Detecting Insulin Resistance in Pakistani Subjects by Fasting Blood Samples
M. Zafar Iqbal Hydrie*, 1, 2, Abdul Basit1, Asher Fawwad1, Muhammad Yakoob Ahmedani3, A. Samad Shera1, Akhtar Hussain
Identifiers and Pagination:Year: 2012
First Page: 20
Last Page: 24
Publisher Id: TODIAJ-5-20
Article History:Received Date: 03/03/2011
Revision Received Date: 06/04/2012
Acceptance Date: 10/04/2012
Electronic publication date: 09/7/2012
Collection year: 2012
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Insulin Resistance has been identified as an independent risk factor for a number of chronic diseases such as diabetes and cardiovascular diseases. Thus identifying insulin resistant cases would help to better prevent the progression of these diseases in such individuals.
To identify a simple indirect method for detecting insulin resistance in our population by using fasting blood samples.
Geographical Imaging Systems was used for randomly selecting the subjects during an epidemiological survey done. For visiting the 532 households selected by geographical imaging systems, nine field teams were developed. A total of 871 subjects older than 25 years were approached by these teams out of which 867 agreed to participate in the survey. Insulin resistance was assessed in 227 normal subjects by fasting insulin, Homeostasis model assessment for insulin resistance (HOMA-IR), Quantitative insulin-sensitivity check index (QUICKI) and McAuley Index.
Insulin Resistance was defined at 75th percentile cut off of insulin levels (9.25 U/mL) and at 75th percentile of HOMA-IR (1.82). The 25th percentile cut off was used for defining insulin resistance in QUICKI (0.347) and McAuley Index (6.77).
A common approach towards managing subjects with metabolic risk factors will help identify insulin resistance earlier by this fasting method and using insulin resistance reference values identified from simple indirect methods would be of value in such cases. However larger population based studies are needed to further define and validate these cutoff values for insulin resistance to be used for the general population.