Skip to main content

The Spectrum of Malnutrition

  • Chapter
  • First Online:
Nutrition and Health in a Developing World

Part of the book series: Nutrition and Health ((NH))

Abstract

This chapter presents the indicators that are used to measure the spectrum of malnutrition. It provides information on how to use indicators of food security, dietary intake, anthropometry, and biomarkers to assess the nutritional status at the global, national, household, and individual level. It highlights new areas of research regarding nutritional assessment such as metabolomics and gut microbiota that can help determine differences in nutritional status and dietary patterns. These indicators of nutritional status have significant variation across regions of the world and are used to assess global and national prevalence of different forms of malnutrition as well as to track progress toward improving nutrition. These indicators are also used to assess the impact of nutrition interventions, whether in research studies or for public health programs. When selecting indicators for these purposes it is important to take into account what they indicate, how responsive they are likely to be to the specific intervention(s) and to note that indicators are also affected by a person’s age, sex, and non-nutritional factors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Atalah E, Amigo H, Bustos P. Does Chile’s nutritional situation constitute a double burden? Am J Clin Nutr. 2014;100(6):1623S–7S.

    Article  CAS  PubMed  Google Scholar 

  2. Cheung HC, Shen A, Oo S, Tilahun H, Cohen MJ, Berkowitz SA. Food insecurity and body mass index: a longitudinal mixed methods study, Chelsea, Massachusetts, 2009–2013. Preventing Chronic Dis. 2015;12:E125.

    Google Scholar 

  3. Dubois L, Francis D, Burnier D, Tatone-Tokuda F, Girard M, Gordon-Strachan G, et al. Household food insecurity and childhood overweight in Jamaica and Quebec: a gender-based analysis. BMC Public Health. 2011;11:199.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kac G, Velasquez-Melendez G, Schlussel MM, Segall-Correa AM, Silva AA, Perez-Escamilla R. Severe food insecurity is associated with obesity among Brazilian adolescent females. Public Health Nutr. 2012;15(10):1854–60.

    Article  PubMed  Google Scholar 

  5. Kaur J, Lamb MM, Ogden CL. The association between food insecurity and obesity in children—the National Health and Nutrition Examination Survey. J Acad Nutr Diet. 2015;115(5):751–8.

    Article  PubMed  Google Scholar 

  6. Rosas LG, Guendelman S, Harley K, Fernald LC, Neufeld L, Mejia F, et al. Factors associated with overweight and obesity among children of Mexican descent: results of a binational study. J Immigr Minor Health. 2011;13(1):169–80.

    Article  PubMed  Google Scholar 

  7. Santos LM. Obesity, poverty, and food insecurity in Brazilian males and females. Cadernos de Saude Publica. 2013;29(2):237–9.

    Article  PubMed  Google Scholar 

  8. Freire WB, Silva-Jaramillo KM, Ramirez-Luzuriaga MJ, Belmont P, Waters WF. The double burden of undernutrition and excess body weight in Ecuador. Am J Clin Nutr. 2014;100(6):1636S–43S.

    Article  CAS  PubMed  Google Scholar 

  9. Abdollahi M, Abdollahi Z, Sheikholeslam R, Kalantari N, Kavehi Z, Neyestani TR. High occurrence of food insecurity among urban Afghan refugees in Pakdasht, Iran 2008: a cross-sectional study. Ecol Food Nutr. 2015;54(3):187–99.

    Article  PubMed  Google Scholar 

  10. Motadi SA, Mbhenyane XG, Mbhatsani HV, Mabapa NS, Mamabolo RL. Prevalence of iron and zinc deficiencies among preschool children ages 3 to 5 y in Vhembe district, Limpopo province South Africa. Nutrition. 2015;31(3):452–8.

    Article  PubMed  Google Scholar 

  11. Tzioumis E, Adair LS. Childhood dual burden of under- and overnutrition in low- and middle-income countries: a critical review. Food Nutr Bull. 2014;35(2):230–43.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Becker C, Orozco M, Solomons NW, Schumann K. Iron metabolism in obesity: how interaction between homoeostatic mechanisms can interfere with their original purpose. Part I: underlying homoeostatic mechanisms of energy storage and iron metabolisms and their interaction. J Trace Elem Med Biol. 2015;30:195–201.

    Article  CAS  PubMed  Google Scholar 

  13. Cherayil BJ. Pathophysiology of iron homeostasis during inflammatory states. J Pediatr. 2015;167(4 Suppl):S15–9.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Osterholm EA, Georgieff MK. Chronic inflammation and iron metabolism. J Pediatr. 2015;166(6):1351–7.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Schmidt PJ. Regulation of iron metabolism by hepcidin under conditions of inflammation. J Biol Chem. 2015;290(31):18975–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. FAO, IFAD, WFP. The state of food insecurity in the world. Meeting the 2015 international hunger targets: taking stock of uneven progress. Rome: Food and Agriculture Organization of the United Nations; 2015.

    Google Scholar 

  17. Jones AD, Ngure FM, Pelto G, Young SL. What are we assessing when we measure food security? A compendium and review of current metrics. Adv Nutr. 2013;4:481–505.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lizumi T, Sakuma H, Yokozawa M, Luo JJ, Challinor AJ, Brown ME, et al. Prediction of seasonal climate-induced variations in global food production. Nat Clim Change. 2013;3:904–8.

    Article  Google Scholar 

  19. Arsenault JE, Hijmans RJ, Brown KH. Improving nutrition security through agriculture: an analytical framework based on national food balance sheets to estimate nutritional adequacy of food supplies. Food Secur. 2015;7:693–707.

    Article  Google Scholar 

  20. World Food Program. Vulnerability analysis and mapping. Food consumption analysis: calculation and use of the food consumption score in food security analysis. Rome: World Food Program; 2008.

    Google Scholar 

  21. Del Gobbo LC, Khatibzadeh S, Imamura F, Micha R, Peilin S, Smith M, et al. Assessing global dietary habits: a comparison of national estimates from the FAO and the Global Dietary Database. Am J Clin Nutr. 2015;101:1038–46.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. IPC Global Partners. Integrated food security phase classification technical manual, version 1.1. Rome; 2008.

    Google Scholar 

  23. Pangaribowo EH, Gerber N, Torero M. Food and nutrition security indicators: a review. Bonn: University of Bonn; 2013.

    Google Scholar 

  24. von Grebmer K, Bernstein J, Prasai N, Yin S, Yohannes Y. Global hunger index. Armed conflict and the challenge of hunger. Bonn, Washington DC, and Dublin: Welthungerhilfe, International Food Policy Research Institute, and Concern Worldwide; 2015.

    Google Scholar 

  25. Economist Intelligence Unit. Global food security index 2012: an assessment of food affordability, availability and quality. London: Economist; 2011.

    Google Scholar 

  26. Masset E. A review of hunger indices and methods to monitor country commitment to fighting hunger. Food Policy. 2011;36:S102–8.

    Article  Google Scholar 

  27. Coates J, Swindale A, Bilinsky P. Household food insecurity access scale (HFIAS) for measurement of household food access: indicator guide. Washington DC: Food and Nutrition Technical Assistance (FANTA) Project, Academy for Educational Development, Project FaNTAF; 2007.

    Google Scholar 

  28. Livingstone MBE, Black A. Markers of the validity of reported energy intake. J Nutr. 2003;133:895S–920S.

    CAS  PubMed  Google Scholar 

  29. Taren DL, Tobar M, Hill A, Howell W, Shisslak C, Bell I, et al. The association of energy intake bias with psychological scores of women. Eur J Clin Nutr. 1999;53:570–8.

    Article  CAS  PubMed  Google Scholar 

  30. Mendez MA. Invited commentary: dietary misreporting as a potential source of bias in diet-disease associations: future directions in nutritional epidemiology research. Am J Epidemiol. 2015;181(4):234–6.

    Article  PubMed  Google Scholar 

  31. Lutomski JE, Broeck J, Harrington J, Shiely F, Perry IJ. Sociodemographic, lifestyle, mental health and dietary factors associated with direction of misreporting of energy intake. Public Health Nutr. 2010;14(3):532–41.

    Article  PubMed  Google Scholar 

  32. Broyles ME, Harris R, Taren DL. Diabetics under report energy intake in NHANES III greater than non-diabetics. Open Nutr J. 2008;2:54–62.

    Article  Google Scholar 

  33. Kennedy G, Ballard T, Dop MC. Guidelines for measuring household and individual dietary diversity. Rome: Food and Agriculture Organization, Nutrition and Consumer Protection Division; 2013.

    Google Scholar 

  34. WHO. Indicators for assessing infant and young child feeding practices. Part 2: measurement. Geneva: World Health Organization; 2010.

    Google Scholar 

  35. IOM. Dietary DRI reference intakes: the essential guide to nutrient requirements. In: Otten JJ, Hellwig J, Meyers L, editors. Washington DC: National Academies Press; 2006.

    Google Scholar 

  36. Trumbo PR, Barr SI, Murphy SP, Yates AA. Dietary reference intakes: cases of appropriate and inappropriate uses. Nutr Rev. 2013;71(10):657–64.

    Article  PubMed  Google Scholar 

  37. The Obesity Expert Panel 2013. Executive summary: guidelines (2013) for the management of overweight and obesity in adults. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Obesity. 2014;22(Suppl 2):S5–39.

    Google Scholar 

  38. Mathus-Vliegen EMH. Obesity and the elderly. J Clin Gastroenterol. 2012;46(7):533–44.

    Article  PubMed  Google Scholar 

  39. Popkin BM, Slining MM. New dynamics in global obesity facing low- and middle-income countries. Obes Rev. 2013;14(Suppl 2):11–22.

    Google Scholar 

  40. Hsu W, Araneta MRG, Kanaya AM, Chiang JL, Fujimoto W. BMI cut points to identify at-risk Asian Americans for type 2 diabetes screening. Diab Care. 2015;38:150–8.

    Article  Google Scholar 

  41. Bhardwaj S, Misra A. Obesity, diabetes and the Asian phenotype. World Rev Nutr Diet. 2015;111:116–22.

    Article  PubMed  Google Scholar 

  42. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63.

    Article  Google Scholar 

  43. Cheong KC, Yusoff AF, Ghazali SM, Lim KH, Selvarajah S, Haniff J, et al. Optimal BMI cut-off values for predicting diabetes, hypertension and hypercholesterolaemia in a multi-ethnic population. Public Health Nutr. 2011;16(3):453–9.

    Article  Google Scholar 

  44. Bays H. Central obesity as a clinical marker of adiposopathy; increased visceral adiposity as a surrogate marker for global fat dysfunction. Curr Opin Endocrinol Diab Obes. 2014;21:345–51.

    Article  CAS  Google Scholar 

  45. Cameron AJ, Magliano DJ, Soderberg S. A systematic review of the impact of including both waist and hip circumference in risk models for cardiovascular diseases, diabetes and mortality. Obes Rev. 2013;14:86–94.

    Article  CAS  PubMed  Google Scholar 

  46. Wang Z, Ma J, Si D. Optimal cut-off values and population means of waist circumference in different populations. Nutr Res Rev. 2010;23:191–9.

    Article  PubMed  Google Scholar 

  47. International Diabetes Federation. International Diabetes Federation (IDF) worldwide definition of the metabolic syndrome. Brussels: Belgium; 2006.

    Google Scholar 

  48. Lear SA, James PT, Ko GT, Kumanyika S. Appropriateness of waist circumference and waist-to-hip ratio cutoffs for different ethnic populations. Eur J Clin Nutr. 2010;64:42–61.

    Article  CAS  PubMed  Google Scholar 

  49. Arabshahi S, Busingye D, Subasinghe AK, Evans RG, Riddell MA, Thrift AG. Adiposity has a greater impact on hypertension in lean than not-lean populations: a systematic review and meta-analysis. Eur J Epidemiol. 2014;29:311–24.

    Article  PubMed  Google Scholar 

  50. WHO. Waist circumference and waist-hip ratio. In: Report of a WHO Expert Consultation, Geneva, 8–11 Dec 2008. Geneva: World Health Organization; 2011.

    Google Scholar 

  51. Fenton TR, Kim JH. A systematic review and meta-analysis to revise the Fenton growth chart for preterm infants. BMC Pediatr. 2013;13.

    Google Scholar 

  52. Lee ACC, Katz J, Blencowe H, Cousens S, Kazuki N, Vogel JP, et al. National and regional estimates of term and preterm babies born small for gestational age in 138 low-income and middle-income countries in 2010. Lancet Glob Health. 2013;1:e26–36.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Grisaru-Granovsky S, Reichman B, Lerner-Geva L, Boyko V, Hammerman C, Samueloff A, et al. Mortality and morbidity in preterm small-for-gestational-age infants: a population-based study. Am J Obstet Gynecol. 2012;150:e1-d7.

    Google Scholar 

  54. Regev RH, Lusky A, Dolan T, Litmanovitz I, Arnon S, Reichman B, et al. Excess mortality and morbidity among small-for-gestational-age premature infants: a population-based study. J Pediatr. 2003;143:186–91.

    Article  PubMed  Google Scholar 

  55. Christian P, Murray-Kolb LE, Tielsch JM, Katz J, LeClerq SC, Khatry SK. Associations between preterm birth, small-for gestational age, and neonatal morbidity and cognitive function among school-age children in Nepal. BMC Pediatr. 2014;14.

    Google Scholar 

  56. Delpisheh A, BrabinL, Drummond S, Brabin B. Prenatal smoking exposure and asymmetric fetal growth restriction. Ann Human Biol. 2008;35(6):573–83.

    Google Scholar 

  57. Zepeda-Monreal J, Rodriquez-Balderrama I, Ochoa-Correa EC, de la O-Cavazos ME, Ambriz-Lopez R. Crecimiento intrauterino. Factores para su restricción. Rev Med Inst Mex Seguro Soc. 2012;50(2):173–81.

    PubMed  Google Scholar 

  58. Pasupathy D, McCowan LME, Poston L, Kenny LC, Dekker GA, North RA, et al. Perinatal outcomes in large infants using customised birthweight centiles and conventional measures of high birthweight. Paediatr Perinat Epidemiol. 2012;26:543–52.

    Article  PubMed  Google Scholar 

  59. Kim SY, Sharma AJ, Sappenfield W, Wilson HG, Salihu HM. Association of maternal body mass index, excessive weight gain, and gestational diabetes mellitus with large-for-gestational-age births. Obstet Gynecol. 2014;123(4):737–44.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Yu Z, Han S, Zhu J, Sun X, Ji C, Guo X. Pre-pregnancy body mass index in relation to infant birth weight and offspring overweight/obesity: a systematic review and meta-analysis. PLoS One. 2013;8(4).

    Google Scholar 

  61. Li N, Liu E, Guo J, Pan L, Li B, Wang P, et al. Maternal prepregnancy body mass index and gestational weight gain on pregnancy outcomes. PLoS ONE. 2013;8(12).

    Google Scholar 

  62. Kozuki N, Katz J, Lee ACC, Vogel JP, Silveira MF, Sania A, et al. Short maternal stature increases risk of small for-gestational-age and preterm births in low and middle-income countries: individual participant data meta-analysis and population attributable fraction. J Nutr. 2015;145:2542–50.

    Article  CAS  PubMed  Google Scholar 

  63. Ververs M, Antierens A, Sackl A, Staderini N, Captier V. Which anthropometric indicators identify a pregnant woman as acutely malnourished and predict adverse birth outcomes in the humanitarian context? PLOS Curr Disasters [Internet]. 2013;1:15p.

    Google Scholar 

  64. Sebayang SK, Dibley MJ, Kelly PJ, Shanker AV, Shanker AJ. The summit study group. Determinants of low birthweight, small-for-gestational-age and preterm birth in Lombok, Indonesia: analyses of the birthweight cohort of the SUMMIT trial. Trop Med Int Health. 2012;17(8):938–50.

    Article  PubMed  Google Scholar 

  65. Ramlal RT, Tembo M, Soko A, Chigwenembe M, Ellington S, Kayira D, et al. Maternal mid-upper arm circumference is associated with birth weight among HIV-infected Malawians. Nutr Clin Pract. 2012;27(3):416–21.

    Article  PubMed  Google Scholar 

  66. Cnattingius S, Villamor E, Lagerros YT, Wikstrom A-K, Granath F. High birth weight and obesity—a vicious circle across generations. Int J Obes. 2012;36:1320–4.

    Article  CAS  Google Scholar 

  67. Siega-Riz AM, Gray GL. Gestational weight gain recommendations in the context of the obesity epidemic. Nutr Rev. 2013;71:S26–30.

    Article  PubMed  Google Scholar 

  68. IOM. Weight gain during pregnancy: reexamining the guidelines. Washington DC: Guidelines NRCCtRIPWG; 2009.

    Google Scholar 

  69. Hinkle SN, Johns AM, Albert PS, Kim S, Grantz K. Longitudinal changes in gestational weight gain and the association with intrauterine fetal growth. Eur J Obstet Gynecol Reprod Biol. 2015;190:41–7.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Sharma AJ, Vesco KK, Bulkley J, Callaghan WM, Bruce FC, Staab J, et al. Associations of gestational weight gain with preterm birth among underweight and normal weight women. Matern Child Health J. 2015;19:2066–73.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Cnattingius S, Villamor E. Weight change between successive pregnancies and risks of stillbirth and infant mortality: a nationwide cohort study. Lancet. 2016;387:558–65.

    Google Scholar 

  72. Ferraro ZM, Barrowman N, Prud’homme D, Walker M, Wen SW, Rodger M, et al. Excessive gestational weight gain predicts large for gestational age neonates independent of maternal body mass index. J Matern-Fetal Neonatal Med. 2012;25(5):538–42.

    Article  CAS  PubMed  Google Scholar 

  73. Godoy AC, do Nascimento SL, Surita FG. A systematic review and meta-analysis of gestational weight gain recommendations and related outcomes in Brazil. Clinics. 2015;70(11):758–64.

    Google Scholar 

  74. Mannan M, Doi SAR, Mamun AA. Association between weight gain during pregnancy and postpartum weight retention and obesity: a bias-adjusted meta-analysis. Nutr Rev. 2013;71(6):343–52.

    Google Scholar 

  75. Briend A, Maire B, Fontaine O, Garenne M. Mid-upper arm circumference and weight-for-height to identify high-risk malnourished under-five children. Matern Child Nutr. 2012;8(1):130–3.

    Article  PubMed  Google Scholar 

  76. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity and trends in body mass index among US children and adolescents, 1999–2010. JAMA. 2012;307(5):483–90.

    Article  PubMed  Google Scholar 

  77. de Onis M, Martinez-Costa C, Nunez F, Nguefack-Tsague G, Montal A, Brines J. Association between WHO cut-offs for childhood overweight and obesity and cardiometabolic risk. Public Health Nutr. 2012;16(4):625–30.

    Article  PubMed  Google Scholar 

  78. Burgess HJL, Burgess AP. The arm circumference as a public health index of protein-calorie malnutrition of early childhood. J Trop Pediatr. 1969:189–92.

    Google Scholar 

  79. Shakir A, Morley D. Measuring malnutrition. Lancet. 1974:758–9.

    Google Scholar 

  80. Briend A. Use of MUAC for severe acute malnutrition. CMAM Forum [Internet]. 2012. Available from: http://www.cmamforum.org/Pool/Resources/FAQ-1-Use-of-MUAC-Briend-Eng-June-2012(1).pdf.

  81. Modi P, Nasrin S, Hawes M, Glavis-Bloom J, Alam NH, Hossain MI, et al. Midupper arm circumference outperforms weight-based measures of nutritional status in children with diarrhea. J Nutr. 2015;145:1582–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Dale NM, Myatt M, Proudhon C, Briend A. Using mid-upper arm circumference to end treatment of severe acute malnutrition leads to higher weight gains in the most malnourished children. PLoS ONE. 2013;8(2):e55404.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Ojo O, Deane R, Amuna P. The use of anthropometric and clinical parameters for early identification and categorisation of nutritional risk in pre-school children in Benin City, Nigeria. J R Soc Promot Health. 2000;120(4):230–5.

    Article  CAS  PubMed  Google Scholar 

  84. D’Angelo S, Yajnik CS, Kumaran K, Joglekar C, Lubree H, Crozier SR, et al. Body size and body composition: a comparison of children in India and the UK through infancy and early childhood. J Epidemiol Commun Health. 2015;69(12):1147–53.

    Article  Google Scholar 

  85. Briend A, Khara T, Dolan C. Wasting and stunting—similarities and differences: policy and programmatic implications. Food Nutr Bull. 2015;36(1):S15–23.

    Article  PubMed  Google Scholar 

  86. Laillou A, Prak S, de Groot R, Whitney S, Conkle J, Horton L, et al. Optimal screening of children with acute malnutrition requires a change in current WHO guidelines as MUAC and WHZ identify different patient groups. PLoS ONE. 2014;9(7).

    Google Scholar 

  87. Garenne M, Maire B, Fontaine O, Briend A. Adequacy of child anthropometric indicators for measuring nutritional stress at population level: a study from Niakhar, Senegal. Public Health Nutr. 2012;16(9):1533–9.

    Article  PubMed  Google Scholar 

  88. Arnhold R. The QUAC stick: a field measure use by the Quaker service team in Nigeria. J Trop Pediatr. 1969;15:243–6.

    Article  Google Scholar 

  89. Sommer A, Loewenstein MS. Nutritional status and mortality: a prospective validation of the QUAC stick. Am J Clin Nutr. 1975;28:287–92.

    CAS  PubMed  Google Scholar 

  90. Briend A, Zimicki S. Validation of arm circumference as an indicator of risk of death in one to four year old children. Nutr Res. 1986;6:249–61.

    Article  Google Scholar 

  91. Bogin B, Varela-Silva MI. Leg length, body proportion, and health: a review with a note on beauty. Int J Environ Res Public Health. 2010;7(3):1047–75.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Ball TM, Taren D. Use of arm circumference (AC) measurements and maternal reporting of illness. J Trop Pediatr. 1995;41:250–2.

    Article  CAS  PubMed  Google Scholar 

  93. Jensen GL, Hsiao PY, Wheeler D. Adult nutrition assessment tutorial. J Parenter Enteral Nutr. 2012;2012(3):267–74.

    Article  CAS  Google Scholar 

  94. Secker DJ, Jeejeebhoy KN. How to perform a subjective global nutritional assessment in children. J Acad Nutr Diet. 2012;112(3):424–31.

    Article  PubMed  Google Scholar 

  95. Detsky AS, McLaughlin JR, Baker JP. What is subjective global assessment of nutritional status. JPEN. 1994;11:8.

    Article  Google Scholar 

  96. Guigoz Y, Vellas B, Garry PJ. Mini nutritional assessment: a practical assessment tool for grading the nutritional status of elderly patients. Facts Res Gerontol. 1994;4(Suppl 2):15.

    Google Scholar 

  97. Kaiser MJ, Bauer JM, Ramsch C, Uter W, Guigoz Y, Ceerholm T, et al. Frequency of malnutrition in older adults: a multinational perspective using the mini nutritional assessment. J Am Geriatr Soc. 2010;58:1734–8.

    Article  PubMed  Google Scholar 

  98. McLaren D. Color atlas of nutritional disorders. Chicago: Year Book Medical Publishers; 1981.

    Google Scholar 

  99. Finch CW. Review of trace mineral requirements for preterm infants: what are the current recommendations for clinical practice? Nutr Clin Pract. 2015;30(1):44–58.

    Article  PubMed  CAS  Google Scholar 

  100. Kapoor S. Diagnosis and treatment of acanthosis nigricans. SkinMed. 2010;8(3):161–5.

    PubMed  Google Scholar 

  101. Kutlubay Z, Engin B, Bairamov O, Tuzun Y. Acanthosis nigricans: a fold (intertriginous) dermatosis. Clin Dermatol. 2015;33:466–70.

    Article  PubMed  Google Scholar 

  102. Ter Beek L, Vanhauwaert E, Slinde F, Orrevall Y, Henriksen C, Johansson M, et al. Unsatisfactory knowledge and use of terminology regarding malnutrition, starvation, cachexia and sarcopenia among dietitians. Clin Nutr. 2016.

    Google Scholar 

  103. Williams CD. A nutritional disease of childhood associated with a maize deit. Arch Dis Child. 1933;8:423–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Williams CD, Oxon BM, Lond H. Kwashiorkor. A nutritional disease of children associated with a maize diet. Lancet. 1935:1151–2.

    Google Scholar 

  105. Anonymous. Classification of infantile malnutrition. Lancet. 1970:302–3.

    Google Scholar 

  106. Waterlow JC. Classification and definition of protein-calorie malnutrition. Br Med J. 1972;3:566–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Potischman N, Freudenheim JL. Biomarkers of nutritional exposure and nutritional status: an overview. J Nutr. 2013;133(3):8735–45.

    Google Scholar 

  108. Elmadfa I, Myere AL. Developing suitable methods of nutritional status assessment: a continuous challenge. Adv Nutr. 2014;5:590S–8S.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Raiten DJ, Ashour FAS, Ross AC, Meydani SM, Dawson HD, Stephensen CB, et al. Inflammation and nutritional science for programs/policies and interpretation of research evidence (INSPIRE). J Nutr. 2015;145:1039S–108S.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Thurnham DI, Northrop-Clewes CA, Knowles J. The use of adjustment factors to address the impact of inflammation on vitamin A and iron status in humans. J Nutr. 2015;145:1137S–43S.

    Google Scholar 

  111. Thurnham DI. Inflammation and biomarkers of nutrition. Sight Life. 2015;29(1):51–9.

    Google Scholar 

  112. Fedosov SN, Brito A, Miller JW, Green R, Allen LH. Combined indicator of vitamin B 12 status: modification for missing biomarkers and folate status and recommendations for revised cut-points. Clin Chem Lab Med. 2015;53(8):1215–25.

    Article  CAS  PubMed  Google Scholar 

  113. Cederholm T, Bosaeus I, Barazzoni R, Bauer J, Van Gossum A, Klek S, et al. Diagnostic criteria for malnutrition. An ESPEN consensus statement. Clin Nutr. 2015;34:335–40.

    Article  CAS  PubMed  Google Scholar 

  114. Combs GF, Trumbo PR, McKinley MC, Milner J, Studenski S, Kimura T, et al. Biomarkers in nutrition: new frontiers in research and application. Ann NY Acad Sci. 2013;1278:1–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Combs GF. Biomarkers of selenium status. Nutrients. 2015;7:2209–36.

    Google Scholar 

  116. Martens IBG, Cardoso BR, Hare DJ, Niedzwiecki MM, Lajolo FM, Martens A, et al. Selenium status in preschool children receiving a Brazil nut—enriched diet. Nutrition. 2015;31:1339–43.

    Article  CAS  PubMed  Google Scholar 

  117. Miller AL, Lee HJ, Lumeng JC. Obesity-associated biomarkers and executive function in children running title: obesity and executive function. Pediatr Res. 2015;77(1–2):143–7.

    PubMed  Google Scholar 

  118. Garcia-Calzon S, Moleres A, Marcos A, Campoy C, Moreno LA, Azcona-Sanjulian MC, et al. Telomere length as a biomarker for adiposity changes after a multidisciplinary intervention in overweight/obese adolescents: the EVASYON study. PLoS ONE. 2014;9(2):e89828.

    Google Scholar 

  119. Plasqui G, Bonomi AG, Westerterp KR. Daily physical activity assessment with accelerometers: new insights and validation studies. Obes Rev. 2013;14:451–62.

    Article  CAS  PubMed  Google Scholar 

  120. de Roos B, McArdle HJ. Proteomics as a tool for the modelling of biological processes and biomarker development in nutrition research. Br J Nutr. 2008;99(Suppl 3):S66–71.

    PubMed  Google Scholar 

  121. Cole RN, Ruczinski I, Schulze K, Christian P, Herbrich S, Wu L, et al. The plasma proteome identifies expected and novel proteins correlated with micronutrient status in undernourished Nepalese children. J Nutr. 2013;143:1540–8.

    Article  CAS  PubMed  Google Scholar 

  122. West KP, Cole RN, Shrestha S, Schulze KJ, Lee SE, Betz J, et al. A plasma a-tocopherome can be identified from proteins associated with vitamin E status in school-aged children of Nepal. J Nutr. 2015;145:2646–56.

    Article  CAS  PubMed  Google Scholar 

  123. Scalbert A, Brennan L, Manach C, Andres-Lacueva C, Dragsted LO, Draper J, et al. The food metabolome: a window over dietary exposure. Am J Clin Nutr. 2014;99:1286–308.

    Article  CAS  PubMed  Google Scholar 

  124. Gibbons H, McNulty BA, Nugent AP, Walton J, Flynn A, Gibney MJ, et al. A metabolomics approach to the identification of biomarkers of sugar-sweetened beverage intake. Am J Clin Nutr. 2015;101:471–7.

    Article  CAS  PubMed  Google Scholar 

  125. Jahren AH, Bostic JN, Davy BM. The potential for a carbon stable isotope biomarker of dietary sugar intake. J Anal At Spectrom. 2014;29:795–816.

    Article  CAS  Google Scholar 

  126. Subramanian S, Blanton LV, Frese SA, Charbonneau M, Mills DA, Gordon JI. Cultivating healthy growth and nutrition through the gut microbiota. Cell. 2015;161:36–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Smith MI, Yatsunenko T, Manary MJ, Trehan I, Mkakosya R, Cheng J, et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science. 2013;339:548–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Aguilar SS, Wengreen HJ, Lefevre M, Madden GJ, Gast J. Skin carotenoids: a biomarker of fruit and vegetable intake in children. J Acad Nutr Diet. 2014;114:1174–80.

    Article  PubMed  Google Scholar 

  129. Nguyen LM, Scherr RE, Linnell JD, Ermakov IV, Gellermann W, Jahns L, et al. Evaluating the relationship between plasma and skin carotenoids and reported dietary intake in elementary school children to assess fruit and vegetable intake. Arch Biochem Biophys. 2015;572:73–80.

    Article  CAS  PubMed  Google Scholar 

  130. FAOSTAT [Internet]. Food and Agriculture Organization of the United Nations. 2016 [cited 15 Mar 2016]. Available from: http://faostat3.fao.org/home/E.

  131. Stoltzfus R, Rasmussen KM. The dangers of being born too small or too soon. Lancet. 2013;382:380–1.

    Article  PubMed  Google Scholar 

  132. Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 2013;382(9890):427–51.

    Article  PubMed  Google Scholar 

  133. National Center for Environmental Sciences. Second national report on biochemical indicators of diet and nutrition in the U.S. population. Atlanta: United States Centers for Disease Control and Prevention, Division of Laboratory Sciences; 2012.

    Google Scholar 

  134. Andersson M, KarumbunathanV, Zimmermann MB. J Nutr. 2012;142:744–50.

    Google Scholar 

  135. Wessells KR, Brown KH. PLoS One. 2012;7:e50568.

    Google Scholar 

  136. Gallagher ML. Intake: the nutrients and their metabolism. In: Mahan LK, Escott-Stump S, Raymond JL, editors. Krause’s food and the nutrition care process, 13th ed. St. Louis: Elsevier Saunders; 2012. p. 32–128.

    Google Scholar 

  137. Marks J. The vitamins: their role in medical practice. Boston: Springer MTP Press Limited; 1985.

    Book  Google Scholar 

  138. Allen L, de Benoist B, Dary O, Hurrell R. Guidelines on food fortification with micronutrients. Geneva: World Health Organization and Food and Agricultural Organization of the United Nations; 2006.

    Google Scholar 

  139. Tulchinsky TH. Micronutrient deficiency conditions: global health issues. Public Health Rev. 2010;32:243–55.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Douglas Taren .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this chapter

Cite this chapter

Taren, D., de Pee, S. (2017). The Spectrum of Malnutrition. In: de Pee, S., Taren, D., Bloem, M. (eds) Nutrition and Health in a Developing World . Nutrition and Health. Humana Press, Cham. https://doi.org/10.1007/978-3-319-43739-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-43739-2_5

  • Published:

  • Publisher Name: Humana Press, Cham

  • Print ISBN: 978-3-319-43737-8

  • Online ISBN: 978-3-319-43739-2

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics