Literature DB >> 25234237

Use of a urinary sugars biomarker to assess measurement error in self-reported sugars intake in the nutrition and physical activity assessment study (NPAAS).

Natasha Tasevska1, Douglas Midthune2, Lesley F Tinker3, Nancy Potischman2, Johanna W Lampe3, Marian L Neuhouser3, Jeannette M Beasley4, Linda Van Horn5, Ross L Prentice3, Victor Kipnis2.   

Abstract

BACKGROUND: Measurement error in self-reported sugars intake may be obscuring the association between sugars and cancer risk in nutritional epidemiologic studies.
METHODS: We used 24-hour urinary sucrose and fructose as a predictive biomarker for total sugars, to assess measurement error in self-reported sugars intake. The Nutrition and Physical Activity Assessment Study (NPAAS) is a biomarker study within the Women's Health Initiative (WHI) Observational Study that includes 450 postmenopausal women ages 60 to 91 years. Food Frequency Questionnaires (FFQ), four-day food records (4DFR), and three 24-hour dietary recalls (24HRs) were collected along with sugars and energy dietary biomarkers.
RESULTS: Using the biomarker, we found self-reported sugars to be substantially and roughly equally misreported across the FFQ, 4DFR, and 24HR. All instruments were associated with considerable intake- and person-specific bias. Three 24HRs would provide the least attenuated risk estimate for sugars (attenuation factor, AF = 0.57), followed by FFQ (AF = 0.48) and 4DFR (AF = 0.32), in studies of energy-adjusted sugars and disease risk. In calibration models, self-reports explained little variation in true intake (5%-6% for absolute sugars and 7%-18% for sugars density). Adding participants' characteristics somewhat improved the percentage variation explained (16%-18% for absolute sugars and 29%-40% for sugars density).
CONCLUSIONS: None of the self-report instruments provided a good estimate of sugars intake, although overall 24HRs seemed to perform the best. IMPACT: Assuming the calibrated sugars biomarker is unbiased, this analysis suggests that measuring the biomarker in a subsample of the study population for calibration purposes may be necessary for obtaining unbiased risk estimates in cancer association studies. ©2014 American Association for Cancer Research.

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Year:  2014        PMID: 25234237      PMCID: PMC4257863          DOI: 10.1158/1055-9965.EPI-14-0594

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  33 in total

1.  Epidemiological assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium.

Authors:  N Day; N McKeown; M Wong; A Welch; S Bingham
Journal:  Int J Epidemiol       Date:  2001-04       Impact factor: 7.196

2.  Commentary: Dietary diaries versus food frequency questionnaires-a case of undigestible data.

Authors:  W Willett
Journal:  Int J Epidemiol       Date:  2001-04       Impact factor: 7.196

Review 3.  Biomarkers in nutritional epidemiology: applications, needs and new horizons.

Authors:  Mazda Jenab; Nadia Slimani; Magda Bictash; Pietro Ferrari; Sheila A Bingham
Journal:  Hum Genet       Date:  2009-04-09       Impact factor: 4.132

4.  Evaluation and comparison of food records, recalls, and frequencies for energy and protein assessment by using recovery biomarkers.

Authors:  Ross L Prentice; Yasmin Mossavar-Rahmani; Ying Huang; Linda Van Horn; Shirley A A Beresford; Bette Caan; Lesley Tinker; Dale Schoeller; Sheila Bingham; Charles B Eaton; Cynthia Thomson; Karen C Johnson; Judy Ockene; Gloria Sarto; Gerardo Heiss; Marian L Neuhouser
Journal:  Am J Epidemiol       Date:  2011-07-15       Impact factor: 4.897

5.  Sugars and risk of mortality in the NIH-AARP Diet and Health Study.

Authors:  Natasha Tasevska; Yikyung Park; Li Jiao; Albert Hollenbeck; Amy F Subar; Nancy Potischman
Journal:  Am J Clin Nutr       Date:  2014-02-19       Impact factor: 7.045

6.  The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes.

Authors:  Alanna J Moshfegh; Donna G Rhodes; David J Baer; Theophile Murayi; John C Clemens; William V Rumpler; David R Paul; Rhonda S Sebastian; Kevin J Kuczynski; Linda A Ingwersen; Robert C Staples; Linda E Cleveland
Journal:  Am J Clin Nutr       Date:  2008-08       Impact factor: 7.045

7.  Interrelationships of added sugars intake, socioeconomic status, and race/ethnicity in adults in the United States: National Health Interview Survey, 2005.

Authors:  Frances E Thompson; Timothy S McNeel; Emily C Dowling; Douglas Midthune; Meredith Morrissette; Christopher A Zeruto
Journal:  J Am Diet Assoc       Date:  2009-08

8.  Urinary sucrose and fructose as biomarkers of sugar consumption: comparison of normal weight and obese volunteers.

Authors:  A M C P Joosen; G G C Kuhnle; S A Runswick; S A Bingham
Journal:  Int J Obes (Lond)       Date:  2008-08-26       Impact factor: 5.095

9.  Use of the predictive sugars biomarker to evaluate self-reported total sugars intake in the Observing Protein and Energy Nutrition (OPEN) study.

Authors:  Natasa Tasevska; Douglas Midthune; Nancy Potischman; Amy F Subar; Amanda J Cross; Sheila A Bingham; Arthur Schatzkin; Victor Kipnis
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2011-01-28       Impact factor: 4.254

10.  Epidemiologic assessment of sugars consumption using biomarkers: comparisons of obese and nonobese individuals in the European prospective investigation of cancer Norfolk.

Authors:  Sheila Bingham; Robert Luben; Ailsa Welch; Natasa Tasevska; Nick Wareham; Kay Tee Khaw
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2007-08       Impact factor: 4.254

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  18 in total

1.  Addressing Current Criticism Regarding the Value of Self-Report Dietary Data.

Authors:  Amy F Subar; Laurence S Freedman; Janet A Tooze; Sharon I Kirkpatrick; Carol Boushey; Marian L Neuhouser; Frances E Thompson; Nancy Potischman; Patricia M Guenther; Valerie Tarasuk; Jill Reedy; Susan M Krebs-Smith
Journal:  J Nutr       Date:  2015-10-14       Impact factor: 4.798

Review 2.  Stable Isotope Ratios as Biomarkers of Diet for Health Research.

Authors:  Diane M O'Brien
Journal:  Annu Rev Nutr       Date:  2015-05-27       Impact factor: 11.848

3.  Identifying biomarkers of dietary patterns by using metabolomics.

Authors:  Mary C Playdon; Steven C Moore; Andriy Derkach; Jill Reedy; Amy F Subar; Joshua N Sampson; Demetrius Albanes; Fangyi Gu; Jukka Kontto; Camille Lassale; Linda M Liao; Satu Männistö; Alison M Mondul; Stephanie J Weinstein; Melinda L Irwin; Susan T Mayne; Rachael Stolzenberg-Solomon
Journal:  Am J Clin Nutr       Date:  2016-12-28       Impact factor: 7.045

4.  Consumption of Sugars, Sugary Foods, and Sugary Beverages in Relation to Adiposity-Related Cancer Risk in the Framingham Offspring Cohort (1991-2013).

Authors:  Nour Makarem; Elisa V Bandera; Yong Lin; Paul F Jacques; Richard B Hayes; Niyati Parekh
Journal:  Cancer Prev Res (Phila)       Date:  2018-04-19

5.  Tolerable upper intake level for dietary sugars.

Authors:  Dominique Turck; Torsten Bohn; Jacqueline Castenmiller; Stefaan de Henauw; Karen Ildico Hirsch-Ernst; Helle Katrine Knutsen; Alexander Maciuk; Inge Mangelsdorf; Harry J McArdle; Androniki Naska; Carmen Peláez; Kristina Pentieva; Alfonso Siani; Frank Thies; Sophia Tsabouri; Roger Adan; Pauline Emmett; Carlo Galli; Mathilde Kersting; Paula Moynihan; Luc Tappy; Laura Ciccolallo; Agnès de Sesmaisons-Lecarré; Lucia Fabiani; Zsuzsanna Horvath; Laura Martino; Irene Muñoz Guajardo; Silvia Valtueña Martínez; Marco Vinceti
Journal:  EFSA J       Date:  2022-02-28

Review 6.  New markers of dietary added sugar intake.

Authors:  Brenda Davy; Hope Jahren
Journal:  Curr Opin Clin Nutr Metab Care       Date:  2016-07       Impact factor: 4.294

7.  Biomarker-predicted sugars intake compared with self-reported measures in US Hispanics/Latinos: results from the HCHS/SOL SOLNAS study.

Authors:  J M Beasley; M Jung; N Tasevska; W W Wong; A M Siega-Riz; D Sotres-Alvarez; M D Gellman; J R Kizer; P A Shaw; J Stamler; M Stoutenberg; L Van Horn; A A Franke; J Wylie-Rosett; Y Mossavar-Rahmani
Journal:  Public Health Nutr       Date:  2016-06-24       Impact factor: 4.022

8.  Association between per capita sugar consumption and diabetes prevalence mediated by the body mass index: results of a global mediation analysis.

Authors:  Alexander Lang; Oliver Kuss; Tim Filla; Sabrina Schlesinger
Journal:  Eur J Nutr       Date:  2020-10-09       Impact factor: 5.614

9.  Associations of Biomarker-Calibrated Intake of Total Sugars With the Risk of Type 2 Diabetes and Cardiovascular Disease in the Women's Health Initiative Observational Study.

Authors:  Natasha Tasevska; Mary Pettinger; Victor Kipnis; Douglas Midthune; Lesley F Tinker; Nancy Potischman; Marian L Neuhouser; Jeannette M Beasley; Linda Van Horn; Barbara V Howard; Simin Liu; JoAnn E Manson; James M Shikany; Cynthia A Thomson; Ross L Prentice
Journal:  Am J Epidemiol       Date:  2018-10-01       Impact factor: 4.897

10.  The carbon isotope ratios of nonessential amino acids identify sugar-sweetened beverage (SSB) consumers in a 12-wk inpatient feeding study of 32 men with varying SSB and meat exposures.

Authors:  Jessica J Johnson; Pamela A Shaw; Eric J Oh; Matthew J Wooller; Sean Merriman; Hee Young Yun; Thomas Larsen; Jonathan Krakoff; Susanne B Votruba; Diane M O'Brien
Journal:  Am J Clin Nutr       Date:  2021-05-08       Impact factor: 7.045

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