Literature DB >> 12633522

Research strategies and the use of nutrient biomarkers in studies of diet and chronic disease.

Ross L Prentice1, Elizabeth Sugar, C Y Wang, Marian Neuhouser, Ruth Patterson.   

Abstract

OBJECTIVE: To provide an account of the state of diet and chronic disease research designs and methods; to discuss the role and potential of aggregate and analytical observational studies and randomised controlled intervention trials; and to propose strategies for strengthening each type of study, with particular emphasis on the use of nutrient biomarkers in cohort study settings.
DESIGN: Observations from diet and disease studies conducted over the past 25 years are used to identify the strengths and weaknesses of various study designs that have been used to associate nutrient consumption with chronic disease risk. It is argued that a varied research programme, employing multiple study designs, is needed in response to the widely different biases and constraints that attend aggregate and analytical epidemiological studies and controlled intervention trials. Study design modifications are considered that may be able to enhance the reliability of aggregate and analytical nutritional epidemiological studies. Specifically, the potential of nutrient biomarker measurements that provide an objective assessment of nutrient consumption to enhance analytical study reliability is emphasised. A statistical model for combining nutrient biomarker data with self-report nutrient consumption estimates is described, and related ongoing work on odds ratio parameter estimation is outlined briefly. Finally, a recently completed nutritional biomarker study among 102 postmenopausal women in Seattle is mentioned. The statistical model will be applied to biomarker data on energy expenditure, urinary nitrogen, selected blood fatty acid measurements and various blood micronutrient concentrations, and food frequency self-report data, to identify study subject characteristics, such as body mass, age or socio-economic status, that may be associated with the measurement properties of food frequency nutrient consumption estimates. This information will be crucial for the design of a potential larger nutrient biomarker study within the cohort study component of the Women's Health Initiative. SETTING AND
SUBJECTS: The methodology under study is expected to be pertinent to a wide variety of diet and chronic disease association studies in the general population. Ongoing work focuses on statistical methods developed using computer simulations motivated by studies of dietary fat in relation to breast and colon cancer among post-menopausal women, and ongoing pilot studies to be described in detail elsewhere, involving post-menopausal women living in the Seattle area. RESULTS AND
CONCLUSION: A varied research programme appears to be needed to make progress in the challenging diet and chronic disease research area. Such progress may include aggregate studies of diet and chronic disease that include sample surveys in diverse population groups world-wide, analytical epidemiological studies that use nutrient biomarker data to calibrate self-report nutrient consumption estimates, and randomised controlled intervention trials that arise from an enhanced infrastructure for intervention development. New innovative designs, models and methodologies are needed for each such research setting.

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Year:  2002        PMID: 12633522     DOI: 10.1079/PHN2002382

Source DB:  PubMed          Journal:  Public Health Nutr        ISSN: 1368-9800            Impact factor:   4.022


  32 in total

1.  Biomarker-calibrated dietary energy and protein intake associations with diabetes risk among postmenopausal women from the Women's Health Initiative.

Authors:  Lesley F Tinker; Gloria E Sarto; Barbara V Howard; Ying Huang; Marian L Neuhouser; Yasmin Mossavar-Rahmani; Jeannette M Beasley; Karen L Margolis; Charles B Eaton; Lawrence S Phillips; Ross L Prentice
Journal:  Am J Clin Nutr       Date:  2011-11-09       Impact factor: 7.045

2.  Measurement error models with interactions.

Authors:  Douglas Midthune; Raymond J Carroll; Laurence S Freedman; Victor Kipnis
Journal:  Biostatistics       Date:  2015-11-03       Impact factor: 5.899

3.  Hazard ratio estimation for biomarker-calibrated dietary exposures.

Authors:  Pamela A Shaw; Ross L Prentice
Journal:  Biometrics       Date:  2011-10-17       Impact factor: 2.571

Review 4.  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

5.  Connections between survey calibration estimators and semiparametric models for incomplete data.

Authors:  Thomas Lumley; Pamela A Shaw; James Y Dai
Journal:  Int Stat Rev       Date:  2011-08       Impact factor: 2.217

6.  Predictors of measurement error in energy intake during pregnancy.

Authors:  Eric Nowicki; Anna-Maria Siega-Riz; Amy Herring; Ka He; Alison Stuebe; Andy Olshan
Journal:  Am J Epidemiol       Date:  2011-01-27       Impact factor: 4.897

7.  Physical activity assessment: biomarkers and self-report of activity-related energy expenditure in the WHI.

Authors:  Marian L Neuhouser; Chongzhi Di; Lesley F Tinker; Cynthia Thomson; Barbara Sternfeld; Yasmin Mossavar-Rahmani; Marcia L Stefanick; Stacy Sims; J David Curb; Michael Lamonte; Rebecca Seguin; Karen C Johnson; Ross L Prentice
Journal:  Am J Epidemiol       Date:  2013-02-22       Impact factor: 4.897

Review 8.  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

Review 9.  Self-Report Dietary Assessment Tools Used in Canadian Research: A Scoping Review.

Authors:  Sharon I Kirkpatrick; Lana Vanderlee; Amanda Raffoul; Jackie Stapleton; Ilona Csizmadi; Beatrice A Boucher; Isabelle Massarelli; Isabelle Rondeau; Paula J Robson
Journal:  Adv Nutr       Date:  2017-03-15       Impact factor: 8.701

Review 10.  STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.

Authors:  Ruth H Keogh; Pamela A Shaw; Paul Gustafson; Raymond J Carroll; Veronika Deffner; Kevin W Dodd; Helmut Küchenhoff; Janet A Tooze; Michael P Wallace; Victor Kipnis; Laurence S Freedman
Journal:  Stat Med       Date:  2020-04-03       Impact factor: 2.373

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