Assessment of Cardiovascular Disease Risk in Prediabetes

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International Journal of Biochemistry Research & Review
29(2): 1-8, 2020; Article no.IJBCRR.55491 ISSN: 2231-086X, NLM ID: 101654445

Assessment of Cardiovascular Disease Risk in Prediabetes
Ashish Agarwal1, Anupama Hegde2*, Afzal Ahmad1, Charu Yadav3, Poornima A. Manjrekar2 and M. S. Rukmini2
1Department of Biochemistry, GS Medical College and Hospital, Pilkhuwa, U.P., India. 2Department of Biochemistry, Kasturba Medical College, Mangalore, MAHE, Karnataka, India.
3Department of Biochemistry, Lady Harding Medical College, New Delhi, India.
Authors’ contributions
This work was carried out in collaboration among all authors. Authors Ashish Agarwal and AH gave the concept/design and drafted the article. Author Afzal Ahmad did the data acquisition, analysis and interpretation. Authors CY and PAM critically revised the article. Author MSR managed accountability.
All authors finally approved manuscript version to be published.
Article Information
DOI: 10.9734/IJBCRR/2020/v29i230166 Editor(s):
(1) Dr. Chunying li, Georgia State University, USA. Reviewers:
(1) Celestino Sardu, University of Campania Luigi Vanvitelli, Italy. (2) Arthur N. Chuemere, University of Port Harcourt, Nigeria.
Complete Peer review History:

Original Research Article

Received 08 January 2020 Accepted 13 March 2020 Published 19 March 2020

Introduction: Prediabetes is associated with dysglycemia, endothelial dysfunction, obesity and inflammation, placing them at an increased risk of cardiovascular events. Aims: The present study aimed to investigate the risk of cardiovascular disease associated with prediabetes by estimation of serum interleukin-6, myeloperoxidase and urine microalbumin and their correlation with fasting plasma glucose and anthropometric measurements. Study Design: Cross sectional study. Place and Duration of Study: Study was conducted at Department of Biochemistry, Kasturba Medical College Hospitals, Mangaluru between 2014 and 2015. Methodology: Eighty subjects were categorised into prediabetes and healthy controls based on their fasting plasma glucose values. Anthropometric data (weight, body mass index, waist circumference, hip circumference and waist-to-hip ratio from all subjects were recorded. Interleukin-6 & myeloperoxidase were estimated in serum sample whereas microalbumin was estimated in random urine sample.
*Corresponding author: E-mail: [email protected];

Agarwal et al.; IJBCRR, 29(2): 1-8, 2020; Article no.IJBCRR.55491
Results: The mean anthropometric measurements and cardiovascular disease risk markers (interleukin-6, myeloperoxidase and urine microalbumin) were found to be significantly higher (p < 0.05) in prediabetes group. Myeloperoxidase had significant correlation with fasting plasma glucose (r-0.388) in the prediabetes group. Interleukin-6 and myeloperoxidase also showed a positive correlation with body mass index (r - 0.339, r - 0.327), waist circumference (r - 484, r - 0.493) and waist-to-hip ratio (r - 0.430, r - 0.493) while urine microalbumin did not correlate with fasting plasma glucose and anthropometric measurements in prediabetes group. Conclusion: This study suggests that prediabetes is associated with central adiposity and have an increased risk for cardiovascular disease.

Keywords: Interleukin-6(IL-6); Myeloperoxidase (MPO); Microalbuminuria (MA); cardiovascular risk; prediabetes.



: Cardiovascular Disease : Interleukin-6 : Myeloperoxidase : Microalbumin :Fasting Plasma Glucose :Body Mass Index :Waist Circumference :Hip Circumference :Waist-To-Hip Ratio :Pearson’s Correlation :Impaired Fasting Glucose :Impaired Glucose Tolerance :Blood Pressure :Enzyme-Linked Immune Sorbent Assay :Statistical Package For Social Science : Prediabetes Group :Healthy Controls :Reactive Oxygen Species :Hydrogen Peroxide :Nitric Oxide : Hypochlorus Acid :Research Society For Study of Diabetes In India : Manipal University


disease (CVD). A major pro-inflammatory cytokine interleukin-6 (IL-6), contributes in the initiation & acceleration of chronic low grade inflammation resulting in endothelial dysfunction and atherosclerotic plaque formation in type 2 diabetes [2]. Myeloperoxidase (MPO) is an enzyme linked to both oxidative stress and inflammation and has been implicated in the pathogenesis of atherosclerosis and is associated with an increased CVD risk in diabetes population [3].
Microalbuminuria (MA) i.e. increased albumin excretion than normal in urine is associated with oxidative stress and endothelial dysfunction. It is a predictor of cardiovascular mortality and an independent risk factor for the development of CVD in the diabetic population [4].
IL-6, MPO and microalbuminuria are associated with endothelial dysfunction, low grade inflammation and oxidative stress which are the mechanisms for the development of CVD in diabetes patients but their role in prediabetes associated CVD is still debatable. Therefore, estimation of IL-6, MPO and microalbuminuria as indicators of CVD risk [3,5,6] in prediabetes and their correlation with fasting glucose and anthropometric measurements forms the basis of this study.

Pre-diabetes is generally defined as impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or both. It is associated with dyslipidaemia, endothelial dysfunction, obesity, dysglycemia, pro-coagulant state, insulin resistance, hypertension and inflammation placing individuals with prediabetes at an increased risk of cardiovascular events [1].
Low grade inflammation is one of the major underlying pathophysiologic mechanisms responsible for development of cardiovascular

2.1 Study Design
A cross sectional study was conducted for study subjects who came with requisition for fasting plasma glucose test in a tertiary care hospital, Mangalore. Around 300 patients were screened over a period of one year out of which 80 subjects aged 25-45 years who met the inclusion criteria i.e. FPG of 101-125 mg/dl or 70-100 mg/dl were selected and categorised into prediabetes and


Agarwal et al.; IJBCRR, 29(2): 1-8, 2020; Article no.IJBCRR.55491

healthy controls respectively. Subjects with history of diabetes, endocrine disorders, kidney diseases, cardiac diseases, any infectious disease in the past two weeks and pregnant women were excluded.

weight, BMI, WC, HC and WHR were found to be significantly increased in prediabetes group when compared with the healthy controls. The mean age and height were comparable between the groups.

This study was carried out with the approval of the institutional Ethics Committee. After obtaining informed consent, history of the subjects was taken through a structured interview and a thorough physical examination of the subjects was done which included measuring the BP, pulse rate, height, weight, BMI, hip circumference, waist circumference and waist-tohip ratio followed by systemic examination.
2.2 Biochemical Measurements

3.2 CVD Risk Markers
Table 2 shows the central values of cardiovascular disease risk markers (IL-6, MPO and MA) in both the groups. The mean serum IL6, MPO and median urinary MA levels were found to be significantly increased in prediabetes group when compared with healthy controls.
3.3 FPG and CVD Risk Markers Correlation

Along with sample for fasting plasma glucose in fluoride vacutainer, additional blood sample was collected in plain vacutainer for IL-6 & MPO and random urine sample was collected in sterile container for MA estimation. Serum samples for IL-6, MPO and urine for MA were stored at -20°C until further analysis.
Serum IL-6 and MPO were analysed using solid phase enzyme-linked immunosorbent assay (ELISA) based on sandwich principle in ELx 800 by BIO TEK® instruments, Inc. using commercially available kits provided by RayBiotech, Inc. and Immunology Consultants Laboratory, Inc. respectively. Urine MA was analysed by Latex-turbudimetric method in STAR 21 Plus semiautoanalyser using commercially available kit provided by Euro Diagnostic Systems Pvt. Ltd.
2.3 Statistical Analysis
Statistical package SPSS vers.16.0 was used and P < 0.05 was considered significant. Comparison between the groups was done by independent sample ‘t’ test for normal distribution data and for data with skewed distribution MannWhitney U test was used. Correlation was done by Pearson’s correlation for normal distribution data and for data with skewed distribution Spearman’s correlation was used.
3.1 Baseline Characteristics
Following the inclusion criteria a total of eighty patients were enrolled in the study. Table 1 shows the baseline characteristics of the patients from both the groups. The mean serum glucose,

When Pearson’s correlation was applied for FPG, versus serum IL-6, MPO and Spearman’s correlation for urinary MA in both the groups as shown in Fig. 1, only MPO had a significant positive correlation (r- 0.388, P-0.013) with FPG in prediabetes group but had no correlation in the healthy controls. IL-6 and Urine MA had no correlation with FPG in both the groups.
3.4 Anthropometric Measurements and CVD Risk Markers Correlation
When Pearson’s correlation was applied for serum IL-6 and MPO and Spearman’s correlation for urinary MA versus anthropometric measurements i.e weight, BMI, WC, HC and WHR in both the groups as shown in Table 3, IL6 were found to be positively correlated with weight, BMI, WC and WHR, MPO was found to be positively correlated with BMI, WC and WHR and Urine MA was not correlated with anthropometric measurements in prediabetes group.
Prediabetes is associated with microangiopathy and also with more advanced atherosclerotic vascular damage than normoglycemia. Contribution of both glycemic and non-glycemic factors in the development of CVD during prediabetes is supported by the different pathophysiologic pathways leading to vasculopathy [1]. The risk of cardiovascular disease in prediabetes as compared to healthy population was evaluated using serum interleukin-6, myeloperoxidase and urine microalbumin in the current study. At baseline, it was observed that the subjects in prediabetes group had increased weight, BMI, waist


Agarwal et al.; IJBCRR, 29(2): 1-8, 2020; Article no.IJBCRR.55491

circumference, hip circumference and waist-tohip ratio in contrast to healthy group (Table 1). We found increased levels of IL-6, MPO and urine MA in prediabetes group as compared to healthy group. Further, we found a correlation of MPO with fasting glucose in prediabetes. We also found correlation of IL-6 and MPO, but not urine MA with anthropometric measurements in prediabetes.

The increase in anthropometric measurements found in prediabetes subjects in the present study is supported by the study conducted by Ferrannini which suggested that prediabetes individuals, aside from having mild hyperglycemia, have a higher BMI as well as more central fat distribution and higher waist-tohip ratio compared with normoglycemic subjects [7].

Table 1. Baseline characteristics of the prediabetes and healthy group


Prediabetes group (n=40) Healthy controls (n=40)

P -value

Age (years)

37.95 ± 6.08(0.96)

36.05 ± 5.89(0.93)


FPG (mg/dl)

109.18 ± 7.51(1.18)

92.98 ± 4.23(0.66)


Height (cm)

158.22 ± 5.80(0.91)

159.72 ± 8.09(1.28)


Weight (kg) BMI (kg/m2)

68.55 ± 7.59(1.20) 27.29 ± 1.38(0.21)

58.33 ± 7.06(1.11) 22.81 ± 1.50(0.23)

<0.05* <0.05*

WC (cm)

99.10 ± 4.74(0.75)

87.22 ± 7.44(1.17)


HC (cm)

104.62 ± 3.45(0.54)

102.53 ± 4.55(0.71)



0.94 ± 0.04(0.006)

0.85 ± 0.05(0.008)


Results are shown as Mean ± SD(SE- standard error of mean), n – number of subjects, FPG-Fasting Plasma Glucose, BMI – Body Mass Index, WC – waist circumference, HC – hip circumference, WHR – waist-to-hip ratio,
* P <0.05 was considered significant

Table 2. Comparison of cardiovascular disease risk markers between the two groups


Prediabetes group

Healthy controls

P value

IL-6 (pg/ml)

66.29 ± 15.39( 2.43 )

12.59 ± 2.69( 0.42 )


MPO (ng/ml)

67.46 ± 13.77( 2.17)

46.78 ± 9.93( 1.57 )


MA (mg/L)


12.60(9.64, 15.81)*


Results are shown as Mean ± SD (SE-standard error of mean),* median (interquartile range), IL-6 - Interleukin 6,

MPO –Myeloperoxidase, MA-microalbumin, P <0.05 was considered significant

Table 3. Correlation of serum IL-6, MPO and urinary MA with anthropometric measurements between the groups


IL6 (pg/ml)

MPO (ng/ml)

Urine MA (mg/L) #

Weight (Kg)


H BMI (Kg/m2) PD


WC (cm)



HC (cm)






r value (p value) 0.341 (<0.05*) 0.113 (>0.05) 0.339 (<0.05*) -0.044 (>0.05) 0.484 (<0.05*) 0.225 (>0.05) 0.141 (>0.05) 0.240 (>0.05) 0.430 (<0.05*) 0.145 (>0.05)

r value (p value) 0.274 (>0.05) 0.033 (>0.05) 0.327 (<0.05*) 0.121 (>0.05) 0.493 (<0.05*) -0.083 (>0.05) 0.074 (>0.05) 0.175 (>0.05) 0.493 (<0.05*) -0.249 (>0.05)

r value (p value) 0.178# (>0.05) -0.217# (>0.05) 0.214# (>0.05) -0.048# (>0.05) -0.116# (>0.05) 0.161# (>0.05) -0.110# (>0.05) 0.147# (>0.05) -0.076# (>0.05) 0.100# (>0.05)

BMI – Body Mass Index, WC – waist circumference, HC – hip circumference, WHR – waist-to-hip ratio, PD –

Prediabetes group, H – Healthy controls, IL-6 – Interleukin-6, MPO- Myeloperoxidase, MA- microalbumin, r value – Pearson’s correlation, # - Spearman’s correlation, *P<0.05 considered significant


Agarwal et al.; IJBCRR, 29(2): 1-8, 2020; Article no.IJBCRR.55491
r-0.227, p-0.158

Serum IL-6



45 100





r-0.388, p-0.013*

Serum MPO









100 r- -0.059,p-0.716


Urinary MA










Fig. 1. Scatter plot showing correlation between serum IL-6, MPO and urinary MA with FPG in prediabetes group
FPG-Fasting Plasma Glucose, IL-6 - Interleukin 6, MPO –Myeloperoxidase, MA-microalbumin, r- correlation coefficient, * P <0.05 was considered significant


Agarwal et al.; IJBCRR, 29(2): 1-8, 2020; Article no.IJBCRR.55491

Chronic inflammation could be one of the reasons of endothelial dysfunction and atherosclerotic plaque formation, processes which contribute to the development of vascular complications in patients with diabetes [8]. Elevated serum IL-6 concentration in the prediabetes group (Table 2) indicates the presence of chronic ongoing inflammatory process in this group [9], which has been confirmed by the results of a study conducted by Sommer et al., where it has been found that hyperglycemia induces IL-6 production [10]. This can be attributed to the formation of advanced glycation end products by persistent hyperglycemia, contributing in the development of chronic inflammation [11]. No correlation could be established between IFG and IL-6 concentration in the present study (Table 2). Hossain et al. [12] reported correlation of IL-6 with IGT but not with IFG. In present study only IFG subjects were enrolled which could be a reason for this result.

A corrrelation between IL-6 concentration with

weight and BMI in prediabetes subjects indicates

that increased weight strongly contributes to the

development of chronic inflammation (Table 3)

[13]. The results of an in vitro study has

demonstrated that after adding the extract of

adipocytes to human umbilical venous

endothelial cells, there is increased production

of IL-6 by these cells [14]. A strong corrrelation

between IL-6 concentration and abdominal

obesity (suggested by waist circumference (WC)

and waist-hip ratio (WHR) was observed in our

study. Previously documented findings [8,15]

indicate that IL-6 is produced by adipose tissue

macrophages, which may have an important role

in the development of obesity and insulin





phosphorylation, impairing insulin sensitivity and

increases serine phosphorylation leading to

insulin resistance in target tissues, increasing

lipolysis and decreasing glucose uptake in the

adipose tissue [16].

Recent advances in diabetic research suggest that hyperglycemia-mediated endothelial dysfunction and micro and macrovascular complications can be attributed to reactive oxygen species (ROS). Positive correlation was observed between MPO with FPG only in the prediabetes group (Fig. 1). Earlier studies have demonstrated a similar correlation in diabetes [17] and higher levels of MPO with poor glycemic control [18]. There are two proposed mechanisms that may be involved in endothelial dysfunction by MPO. Firstly, H2O2 mediated

consumption of NO by MPO [19] and secondly production of HOCl- and its chlorinating species by reaction with high-glucose–stimulated H2O2 [20] resulting in reduced NO bioavailability. Elevated serum MPO concentration in the prediabetes group (Table 2) indicate the presence of chronic inflammation and endothelial dysfunct ion in them [19]. MPO levels also correlates with anthropometric measures like BMI, WC and WHR in prediabetes group (Table 3) Increased BMI is related to inflammation and oxide tive stress [21]. Similarly abdominal obesity as suggested by increased WC and WHR is also associated with systemic oxidative stress [22]. Thus it can be deduced that prediabetes group had raised levels of MPO as a marker of oxidative stress and signs of central obesity seen in this group predisposes them to dyslipidemia and cardiovascular diseases.
Proteinuria is a sign of more advanced renal disease and is a precursor to renal failure. Importantly, albuminuria is a strong and independent predictor of cardiovascular and all cause mortality [23]. In the present study Urinary Albumin concentration was significantly increased in prediabetes group (Table 2) suggesting that hyperglycemia of prediabetes also leads to renal damage [24]. A recent study of the Korean general population also showed that subjects with urine albumin in microalbuminuria range had a higher fasting plasma glucose than subjects without microalbuminuria [25]. Bahar et al. reported a significant correlation between FPG and urine albumin excretion (r - 0.32, p < 0.001) in prediabetes patients, where the prevalence of MA was 18% in IFG group. In the current study though 30% of prediabetes group were found to have MA. No correlation was observed between FPG and MA in this group (Fig. 1). In the AusDiab Study, [26] the prevalence of albuminuria increased significantly with increasing glycaemia, particularly postprandial glycaemia [27]. Current study had enrolled people with IFG only, hence there was no correlation observed between FPG and MA.
Urinary Albumin concentration has been linked to obesity in earlier studies [28,29]. The prediabetes group had higher BMI, WC, WHR and increased MA level. But MA did not correlate with anthropometric measures of this group because of low prevalence in prediabetes subjects.
Therefore, our data suggest that prediabetes may play a role in the development and


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Assessment of Cardiovascular Disease Risk in Prediabetes