Background and Objective: Metabolic syndrome (MS) is a health condition associated with several factors including central obesity, glucose intolerance, dyslipidaemia, (low HDL-C, hypertriglyceridaemia) and elevated blood pressure. The aim of this study was to determine the pattern of clustering of these factors in diabetes, hypertension (HBP), chronic kidney disease, (CKD) and apparently healthy subjects, (AH), with the MS. Materials and Methods: A cross-sectional study was carried out. A total of 531 subjects including 174 diabetics, 136 hypertensive, 84 with CKD and 137 apparently healthy (37-72 years of age) participated in this study. Fasting plasma glucose level and lipids were analyzed while waist circumference and blood pressure was measured using standard procedures. MS was diagnosed according to the National Cholesterol Education/Adult Treatment Panel 111, (ATP 111), criteria. Graphs and logical binary regression were utilized to assess the validity of each parameter and metabolic syndrome. Results: Frequency of MS in this study were 68, 47, 35 and 26% for diabetes, hypertension, CKD and AH subjects respectively. Among the subjects with the MS, 52, 71, 65.5 and 78.8% of DM, HBP, CKD and AH subjects had three factors; correspondingly 39.7, 27.4, 34.5 and 21.2% had four factors and 8.2, 1.6, 0, 0 had five factors. Central obesity was diagnosed in 96, 76, 60 and 61%; hyperglycaemia was diagnosed in 100, 65, 76 and 77%; hypertriglyceridaemia was diagnosed in 82, 73, 54 and 43%; low HDL-C in 88, 85, 65 and 52% while elevated blood pressure was diagnosed in 84, 100, 43 and 36% of the DM, HBP, CKD and AH subjects with the MS respectively. Conclusion: Results suggested that MS could be caused by the central obesity and since waist circumference can easily and accurately be measured, it is an easier and definite method of screening for the MS in the population.
Okeke, Nduka Jude, I.S.I. Ogbu, Felix Edegbe, Eze Clementina N. and Obi Ihuoma A., 2021. Pattern of Risk Factors Clustering Among Diabetes, Hypertension, Chronic Kidney Disease and Apparently Healthy Subjects with the Metabolic Syndrome. Pakistan Journal of Nutrition, 20: 112-118.
Metabolic syndrome (MS) is a combination of medical disorders that increase the risk of developing cardiovascular disease and type 2 diabetes mellitus (T2DM). The disorders include central obesity, glucose intolerance, atherogenic dyslipidaemia of high triglyceride, low high density lipoprotein cholesterol and elevated blood pressure among others. The presence of three out of five risk factors is diagnostic of MS 1. The 5 risk factors include central obesity/excess waist circumference, (men >102 cm , women >88 cm ), elevated fasting blood glucose (≥5.6 mmol L1), elevated serum triglyceride [≥1.7 mmol L1, low HDL-C (men ≤1.0 mmol L1, women ≤1.3 mmol L1)] and elevated blood pressure, (≥130/85 mm Hg2) (Table 1).
MS promotes 2-fold increased risk for cardiovascular disease and a 5-fold risk of developing T2DM compared with those without the syndrome. It also increases risk of fatty liver disease, cancer and stroke3. The prevalence of MS in most western countries is between 20 and 30% of the adult population and in some populations or segments of the population is even higher but in developing world, the prevalence is lower but with increasing affluence, dietary changes and aging of the population, the prevalence undoubtedly will be elevated4. In a previous study conducted on rural community in Nigeria, prevalence of metabolic syndrome was found to be 12.1% but another study conducted on teachers in semi urban town showed a prevalence rate of 20% hence showing a parallel rise with worldwide value5,6. The global spread of metabolic syndrome are the end result from the years of taking ‘’a modern diet’’ (high calorie-low fiber fast food) high in refined carbohydrate such as breads, starches and sugars, as well as decreased physical activity occasioned by lack of exercise, sedentary lifestyles and mechanized transportation. In view of enormity of potential health implication of MS, early screen is important therefore, the clustering of risk factors assessment in the early detection, monitoring/follow up of individuals who are at great risk of developing MS is pertinent when we consider the cost implication for developing country in assessing all the risk factors before a diagnosis is made. Most worked one on MS have been on diagnosis, prevalence but none on screening. This study, therefore, assessed the pattern of clustering of these risk factors among diabetes, hypertension, chronic kidney disease (CKD) and apparently healthy (AH) subjects with the MS in Enugu Eastern Nigeria, with a view of advocating possible risk factor that should be used in screening for MS.
MATERIALS AND METHODS
Experimental design: This cross-sectional study was carried out at the department of chemical pathology and internal medicine. Data from a previous report (The Frequency of Metabolic Syndrome among Hospital-based Patients in the University of Nigeria Teaching Hospital and Apparently Healthy People in Enugu Metropolis, PhD thesis by I.S. I Ogbu, University of Calabar Nigeria) were analyzed. The study population (total 531) was made up of 174 (men 96, women 78) diabetics, 136 (men 76, women 60) hypertensive, 84 (men 43, women 41) patients with chronic kidney disease(CKD) and 137 (men 68, women 69) apparently healthy subjects. The patients were attending respective outpatient clinics of the University of Nigeria Teaching Hospital, Enugu while the apparently healthy subjects were mostly hospital workers who were not on any drug therapy at the time of sample collection. Ethical Clearance was obtained from the Ethical Committee of the University of Nigeria Teaching Hospital. Informed consent was obtained from participants after detailed explanation of the study protocol before commencement of the study.
Materials and research Tools: Trained laboratory assistants and a trained qualified nurses were employed to assist the researchers in collection of blood and waist circumference/blood pressure measurement respectively. Waist circumference and blood pressure were measured using tape (in cm) and Accuson sphygmomanometer (in mmHg) respectively. EDTA bottles for plasma lipid estimation and fluoride oxalate bottles for glucose estimation were made available. KD780 Semi-autochemistry analyzer was used for measurement of concentration of parameters.
Data collection and processing: Waist circumference (cm), of subjects were measured at the umbilicus using ordinary tailor’s tapes at end of expiration7. Samples were collected after overnight fasting. The site from which the blood sample was taken was disinfected with methylated spirit and dried. Only 10 mL venous blood was collected from the ante-cubital vein. The blood was collected into EDTA bottles for plasma lipid estimation and fluoride oxalate bottles for glucose estimation. These were centrifuged for 10 min at 3000 rpm. Plasma was separated into clean sterile containers and stored at -20°C . Grossly haemolyzed or icteric samples were discarded.
Parameters measured: Anthropometric measurement (waist circumference) and blood pressure was measured and recorded side by side. Plasma glucose was measured using the glucose oxidase method8, The plasma triglyceride was measured using the enzymatic method9. HDL-C was estimated in the plasma supernatant after precipitating β-apoprotein containing lipoproteins using the precipitation technique10. Analytical accuracy and precision was ensured by simultaneous analysis of commercially prepared control sera obtained from Randox Laboratories Ltd, Finsbury House, 23 Finsbury Circus, London with each batch of sample analysed. MS was diagnosed according to the National Cholesterol Education/Adult Treatment Panel 111, (ATP 111), criteria2.
Statistical analysis: Data were analyzed using statistical software program GraphPad Prism Version 2. Result of analysis of patients and apparently healthy subjects were grouped separately and prevalence rates were calculated. The data were further segregated into diseases(diabetes, hypertension and renal failure) and prevalence rates were calculated. Logical binary regression was performed to assess the validity of each parameter and metabolic syndrome.
The rate of occurrence of MS among the different classes of patients: The result showed that among the subjects with the MS, 52% of DM, 71% of HBP, 65.5% of CKD and 78.8% of AH subjects had three risk factors while correspondingly 39.7% of DM, 27.4% of HBP, 34.5% of CKD and 21.2% of AH had four risk factors. Negligible numbers (8.2, 1.6, 0 and 0% respectively) had five risk factors (Fig. 1). The frequency of MS in this study were 68% for DM, 47% for HBP, 35% for CKD and 26% for AH subjects. Also, truncal obesity (excess WC) was diagnosed in 96% of the DM, 76% of the HBP, 60% of the CKD and 61% of the AH subjects with the MS. Corresponding figures for hyperglycaemia were 100, 65, 76 and 77%, for hypertriglyceridaemia; 82, 73, 54 and 43%; for low HDL-C corresponding figures were 88, 85, 65 and 52% while for HBP the figures were 84, 100, 43 and 36%, respectively (Fig. 1).
Meanwhile, apart from hyperglycaemia in DM and elevated blood pressure in hypertension which were expectedly constant (100%), the 3 most prevalent risk factors for DM subjects were excess WC, low HDL-C and elevated blood pressure; for subjects with hypertension the risk factors were: low HDL-C, excess WC and hypertriglyceridaemia; for subjects with CKD: hyperglycaemia, Low HDL-C and excess WC and for AH subjects the risk factors were hyperglycaemia, excess WC and low HDL-C, (Fig. 2).
From Fig. 3, it is obvious that the order of prevalence of risk factors in all the subjects was excess WC (74.5%), hyperglycaemia (71%), low HDL-C (70.1%), hypertriglyceridaemia (63%) and finally elevated blood pressure (54%) (Hyperglycaemia and elevated blood pressure in DM and hypertension respectively were not counted). None of the risk factors was absent in any of the group but their prevalence varied considerably (Fig. 3).
Effect of the parameters on the outcome of MS among diabetic participants: The logical binomial regression model was used to determine the effect of WC, blood pressure, fasting blood glucose (FBG), triglyceride (TG) and high density lipoprotein (HDL-C) on the occurrence of MS among diabetic patients. The binary logistic model showed that 79.7% cases were diagnosed with metabolic syndrome (Table 2-4). Results showed that increase in FBG and low HDL-C is significantly (p<0.00) associated with the incidence of metabolic syndrome among the diabetic patients but increase in blood pressure was not significantly associated with occurrence of metabolic syndrome among the diabetic patients (Table 5).
Incidence of metabolic syndrome among patients diagnosed with hypertension: The logical binomial regression model was used to determine the effect of WC, blood pressure, FBG, triglyceride (TG), high density lipoprotein (HDL-C) on the incidence of MS among patients diagnosed with hypertension. The binary logical regression model showed that 68.1% cases were diagnosed with metabolic syndrome (Table 6-8). It also revealed that increase in FBG and HDL-C is significantly (p<0.005) associated with increased likelihood of having metabolic syndrome (Table 9-11).
Incidence of metabolic syndrome among patients diagnosed with renal failure: The logical binomial regression model was used to determine the association of WC, SPB, DBP, FBG, TG and HDL-C with MS among patients diagnosed with renal failure. Overall 75.6% cases were diagnosed with metabolic syndrome (Table 12). Results showed that increase in FBG is significantly (p<0.005) associated with the occurrence of metabolic syndrome as shown in Table 13.