Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Comparison of the Usefulness of Lipid Ratio Indicators for Prediction of Metabolic Syndrome in the Elderly Aged 65 Years or Older

65세 이상 고령자에서 대사증후군 예측을 위한 지질비율 지표의 유용성 비교

  • Shin, Kyung-A (Dept. of Clinical Laboratory Science, Shinsung University) ;
  • Kim, Eun Jae (Dept. of Medical Laboratory Science, Jeonju Kijeon University)
  • 신경아 (신성대학교 임상병리과) ;
  • 김은재 (전주기전대학교 임상병리과)
  • Received : 2021.10.27
  • Accepted : 2022.01.20
  • Published : 2022.01.28
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Abstract

The purpose of this study was to compare the usefulness of the lipid ratio indicators for the diagnosis of metabolic syndrome in the elderly aged 65 years or older. From January 2018 to December 2020, 1,464 people aged 65 years or older who underwent a health checkup at a general hospital in Seoul were included. Lipid ratio indicators were measured through blood tests. The prevalence of metabolic syndrome according to the quartiles of the lipid ratio index was confirmed by logistic regression analysis. In addition, the metabolic syndrome predictive ability and cutoff value of the lipid ratio indices were estimated with the receiver operating characteristic(ROC) curve. The correlation between atherogenic index of plasma(AIP) and waist circumference was the highest in both men and women(r=0.278, p<0.001 vs r=0.252, p<0.001). As for the lipid ratio indices, the incidence of metabolic syndrome was higher in the fourth quartile than in the first quartile. The area under the ROC curve(AUC) value of AIP was higher at 0.826(95% CI=0.799-0.850) and 0.852(95% CI=0.820-0.881) for men and women, respectively, compared to other lipid ratio indicators, and the optimal cutoff values for both men and women was 0.44(p<0.001). Therefore, the AIP among the lipid ratio indicators was found to be the most useful index for diagnosing metabolic syndrome in the elderly aged 65 years or older.

본 연구에서는 65세 이상 고령자를 대상으로 대사증후군 진단을 위한 지질비율 지표의 유용성을 비교하고자 하였다. 2018년 1월부터 2020년 12월까지 서울지역 종합병원에서 건강검진을 받은 65세 이상 1,464명을 대상으로 하였다. 혈액검사를 통해 혈장 동맥경화 지수를 포함한 지질비율 지표를 측정하였다. 지질비율 지표의 사분위수에 따른 대사증후군 유병률은 로지스틱 회귀분석으로 확인하였다. 또한 수신자 조작 특성(receiver operating characteristic, ROC) 곡선으로 지질비율 지표의 대사증후군 예측능력과 절단값을 추정하였다. 동맥경화 지수와 허리둘레의 상관성이 남녀 모두에서 가장 높았으며(r=0.278, p<0.001 vs r=0.252, p<0.001), 지질비율 지표는 1사분위수와 비교하여 4사분위수에서 대사증후군 발병률이 높았다. 혈장 동맥경화 지수는 다른 지질비율 지표보다 ROC 곡선 아래의 면적(area under the ROC curve; AUC)값이 남녀 각각 0.826(95% CI=0.799-0.850), 0.852(95% CI=0.820-0.881)로 가장 높게 나타났으며, 최적 절단값은 남녀 모두 0.44였다(p<0.001). 따라서 지질비율 지표 중 혈장 동맥경화 지수는 65세 이상 고령자의 대사증후군 진단에 가장 유용한 지표로 나타났다.

Keywords

References

  1. M. Galisteo, J. Duarte & A. Zarzuelo. (2008). Effects of dietary fibers on disturbances clustered in the metabolic syndrome. Journal of Nutritional Biochemistry, 19(2), 71-84. DOI : 10.1016/j.jnutbio.2007.02.009
  2. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. (2001). Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). The Journal of the American Medical Association, 285(19), 2486-2497. DOI : 10.1001/jama.285.19.2486
  3. A. Scuteri et al. (2015). Metabolic syndrome across Europe: different clusters of risk factors. European Journal of Preventive Cardiology, 22(4), 486-491. DOI : 10.1177/2047487314525529
  4. B. Bozkurt et al. (2016). Contributory risk and management of comorbidities of hypertension, obesity, diabetes mellitus, hyperlipidemia, and metabolic syndrome in chronic heart failure: A scientific statement from the American heart association. Circulation, 134(23), e535-e578. DOI : 10.1161/CIR.0000000000000450
  5. M. Aguilar, T. Bhuket, S. Torres, B. Liu & R. J. Wong. (2015). Prevalence of the metabolic syndrome in the United States, 2003-2012. Journal of the American Medical Association, 313(19), 1973-19744. DOI : 10.1001/jama.2015.4260
  6. M. H. Kim et al. (2020). The change of metabolic syndrome prevalence and its risk factors in Korean adults for decade: Korea National Health and Nutrition Examination Survey for 2008-2017. Korean Journal of Family Practice, 10(2), 44-52. DOI : 10.21215/kjfp.2020.10.1.44
  7. E. N. Adejumo et al. (2019). Anthropometric parameter that best predict metabolic syndrome in South west Nigeria. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 13(1), 48-54. DOI : 10.1016/j.dsx.2018.08.009
  8. H. Wang et al. (2017). Comparison of anthropometric indices for predicting the risk of metabolic syndrome and its components in Chinese adults: a prospective, longitudinal study. BMJ Open, 7(9), e016062. DOI : 10.1136/bmjopen-2017-016062
  9. S. D. Banik et al. (2021). Evaluation of anthropometric indices and lipid parameters to predict metabolic syndrome among adults in Mexico. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 14, 691-701. DOI : 10.2147/DMSO.S281894
  10. M. Rezapour, A. Shahesmaeili, A. Hossinzadeh, R. Zahedi, H. Najafipour & M. H. Gozashti. (2018). Comparison of lipid ratios to identify metabolic syndrome. Archives of Iranian Medicine, 21(12), 572-577.
  11. T. T. Wu, Y. Gao, Y. Y. Zheng, Y. T. Ma & X. Xie. (2018). Atherogenic index of plasma (AIP): a novel predictive indicator for the coronary artery disease in postmenopausal women. Lipids in Health and Disease, 17(1), 197. DOI : 10.1186/s12944-018-0828-z
  12. S. Shen et al. (2016). Association between ideal cardiovascular health and the atherogenic index of plasma. Medicine (Baltimore), 95(24), e3866. DOI : 10.1097/MD.0000000000003866
  13. S. Niroumand et al. (2015). Atherogenic Index of Plasma (AIP): A marker of cardiovascular disease. Medical Journal of the Islamic Republic of Iran, 29, 240.
  14. M. Dobiasova & J. Frohlich. (2001). The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clinical Biochemistry, 34(7), 583-588. DOI : 10.1016/s0009-9120(01)00263-6
  15. X. W. Zhu, F. Y. Deng & S. F. Lei. (2015). Meta-analysis of atherogenic index of plasma and other lipid parameters in relation to risk of type 2 diabetes mellitus. Primary Care Diabetes, 9(1), 60-67. DOI : 10.1016/j.pcd.2014.03.007
  16. G. L. Vega, C. E. Barlow, S. M. Grundy, D. Leonard & L. F. DeFina. (2014). Triglyceride-to-high-density-lipoprotein-choleste rol ratio is an index of heart disease mortality and of incidence of type 2 diabetes mellitus in men. Journal of Investigative Medicine, 62(2), 345-349. DOI : 10.2310/JIM.0000000000000044
  17. F. Hadaegh, M. Hatami, M. Tohidi, P. Sarbakhsh, N. Saadat & F. Azizi. (2010). Lipid ratios and appropriate cut off values for prediction of diabetes: a cohort of Iranian men and women. Lipids in Health and Disease, 9, 85. DOI : 10.1186/1476-511X-9-85
  18. G. Zoppini, C. Negri, V. Stoico, S. Casati, I. Pichiri & E. Bonora. (2012). Triglyceride-high-density lipoprotein cholesterol is associated with microvascular complications in type 2 diabetes mellitus. Metabolism, 61(1), 22-29. DOI : 10.1016/j.metabol.2011.05.004
  19. M. P. Hermans, F. M. Sacks, S. A. Ahn & M. F. Rousseau. (2011). Non-HDL-cholesterol as valid surrogate to apolipoprotein B100 measurement in diabetes: Discriminant ratio and unbiased equivalence. Cardiovascular Diabetology. 28(10), 20. DOI : 10.1186/1475-2840-10-20
  20. K. A. Adedokun, J. Olisekodiaka, A. D. Adeyeye & M. A. Muhibi. (2017). Castelli risk index, atherogenic index of plasma, and atherogenic coefficient: emerging risk predictors of cardiovascular disease in HIV-treated patients. Saudi Pharmaceutical Journal, 3(10B), 1101-1110. DOI : 10.21276/sjmps.2017.3.10.15
  21. J. Millan et al. (2009). Lipoprotein ratios: Physiological significance and clinical usefulness in cardiovascular prevention. Vascular Health and Risk Management, 5, 757-765.
  22. D. Gasevic, J. Frohlich, G. B. J. Mancini & S. A. Lear. (2012). The association between triglyceride to high-density-lipoprotein cholesterol ratio and insulin resistance in a multiethnic primary prevention cohort. Metabolism, 61(4), 583-589. DOI : 10.1016/j.metabol.2011.09.009
  23. A. E. Sumner, K. B. Finley, D. J. Genovese, M. H. Criqui & R. C. Boston. (2005). Fasting triglyceride and the triglyceride-HDL cholesterol ratio are not markers of insulin resistance in African Americans. Archives of internal medicine, 165(12), 1395-1400. DOI : 10.1001/archinte.165.12.1395
  24. M. G. Knight et al. (2011). The TG/HDL-C ratio does not predict insulin resistance in overweight women of African descent: a study of South African, African American and West African women. Ethnicity & Disease, 21(4), 490-494.
  25. C. Li, E. S. Ford, Y. X. Meng, A. H. Mokdad & G. M. Reaven. (2008). Does the association of the triglyceride to high-density lipoprotein cholesterol ratio with fasting serum insulin differ by race/ethnicity? Cardiovascular Diabetology, 7, 4. DOI : 10.1186/1475-2840-7-4
  26. M. Dobiasova, J. Frohlich, M. Sedova, M. C. Cheung & B. G. Brown. (2011). Cholesterol esterification and atherogenic index of plasma correlate with lipoprotein size and findings on coronary angiography. Journal of Lipid Research, 52(3), 566-571. DOI : 10.1194/jlr.P011668
  27. W. P. Castelli, R. D. Abbott & P. M. McNamara. (1983). Summary estimates of cholesterol used to predict coronary heart disease. Circulation, 67(4), 730-734. DOI : 10.1161/01.cir.67.4.730
  28. World Health Organization. (2000). The Asia-Pacific perspective: redefining obesity and its treatment. Sydney: Health Communications Australia.
  29. E. Alibasic, E. Ramic, A. Bajraktarevic, F. Ljuca, O. Batic-Mujanovic & M. Zildzic. (2015). Atherogenic dyslipidemia and residual vascular risk in practice of family doctor. Medical Archives, 69(5), 339-341. DOI : 10.5455/medarh.2015.69.339-341
  30. R. SoRelle. (2002). ATP III calls for more intensive low-density lipoprotein lowering in target groups. Circulation, 106(25), e90688. DOI : 10.1161/01.cir.0000052861.81153.18
  31. A. Cordero et al. (2008). Comparison of serum lipid values in subjects with and without the metabolic syndrome. American Journal of Cardiology, 102(4), 424-428. DOI : 10.1016/j.amjcard.2008.03.079
  32. R. Kawamoto et al. (2011). Relationships between lipid profiles and metabolic syndrome, insulin resistance and serum high molecular adiponectin in Japanese community-dwelling adults. Lipids in Health and Disease, 10, 79. DOI : 10.1186/1476-511X-10-79
  33. H. J. Kimm et al. (2010). Associations between lipid measures and metabolic syndrome, insulin resistance and adiponectin. Circulation Journal, 74(5), 931-937. DOI : 10.1253/circj.cj-09-0571
  34. D. Koleva, P. Andreeva-Gateva, M. Orbetzova, I. Atanassova & J. Nikolova. (2015). Atherogenic index of plasma, castelli risk indexes and leptin/adiponectin ratio in women with metabolic syndrome. International Journal of Pharmaceutical and Medical Research, 3(5), 12-18.
  35. F. Essiarab, H. Taki, H. Lebrazi, M. Sabri & R. Saile. (2014). Usefulness of lipid ratios and atherogenic index of plasma in obese Moroccan women with or without metabolic syndrome. Ethnicity & Disease, 24(2), 207-212.
  36. M. Dobiasova. (2006). AIP-atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice. Vnitrni lekarstvi, 52(1), 64-71.
  37. D. Gasevic, J. Frohlich, G. J. Mancini & S. A. Lear. (2014). Clinical usefulness of lipid ratios to identify men and women with metabolic syndrome: a cross-sectional study. Lipids in Health and Disease, 13, 159. DOI : 10.1186/1476-511X-13-159
  38. M. Abbasian, M. Delvarianzadeh, H. Ebrahimi & F. Khosravi. (2017). Lipid ratio as a suitable tool to identify individuals with MetS risk: A casecontrol study. Diabetology & Metabolic Syndrome, 11(Suppl 1), S15-S19. DOI : 10.1016/j.dsx.2016.08.011