Korelasi Pemeriksaan Antropometri Dasar dengan Tekanan Darah, Gangguan Metabolik, dan Fungsi Ginjal
DOI:
https://doi.org/10.55606/jurrike.v5i1.7976Keywords:
Anthropometry, Blood Pressure, Dyslipidemia, Metabolic, Renal FunctionAbstract
The increasing prevalence of metabolic syndrome and chronic kidney disease underscores the need for simple, low-cost, and community-applicable screening indicators. This study aimed to evaluate the association between basic anthropometric parameters and blood pressure, metabolic indicators, and renal function among adults in the Badui Luar community. A cross-sectional design was employed involving 41 participants who underwent anthropometric assessment, biochemical measurements, and blood pressure evaluation. Pearson correlation analysis was used to examine linear associations between variables. The results demonstrated that neck circumference exhibited significant correlations with dyslipidemia components, including LDL (r = 0.377), TC/HDL ratio (r = 0.516), and HDL (r = –0.433), indicating cervical adiposity as a strong marker of atherogenic risk. Calf circumference showed protective correlations with fasting glucose (r = –0.352) and eGFR (r = 0.322), suggesting the metabolic relevance of peripheral muscle mass in glycemic regulation and renal status. Body mass index showed a weak correlation with systolic blood pressure (r = 0.149), whereas waist and hip circumferences exhibited mild, clinically insignificant correlations with triglycerides and total cholesterol. Overall, these findings highlight that simple anthropometric measures—particularly neck and calf circumference—may serve as early indicators of cardiometabolic and renal risk in community-based screening. Further longitudinal studies with larger and more heterogeneous populations are required to validate these associations and determine the predictive power of anthropometric indicators.
Downloads
References
Adil, S. O., Musa, K. I., Uddin, F., Shafique, K., Khan, A., & Islam, M. A. (2023). Role of anthropometric indices as a screening tool for predicting metabolic syndrome among apparently healthy individuals of Karachi, Pakistan. Frontiers in Endocrinology, 14. https://doi.org/10.3389/fendo.2023.1223424
Borel, A.-L., Coumes, S., Reche, F., Ruckly, S., Pépin, J.-L., Tamisier, R., Wion, N., & Arvieux, C. (2018). Waist, neck circumferences, waist-to-hip ratio: Which is the best cardiometabolic risk marker in women with severe obesity? The SOON cohort. PLOS ONE, 13(11), e0206617. https://doi.org/10.1371/journal.pone.0206617
Dahel-Mekhancha, C. C., Rolland-Cachera, M.-F., Botton, J., Karoune, R., Sersar, I., Yagoubi-Benatallah, L., Bouldjedj, I., Benini, A., Fezeu, L. K., Nezzal, L., & Mekhancha, D.-E. (2023). Body composition and anthropometric indicators as predictors of blood pressure: A cross-sectional study conducted in young Algerian adults. British Journal of Nutrition, 129(11), 1993–2000. https://doi.org/10.1017/S0007114522002719
Guligowska, A., Corsonello, A., Pigłowska, M., Roller-Wirnsberger, R., Wirnsberger, G., Ärnlöv, J., Carlsson, A. C., Tap, L., Mattace-Raso, F., Formiga, F., Moreno-Gonzalez, R., Freiberger, E., Sieber, C., Gregorio, P. G., Martínez, S. L., Artzi-Medvedik, R., Yehoshua, I., Fabbietti, P., Lattanzio, F., … Feldreich, T. (2020). Association between kidney function, nutritional status and anthropometric measures in older people. BMC Geriatrics, 20(S1), 366. https://doi.org/10.1186/s12877-020-01699-1
Hall, J. E., Mouton, A. J., da Silva, A. A., Omoto, A. C. M., Wang, Z., Li, X., & do Carmo, J. M. (2021). Obesity, kidney dysfunction, and inflammation: Interactions in hypertension. Cardiovascular Research, 117(8), 1859–1876. https://doi.org/10.1093/cvr/cvaa336
Herningtyas, E. H., & Ng, T. S. (2019). Prevalence and distribution of metabolic syndrome and its components among provinces and ethnic groups in Indonesia. BMC Public Health, 19(1), 377. https://doi.org/10.1186/s12889-019-6711-7
Hidayangsih, P. S., Tjandrarini, D. H., Sukoco, N. E. W., Sitorus, N., Dharmayanti, I., & Ahmadi, F. (2023). Chronic kidney disease in Indonesia: Evidence from a national health survey. Osong Public Health and Research Perspectives, 14(1), 23–30. https://doi.org/10.24171/j.phrp.2022.0290
Ionete, F. I., Vutcanu, O., Chirică, I. S., & Bojinca, M. (2024). AB0120 Epidemiological insights into hyperuricemia prevalence and associated risk factors in adult rural Romanian population: A comprehensive population-based study. Annals of the Rheumatic Diseases, 83(Suppl 1), 1294. https://doi.org/10.1136/annrheumdis-2024-eular.1674
Islam, M. R., Moinuddin, M., Saqib, S. M., & Rahman, S. M. (2021). Relationship of anthropometric indicators of general and abdominal obesity with hypertension and their predictive performance among Albanians: A nationwide cross-sectional study. Nutrients, 13(10), 3373. https://doi.org/10.3390/nu13103373
Jamali, Z., Ayoobi, F., Jalali, Z., Bidaki, R., Lotfi, M. A., Esmaeili-Nadimi, A., & Khalili, P. (2024). Metabolic syndrome: A population-based study of prevalence and risk factors. Scientific Reports, 14(1), 3987. https://doi.org/10.1038/s41598-024-54367-4
Jia, G., Sowers, J. R., & Whaley-Connell, A. T. (2024). Obesity in hypertension: The role of the expanding waistline over the years and insights into the future. Hypertension, 81(4), 687–690. https://doi.org/10.1161/HYPERTENSIONAHA.123.21719
Kosmas, C. E., Rodriguez Polanco, S., Bousvarou, M. D., Papakonstantinou, E. J., Peña Genao, E., Guzman, E., & Kostara, C. E. (2023). The triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio as a risk marker for metabolic syndrome and cardiovascular disease. Diagnostics, 13(5), 929. https://doi.org/10.3390/diagnostics13050929
Kovesdy, C. P. (2022). Epidemiology of chronic kidney disease: An update 2022. Kidney International Supplements, 12(1), 7–11. https://doi.org/10.1016/j.kisu.2021.11.003
Li, Y., & Zeng, L. (2024). Comparison of seven anthropometric indexes to predict hypertension plus hyperuricemia among U.S. adults. Frontiers in Endocrinology, 15. https://doi.org/10.3389/fendo.2024.1301543
Li, Y., Zheng, R., Li, S., Cai, R., Ni, F., Zheng, H., Hu, R., & Sun, T. (2022). Association between four anthropometric indexes and metabolic syndrome in US adults. Frontiers in Endocrinology, 13. https://doi.org/10.3389/fendo.2022.889785
Nie, G., Hou, S., Zhang, M., & Peng, W. (2021). High TG/HDL ratio suggests a higher risk of metabolic syndrome among an elderly Chinese population: A cross-sectional study. BMJ Open, 11(3), e041519. https://doi.org/10.1136/bmjopen-2020-041519
Papastergiou, J., Donnelly, M., Li, W., Sindelar, R. D., & van den Bemt, B. (2020). Community pharmacy-based eGFR screening for early detection of CKD in high-risk patients. Canadian Journal of Kidney Health and Disease, 7. https://doi.org/10.1177/2054358120922617
Qin, Z., Chen, X., Sun, J., & Jiang, L. (2023). The association between visceral adiposity index and decreased renal function: A population-based study. Frontiers in Nutrition, 10. https://doi.org/10.3389/fnut.2023.1076301
Rizk, N., & Yousef. (2012). Association of lipid profile and waist circumference as cardiovascular risk factors for overweight and obesity among school children in Qatar. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 425. https://doi.org/10.2147/DMSO.S39189
Scurt, F. G., Ganz, M. J., Herzog, C., Bose, K., Mertens, P. R., & Chatzikyrkou, C. (2024). Association of metabolic syndrome and chronic kidney disease. Obesity Reviews, 25(1). https://doi.org/10.1111/obr.13649
Semianiv, M., Sydorchuk, L., Biletsky, S., Petrynych, O., Kazantseva, T., Repchuk, Y., Semianiv, I., Sokolenko, A., Yarynych, Y., Voroniuk, K., & Dzhuriak, V. (2023). Linkage between blood pressure, body mass index, and anthropometric, metabolic, hormonal parameters in patients with essential arterial hypertension. Journal of Hypertension, 41(Suppl 3), e214–e215. https://doi.org/10.1097/01.hjh.0000941144.88581.63
Seok Lee, H., Park, Y.-M., Han, K., Yang, J.-H., Lee, S., Lee, S., Yoo, S., & Kim, S. R. (2020). Obesity-related hypertension: Findings from The Korea National Health and Nutrition Examination Survey 2008–2010. PLOS ONE, 15(4), e0230616. https://doi.org/10.1371/journal.pone.0230616
Shen, W., Punyanitya, M., Chen, J., Gallagher, D., Albu, J., Pi‐Sunyer, X., Lewis, C. E., Grunfeld, C., Heshka, S., & Heymsfield, S. B. (2006). Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity, 14(4), 727–736. https://doi.org/10.1038/oby.2006.83
Shrestha, N., Gautam, S., Mishra, S. R., Virani, S. S., & Dhungana, R. R. (2021). Burden of chronic kidney disease in the general population and high-risk groups in South Asia: A systematic review and meta-analysis. PLOS ONE, 16(10), e0258494. https://doi.org/10.1371/journal.pone.0258494
Stamm, E., Marques-Vidal, P., Rodriguez, E. G., Vollenweider, P., Hans, D., & Lamy, O. (2022). Association of adiposity evaluated by anthropometric, BIA, and DXA measures with cardiometabolic risk factors in nonobese postmenopausal women: The CoLaus/OsteoLaus cohort. Menopause, 29(4), 450–459. https://doi.org/10.1097/GME.0000000000001930
Wahyuni, A., Yenni, Y., Utami, A. S., Bachri, Y., & Oktorina, R. (2025). Community health screening for cardiometabolic risk through blood pressure, glucose, and lifestyle assessment. Jurnal Pengabdian Masyarakat Dalam Kesehatan, 7(2), 103–112. https://doi.org/10.20473/jpmk.v7i2.75122
Wardani, N. S., Heriyanto, N., Cahya, M. R. F., Manurung, S., & Parulian, I. (2025). Relationship between hemodialysis frequency and urea reduction ratio (URR) results in CKD patients. Indonesian Journal of Global Health Research, 7(3), 729–740. https://doi.org/10.37287/ijghr.v7i3.5999
Weeks, M., Delgado, A. D., Wood, J., Zhang, B., Pesce, S., Kunces, L., Lili, L., & Putrino, D. (2023). Relationships between body composition, anthropometrics, and standard lipid panels in a normative population. Frontiers in Cardiovascular Medicine, 10. https://doi.org/10.3389/fcvm.2023.1280179
Yang, M., Zhang, Y., Zhao, W., Ge, M., Sun, X., Zhang, G., & Dong, B. (2023). Individual and combined associations of body mass index and waist circumference with components of metabolic syndrome among multiethnic middle-aged and older adults: A cross-sectional study. Frontiers in Endocrinology, 14. https://doi.org/10.3389/fendo.2023.1078331
Zhang, W., Yang, Z., Niu, Y., Li, X., Zhu, L., Lu, S., Zhang, H., Fan, J., Ning, G., Qin, L., & Su, Q. (2017). Association of calf circumference with insulin resistance and non-alcohol fatty liver disease: The REACTION study. BMC Endocrine Disorders, 17(1), 28. https://doi.org/10.1186/s12902-017-0176-4
Zhang, Y., Wu, H., Xu, Y., Qin, H., Lan, C., & Wang, W. (2020). The correlation between neck circumference and risk factors in patients with hypertension. Medicine, 99(47), e22998. https://doi.org/10.1097/MD.0000000000022998
Zhou, J., Ge, H., Zhu, M., Wang, L., Chen, L., Tan, Y., Chen, Y., & Zhu, H. (2013). Neck circumference as an independent predictive contributor to cardio-metabolic syndrome. Cardiovascular Diabetology, 12(1), 76. https://doi.org/10.1186/1475-2840-12-76
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 JURNAL RISET RUMPUN ILMU KEDOKTERAN
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
