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Biomarkers in nutritional epidemiology

Published online by Cambridge University Press:  22 December 2006

Sheila A Bingham
Sheila A Bingham*
Affiliation:
Medical Research Council, Dunn Human Nutrition Unit, Welcome Trust/MRC Building, Hills Road, Cambridge CB2 2XY, UK
*
*Corresponding author: Email [email protected]
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Abstract

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Objective:

To illustrate biomarkers of diet that can be used to validate estimates of dietary intake in the study of gene–environment interactions in complex diseases.

Design:

Prospective cohort studies, studies of biomarkers where diet is carefully controlled.

Setting:

Free–living individuals, volunteers in metabolic suites.

Subjects:

Male and female human volunteers.

Results:

Recent studies using biomarkers have demonstrated substantial differences in the extent of measurement error from those derived by comparison with other methods of dietary assessment. The interaction between nutritional and genetic factors has so far largely gone uninvestigated, but can be studied in epidemiological trials that include collections of biological material. Large sample sizes are required to study interactions, and these are made larger in the presence of measurement errors.

Conclusions:

Diet is of key importance in affecting the risk of most chronic diseases in man. Nutritional epidemiology provides the only direct approach to the quantification of risks. The introduction of biomarkers to calibrate the measurement error in dietary reports, and as additional measures of exposure, is a significant development in the effort to improve estimates of the magnitude of the contribution of diet in affecting individual disease risk within populations. The extent of measurement error has important implications for correction for regression dilution and for sample size. The collection of biological samples to improve and validate estimates of exposure, enhance the pursuit of scientific hypotheses, and enable gene–nutrient interactions to be studied, should become the routine in nutritional epidemiology.

Keywords

DietCancerBiomarkersEpidemiology
Type
Keynote Address
Information
Public Health Nutrition , Volume 5 , Issue 6a , December 2002 , pp. 821 - 827
Copyright
Copyright © CAB International 2002

References

1World Cancer Research Fund, Potter, J, ed. Food, Nutrition and the Prevention of Cancer: A Global Perspective. Washington, DC: American Institute for Cancer Research, 1997.Google Scholar
2Committee on Medical Aspects of Food and Nutrition Policy. Nutritional Aspects of the Development of Cancer. Report of the Working Group on Diet and Cancer of the Committee on Medical Aspects of Food and Nutrition Policy. Report on Health and Social Subjects No. 48. London: Stationery Office [for the Department of Health], 1998.Google Scholar
3D'Errico, A, Taioli, E, Chen, X, Vineis, P. Genetic metabolic polymorphisms and the risk of cancer: a review of the literature. Biomarkers 1996; 1: 149–73.CrossRefGoogle Scholar
4Cotton, SC, Sharp, L, Little, J, Brockton, N. Glutathione S-transferase polymorphisms and colorectal cancer: a HuGE review. Am. J. Epidemiol. 2000; 151: 7–32.CrossRefGoogle ScholarPubMed
5Harty, LC, Caporaso, NE, Hayes, RB, Winn, DM, Bravo-Otero, E, Blot, WJ, et al. Alcohol dehydrogenase 3 genotype and risk of oral cavity and pharyngeal cancers. J. Natl. Cancer Inst. 1997; 89: 1698–705.CrossRefGoogle ScholarPubMed
6Hein, DW, Doll, MA, Fretland, AJ, Leff, MA, Webb, SJ, Xiao, GH, et al. Molecular genetics and epidemiology of the NAT1 and NAT2 acetylation polymorphisms. Cancer Epidemiol. Biomark. Prev. 2000; 9: 29–42.Google ScholarPubMed
7Loktionov, A, Scollen, S, McKeown, N, Bingham, SA. Gene– nutrient interactions: dietary behaviour associated with high coronary heart disease risk particularly affects serum LDL cholesterol in apolipoprotein E1 4-carrying free-living individuals. Br. J. Nutr. 2000; 84: 885–90.CrossRefGoogle Scholar
8Slattery, ML, Potter, JD, Samowitz, W, Schaffer, D, Leppert, M. Methylenetetrahydrofolate reductase, diet, and risk of colon cancer. Cancer Epidemiol. Biomark. Prev. 1999; 8: 513–8.Google ScholarPubMed
9Palli, D, Vineis, P, Russo, A, Berrino, F, Krogh, V, Masala, G, et al. Diet, metabolic polymorphisms and DNA adducts: the EPIC–Italy cross-sectional study. Int. J. Cancer 2000; 87: 444–51.3.0.CO;2-#>CrossRefGoogle ScholarPubMed
10Garcia-Closas, M, Rothman, N, Lubin, J. Misclassification in case–control studies of gene–environment interactions: assessment of bias and sample size. Cancer Epidemiol. Biomark. Prev. 1999; 8: 1043–50.Google ScholarPubMed
11Riboli, E, Kaaks, R. The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition. Int. J. Epidemiol. 1997; 26(Suppl. 1): S6–S14.CrossRefGoogle ScholarPubMed
12Bingham, SA. The dietary assessment of individuals: methods, accuracy, new techniques and recommendations. Nutr. Abstr. Rev. 1987; 57: 705–42.Google Scholar
13Cameron, ME, van Staveren, WA, eds. Manual on Methodology for Food Consumption Studies. Oxford Medical Publications. Oxford: Oxford University Press, 1988.Google Scholar
14Willett, W. Nutritional Epidemiology. Monographs in Epidemiology and Biostatistics, 2nd ed. New York: Oxford University Press, 1988.Google Scholar
15Margetts, BM, Nelson, M. Design Concepts in Nutritional Epidemiology, Oxford Medical Publications, 2nd ed. Oxford: Oxford University Press, 1997.CrossRefGoogle Scholar
16Kipnis, 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
17Beaton, GH, Burema, J, Ritenbaugh, C. Errors in the interpretation of dietary assessments. Am. J. Clin. Nutr. 1997; 65: 1100S–7S.CrossRefGoogle ScholarPubMed
18Qian, GS, Ross, RK, Yu, MC, Yuan, JM, Gao, YT, Henderson, BE, et al. A follow-up study of urinary markers of aflatoxin exposure and liver cancer risk in Shanghai, People's Republic of China. Cancer Epidemiol. Biomark. Prev. 1994; 3: 3–10.Google ScholarPubMed
19Plakke, T, Berkel, J, Beynen, AC, Hermus, RJJ, Katan, MB. Relationship between the fatty acid composition of the diet and that of the subcutaneous adipose tissue in individual human subjects. Hum. Nutr. Appl. Nutr. 1983; 37: 365–72.Google ScholarPubMed
20Wolk, A, Vessby, B, Ljung, H, Barrefors, P. Evaluation of a biological marker of dairy fat intake. Am. J. Clin. Nutr. 1998; 68: 291–5.CrossRefGoogle ScholarPubMed
21Smedman, AEM, Gustafsson, IB, Berglund, LGTM, Vessby, BOH. Pentadecanoic acid in serum as a marker for intake of milk fat: relations between intake of milk fat and metabolic risk factors. Am. J. Clin. Nutr. 1999; 69: 22–9.CrossRefGoogle ScholarPubMed
22Arab, L, Akbar, J. Biomarkers and the measurement of fatty acids. Public Health Nutr 2002; 5: 865–71.CrossRefGoogle ScholarPubMed
23Prentice, AM, Coward, WA, Davies, HL, Murgatroyd, PR, Black, AE, Goldberg, GR, et al. Unexpectedly low levels of energy expenditure in healthy women. Lancet 1985; 1: 1419–22.CrossRefGoogle ScholarPubMed
24Davies, PSW, Coward, WA, Gregory, J, White, A, Mills, A. Total energy expenditure and energy intake in the pre-school child: a comparison. Br. J. Nutr. 1994; 72: 13–20.CrossRefGoogle ScholarPubMed
25Prentice, AM, Black, AE, Coward, WA, Davies, HL, Goldberg, GR, Murgatroyd, PR, et al. High levels of energy expenditure in obese women. Br. Med. J. 1986; 292: 983–7.CrossRefGoogle ScholarPubMed
26Livingstone, MBE, Prentice, AM, Strain, JJ, Coward, WA, Black, AE, Barker, ME, et al. Accuracy of weighed dietary records in studies of diet and health. Br. Med. J. 1990; 300: 708–12.CrossRefGoogle ScholarPubMed
27Schoeller, DA. How accurate is self-reported dietary energy intake? Nutr. Rev. 1990; 48: 373–9.CrossRefGoogle ScholarPubMed
28Black, AE, Goldberg, GR, Jebb, SA, Livingstone, MBE, Cole, TJ, Prentice, AM. Critical evaluation of energy intake data using fundamental principles of energy physiology: 2. Evaluating the results of published surveys. Eur. J. Clin. Nutr. 1991; 45: 583–99.Google ScholarPubMed
29Schofield, WN, Schofield, C, James, WPT. Basal metabolic rate. Hum. Nutr. Clin. Nutr. 1985; 39C(Suppl.): 1–96.Google Scholar
30Black, AE, Coward, WA, Cole, TJ, Prentice, AM. Human energy expenditure in affluent societies: an analysis of 574 doublylabelled water measurements. Eur. J. Clin. Nutr. 1996; 50: 72–92.Google ScholarPubMed
31Goldberg, GR, Black, AE, Jebb, SA, Cole, TJ, Murgatroyd, PR, Coward, WA, et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur. J. Clin. Nutr. 1991; 45: 569–81.Google ScholarPubMed
32Denis, W, Borgstrom, P. A study of the effect of temperature on protein intake. J. Biol. Chem. 1924; 61: 109–16.CrossRefGoogle Scholar
33Isaksson, B. Urinary nitrogen output as a validity test in dietary surveys. Am. J. Clin. Nutr. 1980; 33: 4–5.CrossRefGoogle ScholarPubMed
34Van Staveren, WA, de Boer, JO, Burema, J. Validity and reproducibility of a dietary history method estimating the usual food intake during one month. Am. J. Clin. Nutr. 1985; 42: 554–9.CrossRefGoogle ScholarPubMed
35Bingham, 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
36Kipnis, V, Midthune, D, Freedman, LS, Bingham, SA, Schatzkin, A, Subar, A, et al. Empirical evidence of correlated biases in dietary assessment instruments and its implications. Am. J. Epidemiol. 2001; 153: 394–403.CrossRefGoogle ScholarPubMed
37Bingham, SA, Williams, R, Cole, TJ, Price, CP, Cummings, JH. Reference values for analytes of 24-h urine collections known to be complete. Ann. Clin. Biochem. 1988; 25: 610–9.CrossRefGoogle ScholarPubMed
38Black, AE, Bingham, SA, Johansson, G, Coward, WA. Validation of dietary intakes of protein and energy against 24 hour urinary N and DLW energy expenditure in middle-aged women, retired men and post-obese subjects: comparisons with validation against presumed energy requirements. Eur. J. Clin. Nutr. 1997; 51: 405–13.CrossRefGoogle ScholarPubMed
39Bingham, SA, Cassidy, A, Cole, TJ, Welch, A, Runswick, SA, Black, AE, et al. Validation of weighed records and other methods of dietary assessment using the 24h urine nitrogen technique and other biological markers. Br. J. Nutr. 1995; 73: 531–50.CrossRefGoogle Scholar
40Bandini, LG, Cyr, H, Must, A, Dietz, WH. Validity of reported energy intake in preadolescent girls. Am. J. Clin. Nutr. 1997; 65: 1138S–41S.CrossRefGoogle ScholarPubMed
41Steen, B, Isaksson, B, Svanborg, A. Intake of energy and nutrients and meal habits in 70-year-old males and females in Gothenburg, Sweden. A population study. Acta Med. Scand. 1977; 611: 39–86.CrossRefGoogle ScholarPubMed
42Warnold, I, Carlgren, G, Krotkiewski, M. Energy expenditure and body composition during weight reduction in hyperplastic obese women. Am. J. Clin. Nutr. 1978; 31: 750–63.CrossRefGoogle ScholarPubMed
43Heitmann, BL, Lissner, L. Dietary underreporting by obese individuals – is it specific or non-specific?. Br. Med. J. 1995; 311: 986–9.CrossRefGoogle ScholarPubMed
44Visser, M, de Groot, LCPGM, Deurenberg, P, van Staveren, WA. Validation of dietary history method in a group of elderly women using measurements of total energy expenditure. Br. J. Nutr. 1995; 74: 775–85.Google Scholar
45Kroke, A, Klipstein-Grobusch, K, Voss, S, Möseneder, J, Thielecke, F, Noack, R, et al. Validation of a self-administered food-frequency questionnaire administered in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study: comparison of energy, protein, and macronutrient intakes estimated with the doubly labeled water, urinary nitrogen, and repeated 24-h dietary recall methods. Am. J. Clin. Nutr. 1999; 70: 439–47.CrossRefGoogle Scholar
46Black, AE. The logistics of dietary surveys. Hum. Nutr. Appl. Nutr. 1982; 36: 85–94.Google ScholarPubMed
47Holbrook, JT, Patterson, KY, Bodner, JE, Douglas, LW, Veillon, C, Kelsay, JL, et al. Sodium and potassium intake and balance in adults consuming self-selected diets. Am. J. Clin. Nutr. 1984; 40: 786–93.CrossRefGoogle ScholarPubMed
48Bingham, SA, Goldberg, GR, Coward, WA, Prentice, AM, Cummings, JH. The effect of exercise and improved physical fitness on basal metabolic rate. Br. J. Nutr. 1989; 61: 155–73.CrossRefGoogle ScholarPubMed
49Barlow, RJ, Connell, MA, Milne, FJ. A study of 48-hour faecal and urinary electrolyte excretion in normotensive black and white South African males. J. Hypertens. 1986; 4: 197–200.CrossRefGoogle ScholarPubMed
50Bingham, S. Validation of dietary assessment through biomarkers. In: Kok, FJ, van't Veer, P, eds. Biomarkers of Dietary Exposure, Proceedings of the 3rd Meeting on Nutritional Epidemiology. London: Smith-Gordon, 1991.Google Scholar
51Bingham, SA, Gill, C, Welch, A, Cassidy, A, Runswick, SA, Oakes, S, et al. Validation of dietary assessment methods in the UK arm of EPIC using weighed records, and 24-hour urinary nitrogen and potassium and serum vitamin C and carotenoids as biomarkers. Int. J. Epidemiol. 1997; 26(Suppl. 1): S137–51.CrossRefGoogle ScholarPubMed
52Kaaks, R, Riboli, E. Validation and calibration of dietary intake measurements in the EPIC project: methodological considerations. European Prospective Investigation into Cancer and Nutrition. Int. J. Epidemiol. 1997; 26(Suppl. 1): S15–25.CrossRefGoogle ScholarPubMed
53Slimani, N, Deharveng, G, Charrondiére, RU, van Kappel, AL, Ocké, MC, Welch, A, et al. Structure of the standardized computerized 24-h diet recall interview used as reference method in the 22 centers participating in the EPIC project. European Prospective Investigation into Cancer and Nutrition. Comput. Methods Programs Biomed. 1999; 58: 251–66.CrossRefGoogle ScholarPubMed
54McKeown, NM, Welch, AM, Runswick, SA, Luben, R, Mulligan, A, McTaggart, A, et al. The use of biological markers to validate self reported dietary intake in a random sample of the European Prospective Investigation into Cancer (EPIC) UK Norfolk Cohort. Am. J. Clin. Nutr. 2001; 74: 188–96.CrossRefGoogle Scholar
55Day, NE, McKeown, N, Wong, MY, Welch, A, Bingham, S. Epidemiological assessment of diet: a comparison of a 7 day diary with a food frequency questionnaire. Int. J. Epidemiol. 2001; 30: 309–17.CrossRefGoogle Scholar