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Ethnicity-specific cut-offs that predict co-morbidities: the way forward for optimal utility of obesity indicators

Published online by Cambridge University Press:  04 April 2019

Nitin Kapoor Open the ORCID record for Nitin Kapoor [Opens in a new window] ,
John Furler ,
Thomas V. Paul ,
Nihal Thomas  and
Brian Oldenburg
Nitin Kapoor*
Affiliation:
Department of Endocrinology, Diabetes and Metabolism, Christian Medical College & Hospital, Vellore, Tamil Nadu, India Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Australia
John Furler
Affiliation:
Department of General Practice, Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Australia
Thomas V. Paul
Affiliation:
Department of Endocrinology, Diabetes and Metabolism, Christian Medical College & Hospital, Vellore, Tamil Nadu, India
Nihal Thomas
Affiliation:
Department of Endocrinology, Diabetes and Metabolism, Christian Medical College & Hospital, Vellore, Tamil Nadu, India
Brian Oldenburg
Affiliation:
Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Australia
*
*Corresponding author. Email: [email protected]
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Abstract

Obesity indicators are useful clinical tools in the measurement of obesity, but it is important for clinicians to appropriately interpret their values in individuals with different ethnicities. Future research is needed to identify optimal cut-offs that can predict the occurrence of cardio-metabolic comorbidities in individuals of different ethnic descent. Assessment of more recently developed indicators like the Edmonton Obesity Staging System and visceral adipose tissue are able to appropriately identify metabolically at-risk individuals.

Keywords

Obesity indicatorsNormal-weight obesityEthnicity specific cut-offs
Type
Debate
Information
Journal of Biosocial Science , Volume 51 , Issue 4 , July 2019 , pp. 624 - 626
Copyright
© Cambridge University Press, 2019 

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References

Baxi, R, Vasan, SK, Hansdak, S, Samuel, P, Jeyaseelan, V, Geethanjali, FS et al. (2016) Parental determinants of metabolic syndrome among adolescent Asian Indians: a cross-sectional analysis of parent–offspring trios. Journal of Diabetes 8(4), 494–501.Google Scholar
Kapoor, N, Chapla, A, Furler, J, Paul, TV, Harrap, S, Oldenburg, B and Thomas, N (2019) Genetics of obesity in consanguineous populations – a road map to provide novel insights in the molecular basis and management of obesity. EBioMedicine, doi: https://doi.org/10.1016/j.ebiom.2019.01.004.Google Scholar
Kryst, L, Zeglen, M, Wronka, I, Woronkowicz, A, Bilinska-Pawlak, I, Das, R et al. (2019a) Anthropometric variations in different BMI and adiposity levels among children, adolescents and young adults in Kolkata, India. Journal of Biosocial Science, doi: 10.1017/s0021932018000354.Google Scholar
Kryst, L, Żegleń, M, Wronka, I, Woronkowicz, A, Bilińska-Pawlak, I, Das, R et al. (2019b) BMI and adiposity based approach to obesity: the need for ethnic specificity. A reply to Kapoor et al. (2019). Journal of Biosocial Science, doi:10.1017/S002193201900018XGoogle Scholar
Laforgia, J, Dollman, J, Dale, MJ, Withers, RT and Hill, AM (2009) Validation of DXA body composition estimates in obese men and women. Obesity (Silver Spring) 17(4), 821–826.Google Scholar
Martinez Urbistondo, D and Martinez, JA (2017) Usefulness of the ‘Edmonton Obesity Staging System’ to develop precise medical nutrition. Revista Clínica Española 217(2), 97–98.Google Scholar
Padwal, RS, Pajewski, NM, Allison, DB and Sharma, AM (2011) Using the Edmonton obesity staging system to predict mortality in a population-representative cohort of people with overweight and obesity. Cmaj 183(14), E1059–1066.Google Scholar
Thomas, N, Grunnet, LG, Poulsen, P, Christopher, S, Spurgeon, R, Inbakumari, M et al. (2012) Born with low birth weight in rural Southern India: what are the metabolic consequences 20 years later? European Journal of Endocrinology 166(4), 647–655.Google Scholar
Xin, Z, Liu, C, Niu, WY, Feng, JP, Zhao, L, Ma, YH et al. (2012) Identifying obesity indicators which best correlate with type 2 diabetes in a Chinese population. BMC Public Health 12, 732.Google Scholar
Yajnik, CS and Yudkin, JS (2004) The Y–Y paradox. The Lancet 363(9403), 163.Google Scholar