» Articles » PMID: 19841649

Statistical Aspects of the Use of Biomarkers in Nutritional Epidemiology Research

Overview
Journal Stat Biosci
Date 2009 Oct 21
PMID 19841649
Citations 18
Authors
Affiliations
Soon will be listed here.
Abstract

Few strong and consistent associations have arisen from observational studies of dietary consumption in relation to chronic disease risk. Measurement error in self-reported dietary assessment may be obscuring many such associations. Attempts to correct for measurement error have mostly used a second self-report assessment in a subset of a study cohort to calibrate the self-report assessment used throughout the cohort, under the dubious assumption of uncorrelated measurement errors between the two assessments. The use, instead, of objective biomarkers of nutrient consumption to produce calibrated consumption estimates provides a promising approach to enhance study reliability. As summarized here, we have recently applied this nutrient biomarker approach to examine energy, protein, and percent of energy from protein, in relation to disease incidence in Women's Health Initiative cohorts, and find strong associations that are not evident without biomarker calibration. A major bottleneck for the broader use of a biomarker-calibration approach is the rather few nutrients for which a suitable biomarker has been developed. Some methodologic approaches to the development of additional pertinent biomarkers, including the possible use of a respiratory quotient from indirect calorimetry for macronutrient biomarker development, and the potential of human feeding studies for the evaluation of a range of urine- and blood-based potential biomarkers, will briefly be described.

Citing Articles

Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women.

Navarro S, Gowda G, Bettcher L, Pepin R, Nguyen N, Ellenberger M Metabolites. 2023; 13(4).

PMID: 37110172 PMC: 10143141. DOI: 10.3390/metabo13040514.


Enhancing Capacity for Food and Nutrient Intake Assessment in Population Sciences Research.

Neuhouser M, Prentice R, Tinker L, Lampe J Annu Rev Public Health. 2022; 44:37-54.

PMID: 36525959 PMC: 10249624. DOI: 10.1146/annurev-publhealth-071521-121621.


The Associations of Plasma Carotenoids and Vitamins With Risk of Age-Related Macular Degeneration: Results From a Matched Case-Control Study in China and Meta-Analysis.

Jiang H, Fan Y, Li J, Wang J, Kong L, Wang L Front Nutr. 2022; 9:745390.

PMID: 35223939 PMC: 8873933. DOI: 10.3389/fnut.2022.745390.


Biomarker-Based Methods and Study Designs to Calibrate Dietary Intake for Assessing Diet-Disease Associations.

Huang Y, Zheng C, Tinker L, Neuhouser M, Prentice R J Nutr. 2021; 152(3):899-906.

PMID: 34905061 PMC: 8891186. DOI: 10.1093/jn/nxab420.


Red and processed meat: more with less?.

Neuhouser M Am J Clin Nutr. 2019; 111(2):252-255.

PMID: 31740926 PMC: 9429967. DOI: 10.1093/ajcn/nqz294.


References
1.
Schoeller D . Recent advances from application of doubly labeled water to measurement of human energy expenditure. J Nutr. 1999; 129(10):1765-8. DOI: 10.1093/jn/129.10.1765. View

2.
Beresford S, Johnson K, Ritenbaugh C, Lasser N, Snetselaar L, Black H . Low-fat dietary pattern and risk of colorectal cancer: the Women's Health Initiative Randomized Controlled Dietary Modification Trial. JAMA. 2006; 295(6):643-54. DOI: 10.1001/jama.295.6.643. View

3.
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. 2003; 5(6A):969-76. DOI: 10.1079/phn2002380. View

4.
Prentice R, Thomson C, Caan B, Hubbell F, Anderson G, Beresford S . Low-fat dietary pattern and cancer incidence in the Women's Health Initiative Dietary Modification Randomized Controlled Trial. J Natl Cancer Inst. 2007; 99(20):1534-43. PMC: 2670850. DOI: 10.1093/jnci/djm159. View

5.
Prentice R, Shaw P, Bingham S, Beresford S, Caan B, Neuhouser M . Biomarker-calibrated energy and protein consumption and increased cancer risk among postmenopausal women. Am J Epidemiol. 2009; 169(8):977-89. PMC: 2732977. DOI: 10.1093/aje/kwp008. View