Evaluasi Status Gizi dan Skrining Obesitas pada Populasi Dewasa melalui Indeks Massa Tubuh dan Analisis Komposisi Tubuh
DOI:
https://doi.org/10.69930/scitec.v3i2.791Keywords:
komposisi tubuh, IMT, lemak visceral, PDCA, skrining komunitas, massa ototAbstract
Kelebihan berat badan dan perubahan komposisi tubuh merupakan faktor penting dalam perkembangan gangguan metabolik yang sering tidak terdeteksi pada populasi dewasa. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk melakukan skrining komposisi tubuh guna mengidentifikasi distribusi lemak dan massa otot sebagai indikator risiko metabolik menggunakan pendekatan Plan–Do–Check–Act (PDCA). Pemeriksaan meliputi indeks massa tubuh (IMT), basal metabolic rate (BMR), lemak visceral, lemak tubuh total, lemak subkutan, serta massa otot rangka. Sebanyak 148 partisipan dewasa terlibat dengan rerata usia 42,78±13,44 tahun dan distribusi jenis kelamin yang relatif seimbang. Rerata IMT sebesar 26,45±4,70 kg/m² menunjukkan kecenderungan overweight, dengan proporsi obesitas tingkat 1 (36,5%) dan tingkat 2 (21,6%) yang cukup tinggi. Rerata lemak tubuh total tercatat 30,29±7,67% dan lemak visceral 12,41±11,93, yang mengindikasikan akumulasi lemak yang bermakna. Lemak subkutan lebih tinggi pada perempuan dibandingkan laki-laki, terutama pada ekstremitas (lengan ±45% vs ±26%; kaki ±42% vs ±25%). Sementara itu, massa otot rangka relatif masih terjaga dengan dominasi pada ekstremitas bawah (41,49±6,12%). Temuan ini menunjukkan adanya ketidakseimbangan komposisi tubuh yang berpotensi meningkatkan risiko metabolik, meskipun fungsi otot relatif masih baik. Skrining berbasis komunitas ini efektif dalam memberikan gambaran awal kondisi komposisi tubuh. Integrasi dengan intervensi promotif-preventif seperti pengaturan pola makan, peningkatan aktivitas fisik, dan pemantauan berkala diperlukan untuk mencegah progresivitas gangguan metabolik.
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References
Ahmed, S. K., & Mohammed, R. A. (2025). Obesity: Prevalence, causes, consequences, management, preventive strategies and future research directions. Metabolism Open, 27, 100375. https://doi.org/10.1016/j.metop.2025.100375
Ayuningtyas, D., Kusuma, D., Amir, V., Tjandrarini, D. H., & Andarwati, P. (2022). Disparities in Obesity Rates among Adults: Analysis of 514 Districts in Indonesia. Nutrients, 14(16), 3332. https://doi.org/10.3390/nu14163332
Bansal, D., V. S., M. S., Devi, N., Boya, C., Dhora Babu, K., & Dutta, P. (2024). Trends estimation of obesity prevalence among South Asian young population: a systematic review and meta-analysis. Scientific Reports, 14(1), 596. https://doi.org/10.1038/s41598-023-50973-w
Bianchettin, R. G., Medina-Inojosa, B., Sheffeh, M. A., De Leon, A., Lei, S., Johnson, L. A., Saeidifard, F., Chacin Suarez, A., Medina-Inojosa, J. R., & Lopez-Jimenez, F. (2022). Abstract 15490: The Association Between Measurements of Body Composition and the Prevalence of Hypertension, Diabetes and Dyslipidemia in Patients Without Coronary Artery Disease. Circulation, 146(Suppl_1). https://doi.org/10.1161/circ.146.suppl_1.15490
Bosy-Westphal, A., Braun, W., Geisler, C., Norman, K., & Müller, M. J. (2018). Body composition and cardiometabolic health: the need for novel concepts. European Journal of Clinical Nutrition, 72(5), 638–644. https://doi.org/10.1038/s41430-018-0158-2
Bray, G. A., & Wilson, J. F. (2008). Obesity. Annals of Internal Medicine, 149(7), ITC4-1. https://doi.org/10.7326/0003-4819-149-7-200810070-01004
Lafontant, K., Thiamwong, L., Stout, J., Park, J.-H., Xie, R., & Fukuda, D. (2023). REDEFINING OBESITY: A RATIO OF FAT AND MUSCLE MASS COMPARED TO BODY MASS INDEX IN OLDER ADULTS. Innovation in Aging, 7(Supplement_1), 1109–1109. https://doi.org/10.1093/geroni/igad104.3560
Liu, J., Zhang, Y., Lavie, C. J., & Moran, A. E. (2022). Trends in Metabolic Phenotypes According to Body Mass Index Among US Adults, 1999-2018. Mayo Clinic Proceedings, 97(9), 1664–1679. https://doi.org/10.1016/j.mayocp.2022.02.013
Mongraw-Chaffin, M. L., Anderson, C. A. M., Allison, M. A., Ouyang, P., Szklo, M., Vaidya, D., Woodward, M., & Golden, S. H. (2015). Association Between Sex Hormones and Adiposity: Qualitative Differences in Women and Men in the Multi-Ethnic Study of Atherosclerosis. The Journal of Clinical Endocrinology & Metabolism, 100(4), E596–E600. https://doi.org/10.1210/jc.2014-2934
Moreira-Pais, A., Ferreira, R., Neves, J. S., Vitorino, R., Moreira-Gonçalves, D., & Nogueira-Ferreira, R. (2020). Sex differences on adipose tissue remodeling: from molecular mechanisms to therapeutic interventions. Journal of Molecular Medicine, 98(4), 483–493. https://doi.org/10.1007/s00109-020-01890-2
Pi-Sunyer, X. (2019). Changes in body composition and metabolic disease risk. European Journal of Clinical Nutrition, 73(2), 231–235. https://doi.org/10.1038/s41430-018-0320-x
Potter, A. W., Chin, G. C., Looney, D. P., & Friedl, K. E. (2025). Defining Overweight and Obesity by Percent Body Fat Instead of Body Mass Index. The Journal of Clinical Endocrinology & Metabolism, 110(4), e1103–e1107. https://doi.org/10.1210/clinem/dgae341
Roever-Borges, L. S. (2015). Visceral Fat and Association with Metabolic Risk Factors. Epidemiology: Open Access, 05(01). https://doi.org/10.4172/2161-1165.1000E118
Shackelford, S. (2023). Lifestyle Intervention in Primary Care Patients with Metabolic Syndrome. Journal of Clinical Lipidology, 17(4), e49. https://doi.org/10.1016/j.jacl.2023.05.073
Taylor, R. W., Grant, A. M., Williams, S. M., & Goulding, A. (2010). Sex Differences in Regional Body Fat Distribution From Pre‐ to Postpuberty. Obesity, 18(7), 1410–1416. https://doi.org/10.1038/oby.2009.399
Tylutka, A., Morawin, B., Walas, Ł., Michałek, M., Gwara, A., & Zembron-Lacny, A. (2023). Assessment of metabolic syndrome predictors in relation to inflammation and visceral fat tissue in older adults. Scientific Reports, 13(1), 89.
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