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 Table of Contents  
ORIGINAL ARTICLE
Year : 2014  |  Volume : 1  |  Issue : 2  |  Page : 95-99

A Cross-Sectional Analysis of Metabolic Syndrome Factors in North Indian Adult Population of Kashmir


1 Institute of Liver and Biliary Sciences, New Dehli, India
2 Department of Internal Medicine, Sher-i-Kashmir Institute of Medical Sciences, Srinagar, Jammu and Kashmir, India

Date of Submission28-Mar-2013
Date of Acceptance09-Jun-2014
Date of Web Publication16-Jul-2014

Correspondence Address:
Riyaz Ahmad Bhat
Flat F-18, Married Hostel, Sher-i-Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2384-5147.136822

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  Abstract 

Background: Kashmiri population is ethnically distinct, culturally unique and has distinct lifestyle and dietary habits. There is a high prevalence of obesity in Kashmiri populations and also studies have shown high prevalence of diabetes mellitus in this population. Aim: We designed this study to evaluate important metabolic parameters contributing to the prevalence of metabolic syndrome (MS). Materials and Methods: A prospective study involving 500 participants was designed. An informed consent was obtained from all the subjects before selection and permission was granted by Hospital Ethical Committee beforehand. The subjects were selected from the attendants who accompanied patients in in-patient and out-patient Departments of Sher-i-Kashmir Institute of Medical Sciences, Srinagar Kashmir. A random sampling procedure was adopted for the study. Age, sex, weight, height, waist circumference, hip circumference as well as blood pressure were measured in all study participants. Furthermore, measured were blood glucose and lipids in all participants. Subjects were screened for the components of MS according to criteria given by Adult Treatment Panel-111. Statistical Analysis: Data were analyzed using SPSS Version 11.5. Student's t-test was used to analyze categorical variable, while Chi-square tests was used for categorical variable and Mann-Whitney U-test to define association. The level of significance in each case was considered as P < 0.05. Results: The mean age of both men and women was 37 years. The prevalence of MS in this population is 8.6%, among males the prevalence is 7.4%, while among females it is 9.9% (P = 0.323). The prevalence of hypertension was 24.9% for males and 12.3% for females. The prevalence of hyperglycemia was 9.3% for males and 7.8% for females. About 9.7% males and 25.9% females had low high density lipoprotein-cholesterol (HDL-C). 17.1% males and 13.2% females had elevated triglyceride levels. The prevalence of obesity in males was 1.9% and in females it was 8.6%. Hypertension was the most common factor affecting the estimates of MS in men, whereas central obesity and low HDL-C were the common contributing factors in women. Conclusion: In this population, the components of the MS are more common among females than males. This could be related to the high prevalence of obesity and diabetes among them.

Keywords: Body mass index, high density lipoprotein-cholesterol, metabolic syndrome, waist circumference


How to cite this article:
Wani ZA, Bhat RA. A Cross-Sectional Analysis of Metabolic Syndrome Factors in North Indian Adult Population of Kashmir. Sub-Saharan Afr J Med 2014;1:95-9

How to cite this URL:
Wani ZA, Bhat RA. A Cross-Sectional Analysis of Metabolic Syndrome Factors in North Indian Adult Population of Kashmir. Sub-Saharan Afr J Med [serial online] 2014 [cited 2024 Mar 28];1:95-9. Available from: https://www.ssajm.org/text.asp?2014/1/2/95/136822


  Introduction Top


Metabolic syndrome (MS) is a cluster of traits comprising of obesity, dyslipidemia, hypertension, and insulin resistance. [1] its prevalence is increasing in developing countries like India as a result of change in lifestyle. [2] The first formal definition of the MS was put forth in 1998 by the World Health Organization. [3] The National Cholesterol Education Program: Adult Treatment Panel III (ATP-III) published a new set of criteria based on common clinical measurements such as waist circumference (WC), blood lipids, blood pressure, and fasting glucose. [4] Of late, the International Diabetes Federation (IDF) has proposed new population specific criteria for the MS. [5]

there are suggestions that the ATP-III definition of the MS is not optimal for the identification of risks of type 2 diabetes mellitus (DM) or cardiovascular disease and does not have enough specificity in the South Asian population who are yet to reach the overweight definition (body mass index [BMI] >25 kg/m 2 ). Data from recent studies in India show that WC levels of 90 cm for men and 80 cm for women were associated with higher odds ratios (ORs) for the presence of cardiovascular risk factors. [6]

Therefore, modified ATP-III criteria, which incorporate Asian-specific WC criteria of 90 cm in men and 80 cm in women have been used in recent Asian and Indian studies. [7] Most Indian studies have used ATP-III criteria in their prevalence and factor analysis studies. However, studies show different trends of prevalence when different criteria are used. Different aspects of MS have been studied in many more Indian studies including North Indian studies. [8],[9],[10],[11]

Kashmiri population is ethnically very distinct due to their special dietary intakes. Owing to the temperate climatic conditions of Kashmir valley, the population is habituated to preserve foods in smoked, pickled and sundried form. Besides, Kashmiri populations use high caloric diet in the form of Wazwan. Obesity in Kashmiri population is fairly common in females. [12] However, burden of metabolic abnormalities contributing to MS is not known. This study was therefore designed to analyze factors contributing to MS and estimate its prevalence in adult Kashmiri population.


  Materials and methods Top


The study was conducted among the attendants who accompanied the patients in inpatient and outpatient Departments of a Tertiary Care Hospital in North India. It was a cross-sectional study done over a period of 1 year (from December 2010 to December 2011). A stratified random sampling procedure was adopted for the study. The sample size for the study was calculated from the formula given by Daniel. [13] This formula is based on the assumption of normal approximation.

Persons fulfilling following criteria were included in the study:

  1. Age-20-60 years
  2. No personal history of diabetes, hypertension, obesity, dyslipidemia, coronary artery disease or smoking and
  3. No such family history. Written self-given consent was taken for the study.


The study was approved by Hospital Ethical Committee.

Measurements and Definition

All subjects underwent anthropometric assessment such as measurement of height, weight, BMI, WC and blood pressure. WC was measured in minimal light clothing during midrespiration between the 10 th rib and the iliac crest to the nearest 0.1 cm. Height and weight were taken with a beam balance with minimal clothing and no footwear. The BMI was obtained by dividing weight in kilogram by the square of the height in meters. Blood pressure was estimated in nondominant arm in the sitting position. It was measured 3-5 min after the subject was comfortable using correctly sized cuff. Blood pressure was recorded twice within an interval of 5 min and average of systolic and diastolic blood pressures were taken.

After an overnight fast of at least 8 h, a venous blood sample was taken, serum was separated within 2 h of venepuncture, and analysis was done within 24 h. Biochemical parameters were analyzed with commercially available enzymatic reagents (Audit Diagnostics, Ireland) adapted to the Hitachi 912 autoanalyzer. MS was diagnosed according to the criteria given by ATP-III, when any three of the following were present: Abdominal obesity (WC ≥90 cm in men ≥80 cm in women), raised triglycerides ≥150 mg/dl, low high density lipoprotein-cholesterol (HDL-C) ≤40 mg/dl in men and ≤50 mg/dl in women, blood pressure ≥130/85, and diabetes or fasting blood glucose ≥110 mg/dl.

Statistical Analysis

Data were analyzed using SPSS 11.5 version. The prevalence was reported in percentages. Factor analysis was performed to describe sex-specific clustering of MS factors. Analysis and inference were drawn using Student's t-test for continuous variables, Chi-square test for proportions and Man-Whitney U-test. A two-tailed P value was used for calculating statistical significance. P ≤ 0.05 was considered as statistically significant.


  Results Top


This study population of 500 people consisted of 257 men and 246 women. The mean age ± standard deviation was 38 ± 7.9 in men and 36.7 ± 6.8 in women (P = 0.040). [Table 1] summarizes the anthropometric and metabolic parameters in the study population. Men had significantly higher levels of blood pressure. Serum triglyceride levels were on the higher side in males, but the elevation was not statistically significant. WC was significantly elevated in females and serum HDL-C levels were significantly reduced in females.
Table 1: Clinical characteristics of study population

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Factor Analysis

Factors which were analyzed for significance of relationship with MS include age, blood pressure, WC, serum HDL-C, serum triglyceride levels and serum blood glucose levels. The prevalence of MS was 8.6% and it increased with age peaking at 40-50 years of age [Table 2]. The same trend was shown by WC [Table 3]. Among various factors WC showed a significant positive correlation with the prevalence of MS (P = 0.005). The prevalence of hypertension was 24.9% for males and 12.3% for female (P = 0.001). The prevalence of hyperglycemia was 9.3% for males and 7.4% for females (P = 0.54). 9.7% males and 25.9% females had low HDL-C (P = 0.000). About 17% males and 13.2% females had elevated triglyceride level (P = 0.219) [Table 4].
Table 2: Trend of metabolic syndrome with increasing age

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Table 3: Trend of WC with increasing age

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Table 4: Sex specifi c prevalence of individual components of metabolic syndrome

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High blood pressure showed significant contribution towards MS in case of males (OR = 21.1; P = 0.000), whereas increased WC and low HDL-C had significant contribution in females (OR = 216, P = 0.000; OR = 102.9, P = 0.000, respectively). Hypertriglyceridemia and hyperglycemia were more common in males although statistically insignificant. About 55% subjects among MS patients had DM. Among all these factors, WC was the single most contributing factor towards MS [Table 5].
Table 5: Sex specifi c factor analysis showing contribution of individual parameters to metabolic syndrome

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  Discussion Top


Insulin resistance is common in adult Asian Indians accounting for high prevalence of MS. [14],[15] The prevalence of MS is related to the prevalence of its component factors. Clustering of these component factors define this syndrome complex. [1] Our population is less obese when compared to the Southern and Northern Indian population. [10],[15],[16],[17] This observation has direct effect on the prevalence of MS and its components. Because of higher prevalence of obesity in Kashmiri women, [12] higher tendency toward MS and its components particularly central obesity and dyslipidemia in the form of low HDL-C was observed in female population.

The observation that MS becomes more prevalent with each decade of life increasing in parallel with obesity has been made in many studies. [18],[19],[20],[21],[22] We observed that prevalence increases steadily with age up to 50 years. Highest prevalence was seen in the fifth decade. Women displayed increased prevalence after the age of 40 years (P = 0.000). Similar trend was shown by WC when compared with age. In the National Health and Nutrition Examination Survey cohorts, MS prevalence continued to increase with age into the sixth decade, with prevalence in women catching up to and then exceeding that in men after the age of 60 years. [23],[24] The definition used to estimate prevalence, however, may influence this interaction. Studies that have compared age-related increases in prevalence among different definitions have observed variable prevalence estimates after the sixth or seventh decades. [25] Much of this variability in these later decades of life may be due to a survival effect, because those most susceptible to obesity related mortality have likely died by this point. [26] Finally, whether prevalence estimates plateau or drop off steeply after the age of 60 years also varies according to the MS definition being used. [12],[27]

Clustering of various metabolic abnormalities differ in men and women. Abdominal obesity and abnormal HDL-C levels are more common in women (OR = 216.0, P = 0.000; OR = 102.9, P = 0.000 respectively) whereas high blood pressure is more prevalent in men than women (OR = 21.1, P = 0.000 vs. OR = 14.0, P = 0.000). Abnormal triglyceride levels and high plasma blood glucose although common in men did not show statistically significant difference. Overall, WC emerged to be the most accurate parameter in predicting MS (95%) with a sensitivity of 84.9% and specificity of 95.5%.

Component analysis revealed that WC is the most important factor in defining MS. Globally, an increased prevalence has been seen with IDF definition because it uses lower cut offs for WC. [19],[28] Using modified WC criteria in existing ATP-III definition, we estimate that 52.2% men and 94.4% women qualified for MS as against 21.1% men and 75% women estimated with ATP-III criteria. It is clear that Asian-specific criteria for WC identify more persons having MS as compared to ATP-III criteria.


  Conclusion and limitations Top


Abnormalities in the form of dyslipidemia, hypertension and hyperglycemia are very common, which needs immediate attention. Preventing development of central obesity besides controlling other factors will prevent development of MS in large proportion of population. The results in our study should be taken with some limitations. The sample size in our study was relatively small. Since our study was a hospital based study, errors while selecting patients with low threshold for exclusion can't be wholly ruled out.

 
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    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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