CONICITY INDEX, LINGKAR PINGGANG, DAN RASIO LINGKAR PINGGANG-TINGGI BADAN DENGAN KADAR GLUKOSA DARAH PUASA PADA DEWASA

Main Article Content

Etika Ratna Noer
Fillah Fithra Dieny
Ani - Margawati
destiana - florencia

Abstract

Central obesity is closely related to various metabolic diseases such as diabetes mellitus (DM). Several studies examined the correlation between central obesity parameters such as waist circumference (WC) and waist-to-height ratio (WtHR) with fasting blood glucose (FBG) levels. One of the parameters of central obesity that is still rarely used in Indonesia is a conicity index (CI). This study analyzed the correlation between CI, WC, and WtHR with FBG levels among 59 subjects aged 35 – 59 years who were selected by consecutive sampling. Venous blood samples were collected for the FBG profile. Data on energy intake and physical activity were taken by interview using the SQ-FFQ and GPAQ. Data were analyzed by Rank Spearman, Mann-Whitney, and linear regression test. Most of the subjects (69,5%) were obese but FBG levels (57,62%) were normal. There was a significant correlation between CI, WC, and WtHR with FBG levels (p<0,05, r=0,313, r=0,336, r=0,389) respectively. To date, WC was the most closely related variable to FBG levels (p<0,001).

 

Keywords: conicity index, fasting blood glucose, waist circumference, waist-to-height ratio

 

ABSTRAK

 

Obesitas sentral berkaitan erat dengan berbagai penyakit metabolik seperti diabetes melitus (DM). Beberapa penelitian mengkaji hubungan parameter obesitas sentral seperti lingkar pinggang (LP) dan rasio lingkar pinggang-tinggi badan (RLPTB) dengan kadar glukosa darah puasa (GDP). Salah satu parameter obesitas sentral yang masih jarang digunakan di Indonesia yaitu conicity index (CI). Penelitian bertujuan untuk menganalisis hubungan CI, LP, dan RLPTB dengan kadar GDP pada dewasa dengan subjek 59 orang berusia 35 – 59 tahun yang dipilih secara consecutive sampling. Sampel darah melalui vena diambil untuk mendapatkan profil GDP. Data asupan energi dan aktivitas fisik diambil dengan wawancara menggunakan kuesioner ­SQ-FFQ dan GPAQ. Data dianalisis menggunakan uji korelasi Rank Spearman, Mann-Whitney, dan regresi linear. Sebagian besar subjek (69,5%) memiliki status gizi obesitas, namun kadar GDP (57,62%) tergolong normal. Korelasi signifikan positif ditemukan antara CI, LP, dan RLPTB dengan kadar GDP  (p<0,05, r=0,313, r=0,336, r=0,389). Uji multivariat menunjukkan LP merupakan variabel yang paling berpengaruh terhadap kadar GDP yang dibuktikan dengan nilai p<0,001.

Kata kunci: conicity index, glukosa darah puasa, lingkar pinggang, rasio lingkar pinggang-tinggi badan 

Downloads

Download data is not yet available.

Article Details

Section
Articles

References

D.0000000000008192

Motamed 1. O’Neill D. Measuring obesity in the absence of a gold standard. Economics and Human Biology. 2015; 17:116–28. doi: 10.1016/j.ehb.2015.02.002

WHO. Global health risks: mortality and burden of disease attributable to selected major risks. WHO Press; 2009.

LPB. Laporan nasional riskesdas 2018. Jakarta: Lembaga Penerbit Badan Penelitian dan Pengembangan Kesehatan; 2018.

Souza S de A, Silva AB, Cavalcante UMB, Lima CMBL, Souza TC de. Adult obesity in different countries: an analysis via beta regression models. Cad Saude Publica. 2018; 34(8):1–13. doi: 10.1590/0102-311X00161417

Dhawan D, Sharma S. Abdominal obesity, adipokines and non-communicable diseases. Journal of Steroid Biochemistry Molecular Biology. 2020; 203:105737. doi: 10.1016/j.jsbmb.2020.105737

ADA. Classification and diagnosis of diabetes: standards of medical care in diabetes—2020. Diabetes Care. 2020; 43(Supplement 1):S14–31. doi: 10.2337/dc20-S002

Kemenkes RI. Infodatin tetap produktif, cegah, dan atasi diabetes melitus 2020. Kementerian Kesehatan Republik Indonesia; 2020.

LPB. Laporan Provinsi Jawa Tengah Riskesdas 2018. Jakarta: Lembaga Penerbit Badan Penelitian dan Pengembangan Kesehatan; 2019.

Yakubu IM, Kaoje YS, Jabbe T, Abubakar AA. Best anthropometric predictors of fasting blood sugar, prediabetes, and diabetes. Diabetes Updates. 2020; 6:2-7. doi: 10.15761/DU.1000149

Mayasari N, Wirawanni Y. Hubungan lingkar leher dan lingkar pinggang dengan kadar glukosa darah puasa orang dewasa. Journal of Nutrition College. 2014; 3(4):473-481. doi: 10.14710/jnc.v3i4.6829

Khosravian S, Bayani MA, Hosseini SR, Bijani A, Mouodi S, Ghadimi R. Comparison of anthropometric indices for predicting the risk of metabolic syndrome in older adults. Romanian Journal of Internal Medicine. 2020; 0(0):1–12. doi: 10.2478/rjim-2020-0026

Woldegebriel AG, Fenta KA, Aregay AB, Aregay AD, Mamo NB, Wubayehu TW, et al. Effectiveness of anthropometric measurements for identifying diabetes and prediabetes among civil servants in a regional city of northern ethiopia: a cross-sectional study. Journal of Nutrition Metabolism. 2020; 2020:1–8. doi: 10.1155/2020/8425912

Chen X, Liu Y, Sun X, Yin Z, Li H, Deng K, et al. Comparison of body mass index, waist circumference, conicity index, and waist-to-height ratio for predicting incidence of hypertension: the rural chinese cohort study. Journal of Human Hypertension. 2018; 32(3):228–35. doi: 10.1038/s41371-018-0033-6

A Asif M, Aslam M, Altaf S, Majid A, Atif S. Evaluation of anthropometric parameters of central obesity among professional drivers: A receiver operating characteristic analysis. Kesmas. 2020;15(3):106–12. doi: 10.21109/kesmas.v15i3.3218

Hou X, Chen S, Hu G, Chen P, Wu J, Ma X, et al. Stronger associations of waist circumference and waist-to-height ratio with diabetes than bmi in chinese adults. Diabetes Research and Clinical Practice. 2019; 147: 9–18. doi: 10.1016/j.diabres.2018.07.029

Andrade MD, Freitas MCP de, Sakumoto AM, Pappiani C, Andrade SC de, Vieira VL, et al. Association of the conicity index with diabetes and hypertension in brazilian women. Archives of Endocrinology and Metabolism. 2016; 60(5):436–42.2 doi: 10.1590/2359-3997000000187

Mulyasari I, Purbowati. Conicity index as an indicator of body fat percentage in adolescents. Food Research. 2020; 4(S3):13–7. doi: 10.26656/fr.2017.4(S3).S04

PERKENI. Pedoman pengelolaan dan pencegahan diabetes melitus tipe 2 di indonesia 2019. Jakarta: PB Perkeni; 2019.

Cho S, Shin A, Choi JY, Park SM, Kang D, Lee JK. O ptimal cutoff values for anthropometric indices of obesity as discriminators of metabolic abnormalities in korea: results from a health examinees study. BioMed Central Public Health. 2021;21(1):1–8. doi: 10.1186/s12889-021-10490-9

WHO. Waist circumference and waist–hip ratio: report of a who expert consultation, geneva, 8–11 december 2008. Geneva: WHO; 2008.

Runingsari T. Sensitivity and specificity of waist to height ratio in obesity. Arsip Gizi dan Pangan. 2018;3(2):96–101. https://journal.uhamka.ac.id/index.php/argipa

WHO. Global physical activity questionnaire analysis guide. Geneva; 2010.

Hernández-Vásquez A, Azañedo D, Vargas-Fernández R, Aparco JP, Chaparro RM, Santero M. Cut-off points of anthropometric markers associated with hypertension and diabetes in peru: Demographic and health survey 2018. Public Health Nutrition. 2021;24(4):611–21. doi: 10.1017/S1368980020004036

Quaye L, Owiredu WKBA, Amidu N, Dapare PPM, Adams Y. Comparative abilities of body mass index, waist circumference, abdominal volume index, body adiposity index, and conicity index as predictive screening tools for metabolic syndrome among apparently healthy ghanaian adults. Journal of Obesity. 2019; 2019(Ci):1–10. doi: 10.1155/2019/8143179

Motamed N, Perumal D, Zamani F, Ashrafi H, Haghjoo M, Saeedian FS, et al. Conicity index and waist-to-hip ratio are superior obesity indices in predicting 10-year cardiovascular risk among men and women. Clinical Cardiology Journal. 2015;38(9):527–34. doi: 10.1002/clc.22437

Yang H, Xin Z, Feng J-P, Yang J-K. Waist-to-height ratio is better than body mass index and waist circumference as a screening criterion for metabolic syndrome in han chinese adults. Medicine (Baltimore). 2017;96(39):e8192. doi: 10.1097/M N, Sohrabi M, Poustchi H, Maadi M, Malek M, Keyvani H, et al. The six obesity indices, which one is more compatible with metabolic syndrome? A population based study. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2017;11(3):173–7. doi: 10.1016/j.dsx.2016.08.024

Djap HS, Sutrisna B, Soewondo P, Djuwita R, Timotius KH, Trihono, et al. Waist to height ratio (0.5) as a predictor for prediabetes and type 2 diabetes in indonesia. IOP Conference Series: Materials Science and Engineering. 2018;434:012311. doi: 10.1088/1757-899X/434/1/012311

Adejumo EN, Adejumo AO, Azenabor A, Ekun AO, Enitan SS, Adebola OK, et al. Anthropometric parameter that best predict metabolic syndrome in south west nigeria. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2019;13(1):48–54. doi: 10.1016/ j.dsx.2018.08.009.

Veghari G, Sedaghat M, Joshaghani H, Banihashem S, Moharloei P, Angizeh A, et al. The association of fasting blood glucose (fbg) and waist circumference in northern adults in iran: a population based study. Journal of Diabetes & Metabolic Disorders. 2014;13(1):2–7. doi: 10.1186/2251-6581-13-2

Wen WL, Wu PY, Huang JC, Tu HP, Chen SC. Different curve shapes of fasting glucose and various obesity-related indices by diabetes and sex. International Journal of Environmental Research and Public Health. 2021;18(6):1–13. doi: 10.3390/ijerph18063096

Katulanda GW, Katulanda P, Dematapitiya C, Dissanayake HA, Wijeratne S, Sheriff MHR, et al. Plasma glucose in screening for diabetes and pre-diabetes: how much is too much? Analysis of fasting plasma glucose and oral glucose tolerance test in sri lankans. BMC Endocrine Disorders. 2019;19(1):11. doi: 10.1186/s12902-019-0343-x

Hossain MI, Islam MS, Hasan MR, Akter M, Khoka MSH. Fasting blood glucose level and its association with sex, body mass index and blood pressure: a cross sectional study on a bangladeshi public university students. The International Journal of Community Medicine and Public Health Research. 2017;4(8):2663. doi: 10.18203/2394-6040.ijcmph20173310

Nabila R, Widyastuti N, Murbawani EA. Hubungan lingkar pergelangan tangan dengan kadar glukosa darah wanita obesitas usia 40 – 55 tahun. Journal of Nutrition College. 2018;7(2):92. doi: 10.14710/jnc.v7i2.20828

Yin X, Chen Y, Lu W, Jin T, LI L. Association of dietary patterns with the newly diagnosed diabetes mellitus and central obesity: a community based cross-sectional study. Nutrition & Diabetes. 2020; 10(1):16. doi: 10.1038/s41387-020-0120-y

Wang Q, Zhang X, Fang L, Guan Q, Gao L, Li Q. Physical activity patterns and risk of type 2 diabetes and metabolic syndrome in middle-aged & elderly northern chinese adults. Journal of Diabetes Research. 2018;2018:1–8. doi: 10.1155/2018/719827