Comparison of Methods Used to Correct Self-Reported Protein Intake for Systematic Variation in Reported Energy Intake Using Quantitative Biomarkers of Dietary Intake.


Journal

The Journal of nutrition
ISSN: 1541-6100
Titre abrégé: J Nutr
Pays: United States
ID NLM: 0404243

Informations de publication

Date de publication:
01 05 2020
Historique:
received: 21 08 2019
revised: 30 09 2019
accepted: 08 01 2020
pubmed: 8 2 2020
medline: 15 9 2020
entrez: 8 2 2020
Statut: ppublish

Résumé

Multiple methods of correcting nutrient intake for misreported energy intake have been proposed but have not been extensively compared. The availability of the Women's Health Initiative (WHI) data set, which includes several objective recovery biomarkers, offers an opportunity to compare these corrections with respect to protein intake. We compared 5 energy-correction methods for self-reported dietary protein against urinary nitrogen-derived protein intake. As part of the WHI Nutritional Biomarkers Study (NBS) 544 participants (50- to 80-y-old women) completed a FFQ and biomarker assessments using doubly labeled water (DLW) for total energy expenditure (TEE) and 24-h urinary nitrogen. Correction methods evaluated were as follows: 1) DLW-TEE; 2) the Institute of Medicine's (IOM's) estimated energy requirement (EER) TEE prediction equation based on sex, height, weight, and age; 3) published NBS total energy TEE prediction (WHI-NBS-TEE) using age, BMI, race, and income; 4) reported protein versus reported energy linear regression-based residual method; and 5) a Goldberg cutoff to exclude subjects reporting energy intakes <1.35 times their basal metabolic rate. Efficacy was evaluated using correlations obtained by regressing corrected protein against biomarker protein (6.25 × urinary nitrogen/0.81). Unadjusted self-reported protein intake from the FFQ (mean = 66.7 g) correlated weakly (r = 0.31) with biomarker protein (mean = 74.9 g). DLW-TEE-corrected self-reported protein intake (mean = 90.7 g) had the strongest correlation with biomarker protein (r = 0.47). Other energy corrections yielded lower, but still significant correlations: EER, r = 0.44 (mean = 92.1 g); WHI-NBS-TEE, r = 0.37 (mean = 90.4 g); Goldberg cutoff, r = 0.36 (mean = 88.4 g); and residual method, r = 0.35 (mean = 66.7 g). Our data indicate that proportional correction of reported protein intake using a measure of energy requirement from DLW-TEE or IOM-EER performed modestly better than other methods in this cohort. These energy adjustments, however, yielded corrected protein exceeding the biomarker protein, indicating that energy adjustment alone does not eliminate all self-reported protein reporting bias.

Sections du résumé

BACKGROUND
Multiple methods of correcting nutrient intake for misreported energy intake have been proposed but have not been extensively compared. The availability of the Women's Health Initiative (WHI) data set, which includes several objective recovery biomarkers, offers an opportunity to compare these corrections with respect to protein intake.
OBJECTIVE
We compared 5 energy-correction methods for self-reported dietary protein against urinary nitrogen-derived protein intake.
METHODS
As part of the WHI Nutritional Biomarkers Study (NBS) 544 participants (50- to 80-y-old women) completed a FFQ and biomarker assessments using doubly labeled water (DLW) for total energy expenditure (TEE) and 24-h urinary nitrogen. Correction methods evaluated were as follows: 1) DLW-TEE; 2) the Institute of Medicine's (IOM's) estimated energy requirement (EER) TEE prediction equation based on sex, height, weight, and age; 3) published NBS total energy TEE prediction (WHI-NBS-TEE) using age, BMI, race, and income; 4) reported protein versus reported energy linear regression-based residual method; and 5) a Goldberg cutoff to exclude subjects reporting energy intakes <1.35 times their basal metabolic rate. Efficacy was evaluated using correlations obtained by regressing corrected protein against biomarker protein (6.25 × urinary nitrogen/0.81).
RESULTS
Unadjusted self-reported protein intake from the FFQ (mean = 66.7 g) correlated weakly (r = 0.31) with biomarker protein (mean = 74.9 g). DLW-TEE-corrected self-reported protein intake (mean = 90.7 g) had the strongest correlation with biomarker protein (r = 0.47). Other energy corrections yielded lower, but still significant correlations: EER, r = 0.44 (mean = 92.1 g); WHI-NBS-TEE, r = 0.37 (mean = 90.4 g); Goldberg cutoff, r = 0.36 (mean = 88.4 g); and residual method, r = 0.35 (mean = 66.7 g).
CONCLUSIONS
Our data indicate that proportional correction of reported protein intake using a measure of energy requirement from DLW-TEE or IOM-EER performed modestly better than other methods in this cohort. These energy adjustments, however, yielded corrected protein exceeding the biomarker protein, indicating that energy adjustment alone does not eliminate all self-reported protein reporting bias.

Identifiants

pubmed: 32030414
pii: S0022-3166(22)02137-X
doi: 10.1093/jn/nxaa007
pmc: PMC7198304
doi:

Substances chimiques

Biomarkers 0
Dietary Proteins 0
Oxygen Isotopes 0
Deuterium AR09D82C7G
Nitrogen N762921K75

Types de publication

Comparative Study Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

1330-1336

Subventions

Organisme : NHLBI NIH HHS
ID : HHSN268201600001C
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201600003C
Pays : United States
Organisme : NCRR NIH HHS
ID : S10 RR020915
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201600002C
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA119171
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201600004C
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201600018C
Pays : United States
Organisme : NIDDK NIH HHS
ID : T32 DK007665
Pays : United States

Informations de copyright

Copyright © The Author(s) 2020.

Références

Curr Atheroscler Rep. 2013 Sep;15(9):353
pubmed: 23881548
Public Health Nutr. 2014 May;17(5):1054-60
pubmed: 23701939
Am J Epidemiol. 2015 Feb 15;181(4):225-33
pubmed: 25656533
Eur J Clin Nutr. 1997 Jun;51(6):405-13
pubmed: 9192200
Am J Epidemiol. 2003 Jul 1;158(1):14-21; discussion 22-6
pubmed: 12835281
Eur J Clin Nutr. 2013 Aug;67(8):863-7
pubmed: 23486508
Annu Rev Nutr. 2015;35:565-94
pubmed: 26048703
Int J Obes (Lond). 2007 Jun;31(6):956-61
pubmed: 17299385
Hum Genet. 2009 Jun;125(5-6):507-25
pubmed: 19357868
Am J Epidemiol. 1990 Dec;132(6):1185-95
pubmed: 2135637
J Nutr. 2004 Jul;134(7):1836-43
pubmed: 15226478
Am J Clin Nutr. 1997 Apr;65(4 Suppl):1220S-1228S; discussion 1229S-1231S
pubmed: 9094926
Ann Epidemiol. 2003 Oct;13(9 Suppl):S87-97
pubmed: 14575941
Am J Physiol Endocrinol Metab. 2001 Nov;281(5):E891-9
pubmed: 11595643
Am J Epidemiol. 1986 Jul;124(1):17-27
pubmed: 3521261
Eur J Clin Nutr. 1991 Dec;45(12):569-81
pubmed: 1810719
Eur J Clin Nutr. 2003 Jan;57(1):138-42
pubmed: 12548308
Am J Epidemiol. 2015 Feb 15;181(4):234-6
pubmed: 25656531
Br J Nutr. 2014 Jun 14;111(11):2032-43
pubmed: 24635904
Ann Epidemiol. 1999 Apr;9(3):178-87
pubmed: 10192650
Biometrics. 2012 Jun;68(2):397-407
pubmed: 22004367
Clin Sci (Lond). 1983 Jun;64(6):629-35
pubmed: 6601560
Am J Clin Nutr. 1983 Jun;37(6):986-95
pubmed: 6846242
Am J Clin Nutr. 2000 Jan;71(1):130-4
pubmed: 10617957
JAMA. 2006 Feb 8;295(6):629-42
pubmed: 16467232
Public Health Nutr. 2005 Oct;8(7A):1202-12
pubmed: 16277830
Am J Epidemiol. 2003 Jul 1;158(1):1-13
pubmed: 12835280
Am J Clin Nutr. 2004 May;79(5):795-804
pubmed: 15113717
Mayo Clin Proc. 2015 Jul;90(7):911-26
pubmed: 26071068
Public Health Nutr. 1999 Dec;2(4):587-91
pubmed: 10656479
Am J Epidemiol. 2008 May 15;167(10):1247-59
pubmed: 18344516
Am J Epidemiol. 2014 Jul 15;180(2):172-88
pubmed: 24918187
Am J Clin Nutr. 1994 Jan;59(1 Suppl):227S-231S
pubmed: 8279431
Nutr Res Rev. 2004 Jun;17(1):5-22
pubmed: 19079912
J Appl Physiol (1985). 2002 Mar;92(3):1036-44
pubmed: 11842037

Auteurs

Amy L Korth (AL)

Department of Nutritional Sciences, University of Wisconsin, Madison, WI, USA.
School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.

Surabhi Bhutani (S)

Department of Nutritional Sciences, University of Wisconsin, Madison, WI, USA.
School of Exercise and Nutritional Sciences, San Diego State University, San Diego, CA, USA.

Marian L Neuhouser (ML)

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Shirley A Beresford (SA)

Department of Epidemiology, University of Washington, Seattle, WA, USA.

Linda Snetselaar (L)

Department of Epidemiology, University of Iowa, Iowa City, IA, USA.

Lesley F Tinker (LF)

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Dale A Schoeller (DA)

Department of Nutritional Sciences, University of Wisconsin, Madison, WI, USA.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH