Use of a urinary sugars biomarker to assess measurement error in self-reported sugars intake in the Nutrition and Physical Activity Assessment Study (NPAAS)

Cancer Epidemiology Biomarkers and Prevention

Volumn 23, Issue 12, 2014, Pages 2874-2883

  Tasevska, Natasha ^a,b   Midthune, Douglas ^a   Tinker, Lesley F ^c   Potischman, Nancy ^a   Lampe, Johanna W ^c   Neuhouser, Marian L ^c   Beasley, Jeannette M ^d   Van Horn, Linda ^e   Prentice, Ross L ^c   Kipnis, Victor ^a  

^a NATIONAL CANCER INSTITUTE   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

^c USA   (United States)

^d ALBERT EINSTEIN COLLEGE OF MEDICINE   (United States)

^e NORTHWESTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; FRUCTOSE; SUCROSE; CARBOHYDRATE;

ADULT; AGED; ARTICLE; CALORIC INTAKE; CLINICAL ASSESSMENT TOOL; DIETARY INTAKE; ENERGY CONSUMPTION; ENERGY EXPENDITURE; FEMALE; FOOD FREQUENCY QUESTIONNAIRE; GLUCOSE URINE LEVEL; HUMAN; MAJOR CLINICAL STUDY; MEASUREMENT ERROR; NUTRITION AND PHYSICAL ACTIVITY ASSESSMENT STUDY; NUTRITIONAL ASSESSMENT; OBSERVATIONAL STUDY; POSTMENOPAUSE; RISK FACTOR; SELF REPORT; URINALYSIS; URINARY EXCRETION; URINE VOLUME; VERY ELDERLY; DIET; MIDDLE AGED; MOTOR ACTIVITY; NUTRITIONAL STATUS; PHYSIOLOGY; PROSPECTIVE STUDY; QUESTIONNAIRE; URINE;

AGED; CARBOHYDRATES; DIET SURVEYS; ENERGY INTAKE; FEMALE; HUMANS; MIDDLE AGED; MOTOR ACTIVITY; NUTRITIONAL STATUS; PROSPECTIVE STUDIES; SELF REPORT; SURVEYS AND QUESTIONNAIRES;

EID: 84919360080     PISSN: 10559965     EISSN: 15387755     Source Type: Journal    
DOI: 10.1158/1055-9965.EPI-14-0594     Document Type: Article

References (35)

1. WCRF/AICR. Food, nutrition, physical activity and the prevention of cancer: a global perspective. Washington, DC: AICR; 2007.
2. Tasevska N, Jiao L, Cross A.J., Kipnis V., Subar AF, Hollenbeck A, et al. Sugars in diet and risk of cancer in the NIH-AARP Diet and Health Study. Int J Cancer 2012; 130: 159-69.
3. Tasevska N, Park Y, Jiao L., Hollenbeck A, Subar AF, Potischman N. Sugars and risk of mortality in the NIH-AARP Diet and Health Study. Am J Clin Nutr 2014; 99: 1077-88.
4. Kipnis V, Freedman LS, Brown C.C., Hartman AM, Schatzkin A, Wacholder S. Effect of measurement error on energy-adjustment models in nutritional epidemiology. Am J Epidemiol 1997; 146: 842-55.
5. Schatzkin A, Kipnis V. Could exposure assessment problems give us wrong answers to nutrition and cancer questions? J Natl Cancer Inst 2004; 96: 1564-5.
6. Kristal AR, Peters U, Potter JD Is it time to abandon the food frequency questionnaire? Cancer Epidemiol Biomarkers Prev 2005; 14: 2826-8.
7. Schatzkin A, Kipnis V, Carroll R.J., Midthune D., Subar AF, Bingham S, et al. A comparison of a food frequency questionnaire with a 24-hour recall for use in an epidemiological cohort study: results from the biomarker-based Observing Protein and Energy Nutrition (OPEN) study. Int J Epidemiol 2003; 32: 1054-62.
8. Day N, McKeown N, Wong M., Welch A, Bingham S. Epidemiological assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. Int J Epidemiol 2001; 30: 309-17.
9. Neuhouser ML, Tinker L, Shaw P.A., Schoeller D., Bingham SA, Horn LV, et al. Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women's Health Initiative. Am J Epidemiol 2008; 167: 1247-59.
10. Prentice RL, Mossavar-Rahmani Y, Huang Y., Van Horn L, Beresford SA, Caan B, et al Evaluation and comparison of food records, recalls, and frequencies for energy and protein assessment by using recovery biomarkers. Am J Epidemiol 2011; 174: 591-603.
11. Jenab M, Slimani N, Bictash M., Ferrari P, Bingham SA. Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet 2009; 125: 507-25.
12. Kaaks R, Ferrari P. Dietary intake assessments in epidemiology: can we know what we are measuring? Ann Epidemiol 2006; 16: 377-80.
13. Prentice RL, Tinker LF, Huang Y, Neuhouser ML Calibration of self-reported dietary measures using biomarkers: an approach to enhancing nutritional epidemiology reliability. Curr Atheroscler Rep 2013; 15:353.
14. Kaaks R, Ferrari P, Ciampi A., Plummer M, Riboli E. Uses and limitations of statistical accounting for random error correlations, in the validation of dietary questionnaire assessments. Public Health Nutr 2002; 5: 969-76.
15. Freedman LS, Midthune D, Carroll R.J., Tasevska N., Schatzkin A, Mares J, et al Using regression calibration equations that combine self-reported intake and biomarker measures to obtain unbiased estimates and more powerful tests of dietary associations. Am J Epidemiol 2011; 174: 1238-45.
16. Tasevska N, Midthune D, Potischman N., Subar AF, Cross AJ, Bingham SA, et al Use of the predictive sugars biomarker to evaluate self-reported total sugars intake in the Observing Protein and Energy Nutrition (OPEN) study. Cancer Epidemiol Biomarkers Prev 2011; 20: 490-500.
17. Tasevska N, Runswick SA, McTaggart A, Bingham SA. Urinary sucrose and fructose as biomarkers for sugar consumption. Cancer Epidemiol Biomarkers Prev 2005; 14: 1287-94.
18. Design of the Women's Health Initiative clinical trial and observational study. The Women's Health Initiative Study Group. Control Clin Trials 1998; 19: 61-109.
19. Langer RD, White E, Lewis C.E., Kotchen JM, Hendrix SL, Trevisan M. The Women's Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol 2003; 13:S107-21.
20. Johnson-Kozlow M., Rock CL, Gilpin E.A., Hollenbach KA, Pierce JP. Validation of the WHI brief physical activity questionnaire among women diagnosed with breast cancer. Am J Health Behav 2007; 31: 193-202.
21. Patterson RE, Kristal AR, Tinker L.F., Carter RA, Bolton MP, Agurs-Collins T. Measurement characteristics of the Women's Health Initiative food frequency questionnaire. Ann Epidemiol 1999; 9: 178-87.
22. Moshfegh AJ, Rhodes DG, Baer D.J., Murayi T., Clemens JC, Rumpler WV, et al. The US Department of Agriculture automated multiple-pass method reduces bias in the collection of energy intakes. Am J Clin Nutr 2008; 88: 324-32.
23. Bingham S, Cummings JH The use of 4-aminobenzoic acid as a marker to validate the completeness of 24 h urine collections in man. Clin Sci (Lond) 1983; 64: 629-35.
24. Schoeller DA, Hnilicka JM Reliability of the doubly labeled water method for the measurement of total daily energy expenditure in free-living subjects. J Nutr 1996; 126:348S-54S.
25. Schoeller DA. Recent advances from application of doubly labeled water to measurement of human energy expenditure. J Nutr 1999; 129: 1765-8.
26. Willett W. Commentary: Dietary diaries versus food frequency questionnaires-a case of undigestible data. Int J Epidemiol 2001; 30: 317-9.
27. Mossavar-Rahmani Y., Tinker LF, Huang Y, Neuhouser M.L., McCann SE, Seguin RA, et al. Factors relating to eating style, social desirability, body image and eating meals at home increase the precision of calibration equations correcting self-report measures of diet using recovery biomarkers: findings from the Women's Health Initiative. Nutr J 2013; 12:63.
28. Neuhouser ML, Di C, Tinker L.F., Thomson C., Sternfeld B, Mossavar-Rahmani Y, et al. Physical activity assessment: biomarkers and self-report of activity-related energy expenditure in the WHI. Am J Epidemiol 2013; 177: 576-85.
29. Wacholder S. When measurement errors correlate with truth: surprising effects of nondifferential misclassification. Epidemiology 1995; 6: 157-61.
30. Thiebaut AC, Freedman LS, Carroll R.J., Kipnis V. Is it necessary to correct for measurement error in nutritional epidemiology? Ann Intern Med 2007; 146: 65-7.
31. Ervin RB, Ogden CL Consumption of added sugars among U.S. adults, 2005-2010. Hyattsville, MD: National Center for Health Statistics; 2013.
32. Thompson FE, McNeel TS, Dowling E.C., Midthune D., Morrissette M, Zeruto CA. Interrelationships of added sugars intake, socioeconomic status, and race/ethnicity in adults in the United States: National Health Interview Survey, 2005. J Am Diet Assoc 2009; 109: 1376-83.
33. Bingham S, Luben R, Welch A., Tasevska N, Wareham N, Khaw KT Epidemiologic assessment of sugars consumption using biomarkers: comparisons of obese and nonobese individuals in the European prospective investigation of cancer Norfolk. Cancer Epidemiol Bio-markers Prev 2007; 16: 1651-4.
34. Joosen AM, Kuhnle GG, Runswick S.A., Bingham SA. Urinary sucrose and fructose as biomarkers of sugar consumption: comparison of normal weight and obese volunteers. Int J Obes (Lond) 2008; 32: 1736-40.
35. Song X, Navarro SL, Diep P, Thomas W.K., Razmpoosh EC, Schwarz Y, et al. Comparisonandvalidationof2analyticalmethodsformeasurement of urinary sucrose and fructose excretion. Nutr Res 2013; 33: 696-703.