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JTEMB-25512; No. of Pages 1

Journal of Trace Elements in Medicine and Biology xxx (2014) xxx–xxx

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LETTER TO THE EDITOR Copper and public health: Dietary intakes vs. clinical data Nakatsuka et al. [1] measured mineral elements in food samples representing daily food intakes of nearly 300 children in northeastern Japan to evaluate the accuracy of intakes calculated from food composition tables. Significant differences were detected for all nine minerals suggesting that care should be taken when calculating mineral intakes. Calculated intakes of copper, iron, magnesium and sodium were too high by approximately 20%. Their data on copper help to unearth an important problem in the use of dietary histories and food tables in the estimation of intakes. To date, nine other publications with comparisons similar to those of the authors have been found (for references see [2]). Nine of the ten showed that calculated copper was greater than measured copper (probability by sign test = 0.0098) [3]. Thus there is a systematic, or determinate, error in the estimation of copper intakes by calculation giving results that are falsely high. The authors help to extend this concept to non-Western diets and confirm the findings of Chiplonkar et al. [4]. Together, the average, calculated excess copper for Asian diets is 11%, and is less than the average for eight Western diets, 77%. The source of the copper error(s) must be identified because systematic overestimation of copper intakes of individuals and populations provides a false sense of security. In contrast is the collection [2] of more than 60 medical publications on 2500 people with impaired copper status based on the criteria of the Oxford Textbook of Medicine [5] of low copper concentrations or low activities of enzymes depending on copper. Whether or not the errors in the other minerals evaluated by the authors are important in evaluating public health is unknown. Correction of copper intake data for the 77% error in large surveys of the United States [6] reveals that roughly half the people do not meet the recommendations; even with uncorrected data, nearly 25% of people do not meet the higher standards for Europe,

Australia and New Zealand [2]. It is important to improve intake data so it can be harmonized with clinical data revealing poor copper status to define better the degree to which poor copper nutriture affects public health adversely. Conflict of interest None declared. References [1] Nakatsuka H, Shimbo S, Watanabe T, Yaginuma-Sakurai K, Ikeda M. Applicability of food composition tables as a tool to estimate mineral and trace element intake of pre-school children in Japan: a validation study. J Trace Elem Med Biol 2013;27:339–45. [2] Klevay LM. Is the Western diet adequate in copper. J Trace Elem Med Biol 2011;25:204–12. [3] Handbook of tables for probability and statistics. Cleveland, Ohio: The Chemical Rubber Co.; 1966. p. 42. [4] Chiplonkar SA, Agte VV. Extent of error in estimating nutrient intakes from food tables versus laboratory estimates of cooked foods. Asia Pac J Clin Nutr 2007;16:227–39. [5] Golden MHN. Severe malnutrition. In: Weatherall DJ, Ledingham JG, Warrell DA, editors. Oxford textbook of medicine. 3rd ed. Oxford: Oxford University Press; 1996. p. 1278–96. [6] Anon. Dietary reference intakes for vitamin A, vitamin K, arsenic, boron, chromium, copper, iodine, iron, manganese, molybdenum, nickel, silicon, vanadium, and zinc. Washington, DC: National Academy Press; 2001. p. 622–3, 648–9, 660–1.

Leslie M. Klevay ∗ University of North Dakota, School of Medicine and Health Sciences, 223 27th Ave. South, Grand Forks, ND 58201, United States ∗ Tel.:

+1 701 772 6960; fax: +1 701 777 2477. E-mail address: [email protected] 5 March 2014

http://dx.doi.org/10.1016/j.jtemb.2014.03.002 0946-672X/© 2014 Elsevier GmbH. All rights reserved.

Please cite this article in press as: Klevay LM. Copper and public health: Dietary intakes vs. clinical data. J Trace Elem Med Biol (2014), http://dx.doi.org/10.1016/j.jtemb.2014.03.002

Copper and public health: dietary intakes vs. clinical data.

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