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 Table of Contents  
ORIGINAL ARTICLE
Year : 2017  |  Volume : 4  |  Issue : 2  |  Page : 66-70

Is glucose dysregulation an inflammatory process?


1 Department of Medicine, Federal Medical Centre, Azare, Bauchi State, Nigeria
2 Department of Medicine, College of Health Sciences, University of Abuja, Abuja, Nigeria
3 Department of Medicine, Ahmadu Bello University, Zaria, Kaduna State, Nigeria

Date of Submission09-Nov-2016
Date of Acceptance19-Dec-2016
Date of Web Publication15-Jun-2017

Correspondence Address:
Yakubu Lawal
Department of Medicine, Federal Medical Centre, Azare, Bauchi State
Nigeria
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2394-2010.208120

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  Abstract 

Aim: To determine the relationship between glucose dysregulation and high-sensitive C-reactive protein (hsCRP). Settings and Design: Zaria is a major city located on the high plains of Northern Nigeria, 652.6 m above the sea level, some 950 km away from the coast. Its location is latitude 112°31 N and longitude 7°42 E. This was a cross-sectional observational study. Participants not previously known to have diabetes mellitus (DM) who satisfied the inclusion criteria were enrolled after cluster random sampling. The study was carried out over a period of 12 months. Materials and Methods: Four hundred apparently healthy participants were recruited through cluster sampling from their respective communities after due consent. Relevant biodata were documented, and appropriate examinations including anthropometric measurements were carried out. Plasma glucose and hsCRP levels were subsequently measured. Statistical Analysis Used: Microsoft excel was used for data entry while SPSS software version 19 was used for data analysis. Pearson's Correlation was used to test for association between plasma glucose levels and body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and hsCRP. Multiple logistic regression was used to determine whether BMI, WC, WHR, and hsCRP were significant determinants of glucose dysregulation. Significance level was considered P < 0.05. Results and Conclusions: BMI, WC, WHR, and hsCRP were shown to be significant determinants of glucose dysregulation. Therefore, a chronic low-grade inflammation may contribute to the etiopathogenesis of DM.

Keywords: C-reactive protein, diabetes mellitus, hyperglycemia, inflammation


How to cite this article:
Lawal Y, Anumah FE, Bakari AG. Is glucose dysregulation an inflammatory process?. J Health Res Rev 2017;4:66-70

How to cite this URL:
Lawal Y, Anumah FE, Bakari AG. Is glucose dysregulation an inflammatory process?. J Health Res Rev [serial online] 2017 [cited 2017 Aug 21];4:66-70. Available from: http://www.jhrr.org/text.asp?2017/4/2/66/208120




  Introduction Top


Diabetes mellitus (DM) is a group of metabolic diseases characterized by chronic hyperglycemia due to defects in insulin secretion, insulin action, or both resulting in abnormalities of carbohydrate, fat, and protein metabolism.[1]

DM is the most common endocrine disorders affecting 8.3% of the world's population.[2] Presently, about 415 million people are affected and type 2 diabetes constitutes 90% of this population.[2] It is projected that about 642 million people will be affected in 2040 and three-quarters of this figure will be in nonindustrialized countries including Nigeria.[2]

The prevalence of type 2 diabetes ranges from 0.3% to 17.9% in Africa and 3.9% in Nigeria.[2] Okoro and Oyejola reported that over 95% of Nigerians with diabetes suffer from the type 2 variant.[3] In a study by Dahiru et al., the prevalence of diabetes in a semi-urban community in Zaria, Northern Nigeria was 2.0%.[4]

DM is the sixth leading cause of death by disease and the seventh leading cause of death in the United States.[1] Untreated, DM is crippling and can lead to blindness, kidney failure, amputation, coronary heart disease, stroke, and untimely death.[2] The projected increase in the number of patients with diabetes will strain the capabilities of health-care providers, especially in the developing countries like Nigeria.[2] It is, therefore, necessary to continuously look for better ways that can lead to the early diagnosis of any degree of glucose dysregulation and hence avert or significantly reduce the health and financial burden of DM. We, therefore, explore the relationship between high-sensitivity C-reactive protein (hsCRP) and glucose dysregulation to open way for further research into its use as a tool for early screening of DM and possible future target for therapeutic intervention.

The pathogenesis of type 2 DM is multifaceted and includes defective insulin secretion from pancreatic islet cells, insulin resistance in peripheral tissues, and inadequate suppression of glucagon production.[5] Conventional understanding of the pathogenesis of type 2 diabetes suggests that insulin resistance is an initiating factor which leads to beta-cell hyperfunction subsequently leading to beta-cell failure and therefore overt diabetes.[5],[6]

Recent attention has focused on the involvement of free fatty acids (FFA) and adipocyte-derived bioactive substances (adipokines) in insulin resistance. While tumor necrosis factor (TNF)-alpha, leptin, resistin, and FFA act to increase insulin resistance, adiponectin and omentin improve it.[5]

Elevated production of adipokines such as TNF-alpha and interleukin (IL)-6 leads to an acute phase response with increased hepatic production of C-reactive protein (CRP), a sensitive marker of low-grade systemic inflammation.[5],[7],[8],[9],[10] Inflammatory cytokines (TNF-alpha and IL-6) and CRP exert an endocrine effect conferring insulin resistance in liver and skeletal muscle, ultimately leading to the clinical expression of type 2 DM.[5],[7],[8],[9],[10]

CRP is an acute-phase protein found elevated in blood in response to inflammation in the body. It is synthesized by the liver in response to factors released by macrophages and adipocytes.[5] Chronic inflammation can keep CRP levels elevated, which can increase the risk of diabetes, hypertension, and cardiovascular diseases.[5]

In a study, Guerrero-Romero et al.[7] demonstrated that hsCRP significantly predicted diabetes risk after adjusting for body mass index (BMI) and other lifestyle factors. They suggested that elevated hsCRP can serve as a common target for lifestyle and therapeutic interventions for diabetes and CVD. Similarly, Woo et al.,[8] Thanabalasingham et al.,[9] and Liu and Ho [10] in different studies showed that inflammatory mediators including hsCRP were determinants of prediabetes and diabetes, especially in those where insulin resistance is the predominant defect.

Furthermore, a significant reduction in hsCRP levels postdietary modification among hyperglycemic patients was reported by Rahamon et al.[11] in a Western Nigerian city. Similar studies have shown the impact of glucose lowering on hsCRP level.[12],[13],[14] This further buttresses the position that a direct relationship exists between hsCRP and hyperglycemia.

Aim and objectives

Aim

To determine the relationship between glucose dysregulation and hsCRP.

Objectives

  1. To determine the prevalence of previously undiagnosed glucose dysregulation in a Northern Nigerian population
  2. To determine the relationship between glucose dysregulation and hsCRP.



  Materials and Methods Top


Zaria is a major city in Kaduna State of Northern Nigeria. It is a very large, heterogeneous city whose population of 975,153 comes from different parts of Nigeria.[15] Zaria is located on the high plains of Northern Nigeria, 652.6 m above the sea level, some 950 km away from the coast. Its location is latitude 112°31 N and longitude 7° 42 E.[15]

Among the major ethnic groups are Hausa, Fulani, Jaba, and a host of other southern Kaduna tribes. Settlers from all parts of the country constitute a significant proportion of the populace.

This was a cross-sectional observational study carried out over a period of 12 months. Approval was sought and granted from the Research and Ethical Committee of Ahmadu Bello University Teaching Hospital, Zaria. Four hundred participants not previously known to have DM who satisfied the inclusion criteria were enrolled after cluster random sampling from ten communities, with each community representing a cluster. Forty participants were selected from each community by simple random sampling. Blinding was achieved by writing numbers concealed in small pieces of paper properly mixed, and participants asked to pick. Those that pick the numbers marked as participants were subsequently enrolled into the study. The patient's information sheets were distributed to those who were literate and read/explained to those who were not literate. Written and duly signed or thumb-printed consent was obtained from each prospective participant and consecutively enrolled.

Inclusion criteria include:

  • Apparently healthy participants who were not previously known to have diabetes
  • Age 18–70 years.


Exclusion criteria include:

  • Any form of illness in the participants
  • Known diabetic patients
  • Participants on medications that affect glucose tolerance such as steroids, thiazides, beta blockers, or HIV protease inhibitors
  • Patients and/or relatives who declined consent in whatever form.


Sample size was calculated using Fisher's formula:



Where

N = Minimum sample size

Z = Standardized normal deviation (which corresponds to the specified confidence level) =1.96

p = Best estimation of the population prevalence

q = 1−p

d = Tolerable margin of error =0.05.

Considering the prevalence of glucose intolerance from various large-scale studies done in Nigeria, Africa, Asia, Europe, and America ranging from 2.2% to over 20% consecutively, p shall be put at 10%. Therefore, N = (1.96)2× 0.10× 0.90/(0.05)2 = 138.

With default and nonresponse rate assumed to be 10% =13

N = 138 + 13 = 151

Four hundred apparently healthy participants were subsequently selected for this study through cluster sampling. Four participants were dropped out due to incomplete biodata and laboratory results. Response rate was 99%.

The World Health Organisation 3-step proforma [16] modified to suit the peculiarities of this study was used for the collection of data. These included history (age, sex, ethnic group, occupation, and marital status; medical, medication, social history; and family history of diabetes, hypertension, obesity), physical examination (some anthropometric indices and systemic examinations), and laboratory investigation results (fasting plasma glucose 66, 2 h plasma glucose [2HPG], and hsCRP). Participants were diagnosed as having impaired fasting glucose, impaired glucose tolerance, or DM if they satisfy the WHO/International Diabetes Federation criteria.[1],[2] The pro forma was filled for each participant by the researcher with the help of a trained assistant.

Physical examinations including anthropometric measurements were carried out by the researcher with the help of a trained assistant and in the presence of a chaperone.

Standing height was measured using a stadiometer with a fixed vertical backboard and an adjustable headpiece. Participants were asked to remove any hair ornaments, jewelry, buns, or braids from the top of their heads and directed to stand on the stadiometer platform. They were positioned to stand up straight against the backboard with the body weight evenly distributed, and both feet flat on the platform, heels together, and toes apart pointing slightly outward at approximately 60° angle. The head, shoulder blades, buttocks, and heels were aligned to make contact with the backboard.[17]

Each participant's head was positioned in the Frankfort horizontal plane (i.e., when the horizontal line from the ear canal to the lower border of the orbit of the eye is parallel to the floor and perpendicular to the vertical backboard) and was instructed to look straight ahead. The stadiometer headpiece was then lowered to rest firmly on top of the head, with sufficient pressure to compress the hair.[17] Participants were instructed to take deep breath and hold this position until the height was measured to the nearest 0.1 cm. The act of taking a deep breath helps straighten the spine to yield a more consistent and reproducible stature measurement.[17]

Participants were weighed in kilograms using a beam balance. They were asked to wear only light clothing/undergarments and stand in the center of the scale platform facing the recorder, hands at the sides, looking straight ahead, and weight evenly distributed. The weight was then recorded in kilograms to the nearest 0.5 kg.[17]

Waist circumference (WC) was measured using a measuring tape that is 1 cm in width and made of a material that does not stretch. Each participant was instructed to gather his or her gown or shirt above the waist, lower the pants and underclothing to slightly below the waist, cross the arms, and place the hands on opposite shoulders. From the right side of the participant, the waist was located at a level midway between the iliac crest and the costal margin along the midaxillary line.[17]

The measuring tape was extended around the waist snugly in such a way that it does not compress the skin. An assistant standing on the opposite side helped to ensure the horizontal alignment of the tape. The zero end of the tape was placed below the section containing the measurement value. Each measurement was then taken to the nearest 0.1 cm at the end of normal expiration. Values >94 cm for males and >80 cm for females were used to define abdominal obesity.[17]

To measure the hip circumference, each participant was asked to stand up with feet together and weight evenly distributed on both feet. The examiner stood behind each respondent, moved directly to his/her right side, and squatted down. From the right side of the participant, the measuring tape was placed around the hip at the level of the greater trochanters. If the greater trochanter was not palpable, the largest horizontal girth around the buttocks was used. The sides of the tape were then adjusted to ensure that the plane of the tape was horizontal and each measurement was taken to the nearest 0.1 cm.[17]

Chest, cardiovascular, abdominal, and central nervous system examinations were done for each participant. Those with illnesses that could interfere with the parameters measured were excluded from the study.

The following materials/equipment were used in the study:

  • WHO 3-step pro forma
  • Mercury sphygmomanometer (diplomat-presameter by Rudolf Riester company, Germany)
  • Stethoscope (3M Littmann Classic II S.E. Stethoscope, black tube, 28 inch, 2201, USA)
  • Measuring tape (NON 171330, 72”, Medline industries, Inc., USA)
  • Anhydrous glucose (75 g dissolved in 250 mL of water)
  • Lancet, cotton wool swab, and safety box
  • Distilled water
  • Syringe (2 mL, 10 mL)
  • Test tubes
  • Water bath (Large beaker + Bunsen burner)
  • Stopwatch
  • Cuvette
  • A 5 mmol/L glucose stock solution
  • Stock enzyme working solution (contains phenol, 4-aminoantipyrene, glucose oxidase, and peroxidase in phosphate buffer)
  • 50 mL volumetric flasks
  • Precision pipettes: 5, 10, 50, 100, and 1.0 mL
  • Disposable pipette tips
  • Absorbent paper
  • Graph paper
  • Beam balance (Seca 700 series, Seca North America, USA)
  • Stadiometer (Seca 213 portable stadiometer, Seca North America, USA)
  • Spectrophotometer (Unicam UV9100-visible spectrophotometer, Perkin-Elmer)
  • Microplate reader manufactured by Cortez Diagnostics with wavelength at 450 nm.
  • hsCRP ELISA kit manufactured by Cortez Diagnostics with detection range 0–0.1 mg/L, specificity 96%, sensitivity 0.01 mg/mL
  • Centrifuge (GH 3.8, Rotor 2023964, Beckman, Inc.).


Oral glucose tolerance test (OGTT) was performed by asking participants to take normal diet with no carbohydrate restriction 72 h before the test. They then fasted for 8–10 h overnight before test day (water was allowed). At 0900 h, 3 mL of blood was drawn from each participant's antecubital vein, and 2 mL out of it was gently emptied into a fluoride oxalate bottle for centrifugation and separation of the plasma by the researcher and his assistants.

Each participant was then given a 75 g anhydrous glucose load in 250 mL of water to drink within 5 min. Two hours after the oral glucose load, another 2 mL of venous blood from each participant was again drawn into a fluoride oxalate bottle for centrifugation and separation of the plasma by the researcher and his assistants. The plasma samples were subsequently used for fasting and 2 h postprandial glucose estimation by the glucose oxidase method.

One milliliter of the fasting venous sample drawn was gently emptied into a plain bottle and then centrifuged, separated, and stored as serum at ≤−20°C by the researcher and his assistants. The serum sample was used for subsequent determination of hsCRP levels.

Statistical analysis

Microsoft excel was used for data entry, and SPSS version 19 IBM corp. released 2010 in Armonk, New York, United States was used for data analysis. Results were expressed as mean ± standard deviation at 95% confidence interval. Student's t-test was used to compare continuous variables and Chi-square test for categorical variables. Pearson's correlation was used to test for association between FPG/2HPG and BMI, WC, waist-hip ratio (WHR), and hsCRP. Multiple logistic regression was used to assess whether BMI, WC, WHR, and hsCRP were significant determinants of glucose dysregulation. P value was considered statistically significant when ≤0.05.


  Results Top


This study was carried out to determine the relationship between glucose dysregulation and hsCRP. At the beginning of this study, 400 participants were enrolled with a male: female ratio of 1:1.1. Four respondents were excluded from analysis due to incomplete biodata and laboratory results. Response rate was 99%.

A significant correlation was shown between FPG and hsCRP (P < 0.001) and 2 h postprandial plasma glucose (2 hrPPG) and hsCRP (P < 0.001) among study participants [Table 1].
Table 1: Correlation matrix of some laboratory parameters among study participants

Click here to view


Multiple logistic regression of the determinants of glucose dysregulation was done using the backward stepwise method. Significant determinants of glucose dysregulation among study participants included BMI (P = 0.005), WC (P = 0.032), WHR (P = 0.015), and hsCRP (P < 0.001) [Table 2].
Table 2: Independent determinants of glucose dysregulation among participants

Click here to view



  Discussion Top


The aim of our study was to determine the relationship between glucose dysregulation and hsCRP.

The prevalence of previously undiagnosed glucose dysregulation among the participants was 26.6% (prediabetes 17.2% and diabetes 9.4%). The high prevalence of previously undiagnosed glucose dysregulation in the population studied is in agreement with various studies among Africans and non-Africans alike.[1],[2],[3],[4]

FPG and 2 h PPG load levels were found to be significantly correlated to the mean hsCRP. Furthermore, participants with glucose dysregulation had significantly higher mean hsCRP levels compared to those with normal glucose levels. Further analysis by multiple logistic regression showed that hsCRP in addition to BMI, WC, and WHR were significant independent determinants of glucose dysregulation which is in agreement with reports from several studies.[7],[8],[9],[10],[18],[19],[20] Guerrero-Romero et al., Woo et al., and Thanabalasingham et al. in different studies showed that inflammatory mediators including hsCRP were determinants of glucose dysregulation, especially in those with insulin resistance as their predominant defect.[7],[8],[9],[10]

They also suggested that elevated hsCRP can serve as a common target for lifestyle and therapeutic interventions for diabetes. This was demonstrated by Rahamon et al.[11] in Ibadan Nigeria where a significant reduction in hsCRP level was observed among hyperglycemic participants postdietary modification. Similar studies have shown the impact of glucose lowering on hsCRP level.[12],[13],[14] This relationship between hsCRP and hyperglycemia is due to the underlying low-grade inflammation that is involved in the pathogenesis of glucose dysregulation.[5],[7],[8],[9],[10] Inflammatory cytokines (TNF-alpha and IL-6) and hsCRP exert an endocrine effect conferring insulin resistance in liver, skeletal muscle, and vascular endothelial tissue, ultimately leading to the clinical expression of glucose dysregulation.[5],[7],[8],[9],[10]

This study was a cross-sectional observational type; hence, some limitation in proving the dynamic relationship between glucose dysregulation and hsCRP levels. Furthermore, confounding factors such as the presence of conditions that could cause elevation of hsCRP could not be screened for, however, the recruitment of apparently healthy individuals and further screening by physical examination has helped to reduce the effect of such confounding factor. Suggestions for further research will include an interventional study to elucidate the dynamics of the reduction in the hsCRP levels when glucose dysregulation is been treated.

Considering this role of low-grade inflammation in the pathogenesis of some subsets of type 2 DM, further arguments could be generated on the theoretical possibility of using anti-inflammatory agents in the treatment or prevention of glucose dysregulation and on whether the benefits of such will outweigh its risks. Future research in this direction may be needed.

Furthermore, the eligibility of hsCRP levels as a screening tool could be called to question as to its cost-effectiveness when compared to other screening methods such as the OGTT. Further research may have to prove whether the measurement of hsCRP levels could be more sensitive and hence help in the diagnosis of glucose dysregulation earlier than OGTT. Such early diagnosis can help to reduce the complications and heavy toll on health budget seen in patients with DM.

The attention given to communicable diseases such as malaria and tuberculosis should equally be given to noncommunicable diseases such as DM. This includes putting elaborate strategies in place including research funding by public and private health institutions and nongovernmental organizations to help develop methods for early and highly sensitive screening of high-risk individuals, especially first-degree relatives of persons with type 2 DM. This will help in the early diagnosis and treatment of glucose intolerance, hence averting or significantly reducing the numerous complications of type 2 DM and its heavy toll on health budget.


  Conclusion Top


The attention given to communicable diseases such as malaria and tuberculosis should equally be given to noncommunicable diseases such as DM. This includes putting elaborate strategies in place including research funding by public and private health institutions and nongovernmental organizations to help develop methods for early and highly sensitive screening of high-risk individuals, especially first-degree relatives of persons with type 2 DM. This will help in the early diagnosis and treatment of glucose intolerance, hence averting or significantly reducing the numerous complications of type 2 DM and its heavy toll on health budget.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

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Baynes HW. Classification, pathophysiology, diagnosis, and management of diabetes mellitus. J Diabetes Metab 2015;6:541.  Back to cited text no. 1
    
2.
International Diabetes Federation. IDF Diabetes Atlas. 7th ed. Brussels, Belgium: International Diabetes Federation; 2015.  Back to cited text no. 2
    
3.
Okoro EO, Oyejola BA. Inadequate control of blood pressure in Nigerians with diabetes. Ethn Dis 2004;14:82-6.  Back to cited text no. 3
    
4.
Dahiru T, Jibo A, Hassan AA, Mande AT. Prevalence of diabetes in a semi-urban community in Northern Nigeria. Niger J Med 2008;17:414-6.  Back to cited text no. 4
    
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Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes: Perspectives on the past, present, and future. Lancet 2014;383:1068-83.  Back to cited text no. 5
    
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Bakari AG, Onyemelukwe GC. Total insulin output is low in type-2 diabetic Nigerians. Int J Diabetes Metab 2005;13:93-5.  Back to cited text no. 6
    
7.
Guerrero-Romero F, Simental-Mendía LE, Rodríguez-Morán M. Association of C-reactive protein levels with fasting and postload glucose levels according to glucose tolerance status. Arch Med Res 2014;45:70-5.  Back to cited text no. 7
    
8.
Woo YC, Tso AW, Xu A, Law LS, Fong CH, Lam TH, et al. Combined use of serum adiponectin and tumor necrosis factor-alpha receptor 2 levels was comparable to 2-hour post-load glucose in diabetes prediction. PLoS One 2012;7:e36868.  Back to cited text no. 8
    
9.
Thanabalasingham G, Shah N, Vaxillaire M, Hansen T, Tuomi T, Gašperíková D, et al. A large multi-centre European study validates high-sensitivity C-reactive protein (hsCRP) as a clinical biomarker for the diagnosis of diabetes subtypes. Diabetologia 2011;54:2801-10.  Back to cited text no. 9
    
10.
Liu ZM, Ho SC. The association of serum C-reactive protein, uric acid and magnesium with insulin resistance in Chinese postmenopausal women with prediabetes or early untreated diabetes. Maturitas 2011;70:176-81.  Back to cited text no. 10
    
11.
Rahamon SK, Charles-Davies MA, Akinlade KS, Olaniyi JA, Fasanmade AA, Oyewole OE. Impact of dietary intervention on selected biochemical indices of inflammation and oxidative stress in Nigerians with metabolic syndrome: A pilot study. Eur J Nutr 2014;4:137-49.  Back to cited text no. 11
    
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Giugliano D, Ceriello A, Esposito K. The effects of diet on inflammation: Emphasis on the metabolic syndrome. J Am Coll Cardiol 2006;48:677-85.  Back to cited text no. 12
    
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Roberts CK, Won D, Pruthi S, Kurtovic S, Sindhu RK, Vaziri ND, et al. Effect of a short-term diet and exercise intervention on oxidative stress, inflammation, MMP-9, and monocyte chemotactic activity in men with metabolic syndrome factors. J Appl Physiol 2006;100:1657-65.  Back to cited text no. 13
    
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Esposito K, Marfella R, Ciotola M, Di Palo C, Giugliano F, Giugliano G, et al. Effect of a mediterranean-style diet on endothelial dysfunction and markers of vascular inflammation in the metabolic syndrome: A randomized trial. JAMA 2004;292:1440-6.  Back to cited text no. 14
    
15.
The Geonames Geographical Database. Population of Zaria, Nigeria. Mongabay; 2012. Available from: http://www.population.mongabay.com/population/nigeria/2317765/zaria. [Last accessed on 2015 Mar 14].  Back to cited text no. 15
    
16.
World Health Organization. WHO STEPS Instrument (Version 3.0). Geneva: World Health Organization; 2014. Available from: http://www.who.int/chp/steps/instrument/en/.[Last accessed on 2015 Aug 28].  Back to cited text no. 16
    
17.
Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey III; Anthropometry Procedures Manual. Georgia, USA: Centers for Disease Control and Prevention; 2016. Available from: http://www.cdc.gov/nchs/data/nhanes/nhanes_15_16/manual_an.pdf171.[Last accessed on 2016 Oct 22].  Back to cited text no. 17
    
18.
Hu FB, Stampfer MJ. Is type 2 diabetes mellitus a vascular condition? Arterioscler Thromb Vasc Biol 2003;23:1715-6.  Back to cited text no. 18
    
19.
Festa A, Hanley AJ, Tracy RP, D'Agostino R Jr., Haffner SM. Inflammation in the prediabetic state is related to increased insulin resistance rather than decreased insulin secretion. Circulation 2003;108:1822-30.  Back to cited text no. 19
    
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Steven MH. Insulin resistance, inflammation, and the prediabetic state. Am J Cardiol 2003;92:18-26.  Back to cited text no. 20
    



 
 
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