Risk factor of metabolic syndrome in Javanese population based on determinants of anthropometry and metabolic measurement

https://doi.org/10.19106/JMedSci005302202105

Rosdiana Mus(1*), Ahmad Hamim Sadewa(2), Pramudji Hastuti(3), Anggelia Puspasari(4), Citra Maharani(5), Ika Setyawati(6)

(1) Technology of Medical Laboratory, Faculty of Pharmacy, Hospital Technology and Informatics, Universitas Mega Rezky, Makassar
(2) Department of Biochemistry, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta
(3) Department of Biochemistry, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta
(4) Department of Biochemistry, Faculty of Medicine and Health Science, Universitas Jambi, Jambi
(5) Department of Biochemistry, Faculty of Medicine and Health Science, Universitas Jambi, Jambi
(6) Department of Biochemistry, Faculty of Medicine and Health Science, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia
(*) Corresponding Author

Abstract


The prevalence of metabolic syndrome (MetS) is high worldwide which it can increase the risk of some diseases such as cardiovascular disease, type 2 diabetes mellitus even mortality. The prevalence pattern and determinants of MetS risk factors might differ among ethnics in Indonesia. This study aimed to determine the anthropometry and metabolic measurements determinants to predict the MetS prevalence of the Javanese population in Yogyakarta. It was a case control study conducted from December 2018 to March 2019 involving 214 Javanese subjects aged 20-74 years resided in Yogyakarta Special Region, Indonesia. NCEP ATP III criteria were used to identify MetS as case and not diagnosed with MetS as control. The results showed that BMI, WC, BP, total cholesterol and HDL-C were significantly different between MetS and non MetS patients (p<0.005). In MetS subjects, prevalence of obesity was 75.3%, visceral fat was 75.3%, WC 92.95%, WHtR 97.64% and total cholesterol/HDL-C ratio 553%, which independently increased the risk of MetS 7.30, 5.32, 13.37, 20.75, and 7.16 times, respectively. Result of logistic regression analysis showed central obesity based on WC increased the risk of Met-S by 17.62 time and the ratio of total cholesterol/HDL-C>5 by 9.54 time. In conclusion, WHtR is a better marker for MetS prediction independently. However, the WHtR in combination with WC and total cholesterol/HDL-C ratio are better for MetS prediction in the Javanese population.

Keywords


Anthropometry; Cholesterol Total/HDL ratio; Javanese ethnic; Metabolic syndrome;

Full Text:

PDF


References

  1. Kaur J. A comprehensive review on metabolic syndrome. Cardiol Res Pract 2014; 2014:943162. https://doi.org/10.1155/2014/943162
  2. Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: Pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis 2017; 11(8):215-25. https://doi.org/10.1177/1753944717711379
  3. Kamso S, Purwantyastuti P, Lubis DU, Juwita R, Robbi YK, Besral B. Prevalensi dan determinan sindrom metabolik pada kelompok eksekutif di Jakarta dan sekitarnya. Kesmas Natl Public Heal J 2011; 6(2):85. https://doi.org/10.21109/kesmas.v6i2.110
  4. Puspasari A, Maharani C, Mus R, Hastuti P. Biomarker pengganti HOMA-IR untuk mendeteksi. 2019; 7:122-30.
  5. Lim S, Shin H, Song JH, Kwak SH, Kang SM, Yoon JW, et al. Increasing prevalence of metabolic syndrome in Korea: The Korean National Health and Nutrition Examination Survey for 1998-2007. Diabetes Care 201; 34(6):1323-8. https://doi.org/10.2337/dc10-2109
  6. Jian C hao, Dewi FST, Herningtyas EH. Prevalence of metabolic syndrome and its component based on International Diabetes Federation (IDF) definition in Yogyakarta Special Region, Indonesia. J Med Sci 2017; 49(3):128-40. https://doi.org/10.19106/JMedSci004903201705
  7. Badan Penelitian dan Pengembangan Kesehatan. Riset kesehatan dasar (RISKESDAS) 2018. Jakarta: Kementrian Kesehatan Republik Indonesia, 2018.
  8. Herningtyas EH, Ng TS. Prevalence and distribution of metabolic syndrome and its components among provinces and ethnic groups in Indonesia. BMC Public Health 2019; 19(1):377. https://doi.org/10.1186/s12889-019-6711-7
  9. Zhao Y, Yan H, Yang R, Li Q, Dang S, Wang Y. Prevalence and determinants of metabolic syndrome among adults in a rural area of Northwest China. PLoS One 2014; 9(3):91578. https://doi.org/10.1371/journal.pone.0091578
  10. Mus R, Sadewa AH, Hastuti P, Puspasari A, Maharani C, Setyawati I. Age stratification in genetic variation of lipoprotein lipase in metabolic syndrome javanese ethnics of Indonesia. Open Access Maced J Med Sci 2019; 7(21):3540-5. https://doi.org/10.3889/oamjms.2019.844
  11. Brambilla P, Bedogni G, Heo M, Pietrobelli A. Waist circumference-to-height ratio predicts adiposity better than body mass index in children and adolescents. Int J Obes 2013; 37(7):943-6. https://doi.org/10.1038/ijo.2013.32
  12. Chu SY, Jung JH, Park MJ, Kim SH. Risk assessment of metabolic syndrome in adolescents using the triglyceride/high-density lipoprotein cholesterol ratio and the total cholesterol/high-density lipoprotein cholesterol ratio. Ann Pediatr Endocrinol Metab 2019; 24(1):41-8. https://doi.org/10.6065/apem.2019.24.1.41
  13. Rezapour M, Shahesmaeili A, Hossinzadeh A, Zahedi R, Najafipour H, Gozashti MH. Comparison of lipid ratios to identify metabolic syndrome. Arch Iran Med 2018; 21(12):572-7.
  14. Calling S, Johansson SE, Wolff M, Sundquist J, Sundquist K. The ratio of total cholesterol to high density lipoprotein cholesterol and myocardial infarction in Women’s health in the Lund area (WHILA): A 17-year follow-up cohort study. BMC Cardiovasc Disord 2019; 19(1):239. https://doi.org/10.1186/s12872-019-1228-7
  15. Pratiwi ZA, Hasanbasri M, Huriyati E. Penentuan titik potong skor sindroma metabolik remaja dan penilaian validitas diagnostik parameter antropometri: analisis Riskesdas 2013. J Gizi Klin Indones 2017; 14(2):80. https://doi.org/10.22146/ijcn.25590
  16. Alshamiri MQ, A Habbab FM, Al-Qahtani SS, Alghalayini KA, Al-Qattan OM, El-Shaer F. Waist-to-Height Ratio (WHtR) in predicting coronary artery disease compared to body mass index and waist circumference in a single center from Saudi Arabia. Cardiol Res Pract 2020;2020:4250793. https://doi.org/10.1155/2020/4250793
  17. Gharipour M, Sadeghi M, Dianatkhah M, Bidmeshgi S, Ahmadi A, Tahri M, et al. The cut-off values of anthropometric indices for identifying subjects at risk for metabolic syndrome in iranian elderly men. J Obes 2014; 2014:907149. https://doi.org/10.1155/2014/907149
  18. Dong X, Liu Y, Yang J, Sun Y, Chen L. Efficiency of anthropometric indicators of obesity for identifying cardiovascular risk factors in a Chinese population. Postgrad Med J 2011; 87(1026):251-6. https://doi.org/10.1136/pgmj.2010.100453.
  19. Shen S, Lu Y, Qi H, Li F, Shen Z, Wu L, et al. Waist-To-height ratio is an effectiveindicator for comprehensive cardiovascular health. Sci Rep 2017; 7:43046. https://doi.org/10.1038/srep43046



DOI: https://doi.org/10.19106/JMedSci005302202105

Article Metrics

Abstract views : 1360 | views : 1891


Bookmark and Share


Copyright (c) 2021 Rosdiana Mus, Ahmad Hamim Sadewa, Pramudji Hastuti, Anggelia Puspasari, Citra Maharani, Ika Setyawati

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