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New challenges in studying nutrition-disease interactions in the developing world
Andrew M. Prentice, … , Cesar G. Victora, Jeffrey I. Gordon
Andrew M. Prentice, … , Cesar G. Victora, Jeffrey I. Gordon
Published April 1, 2008
Citation Information: J Clin Invest. 2008;118(4):1322-1329. https://doi.org/10.1172/JCI34034.
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Review Series

New challenges in studying nutrition-disease interactions in the developing world

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Abstract

Latest estimates indicate that nutritional deficiencies account for 3 million child deaths each year in less-developed countries. Targeted nutritional interventions could therefore save millions of lives. However, such interventions require careful optimization to maximize benefit and avoid harm. Progress toward designing effective life-saving interventions is currently hampered by some serious gaps in our understanding of nutrient metabolism in humans. In this Personal Perspective, we highlight some of these gaps and make some proposals as to how improved research methods and technologies can be brought to bear on the problems of undernourished children in the developing world.

Authors

Andrew M. Prentice, M. Eric Gershwin, Ulrich E. Schaible, Gerald T. Keusch, Cesar G. Victora, Jeffrey I. Gordon

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Figure 3

Influence of malnutrition on hospital case fatality rates for various diseases.

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Influence of malnutrition on hospital case fatality rates for various di...
Malnutrition is rarely identified as a specific cause of mortality, but it is an underlying contributory factor in at least one third of child deaths in developing countries. Depicted here are hospital case fatality rates from children admitted to Gambian hospitals with various primary diagnoses. The children analyzed were all malnourished, i.e., had low weight-for-height (WFH). As shown, the extent of malnutrition (WFH z score < –4 being the most severely malnourished) correlates with the chance of an adverse survival outcome in most diseases. WFH z score < –4, patients who are more than 4 standard deviations below the mean WFH; WFH z score = –3 to –4, patients who are 3–4 standard deviations below the mean WFH; WFH z score = –2 to –3, patients who are 2–3 standard deviations below the mean WFH; WFH z score > –2, patients who are less than 2 standard deviations below the mean WFH. Data collated by Man et al. (12).

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ISSN: 0021-9738 (print), 1558-8238 (online)

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