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

Published online by Cambridge University Press:  22 December 2006

Ross L Prentice*
Affiliation:
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA Department of Biostatistics, University of Washington, Seattle, WA, USA
Elizabeth Sugar
Affiliation:
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA Department of Statistics, University of Washington, Seattle, WA, USA
CY Wang
Affiliation:
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA Department of Biostatistics, University of Washington, Seattle, WA, USA
Marian Neuhouser
Affiliation:
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA
Ruth Patterson
Affiliation:
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA Department of Epidemiology, University of Washington, Seattle, WA, USA
*
*Corresponding author: Email rprentic@fhcrc.org
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Abstract

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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.

Type
Research Article
Copyright
Copyright © CAB International 2002

References

1World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR). Food, Nutrition and the Prevention of Cancer: A Global Perspective. Washington, DC: WCRF/AICR, 1997.Google Scholar
2Greenwald, P. Role of dietary fat in the causation of breast cancer: point. Cancer Epidemiol. Biomark. Prev. 1999; 8: 37.Google ScholarPubMed
3Hunter, DJ. Role of dietary fat in the causation of breast cancer: counterpoint. Cancer Epidemiol. Biomark. Prev. 1999; 8: 913.Google Scholar
4Shepherd, J, Cobbe, SM, Ford, I, Isles, CG, Lorimer, AR, Macfarlane, PW, et al. Prevention of coronary heart disease with pravastatin in men with hypercholesterolemia. West of Scotland Coronary Prevention Study Group. N. Engl. J. Med. 1995; 333: 1301–7.CrossRefGoogle ScholarPubMed
5Chapuy, MC, Arlot, ME, Duboeuf, F, Brun, J, Crouzet, B, Arnaud, S, et al. Vitamin D3 and calcium to prevent hip fractures in the elderly women. N. Engl. J. Med. 1992; 327: 1637–42.CrossRefGoogle ScholarPubMed
6Cummings, SR, Black, DM, Thompson, DE, Applegate, WB, Barrett-Connor, E, Musliner, TA, et al. Effect of alendronate on risk of fracture in women with low bone density but without vertebral fractures – results from the fracture intervention trial. Fracture Intervention Trial Research Group. J. Am. Med. Assoc. 1998; 280: 2077–82.CrossRefGoogle Scholar
7Food and Agriculture Organization (FAO). Food Balance Sheets, 1975–1977 Average: and Per Caput Food Supplies, 1961–65 Average, 1967 to 1977. Rome: FAO, 1980.Google Scholar
8Armstrong, B, Doll, R. Environmental factors and cancer incidence and mortality in different countries, with special reference to dietary practices. Int. J. Cancer 1975; 15: 617–31.CrossRefGoogle ScholarPubMed
9Gray, GE, Pike, MC, Henderson, BE. Breast-cancer incidence and mortality rates in different countries in relation to known risk factors and dietary practices. Br. J. Cancer 1979; 39: 17.CrossRefGoogle ScholarPubMed
10Prentice, RL, Sheppard, L. Dietary fat and cancer: consistency of the epidemiologic data, and disease prevention that may follow from a practical reduction in fat consumption. Cancer Causes Control 1990; 1: 8197.CrossRefGoogle ScholarPubMed
11Heitmann, BL, Lissner, L. Dietary underreporting by obese individuals: is it specific or non-specific?. Br. Med. J. 1995; 311: 986–9.CrossRefGoogle ScholarPubMed
12Hebert, JR, Clemow, L, Pbert, L, Ockene, IS, Ockene, JK. Social desirability bias in dietary self-report may compromise the validity of dietary intake measures. Int. J. Epidemiol. 1995; 24: 389–98.CrossRefGoogle ScholarPubMed
13Hebert, JR, Ma, Y, Clemow, L, Ockene, IS, Saperia, G, Stanek, EJ II, et al. Gender differences in social desirability and social approval bias in dietary self-report. Am. J. Epidemiol. 1997; 146: 1046–55.CrossRefGoogle ScholarPubMed
14Prentice, RL, Sheppard, L. Aggregate data studies of disease risk factors. Biometrika 1995; 82: 113–25.CrossRefGoogle Scholar
15Sheppard, L, Prentice, RL. On the reliability and precision of within- and between-population estimates of relative risk parameters. Biometrics 1995; 51: 853–63.CrossRefGoogle Scholar
16Satia, JA, Patterson, RE, Herrero, R, Jin, F, Dai, Q, King, IB, et al. Study of diet, biomarkers and cancer risk in the United States, China and Costa Rica. Int. J. Cancer 1999; 82: 2832.3.0.CO;2-X>CrossRefGoogle ScholarPubMed
17Baron, JA, Beach, M, Mandel, JS, van Stolk, RU, Haile, RW, Sandler, RS, et al. Calcium supplements for the prevention of colorectal adenomas. Calcium Polyp Prevention Study Group. N. Engl. J. Med. 1999; 340: 101–7.CrossRefGoogle ScholarPubMed
18Alberts, DS, Martínez, ME, Roe, DJ, Guillén-Rodríguez, JM, Marshall, JR, Van Leeuwen, JB, et al. The Phoenix Colon Cancer Prevention Physician's Network. Lack of effect of a high-fiber cereal supplement on the recurrence of colorectal adenomas. N. Engl. J. Med. 2000; 342: 1156–62.CrossRefGoogle ScholarPubMed
19Schatzkin, A, Lanza, E, Corle, D, Lance, P, Iber, F, Caan, B, Shike, M, et al. Lack of effect of a low-fat, high-fiber diet on the recurrence of colorectal adenomas. N. Engl. J. Med. 2000; 342: 1149–55.CrossRefGoogle ScholarPubMed
20The Women's Health Initiative Study Group. Design of the Women's Health Initiative clinical trial and observational study. Control. Clin. Trials 1998; 19: 61109.CrossRefGoogle Scholar
21Brown, PO, Hartwell, L. Genomics and human disease – variations on variation. Nat. Genet. 1998; 18: 91–3.CrossRefGoogle ScholarPubMed
22Prentice, RL. Measurement error and results from analytic epidemiology: dietary fat and breast cancer. J. Natl. Cancer Inst. 1996; 88: 1738–47.CrossRefGoogle ScholarPubMed
23Kipnis, V, Carroll, RJ, Freedman, LS, Li, L. Implications of a new dietary measurement error model for estimation of relative risk: application to four calibration studies. Am. J. Epidemiol. 1999; 150: 642–51.CrossRefGoogle ScholarPubMed
24Carroll, RJ, Ruppert, D, Stefanski, LA. Measurement Error in Nonlinear Models. London: Chapman & Hall, 1995.CrossRefGoogle Scholar
25Patterson, RE, Kristal, AR, Fels-Tinker, L, Carter, RA, Bolton, MP, Agurs-Collins, T. Measurement characteristics of the Women's Health Initiative Food Frequency Questionnaire. Ann. Epidemiol. 1999; 9: 178–97.CrossRefGoogle ScholarPubMed
26Schoeller, DA. Recent advances from application of doubly labeled water to measurement of human energy expenditure. J. Nutr. 1999; 129: 1765–8.CrossRefGoogle ScholarPubMed
27Horner, NK, Patterson, RE, Neuhouser, ML, Mape, JW, Beresford, SA, Prentice, RL. Participant characteristics associated with error in self-reported energy intake from the Women's Health Initiative food frequency questionnaire. Am. J. Clin. Nutr. 2002 [in press].CrossRefGoogle ScholarPubMed
28Bingham, SA, Cummings, JH. Urine nitrogen as an independent validatory measure of dietary intake: a study of nitrogen balance in individuals consuming their normal diet. Am. J. Clin. Nutr. 1985; 42: 1276–89.CrossRefGoogle ScholarPubMed
29Kestin, M, King, I, Yasui, Y. Combinations of plasma phospholipid fatty acids as markers of total diet fat intake. FASEB J. 1998; 12: A344Google Scholar