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Food patterns defined by cluster analysis and their utility as dietary exposure variables: a report from the Malmö Diet and Cancer Study

Published online by Cambridge University Press:  02 January 2007

Elisabet Wirfält ,
Irene Mattisson ,
Bo Gullberg  and
Göran Berglund
Elisabet Wirfält*
Affiliation:
Department of Medicine, Surgery and Orthopaedics, Lund University, SE- 20502: Malmö, Sweden
Irene Mattisson
Affiliation:
Department of Medicine, Surgery and Orthopaedics, Lund University, SE- 20502: Malmö, Sweden
Bo Gullberg
Affiliation:
Department of Community Medicine, Lund University, SE-20502: Malmö, Sweden
Göran Berglund
Affiliation:
Department of Medicine, Surgery and Orthopaedics, Lund University, SE- 20502: Malmö, Sweden
*
*Corresponding author: Email elisabet.wirfalt@smi.mas.lu.se
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Abstract

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Objective

To explore the utility of cluster analysis in defining complex dietary exposures, separately with two types of variables.

Design

A modified diet history method, combining a 7-day menu book and a 168-item questionnaire, assessed dietary habits. A standardized questionnaire collected information on sociodemographics, lifestyle and health history. Anthropometric information was obtained through direct measurements. The dietary information was collapsed into 43 generic food groups, and converted into variables indicating the per cent contribution of specific food groups to total energy intake. Food patterns were identified by the QUICK CLUSTER procedure in SPSS, in two separate analytical steps using unstandardized and standardized (Z-scores) clustering variables.

Setting

The Malmö Diet and Cancer (MDC) Study, a prospective study in the third largest city of Sweden, with baseline examinations from March 1991 to October 1996.

Subjects

A random sample of 2206 men and 3151 women from the MDC cohort (n=28098).

Results

Both variable types produced conceptually well separated clusters, confirmed with discriminant analysis. ‘Healthy’ and ‘less healthy’ food patterns were also identified with both types of variables. However, nutrient intake differences across clusters were greater, and the distribution of the number of individuals more even, with the unstandardized variables. Logistic regression indicated higher risks of past food habit change, underreporting of energy and higher body mass index (BMI) for individuals falling into ‘healthy’ food pattern clusters.

Conclusions

The utility in discriminating dietary exposures appears greater for unstandardized food group variables. Future studies on diet and cancer need to recognize the confounding factors associated with ‘healthy’ food patterns.

Keywords

Dietary patternsFood patternsDietary exposure categoriesNutrient densityStandardizationZ-scoresDiet historyEpidemiology
Type
Research Article
Information
Public Health Nutrition , Volume 3 , Issue 2 , June 2000 , pp. 159 - 173
Copyright
Copyright © CABI Publishing 2000

References

1Schatzkin, A, Dorgan, J, Swanson, C and Potischman, N.Diet and cancer: future etiologic research. Environ. Health Perspect. 1995;103 (Suppl. 8): 171–5.Google ScholarPubMed
2Kohlmeier, L, Simonsen, N, Mottus, K.Dietary modifiers of carcinogenesis. Environ. Health Perspect. 1995; 103 (Suppl. 8):177–84.Google ScholarPubMed
3Potter, JD. Food and phytochemicals, magic bullets and measurement error: a commentary. Am. J. Epidemiol. 1996; 144(11): 1026–7.CrossRefGoogle ScholarPubMed
4Byers, T, Gieseker, K.Issues in the design and interpretation of studies of fatty acids and cancer in humans. Am. J. Clin. Nutr. 1997; 66 (Suppl.): S1541–7.CrossRefGoogle ScholarPubMed
5Palmgren, J.Controlling for total energy intake in regression models for assessing macronutrient effects on disease. Eur. J. Clin. Nutr. 1993; 47 (Suppl. 2): S46–50.Google ScholarPubMed
6Randall, E, Marshall, JR, Brasure, J, Graham, S.Dietary patterns and colon cancer in western New York. Nutr. Cancer. 1992; 18: 265–76.CrossRefGoogle ScholarPubMed
7Farchi, G, Mariotti, S, Menotti, A, Seccareccia, F, Torsello, S, Fidanza, F.Diet and 20-y mortality in two rural population groups of middle-aged men in Italy. Am. J. Clin. Nutr. 1989; 50: 1095–103.CrossRefGoogle ScholarPubMed
8Huijbregts, P, Feskens, E, Kromhout, D.Dietary patterns and cardiovascular risk factors in elderly men: the Zutpen Elderly Study. Int. J. Epidemiol. 1995; 24: 313–20.CrossRefGoogle Scholar
9Slattery, ML, Boucher, KM, Caan, BJ, Potter, JD, Ma, K-N. Eating patterns and colon cancer. Am. J. Epidemiol. 1998; 148(1): 4–16.CrossRefGoogle Scholar
10Tucker, K, Dallal, G, Rush, D.Dietary patterns of elderly Boston-area residents defined by cluster analysis. J. Am. Diet. Assoc. 1992; 92: 1487–91.CrossRefGoogle ScholarPubMed
11Jacobson, H, Stanton, J.Pattern analysis in nutrition. Clin. Nutr. 1986; 5: 249–53.Google Scholar
12Aldenderfer, MS, Blashfield, RK. Cluster Analysis. Quantitative Applications in the Social Sciences. Newbury Park, CA: Sage Publications, 1984.Google Scholar
13Hulshof, K, Wedel, M, Löwik, M.Clustering of dietary variables and other lifestyle factors (Dutch Nutritional Surveillance System). J. Epidemiol. Community Health 1992; 46: 417–24.CrossRefGoogle ScholarPubMed
14Patterson, RE, Haines, PS, Popkin, BM. Health lifestyle patterns of US adults. Prev. Med. 1994; 23: 453–60.CrossRefGoogle Scholar
15Tucker, K, Hannan, MT, Chen, H, et al. Diet pattern groups relate to bone mineral density (BMD) among elders in the Framingham heart study. Eur. J. Clin. Nutr. 1998; 52 (Suppl. 2), S78 (abstract).Google Scholar
16Wirfält, AKE, Jeffery, RW, Elmer, PJ. Using cluster analysis to examine dietary patterns: nutrient intakes, gender, and weight status differ across food pattern clusters. J. Am. Diet. Assoc. 1997; 97: 272–9.CrossRefGoogle ScholarPubMed
17Haveman-Nies, A, de Groot, LCPGM, van Staveren, WA. Snack patterns of older Europeans. J. Am. Diet. Assoc. 1998; 98: 1297–302.CrossRefGoogle ScholarPubMed
18Berglund, G, Elmståhl, S, Janzon, L, Larsson, SA. The Malmö Diet and Cancer Study: design and feasibility. J. Intern. Med. 1993; 233: 45–51.CrossRefGoogle Scholar
19Callmer, E, Riboli, E, Saracci, R, Åkesson, B, Lindgärde, F.Dietary assessment methods evaluated in the Malmö food study. J. Intern. Med. 1993; 233: 53–7.CrossRefGoogle ScholarPubMed
20Elmståhl, S, Gullberg, B, Riboli, E, Saracci, R, Lindgärde, F.The Malmö Food Study: the reproducibility of a novel diet history method and an extensive food frequency questionnaire. Eur. J. Clin. Nutr. 1996; 50: 134–42.Google Scholar
21Elmståhl, S, Riboli, E, Lindgärde, F, Gullberg, B, Saracci, R.The Malmö Food Study: the relative validity of a modified diet history method and an extensive food frequency questionnaire for measuring food intake. Eur. J. Clin. Nutr. 1996; 50: 143–51.Google Scholar
22Riboli, E, Elmståhl, S, Saracci, R, Gullberg, B, Lindgärde, F.The Malmö Food Study: validity of two dietary assessment methods for measuring nutrient intake. Int. J. Epidemiol. 1997; 26 (Suppl. 1): S161–73.CrossRefGoogle ScholarPubMed
23Willett, W. Nutritional Epidemiology, 2nd edn., New York: Oxford University Press, 1998.CrossRefGoogle Scholar
24Goldberg, GR, Black, AE, Jebb, SA. 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
25Black, 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
26FAO/WHO/UNU. Energy and Protein Requirements Report of a Joint FAO/WHO/UNU Expert Consultation. Technical Report Series No. 724. Geneva: World Health Organization, 1985.Google Scholar
27Norusis, MJ. SPSS for Windows Professional Statistics Release 6.0. Chicago: SPSS Inc., 1993.Google Scholar
28Willett, WC, Howe, GR, Kushi, LH. Adjustment for total energy intake in epidemiologic studies. Am. J. Clin. Nutr. 1997; 65 (Suppl.): S1220–8.CrossRefGoogle ScholarPubMed
29Kim, J-O, Mueller, CW. Factor Analysis Statistical Methods and Practical Issues. Newbury Park, CA: Sage Publications, 1978.Google Scholar
30Coombs, CH. A Theory of Data. New York: John Wiley & Sons, 1964.Google Scholar
31Herbert, JR, Stoddard, AM, Harris, DR. Measuring the effect of a worksite-based nutrition intervention on food consumption. Ann. Epidemiol. 1993; 3: 629–35.CrossRefGoogle Scholar
32Herbert, JR, Kabat, GC. Implications for cancer epidemiology of differences in dietary intake associated with alcohol consumption. Nutr. Cancer 1991; 15: 107–19.CrossRefGoogle Scholar
33Krebs-Smith, SM, Cronin, FJ, Haytowit, DB, Cook, DA. Contributions of food groups to intakes of energy, nutrients, cholesterol, and fiber in women's diets: effect of method of classifying food mixtures. J. Am. Diet. Assoc. 1990; 90: 1541–6.CrossRefGoogle ScholarPubMed
34Serdula, M, Byers, T, Coates, R, Mokdad, A, Simoes, E, Eldridge, L.Assessing consumption of high-fat foods: the effect of grouping foods into single questions. Epidemiology. 1992; 3(6): 503–8.CrossRefGoogle ScholarPubMed
35Becker, W. Food Habits and Nutrient Intake in Sweden 1989. Uppsala, Sweden: The National Food Administration, 1994.Google Scholar
36Johansson, L, Solvoll, K, Björneboe, G-EA, Drevon, CA. Under- and overreporting of energy intake related to weight status and lifestyle in a nationwide sample. Am. J. Clin. Nutr. 1998; 68: 266–74.CrossRefGoogle ScholarPubMed
37Braam, LAJL, Ocké, MC, Bueno-de-Mesquita, HB, Seidell, JC. Determinants of obesity-related underreporting of energy intake. Am. J. Clin. Nutr. 1998; 147(11), 1081–6.Google ScholarPubMed
38Lafay, L, Basdevant, A, Charles, M-A, et al. Determinants and nature of delivery underreporting in a free-living population: the Fleurbaix Laventie Ville Santé (FLVS) study. Int. J. Obes. 1997; 21: 567–73.CrossRefGoogle Scholar
39Stallone, DD, Brunner, EJ, Bingham, S.Dietary assessment in Whitehall II: the influence of reporting bias on apparent socioeconomic variation in nutrient intakes. Eur. J. Clin. Nutr. 1997; 51: 1–11.CrossRefGoogle ScholarPubMed
40Pryer, JA, Vrijheid, M, Nichols, R, Kiggins, M, Elliott, P.Who are the ‘low energy reporters’ in the Dietary and Nutritional Survey of British Adults?. Int. J. Epidemiol. 1997; 26(1): 146–54.CrossRefGoogle ScholarPubMed
41Klesges, RC, Eck, LH, Ray, JW. Who underreports dietary intake in a dietary recall? Evidence from the second National Health and Nutrition Examination Survey. J. Consult. Clin. Psychol. 1995; 63(3): 438–44.CrossRefGoogle Scholar
42Poppitt, SD, Swann, D, Black, AE, Prentice, AM. Assessment of selective under-reporting of food intake by both obese and non-obese women in a metabolic facility. Int. J. Obes. 1998; 22: 203–11.CrossRefGoogle Scholar
43Lilienthal Heitmann, B, Lissner, L.Dietary underreporting by obese individuals – is it specific or non-specific?. BMJ 1995; 311: 986–9.CrossRefGoogle Scholar
44Price, GM, Paul, AA, Cole, TJ, Wadsworth, MEJ. Characteristics of the low-energy reporters in a longitudinal national dietary survey. Br. J. Nutr. 1997; 77: 833–51.CrossRefGoogle Scholar
45Hirvonen, T, Männistö, S, Roos, E, Pietinen, P.Increasing prevalence of underreporting does not necessarily distort dietary surveys. Eur. J. Clin. Nutr. 1997; 51: 297–301.CrossRefGoogle Scholar
46Krebs-Smith, SM, Heimdinger, J, Subar, AF, Patterson, BH, Pivonka, E.Using food frequency questionnaires to estimate fruit and vegetable intake: association between the number of questions and total intake. J. Nutr. Educ. 1995; 27: 80–5.CrossRefGoogle Scholar
47Boutron, MC, Faivre, J, Milan, C, Lorcerie, B, Esteve, J.A comparison of two diet history questionnaires that measure usual food intake. Nutr. Cancer. 1989; 12: 83–91.CrossRefGoogle ScholarPubMed
48Wolk, A, Bergström, R, Adami, H-O, et al. Self-administered food frequency questionnaire: the effect of different designs on food and nutrient intake estimates. Int. J. Epidemiol. 1994; 23(3): 570–6.CrossRefGoogle ScholarPubMed
49Kuskowska-Wolk, A, Holte, S, Ohlander, E-M, et al. Effects of different designs and extension of a food frequency questionnaire on response rate, completeness of data and food frequency responses. Int. J. Epidemiol. 1992; 21(6): 1144–50.CrossRefGoogle ScholarPubMed
50Körtzinger, I, Bierwag, A, Mats, M, Müller, MJ. Dietary underreporting: validity of dietary measurements of energy intake using a 7-day dietary record and a diet history in non-obese subjects. Nutr. Metab. 1997; 41: 37–44.CrossRefGoogle Scholar
51Wirfält, AKE, Jeffery, RW, Elmer, PJ. Comparison of food frequency questionnaires: the reduced Block and Willett questionnaires differ in ranking on nutrient intakes. Am. J. Epidemiol. 1998; 148(12): 1148–56.CrossRefGoogle Scholar
52Margetts, BM, Nelson, M. Design Concepts in Nutritional Epidemiology, 2nd edn.Oxford: Oxford University Press, 1998.Google Scholar