OPEN about obesity: recovery biomarkers, dietary reporting errors and BMI (original) (raw)

International Journal of Obesity volume 31, pages 956–961 (2007)Cite this article

Abstract

Objective:

Obesity-related under-reporting of usual dietary intake is one of the most persistent sources of bias in nutrition research. The aim of this paper is to characterize obese and non-obese individuals with respect to reporting errors observed with two common dietary instruments, using energy and protein recovery biomarkers as reference measures.

Population and methods:

This report employs data from the Observing Protein and Energy Nutrition (OPEN) study. Analyses are based on stratified samples of 211 (57 obese) men and 179 (50 obese) women who completed 24-h recalls (24HR), food frequency questionnaires (FFQ), doubly labelled water (DLW) and urinary nitrogen (UN) assessments.

Results:

In obese and non-obese subgroups, FFQ yielded lower energy and protein intake estimates than 24HR, although biomarker-based information indicated under-reporting with both dietary instruments. Gender differences in obesity-related bias were noted. Among women, the DLW-based energy requirement was 378 kcal greater in obese than in non-obese groups; the FFQ was able to detect a statistically significant portion of this extra energy, while the 24HR was not. Among men, the DLW-based energy requirement was 485 kcal greater in the obese group; however, neither FFQ nor 24HR detected this difference in energy requirement. Combining protein and energy estimates, obese men significantly over-reported the proportion of energy from protein using the 24HR, but not with the FFQ. In obese women, no significant reporting error for energy percent protein was observed by either method. At the individual level, correlations between energy expenditure and reported energy intake tended to be weaker in obese than non-obese groups, particularly with the 24HR. Correlations between true and reported protein density were consistently higher than for protein or energy alone, and did not vary significantly with obesity.

Conclusion:

This work adds to existing evidence that neither of these commonly used dietary reporting methods adequately measures energy or protein intake in obese groups. The 24HR, while capturing more realistic energy distributions for usual intake, may be particularly problematic in the obese.

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References

  1. Hebert J, Clemow L, Pbert L, Ockene IS, Ockene JK . Social desirability bias in dietary self-report may compromise the validity of dietary intake measures. Int J Epidemiol 1995; 24: 389–398.
    Article CAS Google Scholar
  2. Roth DL, Snyder CR, Pace LM . Dimensions of favourable self-presentation. J Pers Soc Psychol 1986; 51: 867–874.
    Article Google Scholar
  3. Muhlheim LS, Allison DR, Heshka S, Heymsfield SB . Do unsuccessful dieters intentionally underreport food intake? Int J Eat Disord 1998; 24: 259–266.
    Article CAS Google Scholar
  4. Prentice AM, Black AE, Coward WA, Davies HL, Goldberg GR, Murgatroyd PR et al. High levels of energy expenditure in obese women. BMJ 1986; 292: 983–987.
    Article CAS Google Scholar
  5. Hultén B, Bengtsson C, Isaksson B . Some errors in a longitudinal dietary survey revealed by the urine nitrogen test. Eur J Clin Nutr 1990; 44: 169–174.
    PubMed Google Scholar
  6. Heitmann B, Lissner L . Dietary under-reporting by obese individuals: is it specific or nonspecific? BMJ 1995; 311: 986–989.
    Article CAS Google Scholar
  7. Kaaks R, Ferrari P, Ciampi A, Plummer M, Riboli E . Advances in the statistical evaluations and interpretation of dietary data. Uses and limitations of statistical accounting for random error correlations in the validity of dietary questionnaire assessments. Public Health Nutr 2002; 5: 969–976.
    Article Google Scholar
  8. Subar AF, Kipnis V, Troiano RP, Midthune D, Schoeller DA, Bingham S et al. Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN Study. Am J Epidemiol 20003; 158: 1–13.
    Article Google Scholar
  9. Tooze JA, Subar AF, Thompson FE, Troiano RP, Schatzkin A . Psychososcial predictors of energy underreporting in a large doubly labelled water study. Am J Clin Nutr 2004; 79: 795–804.
    Article CAS Google Scholar
  10. Kipnis V, Subar AF, Midthune D, Freedman LS, Ballard-Barbash R, Troiano RP et al. Structure of dietary measurement error: results of the OPEN Biomarker Study. Am J Epidemiol 2003; 158: 14–21.
    Article Google Scholar
  11. Raper N, Perloff B, Ingwersen L, Steinfeldt L, Anand J . An overview of USDA's dietary intake data system. J Food Compos Analysis 2004; 17: 545–555.
    Article Google Scholar
  12. Subar AF, Thompson FE, Kipnis V Midthune D, Hurwitz P, McNutt S et al. Comparitive validation of the Block, Willett, and National Cancer Institute food frequency questionnaires: the Eating at America's Table Study. Am J Epidemiol 2001; 154: 1089–1099.
    Article CAS Google Scholar
  13. Coward A, Cole T . Precision and accuracy of the doubly labelled water energy expenditure by multipoint and two-point methods. Am J Physiol 1992; 263: E965–E973.
    PubMed Google Scholar
  14. Johansson G, Bingham S, Vahter M . A method to compensate for incomplete 24 h urine collections in nutritional epidemiology studies. Public Health Nutr 1999; 2: 587–591.
    Article CAS Google Scholar
  15. Freedman LS, Midthune D, Carrol RJ, Krebs-Smith S, Subar AF, Troiano RP et al. Adjustments to improve the estimation of usual dietary intake distributions in the population. J Nutr 2004; 134: 1836–1843.
    Article CAS Google Scholar
  16. Hedley AA, Ogden CL, Jonhson CL, Carroll MD, Curtin LR Flegal KM . Prevalence of overweight and obesity among US children, adolescents and adults 1999-2002. JAMA 2004; 291: 2847–2850.
    Article CAS Google Scholar
  17. Lissner L, Heitmann B, Lindroos AK . Measuring intake in free-living humans: a question of bias. Proc Nutr Soc 1998; 57: 333–339.
    Article CAS Google Scholar
  18. Heitmann BL, Lissner L . Can adverse effets of dietary fat intake be overestimated as a consequence of dietary fat underreporting. Public Health Nutr 2005; 8: 1332–1337.
    Article Google Scholar
  19. Rosner B, Willett WC, Spiegelman D . Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Stat Med 1989; 8: 1051–1069.
    Article CAS Google Scholar
  20. Poppitt S, Swann D, Black AE, Prentice AM . Assessment of selective under-reporting of food intake by both obese and non-obese women in a metabolic facility. Int J Obes 1992; 22: 303–311.
    Article Google Scholar

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Authors and Affiliations

  1. Department of Public Health and Community Medicine, Sahlgrenska Academy at Göteborg University, Göteborg, Sweden
    L Lissner
  2. Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA
    R P Troiano, A F Subar & N Potischman
  3. Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
    D Midthune & V Kipnis
  4. Research Unit for Dietary Studies at Institute of Preventive Medicine, Copenhagen University Hospital, Copenhagen, Denmark
    B L Heitmann

Authors

  1. L Lissner
  2. R P Troiano
  3. D Midthune
  4. B L Heitmann
  5. V Kipnis
  6. A F Subar
  7. N Potischman

Corresponding author

Correspondence toL Lissner.

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Lissner, L., Troiano, R., Midthune, D. et al. OPEN about obesity: recovery biomarkers, dietary reporting errors and BMI.Int J Obes 31, 956–961 (2007). https://doi.org/10.1038/sj.ijo.0803527

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