| Article Access Statistics | | | Viewed | 7474 | | | Printed | 341 | | | Emailed | 3 | | | PDF Downloaded | 36 | | | Comments | [Add] | | | Cited by others | 1 | | |
|
Click on image for details.
|
|
 |
| ORIGINAL ARTICLE |
|
|
|
| Year : 2011 | Volume
: 57
| Issue : 1 | Page : 4-8 |
Study of impaired glucose tolerance, dyslipidemia, metabolic syndrome, and cardiovascular risk in a south Indian population
S Martha1, S Ramreddy2, N Pantam3
1 Department of Pharmacology and Clinical Pharmacy, University College of Pharmaceutical Sciences, Kakatiya University, Warangal, India; Division of Weight Management & Wellness Center, Pediatric Endocrinology, Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, USA
2 Department of Pharmaceutics, University College of Pharmaceutical Sciences, Kakatiya University, Warangal, India
3 Department of Internal Medicine, Mahatma Gandhi Memorial Hospital, Kakatiya Medical College, Warangal, India
| Date of Submission | 11-Jun-2010 |
| Date of Decision | 17-Jul-2010 |
| Date of Acceptance | 11-Oct-2010 |
| Date of Web Publication | 31-Jan-2011 |
Correspondence Address:
S Martha
Department of Pharmacology and Clinical Pharmacy, University College of Pharmaceutical Sciences, Kakatiya University, Warangal, India; Division of Weight Management & Wellness Center, Pediatric Endocrinology, Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/0022-3859.74283
Background: In developing countries, obesity is the most prevalent metabolic disease and leads to an important cardiovascular and global mortality rate, either directly or indirectly through cardiovascular risk factors. Aim: We sought to study the prevalence of impaired glucose tolerance (IGT), dyslipidemia, metabolic syndrome (MS), and cardiovascular risk (CVR) in a south Indian population. Setting and Design: A cross-sectional, single-center observational study in a cohort of 96 healthy male subjects. Materials and Methods: Age, body mass index (BMI), blood pressure (BP), total lipid profiles, fating plasma glucose (FPG), post lunch plasma glucose (PLPG), glycated hemoglobin (HbA1c), creatinine and insulin were measured by standard methods. Statistical Analysis: Student's t-test and Chi-square test were used to determine differences between mean and frequency values of continuous and categorical variables. Results: Significant differences were observed in the means of BMI (28.89 kg/m 2 ) (P<0.0001), FPG (102.41 mg/dL) (P<0.0001), insulin (18.1 μU/L) (P<0.0001), PLPG (149.05 mg/dL) (P<0.0001), diastolic BP (84.41 mmHg) (P<0.01), total cholesterol (166.72 mg/dL) (P<0.001), low-density lipoprotein (90.65 mg/dL) (P<0.0001) in overweight subjects when compared to normal subjects . The prevalence of dyslipidemia, IGT, MS and CVR was significantly higher in younger (<45years) than middle-aged (46-55years) subjects. Conclusions: The condition of being overweight, expressed as BMI, appears to be a good indicator of risk for IGT, MS, and CVR, particularly in young non-obese subjects (BMI<30).
Keywords: Insulin resistance, metabolic syndrome, obesity, overweight
How to cite this article:
Martha S, Ramreddy S, Pantam N. Study of impaired glucose tolerance, dyslipidemia, metabolic syndrome, and cardiovascular risk in a south Indian population. J Postgrad Med 2011;57:4-8 |
How to cite this URL:
Martha S, Ramreddy S, Pantam N. Study of impaired glucose tolerance, dyslipidemia, metabolic syndrome, and cardiovascular risk in a south Indian population. J Postgrad Med [serial online] 2011 [cited 2023 Jun 3];57:4-8. Available from: https://www.jpgmonline.com/text.asp?2011/57/1/4/74283 |
| :: Introduction | |  |
The condition of being overweight and obesity are recognized as an "escalating epidemic" affecting both developed and developing countries. [1] Obesity and its associated morbidities are leading causes of cardiovascular disease (CVD), Type 2 diabetes, some types of cancer and several other health problems. [2] According to the World Health Organization (WHO) estimates of 2003, 16.7 million people around the globe die of CVD each year [3] and 80% of these deaths occur in low- and middle-income countries. [4] Between 2000 and 2030, the number of years of productive life lost to CVD is predicted to increase by 95% in India. [5] Obesity in the developing world can no longer be considered a disease affecting solely people of higher socioeconomic status, but instead a disease which shifts towards lower socioeconomic status groups as that country's gross national product increases. [6] Recent reports indicate that in India, the proportion of overweight people will have increased from 9-24% between 1995 and 2025. [5] Metabolic syndrome (MS) is a cluster of the most dangerous heart attack risk factors: diabetes, abdominal obesity, changes in cholesterol and high blood pressure. People with MS are also at increased risk, being three times as likely to die from heart attack or stroke compared to people without MS. [7] The aim of the present study was to determine the prevalence of impaired glucose tolerance (IGT), dyslipidemia, metabolic syndrome (MS), and cardiovascular risk (CVR) and to assess the relationship between the condition of being overweight, measured by body mass index (BMI), insulin resistance (IR), insulin sensitivity, and components of the MS in a southern Indian population.
| :: Materials and Methods | | |
A cross-sectional, single-center observational study was carried out in 96 men 35-65 years of age who, for various reasons, visited a primary care outpatient clinic in a public, metropolitan hospital over a period of one year. All studies were approved by the Human Ethical Committee of Kakatiya Medical College. Research participants gave written informed consent after thorough explanation of the study.
Inclusion criteria were voluntary participation in the study, normal hepatic and renal function, a normal complete blood count, normal assessment of thyroid hormones, and a normal standard urine analysis. Exclusion criteria were age outside the given range, subjects following a hypo-caloric diet or having experienced weight gain or loss greater than 10% of body weight within the previous three months, hypothyroidism including sub-clinical hypothyroidism, liver, heart, or kidney failure, and neoplasias. Subjects with a previous history of viral hepatitis or cirrhosis were also excluded. Subjects with plasma liver enzyme values twice the upper limits of normal were also excluded, as were diabetic subjects treated with insulin. Subjects were categorized based on BMI into normal and overweight groups.
Subject's age, personal medical history and any toxic habits were recorded. Height was measured to the nearest 0.1 cm with a non-stretchable measuring tape (Gulick, Lafayette Instruments, Lafayette, IN). Subject weight was measured to the nearest 0.1 kg with a digital scale (Omron HBF-500, Omron Healthcare Inc., Bannockburn, IL). Height and weight measurements were performed while subjects were wearing clothing, but no shoes. Body mass index was calculated for each subject in the study (kg/m 2 ). Blood pressure was measured in the supine position and after a rest period of 10 min. A minimum of three blood pressure readings were collected for each subject with an automated blood pressure machine (Omron HEM-780, Omron Healthcare Inc., Bannockburn, IL). For subjects with an arm circumference less than 27 cm or greater than 44 cm, a manual blood pressure monitor was used. To assure consistency, diastolic blood pressure needed to be within 10 mmHg for each of the three readings. Hypertension (HTN) was diagnosed if systolic blood pressure (SBP) values were equal to or above 140 mmHg, if diastolic blood pressure (DBP) was equal to or above 90 mmHg, or if the patient was being treated with antihypertensive drugs. [8] Blood samples were taken after a 12-h overnight fast and plasma was separated immediately by refrigerated centrifugation. The samples were processed immediately or in the first week following preservation at -200C. Fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), creatinine, total cholesterol (TC) and high-density lipoprotein (HDL), and triglycerides (TG) were measured by enzymatic methods (Excel Diagnostics Private Limited, Hyderabad, India). Fasting serum insulin concentrations were determined by enzyme-linked immunosorbent assay (ELISA) (Mercodia AB., Sweden). Fasting serum very low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) cholesterol concentrations were determined using the formulas: VLDL cholesterol = TG/5; and LDL cholesterol = TC-HDL-VLDL cholesterol. Subjects with FPG between 100 and 125 mg/dL were diagnosed as impaired glucose tolerance (IGT), while subjects previously diagnosed with diabetes or having FPG equal to or greater than 126 mg/dL were diagnosed as diabetics (DM). [9]
Insulin resistance (IR) was measured using the homeostasis model assessment index (HOMA) with the formula described by Matthews et al., (HOMA-IR = fasting insulin X fasting glucose/405) [10] and insulin sensitivity was measured by quantitative insulin check index (QUICKI = 1/log fasting insulin + log fasting glucose). [11] In subjects without clinical or biological evidence of IR, the 90th percentile for the HOMA-IR ≥ 3.8 were considered diagnostic of IR. [12] Metabolic syndrome (MS) was defined as the presence of at least two of the following: HTN, IGT (FPG ≥ 100 mg/dL), and dyslipidemia (fasting plasma TG ≥ 150 mg/dL and/or HDL-C < 40mg/dL. [7] Subjects' 10-year CVR was assessed by using Framingham risk score calculations. [13]
Statistical analysis
Sample size was calculated with a two-sided test to be 96 subjects assuming that the minimum difference is 1.5 and standard deviation is 2.6 in HOMA-IR from previous studies with a power of 80% and an alpha risk of 5%. Descriptive statistics included mean and standard deviation for continuous variables and frequencies using percent for categorical variables. Student's t-test was used to determine whether the differences between the mean values of continuous variables based on weight were significantly different. Chi-square tests were performed to determine overall differences in frequencies of categorical variables among the groups and for pair-wise comparisons of frequencies. The correlation between two variables was studied with the Pearson or Spearman test, depending on whether the variables had a parametric or non-parametric distribution. P ≤ 0.05 was considered as statistically significant. All analyses were carried out using SPSS (Version 18, Chicago, IL).
| :: Results | | |
The study population included 96 male subjects aged 35-65 years. [Table 1] shows the general characteristics of the subjects categorized by weight (normal: BMI < 25 kg/m 2 , overweight: ≥ 25 kg/m 2 ). Significant differences were observed in BMI (P<0.0001), FPG (P<0.0001), insulin (P<0.0001), PLPG (P<0.0001), DBP (P<0.01), TC (P<0.001), and LDL (P<0.0001). [Table 2] shows the prevalence of different disorders such as IR (HOMA-IR ≥ 3.8), HTN (SBP ≥ 140, DBP ≥ 90), dyslipidemia (TG ≥ 150 mg/dL or HDL-C < 40 mg/dL), IGT (FPG ≥ 100, PLPG ≥ 130), DM (FPG ≥ 126, PLPG ≥ 200), obesity (BMI ≥ 30) and MS. We found significant percent increase of all these disorders in overweight compared to normal weight subjects, DM (P<0.001), MS (P<0.01) and obesity (P<0.0001) respectively. [Table 3] summarizes Pearson's correlation coefficients between all the tested parameters with HOMA-IR and QUICKI. FPG, insulin PLPG, SBP, DBP, TC, HDL, LDL and TG levels correlated significantly with insulin resistance and insulin sensitivity. All the parameters except HDL were positively correlated with insulin resistance and negatively correlated with insulin sensitivity in normal subjects but not overweight subjects. [Figure 1] shows the prevalence of the CVR parameter in overweight subjects compared to the normal subjects. Significantly high CVR was observed in overweight subjects. The prevalence of dyslipidemia, IGT, and MS in the population studied, divided according to five years, is shown in [Figure 2]. All these conditions were significantly higher difference in the younger (less than 45) than the middle-aged (46-55 years) and old-aged subjects (more than 56 years). | Figure 1: Cardiovascular risk profile among normal and overweight subjects
Click here to view |
 | Figure 2: Prevalence of IGT, MS and dyslipidemia in all subjects according to age (five years)
Click here to view |
 | Table 1: General characteristics of the study population based on parameter and weight
Click here to view |
 | Table 3: Pearson's correlation coefficients for insulin resistance (HOMA-IR) and insulin sensitivity (QUICKI) as dependent variables
Click here to view |
| :: Discussion | | |
The main findings of the study are that the prevalence of IR, HTN, IGT, DM, dyslipidemia, obesity and MS was significant in overweight than normal weight subjects and especially, dyslipidemia, IGT and MS were significant in the younger than middle-aged subjects. HOMA-IR and QUICKI were significantly correlated with BP and lipid profiles except HDL. Cardiovascular risk parameters were highly significant in overweight than normal weight subjects.
Our results coincide with reports from different parts of India and suggest a rising trend in the prevalence of diabetes. [14] Serial epidemiological studies conducted in the southern Indian city of Chennai showed a steady increase in the prevalence of diabetes in the urban population. [15] A recent study in south India by Ramachandran et al., [16] has shown that the prevalence of IGT (29.8%) and diabetes (16.6%) was very high, particularly in Hyderabad city was coincides with our results (52%, 8.3%) respectively. Prevalence of IGT and MS was higher in the younger than middle-aged subjects were coincide with the Ramachandran et al study, shown that subjects under 40 years of age had a higher prevalence of IGT than diabetes (12.8% vs. 4.6%, P < 0.0001). [16]
Our findings coincide with those proposed in the previous study, which defined hyperinsulinemia as basal plasma insulin ≥ 16 μU/ml or ≥ 62 μU/ml 2 h after an oral glucose load. [17] This definition of hyperinsulinemia was associated with a 1.6 times greater CVR, independent of plasma glucose levels. [17] In the present study, fasting plasma insulin levels were significantly greater in the overweight group compared to the normal subjects (13.29±4.33 vs. 18.10±5.72 mU/ml in men, P<0.0001).
We have also defined having IR by a HOMA-IR index ≥ 3.8, which corresponds to 39.6% of the total group of our population. This finding coincides with that found by other authors in the upper fifth of a population of normal weight (HOMA-IR, 95 percentile, CI 2.77-36.4). [12] In our whole group, the mean HOMA-IR value of 3.49 ± 1.12 was similar to the 3.8 (0.02-72.7) value obtained by Tatjana et al. [18] In our normal and overweight group, the HOMA-IR was 3.30 ± 1.04 and 3.68 ± 1.21, respectively. IR (HOMA-IR ≥ 3.8) was found in 31.8% of normal weight subjects and in 46.2% of overweight subjects.
When studying the main components of the MS, we found a prevalence of 21% in overweight subjects and only 2% in those with normal weight (P<0.01). The prevalence in other populations varied from 0.8 to 35.3%, depending on the criteria used in establishing the diagnosis of the MS. [19] According to our results, the condition being overweight is a good predictor of risk for MS and probably for heart disease, which has been shown recently in different ethnic groups. [20]
The limitations of the present study, recruiting only male subjects and the power needed to demonstrate a significant difference in HOMA index based on sample size study . Thus, the study may be underpowered to detect a significant difference between overweight and normal weight individuals in the other parameters measured.
We conclude that the condition of being overweight is a good predictor of risk of IGT, MS, and CVR, especially in non-obese (BMI<30) south Indian men. IGT is especially present at an early age, during their earning stages, preceding the different components of MS that emerge at a later stage. Additional studies are required to support this hypothesis.
| :: References | | |
| 1. | Parvez H, Bisher K, Meguid EL. Obesity and diabetes in the developing world: A growing challenge. N Engl J Med 2007;356:213-5. 
|
| 2. | Luc FV, Ilse LM, Christophe ED. Mechanisms linking obesity with cardiovascular disease. Nature 2006;444:875-80.
|
| 3. | World Health Organization. Global strategy on diet, physical activity and health. Cardiovascular disease: Prevention and control; 2010. Available from: http://www.who.int/dietphysicalactivity/publications/facts/cvd/en/ [last cited on 2010 May 25].
|
| 4. | World Health Organization. Chronic diseases and their common risk factors. Oct 2005. Available from: http://www.who.int/chp/chronic_disease_report/media/Factsheet1.pdf [last cited on 2010 May 25].
|
| 5. | A race against time. The challenge of cardiovascular disease in developing economics. Columbia University, New York 2004. Available from: http://www.earth.columbia.edu/news/2004/images/raceagainsttime_FINAL_051104.pdf [last cited on 2010 May 25].
|
| 6. | Monteiro CA, Moura EC, Conde WL, Popkin BM. Socioeconomic status and obesity in adult populations of developing countries: A review. Bull World Health Organ 2004;82:940-6.
[PUBMED] [FULLTEXT] |
| 7. | International Diabetes Federation. IDF worldwide definition of the metabolic syndrome. International diabetes federation, 2005. Available from: http://www.idf.org/node/1254?unode=32EF2063-B966-468F-928C-A5682A4E3910 [last cited on 2010 May 25].
|
| 8. | Hall G. Textbook of Medical Physiology. 7 th ed. New York: Elsevier-Saunders; 2005.
|
| 9. | Guillermo E. Umperrez. Forefront. Research / type-2, summer/fall 2004. p. 15-7. Available from: http://www.diabetes.org/assets/pdfs/forefront/forefront-summer-fall-2004.pdf [last cited on 2010 May 25].
|
| 10. | Chari ST, Zapiach M, Yadav D, Rizza RA. Beta-cell function and insulin resistance evaluated by HOMA in pancreatic cancer subjects with varying degrees of glucose intolerance. Pancreatology 2005;5:229-33.
[PUBMED] [FULLTEXT] |
| 11. | Wallace T, Levy J, Matthews D. Use and abuse of HOMA modeling. Diabetes Care 2004;27:1487-95.
|
| 12. | Muredach PR, Megan LW, Thomas R, Cynthia G, Nehal M, Daniel JR. Measures of insulin resistance add incremental value to the clinical diagnosis of metabolic syndrome in association with coronary atherosclerosis. Circulation 2004;110:803-9.
|
| 13. | Available from: http://www.hp2010.nhlbihin.net/atpiii/calculator.asp?usertype=prof [last cited on 2010 May 25].
|
| 14. | Mohan V, Shanthirani CS, Deepa R, Sastry NG, Saroja R. Intra urban differences in the prevalence of metabolic syndrome. Diabetes Res Clin Pract 2001;50:514.
|
| 15. | Ramachandran A, Snehalatha C, Latha E, Manoharam M, Vijay V. Impacts of urbanization on the life style and on the prevalence of diabetes in native Asian Indian population. Diabetes Res Clin Pract 1999;44:207-13.
|
| 16. | Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, et al. High prevalence of diabetes and impaired glucose tolerance in India: National Urban Diabetes Survey. Diabetologia 2001;44:1094-101.
[PUBMED] [FULLTEXT] |
| 17. | Lai R. Survival of patients with adult medulloblastoma: A population-based study. Cancer 2008;112:1568-74.
[PUBMED] [FULLTEXT] |
| 18. | Tatjana I, Milan P, Sanja O, Djuro M, Bojana P, Ivana B, et al. Insulin secretion and HOMA-IR in patients who underwent coronary angiography. Endocr Abstr 2010; 22:135.
|
| 19. | Bethene Ervin R. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United states, 2003-2006. Natl Health Stat Report 2009;13:1-8.
|
| 20. | Hayes L, Pearce MS, Unwin NC. Lifecourse predictors of normal metabolic parameters in overweight and obese adults. Int J Obes 2006;30:970-6.
|
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]
| This article has been cited by | | 1 |
Nonalcoholic fatty liver disease in South India |
|
| Pellicano, R., Fagoonee, S. | | Journal of Postgraduate Medicine. 2011; 57(3): 257-258 | | [Pubmed] | |
|
|
|
|
