Mid-upper arm circumference and Neck Circumference as optimal proxies for Body Mass Index in the Assessment of Malnutrition and Metabolic Risks among the Older Adults in the Rural region of Delhi-NCR, India
DOI:
https://doi.org/10.34256/ijk2624Keywords:
Somatometry, Calf circumference, Community-based, Sarcopenia, General ObesityAbstract
Introduction: BMI is traditionally used as a screening tool to assess malnutrition and evaluate metabolic risks among older adults. But it can misclassify individuals with a low muscle-to-fat ratio by masking progressive muscle loss and simultaneous visceral adiposity. Also, the compression of the vertebral column in older adults raises questions about the validity of height-dependent BMI calculations. Therefore, this study aims to evaluate the utility of MUAC and NC as practical somatometric proxies for BMI among community-dwelling older adults in rural Delhi-NCR, India. Methods: A total of 404 participants, aged 60 years or older, were included in this study. Anthropometric variables were assessed using standardized instruments following the International Society for the Advancement of Kinanthropometry (ISAK) protocols, i.e., height using an Anthropometer rod; weight using an Omron Karada Scan Body Composition Monitor HBF-375; and MUAC, NC, and CC using a Cescorf anthropometric tape. Statistical significance was defined as ρ>0.60 and p<0.05. Low CC was treated as an indicator of a low muscle-to-fat ratio, a measure of malnutrition, and NC as a biomarker of metabolic risk. Results: NC was significantly correlated with BMI in both males and females (p<0.001). Similarly, among individuals with low CC (n=250), a significantly strong correlation between CC and MUAC was observed (p<0.001) when stratified by sex. Further, 40.40% of the individuals with low CC were misclassified as normal weight, regardless of having a low muscle-to-fat ratio. Conclusion: BMI masks progressive loss in muscle mass and can misclassify older adults while screening for metabolic risks. Hence, it may be an inadequate tool for assessing malnutrition and metabolic risks in the geriatric population. Therefore, using tape measurements such as MUAC and NC in screening older adults for malnutrition and metabolic risks may yield better results.
References
Coelho, H.J., Sampaio, R.A.C., Gonçalvez, I.D.O., Aguiar, S.D.S., Palmeira, R., Oliveira, J.F.D., Asano, R.Y., Sampaio, P.Y.S., Uchida, M.C. (2016). Cutoffs and Cardiovascular Risk Factors Associated with Neck Circumference Among Community-Dwelling Elderly Adults: A Cross-Sectional Study. São Paulo Medical Journal, 134(6): 519–527. https://doi.org/10.1590/1516-3180.2016.0160110906
Cruz-Jentoft, A.J., Bahat, G., Bauer, J., Boirie, Y., Bruyère, O., Cederholm, T., Cooper, C., Landi, F., Rolland, Y., Sayer, A.A., Schneider, S.M., Sieber, C.C., Topinkova, E., Vandewoude, M., Visser, M., Zamboni, M. (2019). Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2. Sarcopenia: Revised European Consensus on Definition and Diagnosis. Age and Ageing, 48(1): 16–31. https://doi.org/10.1093/ageing/afy169
Das, P., Khatun, A., Bose, K., Chakraborty, R. (2018). The Validity of Mid-Upper Arm Circumference as an Indicator of low BMI in Population Screening for Undernutrition: A Study among Adult Slum Dwellers in Eastern India. Public Health Nutrition, 21(14): 2575–2583. https://doi.org/10.1017/S1368980018001301
Díaz, D. A., Lera, L., Márquez, C., Valenzuela, A., Saguez, R., Weisstaub, G., Albala, C. (2024). Neck Circumference Cut-Off Points for Identifying Adiposity: Association with Chronic Metabolic Diseases in Older People. Journal of Personalized Medicine, 14(7): 710. https://doi.org/10.3390/jpm14070710
Goswami, A.K., Sathiyamoorthy, R., Kalaivani, M., Kant, S., Gupta, S.K. (2022). Could Mid-Upper Arm Circumference be a Valid Proxy to the Body Mass Index for Elderly Persons? Indian Journal of Community and Family Medicine, 8(2): 143–147. https://doi.org/10.4103/ijcfm.ijcfm_23_22
Guo, J., Huang, X., Dou, L., Yan, M., Shen, T., Tang, W., Li, J. (2022). Aging and Aging-Related Diseases: From Molecular Mechanisms to Interventions and Treatments. Signal Transduction and Targeted Therapy, 7(1): 391. https://doi.org/10.1038/s41392-022-01251-0
Holt, P.R., Altayar, O., Alpers, D.H. (2023). Height with Age Affects Body Mass Index (BMI) Assessment of Chronic Disease Risk. Nutrients, 15(21): 4694. https://doi.org/10.3390/nu15214694
International Society for the Advancement of Kinanthropometry. (2026). Home. https://www.isak.global
Kalra, S., Kapoor, N., Verma, M., Shaikh, S., Das, S., Jacob, J., Sahay, R. (2023). Defining and Diagnosing Obesity in India: A Call for Advocacy and Action. Journal of Obesity, 2023(1): 4178121. https://doi.org/10.1155/2023/4178121
Kalra, S., Shaikh, I.A., Shende, S., Kapoor, N., Unnikrishnan, A.G., Sharma, O.P., Tiwaskar, M.H., Vora, A., Verma, S.K., Kantroo, V., Mehta, P., Lovesley, D., Sivakumar, N., Kukreja, B. B., Kulkarni, K., Deora, A. (2025). An Indian Consensus on Sarcopenia: Epidemiology, Etiology, Clinical Impact, Screening, and Therapeutic Approaches. International Journal of General Medicine, 18: 1731–1745. https://doi.org/10.2147/IJGM.S510412
Kaur, I., Das, S., Chandel, S., Chandel, S. (2025). Possible sarcopenia, sarcopenic obesity phenotypes and their association with diabetes: Evidence from LASI wave-1 (2017-18). Diabetes and Metabolic Syndrome, 19(2): 103185. https://doi.org/10.1016/j.dsx.2025.103185
Kroll, C., Mastroeni, S.S.B.S., Czarnobay, S.A., Ekwaru, J.P., Veugelers, P.J., Mastroeni, M. F. (2017). The Accuracy of Neck Circumference for Assessing Overweight and Obesity: A Systematic Review and Meta-Analysis. Annals of Human Biology, 44(8): 667–677. https://doi.org/10.1080/03014460.2017.1390153
Kshatriya, G.K., Chakraborty, R., Mondal, N., Bose, K. (2021). Validating Mid-Upper Arm Circumference (MUAC) cut off Points as an Indicator of Nutritional Status among Nine Tribal Populations of India. Anthropological Review, 84(3): 301–315. https://doi.org/10.2478/anre-2021-0018
Lee, S.B., Oh, J.H., Park, J.H., Choi, S.P., Wee, J.H. (2018). Differences in Youngest-Old, Middle-Old, and Oldest-Old Patients who Visit the Emergency Department. Clinical and experimental emergency medicine, 5(4): 249–255. https://doi.org/10.15441/ceem.17.261
Malik, R., Goel, H. (2024). Clinical Validation of Calf Circumference with DEXA Scans as a Measure of Muscle Mass to Assess Sarcopenia in Community Settings in Indian Postmenopausal Women. Journal of Mid-Life Health, 15(2): 99–103. https://doi.org/10.4103/jmh.jmh_226_23
Ministry of Statistics and Programme Implementation. (2016). Elderly in India 2016. Government of India.
Reyes-Perez, S.D., Valencia-Sosa, E., Rodriguez-Echevarria, R., Torres-Vanegas, J., Cambron-Mora, D., Martínez-López, E. (2026). Neck Circumference as a Surrogate Marker of Upper-Body Adiposity: Associations with Anthropometric, Biochemical and Inflammatory Cytokines in Mexican Adults with Obesity. Nutrients, 18(9): 1366. https://doi.org/10.3390/nu18091366
Ros, F.E., Vaquero-Cristóbal, R., Marfell-Jones, M. (2019). International Standars for Anthropometric Assessment, 2019. International Society for the Advancement of Kinanthropometry (ISAK).
Rose Berlin Piodena-Aportadera, M., Lau, S., Chew, J., Lim, J.P., Ismail, N.H., Ding, Y.Y., Lim, W.S. (2022). Calf Circumference Measurement Protocols for Sarcopenia Screening: Differences in Agreement, Convergent Validity and Diagnostic Performance. Annals of geriatric medicine and research, 26(3): 215–224. https://doi.org/10.4235/agmr.22.0057
Sato, R., Sawaya, Y., Ishizaka, M., Yin, L., Shiba, T., Hirose, T., Urano, T. (2024). Neck Circumference is a Highly Reliable Anthropometric Measure in Older Adults Requiring Long-Term Care. PeerJ, 12: e16816. https://doi.org/10.7717/peerj.16816
Selvaraj, K., Jayalakshmy, R., Yousuf, A., Singh, A.K., Ramaswamy, G., Palanivel, C. (2017). Can Mid-Upper Arm Circumference and Calf Circumference be the Proxy Measures to Detect Undernutrition among Elderly? Findings of a Community-Based Survey in Rural Puducherry, India. Journal of Family Medicine and Primary Care, 6(2): 356–359. https://doi.org/10.4103/jfmpc.jfmpc_357_16
Simpson, L., Mukherjee, S., Cooper, M.N., Ward, K.L., Lee, J.D., Fedson, A.C., Potter, J., Hillman, D.R., Eastwood, P., Palmer, L.J., Kirkness, J. (2010). Sex Differences in the Association of Regional Fat Distribution with the Severity of Obstructive Sleep Apnea. Sleep, 33(4): 467–474. https://doi.org/10.1093/sleep/33.4.467
Singh, A., Chattopadhyay, A. (2024). Age-Appropriate BMI cut-offs for Malnutrition among Older Adults in India. Scientific Reports, 14(1): 15072. https://doi.org/10.1038/s41598-024-63421-0
Sperrin, M., Marshall, A.D., Higgins, V., Renehan, A.G., Buchan, I.E. (2016). Body mass index relates weight to height differently in women and older adults: Serial cross-sectional surveys in England (1992–2011). Journal of Public Health, 38(3): 607–613. https://doi.org/10.1093/pubmed/fdv067
Srikrajang, S., Komolsuradej, N. (2024). Association between Height-Changing Scores and Risk of Sarcopenia Estimated from Anthropometric Measurements in Older Adults: A Cross-Sectional Study. Healthcare, 12(10): 1005. https://doi.org/10.3390/healthcare12101005
Verma, M., Rajput, M., Sahoo, S.S., Kaur, N. (2017). Neck Circumference: Independent Predictor for Overweight and Obesity in Adult Population. Indian Journal of Community Medicine : Official Publication of Indian Association of Preventive and Social Medicine, 42(4): 209–213. https://doi.org/10.4103/ijcm.IJCM_196_16
Zhao, Y., Ye, J., Yu, W., Du, Q., Liu, D. (2026). Body Composition Characteristics in Aging Populations: Clinical. Limitations of BMI and Implications for Chronic Metabolic Diseases. Body Mass Index in Clinical Practice, 145. https://doi.org/10.5772/intechopen.1013977
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Articles published in the International Journal of Kinanthropometry are licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, provided appropriate credit is given to the original authors and the source.