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 Table of Contents  
ORIGINAL ARTICLE
Year : 2020  |  Volume : 18  |  Issue : 4  |  Page : 380-384

Association between growth differentiation factor-15 and cardiovascular risk in patients with type 2 diabetes mellitus


Department and Institution Clinical Pathology, Faculty of Medicine, Al-Azhar University, Assiut, Egypt

Date of Submission02-Jul-2020
Date of Decision15-Jul-2020
Date of Acceptance13-Aug-2020
Date of Web Publication29-Dec-2020

Correspondence Address:
Fatma E.Z.M Abu-Bakr
Department and Institution Clinical Pathology, Faculty of Medicine, Al-Azhar University, 10 Street, Salama Hegazy, Assiut, 71511
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/AZMJ.AZMJ_101_20

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  Abstract 


Background and aim As most diabetic patients have high risk of cardiovascular hazards, the aim was to assess an indicator biomarker that can help us to estimate these hazards in patients with type 2 diabetes (T2D).
Patients and methods A total of 80 participants were examined for T2D and divided into two groups: T2D group and control group. To estimate the 10-year risk of atherosclerotic cardiovascular disease, we utilized the Framingham risk score (FRS), the New Pooled Cohort Equation score, Tch/high-density lipoprotein (HDL) ratio, HDL/low-density lipoprotein (LDL) ratio, and serum growth differentiation factor-15 (GDF15).
Results The T2D group had higher levels of systolic blood pressure and diastolic blood pressure and lower levels of weight and BMI than control. The T2D group had higher levels of random blood glucose (RBG), glycosylated hemoglobin, and urea. Both the total cholesterol/HDL and LDL/HDL were high significantly in the T2D, whereas FRS was high in the T2D group than control group. The level of serum GDF15 was higher in the T2D group (P=0.000). We found a positive correlation between level of serum GDF15 and BMI (r=0.336, P=0.017), systolic blood pressure (r=0.622, P=0.000), diastolic blood pressure (r=0.572, P=0.000), RBG (r=0.298, P=0.035), and FRS (r=0.415, P=0.003).
Conclusion GDF15 can be used as an indicator for measuring cardiovascular hazards in patients with T2D.

Keywords: Keywords, cardiovascular disease, growth differentiation factor-15, type 2 diabetes mellitus


How to cite this article:
Abu-Bakr FE, Abdel-Meguid MM, Qenawy EM. Association between growth differentiation factor-15 and cardiovascular risk in patients with type 2 diabetes mellitus. Al-Azhar Assiut Med J 2020;18:380-4

How to cite this URL:
Abu-Bakr FE, Abdel-Meguid MM, Qenawy EM. Association between growth differentiation factor-15 and cardiovascular risk in patients with type 2 diabetes mellitus. Al-Azhar Assiut Med J [serial online] 2020 [cited 2021 May 8];18:380-4. Available from: http://www.azmj.eg.net/text.asp?2020/18/4/380/305207




  Introduction Top


The propagation of diabetes has increased worldwide owing to elongated life expectancy, increased the incidence of corpulence, and has led to modification of lifestyles. Cardiovascular disease (CVD), the main complication of diabetes, is consequential to all of these hazard factors. The occurrence and spread of, and death from, CVD are higher in people with diabetes by 2-fold to 8-fold than in those without diabetes [1]. Therefore, cardiovascular risk evaluation of diabetic patients is needed for patient management.

The Framingham risk score (FRS) equation was sophisticated in 1979. The FRS equation in nondiabetic patients included some parameters, which are age, systolic blood pressure, hypertension treatment necessity, smoking situation, high-density lipoprotein cholesterol (HDL-C) level, and total cholesterol (TC) [2]. The New Pooled Cohort Equation, which foretells the risk of atherosclerotic CVD, was developed in 2013 by the American Heart Association and the American College of Cardiology[3]. These two cardiovascular risk scoring systems are used to estimate the 10-year risk of CVD. However, these two risks are of restricted utilization when used in patients with diabetes; consequently, diabetes-specific equations must be utilized. Therefore, there is a need for new biomarkers of cardiovascular risk in patients with diabetes.

Growth differentiation factor-15 (GDF15) is a member of the transforming growth factor-β superfamily and also known as macrophage-inhibiting cytokine 1 [4]. GDF15 under stressful situations is liberated from macrophages, cardiomyocytes, and adipocytes [5],[6]. GDF15 levels in the blood ranges from 150 to 1150 pg/ml, which increases when disease develops [7]. The serum GDF15 levels rise with age, BMI, evolvement of type 2 diabetes (T2D), evolvement of insulin resistance, evolvement of atrial fibrillation, heart failure, and acute coronary syndrome [8-13].


  Patients and methods Top


A total of 80 participants attending the outpatient clinic of the Internal Medicine Department of Al-Azhar Assiut University Hospital from September 2017 to April 2018 were recruited for the study. This study was approved by the ethical committee of Al-Azhar Faculty of Medicine, Assiut, Egypt. Informed consents were taken from all patients and healthy controls before performing the research. The patients were of age more than 18 years. Our target group was T2D not T1D. Exclusion criteria were malignancies and a fatty liver. Finally, 80 participants were evaluated using the diagnostic criteria of the American Diabetes Association [14]. The participants were divided into two groups: control (n=30) and T2D (n=50).

All participants were subjected to physical examinations. Weight (kg), height (cm), blood pressure, and pulse were measured. BMI was calculated as the body weight (kg) divided by the square of the height (m2). We categorized components of metabolic syndrome (MS) using International Diabetes Federation according to the presence or absence of the following criteria: central obesity [waist circumference ≥94 cm in male and ≥80 cm in female (already required)], fasting glucose greater than or equal to 100 mg/dl, TG greater than or equal to 150 mg/dl, HDL cholesterol less than 40 mg/dl in male and less than 50 mg/dl in female, and systolic blood pressure greater than 130 mmHg, or diastolic blood pressure greater than 85 mmHg [15]. All participants provided blood samples, where 7 ml was drawn from each participant. The samples were divided into the following: 2 ml was put in EDTA tube for glycosylated hemoglobin (HbA1c) assay, and the rest of the blood kept in a plain tube and left to clot, and then centrifuged at 3000 rpm for 10 min. The serum is separated, and routine laboratory investigations were done immediately on some of the serum, whereas the rest of serum is aliquoted into Eppendorf tubes and stored at −80°C till assay of GDF15: Boster Bio (Pleasanton, CA, USA). We measured random blood glucose, triglycerides, TC, low-density lipoprotein cholesterol (LDL-C), HDL-C, aspartate aminotransferase, alanine aminotransferase, blood urea, creatinine, and HbA1c. We also collected urine sample from each participant and performed an assay for proteinuria using ComboStik10: Dream Future Innovation (DFI), Korea.

Assessment of cardiovascular disease risk scores

We used the Framingham risk equation (2), the New Pooled Cohort Equation for atherosclerotic CVD risk prediction (3), Tch/HDL ratio, and LDL/HDL ratio [16].

GDF15 measurements

It was measured by ELISA technique using Human GDF15 Picokine ELISA kit which is Sandwich High Sensitivity ELISA kit. The kit was provided by ELISA (lot number EK 0767; USA).

Statistical analysis

Statistical Package for the Social Sciences version 19 ‘IBM SPSS Statistics for Windows, version XX (IBM Corp., Armonk, NY, USA) was used for data entry and analysis. Data were presented as number, percentage, mean, median, and SD. Independent samples t-test was used to differentiate quantitative variables between diabetic and control groups in case of parametric data, whereas median and range were used for nonparametric data. Spearman correlation was done to measure correlation between quantitative variables. Med calc was used for calculation of specificity, sensitivity, positive predictive values, negative predictive values, and receiver operating characteristics (ROC) curves. P value is statistically significant when was less than 0.05.


  Results Top


Clinical data and laboratory investigations of the participants are shown in [Table 1]. The T2D group had higher levels of systolic blood pressure and diastolic blood pressure and lower levels of weight and BMI than control. The T2D group had higher levels of random blood glucose (RBG), HBA1c, and urea. The T2D group had lower levels of HDL-C, TC, and TG than control group. Both the TC/HDL and LDL/HDL were high significantly in the T2D, whereas FRS was high in the T2D group than control group. The level of GDF15 was highly significant in the T2D group (291.83 vs 51.78 pg/ml).
Table 1 Clinical data and laboratory investigations of patients with T2D and the control

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Correlation analyses were done to detect associations between GDF15 level, clinical data, laboratory investigations and risk ratios including the FRS, TC/HDL, and LDL/HDL ([Table 2]). In the diabetic group, there was a significant positive correlation between GDF15 and BMI, systolic blood pressure, and diastolic blood pressure (r=0.336, P=0.017; r=0.622, P=0.000; and r=0.572, P=0.000, respectively). Moreover, there was a significant positive correlation between GDF15 and RBG (r=0.298, P=0.035) regarding laboratory investigations. Furthermore, there was a significant positive correlation regarding risk ratios in diabetics group between GDF15 and FRS (r=0.415, P=0.003) ([Figure 1]). On the contrary, there was no significant correlation between GDF15 and age, pulse, creatinine, TC, TG, HDL, LDL, HBA1c, alanine aminotransferase, aspartate aminotransferase, urea, TC/HDL, and LDL/HDL.
Table 2 Correlation of GDF15 with clinical data, laboratory investigations, and risk ratios in diabetic group

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Figure 1 Growth differentiation factor-15 and Framingham risk score.

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We used the ROC curve for differentiation between diabetic patients with and without ischemia ([Figure 2]). The GDF15 could differentiate between diabetic patients with and without ischemia at cutoff more than 298.7 pg/ml, with sensitivity of 100%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 100% ([Table 3]). Moreover, GDF15 could differentiate between diabetic patients with and without MS at cutoff of more than 255.18 pg/ml, with sensitivity of 82.35%, specificity of 54.55%, positive predictive value of 48.3%, and negative predictive value of 85.7% ([Table 4] and [Figure 3]).
Figure 2 Receiver operating characteristics curve for differentiation between diabetic patients with and without ischemia.

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Table 3 Receiver operating characteristics curve for differentiation between diabetic patients with and without ischemia

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Table 4 Receiver operating characteristics for growth differentiation factor-15 to differentiate between diabetic patients with and without metabolic syndrome

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Figure 3 Receiver operating characteristics for growth differentiation factor-15 to differentiate between diabetic patients with and without metabolic syndrome.

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  Discussion Top


We found that there is a significant increase in GDF15 in diabetic patients than in control group and consistent with the results of previous studies [10]. We found that RBG and HBA1c are significantly increased in diabetic patients than control group. Moreover, cardiovascular risk ratios such as TC/HDL and LDL/HDL were significantly increase in diabetic than in control group, but FRS is not significantly increased. Although diabetic patients were taking lipid-lowering drugs, there was no difference in lipid profile levels between diabetic and control groups, except HDL, which was significantly lower in diabetics than controls. These results are the same as the results of a previous study [17]. Correlation analyses between growth differentiation factor-15 and cardiovascular risk ratios were performed, and we estimate a positive correlation between GDF15 and FRS but not with TC/HDL and LDL/HDL. Serum GDF15 levels were positively correlated with RBG. These results are the same as the results of previous studies [17],[18]. However, there is no relation between growth differentiation factor-15 and LDL-C.

GDF15 level is significantly higher in complicated diabetic patients with ischemia, retinopathy, neuropathy, nephropathy, and MS than their counterparts without these complications. To the best of our knowledge, there is no research correlating GDF15 and complicated diabetics, so comparisons with other results were difficult.

In our study, GDF15 was useful in early diagnosis of type 2 diabetic nephropathy as its level is increased in this group. This results is the same as a previous study [19].

We found that GDF15 level at a cutoff more than 298.7 pg/ml. So, it can be used to differentiate between diabetic patients with and without ischemia with sensitivity level of 100% and specificity level of 100%. On the contrary, the ROC curve for GDF15 to differentiate between diabetic patients with and without MS has a cutoff of more than 255.18 pg/ml but at lower sensitivity and specificity.


  Conclusion Top


An association between the serum GDF15 level and cardiovascular risk ratios was observed in type 2 diabetic patients. So, GDF15 can be used as an indicator for measuring cardiovascular hazards in patients with T2D. Finally, GDF15 can be used as a marker for the cardiovascular and diabetic diseases, and it can be used for identification of the severity of the disease.

Recommendation

A wide range of prospective studies are needed to indicate that serum GDF15 level predicts cardiovascular complications in patients with type 2 diabetes mellitus. Long- term follow-up analysis is needed to know the exact mechanism of GDF15 expression and the signaling pathways, as there is very little information regarding pathophysiological role of GDF15 in diabetes, CAD, hypertension, and diabetes associated with CVD s. Moreover, more studies must be done to correlate between GDF15 and diabetic complications such as ischemia, neuropathy, nephropathy, and MS.


  Financial support and sponsorship Top


Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

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    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

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