Published by : PROFESSIONAL MEDICAL PUBLICATIONS
|October - December 2006||
Correlation of fasting insulin resistance
indices with clinical parameters of metabolic
syndrome in type 2 diabetic subjects
Asher Fawwad1, Rashida Qasim2, M. Zafar Iqbal Hydrie3,
Abdul Basit4, Zahid Miyan5, Asma Gul6
Objectives: To identify the insulin resistance by various available insulin resistance indices and to see their correlation with clinical parameters of metabolic syndrome in type2 diabetic population.
Methods: All type 2 diabetic subjects having age 30 years or more were selected with exclusion criteria (hepatic, renal or cardiac impairment). Insulin resistance was assessed by components of metabolic syndrome as defined by ATP III and by using fasting insulin resistance indices i.e. HOMA, QUICKI and McAuley. The correlation between clinical parameters of metabolic syndrome and insulin resistance indices were seen by calculating Pearson’s coefficient.
Results: One hundred eighteen subjects were selected (62.4% males and 37.6% females). Mean BMI was 27.8±4.9kg/m2. Mean systolic B.P was 129.7±19.7. Mean diastolic B.P was 80.9±10.8. Mean LDL level was 103 ± 27.6. Mean HOMA was 4.1. Mean QUICKI index was 0.3276. Mean McAuley was 6.79. The results indicate that various Insulin resistance indices have strong and significant correlations in between them but not significantly correlated with all clinical parameters of metabolic syndrome.
Conclusion: Correlation of fasting insulin resistance with clinical parameters of metabolic syndrome is lost when people develop diabetes.
Key Words: HOMA, QUICKI, McAuley, Insulin Resistance, Type 2 diabetes.
Pak J Med Sci October - December 2006 Vol. 22 No. 4 433-437
1. Dr. Asher Fawwad MBBS
Incharge Research Department,
2. Dr. Rashida Qasim, PhD.
Chairperson, Z.A School of Medical Technology
Sindh Institute of Urology& Transplantation, Karachi.
3. Dr. M. Zafar Iqbal Hydrie MBBS
4. Dr. Abdul Basit FRCP
Professor of Medicine, Head of Department,
Medical Unit - IV, Baqai Medical University
Baqai Institute of Diabetology and Endocrinology.
5. Dr. Zahid Miyan MCPS
6. Ms. Asma Gul MSc.
1,3-6: Research Department,
Baqai Institute of Diabetology and Endocrinology
Plot 1-2, II-B, Block 2, Nazimabad-2,
Dr. Asher Fawwad MBBS
* Received for Publication: February 4, 2006
* Accepted: June 20, 2006
Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both.1 Insulin resistance appears to precede the development of type 2 diabetes and is associated with an increased risk of developing atherosclerosis, microvascular disease and hypertension.2,3
A large number of epidemiological and clinical studies have established consistent correlation between certain anthropometric, metabolic and hemodynamic variables of insulin resistance. These variables include obesity, unfavorable body fat distribution, glucose intolerance or type 2 diabetes, hyperinsulinemia, low levels of HDL, hypertriglyceridemia, high levels of LDL and hypertension.4 Therapeutic reduction of insulin resistance has been shown to improve glycemic control and has the potential to favorably modify the other components of metabolic syndrome; thereby reducing long-term cardiovascular squealae.5 Insulin resistance is the central pathology in patients with various components of metabolic syndrome.
Obesity is strongly associated with decreased insulin sensitivity in type 2 diabetic subjects.6 In most reports, type 2 diabetic subjects are insulin resistant.7,8 An association of insulin resistance with hypertension and dyslipidemia was also found in type 2 diabetic subjects.9 Dyslipidemia (increased triglyceride and decreased HDL cholesterol levels) has also been associated with insulin resistance in type 2 diabetic subjects.10,11
Relatively little data is available regarding the assessment of insulin resistance in type 2 diabetic Pakistani subjects. Metabolic syndrome was assessed in type 2 diabetic subjects and in one study obesity has been seen in 72.6%, hypertension in 42.8% while dyslipidemia was seen in 68.5% of the subjects. The metabolic syndrome was observed in 20.7% of the total subjects.12 In another study comparing diabetic subjects with non diabetic subjects it was found that serum insulin and cholesterol levels were significantly raised in the diabetic group (P<0.01).13 During the last few decades there has been a surge of interest in the assessment of insulin resistance both as an etiological factor in the pathogenesis of type2 diabetes and also as a key component of the insulin resistance syndrome i.e. metabolic syndrome.
The euglycaemic-hyperinsulinaemic glucose clamp is considered the gold standard reference method for evaluating insulin sensitivity; It is conceptually a simple test, although technically, somewhat more complex.14 To avoid complex procedures as well as widely changing glucose level ranges, few mathematical formulas has been derived by researchers that focus on basal fasting glucose, insulin and triglyceride levels. HOMA was developed by Matthews et al. (1985) as a method for estimating insulin sensitivity from fasting serum insulin and fasting plasma glucose. Low HOMA values indicate high insulin sensitivity, whereas high HOMA values indicate low insulin sensitivity.
HOMA yields the following formula for insulin resistance.15
HOMA-R = Insulin (mU/ml) × glucose (mmol/l)/22.5
In 2000, a new index, termed the quantitative insulin sensitivity check index (QUICKI), has been proposed as a formula for insulin resistance.16
QUICKI = 1/ [log (fasting insulin) + log (fasting glucose)]
While in 2001 McAuley proposed the formula for predicting insulin resistance by adding the triglyceride levels.17
Exp [2.63-028 ln (Insulin in Mu/l) -0.31 ln (triglyceride in mmol/l)
There are variations in the cut off values of insulin resistance due to the lack of established standards for insulin assay procedures. Furthermore the secretion of insulin varies significantly throughout 24 hours especially in type 2 diabetic subjects. These indicate that fasting plasma insulin levels are of limited value for clinical purposes, but has some utility as a research tool in population-based studies.18
Our study aims to identify the insulin resistance by using fasting insulin resistance indices and to see their correlation with clinical parameters of metabolic syndrome in type2 diabetic subjects.
Research Design and Methods
All type 2 diabetic subjects aged 30 years or more were selected from the OPD of Baqai Institute of Diabetology & Endocrinology (BIDE).This study was done from December 2003 to July 2004. Subjects having hepatic, renal or cardiac impairment were excluded from the study.
Sampling and Data collection: Ethical clearance was taken from the Institutional Review Board of BIDE. After taking informed consent from the subjects, fasting plasma samples were collected. Fasting plasma glucose, fasting serum insulin levels, fasting lipid profile (cholesterol, triglyceride, HDL and LDL), were done by using stat fax (1904 by mosquito) and ELISA (303 by sandwich technique).
Fasting plasma glucose was estimated by GOD-PAP Method, serum total Cholesterol, LDL cholesterol and HDL cholesterol were estimated by CHOD-PAP method and Serum triglycerides was estimated by GPO-PAP method.
The height and weight of all the subjects were recorded by a stadiometer with subjects in light clothing and without shoes. Height was recorded to the nearest centimeter and weight to the nearest 0.1 kg. Body Mass Index (BMI) was calculated by: Weight (kg)/height meter2. Blood pressure was measured using mercury sphygmomanometer.
Assessment of insulin resistance: Clinically insulin resistance was assessed by components of metabolic syndrome as defined by ATP III i.e. BMI >25, high triglyceride level >150mg/dl, HDL cholesterol (<40mg/dl) in males (<50mg/dl) in females, high blood pressure >130/85mmhg. Insulin resistance was assessed by using fasting insulin resistance indices i.e. HOMA, QUICKI and McAuley.
Statistical analysis: All statistical analyses were performed using the statistical program SPSS (version 11). Mean and standard deviations of continuous variables were calculated. Percentiles of insulin resistance indices were also reported. The correlations were seen by applying Pearson’s correlation coefficient. Statistical significance was assessed by using p value <0.05.
A total of 118 subjects were recruited in the study over a period of eight months. Mean age of subjects was 49.1±10.2 years. Mean BMI was 27.8±4.9kg/m2. The mean fasting plasma glucose levels was 155.3±48.7 mg/dl. The clinical and biochemical characteristics of subjects comprising of 73 males and 45 females are shown in Table-I. Mean HOMA in our Type 2 diabetic subjects was 4.1. Mean value of QUICKI index was 0.3276. Mean McAuley in our Type 2 diabetic subjects was 6.79 as shown in Table-II. Mean Insulin Resistance indices at different percentiles, is shown in Table-III. The 25th percentile values for QUICKI and McAuley indexes are 0.29 and 5.4 respectively and 75th percentile value for HOMA is 5.36.
Table-IV shows the correlation between clinical parameters of metabolic syndrome and insulin resistance indices in type 2 diabetic subjects. None of the clinical parameters of metabolic syndrome (BMI, blood pressure, HDL and triglyceride) had significant correlation with fasting insulin resistance indices (HOMA, QUICKI and McAuley) in our study. Insulin resistance indices i.e. HOMA QUICKI and McAuley have strong and significant correlations in between them. There was a strong positive correlation QUICKI and McAuley (r=0.888), but an inverse relationship of HOMA was seen with both QUICKI (r= -0.828) and McAuley (r= -0.703).
In this study we tried to assess insulin resistance by using simple fasting measurements. Assessment of insulin resistance by using fasting insulin resistance indices is time consuming and costly. Furthermore facilities to perform such tests are not available everywhere especially in the rural areas of Pakistan. On the other hand clinical parameters of insulin resistance can easily be assessed by physicians in primary care health clinics or facilities.
Our results showed that in type 2 diabetic subjects the mean fasting insulin levels were 10.79±7.84mu/L. The normal range for insulin levels using RIA is 3 to 32 mu/L. of weak format correlation between fasting insulin levels and clinical parameters of metabolic syndrome was seen in this study. Nevertheless it was found that all insulin resistance indices have strong correlation in-between them as shown in Table-IV. Fasting serum insulin levels were significantly correlated with fasting insulin resistance indices and the reason is that in all these mathematical calculations, values of fasting serum insulin levels was used. Mean value of HOMA in our Type 2 diabetic subjects was 4.1. Weak correlation was found between HOMA and clinical parameters of metabolic syndrome i.e. BMI, blood pressure, HDL and triglyceride. Insulin sensitivity measured by using QUICKI showed a mean value of 0.3276 in our type 2 diabetic subjects. Weak correlations were found between QUICKI and clinical parameters of metabolic syndrome also as seen in HOMA values. Mean value of Mcauley in our Type 2 diabetic subjects was 6.79. Weak correlations were found between Mcauley and clinical parameters of metabolic syndrome.
Insulin resistance indices (HOMA, QUICKI and McAuley) and clinical parameters of metabolic syndrome are not strongly correlated in this study because once diabetes is diagnosed, altered glucose metabolism and abnormal insulin levels probably due to treatment masks the true picture of insulin resistance especially in subjects having component of insulin deficiency. Moreover insulin resistances markers may be also influenced by various other associated factors; such as treatment taken for diabetes, hypertension and dyslipidemia etc. All have their role in altering the clinical and biochemical parameters of insulin resistance especially due to their prolonged use.
Any of the three insulin resistance indices can be used alone to assess insulin resistance with their potential limitations of being not having universal cut off values. Most of the studies use 75th percentile of HOMA and 25th percentile for QUICKI and McAuley to determine insulin resistance in their population.20 The 25th percentile values for QUICKI and McAuley indexes are 0.29 and 5.4 respectively. These findings of our study are in agreement with results published by Juan F Ascaso et al.20 The 75th percentile value of HOMA in our study was 5.36 which is higher to other published studies, such as a HOMA value of 2.6 in non- diabetic subjects.20 The reason for this difference is probably that we applied HOMA to Type 2 diabetic subjects while other researchers used either non-diabetic or impaired glucose tolerant groups in their studies.
Therefore, in summary, correlation of fasting insulin resistance with clinical parameters of metabolic syndrome is lost when people develop diabetes and hence is not clinically feasible.
We acknowledge the co-operation of PharmEvo Pakistan for providing support to the Research Department (BIDE) as well as of the work of Mr. Imran Waheed (Research Statistician) of BIDE.
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