¹Ogbu, Innocent S.I; Ph.D,²Ogbu Chinemerem C.; B.MLS,³Ndukwe, Mbrey; MSc,⁴Agunwah, Elizabeth; MSc, ⁵Eze, Clementina MSc,⁶Okeke, Nduka J; FMC.Path

^1,2,3Department of Medical Laboratory Science, Evangel University. Akaeze. Ebonyi State. Nigeria

⁴Department of Nursing Sciences. Evangel University. Akaeze. Ebonyi State, Nigeria.

⁵School of Basic Midwifery, Alex Ekwueme Federal Teaching Hospital, Abakaliki. Ebonyi State.

^1,6Department of Chemical Pahology. Faculty of Clinical Medicine. Ebonyi State University. Abakalilki, Ebonyi State. Nigeria.

Background: There is dyslipidaemia in metabolic syndrome (MetS). Lipid profiles in MetS associated with different health conditions may not be obvious. This study investigated lipid profiles in MetS associated with type 2 diabetes (DM-MetS), hypertension (HBP-MetS), chronic kidney disease (CKD-MetS) and apparent health (AH-MetS).

Methods: 540 patients were recruited for this study; 183 T2D, 136 HBP, 84 CKD patients and 137 AH subjects. They were outpatients and workers in the University of Nigeria Teaching Hospital Enugu. Nigeria. FPG, TC, HDL-C, TG as well as anthropometric measurements were determined using standard methods. Data analyses were done using GraphPad Prism version 2 statistical programme. MetS was diagnosed using NCEP-ATP 111 criteria

Results: Study showed 135 DM, 64 HBP, 31 CKD and 52 AH subjects had MetS, (prevalence rates: 36.9 %, 14.7 %, 18.4 %, and 37.9 % respectively). Only 38% MetS subjects had hypertriglyceridaemia while 66% with hypertriglyceridaemia had MetS. Corresponding figures for low HDLC were 40, and 77%. CKD-MetS had higher mean value of TG and TC than others; (2.65 ± 0.16 mmol/l; F = 11.4; P =0.0001; 6.07±0.02mmol/l; p = 0.001). Variations in TC were observed across groups, (p =0.0001). HDL-C was highest in AH-MetS, (1.51 ±0.07 mmol/l) and differed with mean value of DM-MetS, (1.23 ± 0.04mmol/l, p=0.01) only. Following the pattern of TC, LDLC was lowest among DM-MetS, (2.75 ± 0.06 mmol/l) and highest among CKD-MetS, (3.65 ± 0.28 mmol/l, p =0.001) with variations across groups, (F = 6.35; p= 0.0004).

Conclusion: Dyslipidemia profile varied with associated disorders. Presence of MetS is not a strong factor for development of lipid disorders in the study population.

metabolic syndrome, diabetes, chronic kidney disease, hypertension, lipid profile

1) Reilly MP, Rader DJ. The metabolic syndrome: more than the sum of its parts?

2) Circulation 2003; 108:1546–51.

3) Ayyobi AF, Brunzell JD. Lipoprotein distribution in the metabolic syndrome, type 2 diabetes mellitus, and familial combined hyperlipidemia. Am J Cardiol 2003; 92:27–33J.

4) De-Fronzo, R.A; Frrrannim, E; (1991). Insulin resistance: a multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidaemia and atherosclerotic cardiovascular disease. Diabetes Care 14 (3): 173 – 174.

5) Balkau, B; Charles, M.A; (1999). Comments on the provisional report from the WHO Consultation. European Group for the Study of Insulin Resistance, (EGIR). Diab Med 16(3): 442 – 443.

6) National Cholesterol Education Programme. 3rd Report of the NCEP on Detection and treatment of high blood Cholesterol in Adults (Adult Treatment Panel 111): final report. Circulation 2002; 106: 3143 -3421.

7) Flegal, K.M; Caroll, M.D; Ogden, C.L; Johnson, C.L. Prevalence and trend in obesity among US adults 1999-2000. JAMA (2002); 288: 1723 – 1727.

8) Alexander, C.M; Landsman, T.B; Teutsh, S.M; Haffner, S.M;. National Cholesterol Education Programme, (NCEP) – defined metabolic syndrome, diabetes and prevalence of coronary heart disease among National Health and Nutrition Examination Survey, (NHANES) 111 participants age 50 years and older. Diabetes (2003); 52 (8): 1210 – 1214.

9) Kaplan, N.M; (1989). The deadly quartet: upper body adiposity, glucose intolerance, hypertriglyceridaemia and hypertension. Arch Intern Med 149: 1514 – 1520.

10) Stamler, J; Stamler, A; Neaon, J.D; (1993). Blood pressure, systolic and diastolic and cardiovascular risk; US Population Data. Arch Intern Med 153: 598 – 615.

11) World Health Organization (1999). Definition, diagnosis and classification of diabtes mellitus. Geneva, WHO department of Non-Communicable disease Surveillance

12) Mykkanen, L; Kuusisto, J; Pyoralas, K; Laakso, M; (1993). Cardiovascular risk factors as predictors of type 2 (non-insulin dependent) diabetes mellitus in the elderly subjects. Diabetologia 36: 553 – 559.

13) Alexander, C.M; Landsman, T.B; Teutsh, S.M; Haffner, S.M; (2003). National Cholesterol Education Programme, (NCEP) – defined metabolic syndrome, diabetes and prevalence of coronary heart disease among National Health and Nutrition Examination Survey, (NHANES) 111 participants age 50 years and older. Diabetes 52 (8): 1210 – 1214.

14) Summer, S.A; & Nelson, D.H; (2005). A role for sphingolipids in producing the common features of type 2 diabetes, metabolic syndrome X and Cushing’s syndrome. Diabetes 53(30: 591- 602.

15) Sophia S.K, Yu, Darleen C Casttilo, Amber B Courville, Anne E Summer. The Triglyceride Paradox in people of African descent. Metab Syndr Relat Disord. (2012);10(2): 77 – 82

16) Meigs, J.B; D’Agostino, R.B.S; Wilson, P.W; Gupples, L.A; Nathan, D.M; Singer, D.E; (1977). Risk variable clustering in the insulin resistance syndrome: the Framingham Offspring Study. Diabetes 46 (10): 1594 – 1600.

17) Gray, R.S; Fabsitz, R.R; Cowan, L.D; Lee, E.T; Howard, B.V; Savage, P.J; (1998). Risk factor clustering in the insulin resistance syndrome: The Strong Heart Study. Am J Epidemiol 148:869 – 878.

18) Chen, W; Srinivasan, S.R; Elkasabany, A; Berenson, G.S; (1999). Cardiovascular risk factor clustering features of insulin resistance syndrome, (Syndrome X) in a biracial (blck-white) population of children, adolescents and young adults: The Bogahisa Heart Study. Am J Epidemiol 150: 667 – 674.

19) Sakkinen, PA, Wahl, P, Cushman, M, Lewis, MR, Tracy, RP, (2001). Clustering of procoagulation, inflammation and fibrinolysis variables with metabolic factor in insulin resistance syndrome. Am J Epidemiol 152: 897 – 907.

20) Mykkanen, L; Kuusisto, J; Pyoralas, K; Laakso, M; (1993). Cardiovascular risk factors as predictors of type 2 (non-insulin dependent) diabetes mellitus in the elderly subjects. Diabetologia 36: 553 – 559.

21) Dacie, J.V; Lewis, S.M; (1975). Practical Haematology. 5th Edition ELBS and Churchill Livingstone. P. 1-20

22) Friedewald, W.T; Levy, R. I; Fredrickson, D.S. Estimation of the concentration of low density lipoprotein cholesterol without the use of the preparatory ultracentrifuge. Clin Chem . (1972);18: 499-502

23) Buccolo, G; David, H. Quatitative determination of serum triglycerides by the use of enzymes. Clin Chem (1973); 19(4): 476 – 482.

24) .Allain, C.C; Poon, L.S; Clau, C.G.S; Richmond, W; Fu, P.D. Enzymatic determination of total cholesterol. Clin Chem (1974); 20(3): 470 – 475.

25) National Cholesterol Education Programme (NCEP).Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in adults, (Adult Treatment Panel 111) final report. Circulation (2002); 106 (25): 3143 – 3421.

26) Dows, J.R. Clerafeld, M. Weiss, S. Whitney, E. Shapiro, D.R. Beere, P.A. Langendorf, A. Stein,

27) Libby, P. Current concepts of the pathogenesis of the acute coronary syndrome. Circulation (2001); 104 (3): 365 – 372.

28) Krentz, A.J. Lipoprotien abnormalities and their consequences for patients with type 2 diabetes. Diab Obes Metab (2003); 5: 519 – 527.

29) Choi SH and Ginsberg HN. Increased very low density lipoprotein (VLDL) secretion, hepatic steatosis, and insulin resistance. Trends in Endocrinol Metab. 22:353-363

30) Pedroza-Tobias, A., Trejo-Valdivia, B., Sanchez-Romero, L.M. et al. Classification of metabolic syndrome according to lipid alterations: analysis from the Mexican National Health and Nutrition Survey 2006. BMC Public Health (2014); 14, 1056

31) Jeppesen, J; Hein, H.O; Saudicani, P; Cyntelberg, F. High triglyceride and low high density lipoprotein cholesterol and blood pressure and risk of ischaemic heart disease. Hypertension (2000); 36: 226 – 229.

32) Mazza A, Tikhonoff V, Schiavon L, Casiglia E: Triglycerides + high-density-lipoprotein-cholesterol dyslipidaemia, a coronary risk factor in elderly women: the CArdiovascular Study in the ELderly. Intern Med J. (2005); 35 (10): 604-610.

33) McLaughlin T, Reaven G, Abbasi F, Lamendola C, Saad M, Waters D, Simon J, Krauss RM: Is there a simple Way to identify insulin-resistant individuals at increased risk of cardiovascular disease?. Am J Cardiol. (2005); 96: 399-404

34) McLaughlin T, Abbasi F, Cheal K, Chu J, Lamendola C, Reaven G: Use of metabolic markers to identify overweight individuals who are insulin resistant. Ann Intern Med. (2003); 139 (10): 802-809.

35) Giannini E. Testa R. The metabolic syndrome: All criteria are equal, but some criteria are more equal than others. Arch Intern Med. 2003; 163:2787–2788.

36) Osei K. Metabolic syndrome in blacks: Are the criteria right? Curr Diab Rep. 2010; 10:199–208.

37) Sumner AE. Zhou J. Doumatey A, et al. Low HDL-cholesterol with normal triglyceride levels is the most common lipid pattern in West Africans and African Americans with metabolic syndrome: Implications for cardiovascular disease prevention. CVD Prev Control. 2010; 5:75–80.

38) Ervin RB. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States, 2003–2006. Natl Health Stat Report. 2009:1–7.

39) Park YW. Zhu S. Palaniappan L, et al. The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988–1994. Arch Intern Med. 2003; 163:427–436.

Volume 05 Issue 10 October 2022

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