Background

Metabolic syndrome (MetS) is a cluster of interconnected metabolic disorders that includes insulin resistance, dysglycemia, central obesity, high triglycerides, low high density lipoprotein cholesterol and hypertension [1]. Recent inclusion of additional metabolic disorders such as chronic pro-inflammatory and prothrombotic states, non-alcoholic fatty liver disease and sleep apnea has made this definition even more complex. Existence of three or more of these disorders warrants the diagnosis of this syndrome. Metabolic syndrome has been shown to increase the risk of cardiovascular diseases (CVDs) by two fold and type 2 diabetes mellitus (DM) approximately by five folds over 5 to 10 years [2]-[4].

There is still lack of clearly defined pathophysiology and universal definition of MetS. Many researchers question its own existence as a specific syndrome and believe that it is instead a surrogate of combined syndrome that predisposes an individual to particular risk. This has led to several definitions for MetS being proposed by various international regulatory bodies [1]. World Health Organization (WHO) defines this syndrome as the presence of glucose intolerance or insulin resistance or diabetes mellitus with any two of the following components: obesity, high serum triglycerides, low serum high density lipoprotein cholesterol and hypertension [5]. The National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) describes metabolic syndrome as the presence of any three of the following components: abdominal obesity, dyslipidemia (high levels of triglycerides, low HDL), hypertension, and elevated fasting glucose [6]. The International Diabetes Federation (IDF) takes central obesity as a mandatory component for the diagnosis of MetS along with any two of the other components: hypertension, abnormal blood glucose, high serum triglycerides and low high density lipoprotein cholesterol [7]. Recently, IDF, National Heart, Lung and Blood Institute (NHLBI), American Heart Association (AHA), World Heart Federation (WHF), International Atherosclerosis Society (IAS) and International Association for the Study of Obesity (IASO) have proposed a new harmonized definition which requires any three of the five components included in the IDF definition for the diagnosis of MetS and do not consider central obesity as an obligatory component [8].

Prevalence of type 2 diabetes mellitus is increasing very rapidly, particularly in developing countries of the world resulting in a substantial burden on the healthcare services [9]. Approximately 9.5% of the Nepalese population suffers from the type 2 diabetes mellitus [10]. Majority of the type 2 diabetic patients also have MetS and are predisposed to higher risk of cardiovascular diseases, strokes and premature death compared to both non-diabetic individuals and diabetic individuals without MetS [11]-[13]. Presence of MetS in the type 2 diabetic patients has been shown to decrease the survival rate at least by 10 years [14]. Despite controversies on its own existence and universally accepted definition, metabolic syndrome is still a useful concept which helps identify diabetic patients at high risk of developing atherosclerotic CVDs and stroke and predict all cause mortality [1],[14]. Moreover, it also helps clinical researchers better understand the pathophysiology that culminates in the CVDs and stroke and formulate preventive and therapeutic approaches. Except for a few preliminary studies [15]-[18], there has been no systematic study in Nepal that determines the prevalence of MetS in type 2 diabetic populations and diagnostic performance of the available defining criteria. The aim of this study is therefore to estimate the prevalence of MetS in type 2 diabetic patients using four most popular diagnostic criteria viz. WHO (1999), NCEP ATP III (2001), IDF (2005) and Harmonized (2009) (Table 1) and determine their level of agreement and disparity in the diagnosis of MetS. The results of this study will provide template epidemiological data for conducting nationwide prevalence surveys, formulation of national strategies for the prevention and control of MetS, type 2 diabetes and CVDs in Nepal.

Table 1 Criteria for clinical diagnosis of the metabolic syndrome (MetS) according to various definitions
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Methods

Study design and patients

This was a cross-sectional study conducted from July 2012 to August, 2013 at Manipal Teaching Hospital (MTH), Pokhara, Nepal. The study protocol was approved by the institutional ethical committee and informed consent was obtained from all the enrolled study patients for their inclusion in the screening and participation in the research. A total of 1061 type 2 diabetic patients without any diabetes related complications and other acute or chronic illness were selected from the various out-patient departments of MTH and enrolled for this study. Presence of type 2 diabetes mellitus was ascertained clinically based on the criteria defined by WHO for diabetes mellitus, age of onset of diabetes and types of medications being prescribed [19]. Patients with established cardiovascular diseases, thyroid dysfunction, excessive alcohol or other drug abuse, current psychiatric treatment and current or recent (up to 4 months) pregnancy were excluded to homogenize the study subjects. The subjects were 30–89 years old and hailed mainly from Gandaki, Dhaulagiri, Lumbini and other adjoining zones of the Western Development Region of Nepal.

Anthropometric, physiological and lifestyle related variables

All the study patients were personally interviewed by the trained interviewers within the hospital premises using a pre-validated set of questionnaire and details of their demographics, clinical and family history, smoking and dietary habits, ethnicity and profession were recorded. Height, weight, waist and hip circumferences of these patients were measured following the standard protocols and were used for calculating their BMI and WHR. Recent WHO guideline for South Asian population (18.5-22.9 kg/m2) was followed to classify their BMI status [20]. Blood pressure was measured in triplicate while on the sitting position using digital sphygmomanometer (TaiDoc Technology Corporation, Taiwan).

Biochemical investigations in the blood samples

Five ml of fasting venous blood sample was drawn from each subject and then divided into fluoride-oxalate vials, EDTA vacutainers and plain test tubes. Plasma fasting glucose was measured by glucose oxidase/peroxidase method using blood collected in fluoride-oxalate vials. Glycated hemoglobin (HbA1C) was measured on EDTA blood by ion-exchange resin method. Serum triglycerides (TG), total cholesterol (TC) and HDL-cholesterol (HDL-C) were directly measured on plain blood and then LDL-cholesterol value was calculated using Friedwald formula [21]. All these parameters were analyzed at the Clinical Biochemistry laboratory of MTH using semi-automated chemistry analyzer (Humalyzer-3500) and ready-to-use reagent kits according to the protocols provided by the manufacturer (Human diagnostics, Germany). For serum lipid reference level, NCEP ATP III guideline was referred [6]. According to this, hypercholesterolemia was defined as TC >200 mg/dl, high LDL-C when value >100 mg/dl, hypertriglyceridemia as TG >150 mg/dl and low HDL-C when value <40 mg/dl. Dyslipidemia was defined by the presence of one or more abnormal serum lipid concentration.

Statistical analysis

Statistical analysis was performed by SPSS, version 17.0 for Windows (SPSS, IL, Chicago, USA). Data for categorical variables are expressed either in number and percentage (N,%) or percentage and 95% confidence intervals (95% CI). 95% CIs for crude and age standardized rates were calculated according to the modified Wald method and Keyfitz formulas, respectively [22],[23]. Standard error values of age standardized rate were calculated by binomial approximation. Numerical data for continuous variables were expressed in the form of mean ± standard deviation. The age-standardized prevalence rates were calculated with the direct method, using the standard population of Nepal estimated by National Population and Housing Census in 2011 [24]. The agreements among the definitions of WHO, NCEP ATP III, IDF and harmonized criteria were assessed with kappa statistics. The level of agreement was categorized as poor with κ ≤0.20, fair with κ =0.21 to 0.40, moderate with κ =0.41 to 0.60, substantial with κ =0.61 to 0.80, and very good with κ >0.80 [25]. Pearson’s Chi-square test (asymp.sig, 2 sided) and Independent sample test (p values, 2 tailed) were used for checking the statistical significance of difference between the proportion and mean values of two or more groups of variables respectively. The tests were considered statistically significant when p <0.05.

Results

Baseline characteristics of the study patients

The frequency analysis of socio-demographic, anthropometric, clinical and biochemical parameters of study patients are given in Table 2. A total of 1061 type 2 diabetic patients (male: 589 and female: 472) were enrolled in the present study, with a male to female ratio of about 1.25:1. The mean age of the patients was 56.4 ±10 years and the durations of type 2 diabetes and hypertension were 6.0 ±4.6 and 3.8 ±5.5 years respectively with no significant difference (p >0.05) between males and females. Their ethnic backgrounds were Brahmin (261, 24.6%), Chhetri (253, 23.8%), Dalit (102, 9.6%), Mongol (348, 32.8%) and Newar (97, 9.1%). Seven hundred forty nine (70.6%) patients were from urban areas and 312 (29.9%) were from villages. Their major occupations were agriculture (509, 48.0%), business (318, 30.0%) and office jobs (234, 22.1%). All subjects were under medication for diabetes mellitus, out of which 524 (49.4%) were also undertaking treatment for hypertension. In terms of BMI statuses, thirty (2.8%) individuals were underweight, 306 (28.8%) were at high risk group, 255 (24.0%) were obese type I and 33 (3.1%) were obese type II. Waist circumference was increased in 305 (28.7%) and substantially increased in 483 (45.5%) subjects. The waist-hip ratio was increased in 861 (81.1%) subjects among which 664 (62.6%) had android and 347 (32.7%) had gyenoid type central obesity. There were 125 (11.8%) vegetarian, 936 (88.2%) non-vegetarian, 184 (17.3%), smoker, 205 (19.3%), ex-smoker and 672 (63.3%) non-smoker patients. 1050 (99.0%) patients had good glycemic control while only 11 (1.0%) patients had suboptimal glycemic control. Serum triglycerides were borderline high in 303 (28.6%), high in 373 (35.2%) and very high in 24 (2.3%) subjects. Likewise, serum total cholesterol level was borderline high in 323 (30.4%) and very high in 373 (35.2%) subjects. Serum HDL cholesterol was lower than normal in 842 (79.4%) subjects. There were 263 (24.8%) subjects in pre-hypertensive stage, 343 (32.3%) in hypertensive stage I and 212 (20.0%) in hypertensive stage II and 524 (49.4%) patients were taking medicine for hypertension. The proportion of these parameters differed significantly (p <0.010) between male and female subjects.

Table 2 Frequency analysis of socio-demographic and anthropometric parameters of the study subjects
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The mean values of various anthropometric, clinical and biochemical parameters of the male and female study patients are presented in Table 3. When compared to male patients, female patients had significantly higher mean values of BMI, waist circumference, estimated average glucose, HbA1c, triglycerides, very low density lipoprotein, high density lipoprotein cholesterol and diastolic blood pressure (p <0.010). Only the waist to hip ratio was significantly higher (p <0.001) in males compared to females. The gender wise difference was not statistically significant for other parameters (p >0.05).

Table 3 Anthropometric, biochemical and clinical parameters of the type 2 diabetic patients
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Crude, sex and age-adjusted prevalence of metabolic syndrome

Both age and sex-specific crude and age adjusted prevalence of MetS estimated by using WHO, NCEP ATP III, IDF and Harmonized criteria are given in the Table 4. Most patients met the criteria for metabolic syndrome, and the proportion of patients without metabolic syndrome was relatively small. The total crude prevalence was 81.1%, 83.0%, 80.5% and 91.6% according to WHO, NCEP ATP III, IDF and Harmonized criteria, respectively. The corresponding age-adjusted total prevalence was 69.9%, 73.9%, 66.8% and 80.3% respectively. The Harmonized definition outperformed other definitions in diagnosing the cases of MetS. The prevalence was significantly higher in females (p <0.001) than in males except for the one diagnosed by IDF definition. Prevalence generally increased with the increase in age and remained highest in the age range of 50–69 years in both the sexes. The peak prevalence age group, however, was different for males (50–59 years) and females (70–79 years). Prevalence was at the lower end in the age groups of 30–40 and 80–89 years for both the sexes. Total prevalence of metabolic syndrome was higher among patients living in the urban area according to WHO, NCEP ATPIII and Harmonized definitions and in villages according to IDF definition. However, the difference was significant only with WHO estimated prevalence (p <0.002) (Table 5). On the other hand, according to NCEP ATPIII, IDF and Harmonized definitions, prevalence was much higher (p <0.020) among the female patients living in the villages compared to their urban counterparts. Females had also higher prevalence compared to their male counterparts living in the same places except for the WHO defined prevalence in the urban patients. Likewise, Dalit patients had the highest total prevalence (p <0.05) according to NCEP ATPIII and Harmonized definitions while Mongol and Newar patients had the highest (p <0.010) total prevalence according to WHO and IDF definitions respectively. Sex-wise analysis revealed that Mongol and Dalit male patients had the highest (p <0.010) prevalence according to WHO, NCEP ATPIII and Harmonized definitions whereas Dalit female patients had the highest (p <0.001) total prevalence according to all four definitions. Prevalence was also highest among Chhetri and Newar female patients (p <0.001) according to IDF and Harmonized definitions (Table 6). Depending on the definition used, the prevalence of MetS was also found to have relation with occupation of the study patients. Total prevalence was significantly higher (p <0.05) in patients engaged in agricultural activity according to all four definitions and found to have relation with the gender. Male patients engaged in agricultural activity had the highest prevalence according to WHO, NCEP ATPIII and Harmonized definitions while it was highest among female patients involved in business occupation according to NCEP ATPIII and Harmonized definitions and office jobs according to WHO and IDF definitions (Table 7). However, the difference in prevalence among females of different occupations was not statistically significant (p >0.05) except for the one defined by IDF criteria (p <0.001).

Table 4 Age and sex-specific and age-adjusted prevalence of metabolic syndrome in the type 2 diabetic patients
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Table 5 Prevalence of metabolic syndrome in the type 2 diabetic patients according to their place of residence
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Table 6 Prevalence of metabolic syndrome in the type 2 diabetic patients according to their ethnic background
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Table 7 Prevalence of metabolic syndrome in the type 2 diabetic patients according to their occupations
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Prevalence of individual components of metabolic syndrome

The frequencies of the number of MetS components present in male and female patients are summarized in Table 8. Majority of the Subjects had the cluster of four metabolic abnormalities and the overall prevalence differed significantly (p <0.05) between male and females. The prevalence of individual MetS components included in the WHO, NCEP ATPIII, IDF and Harmonized criteria is shown in Table 9. The most prevalent component was the central obesity according to WHO (98.8%) and IDF (99.9%) definitions. Decreased HDL-cholesterol was the second most prevalent component according to NCEP ATPIII (95.2%) and Harmonized (94.2%) definitions. Increased BMI (≥30 kg/m2) was the least prevalent component (4.0%) according to WHO definition while hypertension was the least frequent component by the NCEP ATPIII (72.1%), IDF (65.9%) and Harmonized criteria (67.0%).

Table 8 Frequency of individual components of metabolic syndrome in the type 2 diabetic patients
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Table 9 Prevalence of individual metabolic abnormalities in the type 2 diabetic patients with metabolic syndrome
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Comparison between groups with and without metabolic syndrome

The subjects with metabolic syndrome were comparatively older, more overweight or obese, hyperglycemic, insulin resistant and suffering from diabetes for longer duration. They had relatively poor glycemic control, increased serum triglycerides, decreased serum HDL-cholesterol and hypertension of at least four year duration. BMI, waist circumference, HbA1C, serum triglycerides and number of metabolic components were significantly higher (p <0.05) in males with MetS whereas waist-hip ratio and insulin resistance level were significantly higher (p <0.05) in females with MetS diagnosed by three or more definitions of metabolic syndrome. There was no significant difference (p >0.05) in duration of diabetes and hypertension, systolic and diastolic blood pressure, fasting glucose, insulin and HDL-cholesterol levels between male and female with MetS defined at least by three definitions. Detailed comparison of clinical and biochemical parameters between groups with and without MetS is presented in Table 10.

Table 10 Comparison of anthropometric, biochemical and clinical parameters in the type 2 diabetic patients with and without metabolic syndrome
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Agreement and disparity among MetS definitions

The agreement and disparity in the diagnosis of MetS among WHO, NCEP ATP III, IDF and Harmonized definitions is presented in the Table 11. The agreement among these four definitions was substantial to slight. The agreement was substantial between NCEP ATP III-Harmonized (κ =0.62 (0.55-0.69), p <0.001), moderate between WHO-NCEP ATPIII (κ =0.51 (0.45-0.58), p <0.001) and IDF-Harmonized (κ =0.51 (0.47-0.61), p <0.001), fair between WHO-Harmonized (κ =0.37 (0.30-0.44), p <0.001) and NCEP ATPII-IDF (κ =0.33 (0.26-0.40), p <0.001) and slight between WHO-IDF definitions (κ =0.27 (0.19-0.33), p <0.001). The Harmonized-NCEP ATPIII and IDF definitions had the highest sensitivity (99.9%) and negative predictive value (98.9%) whereas NCEP ATPIII-Harmonized definitions had the highest specificity (98.9%) and positive predictive value (99.9%).

Table 11 The concordance and diagnostic accuracy of WHO, NCEP ATPIII, IDF and Harmonized definitions in identifying the cases of metabolic syndrome in the type 2 diabetic patients
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Discussion

Irrespective of the defining criteria, our study revealed a very high prevalence of MetS in Nepalese type 2 diabetic patients, far higher than those reported in the general population of Nepal and elsewhere [14],[15],[26]-[28]. Among our diabetic patients, the highest prevalence rate was estimated by Harmonized criteria (crude: 91.6%, age adjusted: 80.3%) followed by NCEP ATPIII (crude: 83.0%, age adjusted: 73.9%), WHO (crude: 81.1%, age adjusted: 69.9%) and IDF criteria (crude: 80.5%, age adjusted: 66.8%), respectively. These seemingly different prevalence rates arise due to the different cut-off points and sets of criteria used by these four different definitions. The performance of Harmonized criteria was the highest due to the removal of central obesity as an obligatory component and inclusion of any three of the five criteria present. This definition was introduced very recently in 2009 to bring the harmony in the several existing definitions of the MetS [8]. Studies that have used this definition also reported very high prevalence of MetS confirming its improved performance in other diabetic population too [29],[30]. Because of the very high cut-off points for waist circumference, NCEP definition could identify relatively low number of patients with central obesity. Similar studies conducted in other subset of Nepalese diabetic population have also reported relatively lower prevalence using this definition [17],[18]. However, there was little effect on the total prevalence rate due to freedom of including any three components. The high prevalence of MetS among our patients was not surprising as they were suffering from type 2 diabetes which itself was an entity of the MetS. Several studies around the globe have reported very high prevalence of MetS in type 2 diabetic patients irrespective of the definitions used, ethnicity and geographical area highlighting the common etiology of MetS [17],[29]-[31].

The gender distribution of the prevalence differed in our subjects when based on NCEP ATP III, IDF and Harmonized definitions, with higher prevalence in females. However, no such difference was noted for the prevalence estimated by WHO definition. Our female patients were suffering from diabetes and hypertension for longer duration, had relatively poor glycemic control and were more obese and dyslipidemic than their male counterpart which might explain why they have increased prevalence of MetS. The less apparent gender difference in prevalence by WHO definition might be due to narrow differences in the sex–specific cut-off values of waist-hip ratio and HDL-cholesterol as they were the more prevalent components of MetS in our patients. It is possible that female patients of our study sought medical treatment later in the disease than men due to less access to finances and lack of awareness and self-determination about their own health status. The prevalence of MetS sharply increased with the age in both men and women and remained highest in 50–69 years age range. This is expected because predisposition of MetS in both men and women is strongly favored by age related processes such as gradual decrease in the basal metabolic rate, decreased growth hormone secretion, hypogonadism, stress induced hypercortisolism, abdominal fat deposition and concomitant insulin resistance [1]. The sharp decline of the prevalence at very high age group, on the other hand, might be due to increased frequency of death of individuals who were most susceptible to obesity related mortality such as coronary artery diseases and cerebrovascular events [26],[32].

Analysis of prevalence according to the place of residence revealed that male patients living in urban areas had the much higher prevalence according to WHO and NCEP ATPIII definitions while the opposite was true according to NCEP ATPIII, IDF and Harmonized definitions. The gender distribution of MetS within patients residing in villages was also significant, with higher prevalence in females by all four definitions. Similarly, there was a significant difference in the prevalence between females residing in urban and village areas. Females residing in village area had higher prevalence than the urban females by all four definitions, although the difference was not significant for the WHO defined prevalence. We also analyzed distribution of prevalence according to ethnicity and occupational activity of the patients. Nepalese society is mainly agrarian in nature and a mixture of two major ethnic groups: Indo-Aryans and Mongoloids. Indo-Aryans are basically Hindus and are further divided into several social caste systems such as Brahmins, Chhetris, Vaishyas and Shudras or Dalits (Kami, Damai, Sarki, Gandharva etc.), while Mongoloids, although not divided into castes, have several tribes and lineages [33]. Analysis based on ethnicity revealed that total prevalence was the highest among Mongol, Dalit and Newar patients. Dalit female patients had consistently higher prevalence of MetS by all four definitions. In addition, prevalence was also highest among Chhetri and Newar female patients according to IDF and Harmonized definitions. However, it was highest among Mongoloid male patients by WHO, Dalit patients by NCEP ATPIII and Harmonized and Newar patients by IDF definitions. Likewise, prevalence was higher among patients who were associated with agriculture and particularly among males. In females, prevalence was higher among those who were involved either in business or office job. It has been established by several studies that low socioeconomic status, urban habitat, illiteracy, blue collar occupation and certain ethnicity are strongly associated with increased prevalence of MetS [28],[34]-[36]. Majority of our patients associated with agriculture profession were residing in the village area and were either illiterate or moderately educated and belonged to the family of low socioeconomic status. The reasons for increased prevalence of MetS in urban patients were seemed to be sedentary life-style due to their involvement in business and office jobs, increased intake of calorie rich foods and android type central obesity.

We found substantial level of agreement between NCEP ATPIII and Harmonized definitions. NCEP ATPIII definition missed out only one diabetic patient having MetS according to Harmonized definition. This high level of concordance is not surprising given their identical criteria except the waist circumference. The second highest agreement was observed between NCEP ATPIII and WHO definitions and the lowest was observed between IDF and WHO definitions. The agreement was only fair between WHO and harmonized definitions. Almost similar pattern of agreement has been found between these pairs of definitions when used for other diabetic population [30],[37]. From these observations, we can conclude that WHO, NCEP ATPIII and Harmonized definitions can identify patients with higher degree of insulin resistance and increased risk of cardiovascular diseases, but IDF definition can identify additional patients, not identified by the earlier definitions. These additional patients were those who are at increased risk of future CVDs but have lesser degree of insulin resistance. Among the four definitions used, harmonized definition was found to be the most sensitive while NCEP ATP III and IDF definitions were found to be most specific in identifying the cases of MetS.

Majority of our patients had a cluster of four components of MetS included by all four definitions: central obesity, high serum TG, low serum HDL-C and hypertension. This type of clustering is a commonly observed phenomenon among type 2 diabetic patients which significantly increases the risk of CVDs [13],[38]. We found central obesity as the most prevalent component according to WHO and IDF criteria and decreased HDL-cholesterol according to NCEP and Harmonized criteria. The lower prevalence of central obesity according to the NCEP ATP III criteria must be due to its relatively higher cut-off values for waist circumference and that makes it less applicable criterion to the Nepalese population because of their smaller body sizes. Prevalence of BMI, central obesity and low HDL-C was significantly higher in females. South Asians, which also includes Nepali population, are shown to have increased visceral fat, central obesity, dyslipidemia and insulin resistance even at younger age group compared to their Western counterparts which predisposes them to very high risk of MetS, type 2 DM and cardiovascular diseases than any other population in the world [39]. In order to address these issues, WHO and IDF have set lower cut-off points for the BMI, waist circumference and waist hip ratio for South Asians [7],[20]. It may thus be argued that central obesity and decreased HDL level can be used as the strong diagnostic markers of MetS also in Nepalese population. Hypertension, on the other hand, was found to be the least prevalent component in our study patients according to all four criteria and no significant relation was found with gender except for the prevalence estimated by NCEP ATP III definition. These findings are consistent with the reports of earlier studies conducted in Nepal [15],[17],[18] but conflicting with many other international studies [30],[37],[40]. In our study, patients with MetS were found to be older, more dyslipidemic, obese and hypertensive for longer duration than those without MetS which was quite expected and supported by many other studie [29],[40]. These patients, thus, bore very high cardiometabolic risk even when compared to their diabetic counterparts without MetS [41].

Our study was a cross-sectional study conducted among uncomplicated type 2 diabetic patients attending a tertiary care teaching hospital located in the Western Development Region of Nepal. It enrolled mainly those patients who hailed from the different districts of this region of Nepal and therefore, may not necessarily represent entire Nepalese diabetic population. This study also did not cover the effect of treatment in the variation of MetS prevalence among the study patients. Despite these limitations, our study is the first of its kind in Nepal and reports the age adjusted prevalence rates of MetS among type 2 diabetics using four most used defining criteria. It also analyzes the prevalence rates according to the age-group, gender, place of residence, occupations, ethnicity and smoking and dietary habits of the study patients and describes the relative similarities and differences of the definitions used in identifying the cases of MetS. Moreover, it contributes to the mapping of epidemiology of MetS in Nepal and serves as comparable baseline data for health policy makers and researchers.

Conclusion

Our study highlights the alarmingly high prevalence of MetS and the increased risk of strokes and cardiovascular diseases among Nepalese type 2 diabetic patients. It also suggests that Harmonized and NCEP ATP III definitions are better than WHO and IDF definitions in identifying the cases of MetS among Nepalese diabetic patients. We expect that these finding will prompt the concerned authorities of Nepal to formulate strategies to prevent and delay the onset of future complications among the diabetic patients. These strategies might include launching aggressive health education programs to increase the public awareness about the preventive measures and negative consequences of MetS and type 2 DM, investing more resources on the health care services making it more accessible to the general public and minimizing the individual risk factors by active therapeutic intervention in already affected individuals.