For measurement in female subjects, a female attendant was taken to stand by the side. The blood samples were collected from the antecubital vein after 12 h of fasting and avoiding of alcohol. The plasma sugar was determined using the glucose oxidase enzymatic method Trinder, Serum HDL-C level was measured by using the phosphotungstate precipitation method.
An internal quality control was in place for assessing the validity of glucose, triglyceride and HDL methods. Insulin sensitivity was assessed by the calculation of the HOMA approach.
INS - Clinical: Insulin, Serum
Continuous variables are presented as mean values standard deviation , while qualitative variables are presented as relative frequencies. Comparisons between normally distributed continuous variables and categorical were performed by the calculation of Student's t -test and one-way or multi-way analysis of variance, after testing for equality of variances homoscedasticity using IBMSPSS [Statistical Package for the Social Sciences] version 20 software Chicago, IL, USA.
SPSS version The cut-off values for IR were based on the 90 th percentile in the study population and a receiver operating characteristic ROC curve was generated. The optimal cut-off value was denoted by the value that had the largest sum of sensitivity and specificity. Informed consent was obtained from the participants. Participation in the study was voluntary and guarantee of confidentiality and anonymity of data was ensured. Ethical clearance was obtained from the Institutional Ethics Committee. Among the individuals who participated in the study, there were 43 males and 69 females.
The mean and standard deviation of both biological and biochemical parameters between males and females were calculated and presented in Table 1. The parameters for assessment of IR by HOMA2-IR among the study population were studied and it was found that the fasting plasma glucose had a mean From the regression results, it was observed that among the components of MS, waist circumference had the highest contribution toward the dependent variable IR, followed by serum triglycerides, fasting blood glucose, serum HDL-C, systolic BP in mm of Hg and lastly, diastolic BP in mm of Hg.
The regression model for this purpose can be best expressed as follows:. Applying the above model, Sensitivity and specificity were Receiver operating characteristics curve of homeostatic model assessment 2 insulin resistance to predict insulin resistance.
IR has been suggested as the primary cause leading to the clustering of risk factors such as glucose intolerance, hypertension, elevated serum triglycerides, low serum HDL-C and central obesity which together have been labeled as MS.
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In the present study, IR was calculated by HOMA2-IR method, which is a more accurate representation of metabolic process because it models the feedback relationship between insulin and glucose in various organs of the body. The present study demonstrated the magnitude of IR and its associated metabolic risk factors among the attendees of an out-patient department of a tertiary care hospital. There were 43 males and 69 females and the mean age of the study population were The differences between males and females were evident in the study population despite similarities in mean age, fasting blood glucose, triglycerides and C-peptide.
In a study, among Peruvian adults by Gelaye et al. Women had significantly higher mean age and high density lipoprotein-cholesterol; there was no significant gender difference in the values of CRP, fasting insulin and fasting glucose. CRP, a systemic inflammatory marker, when measured in the blood with high sensitivity assay has been reported to be a strong and independent predictor of future cardiovascular disease CVD risk including IR.
Gelaye et al. The relationship of obesity to IR and type 2 diabetes is a long-recognized phenomenon with fundamentally important scientific and clinical implications. According to Reaven, IR is the central pathophysiological feature of the cluster of metabolic abnormalities, which are associated with MS.
Similar findings were observed by Yamada et al. The presence of hypertriglyceridemia always associated with IR in these studies may be due to the fact that insulin affects triglyceride and HDL-C metabolism. Determining cut-off values of IR by indirect measures could help in identifying insulin resistant subjects in clinical practice on account of their simplicity and clinicians may be able to use this simple test as an initial screening tool to identify such subjects in the future.
The optimal cut-off value to detect IR was 1. The strength of the study is that it is one of the first study in this part of the country which measures IR based on HOMA2-IR among apparently healthy individuals. However, the limitations of our study must also be considered. The number of subjects studied was very small and they may not be representative of the general population. Due to the cross-sectional nature of the present study, the cause-effect relationship of our findings cannot be proven and a large scale, prospective study is required.
The findings of the present study reveal that IR is associated with various clinico-metabolic risk factors.
http://gelatocottage.sg/includes/2020-06-13/533.php In conclusion, IR can be viewed as a large iceberg where unknown morbidity exceeds the known morbidity. With the recognition that IR is a multifaceted syndrome that can express itself in many ways, it is important for the scientific community to focus their attention on defining the mechanism s responsible for this defect. Source of Support: Nil. Conflict of Interest: None declared. National Center for Biotechnology Information , U.
Ann Med Health Sci Res. Author information Copyright and License information Disclaimer. Address for correspondence: Dr.
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E-mail: moc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3. This article has been cited by other articles in PMC. Abstract Background: Insulin resistance IR , as a result of unhealthy life-styles and westernization, most likely contributes to the increased incidence of metabolic abnormalities and consequently, the development of metabolic syndrome MS. Aim: The present study was undertaken to determine the magnitude of IR and associated clinico-metabolic risk factors among the out-patients of a tertiary care hospital in Bihar, India.
Subjects and Methods: Anthropometric profile, lipid profile, fasting blood glucose, C-reactive protein CRP and C-peptide of individuals were measured using the standard procedures.
Results: The mean IR was 1. Conclusion: IR was found to have a strong association with various clinico-metabolic risk factors. Keywords: C-reactive protein, Homeostatic model assessment insulin resistance, Insulin resistance, Metabolic syndrome. Introduction The new millennium has witnessed the emergence of the epidemic of non-communicable diseases, with frightful consequences to the health of people world-wide.
Subjects and Methods A hospital-based cross-sectional study was conducted among purposively selected apparently healthy individuals aged 20 years and above, attending the out-patient clinic of the Mata Gujri Memorial and LSK Hospital, Kishanganj, during the period of June to November Ethical clearance for this study was obtained from the MGM Medical college Kishanganj Bihar Biophysical parameters Body weight was measured by portable weighing machine setting the pointer at zero reset with the subject wearing light clothes and without shoes.
Biochemical measurements The blood samples were collected from the antecubital vein after 12 h of fasting and avoiding of alcohol. Statistical analyses Continuous variables are presented as mean values standard deviation , while qualitative variables are presented as relative frequencies.
Results Background factors of subjects categorized by sex Among the individuals who participated in the study, there were 43 males and 69 females.
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Open in a separate window. Relationship between components of MS and IR From the regression results, it was observed that among the components of MS, waist circumference had the highest contribution toward the dependent variable IR, followed by serum triglycerides, fasting blood glucose, serum HDL-C, systolic BP in mm of Hg and lastly, diastolic BP in mm of Hg. Figure 1. Discussion IR has been suggested as the primary cause leading to the clustering of risk factors such as glucose intolerance, hypertension, elevated serum triglycerides, low serum HDL-C and central obesity which together have been labeled as MS.
Conclusion The findings of the present study reveal that IR is associated with various clinico-metabolic risk factors. Footnotes Source of Support: Nil. References 1. Kelly GS. Insulin resistance: Lifestyle and nutritional interventions. Altern Med Rev. Krentz AJ. Insulin Resistance: A Clinical Handbook. Perwez Asim. Cardiodiabetes, insulin resistance, obesity, dyslipidemia, hypertension, hypercoagulability, type 2 diabetes, metabolism. Abstract Abstract Insulin resistance is not simply a problem of deficient glucose uptake in response to insulin, but a multifaceted syndrome that increases significantly the risk for cardiovascular disease.
The links between insulin resistance and the associated dyslipidemia, hypertension, hypercoagulability, and atherosclerosis are numerous and complex. Insulin resistance in obesity and type 2 diabetes is manifested by decreased insulin-stimulated glucose transport and metabolism in adipocytes and skeletal muscle and by impaired suppression of hepatic glucose output. Related Books.
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