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THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus
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DSX-492; No. of Pages 6 Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx
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Original Article
THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus A. Ipadeola a,*, J.O. Adeleye b a b
Department of Medicine, University College Hospital, Ibadan, Nigeria Department of Medicine, College of Medicine, University of Ibadan, Nigeria
A R T I C L E I N F O
A B S T R A C T
Keywords: Type 2 diabetes mellitus Metabolic syndrome Calculated absolute cardiovascular risk UKPDS risk engine
Background: Persons with type 2 DM have a cardiovascular risk 2–4 times that of the normal population. Co-occurrence of metabolic syndrome (MS) with type 2 DM is associated with an increased risk of development of cardiovascular disease. The aim of this study was to determine the prevalence of the MS in patients with type 2 diabetes mellitus and to compare absolute cardiovascular risk in type 2 DM Patients with MS with those without MS. Methods: Anthropometric measurements and Blood Pressure of 340 eligible patients with type 2 DM recruited into the study were taken. Participants’ FPG, FLP and glycated haemoglobin were also estimated. Cardiovascular risk score was calculated using the United Kingdom Prospective Diabetes Study (UKPDS) risk engine. Diagnosis of the MS was the International Diabetes Federation Criteria (IDF). Results: Over 66% participants had MS. The absolute cardiovascular risk score was found to be similar in persons with type 2 DM whether they fulfilled the criteria for diagnosis metabolic syndrome or not. Conclusion: The absolute cardiovascular risk score was similar in type 2 DM patients with or without the metabolic syndrome. ß 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.
1. Introduction Type 2 diabetes mellitus (type 2 DM) accounts for 90–95% of all cases of diabetes mellitus (DM) and is characterized by insulin secretory defects and insulin resistance of varying degrees [1]. It is considered an independent risk factor for cardiovascular disease, and persons with type 2 DM are reported to have a cardiovascular risk two to four times greater than that of the normal population [2]. The metabolic syndrome (MS) refers to an aggregation of cardiovascular risk factors in a single individual with insulin resistance central in its pathogenic paradigm [3]. The metabolic syndrome is also associated with an increased risk for the
Abbreviations: ADA, American Diabetic Association; CHD, coronary heart disease; CVD, cardiovascular disease; DCCT, Diabetes Control and Complications Trial; DECODE, collaborative analysis of diagnostic criteria in Europe study group; DPP, Diabetes Prevention Program; DM, diabetes mellitus; EDTA, ethylenediamine tetra acetic acid; HDL-c, high density lipoprotein cholesterol; HbA1c, glycated haemoglobin; IDF, International Diabetes Federation; IR, insulin resistance; LDLc, low density lipoprotein cholesterol; MS, metabolic syndrome; MOP, medical outpatient clinic; NCEP-ATP III, national cholesterol education project adult treatment panel; Type 1 DM, type 1 diabetes mellitus; Type 2 DM, type 2 diabetes mellitus; UKPDS, United Kingdom Prospective Diabetes Study; WHO, World Health Organization. * Corresponding author. Tel.: +234 8037024916. E-mail address: [email protected] (A. Ipadeola).
development of cardiovascular disease [4]. People with this syndrome are twice as likely to die from a macrovascular event and three times as likely to have ischaemic heart disease and stroke compared with people without the syndrome [4]. The above, therefore, makes an assessment of its association with type 2 DM crucial. Accurate assessment of cardiovascular risk in persons with diabetes mellitus is a major first step for developing a plan for risk reduction in such persons with diabetes and has become necessary to inform the choice of therapeutic strategies for individual patients [5]. The use of cardiovascular risk calculation algorithms provide a method for accurate estimation of the cardiovascular risk in such patients. A number of risk calculation algorithms exist but have been found to underestimate the cardiovascular risk in persons with type 2 DM [5]. The UKPDS risk engine has been found to be quite accurate in persons with type 2 DM because it includes glycaemic control in its analysis [6,7]. The aim of this study was to determine the prevalence of the MS in patients with type 2 diabetes mellitus and to compare absolute cardiovascular risk in type 2 DM Patients with MS with those without MS. 2. Methods This was a cross-sectional study of 340 consecutive persons with type 2 diabetes mellitus carried out at the University College
http://dx.doi.org/10.1016/j.dsx.2015.08.011 1871-4021/ß 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Ipadeola A, Adeleye JO. THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.08.011
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Hospital, Ibadan over a four month period May 2008–September 2008. Ethical clearance was obtained from the Joint Institution Review Committee (IRC) of the University College Hospital and the College of Medicine, University of Ibadan. 2.1. Clinical evaluation Demographic and clinical data were obtained from all subjects using a data collection form. Clinical data included the time of diagnosis of diabetes mellitus, duration of treatment, history of hypertension, previous or current history of ischaemic heart disease (angina, myocardial infarction or coronary revascularization procedure), cerebrovascular disease (e.g. TIA or stroke) or peripheral vascular disease, (e.g. intermittent claudication, gangrene of the lower extremities or revascularization procedure). History of cigarette smoking and alcohol use was also obtained. Family history of diabetes mellitus, hypertension, coronary heart disease, stroke and sudden unexpected death was also inquired about. The radial pulse was examined in all subjects; persons found to have irregular pulse rhythm had an electrocardiogram to rule out atrial fibrillation. All clinical information was imputed into the UKPDS risk engine along with other parameters to obtain an absolute cardiovascular risk score for each patient. 2.2. Diagnosis of the metabolic syndrome Patients were categorized as having the metabolic syndrome based on the International Diabetes Federation (IDF) definition [8]; as follows – Central obesity (defined as waist circumference 94 cm for men and 80 cm for women of African descent as European values are to be used in the absence of figures for Africans), plus any two of the following four factors: 1. Triglyceride level greater than 150 mg/dl, or on specific treatment for this lipid abnormality. 2. HDL cholesterol: less than 40 mg/dl in men and less than 50 mg/ dl in female or on specific treatment for this abnormality. 3. Elevated blood pressure with systolic 130 or diastolic 85 mmHg or on treatment for previously diagnosed hypertension. 4. Fasting plasma glucose of 100 mg/dl or previously diagnosed type2 diabetes mellitus.
2.3. Waist circumference measurement Waist circumference was measured using the protocol recommended by the World Health Organization [28]. A waist circumference of 80 cm in females and 94 cm in males was taken as indicative of truncal obesity [8]. 2.4. Weight and height Measurement Weight was measured (in kilograms) using a beam balance scale with subjects in light clothing and without shoes on. Height was measured (in metres) using a portable height/length measuring board without the subjects wearing footwear, caps or other head gear. Body mass index (BMI) was subsequently calculated using the formula: 2
2.5. Blood pressure Blood pressure was measured with the patients seated after at least 5 min rest using a mercury sphygmomanometer with a standard adult cuff size of 12 cm was wrapped round the patient’s arm and placed at the heart level. The cuff was applied closely to the upper arm with the lower end about 2.5 cm from the cubital fossa. The patient was seated with the back resting on the chair, the non-dominant arm used was resting on the table while both feet were resting on the floor. Korotkoff sounds phases I and V were taken as the systolic and diastolic blood pressures respectively and values were recorded to the nearest 2 mmHg. Two readings were taken 2 min apart and the average was recorded. Patients with systolic or diastolic blood pressure of 130 and 85 mmHg respectively were grouped as having hypertension according to the IDF criteria [8]. In addition, patients on treatment for previously diagnosed hypertension were also regarded as having hypertension. 2.6. Laboratory evaluation Measurements were taken after an overnight fast of 8–14 h. Patients were seated and allowed to rest for 5 min, and then a venipuncture was performed after cleaning the skin with 70% methylated spirit, using a new sterile disposable needle and syringe. Ten millilitres (ml) of blood was collected out of which 2 ml was put into a fluoride oxalate bottle for fasting blood glucose estimation. The remaining 8 ml was transferred into separate potassium EDTA bottle for lipid profile and glycated haemoglobin analysis. 2.7. Plasma glucose measurement Samples were spun after collection to obtain plasma and analysis was carried out in the Department of Medicine laboratory by a trained laboratory scientist and the investigator using the glucose oxidase enzymatic method. 2.8. Plasma lipid assay Total cholesterol and Triglycerides were analysed in the same laboratory stated above using enzymatic methods and values were read off a colorimeter. High-density lipoprotein cholesterol (HDLC) was determined using selective precipitation; followed by enzymatic method for measuring cholesterol. All reagents used were from ‘‘DIALAB Austria’’. Calculated values for low-density lipoprotein cholesterol (LDL-C) were obtained using the FriedWald’s equation as follows (provided the plasma triglycerides are not greater than 400 mg/dl) [10]. 2.9. Glycated haemoglobin estimation Samples were stored at 2–8 8C. Analysis was done within one week of storage using the HbA1c ionic exchange chromatographic method (DIALAB, AUSTRIA). When this method was compared to the US National Glycohaemoglobin Standardization Program certified method (NGSP) which is DCCT referenced some correlation was obtained. The following formula was used by the manufacturer of the kit used (DIALAB, AUSTRIA) to obtain DCCT referenced values: HbA1c (NGSP) (%) = 0.86 HbA1cDialab (%) + 0.24. HbA1c of 6.5% was taken as good control according to the IDF, while >6.5% was taken as poor glycaemic control [8].
BMI ¼ Weight=height ðkg=m2 Þ 3. Calculated cardiovascular risk A BMI of 18.5–24.9 kg/m2 was considered as normal. Values between 25 and 29.9 kg/m2 implied overweight, while obesity was defined as a BMI 30 kg/m2 [9].
A validated diabetes specific risk calculator known as the United Kingdom Prospective Diabetes Study (UKPDS) risk engine
Please cite this article in press as: Ipadeola A, Adeleye JO. THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.08.011
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was used in calculation of absolute cardiovascular risk in these patients with type 2 DM. This risk engine was developed following a randomized controlled trial involving 5102 patients with type 2 DM who were followed up for 20 years and has been found to be superior to other previously used risk engines [6,7]. The model provides equations for absolute risk, incorporating the effect of multiple risk factors to give overall event rates, rather than relative risk [6,7]. Data necessary for the calculation-age, duration of diabetes, gender, presence or absence of atrial fibrillation, ethnicity, haemoglobin A1c, cigarette smoking, systolic blood pressure, total cholesterol and high density lipoprotein cholesterol was imputed as appropriate. The inputs required by the model had been measured during the course of the study. Model fitting was carried out by maximum likelihood estimation, for which the Newton– Raphson method was used with numerical derivatives. The model has been incorporated in the UKPDS risk engine software, which was available without charge from the UKPDS website [7]. A risk score was obtained which estimated the risk of development of cardiovascular disease in the following 10 years. All patients who participated in the study had their absolute cardiovascular risk score calculated using the UKPDS risk engine and were categorized into the following sub-groups: low risk – 0– 15%, moderate risk – >15–<30% and high risk – >30% according to the model [7]. 4. Data analysis The data obtained was coded and entered on a computer worksheet. Data analysis was carried out using the SPSS software, windows version 16.0 version. Descriptive variables are presented using frequency tables, pie charts and bar charts. Continuous variables are expressed as means (standard deviation, SD) while categorical variables are expressed as percentages. The association between variables was tested using the Chi Square for categorical variables and Student t-test for quantitative variables. Statistical significance of the tests of association was set at a p-value of less than 0.05. 5. Results 5.1. Characteristics of study subjects Three hundred and forty persons with type 2 DM were recruited, of whom 140 (41.2%) were males. The mean age (SD) of the patients was 60.5 (9.89) years. Majority of the patients were in the age group 60–69 years which constituted about 35%, while the least number of patients were in the age group >80years constituting 2.6% of the total patients. The mean (SD) duration of diabetes from onset was 7.65 (7.19) years. Majority of the subjects were of the Yoruba tribe. Trading was the predominant occupation amongst the subjects (45%), while 31.2% of subjects were pensioners. The socio-demographic data of the study group is shown in Table 1.
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Table 1 Sociodemographic data of study participants (all persons with type 2 diabetes mellitus). Metabolic syndrome present n (%) n = 225 Age groups 40–49 50–59 60–69 70–79 >80 Gender Male Female Tribe Yoruba Ibo Hausa Edo Others Marital status Married Divorced Separated Widowed Religion Christianity Islam Occupation Trader Pensioners Clergy Civil servants Teachers/lecturers Other professionals Others
32 65 76 46 6
(14.2) (28.9) (33.8) (20.4) (2.7)
Metabolic syndrome absent n (%) n = 115 19 31 43 19 3
(16.5) (27.0) (37.4) (16.5) (2.6)
Total
51 96 119 65 9
(15.0) (28.2) (35.0) (19.1) (2.6)
50 (22.2) 175 (77.8)
90 (26.5) 25 (21.7)
140 (41.2) 200 (58.8)
207 8 2 4 4
(92.0) (3.6) (0.9) (1.8) (1.7)
99 6 0 6 4
(86.1) (5.2) (0) (5.2) (3.5)
306 14 2 10 8
(90) (4.1) (0.6) (2.9) (2.4)
170 1 3 51
(75.6) (004) (1.3) (22.7)
102 0 1 12
(88.7) (0) (0.9) (10.4)
272 1 4 63
(80) (0.3) (1.2) (18.5)
156 (69.3) 69 (30.7)
80 (69.9) 35 (30.4)
236 (69.4) 104 (30.6)
115 61 2 10 15 10 12
38 45 4 6 7 4 11
153 106 6 16 22 4 23
(51.1) (27.1) (0.9) (4.4) (6.7) (4.4) (5.3)
(33.0) (39.1) (3.5) (5.2) (6.1) (3.5) (9.6)
(45.0) (31.2) (1.8) (4.7) (6.5) (4.1) (6.8)
proportion. However the prevalence of the metabolic syndrome did not differ significantly within the age groups (p = 0.867). 5.2.2. History of cardiovascular disease Fourteen out of twenty one patients who had previous history or clinical features suggestive of angina or myocardial infarction were found to have the metabolic syndrome. Amongst 15 patients with a previous history of stroke, 11 had metabolic syndrome. Also, the frequency of the metabolic syndrome was higher amongst patients with history suggestive of peripheral vascular disease (73.2%). None of these findings were statistically significant (p = 0.95, p = 0.549, p = 0.223 respectively).
5.2. Prevalence of the metabolic syndrome The prevalence of metabolic syndrome, using the IDF criteria was 66.2%. Amongst this group, 50 (22.2%) were men. 87.5% of the women had the metabolic syndrome while amongst the men the proportion was 37.5% (p = 0.001) (Figs. 1 and 2). 5.2.1. Age-groups Thirty four percent of the patients with the metabolic syndrome were in the 60–69 years age group, which forms the largest
Fig. 1. Percentage of patients with metabolic syndrome amongst type 2 diabetes mellitus subjects This is a pie-chart showing the percentage or persons with type 2 diabetes mellitus with the metabolic syndrome.
Please cite this article in press as: Ipadeola A, Adeleye JO. THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.08.011
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A. Ipadeola, J.O. Adeleye / Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx Table 3 Association between metabolic syndrome and the UKPDS CHD absolute risk score. UKPDS CHD absolute risk
Metabolic syndrome present n (%)
Metabolic syndrome absent n (%)
Low risk Moderate risk High risk Total
210 13 2 225
102 11 2 115
(93.3) (5.8) (0.9%) (100%)
(88.7) (9.6) (1.7%) (100%)
x2 = 2.100; d.f. = 2; p = 0.334. Table 4 Association between metabolic syndrome and the UKPDS stroke absolute risk score. UKPDS stroke absolute risk
Metabolic syndrome present n (%)
Metabolic syndrome absent n (%)
Low risk Moderate risk High risk Total
181 (80.4) 29 (12.9) 15 (6.7%) 225 (100%)
90 (78.3) 19 (16.5) 6 (5.2%) 115 (100%)
x2 = 1.006; d.f. = 2; p = 0.605. Fig. 2. Distribution of metabolic syndrome according to gender. This bar chart shows that a significantly higher proportion of women than men had the metabolic syndrome.
5.2.3. Biochemical parameters Table 2 shows that the average fasting plasma glucose, glycated haemoglobin, total cholesterol and LDLc values were higher in patients with the metabolic syndrome. The triglyceride level was largely in the target range in the two groups but was significantly higher in the metabolic syndrome group.
Table 5 Association between metabolic syndrome and the UKPDS FATAL CHD absolute risk score. UKPDS absolute risk score for fatal CHD
Metabolic syndrome present n (%)
Metabolic syndrome absent n (%)
Low risk Moderate risk High risk Total
215(95.6) 10 (4.4) 0 (0) 225 (100%)
109 5 1 115
(94.8) (4.3) (0.9%) (100%)
x2 = 2.174; d.f. = 2; p = 0.375.
5.3. UKPDS absolute risk score 5.3.1. UKPDS coronary heart disease (CHD) absolute risk score In patients with the metabolic syndrome, 93.3% were in the low risk group, 5.8% in the moderate risk group and 0.9% in the high risk group. In those without the metabolic syndrome the proportions for low, moderate and high risk were 88.7%, 9.6% and 1.7% respectively. This finding was not statistically significant as shown in Table 3. 5.3.2. UKPDS stroke absolute risk score Most (80.4%) of the subjects with the metabolic syndrome had a low absolute risk for stroke. The moderate and high risk groups constituted a total of 19.6% of this population. The results were similar in patients without the metabolic syndrome, revealing no statistical difference (Table 4). 5.3.3. UKPDS fatal coronary heart disease absolute risk score Table 5 shows that none of the patients with the metabolic syndrome was at a high risk for a fatal CHD while 95.6% and 4.4% had low and moderate risk respectively. Again, the distribution
was also similar in those without the metabolic syndrome showing no statistical significance. 5.3.4. UKPDS fatal stroke absolute risk score All study subjects had a low risk absolute risk for fatal stroke whether they had the metabolic syndrome or not, as shown in Table 6.
6. Discussion The prevalence of the metabolic syndrome in this study was 66.2%. Studies from various parts of the world have reported the prevalence rate of metabolic syndrome in patients with type 2 DM to be between 30 and 92% [11–16]. This wide range of results suggest that ethnic specific differences, racial fat distribution patterns and the criteria used for the diagnosis of the metabolic syndrome influenced the percentages diagnosed.
Table 2 Comparison of biochemical characteristics amongst patients with and without metabolic syndrome.
Average fasting plasma glucose (mg/dl) HbA1c (%) Total cholesterol (mg/dl) Triglycerides (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl)
Metabolic syndrome present Mean (SD)
Metabolic syndrome absent Mean (SD)
p-Value
136.41 6.36 154.78 77.20 35.61 103.72
133.79 6.24 149.67 69.49 34.95 99.71
0.702 0.577 0.292 0.020 0.536 0.342
(58.53) (1.89) (41.96) (30.57) (9.33) (36.03)
(62.58) (1.69) (42.66) (25.14) (9.39) (38.14)
Only the value for triglycerides was significantly higher amongst persons with the metabolic syndrome.
Please cite this article in press as: Ipadeola A, Adeleye JO. THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.08.011
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DSX-492; No. of Pages 6 A. Ipadeola, J.O. Adeleye / Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx Table 6 Association between metabolic syndrome and the UKPDS fatal stroke absolute risk score. UKPDS absolute risk score for fatal stroke
Metabolic syndrome present (%)
Metabolic syndrome absent (%)
Low risk Moderate risk High risk Total
225 0 0 225
115 0 0 115
(100) (0) (0) (100%)
(100) (0) (0) (100%)
Previously published studies on the prevalence of the metabolic syndrome amongst Nigerians with type 2 DM, have ranged between 20 and 59.1% [17–20]. These figures are however lower than the prevalence in the current study. Alebiosu and Odusan [18], in a study amongst patients with type 2 DM in South West Nigeria reported the prevalence of the metabolic syndrome as 25.2%, using the WHO criteria. Onyemelukwe et al. [17] reported a prevalence of 40% using the WHO criteria for the diagnosis of metabolic syndrome in type 2 DM patients in Zaria. Adediran et al. [19] in a study amongst patients with type 2 DM in Lagos, reported a prevalence of 51% (56% females and 44% males) also using the WHO criteria. All the aforementioned studies on the burden of the metabolic syndrome amongst Nigerians with type 2 diabetes utilized the WHO criteria, while this study utilized the IDF criteria. These different criteria used may have contributed to the differences in prevalence observed amongst Nigerians from the various studies. With the exception of the study by Alebiosu and Odusan [18], other studies on the metabolic syndrome amongst Nigerians with type 2 diabetes revealed that a larger proportion of females had the metabolic syndrome [17,19]. This has also been corroborated in studies on the prevalence of the metabolic syndrome amongst persons without diabetes or hypertension [21,22]. Akbar [11] and Ashraf [14] using the WHO definition, however, found a higher male to female ratio with the metabolic syndrome, amongst Saudi Arabians and Pakistanis with type 2 DM respectively. It is plausible that the observed differences in gender distribution may be due to the fact that these studies were conducted in people of varying ethnicity with different lifestyles and dietary patterns. Central obesity is an obligatory criterion in the diagnosis of the metabolic syndrome using the IDF definition [8]. Women in this study were significantly more overweight and obese (generalized and truncal) when compared with their males counterparts. This may account for the higher prevalence of the metabolic syndrome in female participants in the current study. Obesity also appears to be a problem of urban women. The association between obesity and living in urban areas is well known in West Africa, although it is less clear why obesity is more marked among women [23]. This difference in gender distribution of obesity suggests that behavioural factors may be responsible, because most genetic and environmental factors are shared by men and women [24]. Multiparity, which is common in African women, is associated with overweight and possibly diabetes and may also be a reason why more women in this study were overweight or obese [25]. Due to the strong connection between central obesity and the components of the metabolic syndrome, the IDF consensus has identified an increase in waist circumference as a necessary component for the clinical diagnosis of the metabolic syndrome [8]. Thresholds for the waist circumference have been established for various populations by expert panels based mostly based on data from European and American patients [8,26]. This is yet to be done specifically for the Nigerian population. It has been observed that some ethnic groups may develop the metabolic syndrome, despite levels of abdominal obesity below current diagnostic thresholds, as it has been reported in Asians. This further highlights
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the need for Nigerian specific threshold for measurement on central obesity as the current European values by IDF may require modification for our population. The distribution of the calculated absolute risk for cardiovascular events in persons with and without the metabolic syndrome did not differ significantly, with majority of subjects in the low risk group. Studies carried out to determine if the diagnosis of the metabolic syndrome actually conferred a higher cardiovascular risk have shown conflicting results. Recently, the Metascreen Writing Committee reported that the presence of the metabolic syndrome as defined by the NCEP and IDF guidelines was a strong, independent predictor of clinical complications in both type 1 and type 2 diabetic patients [27]. Alexander et al. [15] also reported that the metabolic syndrome was a significant univariate predictor of prevalent coronary artery disease in diabetic patients. He noted that when type 2 DM and metabolic syndrome were present, the prevalence of CVD was almost twice the prevalence when DM was present alone [15]. In the Casale Monferrato study, it was concluded that the metabolic syndrome by the WHO criteria used, was not a predictor of 11-year all-cause and cardiovascular mortality [13]. It was also reported that categorizing persons with type 2 DM, as having or not having the metabolic syndrome, did not provide further prediction of cardiovascular mortality compared with the knowledge of its single components [13]. In another study, individuals with newly diagnosed type 2 DM exhibited a high prevalence of the metabolic syndrome, and the presence of this syndrome identified diabetic patients at increased risk of future macrovascular complications [16]. However, there was significant overlap in estimated 10-year CVD risks between patients with and without Metabolic syndrome and identification of the metabolic syndrome carried a low positive predictive value for CVD outcomes [16]. The result of the current study suggests that despite the high prevalence of the metabolic syndrome, the diagnosis of the metabolic syndrome using the IDF criteria may not be enough to accurately predict cardiovascular events and outcomes. Other risk factors not accounted for in the definition of the metabolic syndrome such as smoking, the presence or absence of atrial fibrillation, level of blood pressure control and glycated haemoglobin utilized by the risk calculation engine, gives a more global picture of each person’s CVD risk. It is possible that persons who consent to participate in studies such as this may be people who are more regular with clinic visits, are better educated about their health, are more compliant with medications and have better metabolic control, thus attenuating cardiovascular risk. Nonetheless, the metabolic syndrome still remains a simple useful clinical tool for identification of clusters with potent cardiovascular risk factors associated with diabetes. Identification of these clusters of cardiovascular risk factors may stimulate further research on pathological mechanisms responsible for the metabolic syndrome. The risk calculation engines may also not be readily available to all clinicians in this environment and its use, though important, may be difficult in a busy outpatient clinic. It is also important to note that the UKPDS risk engine used in this study as well as other risk calculation models may need to be validated in our environment. A limitation of this study was that it was a cross-sectional study amongst patients. Design of a prospective cohort study with a follow up period would further help to evaluate the metabolic syndrome as a predictor of CVD morbidity and mortality in patients with type 2 DM. 7. Conclusions The prevalence of the metabolic syndrome using IDF criteria amongst persons with type 2 DM in our study was high (66.2%) with females constituting the majority. The frequency of generalized and
Please cite this article in press as: Ipadeola A, Adeleye JO. THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.08.011
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truncal obesity was also significantly higher in females. Despite the high prevalence of the metabolic syndrome, using IDF criteria the absolute cardiovascular risk did not differ significantly in persons with type 2 DM with or without the metabolic syndrome. There was however a tendency towards higher cardiovascular risk scores in those with metabolic syndrome compared with those without. 8. Recommendations There is a need for a multi-centre, prospective study to help determine the average prevalence of the metabolic syndrome in Nigeria and its attendant complications. This will be of immense benefit in the management of persons with type 2 DM, especially with the rising prevalence and growing burden of diabetes mellitus. The routine use of the global cardiovascular risk calculation engines especially the UKPDS risk engine should be encouraged to further determine their importance and relevance in our practice settings. Declaration of competing interests Nothing to declare. Authors’ contribution AI (Department of Medicine, University College Hospital, Ibadan, Nigeria) was responsible for the conception, design, acquisition of data, analysis, and drafting of manuscript. JOA (Department of Medicine, University College Hospital, Ibadan, Nigeria) was involved in design, acquisition of data, drafting of manuscript and revising it critically for important intellectual content. All authors read and approved of the final manuscript. 9. Conflict of interest No conflict of interest. Acknowledgements Sincere appreciation also goes to Dr Obioma Uchendu and Mr Adewole of the Department of Community Medicine for assisting with the statistical analysis. An abstract of this article was published by the American Association of Clinical Endocrinologists in 2011 following presentation at the annual scientific meeting of the association. References [1] American Diabetes Association (ADA). Report of the expert committee on the diagnosis and classification of diabetes. Mellit Diabetes Care 2004;27(Suppl. 1):S5–10. [2] Saydah SH, Eberhardt MS, Loria CM, Brancanti FL. Age and the burden of death attributable to diabetes in the United states. Am J Epidermiol 2002;156:714–9.
[3] Wannamethee S, Shaper A, Lennon L, Morris R. Metabolic Syndrome vs Framinghan Risk Score for prediction of coronary heart disease, stroke, and type 2 diabetes mellitus. Arch Intern Med 2005;165(22):2644–50. [4] Ninomiya JK, L’Italien G, Criqui MH, Whyte JL, Gamst A, Chen RS. Association of the metabolic syndrome with history of myocardial infarction and stroke in the Third National Health and Nutrition Examination Survey. Circulation 2004;109(1):42–6. [5] Coleman RL, Stevens RJ, Retnakaran R, Holman RR. Framingham, SCORE, and DECODE risk equations do not provide reliable cardiovascular risk estimates in type 2 diabetes. Diabetes Care 2007;30(5):1292–3. [6] Stevens RJ, Kothari V, Adler AI, Stratton IM. The UKPDS risk engine: a model for the risk of coronary heart disease in type II diabetes (UKPDS 56). Clin Sci 2001;101(6):671–9. [7] Kothari V, Stevens RJ, Adler AI, Stratton IM, Manley SE, Neil HA, et al. UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine. Stroke 2002;33(7):1776–81. [8] Alberti K, Zimmet P, Shaw J, IDF epidemiology task force consensus group. The metabolic syndrome-a new worldwide definition. Lancet 2005;366:1059–62. [9] WHO. Global strategy on diet, physical activity and health. Geneva: WHO; 2003. [10] Friedwald WT, Levi RI, Friedickson DS. Estimation of the concentration of lowdensity lipoproprtein cholesterol in plasma without the use of preparative ultracentrfugation. Clin Chem 1972;18:499–502. [11] Akbar DH. Metabolic syndrome is common in Saudi type 2 diabetes patients. Diabetes Int 2002;12:47–9. [12] Mansour AA. Prevalence of the metabolic syndrome among patients with type 2 diabetes in Iraq. Diabetes Int 2007;15:12. [13] Bruno G, Merletti F, Biggeri A, Bargero G, Ferrero S, Runzo C, et al. Metabolic syndrome as a predictor of all-cause and cardiovascular mortality in type 2 diabetes: the Casale Monferrato Study. Diabetes Care 2004;11:2689–94. [14] Sohail Ashraf SM, Ziauddin F, Jahangeer U. Metabolic Syndrome in type 2 diabetes mellitus. Pak J Med Sci 2006;22(3):295–9. [15] Alexander CM, Landsman PB, Teutsch SM, Haffner SM. NCEP-defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older. Diabetes 2003;52(5):1210–4. [16] Bonora E, Targher G, Formentini G, Calcaterra F, Lombardi S, Marini F, et al. The Metabolic Syndrome is an independent predictor of cardiovascular disease in type 2 diabetic subjects. Prospective data from the Verona Diabetes Complications Study. Diabet Med 2004;21(1):52–8. [17] Onyemelukwe GC, AG B. The metabolic syndrome amongst Nigerian type 2 diabetic patients. Diabetes Int 2006;1:21–3. [18] Alebiosu C, Odusan B. Metabolic syndrome in subjects with type-2 diabetes mellitus. J Natl Med Assoc 2004;96:817–21. [19] Adediran OS, Edo AE, Jimoh AK, Ohwovoriole AA. Prevalence of the metabolic syndrome among Nigerians with type 2 diabetes. Diabetes Int 2007;15:13–4. [20] Isezuo SA, Ezunu E. Demographic and clinical correlates of metabolic syndrome native African type 2 diabetic patients. J Natl Med Assoc 2005;97:557–63. [21] Eregie A, Edo A. Diagnosing the metabolic syndrome: a comparative evaluation of three diagnostic tools. Diabet Med 2006;23:2107. [22] Bener A, Zirie M, Musallam M, Khader Y, Al-Hamaq A. Prevalence of metabolic syndrome according to Adult Treament Panel and International Diabetes Federation Criteria: a population-based study. Metab Syndr Relat Disord 2009;7:221–9. [23] Van der Sande MAB, Ceesay SM, Milligan PJM, Nyan OA, Banya WAS, Prentice A, et al. Obesity and undernutrition and cardiovascular risk factors in rural and urban gambian communities. Am J Public Health 2001;91(10):1641–4. [24] Wilks R, McFarlane-Anderson N, Bennett F. Obesity in peoples of the African diaspora. Ciba Found Symp 1996;201:37–53. [25] Kritz-silverstein D, Bareltt-Connor E, Wingand D. The effect of parity on later development of non-insulin dependent diabetes mellitus and impaired glucose tolerance. N Engl J Med 1989;321:1214–9. [26] Lakka H-M, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 2002;288:2709–16. [27] The Metascrening Writing Committee. The metabolic syndrome is a risk indicator for microvascular and marovascular complications in diabetes: results from metascreen, a multicenter diabetes clinic based survey. Diabetes Care 2006;29:2701–7. [28] World Health Organization. Physical status: the use and interpretation of anthropometry. Report of a WHO expert committee, WHO Technical Report Series 854. Geneva: WHO; 1995.
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DSX-492; No. of Pages 6 Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx
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Original Article
THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus A. Ipadeola a,*, J.O. Adeleye b a b
Department of Medicine, University College Hospital, Ibadan, Nigeria Department of Medicine, College of Medicine, University of Ibadan, Nigeria
A R T I C L E I N F O
A B S T R A C T
Keywords: Type 2 diabetes mellitus Metabolic syndrome Calculated absolute cardiovascular risk UKPDS risk engine
Background: Persons with type 2 DM have a cardiovascular risk 2–4 times that of the normal population. Co-occurrence of metabolic syndrome (MS) with type 2 DM is associated with an increased risk of development of cardiovascular disease. The aim of this study was to determine the prevalence of the MS in patients with type 2 diabetes mellitus and to compare absolute cardiovascular risk in type 2 DM Patients with MS with those without MS. Methods: Anthropometric measurements and Blood Pressure of 340 eligible patients with type 2 DM recruited into the study were taken. Participants’ FPG, FLP and glycated haemoglobin were also estimated. Cardiovascular risk score was calculated using the United Kingdom Prospective Diabetes Study (UKPDS) risk engine. Diagnosis of the MS was the International Diabetes Federation Criteria (IDF). Results: Over 66% participants had MS. The absolute cardiovascular risk score was found to be similar in persons with type 2 DM whether they fulfilled the criteria for diagnosis metabolic syndrome or not. Conclusion: The absolute cardiovascular risk score was similar in type 2 DM patients with or without the metabolic syndrome. ß 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.
1. Introduction Type 2 diabetes mellitus (type 2 DM) accounts for 90–95% of all cases of diabetes mellitus (DM) and is characterized by insulin secretory defects and insulin resistance of varying degrees [1]. It is considered an independent risk factor for cardiovascular disease, and persons with type 2 DM are reported to have a cardiovascular risk two to four times greater than that of the normal population [2]. The metabolic syndrome (MS) refers to an aggregation of cardiovascular risk factors in a single individual with insulin resistance central in its pathogenic paradigm [3]. The metabolic syndrome is also associated with an increased risk for the
Abbreviations: ADA, American Diabetic Association; CHD, coronary heart disease; CVD, cardiovascular disease; DCCT, Diabetes Control and Complications Trial; DECODE, collaborative analysis of diagnostic criteria in Europe study group; DPP, Diabetes Prevention Program; DM, diabetes mellitus; EDTA, ethylenediamine tetra acetic acid; HDL-c, high density lipoprotein cholesterol; HbA1c, glycated haemoglobin; IDF, International Diabetes Federation; IR, insulin resistance; LDLc, low density lipoprotein cholesterol; MS, metabolic syndrome; MOP, medical outpatient clinic; NCEP-ATP III, national cholesterol education project adult treatment panel; Type 1 DM, type 1 diabetes mellitus; Type 2 DM, type 2 diabetes mellitus; UKPDS, United Kingdom Prospective Diabetes Study; WHO, World Health Organization. * Corresponding author. Tel.: +234 8037024916. E-mail address: [email protected] (A. Ipadeola).
development of cardiovascular disease [4]. People with this syndrome are twice as likely to die from a macrovascular event and three times as likely to have ischaemic heart disease and stroke compared with people without the syndrome [4]. The above, therefore, makes an assessment of its association with type 2 DM crucial. Accurate assessment of cardiovascular risk in persons with diabetes mellitus is a major first step for developing a plan for risk reduction in such persons with diabetes and has become necessary to inform the choice of therapeutic strategies for individual patients [5]. The use of cardiovascular risk calculation algorithms provide a method for accurate estimation of the cardiovascular risk in such patients. A number of risk calculation algorithms exist but have been found to underestimate the cardiovascular risk in persons with type 2 DM [5]. The UKPDS risk engine has been found to be quite accurate in persons with type 2 DM because it includes glycaemic control in its analysis [6,7]. The aim of this study was to determine the prevalence of the MS in patients with type 2 diabetes mellitus and to compare absolute cardiovascular risk in type 2 DM Patients with MS with those without MS. 2. Methods This was a cross-sectional study of 340 consecutive persons with type 2 diabetes mellitus carried out at the University College
http://dx.doi.org/10.1016/j.dsx.2015.08.011 1871-4021/ß 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Ipadeola A, Adeleye JO. THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.08.011
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Hospital, Ibadan over a four month period May 2008–September 2008. Ethical clearance was obtained from the Joint Institution Review Committee (IRC) of the University College Hospital and the College of Medicine, University of Ibadan. 2.1. Clinical evaluation Demographic and clinical data were obtained from all subjects using a data collection form. Clinical data included the time of diagnosis of diabetes mellitus, duration of treatment, history of hypertension, previous or current history of ischaemic heart disease (angina, myocardial infarction or coronary revascularization procedure), cerebrovascular disease (e.g. TIA or stroke) or peripheral vascular disease, (e.g. intermittent claudication, gangrene of the lower extremities or revascularization procedure). History of cigarette smoking and alcohol use was also obtained. Family history of diabetes mellitus, hypertension, coronary heart disease, stroke and sudden unexpected death was also inquired about. The radial pulse was examined in all subjects; persons found to have irregular pulse rhythm had an electrocardiogram to rule out atrial fibrillation. All clinical information was imputed into the UKPDS risk engine along with other parameters to obtain an absolute cardiovascular risk score for each patient. 2.2. Diagnosis of the metabolic syndrome Patients were categorized as having the metabolic syndrome based on the International Diabetes Federation (IDF) definition [8]; as follows – Central obesity (defined as waist circumference 94 cm for men and 80 cm for women of African descent as European values are to be used in the absence of figures for Africans), plus any two of the following four factors: 1. Triglyceride level greater than 150 mg/dl, or on specific treatment for this lipid abnormality. 2. HDL cholesterol: less than 40 mg/dl in men and less than 50 mg/ dl in female or on specific treatment for this abnormality. 3. Elevated blood pressure with systolic 130 or diastolic 85 mmHg or on treatment for previously diagnosed hypertension. 4. Fasting plasma glucose of 100 mg/dl or previously diagnosed type2 diabetes mellitus.
2.3. Waist circumference measurement Waist circumference was measured using the protocol recommended by the World Health Organization [28]. A waist circumference of 80 cm in females and 94 cm in males was taken as indicative of truncal obesity [8]. 2.4. Weight and height Measurement Weight was measured (in kilograms) using a beam balance scale with subjects in light clothing and without shoes on. Height was measured (in metres) using a portable height/length measuring board without the subjects wearing footwear, caps or other head gear. Body mass index (BMI) was subsequently calculated using the formula: 2
2.5. Blood pressure Blood pressure was measured with the patients seated after at least 5 min rest using a mercury sphygmomanometer with a standard adult cuff size of 12 cm was wrapped round the patient’s arm and placed at the heart level. The cuff was applied closely to the upper arm with the lower end about 2.5 cm from the cubital fossa. The patient was seated with the back resting on the chair, the non-dominant arm used was resting on the table while both feet were resting on the floor. Korotkoff sounds phases I and V were taken as the systolic and diastolic blood pressures respectively and values were recorded to the nearest 2 mmHg. Two readings were taken 2 min apart and the average was recorded. Patients with systolic or diastolic blood pressure of 130 and 85 mmHg respectively were grouped as having hypertension according to the IDF criteria [8]. In addition, patients on treatment for previously diagnosed hypertension were also regarded as having hypertension. 2.6. Laboratory evaluation Measurements were taken after an overnight fast of 8–14 h. Patients were seated and allowed to rest for 5 min, and then a venipuncture was performed after cleaning the skin with 70% methylated spirit, using a new sterile disposable needle and syringe. Ten millilitres (ml) of blood was collected out of which 2 ml was put into a fluoride oxalate bottle for fasting blood glucose estimation. The remaining 8 ml was transferred into separate potassium EDTA bottle for lipid profile and glycated haemoglobin analysis. 2.7. Plasma glucose measurement Samples were spun after collection to obtain plasma and analysis was carried out in the Department of Medicine laboratory by a trained laboratory scientist and the investigator using the glucose oxidase enzymatic method. 2.8. Plasma lipid assay Total cholesterol and Triglycerides were analysed in the same laboratory stated above using enzymatic methods and values were read off a colorimeter. High-density lipoprotein cholesterol (HDLC) was determined using selective precipitation; followed by enzymatic method for measuring cholesterol. All reagents used were from ‘‘DIALAB Austria’’. Calculated values for low-density lipoprotein cholesterol (LDL-C) were obtained using the FriedWald’s equation as follows (provided the plasma triglycerides are not greater than 400 mg/dl) [10]. 2.9. Glycated haemoglobin estimation Samples were stored at 2–8 8C. Analysis was done within one week of storage using the HbA1c ionic exchange chromatographic method (DIALAB, AUSTRIA). When this method was compared to the US National Glycohaemoglobin Standardization Program certified method (NGSP) which is DCCT referenced some correlation was obtained. The following formula was used by the manufacturer of the kit used (DIALAB, AUSTRIA) to obtain DCCT referenced values: HbA1c (NGSP) (%) = 0.86 HbA1cDialab (%) + 0.24. HbA1c of 6.5% was taken as good control according to the IDF, while >6.5% was taken as poor glycaemic control [8].
BMI ¼ Weight=height ðkg=m2 Þ 3. Calculated cardiovascular risk A BMI of 18.5–24.9 kg/m2 was considered as normal. Values between 25 and 29.9 kg/m2 implied overweight, while obesity was defined as a BMI 30 kg/m2 [9].
A validated diabetes specific risk calculator known as the United Kingdom Prospective Diabetes Study (UKPDS) risk engine
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was used in calculation of absolute cardiovascular risk in these patients with type 2 DM. This risk engine was developed following a randomized controlled trial involving 5102 patients with type 2 DM who were followed up for 20 years and has been found to be superior to other previously used risk engines [6,7]. The model provides equations for absolute risk, incorporating the effect of multiple risk factors to give overall event rates, rather than relative risk [6,7]. Data necessary for the calculation-age, duration of diabetes, gender, presence or absence of atrial fibrillation, ethnicity, haemoglobin A1c, cigarette smoking, systolic blood pressure, total cholesterol and high density lipoprotein cholesterol was imputed as appropriate. The inputs required by the model had been measured during the course of the study. Model fitting was carried out by maximum likelihood estimation, for which the Newton– Raphson method was used with numerical derivatives. The model has been incorporated in the UKPDS risk engine software, which was available without charge from the UKPDS website [7]. A risk score was obtained which estimated the risk of development of cardiovascular disease in the following 10 years. All patients who participated in the study had their absolute cardiovascular risk score calculated using the UKPDS risk engine and were categorized into the following sub-groups: low risk – 0– 15%, moderate risk – >15–<30% and high risk – >30% according to the model [7]. 4. Data analysis The data obtained was coded and entered on a computer worksheet. Data analysis was carried out using the SPSS software, windows version 16.0 version. Descriptive variables are presented using frequency tables, pie charts and bar charts. Continuous variables are expressed as means (standard deviation, SD) while categorical variables are expressed as percentages. The association between variables was tested using the Chi Square for categorical variables and Student t-test for quantitative variables. Statistical significance of the tests of association was set at a p-value of less than 0.05. 5. Results 5.1. Characteristics of study subjects Three hundred and forty persons with type 2 DM were recruited, of whom 140 (41.2%) were males. The mean age (SD) of the patients was 60.5 (9.89) years. Majority of the patients were in the age group 60–69 years which constituted about 35%, while the least number of patients were in the age group >80years constituting 2.6% of the total patients. The mean (SD) duration of diabetes from onset was 7.65 (7.19) years. Majority of the subjects were of the Yoruba tribe. Trading was the predominant occupation amongst the subjects (45%), while 31.2% of subjects were pensioners. The socio-demographic data of the study group is shown in Table 1.
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Table 1 Sociodemographic data of study participants (all persons with type 2 diabetes mellitus). Metabolic syndrome present n (%) n = 225 Age groups 40–49 50–59 60–69 70–79 >80 Gender Male Female Tribe Yoruba Ibo Hausa Edo Others Marital status Married Divorced Separated Widowed Religion Christianity Islam Occupation Trader Pensioners Clergy Civil servants Teachers/lecturers Other professionals Others
32 65 76 46 6
(14.2) (28.9) (33.8) (20.4) (2.7)
Metabolic syndrome absent n (%) n = 115 19 31 43 19 3
(16.5) (27.0) (37.4) (16.5) (2.6)
Total
51 96 119 65 9
(15.0) (28.2) (35.0) (19.1) (2.6)
50 (22.2) 175 (77.8)
90 (26.5) 25 (21.7)
140 (41.2) 200 (58.8)
207 8 2 4 4
(92.0) (3.6) (0.9) (1.8) (1.7)
99 6 0 6 4
(86.1) (5.2) (0) (5.2) (3.5)
306 14 2 10 8
(90) (4.1) (0.6) (2.9) (2.4)
170 1 3 51
(75.6) (004) (1.3) (22.7)
102 0 1 12
(88.7) (0) (0.9) (10.4)
272 1 4 63
(80) (0.3) (1.2) (18.5)
156 (69.3) 69 (30.7)
80 (69.9) 35 (30.4)
236 (69.4) 104 (30.6)
115 61 2 10 15 10 12
38 45 4 6 7 4 11
153 106 6 16 22 4 23
(51.1) (27.1) (0.9) (4.4) (6.7) (4.4) (5.3)
(33.0) (39.1) (3.5) (5.2) (6.1) (3.5) (9.6)
(45.0) (31.2) (1.8) (4.7) (6.5) (4.1) (6.8)
proportion. However the prevalence of the metabolic syndrome did not differ significantly within the age groups (p = 0.867). 5.2.2. History of cardiovascular disease Fourteen out of twenty one patients who had previous history or clinical features suggestive of angina or myocardial infarction were found to have the metabolic syndrome. Amongst 15 patients with a previous history of stroke, 11 had metabolic syndrome. Also, the frequency of the metabolic syndrome was higher amongst patients with history suggestive of peripheral vascular disease (73.2%). None of these findings were statistically significant (p = 0.95, p = 0.549, p = 0.223 respectively).
5.2. Prevalence of the metabolic syndrome The prevalence of metabolic syndrome, using the IDF criteria was 66.2%. Amongst this group, 50 (22.2%) were men. 87.5% of the women had the metabolic syndrome while amongst the men the proportion was 37.5% (p = 0.001) (Figs. 1 and 2). 5.2.1. Age-groups Thirty four percent of the patients with the metabolic syndrome were in the 60–69 years age group, which forms the largest
Fig. 1. Percentage of patients with metabolic syndrome amongst type 2 diabetes mellitus subjects This is a pie-chart showing the percentage or persons with type 2 diabetes mellitus with the metabolic syndrome.
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A. Ipadeola, J.O. Adeleye / Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx Table 3 Association between metabolic syndrome and the UKPDS CHD absolute risk score. UKPDS CHD absolute risk
Metabolic syndrome present n (%)
Metabolic syndrome absent n (%)
Low risk Moderate risk High risk Total
210 13 2 225
102 11 2 115
(93.3) (5.8) (0.9%) (100%)
(88.7) (9.6) (1.7%) (100%)
x2 = 2.100; d.f. = 2; p = 0.334. Table 4 Association between metabolic syndrome and the UKPDS stroke absolute risk score. UKPDS stroke absolute risk
Metabolic syndrome present n (%)
Metabolic syndrome absent n (%)
Low risk Moderate risk High risk Total
181 (80.4) 29 (12.9) 15 (6.7%) 225 (100%)
90 (78.3) 19 (16.5) 6 (5.2%) 115 (100%)
x2 = 1.006; d.f. = 2; p = 0.605. Fig. 2. Distribution of metabolic syndrome according to gender. This bar chart shows that a significantly higher proportion of women than men had the metabolic syndrome.
5.2.3. Biochemical parameters Table 2 shows that the average fasting plasma glucose, glycated haemoglobin, total cholesterol and LDLc values were higher in patients with the metabolic syndrome. The triglyceride level was largely in the target range in the two groups but was significantly higher in the metabolic syndrome group.
Table 5 Association between metabolic syndrome and the UKPDS FATAL CHD absolute risk score. UKPDS absolute risk score for fatal CHD
Metabolic syndrome present n (%)
Metabolic syndrome absent n (%)
Low risk Moderate risk High risk Total
215(95.6) 10 (4.4) 0 (0) 225 (100%)
109 5 1 115
(94.8) (4.3) (0.9%) (100%)
x2 = 2.174; d.f. = 2; p = 0.375.
5.3. UKPDS absolute risk score 5.3.1. UKPDS coronary heart disease (CHD) absolute risk score In patients with the metabolic syndrome, 93.3% were in the low risk group, 5.8% in the moderate risk group and 0.9% in the high risk group. In those without the metabolic syndrome the proportions for low, moderate and high risk were 88.7%, 9.6% and 1.7% respectively. This finding was not statistically significant as shown in Table 3. 5.3.2. UKPDS stroke absolute risk score Most (80.4%) of the subjects with the metabolic syndrome had a low absolute risk for stroke. The moderate and high risk groups constituted a total of 19.6% of this population. The results were similar in patients without the metabolic syndrome, revealing no statistical difference (Table 4). 5.3.3. UKPDS fatal coronary heart disease absolute risk score Table 5 shows that none of the patients with the metabolic syndrome was at a high risk for a fatal CHD while 95.6% and 4.4% had low and moderate risk respectively. Again, the distribution
was also similar in those without the metabolic syndrome showing no statistical significance. 5.3.4. UKPDS fatal stroke absolute risk score All study subjects had a low risk absolute risk for fatal stroke whether they had the metabolic syndrome or not, as shown in Table 6.
6. Discussion The prevalence of the metabolic syndrome in this study was 66.2%. Studies from various parts of the world have reported the prevalence rate of metabolic syndrome in patients with type 2 DM to be between 30 and 92% [11–16]. This wide range of results suggest that ethnic specific differences, racial fat distribution patterns and the criteria used for the diagnosis of the metabolic syndrome influenced the percentages diagnosed.
Table 2 Comparison of biochemical characteristics amongst patients with and without metabolic syndrome.
Average fasting plasma glucose (mg/dl) HbA1c (%) Total cholesterol (mg/dl) Triglycerides (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl)
Metabolic syndrome present Mean (SD)
Metabolic syndrome absent Mean (SD)
p-Value
136.41 6.36 154.78 77.20 35.61 103.72
133.79 6.24 149.67 69.49 34.95 99.71
0.702 0.577 0.292 0.020 0.536 0.342
(58.53) (1.89) (41.96) (30.57) (9.33) (36.03)
(62.58) (1.69) (42.66) (25.14) (9.39) (38.14)
Only the value for triglycerides was significantly higher amongst persons with the metabolic syndrome.
Please cite this article in press as: Ipadeola A, Adeleye JO. THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.08.011
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DSX-492; No. of Pages 6 A. Ipadeola, J.O. Adeleye / Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx Table 6 Association between metabolic syndrome and the UKPDS fatal stroke absolute risk score. UKPDS absolute risk score for fatal stroke
Metabolic syndrome present (%)
Metabolic syndrome absent (%)
Low risk Moderate risk High risk Total
225 0 0 225
115 0 0 115
(100) (0) (0) (100%)
(100) (0) (0) (100%)
Previously published studies on the prevalence of the metabolic syndrome amongst Nigerians with type 2 DM, have ranged between 20 and 59.1% [17–20]. These figures are however lower than the prevalence in the current study. Alebiosu and Odusan [18], in a study amongst patients with type 2 DM in South West Nigeria reported the prevalence of the metabolic syndrome as 25.2%, using the WHO criteria. Onyemelukwe et al. [17] reported a prevalence of 40% using the WHO criteria for the diagnosis of metabolic syndrome in type 2 DM patients in Zaria. Adediran et al. [19] in a study amongst patients with type 2 DM in Lagos, reported a prevalence of 51% (56% females and 44% males) also using the WHO criteria. All the aforementioned studies on the burden of the metabolic syndrome amongst Nigerians with type 2 diabetes utilized the WHO criteria, while this study utilized the IDF criteria. These different criteria used may have contributed to the differences in prevalence observed amongst Nigerians from the various studies. With the exception of the study by Alebiosu and Odusan [18], other studies on the metabolic syndrome amongst Nigerians with type 2 diabetes revealed that a larger proportion of females had the metabolic syndrome [17,19]. This has also been corroborated in studies on the prevalence of the metabolic syndrome amongst persons without diabetes or hypertension [21,22]. Akbar [11] and Ashraf [14] using the WHO definition, however, found a higher male to female ratio with the metabolic syndrome, amongst Saudi Arabians and Pakistanis with type 2 DM respectively. It is plausible that the observed differences in gender distribution may be due to the fact that these studies were conducted in people of varying ethnicity with different lifestyles and dietary patterns. Central obesity is an obligatory criterion in the diagnosis of the metabolic syndrome using the IDF definition [8]. Women in this study were significantly more overweight and obese (generalized and truncal) when compared with their males counterparts. This may account for the higher prevalence of the metabolic syndrome in female participants in the current study. Obesity also appears to be a problem of urban women. The association between obesity and living in urban areas is well known in West Africa, although it is less clear why obesity is more marked among women [23]. This difference in gender distribution of obesity suggests that behavioural factors may be responsible, because most genetic and environmental factors are shared by men and women [24]. Multiparity, which is common in African women, is associated with overweight and possibly diabetes and may also be a reason why more women in this study were overweight or obese [25]. Due to the strong connection between central obesity and the components of the metabolic syndrome, the IDF consensus has identified an increase in waist circumference as a necessary component for the clinical diagnosis of the metabolic syndrome [8]. Thresholds for the waist circumference have been established for various populations by expert panels based mostly based on data from European and American patients [8,26]. This is yet to be done specifically for the Nigerian population. It has been observed that some ethnic groups may develop the metabolic syndrome, despite levels of abdominal obesity below current diagnostic thresholds, as it has been reported in Asians. This further highlights
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the need for Nigerian specific threshold for measurement on central obesity as the current European values by IDF may require modification for our population. The distribution of the calculated absolute risk for cardiovascular events in persons with and without the metabolic syndrome did not differ significantly, with majority of subjects in the low risk group. Studies carried out to determine if the diagnosis of the metabolic syndrome actually conferred a higher cardiovascular risk have shown conflicting results. Recently, the Metascreen Writing Committee reported that the presence of the metabolic syndrome as defined by the NCEP and IDF guidelines was a strong, independent predictor of clinical complications in both type 1 and type 2 diabetic patients [27]. Alexander et al. [15] also reported that the metabolic syndrome was a significant univariate predictor of prevalent coronary artery disease in diabetic patients. He noted that when type 2 DM and metabolic syndrome were present, the prevalence of CVD was almost twice the prevalence when DM was present alone [15]. In the Casale Monferrato study, it was concluded that the metabolic syndrome by the WHO criteria used, was not a predictor of 11-year all-cause and cardiovascular mortality [13]. It was also reported that categorizing persons with type 2 DM, as having or not having the metabolic syndrome, did not provide further prediction of cardiovascular mortality compared with the knowledge of its single components [13]. In another study, individuals with newly diagnosed type 2 DM exhibited a high prevalence of the metabolic syndrome, and the presence of this syndrome identified diabetic patients at increased risk of future macrovascular complications [16]. However, there was significant overlap in estimated 10-year CVD risks between patients with and without Metabolic syndrome and identification of the metabolic syndrome carried a low positive predictive value for CVD outcomes [16]. The result of the current study suggests that despite the high prevalence of the metabolic syndrome, the diagnosis of the metabolic syndrome using the IDF criteria may not be enough to accurately predict cardiovascular events and outcomes. Other risk factors not accounted for in the definition of the metabolic syndrome such as smoking, the presence or absence of atrial fibrillation, level of blood pressure control and glycated haemoglobin utilized by the risk calculation engine, gives a more global picture of each person’s CVD risk. It is possible that persons who consent to participate in studies such as this may be people who are more regular with clinic visits, are better educated about their health, are more compliant with medications and have better metabolic control, thus attenuating cardiovascular risk. Nonetheless, the metabolic syndrome still remains a simple useful clinical tool for identification of clusters with potent cardiovascular risk factors associated with diabetes. Identification of these clusters of cardiovascular risk factors may stimulate further research on pathological mechanisms responsible for the metabolic syndrome. The risk calculation engines may also not be readily available to all clinicians in this environment and its use, though important, may be difficult in a busy outpatient clinic. It is also important to note that the UKPDS risk engine used in this study as well as other risk calculation models may need to be validated in our environment. A limitation of this study was that it was a cross-sectional study amongst patients. Design of a prospective cohort study with a follow up period would further help to evaluate the metabolic syndrome as a predictor of CVD morbidity and mortality in patients with type 2 DM. 7. Conclusions The prevalence of the metabolic syndrome using IDF criteria amongst persons with type 2 DM in our study was high (66.2%) with females constituting the majority. The frequency of generalized and
Please cite this article in press as: Ipadeola A, Adeleye JO. THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.08.011
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DSX-492; No. of Pages 6 A. Ipadeola, J.O. Adeleye / Diabetes & Metabolic Syndrome: Clinical Research & Reviews xxx (2015) xxx–xxx
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truncal obesity was also significantly higher in females. Despite the high prevalence of the metabolic syndrome, using IDF criteria the absolute cardiovascular risk did not differ significantly in persons with type 2 DM with or without the metabolic syndrome. There was however a tendency towards higher cardiovascular risk scores in those with metabolic syndrome compared with those without. 8. Recommendations There is a need for a multi-centre, prospective study to help determine the average prevalence of the metabolic syndrome in Nigeria and its attendant complications. This will be of immense benefit in the management of persons with type 2 DM, especially with the rising prevalence and growing burden of diabetes mellitus. The routine use of the global cardiovascular risk calculation engines especially the UKPDS risk engine should be encouraged to further determine their importance and relevance in our practice settings. Declaration of competing interests Nothing to declare. Authors’ contribution AI (Department of Medicine, University College Hospital, Ibadan, Nigeria) was responsible for the conception, design, acquisition of data, analysis, and drafting of manuscript. JOA (Department of Medicine, University College Hospital, Ibadan, Nigeria) was involved in design, acquisition of data, drafting of manuscript and revising it critically for important intellectual content. All authors read and approved of the final manuscript. 9. Conflict of interest No conflict of interest. Acknowledgements Sincere appreciation also goes to Dr Obioma Uchendu and Mr Adewole of the Department of Community Medicine for assisting with the statistical analysis. An abstract of this article was published by the American Association of Clinical Endocrinologists in 2011 following presentation at the annual scientific meeting of the association. References [1] American Diabetes Association (ADA). Report of the expert committee on the diagnosis and classification of diabetes. Mellit Diabetes Care 2004;27(Suppl. 1):S5–10. [2] Saydah SH, Eberhardt MS, Loria CM, Brancanti FL. Age and the burden of death attributable to diabetes in the United states. Am J Epidermiol 2002;156:714–9.
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Please cite this article in press as: Ipadeola A, Adeleye JO. THE metabolic syndrome and accurate cardiovascular risk prediction in persons with type 2 diabetes mellitus. Diab Met Syndr: Clin Res Rev (2015), http://dx.doi.org/10.1016/j.dsx.2015.08.011