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Sources of variation in nutrient intakes among men in Shanghai, China

Published online by Cambridge University Press:  02 January 2007

Hui Cai
Affiliation:
Department of Medicine, Center for Health Services Research and Vanderbilt–Ingram Cancer Center, 6009 MCE, Vanderbilt University, 1215 21st Avenue, Nashville, TN 37232-8300, USA
Gong Yang
Affiliation:
Department of Medicine, Center for Health Services Research and Vanderbilt–Ingram Cancer Center, 6009 MCE, Vanderbilt University, 1215 21st Avenue, Nashville, TN 37232-8300, USA
Yong-Bing Xiang
Affiliation:
Department of Epidemiology, Shanghai Cancer Institute, Shanghai, 200032, People's Republic of China
James R Hebert
Affiliation:
Department of Epidemiology and Biostatistics, Arnold School of Public Health and South Carolina Cancer Center, University of South Carolina, Columbia, SC 29203, USA
Da-Ke Liu
Affiliation:
Department of Epidemiology, Shanghai Cancer Institute, Shanghai, 200032, People's Republic of China
Wei Zheng
Affiliation:
Department of Medicine, Center for Health Services Research and Vanderbilt–Ingram Cancer Center, 6009 MCE, Vanderbilt University, 1215 21st Avenue, Nashville, TN 37232-8300, USA
Xiao-Ou Shu*
Affiliation:
Department of Medicine, Center for Health Services Research and Vanderbilt–Ingram Cancer Center, 6009 MCE, Vanderbilt University, 1215 21st Avenue, Nashville, TN 37232-8300, USA
*
*Corresponding author: Email [email protected]
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Abstract

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Background and objective

Random errors, from any source, will attenuate epidemiological risk estimates. Before we launched the Shanghai Men's Health Study (SMHS), a large population-based cohort study investigating the diet–cancer association among Chinese men, a dietary calibration study was conducted among 96 men aged 40–75 years (mean age 56.5 years), with biweekly 24-hour dietary recalls (24HDRs) implemented over a 1-year period. Data from this study were analysed to evaluate the nature and magnitude of variances for intake of 26 nutrients among SMHS participants, to compare variance ratios of 26 nutrients among Chinese men and women and individuals in other studies, and to estimate the number of 24HDRs required for future dietary calibration studies in similar populations.

Design

Ninety-six healthy, free-living men in Shanghai were administered biweekly 24HDR interviews 24 times over a 1-year period. To assess between-individual and within-individual contributions to variance, a mixed effects model was fitted and ratios of within-individual to between-individual dietary intake variances were computed.

Setting

Shanghai, China.

Results

In agreement with reports from studies conducted in the USA and many other countries, we found that within-individual variances were usually larger than between-individual variances in dietary intake for all nutrients. The sum of all other variation (e.g. weekday and weekend, seasonal, interviewer) accounted for less than 5% of total variation. Ratios of within- to between-individual variances (for log-transformed data) ranged from 1.25 for carbohydrate intake to near 8 for δ-tocopherol intake.

Conclusions

The results of this study suggest that among middle-aged and elderly Chinese men in Shanghai, within- and between-individual variation account for more than 95% of the total variation for 26 nutrients. Further dietary validation studies in the same population could be adequately carried out with only 12 days of dietary recalls, if 100 participants were enrolled.

Type
Research Article
Copyright
Copyright © The Authors 2005

References

1Shikany, JM, White, GL Jr. Dietary guidelines for chronic disease prevention. Southern Medical Journal 2000; 93: 1138–51.Google Scholar
2Willett, WC. Diet and cancer: one view at the start of the millennium. Cancer Epidemiology, Biomarkers & Prevention 2001; 10: 38.Google ScholarPubMed
3Bingham, SA, Gill, C, Welch, A, Day, K, Cassidy, A, Khaw, KT, et al. Comparison of dietary assessment methods in nutritional epidemiology; weighed records v. 24h recalls, food frequency questionnaires and estimated-diet records. British Journal of Nutrition 1994; 72: 619–43.Google Scholar
4Subar, AF, Thompson, FE, Kipnis, V. Subar et al. respond to ‘A further look at dietary questionnaire validation’ and ‘Another perspective on food frequency questionnaires’. American Journal of Epidemiology 2001; 154: 1105–6.Google Scholar
5Hebert, JR, Miller, DR. Methodologic considerations for investigating the diet–cancer link. American Journal of Clinical Nutrition 1988; 47: 1068–77.Google Scholar
6Hebert, JR, Clemow, L, Pbert, L, Ockene, IS, Ockene, JK. Social desirability bias in dietary self-report may compromise the validity of dietary intake measures. International Journal of Epidemiology 1995; 24: 389–98.Google Scholar
7Willett, W. Nutritional Epidemiology. 2nd ed. New York: Oxford University Press, 1998.Google Scholar
8Stram, DO, Hankin, JH, Wilkens, LR, Pike, MC, Monroe, KR, Park, S, et al. Calibration of the dietary questionnaire for a multiethnic cohort in Hawaii and Los Angeles. American Journal of Epidemiology 2000; 151: 358–70.Google Scholar
9Beaton, GH, Milner, J, McGuire, V, Feather, TE, Little, JA. Source of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. Carbohydrate sources, vitamins, and minerals. American Journal of Clinical Nutrition 1983; 37: 986–95.Google ScholarPubMed
10Hunt, WC, Leonard, AG, Garry, PJ, Goodwin, JS. Components of variance in dietary data for an elderly population. Nutrition Research 1983; 3: 433–44.Google Scholar
11Sempos, CT, Johnson, NE, Smith, EL, Gilligan, C. Effects of intraindividual and interindividual variation in repeated dietary records. American Journal of Epidemiology 1985; 121: 120–30.Google Scholar
12Hebert, JR, Hurley, TG, Chiraboga, DE, Barone, J. A comparison of selected nutrient intakes derived from three diet assessment methods used in a low-fat maintenance trial. Public Health Nutrition 1988; 1: 207–14.Google Scholar
13Ziegler, RG, Wilcox, HB, Mason, TJ, Bill, JS, Virgo, PW. Seasonal variation in intake of carotenoids and vegetables and fruits among white men in New Jersey. American Journal of Clinical Nutrition 1987; 45: 107–14.Google Scholar
14Liu, K. Consideration of and compensation of intra-individual variability in nutrient intakes. In: Kohlmeier, L, Helsing, E, eds. Epidemiology, Nutrition and Health. London/Niigate, Japan: Smith-Gordon/Nishimura, 1989.Google Scholar
15Tarasuk, V, Beaton, GH. The nature and individuality of within-subject variation in energy intake. American Journal of Clinical Nutrition 1991; 54: 464–70.Google Scholar
16Hebert, JR, Gupta, PC, Mehta, H, Ebbeling, CB, Bhonsle, RR, Varghese, F. Sources of variability in dietary intake in two distinct regions of rural India: implications for nutrition study design and interpretation. European Journal of Clinical Nutrition 2000; 54: 479–86.Google Scholar
17Cai, H, Shu, XO, Hebert, JR, Jin, F, Yang, G, Liu, DK, et al. Variation in nutrient intakes among women in Shanghai, China. European Journal of Clinical Nutrition 2004; 58: 1604–11.Google Scholar
18Shu, OX, Yang, G, Jin, F, Liu, DK, Kushi, L, Wen, WQ, et al. Validity and reproducibility of the food frequency questionnaire used in the Shanghai Women's Health Study. European Journal of Clinical Nutrition 2004; 58: 1723.Google Scholar
19Wang, GY, Shen, ZP, ed. Chinese Food Composition Table. Beijing: People's Health Publishing House, 1991.Google Scholar
20Liu, K, Stamler, J, Dyer, A, McKeever, J, McKeever, P. Statistical methods to assess and minimize the role of intra-individual variability in obscuring the relationship between dietary lipids and serum cholesterol. Journal of Chronic Disease 1978; 31: 399418.Google Scholar
21McGee, D, Rhoads, G, Hankin, J, Yano, K, Tillotson, J. Within-person variability of nutrient intake in a group of Hawaiian men of Japanese ancestry. American Journal of Clinical Nutrition 1982; 36: 657–63.CrossRefGoogle Scholar
22Hartman, AM, Brown, CC, Palmgrem, J, Pietinen, P, Verkasalo, M, Myer, D, et al. Variability in nutrient and food intakes among older middle-aged men. American Journal of Epidemiology 1990; 132: 9991012.CrossRefGoogle ScholarPubMed
23Nelson, M, Black, AE, Morns, JA, Cole, TJ. Between- and within- subject variation in nutrient intake from infancy to old age: estimating the number of days required to rank dietary intakes with desired precision. American Journal of Clinical Nutrition 1989; 50: 155–67.Google Scholar
24Beaton, GH, Milner, J, Corey, P, McGuire, V, Cousins, M, Stewart, E, et al. Source of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. American Journal of Clinical Nutrition 1979; 32: 2546–9.CrossRefGoogle ScholarPubMed
25Ogawa, K, Tsubono, Y, Nishino, Y, Watanabe, Y, Ohkubo, T, Watanabe, T, et al. Inter- and Intra-individual variation of food and nutrient consumption in a rural Japanese population. European Journal of Clinical Nutrition 1999; 52: 781–5.CrossRefGoogle Scholar
26McAvay, G, Rodin, J. Interindividual and intraindividual variation in repeated measures of 24-hour dietary recall in the elderly. Appetite 1998; 11: 97110.CrossRefGoogle Scholar
27Oh, SY, Hong, MH. Within- and between-person variation of nutrient intakes of older people in Korea. European Journal of Clinical Nutrition 1999; 53: 625–9.Google Scholar