; Jason G Umans; Amanda Fretts; Ana Navas-Acien Journal: Food Chem Toxicol Date: 2017-05-04 Impact factor: 6.023
Authors: Miranda J Spratlen; Maria Grau-Perez; Jason G Umans; Joseph Yracheta; Lyle G Best; Kevin Francesconi; Walter Goessler; Poojitha Balakrishnan; Shelley A Cole; Mary V Gamble; Barbara V Howard; Ana Navas-Acien Journal: Environ Int Date: 2018-10-12 Impact factor: 9.621
Authors: Rita Hamad; Akansha Batra; Deborah Karasek; Kaja Z LeWinn; Nicole R Bush; Robert L Davis; Frances A Tylavsky Journal: Am J Epidemiol Date: 2019-08-01 Impact factor: 4.897
Authors: Ann F Brown; Carla M Prado; Sunita Ghosh; Shawn M Leonard; Paul J Arciero; Katherine L Tucker; Michael J Ormsbee Journal: Clin Nutr ESPEN Date: 2019-01-25