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Journal of Diabetes and Metabolic Disorders logoLink to Journal of Diabetes and Metabolic Disorders
. 2014 Nov 23;13:104. doi: 10.1186/s40200-014-0104-3

Prevalence of metabolic syndrome in Nepalese type 2 diabetic patients according to WHO, NCEP ATP III, IDF and Harmonized criteria

Daya Ram Pokharel 1,, Dipendra Khadka 3, Manoj Sigdel 1, Naval Kishor Yadav 1, Shreedhar Acharya 4, Ram Chandra Kafle 2, Pramod Shankar Shukla 1
PMCID: PMC4251856  PMID: 25469328

Abstract

Background

Metabolic syndrome (MetS) present in type 2 diabetic patients greatly increases the risk of strokes and cardiovascular diseases. Timely detection and mapping of MetS facilitates appropriate preventive and therapeutic approaches to minimize these risks. Our study aimed to determine the prevalence of MetS among Nepalese type 2 diabetic patients using WHO (1999), NCEP ATP III (2001), IDF (2005) and Harmonized (2009) definitions and identify the diagnostic concordance and disparity resulting from these four definitions.

Methods

Clinical and biochemical data were collected for 1061 type 2 diabetic patients at Manipal Teaching Hospital, Pokhara, Nepal. The data was analyzed in order to identify prevalence of MetS in these patients. Statistical analysis included usage of Student’s t- and Chi-square tests, kappa statistics and 95% confidence intervals.

Results

The total age adjusted prevalence rates of MetS were 80.3%, 73.9%, 69.9% and 66.8% according to Harmonized, NCEP ATP III, WHO and IDF definitions, respectively. Prevalence increased with the age and was higher in females (p <0.001) according to WHO, NCEP ATP III and Harmonized definitions. Patients of Dalit community had the highest prevalence (p<0.05) according to NCEP ATP III and Harmonized definitions while Mongoloid and Newar patients had the highest prevalence (p <0.05) according to WHO and IDF definitions, respectively. Prevalence was also highest among patient engaged in agriculture occupation. Central obesity and hypertension were respectively the most and the least prevalent components of MetS. The highest overall agreement was between Harmonized and NCEP ATP III definitions (κ =0.62, substantial) and the lowest between WHO & IDF definitions (κ=0.26, slight). The Harmonized definition had the highest sensitivity (99.9%) and negative predictive value (98.9%) while NCEP ATP III definition had the highest specificity (98.9%) and positive predictive values (99.9%) in identifying the cases of MetS.

Conclusions

The prevalence of MetS among Nepalese type 2 diabetic patients was very high suggesting that these patients were at increased risk of strokes, cardiovascular diseases and premature death. The Harmonized definition was the most sensitive while NCEP ATP III and IDF definitions were the most specific in detecting the presence of MetS in Nepalese type 2 diabetic patients.

Keywords: Prevalence, Metabolic syndrome, Type 2 diabetes mellitus, Nepal, Pokhara, Manipal Teaching Hospital

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

Criteria WHO (1998) NCEP (2001) IDF (2005) Harmonized (2009)
Prerequisite DM, IFG, IGT, IR None WC: ≥90 cm (men) & ≥80 cm (women)† None
No. of other criteria and ≥2 of: ≥3 of: and ≥2 of: ≥3 of:
Obesity BMI: ≥30 &/or WHR: >0.9 (men) & >0.85 (women) WC: ≥102 cm (men) & ≥88 cm (women) Already considered as perquisite criterion WC: ≥90 cm (men) & ≥80 cm (women)†
BP (mmHg) ≥140/90 ≥130/85 or Rx ≥130/85 or Rx ≥130/85 or Rx
HDL-C (mg/dl) <35 (men) & <39 (women) or <40 (men) & <50 (women) or Rx <40 (men) & <50 (women) or Rx <40 (men) & <50 (women) or Rx
TG (mg/dl) ≥150 ≥150 or Rx ≥150 or Rx ≥150 or Rx
Fasting glucose (mg/dl) ≥110, IGT ≥100 or Rx ≥100 or Rx ≥100 or Rx
Microalbuminuria Urinary albumin ≥20 μg/min or albumin-creatinine ratio >30 mg/g - - -

†Recommended waist circumference thresholds for the abdominal obesity in people of Asian origin.

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

Characteristic variables Male Female p-value* Total
n (%) 589 (55.5) 472 (44.5) 1061 (100)
Ethnic groups
  Brahmin 145 (24.6) 116 (24.6) <0.010 261 (24.6)
  Chhetri 163 (27.7) 90 (19.1) 253 (23.8)
  Dalit 57 (9.7) 45 (9.5) 102 (9.6)
  Mongol 169 (28.7) 179 (37.9) 348 (32.8)
  Newars 55 (9.3) 42 (8.9) 97 (9.1)
Occupation
  Agriculture 164 (27.8) 345 (73.1) <0.001 509 (48.0)
  Business 251 (42.6) 67 (14.2) 318 (30.0)
  Office job 174 (29.5) 60 (12.7) 234 (22.1)
Place of residence
  Urban 428 (72.7) 321 (68.0) <0.001 749 (70.6)
  Village 161 (27.3) 151 (32.0) 312 (29.4)
Smoking habit
  Non-smokers 338 (54.7) 334 (70.8) 672 (63.3)
  Current smokers 108 (18.3) 76 (16.1) <0.001 184 (17.3)
  Ex-smokers 143 (24.3) 62 (13.1) 205 (19.3)
Dietary habit
  Vegetarian 62 (10.5) 63 (13.3) <0.001 125 (11.8)
  Non-vegetarian 527 (89.5) 409 (86.7) 936 (88.2)
General obesity 127 (21.6) 161 (34.1) <0.001 288 (27.1)
Central obesity
  Android 448 (76.1) 216 (45.8) <0.001 664 (62.6)
  Gyenoid 111 (18.8) 236 (50.0) 347 (32.7)
Glycemic control
  Good (≤6.5%) 589 (100.0) 461 (97.7) <0.001 1050 (99.0)
  Suboptimal (≥6.5%) 0 (0) 11 (2.3) 11 (1.0)
Dyslipidemia
  High TG 377 (64.0) 323 (68.4) <0.001 700 (66.0)
  Low HDL-C 464 (78.8) 378 (80.1) 842 (79.4)
Hypertension (HTN) 410 (52.4) 357 (52.1) <0.001 555 (32.3)
Treatment for HTN 281 (47.7) 243 (51.5) <0.001 524 (49.4)

Values are presented as n (%). *p-values (2-tailed): Chi-square test was performed to compare the proportions of selected demographic, anthropometric and biochemical characteristics between male and female diabetic patients.

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

Characteristic variables Male Female p-value* Total
n 589 472 - 1061
Age (years) 56.4 ±9.6 56.4 ±10.4 >0.050 56.4 ±10.0
BMI (m/kg2) 23.2 ±2.7 24.1 ±2.8 <0.001 23.6 ±2.8
WC (cm) 95.0 ±8.5 96.7 ±10.1 <0.010 95.8 ±9.3
WHR 1.1 ±0.1 1.0 ±0.1 <0.001 1.0 ±0.1
FPG (mg/dl) 132.2 ±37.6 129.8 ±28.3 >0.05 131.1 ±33.8
2 hr PMG (mg/dl) 217.2 ±70.7 219.1 ±50.3 >0.05 218.1 ±62.4
EAG (mg/dl) 131.1 ±19.3 135.8 ±22.7 <0.001 133.2 ±21.0
HbA1C (%) 6.2 ±0.7 6.4 ±0.8 <0.001 6.3 ±0.7
Duration of DM (year) 5.8 ±4.7 6.3 ±4.6 >0.050 6.0 ±4.6
TG (mg/dl) 188.3 ±117.4 229.6 ±236.7 <0.010 206.7 ±181.6
TC (mg/dl) 227.4 ±59.1 231.3 ±74.5 >0.050 229.1 ±66.4
VLDL (mg/dl) 37.7 ±23.4 45.8 ±27.4 <0.010 41.3 ±36.3
HDL-C (mg/dl) 31.9 ±7.6 30.7 ±8.2 <0.050 31.4 ±7.9
LDL-C (mg/dl) 157.8 ±63.7 154.8 ±55.7 >0.050 156.5 ±60.2
SBP (mmHg) 132.4 ±13.8 132.6 ±17.4 >0.050 132.5 ±15.5
DBP (mmHg) 83.4 ±7.7 81.9 ±9.9 <0.010 82.8 ±8.8
Duration of HTN (year) 3.6 ±4.9 4.2 ±6.2 >0.050 3.8 ±5.5

Values are presented as mean ± standard deviation. *p-values (2-tailed): Student’s t-test was used to compare the mean values of selected biochemical and anthropometric characteristics between male and female diabetic patients.

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

Age group (years) Prevalence of MetS (WHO) (%) Prevalence of MetS (NCEP ATPIII) (%)
Male Female *p-value Male Female *p-value
n 589 472 - 589 472 -
30-39 14 (50.0) 15 (50.0) >0.050 14 (50.0) 16 (53.3) >0.050
40-49 88 (67.2) 60 (67.4) >0.050 103 (78.6) 82 (92.1) <0.010
50-59 157 (84.4) 148 (95.5) <0.010 145 (78.0) 147 (94.8) <0.001
60-69 151 (83.9) 111 (82.2) >0.050 143 (79.4) 122 (90.4) <0.010
≥70 61 (95.3) 56 (88.9) >0.050 57 (89.1) 55 (87.3) >0.050
Sex-specific crude 471 (80.0) 390 (82.6) >0.050 462 (78.4) 422 (89.4) <0.001
Total crude 861 (81.1) 884 (83.3)
Total age-adjusted 69.9 73.9
Age group (years) Prevalence of MetS (IDF) (%) Prevalence of MetS (Harmonized) (%)
Male Female *p-value Male Female *p-value
n 589 472 - 589 472 -
30-39 4 (14.3) 18 (60.0) <0.001 14 (50.0) 18 (60.0) >0.050
40-49 91 (69.5) 77 (86.5) <0.010 110 (84.0) 87 (97.8) <0.010
50-59 152 (81.7) 153 (98.7) <0.001 182 (97.8) 153 (98.7) >0.050
60-69 121 (67.2) 135 (100) <0.001 150 (83.3) 135 (100) <0.001
≥70 37 (63.8) 58 (98.3) <0.001 57 (98.3) 58 (98.3) >0.050
Sex-specific crude 410 (69.6) 444 (94.1) <0.001 518 (87.9) 454 (96.2) <0.001
Total crude 854 (80.5) 972 (91.6)
Total age-adjusted 66.8 80.3

Values are expressed as n (%) or only%. *p-values (2-tailed): Chi square test was used to compare the prevalence of metabolic syndrome between male and female diabetic patients.

Table 5.

Prevalence of metabolic syndrome in the type 2 diabetic patients according to their place of residence

Defining bodies Sex n Place of residence *p-value
Urban Village
Total 860 626 (83.5) 234 (75.2) <0.010
WHO Male 470 368 (85.8) 102 (63.8) <0.001
Female 390 258 (80.4) 132 (87.4) >0.050
p-value <0.050 <0.001
Total 884 633 (84.4) 251 (80.7) >0.050
NCEP ATP III Male 462 353 (82.3) 109 (68.1) <0.001
Female 422 280 (87.2) 142 (94.0) <0.050
p-value >0.050 <0.001
Total 854 589 (78.5) 265 (85.2) <0.050
IDF Male 410 295 (68.8) 115 (71.9) >0.050
Female 444 294 (91.6) 150 (99.3) <0.010
p-value <0.001 <0.001
Total 972 687 (91.6) 285 (91.6) >0.050
Harmonized Male 518 383 (89.3) 135 (84.4) >0.050
Female 454 304 (94.7) 150 (99.3) <0.050
p-value <0.010 <0.001

Values are presented as n (%). *p-values (2-tailed): Chi-square tests were performed to compare the prevalence of MetS between male and female patients residing in urban and village areas (rows) and within the individual place of residence (column).

Table 6.

Prevalence of metabolic syndrome in the type 2 diabetic patients according to their ethnic background

Ethnic groups
Defining bodies Sex n Brahman Chhetri Mongol Dalit Newar *p-value
WHO Total 860 181 (69.3) 187 (73.9) 331 (95.1) 92 (90.2) 69 (71.1) <0.001
Male 470 124 (85.5) 109 (66.9) 159 (94.1) 47 (82.5) 31 (56.4) <0.001
Female 390 57 (49.1) 78 (86.7) 172 (96.1) 45 (100.0) 38 (90.5) <0.001
*p-value <0.001 <0.010 >0.050 <0.010 <0.001
NCEP ATPIII Total 884 204 (78.2) 196 (77.5) 309 (88.5) 98 (96.1) 77 (79.4) <0.010
Male 462 109 (75.2) 117 (71.8) 141 (83.4) 55 (96.5) 40 (72.7) <0.010
Female 422 95 (81.9) 79 (87.8) 168 (93.9) 43 (95.6) 37 (88.1) <0.050
p-value >0.050 <0.010 <0.010 >0.050 >0.050
IDF Total 854 178 (68.2) 214 (84.6) 296 (85.1) 82 (80.4) 84 (86.6) <0.001
Male 410 87 (60.0) 124 (76.1) 120 (71.0) 37 (64.9) 42 (76.4) <0.050
Female 444 91 (78.4) 90 (100.0) 176 (98.3) 45 (100.0) 42 (100.0) <0.001
p-value <0.010 <0.001 <0.001 <0.001 <0.010
Harmonized Total 972 217 (83.1) 233 (92.1) 336 (96.6) 102 (100.0) 84 (86.6) <0.001
Male 518 116 (80.0) 143 (87.7) 160 (94.7) 57 (100.0) 42 (76.4) <0.001
Female 454 101 ((87.1) 90 (100.0) 176 (98.3) 45 (100.0) 42 (100.0) <0.001
p-value >0.050 <0.010 >0.050 ǂ <0.010

Values are presented as n (%). *p-values (2-tailed): Chi-square tests were performed to compare the prevalence of MetS between the male and female patients of different ethnic groups (rows) and within the individual ethnic groups (column). ǂNo statistics were computed because metabolic syndrome defined by Harmonized definition is a constant.

Table 7.

Prevalence of metabolic syndrome in the type 2 diabetic patients according to their occupations

Defining bodies Sex n Agriculture Business Office job *p-value
WHO Total 860 422 (82.9) 260 (81.8) 178 (76.1) >0.050
Male 470 137 (83.5) 206 (82.1) 127 (73.0) <0.050
Female 390 285 (82.6) 54 (80.6) 51 (85.0) >0.050
p-value - >0.050 >0.050 >0.050 -
NCEP ATPIII Total 884 447 (87.8) 241 (75.8) 196 (83.8) <0.001
Male 462 141 (86.0) 177 (70.5) 144 (82.8) <0.001
Female 422 306 (88.7) 64(95.5) 52 (86.7) >0.050
p-value - >0.050 <0.001 >0.050 -
IDF Total 854 439(86.2) 236(74.2) 179 (76.5) <0.001
Male 410 109 (66.5) 180 (71.7) 121 (69.5) >0.050
Female 444 330 (95.7) 56 (83.6) 58 (96.7) <0.001
p-value - <0.001 <0.050 <0.001 -
Harmonized Total 972 479 (94.1) 286 (89.9) 207 (88.5) <0.050
Male 518 149 (90.9) 220 (87.6) 149 (85.6) >0.050
Female 454 330 (95.7) 66 (98.5) 58 (96.7) >0.050
p-value - <0.050 <0.010 <0.050 -

Values are presented as n (%). *p-values (2-tailed): Chi-square tests were performed to compare the prevalence of MetS between male and female patients of different occupations (rows) and within the individual occupation (column).

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

MetS components WHO (1998) NCEP ATP (2001) IDF (2005) Harmonised (2009)
Male Female* Male Female** Male Female** Male Female**
n 589 472 589 472 589 472 589 472
1 6 (1.0) 1 (0.2) 25 (4.2) 4 (.8) 70 (11.9) 19 (4.0) 70 (11.9) 19 (4.0)
2 102 (17.3) 82 (17.4) 102 (17.3) 46 (9.7) 129 (21.9) 71 (15.0) 128 (21.7) 71 (15.0)
3 230 (39.0) 149 (31.6) 179 (30.4) 65 (13.8) 226 (38.4) 166 (35.2) 227 (38.5) 166 (35.2)
4 251 (42.6) 240 (50.8) 222 (37.7) 152 (32.2) 164 (27.8) 216 (45.8) 164 (27.8) 216 (45.8)
5 - - 61 (10.4) 205 (43.4) - - - -

Values are presented as n (%). *p <0.050, **p <0.001 (two sided). Chi-square test was performed to compare the number of components of metabolic syndrome between male and female patients.

Table 9.

Prevalence of individual metabolic abnormalities in the type 2 diabetic patients with metabolic syndrome

WHO NCEP ATP III
Prevalence (%) *p-value Prevalence (%) *p-value
Components of MetS Male Female Total Male Female Total
n 470 386 856 465 429 894
Increased FGǂ 470 (100) 386 (100) - 856 (100) 465 (100) 429 (100) - 894 (100)
BMI (≥30 kg/m2) 10 (2.1) 24 (6.2) <0.010 34 (4.0) - - -
Central obesity 460 (97.9) 386 (100) <0.010 846 (98.8) 132 (28.4) 368 (85.8) <0.001 500 (55.9)
Increased TG 368 (78.3) 303 (78.5) >0.050 671 (78.4) 363 (78.1) 324 (75.5) >0.050 687 (76.8)
Decreased HDL-C 373 (79.4) 344 (89.1) <0.001 717 (83.8) 425 (91.4) 426 (99.3) <0.001 851 (95.2)
Hypertension 300 (63.8) 267 (69.2) >0.050 567 (66.2) 350 (75.3) 295 (68.8) <0.050 645 (72.1)
IDF Harmonized
Prevalence (%) *p-value Prevalence (%) *p-value
Components of MetS Male Female Total Male Female Total
n 400 434 834 518 454 972
Increased FGǂ 400 (100) 434 (100) - 834 (100) 518 (100) 454 (100) - 972 (100)
Central obesity 400 (100) 433 (99.8) >0.050 833 (99.9) 399 (77.0) 433 (95.4) <0.001 832 (85.6)
Increased TG 268 (67.0) 304 (68.6) >0.050 572 (58.2) 367 (70.8) 324 (71.4) >0.050 691 (71.1)
Decreased HDL-C 349 (87.3) 431 (99.3) <0.001 780 (93.5) 468 (90.3) 451 (99.3) <0.001 819 (94.5)
Hypertension 256 (64.0) 286 (65.9) >0.050 542 (62.0) 355 (68.5) 296 (65.2) >0.050 651 (67.0)

Values are presented as n (%). ǂAll the study patients were considered to have increased fasting glucose as they were diagnosed type 2 diabetic patients and taking medication for blood glucose control. *p-values (2-tailed): Chi-square test was performed to compare the mean proportions of metabolic components between male and female patients.

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

WHO NCEP ATPIII
Variables With MetS Without MetS *p-value With MetS Without MetS *p-value
(n,%) 861 (81.5) 200 (18.5) <0.001 884 (83.2) 177 (16.8) <0.001
Age (year) 57.4 ±9.4 52.1 ±11.2 <0.001 56.9 ±9.5 53.5 ±11.7 <0.001
BMI (kg/m2) 23.8 ±2.9 23.0 ±2.1 <0.001 23.7 ±2.9 23.0 ±2.0 <0.001
WC (cm) 96.9 ±9.2 90.9 ±8.1 <0.001 96.8 ±9.3 90.4 ±6.9 <0.001
WHR 1.05 ±0.1 0.98 ±0.1 <0.001 1.04 ± .01 0.99 ±0.09 <0.001
FPG (mg/dl) 134.2 ±35.4 117.9 ±21.1 <0.001 132.9 ±34.9 122.0 ±25.8 <0.001
2 hr PMG (mg/dl) 226.5 ±63.8 182.1 ±39.4 <0.001 224.7 ±63.3 185.0 ±45.5 <0.001
EAG (mg/dl) 137.1 ±20.0 116.3 ±16.8 <0.001 136.5 ±20.1 116.6 ±17.3 <0.001
Hb1Ac% 6.4 ±0.7 5.7 ±0.6 <0.001 6.4 ±0.7 5.7 ±0.6 <0.001
Duration of DM (year) 6.5 ±4.7 4.0 ±3.6 <0.001 6.3 ±4.7 4.6 ±3.7 <0.001
TG (mg/dl) 223.0 ±195.8 136.3 ±61.3 <0.001 222.6 ±190.4 127.1 ±95.0 <0.001
TC (mg/dl) 239.9 ±67.1 183.1 ±37.4 <0.001 239.9 ±66.2 175.4 ±33.0 <0.001
VLDL (mg/dl) 44.5 ±29.2 27.5 ±12.3 <0.001 44.4 ±38.1 25.4 ±18.9 <0.001
HDL-C (mg/dl) 29.5 ±6.7 39.5 ±7.5 <0.001 29.5 ±6.8 40.7 ±6.3 <0.001
LDL-C (mg/dl) 165.9 ±61.0 116.2 ±35.3 <0.001 166.0 ±59.9 109.2 ±34.2 <0.001
SBP (mmHg) 135.2 ±15.0 120.8 ±11.6 <0.001 134.5 ±14.9 122.4 ±14.2 <0.001
DBP (mmHg) 84.1 ±8.8 76.9 ±5.5 <0.001 83.9 ±8.6 76.9 ±7.0 <0.001
Duration of HTN (year) 4.7 ±5.8 0.0 ±0.0 <0.001 4.5 ±5.8 0.6 ±1.9 <0.001
No. of MetS components 3.6 ±0.5 2.0 ±0.3 <0.001 4.0 ±0.8 1.8 ±0.4 <0.001
IDF Harmonized
Variables With MetS Without MetS *p-value With MetS Without MetS *p-value
n (%) 854 (80.5) 207 (19.5) <0.001 972 (91.6) 89 (8.4) <0.001
Age (year) 57.1 ±9.4 53.5 ±11.8 <0.001 56.9 ±9.5 50.5 ±12.7 <0.001
BMI (kg/m2) 24.1 ±2.6 21.6 ±2.2 <0.001 23.7 ±2.8 22.7 ±2.2 <0.001
WC (cm) 98.2 ±8.0 85.6 ±6.7 <0.001 96.4 ±9.2 88.8 ±7.5 <0.001
WHR 1.05 ±0.09 0.96 ±0.08 <0.001 1.0 ±0.1 0.95 ±0.1 <0.001
FPG (mg/dl) 131.2 ±30.8 130.9 ±43.9 >0.050 132.4 ±34.5 117.1 ±20.0 <0.001
2 hr PMG (mg/dl) 220.7 ±57.3 207.2 ±79.3 <0.050 222.6 ±62.6 167.9 ±30.5 <0.001
EAG (mg/dl) 135.9 ±20.6 122.0 ±19.0 <0.001 135.5 ±20.2 108.3 ±12.8 <0.001
Hb1Ac% 6.4 ±0.7 5.9 ±0.7 <0.001 6.3 ±0.7 5.4 ±0.5 <0.001
Duration of DM (year) 6.4 ±4.7 4.3 ±4.0 <0.001 6.2 ±4.7 3.7 ±3.6 <0.001
TG (mg/dl) 217.7 ±99.2 161.2 ±51.8 <0.001 214.3 ±187.7 123.2 ±25.7 <0.001
TC (mg/dl) 233.4 ±68.6 211.7 ±53.2 <0.001 234.3 ±66.4 173.2 ±32.6 <0.001
VLDL (mg/dl) 43.4 ±39.9 32.4 ±10.3 <0.001 42.8 ±37.6 25.1 ±5.5 <0.001
HDL-C (mg/dl) 30.7 ±7.4 34.2 ±9.2 <0.001 30.3 ±7.1 43.1 ±5.8 <0.001
LDL-C (mg/dl) 159.3 ±60.8 145.0 ±56.4 >0.010 161.2 ±60.0 105.0 ±33.7 <0.001
SBP (mmHg) 132.4 ±15.7 132.7 ±14.6 >0.050 132.9 ±15.3 128.3 ±16.9 <0.050
DBP (mmHg) 83.0 ±9.0 81.6 ±7.5 <0.050 83.1 ±8.9 78.7 ±6.7 <0.001
Duration of HTN (year) 4.3 ±5.8 2.0 ±3.5 <0.001 4.1 ±5.7 0.7 ±1.4 <0.001
No. of MetS components 4.2 ±0.7 3.0 ±0.9 <0.001 4.2 ±0.8 2.0 ±0.2 <0.001

Values are expressed as mean ± standard deviation.*p-values (2-tailed): Students t-test comparing the mean values of selected characteristics between patients with and without MetS.

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

Concordance Diagnostic Accuracy (%)
Definitions κ-value (95% CI) *p-value Agreement Sensitivity Specificity PPV NPV
WHO vs NCEP ATPIII 0.53 (0.45-0.58) <0.001 Moderate 90.0 71.4 92.8 63.5
NCEP ATPIII vs WHO 0.53 (0.45-0.58) <0.001 Moderate 92.8 57.5 90.4 65.0
WHO vs IDF 0.27 (0.19-0.33) <0.001 Slight 86.2 39.6 85.5 41.0
IDF vs WHO 0.27 (0.19-0.33) <0.001 Slight 85.5 41.0 86.2 39.6
WHO vs Harmonized 0.37 (0.30-0.44) <0.001 Fair 86.0 71.9 97.1 32.0
Harmonized vs WHO 0.37 (0.30-0.44) <0.001 Fair 97.1 32 86 71.9
NCEP ATPIII vs IDF 0.33 (0.26-0.40) <0.001 Fair 89.5 42.0 86.4 49.2
IDF vs NCEP ATPIII 0.33 (0.26-0.40) <0.001 Fair 86.4 49.2 89.5 42.0
NCEP ATPIII vs Harmonized 0.62 (0.55-0.69) <0.001 Substantial 90.8 98.9 99.9 49.7
Harmonized vs NCEP ATP III 0.62 (0.55-0.69) <0.001 Substantial 99.9 49.7 90.8 98.9
IDF vs Harmonized 0.54 (0.47-0.61) <0.001 Moderate 87.8 98.9 99.5 42.5
Harmonized vs. IDF 0.54 (0.47-0.61) <0.001 Moderate 99.9 42.5 87.8 98.9

Level of agreement and diagnostic accuracy are presented as κ-value (95% CI) and%, respectively. *p-values (approx. sig.). Chi-square test was used to compare the level of agreement between two different definitions of MetS.

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.

Acknowledgements

The authors would like to thank the all the laboratory technicians of the Department of Clinical Biochemistry, Manipal Teaching Hospital, Phoolbari, Pokhara for providing their cooperation in the enrollment and interview of patients, sample collection, processing, storage and their laboratory analyses. We would also like to extend our special thank to Mr. Ravindra Mohan Sapkota, PhD Research Scholar, University of Zurich, Switzerland for his critical comments and help in the English language improvement.

Abbreviations

AHA

American Heart Association

BMI

Body mass index

BP

Blood pressure

CVDs

Cardiovascular diseases

DBP

Diastolic blood pressure

DM

Diabetes mellitus

FPG

Fasting plasma glucose

HDL-C

High density lipoprotein cholesterol

HTN

Hypertension

IDF

International Diabetes Federation

IAS

International Atherosclerosis Society

IASO

International Association for the Study of Obesity

IFG

Impaired fasting glucose

IGT

Impaired glucose tolerance

IR

Insulin resistance

LDL-C

Low density lipoprotein cholesterol

MetS

Metabolic syndrome

NCEP ATPIII

National Cholesterol Education Program Adult Treatment Panel III

NHLBI

National Heart Lung and Blood Institute

NPV

negative predictive value

PPV

Positive predictive value

PMG

Post meal glucose

Rx

Under medical treatment

SBP

Systolic blood pressure

TC

Total cholesterol

TG

Triglycerides

VLDL

Very low density lipoprotein

WC

Waist circumference

WHF

World Heart Federation

WHO

World Health Organization

WHR

Waist hip ratio

Footnotes

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

DRP designed the study, analyzed the data and wrote the manuscript. DK, MS and NKY contributed in the patient enrollment, data collection and management. RCK screened and confirmed the presence of type 2 diabetes in the study patients. SA provided help in the statistical analysis and interpretation of the analyzed data. PSS contributed in the critical reading, correction of the English and improvement of the manuscript. All authors read and approved the final manuscript.

Contributor Information

Daya Ram Pokharel, Email: drpokharel09@gmail.com.

Dipendra Khadka, Email: dipen.khadka15@gmail.com.

Manoj Sigdel, Email: manoj.sigdel@hotmail.com.

Naval Kishor Yadav, Email: naval.rhythm@gmail.com.

Shreedhar Acharya, Email: shreedhar.acharya@cambriancollege.ca.

Ram Chandra Kafle, Email: kafle30@hotmail.com.

Pramod Shankar Shukla, Email: pramodkshukla@hotmail.com.

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