jimi

Article Details

Vol. 5 No. 2 (2026): Maret

Articles

Anthropometric Indicators and Type 2 Diabetes Mellitus Among Older Adults in a Primary Care Setting

S Safrina Oksidriyani P Putri Tiara Rosha R Refani Alycia Kusuma A Anastu Regita Nareswara N Nur Ahmad Habibi B Bintang Rizqi Kurniawan E Ela Sri Faniyanti B Bela Prasasti
26 March 2026 Vol. 5 No. 2 (2026): Maret https://doi.org/10.35912/jimi.v5i2.6576
Abstract
26 Mar 2026

Purpose: This study aimed to evaluate the association between five anthropometric indicators and type 2 diabetes mellitus (T2DM) prevalence among older adults in a primary care setting in Semarang, Indonesia.

Research Methodology: A cross-sectional study enrolled 55 individuals aged ?45 years at Kedungmundu Primary Health Center. Anthropometric measurements and capillary blood glucose were collected from routine health examinations. T2DM was defined as random blood glucose ?200 mg/dL or prior diagnosis. Independent t-tests, Mann–Whitney U tests, and multivariate logistic regression were applied.

Results: Of 55 participants (mean age 59.95 ± 6.76 years; 9.1% T2DM), no significant differences were found in any anthropometric indicator (all p > 0.05), although WC (92.00 vs. 89.50 cm), BMI (29.33 vs. 26.28 kg/m²), WHtR (0.61 vs. 0.58), and BRI (5.86 vs. 5.15) were consistently higher in the DM group. Multivariate analysis identified age as the only variable approaching significance (OR = 0.87, 95% CI: 0.75–1.01, p = 0.062), suggesting T2DM was more frequent among younger elderly individuals.

Conclusions: Consistent directional trends support the exploratory utility of WC, BMI, and WHtR as initial metabolic screening tools in primary care for younger elderly individuals, pending confirmation through larger longitudinal studies.

Limitations: The small DM case count (n = 5), cross-sectional design, and single-site recruitment limit statistical power and generalizability.

Contributions: This study provides original evidence on the utility of five anthropometric screening indicators for T2DM risk identification in elderly Indonesian primary care populations, informing clinical practice and screening strategies in resource-limited health settings.

Keywords: Anthropometric Indicators Central Obesity NonCommunicable Diseases Primary Health Care Type 2 Diabetes Mellitus
How to Cite
“
Oksidriyani, S., Rosha, P. T. ., Kusuma, R. A. ., Nareswara, A. R., Habibi, N. A., Kurniawan, B. R., Faniyanti, E. S., & Prasasti, B. (2026). Anthropometric Indicators and Type 2 Diabetes Mellitus Among Older Adults in a Primary Care Setting. Jurnal Ilmu Medis Indonesia, 5(2), 109–119. https://doi.org/10.35912/jimi.v5i2.6576
Endnote/Zotero/Mendeley (RIS) BibTeX
References
  1. Azam, M., Sakinah, L. F., Kartasurya, M. I., Fibriana, A. I., Minuljo, T. T., & Aljunid, S. M. (2023). Prevalence and determinants of obesity among individuals with diabetes in Indonesia. F1000Res, 11, 1063. doi:https://doi.org/10.12688/f1000research.125549.4
  2. Bender, R., & Lange, S. (2001). Adjusting for multiple testing—when and how? Journal of Clinical Epidemiology, 54(4), 343-349. doi:https://doi.org/10.1016/S0895-4356(00)00314-0
  3. Boonpor, J., Parra-Soto, S., Talebi, A., Zhou, Z., Carrasco-Marin, F., Petermann-Rocha, F., Ho, F. K. (2023). Associations and predictive performance of 11 anthropometric measures with incident type 2 diabetes: A prospective cohort study from the UK Biobank. Obesity, 31(10), 2648-2657. doi:https://doi.org/10.1002/oby.23849
  4. Chang, Y., Guo, X., Li, T., Li, S., Guo, J., & Sun, Y. (2016). A Body Shape Index and Body Roundness Index: Two New Body Indices to Identify left Ventricular Hypertrophy among Rural Populations in Northeast China. Heart, Lung and Circulation, 25(4), 358-364. doi:https://doi.org/10.1016/j.hlc.2015.08.009
  5. Dhokte, S., & Czaja, K. (2024). Visceral Adipose Tissue: The Hidden Culprit for Type 2 Diabetes. Nutrients, 16(7). doi:https://doi.org/10.3390/nu16071015
  6. ElSayed, N. A., Aleppo, G., Aroda, V. R., Bannuru, R. R., Brown, F. M., Bruemmer, D., Association, o. b. o. t. A. D. (2022). Introduction and Methodology: Standards of Care in Diabetes—2023. Diabetes Care, 46(Supplement_1), S1-S4. doi:https://doi.org/10.2337/dc23-Sint
  7. Feng, X., Wang, J., Wang, S., Wang, Z., Wu, S., Wei, Y., Chen, Q. (2023). Correlation analysis of anthropometric indices and type 2 diabetes mellitus in residents aged 60?years and older. Frontiers in Public Health, Volume 11 - 2023. doi:https://doi.org/10.3389/fpubh.2023.1122509
  8. Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol, 31(4), 337-350. doi:https://doi.org/10.1007/s10654-016-0149-3
  9. Harbuwono, D. S., Tahapary, D. L., Tarigan, T. J. E., & Yunir, E. (2020). New proposed cut-off of waist circumference for central obesity as risk factor for diabetes mellitus: Evidence from the Indonesian Basic National Health Survey. PLoS One, 15(11), e0242417. doi:https://doi.org/10.1371/journal.pone.0242417
  10. Hu, S., Zhang, Y., Poon, A. C. Y., Ji, Y., Hou, B., Zhang, C., Sun, Y. (2025). Risk factors of early-onset type 2 diabetes: A systematic review and meta-analysis. Diab Vasc Dis Res, 22(6), 14791641251400358. doi:https://doi.org/10.1177/14791641251400358
  11. Huang, Q., Li, Y., Yu, M., Lv, Z., Lu, F., Xu, N., Jiang, H. (2025). Global burden and risk factors of type 2 diabetes mellitus from 1990 to 2021, with forecasts to 2050. Front Endocrinol (Lausanne), 16, 1538143. doi:https://doi.org/10.3389/fendo.2025.1538143
  12. Karnchanasorn, R., Huang, J., Ou, H. Y., Feng, W., Chuang, L. M., Chiu, K. C., & Samoa, R. (2016). Comparison of the Current Diagnostic Criterion of HbA1c with Fasting and 2-Hour Plasma Glucose Concentration. J Diabetes Res, 2016, 6195494. doi:https://doi.org/10.1155/2016/6195494
  13. Ke, P., Wu, X., Xu, M., Feng, J., Xu, H., Gan, Y., Lu, Z. (2022). Comparison of obesity indices and triglyceride glucose-related parameters to predict type 2 diabetes mellitus among normal-weight elderly in China. Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity, 27(3), 1181-1191. doi:https://doi.org/10.1007/s40519-021-01238-w
  14. Kementerian Kesehatan RI. (2023). Survei Kesehatan Indonesia Tahun 2023. Retrieved from https://www.badankebijakan.kemkes.go.id/hasil-ski-2023/
  15. Khasanah, U., Kelliat, B. A., Afiyanti, Y., Besral, B., & Sari, D. W. (2024). The Problems and Needs of Self-Management Among Indonesian Older Adults With Hypertension: A Qualitative Study. SAGE Open Nurs, 10, 23779608241282915. doi:https://doi.org/10.1177/23779608241282915
  16. Konstantinova, E. D., Maslakova, T. A., & Ogorodnikova, S. Y. (2024). The predictive capability of several anthropometric indices for identifying the risk of metabolic syndrome and its components among industrial workers. Sci Rep, 14(1), 15327. doi:https://doi.org/10.1038/s41598-024-66262-z
  17. Krakauer, N. Y., & Krakauer, J. C. (2012). A new body shape index predicts mortality hazard independently of body mass index. PLoS One, 7(7), e39504. doi:https://doi.org/10.1371/journal.pone.0039504
  18. Kurniawaty, Y. (2020). Hubungan Indeks Massa Tubuh dengan Risiko Diabetes Melitus Tipe 2. Media Ilmu Kesehatan, 9(3), 237-242. doi:https://doi.org/10.30989/mik.v9i3.508
  19. Lee, C. M. Y., Huxley, R. R., Wildman, R. P., & Woodward, M. (2008). Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. Journal of Clinical Epidemiology, 61(7), 646-653. doi:https://doi.org/10.1016/j.jclinepi.2007.08.012
  20. Liu, Y., Liu, X., Guan, H., Zhang, S., Zhu, Q., Fu, X., Kuang, J. (2021). Body Roundness Index Is a Superior Obesity Index in Predicting Diabetes Risk Among Hypertensive Patients: A Prospective Cohort Study in China. Frontiers in Cardiovascular Medicine, Volume 8 - 2021. doi:https://doi.org/10.3389/fcvm.2021.736073
  21. Luk, A., Wild, S. H., Jones, S., Anjana, R. M., Hivert, M.-F., McCaffrey, J., Misra, S. (2025). Early-onset type 2 diabetes: the next major diabetes transition. The Lancet, 405(10497), 2313-2326. doi:https://doi.org/10.1016/S0140-6736(25)00830-X
  22. Neeland, I. J., Turer, A. T., Ayers, C. R., Berry, J. D., Rohatgi, A., Das, S. R., de Lemos, J. A. (2015). Body Fat Distribution and Incident Cardiovascular Disease in Obese Adults. Journal of the American College of Cardiology, 65(19), 2150-2151. doi:https://doi.org/10.1016/j.jacc.2015.01.061
  23. Pagehgiri, E. S., Irawati, D., & Josafat, A. (2019). Hubungan antara Rasio Lingkar Pinggang terhadap Tinggi Badan dengan Glukosa Darah Puasa pada Lansia. Jurnal Kedokteran, 8(2), 17-23. doi:https://doi.org/10.29303/jk.v8i2.4546
  24. Park, J., Byun, Y., & Kim, S. (2025). Predictive Diagnostic Power of Anthropometric Indicators for Metabolic Syndrome: A Comparative Study in Korean Adults. J Clin Med, 14(2). doi:https://doi.org/10.3390/jcm14020448
  25. Rachmi, C. N., Agho, K. E., Li, M., & Baur, L. A. (2016). Stunting, Underweight and Overweight in Children Aged 2.0-4.9 Years in Indonesia: Prevalence Trends and Associated Risk Factors. PLoS One, 11(5), e0154756. doi:https://doi.org/10.1371/journal.pone.0154756
  26. Rizvi, A. A., & Rizzo, M. (2024). Age-Related Changes in Insulin Resistance and Muscle Mass: Clinical Implications in Obese Older Adults. Medicina (Kaunas), 60(10). doi:https://doi.org/10.3390/medicina60101648
  27. Rosha, P., Oksidriyani, S., Siyam, N., Meisyaroh, I., Amalia, L., & Fakhira, A. (2024). Exploration of longitudinal data based on integrated elderly health care for developing predictive health status in Semarang, Indonesia. BKM Public Health Community Med, e12504. doi: https://doi.org/10.22146/bkm.v40i08.12504
  28. Sadeghi, E., Khodadadiyan, A., Hosseini, S. A., Hosseini, S. M., Aminorroaya, A., Amini, M., & Javadi, S. (2024). Novel anthropometric indices for predicting type 2 diabetes mellitus. BMC Public Health, 24(1), 1033. doi:https://doi.org/10.1186/s12889-024-18541-7
  29. Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., . . . Williams, R. (2019). Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Research and Clinical Practice, 157, 107843. doi:https://doi.org/10.1016/j.diabres.2019.107843
  30. Sapra, A., & Bhandari, P. (2023, Jun 21). Diabetes. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK551501/
  31. Sartika, R. A. D., Sigit, F. S., Shukri, N. H. M., Purwanto, E., Yunita, J., & Lubis, P. N. (2025). Redefining Obesity in the Indonesian Population: The Critical Role of Waist-to-Height Ratio in Screening for Diabetes Mellitus and Hypertension. J Nutr Metab, 2025, 5815261. doi:https://doi.org/10.1155/jnme/5815261
  32. Sharma, M., & Majumdar, P. K. (2009). Occupational lifestyle diseases: An emerging issue. Indian J Occup Environ Med, 13(3), 109-112. doi:https://doi.org/10.4103/0019-5278.58912
  33. Szukiewicz, D. (2023). Molecular Mechanisms for the Vicious Cycle between Insulin Resistance and the Inflammatory Response in Obesity. International Journal of Molecular Sciences, 24(12), 9818. doi:https://doi.org/10.3390/ijms24129818
  34. Talukder, A., Kelly, M., Gray, D., & Sarma, H. (2024). Prevalence and trends of double burden of malnutrition at household-level in South and Southeast Asia. Discover Public Health, 21(1). doi:https://doi.org/10.1186/s12982-024-00339-y
  35. Twig, G., Yaniv, G., Levine, H., Leiba, A., Goldberger, N., Derazne, E., Kark, J. D. (2016). Body-Mass Index in 2.3 Million Adolescents and Cardiovascular Death in Adulthood. N Engl J Med, 374(25), 2430-2440. doi:https://doi.org/10.1056/NEJMoa1503840
  36. Unnikrishnan, R., Pradeepa, R., Joshi, S. R., & Mohan, V. (2017). Type 2 Diabetes: Demystifying the Global Epidemic. Diabetes, 66(6), 1432-1442. doi:https://doi.org/10.2337/db16-0766
  37. Utami, I. N., Demartoto, A., & Murti, B. (2024). Biopsychosocial Factors Associated with the Risk of Hypertension in Elderly in Klaten, Central Java: A Cross-Sectional Study. Journal of Epidemiology and Public Health, 9(4), 437-447. doi:https://doi.org/10.26911/jepublichealth.2024.09.04.04
  38. Walsh, E. I., Jacka, F. N., Butterworth, P., Anstey, K. J., & Cherbuin, N. (2021). Midlife susceptibility to the effects of poor diet on diabetes risk. European Journal of Clinical Nutrition, 75(1), 85-90. doi:https://doi.org/10.1038/s41430-020-0673-9
  39. WHO Expert Consultation. (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. The Lancet, 363(9403), 157-163. doi:https://doi.org/10.1016/S0140-6736(03)15268-3
  40. Zhao, W., Tong, J., Li, J., & Cao, Y. (2021). Relationship between Body Roundness Index and Risk of Type 2 Diabetes in Japanese Men and Women: A Reanalysis of a Cohort Study. International Journal of Endocrinology, 2021(1), 4535983. doi:https://doi.org/10.1155/2021/4535983
  41. Zhou, B., Lu, Y., Hajifathalian, K., Bentham, J., Di Cesare, M., Danaei, G., Zuñiga Cisneros, J. (2016). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. The Lancet, 387(10027), 1513-1530. doi:https://doi.org/10.1016/S0140-6736(16)00618-8
  42. Zhou, Z., & Liu, J. (2025). Association between body roundness index and incidence of type 2 diabetes in a population-based cohort study. Sci Rep, 15(1), 13186. doi:https://doi.org/10.1038/s41598-025-92652-y
License & Copyright
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.