A Community-Based Study of Metabolic Syndrome and Its Components Among Women of Rural Community in Ballabgarh,Haryana

Abstract

Background:

The metabolic syndrome is an important cluster of coronary heart disease risk factors with common insulin resistance. Although the prevalence of metabolic syndrome is high among Asians, including Indians, no sizeable literature is available about the magnitude of metabolic syndrome in rural areas, especially in women.

Methods:

Blood pressure and anthropometric measurements were noted in 307 women, aged ≥20 years, selected through a multistage sampling technique. Blood samples were collected after overnight fasting and subjected to biochemical quantification such as fasting blood glucose, triglycerides, and high-density lipoprotein cholesterol (HDL-C). Data were analyzed using updated National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) definition by modifying the waist circumference cutoffs as per Asia-Pacific guidelines.

Results:

Overall metabolic syndrome was observed in 12.0% [95% confidence interval (CI); 8.5–16.8] of the rural women population. Women in the age group ≥60 years had the highest prevalence (27.8%), whereas those in the age group 20–39 years had the lowest prevalence (4.2%). At least one component of metabolic syndrome was present in nearly 95% of the study respondents; 41.6% had at least two, 12.0% had at least three, and 2.6% had at least four components of metabolic syndrome. None of the participants had all the five components.

Conclusion:

The current prevalence of metabolic syndrome in women in rural communities of the selected area, although less than that in their urban counterparts, could be a serious problem in the future. It is incumbent on government agencies and the medical community to address this issue to prevent the consequences of its increased burden.

Introduction

T he combination of abdominal obesity, hypertension, dyslipidemia, hyperglycemia, and insulin resistance or type 2 diabetes mellitus defines the metabolic syndrome.¹ Worldwide prevalence of metabolic syndrome ranges from <10% to as much as 84%, depending on age, region, urban or rural environment, ethnicity, and the various definitions of metabolic syndrome used.^2
–4

Recent studies have shown that prevalence rates for cardiovascular diseases and diabetes mellitus have risen in India.^5
–7 Excess weight gain and physical inactivity are two known important determinants of metabolic syndrome, and women are particularly vulnerable due to weight gain that occurs at various physiological transitions like puberty, pregnancy, and menopause.^8,9 Also, in Indian diets, carbohydrate contributes to 60%–70% of energy intake and high carbohydrate intake is associated with hypertriglyceridemia.^10
–12 Thus, Indians have a tendency to develop hypertriglyceridemia.

Metabolic syndrome is associated with a more than 50% increased risk of cardiovascular mortality and an almost 30% enhanced risk of mortality from all causes.^13
–15 Although, over the life span, approximately the same proportion of the female population as the male population die of complications resulting from cardiovascular diseases, cardiovascular disease has been traditionally considered as a middle-aged "male" disease and, as a consequence, women have been excluded from most clinical trial and epidemiologic studies.¹⁶ India is predominantly an agricultural nation, with 72.2% of the population residing in rural areas.¹⁷ A number of studies have been done in urban areas to study the prevalence of metabolic syndrome, but there is a paucity of data regarding the prevalence of metabolic syndrome in the rural population, particularly women from the northern part of India.^18

–24 We studied the prevalence of metabolic syndrome and its components among women from a rural area in Haryana, a state in the northern India.

Materials and Methods

Study area

The study area consisted of 28 villages under the Intensive Field Practice Area (IFPA) of the Comprehensive Rural Health Services Project (CRHSP). These villages are about 50 km from Delhi and represent typical rural communities in Haryana. CRHSP is run by the Centre for Community Medicine, All India Institute of Medical Sciences (AIIMS), New Delhi. The project, comprised one secondary-level hospital and two Primary Health Centres (PHCs). The two PHCs served a population of 85,552, as of December 31, 2008.

Sample size and study participants

The required sample size was 256, considering 20% prevalence with 5% absolute precision. Considering a refusal rate of 20% and after adjusting for refusal, the final sample size was calculated to be 307. All those women who were aged ≥20 years and were living in the study area for past 6 months were eligible to be included in the study. The exclusion criteria comprised of women with severe acute or chronic illness, known human immunodeficiency virus (HIV) seropositivity, diagnosed malignancy, any diagnosed endocrinological disorders including diabetes, presence of severe end organ damage, and pregnancy and lactation. None of the subjects was on any antidiabetic, antihypertensive, or lipid-lowering medications at the time of inclusion in the study.

Study design and sampling framework

This population-based, cross-sectional study was undertaken over a period of 1 year from April, 2008, to March, 2009, using a multistage sampling technique. In the first stage, out of the 28 villages under CRHSP-AIIMS, six villages were selected randomly. The second stage was to select 50 households randomly from each of these villages. In the third stage, if there was more than one eligible woman in selected household, then one woman was selected randomly among them through lottery method.

Data collection

The study personnel consisted of research officers, health workers, and laboratory technicians drawn from the staff of CRHSP, Ballabgarh, India. Administration of interview schedules included data collection for sociodemographic profile and level of physical activity. Revisit was made if the eligible subject was absent or the house was found locked. If on revisit, the selected eligible women was again absent or the house was again found to be locked, then the next house was visited as a replacement. If no eligible women was found in the family, then the adjoining house was visited until an eligible woman was found. The interview and physical examination were done at the domiciliary level, and the collection of blood sample was done mainly through the camp approach.

All measurements were taken by standard guidelines.^25,26 Height was measured to the nearest 0.1 cm using a stadiometer. Weight was measured to the nearest 0.1 kg using a digital weighing scale (Seca). Waist circumference was measured using an inelastic tape at the midpoint between the costal margin and iliac crest. Blood pressure was recorded using a digital manometer.

Blood sample collection and laboratory analysis

The collection of blood samples was done mainly using camp approach. Some house visits were also made to collect blood samples of those who did not turn up in camps. Five milliliters of blood was taken from the antecubital vein, observing universal precautions after overnight fasting. The collected blood samples were rapidly transported to the laboratory at CRHSP, Ballabgarh, where biochemical analysis for fasting glucose, high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) was done using an autoanalyzer (ECO automatic analyzer, Firmware version 3.13). Commercially available enzymatic kits made by Erba Chem were used. Glucose was measured by the oxidase/peroxidase method; TG and HDL-C were measured by glycerol phosphate/oxidase peroxidase aminophenazone and phosphotungstate/MgCl₂ precipitation method. None of the collected blood samples was spoiled.

Quality control

To ensure quality control, two samples with known results (one normal and other with abnormal value) were run with each batch of blood samples to be tested. The samples with known results were provided by manufacturing company of kits used. The coefficient of variation was 0.02.

Ethical review

The Institutional Ethics Committee of AIIMS approved the study. We obtained written informed consent for the questionnaire-based interview, physical measurements, and laboratory tests. Participants were provided with results of the biochemical estimation done. Appropriate treatment and referrals were provided for those in need.

Definition of the metabolic syndrome

Metabolic syndrome was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria.^27,28 The participants were labeled as having metabolic syndrome if they had three or more of the following criteria: TG, ≥150 mg/dL; blood pressure (BP), ≥130/≥85 mmHg; HDL-C, <50 mg/dL; fasting blood glucose (FBG), ≥100 mg/dL; waist circumference, ≥80 cm.^29,30

Statistical methods

The prevalence of the metabolic syndrome and its individuals components was expressed in percentage and 95% confidence interval (CI). Continuous variables were expressed as mean±standard deviation (SD). The independent t-test and chi-squared test/Fisher exact test were used to compare means and to test for any difference in proportions respectively. All analysis were two-tailed, and a P value <0.05 was considered statistically significant. The statistical package SPSS for windows (version17.0) was used for all the analyses.

Results

A total of 307 women were recruited in the study, of which the anthropometric data and the blood pressure were recorded for all the 307 study participants. Only 233 (75.9%) participants gave consent to collect the blood samples. The characteristics of the participants who refused to give their blood samples did not differ from those who gave (Table 1).

Table 1.

Comparison of the Sociodemographic and Clinical Characteristics of Study Participants Who Gave Their Blood Samples and Those Who Refused

Variables	Those who refused for blood samples (n=74)	Those who gave blood samples (n=233)	P value
Age (years)^a	37.93 (12.16)	39.63 (11.83)	0.284
Education^b
Illiterate	47 (63.5)	152 (65.5)	0.396
Up to 8^th	16(21.6)	47 (20.2)
Up to 10^th	6 (8.1)	22 (9.4)
Up to 12^th	2 (2.7)	10 (4.3)
Higher education	3 (4.1)	2 (0.9)
Family income (Rupees per month)^b
<1000	2 (2.7)	5 (2.1)
1000–2000	8 (10.8)	30 (12.9)	0.806
2001–3000	12 (16.2)	47 (20.2)
>3000	52 (70.3)	151 (64.8)
Mean SBP (mmHg)^a	112.32 (16.92)	114.74 (18.66)	0.321
Mean DBP (mmHg)^a	73.95 (10.82)	75.12 (11.60)	0.447
Mean WC (cm)^a	30.90 (5.09)	30.68 (4.40)	0.720
Mean BMI (kg/m²)^a	22.19 (4.52)	22.15 (4.27)	0.940

Data represented as mean (standard deviation, SD). The Student test was used to obtain the P value.

Data are displayed as number, n (%). Chi-squared analysis was used to obtain the P value.

SBP, systolic blood pressure; DBP, diastolic blood pressure; WC, waist circumference; BMI, body mass index.

Description of the study sample

Tables 2 and 3 show the baseline sociodemographic and clinical characteristics of the total study participants. Nearly half (53.4%) were in the age group of 20–39 years and another 2/5^th (38.8%) was in the age group 40–59 years. The mean age of the participants was 39.2±11.9 years. Most of the women were illiterate (64.8%) and of the households selected, about 3/5^th (66.1%) had family monthly income more than 3000 Rupees. The mean waist circumference of the women participating in the study was 78.0±11.6 cm, while the mean systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 114.1±18.2 and 74.8±11.4 mmHg, respectively. The mean body mass index (BMI) was 22.1±4.3 kg/m² whereas the mean HDL-C, TG, and FBG values were 39.3±8.9 mg/dL, 109.8±58.4 mg/dL, and 88.9±28.8 mg/dL, respectively.

Table 2.

Distribution of Baseline Sociodemographic Characteristics Among Study Participants (N=307)

Variables	N (%)
Age (years)
20–39	164 (53.4)
40–59	119 (38.8)
≥60	24 (7.8)
Mean age of the study participants (years)	39.23±11.91
Education
Illiterate	199 (64.8)
Up to 8^th	63 (20.5)
Up to 10^th	28 (9.1)
Up to 12^th	12 (3.9)
Higher education	5 (1.6)
Family income (Rupees per month)
<1000	7 (2.3)
1000–2000	38 (12.4)
2001–3000	59 (19.2)
>3000	203 (66.1)

Table 3.

Clinical Characteristics of the Study Subjects

Variable(s)	Mean (SD)	Range
Waist circumference (cm)	78.08 (11.61)	55.88–127.0
SBP (mmHg)	114.16 (18.26)	77–179
DBP (mmHg)	74.84 (11.41)	45–115
BMI (kg/m²)	22.16 (4.32)	11.70–39.67
HDL-C (mg/dL)^a	39.39 (8.98)	25–112
TG (mg/dL)^a	109.80 (58.40)	26–419
FBG (mg/dL)^a	88.97 (28.8)	57–344

The number of subjects (n) is 233 due to 74 participants refusing to give a blood sample. For the other variables considered, n=307.

SD, standard deviation; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; FBG, fasting blood glucose.

Prevalence of individual metabolic abnormalities

Table 4 reports the prevalence of each component of metabolic syndrome within the study participants. Overall, 88.0% of the women had a low HDL level (<50 mg/dL), nearly 20% had an increased TG level (≥150 mg/dL), and approximately 20% had an increased waist circumference (≥80 cm). The components of the metabolic syndrome varied in their rates of occurrence across the age groups. Out of the three age groups, women aged ≥60 years had the highest prevalence of metabolic abnormalities such as raised TG levels (27.8%), low HDL-C (88.9%), increased blood pressure of ≥130/≥85 mmHg (29.2%), and raised FBG (27.8%).

Table 4.

Age-Specific Prevalence (%) for the Components of the Metabolic Syndrome As Well As Body Mass Index

	Age-groups (years)
	20–39	40–59	≥60	Total
Variables	%	%	%	%	95% CI	P value
TG^a	13.6	26.8	27.8	20.2	15.0–25.3	0.039
HDL-C^a	87.3	88.7	88.9	88.0	83.8–92.2	0.947
DBP	11.0	21.8	29.8	16.6	12.4–20.8	0.012
SBP	6.7	22.7	66.7	17.6	13.3–21.8	0.0001
BP	3.0	13.4	29.2	9.1	5.9–12.3	0.0001
FBG^a	13.6	14.4	27.8	15.0	11.0–20.2	0.284
WC	11.0	31.1	25.0	19.9	15.4–24.3	0.0001
BMI	15.9	31.6	8.3	21.5	16.7–25.9	0.021
Components of metabolic syndrome^a
1	58.5	48.5	38.9	52.8	46.4–59.1	0.002
2	28.8	29.9	33.3	29.6	24.1–5.8	0.641
Metabolic syndrome (≥3 components)^a	4.2	18.6	27.8	12.0	8.5–16.8	0.0001

The denominator for calculating the percentage was 233 due to 74 participants refusing to give a blood sample.

TG, triglycerides ≥150 mg/dL; HDL-C, high-density lipoprotein cholesterol <50 mg/dL; DBP, diastolic blood pressure ≥85 mmHg; SBP, systolic blood pressure ≥130 mmHg; BP, blood pressure ≥130/≥85 mm Hg; FBG, fasting glucose level ≥100 mg/dL; WC, waist circumference ≥80 cm; BMI, body mass index in kg/m² >25. Bold figures are statistically significant.

A larger proportion of the women with increased waist circumference (31.1%) and raised BMI, i.e., >25 kg/m² (31.6%) were in the age group 40–59 years. Taking into consideration that a BMI of >25 kg/m² is considered to be obese (as per the Indian adaptation),³¹ nearly 21% of the women were found to be obese. For low HDL-C level (P=0.947) and increased FBG level (p=0.284), the difference among the age groups was not statistically significant. Nearly 95% of the study respondents (N=233) had at least one component of metabolic syndrome, 41.6% had at least two, 12% had at least three, and 2.6% had at least four components of metabolic syndrome. None of the participants had all the five components (Fig. 1).

FIG. 1.

Cumulative prevalence of components of metabolic syndrome in the rural female population.

Prevalence of metabolic syndrome

The overall prevalence of metabolic syndrome in this study as per the updated National NCEP ATP III criteria was 12% (95% CI 8.5–16.8) (Table 4). Age-specific prevalence of metabolic syndrome differed in the three age categories. Women in the age group ≥60 years had the highest prevalence (27.8%), whereas those in the age group 20–39 years had the lowest prevalence (4.2%). The difference in the prevalence of metabolic syndrome was statistically significant among the age groups studied (P=0.0001).

Comparison of characteristics of participants with and without metabolic syndrome

Table 5 shows a comparison of the characteristics of the participants with and without metabolic syndrome. There were 28 women with metabolic syndrome out of 233 women who consented to give blood samples. Of these, 75% were illiterate and a nearly similar percentage of women in the non-metabolic syndrome group (64%) were also illiterate. The difference in educational level was not statistically significant (P=0.714). Most of the women with metabolic syndrome were in the age group 40–59 years (64.4%), whereas a major proportion was in the age group 20–39 years (55.1%) in the non-metabolic syndrome group. Of those with metabolic syndrome, all had family income above 2,000 Rupees, with majority (85.7%) having income >3,000 Rupees per month. Compared to this, 17% of those without metabolic syndrome had a family income of <2,000 Rupees per month. About 68% of the women with metabolic syndrome were obese, whereas only around 17% fell into the obese category in women without metabolic syndrome. The mean age of the women with metabolic syndrome was 48.9±11.0 years whereas it was 38.4±11.3 years in those without metabolic syndrome. All of the clinical and biochemical characteristics compared were statistically significant between the two groups, i.e., with metabolic syndrome and without metabolic syndrome, except the HDL-C level (P=0.897).

Table 5.

Sociodemographic and Clinical Characteristics of Subjects with Metabolic Syndrome and without Metabolic Syndrome (n=233)

Variables	With metabolic syndrome (n=28)	Without metabolic syndrome (n=205)	P value
Age group (years)^a
20–39	5 (17.8)	113 (55.1)	0.0001 ^b
40–59	18 (64.4)	79 (38.5)
≥60	5 (17.8)	13 (6.4)
Education^a
Illiterate	21(75.0)	131 (63.9)	0.714^b
Up to 8^th	4 (14.3)	43 (21.0)
Up to 10^th	3 (10.7)	19 (9.3)
Up to 12^th	0 (0.0)	10 (4.8)
Higher education	0 (0.0)	2 (1.0)
Family income (Rupees per month)^a
<1000	0 (0.0)	5 (2.4)	0.052^b
1000–2000	0 (0.0)	30 (14.6)
2001–3000	4 (14.3)	43 (21.0)
>3000	24 (85.7)	127 (62.0)
BMI^a
<18.5	1 (3.6)	45 (22.0)	0.0001 ^b
18.5–22.9	2 (7.1)	94 (45.8)
23–24.9	6(21.4)	32 (15.6)
≥25	19 (67.9)	34 (16.6)
Mean age (years)^c	48.92 (11.01)	38.37 (11.39)	0.0001 ^d
Mean TG (mg/dL)^c	169.89 (88.55)	101.59 (48.90)	0.0001 ^d
Mean HDL-C (mg/dL)^c	39.17 (6.82)	39.41 (9.25)	0.897^d
Mean FBG (mg/dL)^c	112.53 (44.14)	85.75 (24.53)	0.0001 ^d
Mean SBP (mmHg)^c	132.50(20.74)	112.32 (17.02)	0.0001 ^d
Mean DBP (mmHg)^c	86.92 (14.23)	73.50 (10.22)	0.0001 ^d
Mean WC (cm)^c	36.42 (4.14)	29.90 (3.82)	0.0001 ^d
Mean BMI (kg/m²)^c	27.02 (4.17)	21.48 (3.83)	0.0001 ^d

Data are displayed as number, n (%).

P value was obtained from the Fisher exact test.

Quantitative data shown as mean values and standard deviation (SD); mean (SD).

P value was obtained from the Student t-test.

BMI, body mass index; TG, triglcyerides; HDL-C, high-density lipoprotein cholesterol; FBG, fasting blood glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; WC, waist circumference. Bold figures are statistically significant.

Discussion

The study was done with the objective of estimating the prevalence of metabolic syndrome in the rural women population because the prevalence data is unavailable for an Indian rural female population. The prevalence of metabolic syndrome was 12% in women aged ≥20 years and residing in a rural area. In the current study, there was an age-related increase in the prevalence of the metabolic syndrome. The prevalence of metabolic syndrome was lowest in age group 20–39 (4.2%), while it progressively increased with age, with the age group ≥60 years (27.8%) having the highest prevalence.

Tandon et al., in their study amongst postmenopausal women in rural areas of Jammu and Kashmir, found that metabolic syndrome was present in 13% of the participants.³² Among the participants aged ≥40 years, there is an expected higher proportion of postmenopausal women, and the present study showed increased prevalence of most of the cardiovascular risk factors, especially hypertension, hypertriglyceridemia, FBG, and increased BMI and waist circumference in these women. In the light of this, it is important to identify these risk factors in postmenopausal patients for early treatment of these conditions along with initiating efforts to increase awareness.

Recognizing the fact that waist circumference is a good marker of abdominal fat and intraabdominal fat, which has a stronger metabolic impact, the finding in the present study that nearly 1/5^th (19.9%) of the women had a waist circumference above the cutoff value points toward the likelihood of future rise in metabolic derangements in the female population.³³ In the study, the single most common abnormality was low HDL-C (overall 88.0%), which is more than what had previously been reported from other studies in India.^34,35 Apart from the influence of environmental factors and genetic predisposition, the key issue is the cutoff considered. Nongkynrih et al., in their study in rural Ballabgarh, found that 22.4% of the women aged 15–64 years had low HDL-C levels, considering a cutoff value of ≤35 mg/dL.³⁴ In our study, the cutoff was higher (<50 mg/dL) and thus, comparatively, there was an increased percentage of those with low HDL-C.

These results are in accordance with findings of studies done in other parts of the country. A study done in rural Wardha in 2007–2008 found the prevalence of metabolic syndrome in women over 18 years old to be 10.7%. ²¹ Prabhakaran et al. in their study looked at the urban–rural differences in the prevalence of metabolic syndrome. The sampling frame consisted of adult residents of urban Delhi and rural Ballabgarh, Haryana (where the present study was done) aged 35–64 years. They found the age-adjusted prevalence of 11.1% for rural Ballabgarh using NCEP ATP III criteria. The prevalence of metabolic syndrome was 11.6% in rural women (9.1%–14.1%).³⁶ Although this study was done in the years 1991–1995 and our study was done 13 years later, our study found a similar prevalence in rural women; yet there are some interesting issues. In the previous study, the mean age of the women was 45.5±8.6 years whereas in our study it was 39.2±11.9 years. Thus, the occurrence of similar prevalence in comparatively younger women underlies the possibility that metabolic syndrome might be gradually involving the younger population as well. Previously, 12.9% of women had no component of metabolic syndrome, whereas in the present study it decreased to 5.6%. Also, those with at least one component increased from 87.1% to 94.4%. The mean BMI and waist circumference also showed a 2-point and 8-point increase, respectively.

Keeping in view that substantial socioeconomic changes have occurred in the Indian rural population and the accompanying transition from a traditional to a western-like urban lifestyle has been associated with adverse changes in lifestyle habits, the results of the study seem to be justified.³⁷ A study done by Sinha S in 2008–2009 found the prevalence of metabolic syndrome in women ≥20 years residing in urban areas of Delhi to be around 30%, which is nearly three times the prevalence in the present study in rural women residing just 50 km away from their urban counterparts.³⁸ This highlights the important contributory role of urbanization in causation of metabolic syndrome. The National Nutrition Monitoring Bureau (NNMB) data for adults showed a moderate increase in the combined prevalence of overweight and obesity between 2000 and 2001 and 2005 and 2006 among women (8.2%–10.9%) in the rural population.^39,40 On comparing National Family Health Survey (NFHS)-2 (1998–1999) with NFHS-3 (2005–2006) report, the prevalence of overweight in women increased from 5.9% to 7.4% and of obesity from 0.9% to 1.3%.^41,42

The study has its share of limitations. First, there was a modest response rate (76%) for the biochemical variables in the rural population, which may contribute to a nonresponder bias and influence the overall generalizability of results. We compared the characteristics such as age, education level, family income, blood pressure, waist circumference, and BMI between responders and nonresponders to blood sampling for biochemical analysis and did not find any significant differences for these variables. There are several reasons for this nonresponse. The important ones include fear associated with giving blood samples, the occupational demands of the rural participants, as farming in India starts early in the morning and women play a pivotal role in it, an inability to give fasting samples, and other prevalent cultural practices. Due to logistic reasons, plasma insulin levels could not be measured and correspondingly homeostasis model assessment (HOMA), which gives an idea of the steady-state beta-cell function and insulin sensitivity, could not be estimated. This was important, especially as the study participants represented a younger Asian population.

Conclusion

The study documents the current prevalence of metabolic syndrome in rural areas. Although it is less than that in the urban setup, it could be a serious problem in the future, and there is a possibility that this could further increase over the next few decades. The prevention and treatment of this condition is of major public concern and requires formulation of appropriate interventions and policies. Educating the people regarding adopting healthy lifestyle measures could prove to be a way of lowering the prevalence of various components of metabolic syndrome and thus avert the risk of cardiovascular complications in the population.

Footnotes

Acknowledgments

P.M. conceived and designed the study; R.P.U., K.A., and S.S. performed the analysis and prepared the manuscript; P.M., N.K.V., and K.A. provided advice on data analysis and revised the final manuscript. All the authors read and approved the final manuscript. No funding was required.

Author Disclosure Statement

The authors declare that they have no competing financial interests of conflict.

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