Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The double burden of ‘malnutrition’: Under-Nutrition & Obesity

Intraindividual double-burden of anthropometric undernutrition and “metabolic obesity” in Indian children: a paradox that needs action

Abstract

Background

Intra-individual coexistence of anthropometrically defined undernutrition and ‘metabolic obesity’, characterised by presence of at least one abnormal cardiometabolic risk factor, is rarely investigated in young children and adolescents, particularly in Low-and-Middle-Income-Countries undergoing rapid nutrition transition.

Methods

Prevalence of biomarkers of metabolic obesity was related to anthropometric and socio-demographic characteristics in 5–19 years old participants from the population-based Comprehensive National Nutrition Survey in India (2016–2018). The biomarkers, serum lipid-profile (total cholesterol (TC), low density lipoprotein (LDL), high density lipoprotein (HDL) and triglycerides), fasting glucose, and glycosylated hemoglobin (HbA1C), and all jointly were analysed in 22567, 23192, 25962 and 19143 participants, respectively.

Results

Overall (entire dataset), the prevalence of abnormalities was low (4.3–4.5%) for LDL and TC, intermediate for dysglycemia (10.9–16.1%), and high for HDL and triglycerides (21.7–25.8%). Proportions with ≥1 abnormal metabolic obesity biomarker(s) were 56.2% overall, 54.2% in thin (BMI-for-age < −2 SD) and 59.3% in stunted (height-for-age < −2 SD) participants. Comparable prevalence was evident in mild undernutrition (−1 to −2 SD). Clustering of two borderline abnormalities occurred in one-third, warranting active life-style interventions. Metabolic obesity prevalence increased with BMI-for-age. Among those with metabolic obesity, only 9% were overweight/obese (>1 SD BMI-for-age). Among poor participants, triglyceride, glucose and HDL abnormalities were higher.

Conclusions

A paradoxical, counter-intuitive prevalence of metabolic obesity biomarker(s) exists in over half of anthropometrically undernourished and normal-weight Indian children and adolescents. There is a crucial need for commensurate investments to address overnutrition along with undernutrition. Nutritional status should be characterized through additional reliable biomarkers, instead of anthropometry alone.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2: Presence of metabolic obesity biomarkers in relation to BMI-for-age categories among 5–19 years old children.
Fig. 3: Presence of metabolic obesity biomarkers in relation to BMI-for-age categories among 5–19 years old children.

Similar content being viewed by others

References

  1. World Health Organization. Guideline: implementing effective actions for improving adolescent nutrition. Geneva: World Health Organization; 2018. p. 1–59.

  2. World Health Organization. Growth reference data for 5–19 years. Growth reference 5–19 years [website]. Geneva: World Health Organization; 2007 (http://www.who.int/growthref/en/, accessed 20 Jul 2020).

  3. World Health Organization. The double burden of malnutrition: policy brief. Geneva: World Health Organization; 2017 (http://apps.who.int/iris/bitstream/10665/255413/1/WHO-NMH-NHD-17.3-eng.pdf?ua=1, accessed 20 Jul 2020).

  4. Wells JC, Sawaya AL, Wibaek R, Mwangome M, Poullas MS, Yajnik CS, et al. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet. 2020;395:75–88.

    Article  Google Scholar 

  5. Popkin BM, Corvalan C, Grummer-Strawn LM. Dynamics of the double burden of malnutrition and the changing nutrition reality. Lancet.2020;395:65–74.

    Article  Google Scholar 

  6. Peng W, Mu Y, Hu Y, Li B, Raman J, Sui Z. Double burden of malnutrition in the Asia-Pacific region- a systematic review and meta-analysis. J Epidemiol Glob Health. 2020;10:16–27.

    Article  Google Scholar 

  7. Mahmudiono T, Segalita C, Rosenkranz RR. Socio-ecological model of correlates of double burden of malnutrition in developing countries: a narrative review. Int J Environ Res Public Health. 2019;16:3730.

    Article  Google Scholar 

  8. Kosaka S, Umezaki M. A systematic review of the prevalence and predictors of the double burden of malnutrition within households. Br J Nutr. 2017;117:1118–27.

    Article  CAS  Google Scholar 

  9. Caleyachetty R, Thomas GN, Kengne AP, Echouffo-Tcheugui JB, Schilsky S, Khodabocus J, et al. The double burden of malnutrition among adolescents: analysis of data from the global school-based student health and health behavior in school-aged children surveys in 57 low- and middle-income countries. Am J Clin Nutr. 2018;108:414–24.

    Article  Google Scholar 

  10. Williams AM, Guo J, Addo OY, Ismaily S, Namaste SML, Oaks BM, et al. Intraindividual double burden of overweight or obesity and micronutrient deficiencies or anemia among women of reproductive age in 17 population-based surveys. Am J Clin Nutr. 2020;112:468S–77S.

    Article  Google Scholar 

  11. Engle-Stone R, Guo J, Ismaily S, Addo OY, Ahmed T, Oaks B, et al. Intraindividual double burden of overweight and micronutrient deficiencies or anemia among preschool children. Am J Clin Nutr. 2020;112:478S–87S.

    Article  Google Scholar 

  12. Ruderman NB, Schneider SH, Berchtold P. The “metabolically-obese,” normal-weight individual. Am J Clin Nutr. 1981;34:1617–21.

    Article  CAS  Google Scholar 

  13. Klitgaard HB, Kilbak JH, Nozawa EA, Seidel AV, Magkos F. Physiological and lifestyle traits of metabolic dysfunction in the absence of obesity. Curr Diab Rep. 2020;20:17.

    Article  CAS  Google Scholar 

  14. Heshmat R, Hemati Z, Payab M, Hamzeh SS, Motlagh ME, Shafiee G, et al. Prevalence of different metabolic phenotypes of obesity in Iranian children and adolescents: the CASPIAN V study. J Diabetes Metab Disord. 2018;17:211–21.

    Article  CAS  Google Scholar 

  15. Li YP, Yang XG, Zhai FY, Piao JH, Zhao WH, Zhang J, et al. Disease risks of childhood obesity in China. Biomed Environ Sci. 2005;18:401–10.

    PubMed  Google Scholar 

  16. Ramachandran A, Snehalatha C, Yamuna A, Murugesan N, Narayan KM. Insulin resistance and clustering of cardiometabolic risk factors in urban teenagers in southern India. Diabetes Care. 2007;30:1828–33.

    Article  CAS  Google Scholar 

  17. Garg P, Kaur S, Gupta D, Osmond C, Lakshmy R, Sinha S, et al. Variability of thinness and its relation to cardiometabolic risk factors using four body mass index references in school children from Delhi, India. Indian Pediatr. 2013;50:1025–32.

    Article  Google Scholar 

  18. Ministry of Health and Family Welfare, Government of India, UNICEF, Population Council. Comprehensive national nutrition survey 2016–2018. national health mission, 2019. https://nhm.gov.in/showfile.php?lid=712. Accessed 20 Jul 2020.

  19. National Family Health Survey 4. International Institute for Population Sciences (IIPS) and ICF. National Family Health Survey (NFHS-4), 2015–16: India. Mumbai; International Institute of Population Science; 2015–16.

  20. Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents; National Heart, Lung, and Blood Institute. Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report. Pediatrics. 2011;128:S213–56.

    Article  Google Scholar 

  21. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33:S62–9.

    Article  Google Scholar 

  22. Leite HP, Rodrigues da Silva AV, de Oliveira Iglesias SB, Koch Nogueira PC. Serum albumin is an independent predictor of clinical outcomes in critically ill children. Pediatr Crit Care Med. 2016;17:e50–7.

    Article  Google Scholar 

  23. Kim S, McClave SA, Martindale RG, Miller KR, Hurt RT. Hypoalbuminemia and clinical outcomes: what is the mechanism behind the relationship? Am Surg. 2017;83:1220–7.

    Article  Google Scholar 

  24. Ogata E, Asahi K, Yamaguchi S, Iseki K, Sato H, Moriyama T, et al. Low fasting plasma glucose level as a predictor of new-onset diabetes mellitus on a large cohort from a Japanese general population. Sci Rep. 2018;8:13927.

    Article  Google Scholar 

  25. Serbis A, Giapros V, Galli-Tsinopoulou A, Siomou E. Metabolic syndrome in children and adolescents: is there a universally accepted definition? Does it matter? Metab Syndr Relat Disord. 2020 Aug 13. https://doi.org/10.1089/met.2020.0076. Epub ahead of print.

  26. Ataie-Jafari A, Heshmat R, Kelishadi R, Ardalan G, Mahmoudarabi M, Rezapoor A, et al. Generalized or abdominal obesity: which one better identifies cardiometabolic risk factors among children and adolescents? The CASPIAN III study. J Trop Pediatr. 2014;60:377–85.

    Article  Google Scholar 

  27. González-Gil EM, Cadenas-Sanchez C, Santabárbara J, Bueno-Lozano G, Iglesia I, Gonzalez-Gross M, et al. Inflammation in metabolically healthy and metabolically abnormal adolescents: the HELENA study. Nutr Metab Cardiovasc Dis. 2018;28:77–83.

    Article  Google Scholar 

  28. Martin L, Oepen J, Reinehr T, Wabitsch M, Claussnitzer G, Waldeck E, et al. Ethnicity and cardiovascular risk factors: evaluation of 40,921 normal-weight, overweight or obese children and adolescents living in Central Europe. Int J Obes (Lond). 2015;39:45–51.

    Article  CAS  Google Scholar 

  29. Kloppenborg JT, Fonvig CE, Nielsen TRH, Mollerup PM, Bøjsøe C, Pedersen O, et al. Impaired fasting glucose and the metabolic profile in Danish children and adolescents with normal weight, overweight, or obesity. Pediatr Diabetes. 2018;19:356–65.

    Article  CAS  Google Scholar 

  30. National Heart Lung and Blood Institute. What is energy balance? Balance Food and Activity. Available from: https://www.nhlbi.nih.gov/health/educational/wecan/healthy-weight-basics/balance.htm#:~:text=What%20is%20Energy%20Balance%3F,physical%20activity%20is%20ENERGY%20OUT. Accessed 13 Nov 2020.

  31. Sachdev HPS. Undersized Indian children: nutrients-starved or hungry for development? Proc Indian Natn Sci Acad. 2018;84:867–75.

    Google Scholar 

  32. Bray GA, Bouchard C. The biology of human overfeeding: a systematic review. Obes Rev. 2020;21:e13040.

    Article  CAS  Google Scholar 

  33. Most J, Tosti V, Redman LM, Fontana L. Calorie restriction in humans: an update. Ageing Res Rev. 2017;39:36–45.

    Article  Google Scholar 

  34. Kraus WE, Bhapkar M, Huffman KM, Pieper CF, Krupa Das S, Redman LM, et al. 2 years of calorie restriction and cardiometabolic risk (CALERIE): exploratory outcomes of a multicentre, phase 2, randomised controlled trial. Lancet Diabetes Endocrinol. 2019;7:673–83.

    Article  Google Scholar 

  35. Eckel RH, Jakicic JM, Ard JD, de Jesus J, Miller NH, Hubbard VS, et al. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;129:S76–99.

    Article  Google Scholar 

  36. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on clinical practice guidelines circulation. Circulation 2019;140:e596–646.

  37. Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on clinical practice guidelines. Circulation. 2019;139:e1082–143.

    PubMed  Google Scholar 

  38. American Diabetes Association. 3. prevention or delay of type 2 diabetes: standards of medical care in diabetes-2019. Diabetes Care. 2019;42:S29–33.

    Article  Google Scholar 

  39. Chooi YC, Ding C, Chan Z, Choo J, Sadananthan SA, Michael N, et al. Moderate weight loss improves body composition and metabolic function in metabolically unhealthy lean subjects. Obes (Silver Spring). 2018;26:1000–7.

    Article  CAS  Google Scholar 

  40. Kelishadi R, Hashemipour M, Sarrafzadegan N, Mohammadifard N, Alikhasy H, Beizaei M, et al. Effects of a lifestyle modification trial among phenotypically obese metabolically normal and phenotypically obese metabolically abnormal adolescents in comparison with phenotypically normal metabolically obese adolescents. Matern Child Nutr. 2010;6:275–86.

    PubMed  Google Scholar 

  41. Van Hulst A, Ybarra M, Mathieu ME, Benedetti A, Paradis G, Henderson M. Determinants of new onset cardiometabolic risk among normal weight children. Int J Obes (Lond). 2020;44:781–9.

    Article  Google Scholar 

  42. Nier A, Brandt A, Baumann A, Conzelmann IB, Özel Y, Bergheim I. Metabolic abnormalities in normal weight children are associated with increased visceral fat accumulation, elevated plasma endotoxin levels and a higher monosaccharide intake. Nutrients. 2019;11:652.

    Article  CAS  Google Scholar 

  43. India State-Level Disease Burden Initiative Collaborators. Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study. Lancet. 2017;390:2437–60.

    Article  Google Scholar 

  44. Kuriyan R, Selvan S, Thomas T, Jayakumar J, Lokesh DP, Phillip MP, et al. Body composition percentiles in Urban South Indian children and adolescents. Obes (Silver Spring). 2018;26:1629–36.

    Article  Google Scholar 

  45. 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 Comm Health. 2015;69:1147–53.

    Article  Google Scholar 

  46. National Sample Survey Office. Nutritional Intake in India: 2011–12, NSS 68th Round. New Delhi, India: National Statistical Organization. Government of India; 2014.

  47. Parks EJ, Hellerstein MK. Carbohydrate-induced hypertriacylglycerolemia: historical perspective and review of biological mechanisms. Am J Clin Nutr. 2000;71:412–33.

    Article  CAS  Google Scholar 

  48. Vos MB, Kaar JL, Welsh JA, Van Horn LV, Feig DI, Anderson CAM, et al. Added sugars and cardiovascular disease risk in children: a scientific statement from the American Heart Association. Circulation. 2017;135:e1017–34.

    Article  CAS  Google Scholar 

  49. WHO, WFP, UNSCN, UNICEF. Community-based management of severe acute malnutrition. A joint statement by the World Health Organization, World Food Programme, United Nations Standing Committee on Nutrition, United Nations Children’s Fund. Geneva, World Health Organization; 2007. Available from: http://www.who.int/nutrition/publications/severemalnutrition/9789280641479/en/. Accessed 20 Aug 2020.

  50. World Health Organization. WHO Recommendations on antenatal care for a positive pregnancy experience. Geneva: World Health Organization; 2016. Available from: https://www.who.int/reproductivehealth/publications/maternal_perinatal_health/anc-positive-pregnancy-experience/en/. Accessed 20 Aug 2020.

  51. von Grebmer K, Bernstein J, Mukerji R, Patterson F, Wiemers M, Ní Chéilleachair R, et al. 2019 Global Hunger Index: The Challenge of Hunger and Climate Change. Bonn: Welthungerhilfe; and Dublin: Concern Worldwide, 2019. Available at: https://www.globalhungerindex.org/pdf/en/2019.pdf. Accessed on 23 Aug 2020.

  52. United Nations. Sustainable development Goals. Goal 2: Zero Hunger. Available at: https://www.un.org/sustainabledevelopment/hunger. Accessed 23 August, 2020.

Download references

Acknowledgements

HSS and AVK are recipients of the Wellcome Trust/Department of Biotechnology India Alliance Clinical/Public Health Research Centre Grant # IA/CRC/19/1/610006. AVK is also supported by the India Alliance through their Margdarshi Fellowship.

Funding

The CNNS was conducted by the Ministry of Health and Family Welfare, Government of India, and the UNICEF, with financial support from the Mittal Foundation. These secondary analyses and paper were not supported by any specific funding.

Data sharing and declaration

The Ministry of Health and Family Welfare (MoHFW), Government of India owns the Comprehensive National Nutrition Survey data. The data used in this paper were released for public use by the MoHFW and United Nations Children Fund, India Country Office. The code book and the analytic code can be made available upon reasonable request to the corresponding author(s).

Author information

Authors and Affiliations

Authors

Contributions

HSS conceived the idea, guided the analysis and drafted the manuscript. AP conducted all statistical analyses. AVK provided analytic insight for the National Sample Survey Office dietary consumption data. All authors were involved at every iteration of analyses and drafting, and approved the final paper. All authors had access to raw data.

Corresponding authors

Correspondence to Harshpal Singh Sachdev or Anura V. Kurpad.

Ethics declarations

Conflict of interest

HSS designed the draft protocol of the CNNS with consultancy support from the UNICEF, India. HSS, AS, UK and AVK were members of the Technical Advisory Committee of the CNNS, constituted by the Ministry of Health and Family Welfare of the Government of India, to oversee its conduct and analysis. HSS is a member of the World Health Organization Nutrition Guidance Expert Advisory Subgroup on Diet and Health and member of Expert Groups of the Ministry of Health and Family Welfare on Nutrition and Child Health. AS, RA, SR and AP were involved in the CNNS study implementation and main analyses. There were no other conflicts to declare.

Ethical approval

This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Population Council’s International Review Board (New York, USA) and ethics committee of Post Graduate Institute of Medical Education and Research (Chandigarh, India). No separate ethical approval was required or taken for this secondary analysis.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sachdev, H.S., Porwal, A., Sarna, A. et al. Intraindividual double-burden of anthropometric undernutrition and “metabolic obesity” in Indian children: a paradox that needs action. Eur J Clin Nutr 75, 1205–1217 (2021). https://doi.org/10.1038/s41430-021-00916-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41430-021-00916-3

This article is cited by

Search

Quick links