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2022, Number 1

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Rev Cub Med Mil 2022; 51 (1)

Combinations of metabolic syndrome and risk of diabetes mellitus

Vera-Ponce VJ, Talavera JE, Torres-Malca JR, De La Cruz-Vargas JA

Language: Spanish
References: 28
Page:
PDF size: 275.82 Kb.


ABSTRACT

Introduction: Metabolic syndrome is associated with an increased risk of diabetes, so its clinical identification helps to identify these patients at high risk.
Objective: To determine the combinations of metabolic syndrome for the risk of type 2 diabetes mellitus in a sample of Peruvian population.
Methods: 5-year cohort study of secondary analysis of the PERU MIGRANT study database. The altered components of the metabolic syndrome were low high-density lipoprotein, hypertriglyceridemia; and elevated glucose, blood pressure and waist circumference. In total 35 subgroups of metabolic syndrome components: 5 groups for each of the 5 components, 10 groups for combinations of 2 components and 3 components, 5 groups for the combination of 4 components.
Results: In the multiple regression analysis, only G as an independent factor presented a statistically significant relative risk (RR= 9.02; 95 % CI 2.45 - 33.24; p= 0.001). In relation to the combination of 2 factors, only the combination of glucose-elevated abdominal waist (RR= 7.28; 95 % CI 1.21 - 43.64; p= 0.030) and glucose-high-density lipoprotein (RR= 10.94; 95 % CI 2.71 - 44.23; p= 0.001) presented a statistically significant relative risk. Finally, patients with the glucose-high-density lipoprotein-abdominal waist combination had 7.80 times the risk of presenting type 2 diabetes mellitus versus those who did not (PR= 7.80; CI: 95 % 1.39 - 43.77; p= 0.020).
Conclusion: The combinations that include at the same time glucose - high density lipoproteins - abdominal waist, were the most associated combinations.


REFERENCES

  1. 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. DOI: 10.1177/1753944717711379

  2. McCracken E, Monaghan M, Sreenivasan S. Pathophysiology of the metabolic syndrome. Clin Dermatol. 2018;36(1):14-20. DOI: 10.1016/j.clindermatol.2017.09.004

  3. Herath HMM, Weerasinghe NP, Weerarathna TP, Amarathunga A. A Comparison of the Prevalence of the Metabolic Syndrome among Sri Lankan Patients with Type 2 Diabetes Mellitus Using WHO, NCEP-ATP III, and IDF Definitions. Int J Chronic Dis. 2018; 2018:7813537. DOI: 10.1155/2018/7813537

  4. Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep. 2018;20(2):12. DOI: 10.1007/s11906-018-0812-z

  5. Li R, Li W, Lun Z, Zhang H, Sun Z, Kanu JS, et al. Prevalence of metabolic syndrome in Mainland China: a meta-analysis of published studies. BMC Public Health. 2016;16:296. DOI: 10.1186/s12889-016-2870-y

  6. Moore JX, Chaudhary N, Akinyemiju T. Metabolic Syndrome Prevalence by Race/Ethnicity and Sex in the United States, National Health and Nutrition Examination Survey, 1988-2012. Prev Chronic Dis. 2017;14:E24. DOI: 10.5888/pcd14.160287

  7. Cuevas A, Alvarez V, Carrasco F. Epidemic of metabolic syndrome in Latin America. Curr Opin Endocrinol Diabetes Obes. 2011;18(2):134-8. DOI: 10.1097/MED.0b013e3283449167

  8. Vizmanos B, Betancourt-Nuñez A, Márquez-Sandoval F, González-Zapata LI, Monsalve-Álvarez J, Bressan J, et al. Metabolic Syndrome Among Young Health Professionals in the Multicenter Latin America Metabolic Syndrome Study. Metab Syndr Relat Disord. 2020;18(2):86-95. DOI: 10.1089/met.2019.0086

  9. Chávez VEJ, Villena Chávez JE Prevalencia de sobrepeso y obesidad en el Perú. Rev Peru Ginecol Obstet. 2017 [acceso: 13/09/2021];63(4):593-8. Disponible en: Disponible en: http://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S2304-51322017000400012

  10. Hudish LI, Reusch JE, Sussel L. β Cell dysfunction during progression of metabolic syndrome to type 2 diabetes. J Clin Invest. 2019;129(10):4001-8. DOI: 10.1172/JCI129188

  11. Noale M, Maggi S, Marzari C, Limongi F, Gallina P, Bianchi D, et al. Components of the metabolic syndrome and incidence of diabetes in elderly Italians: the Italian Longitudinal Study on Aging. Atherosclerosis. 2006;187(2):385-92. DOI: 10.1016/j.atherosclerosis.2005.09.018

  12. Wilson PWF, D’Agostino RB, Parise H, Sullivan L, Meigs JB. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation. 2005;112(20):3066-72. DOI: 10.1161/CIRCULATIONAHA.105.539528

  13. Lee M-K, Han K, Kim MK, Koh ES, Kim ES, Nam GE, et al. Changes in metabolic syndrome and its components and the risk of type 2 diabetes: a nationwide cohort study. Sci Rep. 2020;10(1):2313. DOI: 10.1038/s41598-020-59203-z

  14. Cho A-R, Kwon Y-J, Kim J-K. Pre-Metabolic Syndrome and Incidence of Type 2 Diabetes and Hypertension: From the Korean Genome and Epidemiology Study. J Pers Med. 2021;11(8):700. DOI: 10.3390/jpm11080700

  15. Lee M-K, Han K, Kim MK, Koh ES, Kim ES, Nam GE, et al. Combinations of metabolic syndrome components and the risk of type 2 diabetes mellitus: A nationwide cohort study. Diabetes Res Clin Pract. 2020;165. DOI: 10.1016/j.diabres.2020.108237

  16. Kurotani K, Miyamoto T, Kochi T, Eguchi M, Imai T, Nishihara A, et al. Metabolic syndrome components and diabetes incidence according to the presence or absence of impaired fasting glucose: The Japan Epidemiology Collaboration on Occupational Health Study. J Epidemiol. 2017;27(9):408-12. DOI: 10.1016/j.je.2016.08.015

  17. Miranda JJ, Gilman RH, García HH, Smeeth L. The effect on cardiovascular risk factors of migration from rural to urban areas in Peru: PERU MIGRANT Study. BMC Cardiovasc Disord. 2009;9:23. DOI: 10.1186/1471-2261-9-23

  18. PERU MIGRANT Study | Baseline and 5yr follow-up dataset. Figshare; 2017. [acceso: 13/09/2021] Disponible en: Disponible en: https://figshare.com/articles/dataset/PERU_MIGRANT_Study_Baseline_and_5yr_follow-up_dataset/4832612/3

  19. Heianza Y, Kato K, Kodama S, Ohara N, Suzuki A, Tanaka S, et al. Risk of the development of Type 2 diabetes in relation to overall obesity, abdominal obesity and the clustering of metabolic abnormalities in Japanese individuals: does metabolically healthy overweight really exist? The Niigata Wellness Study. Diabet Med. 2015;32(5):665-72. DOI: 10.1111/dme.12646

  20. American Diabetes Association. 5. Lifestyle Management: Standards of Medical Care in Diabetes-2019. Diabetes Care. 2019;42(Suppl 1):S46-60. DOI: 10.2337/dc19-S005

  21. Mohammad S, Ahmad J. Management of obesity in patients with type 2 diabetes mellitus in primary care. Diabetes Metab Syndr. 2016;10(3):171-81. DOI: 10.1016/j.dsx.2016.01.017

  22. Verma S, Hussain ME. Obesity and diabetes: An update. Diabetes Metab Syndr. 2017;11(1):73-9. DOI: 10.1016/j.dsx.2016.06.017

  23. Thambiah SC, Lai LC. Diabetic dyslipidaemia. Pract Lab Med. 2021;26:e00248. DOI: 10.1016/j.plabm.2021.e00248

  24. Hirano T. Pathophysiology of Diabetic Dyslipidemia. J Atheroscler Thromb. 2018;25(9):771-82. DOI: 10.5551/jat.RV17023

  25. Duclos M. Osteoarthritis, obesity and type 2 diabetes: The weight of waist circumference. Ann Phys Rehabil Med. 2016;59(3):157-60. DOI: 10.1016/j.rehab.2016.04.002

  26. Srivastava RAK. Dysfunctional HDL in diabetes mellitus and its role in the pathogenesis of cardiovascular disease. Mol Cell Biochem. 2018;440(1-2):167-87. DOI: 10.1007/s11010-017-3165-z

  27. Xepapadaki E, Nikdima I, Sagiadinou EC, Zvintzou E, Kypreos KE. HDL and type 2 diabetes: the chicken or the egg? Diabetologia. 2021;64(9):1917-26. DOI: 10.1007/s00125-021-05509-0

  28. Er L-K, Wu S, Chou H-H, Hsu L-A, Teng M-S, Sun Y-C, et al. Triglyceride Glucose-Body Mass Index Is a Simple and Clinically Useful Surrogate Marker for Insulin Resistance in Nondiabetic Individuals. PloS One. 2016;11(3):e0149731. DOI: 10.1371/journal.pone.0149731




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