Open access peer-reviewed chapter

Novel Biomarkers to Understand Cardiovascular Complications in Diabetes

Written By

Ramu Adela and Sanjay K. Banerjee

Submitted: October 26th, 2015 Reviewed: February 18th, 2016 Published: August 17th, 2016

DOI: 10.5772/62595

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Diabetic subjects have shown two- to fourfold increased risk of cardiovascular diseases (CVDs) than without diabetes. Diabetes can be prevented if detected early at prediabetes stage. Progression of diabetes not only causes hyperglycaemia; it also increased the risk of macrovascular and microvascular complications. Different mechanisms, i.e. inflammation, abnormal adipocyte signalling, insulin resistance, endothelial dysfunction, and oxidative stress, are involved in the progression of diabetes and associated cardiovascular complication. These mechanisms alter different signalling molecules in blood and other body fluids. These altered molecules offer potential biomarkers for the identification and early detection of the disease progression. If we are able to detect the early biomarkers based on the alteration of different mediators responsible for cardiac complications in diabetes, we can prevent the cardiac diseases in diabetes by selective therapy. Different kinds of biomarkers, i.e. miRNA, protein, metabolites, cytokines, and adipokines, can be used together to detect the different stages of the disease. In the present book chapter, we are explaining briefly about characteristics of biomarkers and their applications and different approaches that were used to identify biomarkers. Different existing and novel biomarkers and their scope to detect patients with prediabetes, diabetes and cardiovascular complication in diabetes have been discussed.


  • type 2 diabetes
  • biomarkers
  • cardiovascular diseases
  • metabolic syndrome
  • coronary artery diseases

1. Introduction

A biomarker is defined as “any substance, structure, or process that can be measured in the body or its products and influence or predict the incidence of outcome or disease” [1]. Research interest on biomarkers has increased in recent years. In MedLine search in 1990, there were only 21 hits on cardiovascular risk markers, while by 2010, it is increased to 2032 hits, thus indicating huge increase in number of publications in biomarkers in the last decade [2]. There were 37% more biomarker studies in 2014 as compared to 2013 [3]. However, only few biomarkers are routinely used in clinical practice. For example, fasting blood sugar, glycated haemoglobin, cardiac troponin T (cTnT), cardiac troponin I (cTnI), and B-type natriuretic peptide (BNP) are used regularly for diabetes, myocardial infarction, and heart failure. Biomarkers should have specific characteristics, i.e. specific to the particular diseases and easily detectable. Biomarker can be predictive to identify disease progression after treatment. The detection method should be fast, simple, and low cost. It should be stable at any time of the day and samples should be available easily by invasive method (blood and urine). Identified biomarker should be proven of its importance preclinically and clinically. Biomarker can be used for different purposes such as the early detection of disease, evaluation of acute and chronic clinical condition, risk stratification of patients to suspect or confirm the diagnosis, selection of appropriate therapeutic treatment, and observation of patient response for the treatment (Figure 1) [4]. Identification of early biomarkers for noncommunicable chronic diseases such as diabetes is very important for finding appropriate therapeutic strategy.

        Figure 1.

Biomarker characteristics (inner circle) and applications (outer circle).

Prevalence of diabetes is reaching epidemic proportions in developed and developing nations due to increase in life expectancy, sedentary lifestyle, and obesity. As per the International Diabetic Federation (IDF) Diabetes Atlas (Sixth Edition 2013), the number of people with diabetes is 382 million and it is going to rise to 592 million by 2035. Global burden of diabetes is huge and 548 billion dollars was spent in 2013. In India, approximately 65.1 million people are with diabetes. Cardiovascular diseases (CVDs) are the major complications of diabetes. The prevalence, incidence, and mortality of cardiovascular diseases are two- to fourfold higher in persons having diabetes than those without diabetes [5]. Prediction of cardiovascular disease (CVD) risk among people with diabetes is important not only to give better clinical therapy but also to distinguish higher risk patients for extra care. Biomarkers may help in early detection of diseases, distinguishing patients based on disease severity, and find the cardiovascular risk among diabetic patients.

1.1. Diabetes and its cardiovascular complications

Diabetes is characterised by high glucose level in blood due to either less insulin secretion from pancreas or developing insulin resistance in skeletal muscle. Type 2 diabetes (T2DM) is the commonest form and it is characterised by insulin resistance mostly in skeletal muscle and deficiency of insulin release at end stage. In general, T2DM causes elevation of blood glucose level and other components of metabolic syndrome. Parameters of metabolic syndrome are elevated blood pressure, increased triglycerides, reduced high density lipoprotein levels, and abdominal obesity [5]. In obese condition, increased adipocytes secrete adipocytokines. Released adipocytokines integrate the endocrine, autocrine, and paracrine signals to mediate the insulin sensitivity, oxidative stress, energy metabolism, blood coagulation, and inflammatory responses. Elevated levels of free fatty acids (FFAs) induce insulin resistance and increase fibrinogen and plasminogen activator inhibitor-1 (PAI-1). In the long run, high FFA and glucose together impair beta-cell function through lipotoxicity and glucotoxicity and develop macro- and microvascular complications [6,7].

        Figure 2.

Development of biomarkers with the help of different omics approaches.

Diabetes and cardiovascular diseases are involved in different abnormalities in genes, proteins, metabolites, and lipids by different mechanisms such as oxidative stress, inflammation, and endothelial dysfunction. Identification of highly sensitive and specific potential biomarkers would be beneficial for the detection of cardiovascular diseases risk among diabetic patients with different advanced omics approaches such as genomics (genes), metabolomics (metabolites), proteomics (proteins), transcriptomics (mRNA), and lipidomics (lipids) (Figure 2). These new techniques are useful for simultaneous investigation of multiple molecules and to identify different kinds of biomarkers (Table 1).

Different omics approaches Study of different molecules Technology used
Genomics DNA DNA microarray
Single nucleotide polymorphism
Hot spot mutation
Transcriptomics mRNA
Noncoding RNA
RNA microarray
New-generation sequencing (NGS)
Exome sequencing
Proteomics Proteins and their
abundance, variation, modific
ations, and interactions
Protein microarray
Mass spectrometry
Metabolites NMR
Mass spectrometry–LC–MS
Lipids Mass spectrometry–LC–MS/MS

Table 1.

Omics approaches target for different molecules.


2. Different omics approaches used for the identification of biomarkers

(i) Genomics is a systematic study of structure, function, and expression of organism’s genome. This involves DNA sequencing, assembly, as well as analysis of an annotation of structure and function of the gene. The single nucleotide polymorphism array (SNP array) is a type of DNA microarray that can be used to detect polymorphisms within the whole genome. Next-generation sequencing (NGS) has gained considerable attention for investigations at the nucleotide levels including both DNA and RNA sequences. (ii) Transcriptomics is a study of total set of RNA including mRNA, tRNA, rRNA, and microRNA. Single gene was analysed by single-gene detection method individually, and thousands of gene expression are analysed simultaneously by high-throughput analysis such as DNA microarrays. (iii) Proteomics is a study of all expressed proteins and it gives information about protein abundance, variation, modification, and interaction through signalling pathway and network analysis. Initially two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) technique was used to determine whole protein expression. In this method we can separate a large number of protein mixture based on molecular weight and isoelectric point. This technique has initially been used to find global changes of protein expression. However, recently mass spectrophotometer has been used to identify protein alterations. Mass spectrometry has been utilised to separate ions from proteins, peptides, or metabolites according to their mass-to-charge ratio (m/z) and to provide data as mass spectrum that can be further analysed to determine characteristics of molecular mass and structure. However, proteomics studies are more complicated due to presence of high-abundant proteins such as albumin and immunoglobulins (serum) that may mask the important biomarker candidates. To overcome this problem, different depletion columns are available for removing high-abundant proteins and immunoglobulins. With mass spectrometry the two types of approaches were targeted (preselected panel of proteins) and untargeted (without any assumptions total proteins are captured). Protein microarray has also been developed to detect thousands of proteins based on specific antibody detection. (iv) Metabolomics is a systemic approach to evaluate the metabolic profile, which can be useful in biomarker discovery. Metabolites are usually considered as good biomarkers due their stability. Metabolome analysis can be performed using a variety of techniques such as nuclear magnetic resonance (NMR) as well as mass spectrometry. Metabolites were also studied in two ways as same as proteomics, i.e. targeted and untargeted [8]. (v) Lipidomics is systemic approach to study large-scale changes in lipids and understand the regulation of lipid metabolism. This will be analysed with the help of LC–MS/MS. These omics approaches offer simultaneous estimation of different molecules through high-throughput screening. After identification of set of proteins/genes/metabolites/lipids by omics approach, there is a need for validation of each marker by other methods. Table 2 shows a list of biomarkers identified by different omics approaches.

Diseases Study name Approach Sample type No. of
Biomarkers identified Reference
Prediabetes KORA Metabo
Serum 4297 Three metabolites
(glycine, lysoph
osphatid', and
that altered significant
levels in impaired
glucose tolerance (IGT)
as compared
with normal glucose
Type 2
Saliva 40 (10
10 I
FG, 10
IGT, 10
487 biomarkers identified [99]
T2DM Frami
ngham Off
Plasma Rando
mly se
1561 ind
Out of 70 metabolites
2-AAA (2-amino
adipic acid) had the
strongest association
with risk of
future diabetes
Plasma 685 subj
ects with
Cholesterol esters (CEs)
, lysophosphatidylcholines,
(PEs), sphingomyelins,
and triacylglycerols (TAGs)
were associated
with cardiovascular disease
and insulin
The Western
Plasma 1126
Sphingomyelins, particularly
those with two double
bonds, and lysophosphatidylcholines
were identified between
subjects with normal
weight and obesity
independent of LDL-C
and HDL-C concentrations
(n = 7256;
800 events),
(n = 
2622; 573
(n = 3563;
368 events)
Higher phenylalanine
and monounsaturated
fatty acid levels
were associated
with increased
cardiovascular risk, while
higher omega-6 fatty
acids and docos
ahexaenoic acid levels
were associated
with lower risk
scular dis
Plasma 2023
Five metabolite
factors were
independently associated
with mortality:
factor 1
(medium-chain acylcarnitines,
short-chain dic
long-chain dicarboxylacyl
carnitines), factor 6 (branched-cha
in amino acids)
Type 2
Lipidomics Plasma 104 wo
with previ
ous ges
diabetes; 21
period of
8.5 years
Cholesteryl ester
species CE 20:4
, alkenylphosphatidylethan
olamine species PE
(P-36:2), and the phosphatidyls
erine species PS 38:4
were independently and
positively associated
with the development of
type 2 diabetes

Table 2.

Biomarkers identified by different omics approaches in diabetes and cardiovascular diseases.

Although there are a high number of research articles describing existing and promising biomarkers for diabetes and cardiovascular disease, here we are providing an overview of a few standard and exciting biomarkers that regularly used in clinic.


3. Existing and standard biomarkers in diabetes and cardiovascular diseases

Glycated haemoglobin (HbA1c) and glucose levels are mostly used for the diagnosis of diabetes. These two tests give idea about sugar levels in the body in the presence and absence of medication. However, these tests can be used to predict the disease in the later stages not in the early stages. So, there is an urgent need to identify novel biomarkers to detect the early stage of diabetes, i.e. prediabetic stage.

Troponin T (cTnT), troponin I (cTnI), and creatinine kinase-MB (CK-MB) are the common markers for the diagnosis of myocardial injury and stratification of the risk in acute coronary syndrome. cTnI is as effective as cTnT in diagnosing myocardial necrosis in the setting of trauma and coronary bypass grafting [9]. Increased hs-cTnT concentrations are associated with extent and complexity of CAD as well as diabetic patients with stable CAD [10]. Recently, Brendan et al. reported that cardiac troponin T concentration was an independent predictor of death from cardiovascular causes, myocardial infarction, or stroke in patients who had both type 2 diabetes and stable ischemic heart disease [11]. European Society of Cardiology (ESC) and the American College of Cardiology (ACC) guidelines have recommended cardiac troponins are markers for the acute myocardial infarction. These sensitive markers (cTnT and cTnI) begin to rise in the first 4–8 h following injury and peak at 12–24 h. However, cTnT may remain raised for more than two weeks while cTnI for more than 5–7 days. These two detect myocardial injury below the detection limit of CK-MB. Some of the clinicians measured CK-MB to rule out myocardial infarction and to monitor for additional cardiac muscle injury over time [9].

Natriuretic peptides [brain natriuretic peptide (BNP), atrial natriuretic peptide (ANP)] and their N-terminal pro-hormones [N-terminal pro-atrial natriuretic peptide (NT-pro-ANP) and N-terminal pro-brain natriuretic peptide (NT-pro-BNP)] were increased in patients with heart failure, i.e. left ventricular dysfunction. In general, these markers are produced initially within the heart and released into the circulation in response to increased wall tension. BNP and ANP are secreted not only from the atria but also from the ventricles, especially in patients with heart failure. BNP and NT-pro-BNP may be superior to ANP and NT-pro-ANP in the detection of left ventricular dysfunction [12].

C-reactive protein (CRP) is a liver-derived pattern recognition molecule and systemic inflammatory marker that is increased in inflammatory states. It releases rapidly after immediate tissue injury and work as host defence [13]. Interleukin-6 (IL-6) is the most potent inducer of CRP production, and hsCRP (high-sensitivity C-reactive protein) is released from activated leukocytes in response to infection or trauma and from vascular smooth muscle cells in response to atherosclerosis [14]. Inflammation plays a major role in type 2 diabetes and cardiovascular diseases. Inflammation is the one of the risk factors for the development of T2DM and CVD. Researchers have identified that increased hsCRP levels were associated with obesity, metabolic syndrome, type 1 diabetes, type 2 diabetes, atherosclerosis, and coronary artery diseases (CADs) [15]. Increased blood hsCRP levels indicate the coexistence of subclinical systemic inflammation and insulin resistance and correlated with elevated insulin, C-peptide, and HOMA-IR (Homeostatic Model Assessment-insulin resistance) [16]. Treatment with aspirin, statins, cyclooxygenase-2 inhibitors, and fibrates are able to reduce hsCRP levels [13]. Treatment with peroxisome proliferator-activated receptor gamma (PPAR-γ) agonist pioglitazone also decreased hsCRP along with other cardiovascular risk markers [17].

Pro-inflammatory cytokines, i.e. tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), IL-1-beta, and IL-8 and monocyte chemoattractant protein (MCP-1); cell adhesion molecules, i.e. intra-cell adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1); and markers of cardiovascular risk, i.e. C-reactive protein (CRP), homocysteine, and plasminogen activator inhibitor-1 (PAI-1) and reactive oxygen species (ROS) are the common markers for inflammation and oxidative stress. Metabolic hormones such as resistin, leptin, adiponectin, ghrelin, and visfatin were also altered in diabetes and metabolic disorder and considered as important biomarkers for understanding the diabetic complication.

All these above markers are used to predict diabetes and cardiovascular diseases independently but not in a combination to understand the disease complexity. Even these biomarkers are not able to predict the disease in the early stages. Thus, there is an urgent need to identify novel biomarkers to predict the early detection of the disease and disease progression.


4. Exploring novel biomarkers for diagnostic and prognostic biomarkers for diabetes and its cardiovascular complications

4.1. Galectin-3

Galectin-3 (Gal-3) a 30-kDa β-galactoside-binding lectin mainly present in the cytoplasm, and also in the nucleus, is expressed by different types of cells and regulates various T-cell functions and innate immune responses. Expression of galectin-3 increased in activated macrophages and involved in inflammation, tumour growth, and fibrosis [18,19]. Gal-3 protects β-cells from the cytotoxic effect of IL-1β [20] and advanced glycation end product (AGE)-induced tissue injury. Removal of Gal-3 accelerates AGE-induced kidney injury in diabetes [21], enhances atherogenesis [22], accelerates high-fat diet-induced obesity, and increases inflammation in adipose tissue and pancreatic islets. Gal-3 shows protective effect in obesity-induced inflammation and diabetes [18]. Ohkura et al. reported that low levels of Gal-3 were associated with insulin resistance in T2DM patients [23]. On the contrary, elevated levels of Gal-3 are associated with increased risk of heart failure and mortality [24]. Recently, Ozturk et al. identified that Gal-3 concentrations were significantly higher in the coronary artery disease (CAD) group than in the non-CAD group. Gal-3 levels were correlated positively with BMI (Body mass index), high-sensitivity C-reactive protein, the total number of diseased vessels, the number of plaques, and the calcified plaque type. In addition, galectin-3 levels were found to be independent predictor of coronary atherosclerosis in type 2 diabetic patients. Gal-3 is a novel and promising biomarker that may help to identify type 2 diabetic patients who may require early CAD intervention because of the potential risk of coronary atherosclerosis [25]. Jin et al. reported that elevated galectin levels were associated with increase in vascular complications, i.e. the heart failure, nephropathy, and peripheral artery diseases. Several research articles have shown association of Gal-3 with diabetes and cardiovascular diseases. However, many questions still need to be answered before considering Gal-3 as a biomarker in diabetes and cardiovascular diseases: Can Gal-3 specifically be used as a biomarker for diabetes-associated cardiovascular diseases (Gal-3 is usually expressed in the inflammatory and fibrinolytic conditions in the liver, kidney, and lungs; this shows lack of tissue specificity)? Can Gal-3 alone predict the disease prevention strategy? Can it be used with any other established marker to predict diabetes-associated cardiovascular complications?

4.2. Irisin

Irisin is a newly discovered hormone which is mainly secreted by the heart, skeletal muscle, liver, and kidneys. Bostrom et al. reported that cardiac muscle produces more irisin than skeletal muscle. Irisin is mainly produced within heart and skeletal muscle [26]. Irisin is essential to convert white adipose tissue to brown adipose tissue. He et al. found that irisin levels were decreased and urotensin II (UII) levels were increased in type 2 diabetic subjects. Circulating urotensin II levels were increased in diabetes and could inhibit the glucose transport in skeletal muscle in diabetic mouse and aggravated the insulin resistant [27]. The study found the association between both irisin and urotensin II and concluded that urotensin II and high glucose may inhibit the release of irisin from skeletal muscle in diabetic patients [28]. Increased circulating irisin predicts the insulin resistance onset in association with weight regain and authors concluded that irisin could be secreted as an adaptive response to counteract the deleterious effect of excess adiposity on glucose homeostasis [29]. Two-week treatment with simvastatin increased circulating irisin concentrations in healthy individuals and also in primary human skeletal muscle cells. Simvastatin induces both cellular stress markers as well as protective response markers. Simvastatin-induced irisin secretion can block mitochondrial oxidative stress and thus play an important role in the regulation of oxidative stress in human skeletal muscle [30]. Irisin is also secreted in response to the activation of PGC-1α. Previous studies have explained well regarding the regulation of PGC-1α in mitochondrial biogenesis, oxidative metabolism, mitochondrial function, and modulation of insulin resistance. Decreased PGC-1α levels in type 2 diabetic subjects as reported earlier [3133] might be responsible for reduced irisin levels. There is a controversy regarding irisin levels in obesity, insulin resistance, and metabolic syndrome in type 2 diabetic patients. While some reported higher irisin levels in diabetic patients, others reported the opposite [3436]. These discrepancies were due to the data analysed from different stages of diseases and the presence of other complications [26]. Researchers have reported that serum irisin levels were decreased in type 2 diabetes and myocardial infarction patients [37,38]. Aronis et al. reported that increased irisin levels predict the development of major cardiovascular events, especially unstable angina, in patients with CAD after percutaneous intervention (PCI) [39]. Hanatani et al. also reported that irisin is a novel biomarker providing prognostic information in patients with heart failure with reduced ejection fraction. However, further clinical studies are needed to find whether irisin is associated with cardiometabolic disease and evaluate whether circulatory irisin levels could serve as an independent prognostic marker in diabetic patients with cardiovascular complications and also elucidate beneficial effects by finding molecular mechanism and intervention studies in different animal models.

4.3. Apelin

Apelin is identified as a 36-amino acid peptide and is an endogenous ligand of G-protein-coupled receptors (GPCRs) of apelin receptor. Recently, apelin was recognised as adipokine and secreted from white adipose tissue. Apelin receptor is present in ventricular cardiomyocytes, vascular smooth muscle cells (VSMCs), and intra-myocardial endothelial cells (ECs) [40]. Apelin stimulates endothelium-dependent nitric oxide-mediated vasorelaxation and reduces arterial blood pressure. Apelin synthesis in adipocytes is stimulated by insulin, and apelin plasma levels are markedly increased in obesity associated with insulin resistance [41]. Apelin shows antioxidant effects and attenuates reactive oxygen species (ROS)-induced adipogenesis, lipogenesis, lipolysis, and release of free fatty acids [42]. Apelin knockout mice show diminished insulin sensitivity [43]. Tumour necrosis factor-α (TNF-α) induces the expression of apelin via phosphatidylinositol 3-kinase (PI3K), c-Jun N-terminal kinase (JNK) and MEK1/2 signalling pathways in adipocytes [44]. Chronic treatment with apelin ameliorates both glucose and lipid metabolism and also increases muscle mitochondrial performance through increased mitochondrial biogenesis and a tighter matching between fatty acid oxidation and the tricarboxylic acid cycle. Therefore, chronic apelin treatment can be a targeted therapy for type 2 diabetes and its complications [45]. Furthermore, in studies using lipopolysaccharide (LPS) and cytokines to elicit an immune response in rodents, the expression of apelin mRNA has been reported to be upregulated, involving the JAK/STAT pathway [40]. Ma et al. reported that plasma apelin is a novel biomarker for predicting type 2 diabetes in men [46]. It was reported that suppressed apelin levels were associated with increased cardiovascular risk in women with previous history of gestational diabetes [47]. Recently, Abd-Elbaky et al. reported that omentin levels were significantly lower and serum apelin and IL-1β concentrations were significantly higher in obese diabetic groups compared to nonobese controls. This study concluded that abnormal production of omentin and apelin can contribute to the pathogenesis of obesity-related complications including T2DM and cardiovascular disease [48]. However, further research is needed to confirm whether apelin can be used as biomarker in diabetes and cardiovascular diseases.

4.4. Growth differentiation factor-15

Growth differentiation factor-15 (GDF-15) is a stress-responsive cytokine produced as a ≈40 kDa propeptide form and N-terminal cleaved to release as a ≈30 kDa disulphide-linked dimeric active protein form. It is highly expressed in cardiomyocytes, adipocytes, macrophages, endothelial cells, and vascular smooth muscle cells in normal and pathological condition. GDF-15 increases during tissue injury and inflammatory states and is associated with cardiometabolic risk. Increased GDF-15 levels are associated with cardiovascular diseases such as hypertrophy, heart failure, atherosclerosis, endothelial dysfunction, obesity, insulin resistance, diabetes, and chronic kidney diseases in diabetes. Researchers have reported that GDF-15 shows cardioprotective effect through activation of ALK (Activin receptor-like kinase) type 1 receptor (ALK 1–7) and GDF-15 phosphorylates Smad2/3 and Smad1/5/8 which translocate to the nucleus in the form of heteromeric complex with Smad4 and activates PI3K/AKT/eNOS/NO pathway. It also inhibits epidermal growth factor receptor (EGFR) transactivation and NF-kB/JNK/caspase-3 pathway. Many patents have been filed reporting GDF-15 as a marker for the diabetes and cardiovascular diseases. Patent no. EP2439535A1 claimed that GDF-15 can be distinguished between diabetes and diabetes with coronary artery diseases subjects. Recently, we have also shown that GDF-15 levels can be useful to distinguish diabetic patients from cardiovascular complications [5]. However, a large multinational study has to be conducted to validate GDF-15 as a biomarker to detect specific cardiovascular complication in diabetes.

4.5. Growth differentiation factor-11

Growth differentiation factor-11 (GDF-11) is a cytokine that belongs to TGF-β super family and also known as bone morphogenetic protein-11 (BMP-11). GDF-11 works like myostatin to modulate metabolic function [49]. Previous scientific literature claimed that GDF-11 is an anti-ageing factor. Fadini et al. showed that circulating GDF-11 levels were decreased with age [50]. Peripheral supplementation of GDF-11 protein in mice attenuated the age-related dysfunction of skeletal muscle [51]. In recent years, researchers are focusing their interest on circulatory GDF-11 levels in heart diseases. GDF-11 levels reversed the age-related hypertrophied heart into a young heart [52]. GDF-11 is also an essential factor for the regeneration of pancreatic islets in diabetic patients [53]. The plasma GDF-11 levels with age and disease condition remain controversial. Egerman et al. in his study showed that an increased GDF-11 protein level was observed with age in rat skeletal muscle. However, serum GDF-11 levels in rat and human were not significantly increased [54]. In contrast, Poggioli et al. explained age-dependent decline in GDF-11 levels in multiple mammalian species such as mice, rats, horses, and sheep. They also showed that exogenous GDF-11 administration rapidly activates SMAD signalling to reduce cardiomyocyte size [55]. This property of reducing cardiomyocytes can be useful against cardiac hypertrophy. Two more recent studies supported the above statement. Heidecker et al. showed that low levels of GDF-11 and high levels of its inhibitor follistatin-like 3 are associated with adverse cardiovascular outcomes in humans [56]. Similarly Olson et al. reported that high levels of GDF-11 are associated with lower prevalence of left ventricular hypertrophy [57]. Recently, Adela et al. reported that plasma GDF-11 levels were decreased in diabetes and diabetes with cardiovascular complications as compared with control subjects [58]. To use GDF-11 as a biomarker for diabetes and diabetes associated with cardiovascular diseases, more research needs to be carried out with a different population. GDF-11 could be used as a biomarker or as an intervention therapy to reduce the disease progression.

4.6. Cyclophilin A

Cyclophilin A (CyPA) was discovered three decades ago as the intracellular receptor of the immunosuppressive drug cyclosporine. CyPA is secreted from vascular cell components of endothelial cells and vascular smooth muscle cells in response to the reactive oxygen species (ROS) and also expressed in T cells, neutrophils, monocytes, macrophages, and foam cells and shows cellular effects such as proliferation, migration, activation of NF-kB, induction of matrix metalloproteinases, adhesion of molecules, and induction of ROS [59]. Extracellular CyPA initiates expression of adhesion molecules in endothelial cells (EC), induces apoptosis, and works as a chemoattractant for inflammatory cells. Intracellular and extracellular CyPA promotes intimal thickening, abdominal aortic aneurysms, atherosclerosis, and cardiac hypertrophy in mice [60]. Recently Tsai et al. reported that hyperglycaemia causes release of CyPA in mesangial (MES-13) and tubular (HK-2) cells. Urinary CyPA correlated with the progression of renal function. Significant increase in urinary CyPA was noted in stage 2 diabetic nephropathy and persisted in later stages of the disease. This study concluded that CyPA is a new biomarker for diabetic nephropathy and can be used as an early maker [61]. Type 2 diabetes subjects have increased circulating levels of CyPA than the healthy subjects. CyPA is secreted by monocytes in response to high glucose treatment and responsible for the progression of atherosclerosis in type 2 diabetes [62]. Further authors found that plasma CyPA levels were increased in diabetes subjects with coronary artery disease. This study concluded that CyPA play important role to progress vascular disease in type 2 diabetes subjects. The scientific literature thus provides strong evidence that CyPA work as inflammatory mediator in the progression of atherogenesis [63]. Therefore, all data indicate that CyPA is a promising and potential biomarker for the detection of vascular diseases in type 2 diabetes [64].

4.7. Prolactin

Prolactin is a polypeptide released as a pituitary hormone. Prolactin is named so for its ability to promote lactation in post pregnancy in female mammals. Other than lactogenic property, prolactin plays important role in the regulation of reproduction, growth and development, metabolism, immune regulation, brain function, and behaviour [65]. Prevalence of obesity was increased in hyperprolactinaemic patients [66]. Circulating levels of prolactin increase in diabetic patients. Increased prolactin levels were associated with lower prevalence of diabetes and impaired glucose regulation [65,67]. However, Balbach et al. reported that low circulatory prolactin concentration is associated with increased T2DM risk. However, this study did not show any evidence to prove prolactin as a cardiometabolic risk factor [68]. Prolactin levels were increased in essential hypertension, acute coronary syndromes, ischemic strokes, transient ischemic attacks, pre-eclampsia, and heart failure. Carrero et al. also reported that increased prolactin levels were associated with endothelial dysfunction, increased risk of cardiovascular events, and increased mortality in chronic kidney disease (CKD) patients [69]. In vitro studies show that prolactin stimulates integrin-mediated adhesion of circulating mononuclear cells to endothelium and induces vascular smooth muscle cell proliferation. Reuwer et al. study did not predict the prolactin as predictor for the coronary artery diseases in spite of presence of prolactin receptors in human coronary artery plaques [70]. On the other hand, increased plasma prolactin can protect rat cardiomyocytes against hypoxia through the p-JAK2 and p-STAT5 pathways and the PI3Kα/AKT and MAPK survival pathways [71]. Landberg et al. reported that prolactin concentrations were not associated with cardiovascular mortality and thus not a marker of heart failure [72]. However, a cathepsin D-cleaved 16 kDa form of prolactin mediates postpartum cardiomyopathy and authors claimed that inhibition of prolactin may be a new therapeutic strategy for the paripartum cardiomyopathy [73].

4.8. Vitamin D

Vitamin D is a secosteroid that exists in two forms, i.e. ergocalciferol (D2) and cholecalciferol (D3). Ergocalciferol (D2) is synthesised from the vegetable sources. Unlike D2, cholecalciferol (D3) is synthesised by the epidermis on exposure to the UV radiation (sunlight) and also from oily fish supplementation. Vitamin D (D2 and D3) is converted into active metabolite 1, 25(OH)2 D by the two hydroxylation steps. These active metabolites bind with the vitamin D receptor and exert its biological action [74]. Vitamin D receptors are present in many cells such as pancreatic β cells, cardiomyocytes, endothelial cells, and vascular smooth muscle cells. Vitamin D plays a pivotal role in the bone and mineral metabolism. Vitamin D deficiency is a common health problem worldwide and is the cause for osteoporosis and osteomalacia, rickets, and other bone-related disorders. In the recent decades, researchers have also identified that lower vitamin D levels were associated with metabolic diseases such as type 1 diabetes, obesity, insulin resistance, hypertension, cardiovascular diseases, and cancer [75,76]. Many epidemiological studies have reported that people from different countries are more prevalence to vitamin D deficiency [7782]. Eight-week vitamin D replacement therapy in type 2 diabetic patients potentially has beneficial effects on cardiovascular disease risk factors such as HbA1c, total cholesterol, LDL-C, and diastolic blood pressure [83]. Tarcin et al. reported that 25(OH)D-deficient subjects has lower flow-mediated dilatation (FMD) which is useful to measure endothelial dysfunction and was improved after acute treatment with calcitriol [84]. Vitamin D is a negative regulator of renin–angiotensin system and blood pressure [85]. Recently Jisu et al. reported that deletion of macrophage vitamin D receptor promotes insulin resistance and monocyte cholesterol transport to accelerate atherosclerosis in mice. This study suggested that vitamin D plays an important role in inflammation and thus responsible for the development of type 2 diabetes and atherosclerosis [86]. Vitamin D can be used as a biomarker to predict the disease severity of diabetes and cardiovascular complications. However, for better understanding the role of vitamin D in pathophysiology of diabetes and cardiovascular diseases, more intervention studies with long-term follow-up are required.

4.9. Pregnancy-associated plasma protein-A (PAPP-A)

PAPP-A is a zinc-binding matrix metalloproteinase that regulates extracellular matrix remodelling. PAPP-A degrades IGFBP-4 and increases the levels of local IGF-1 in response to injury and involved in the pathogenesis of atherosclerosis. Two inflammatory cytokines, i.e. TNF-α and IL-1, are involved in insulin resistance development and most potent stimulators of PAPP-A [95]. Many researchers reported that elevated levels of PAPP-A were associated 36 with coronary artery diseases, e.g. acute coronary syndrome [[88]-[93]]. On this contrary, Pellitero et al. reported in their study that serum PAPP-A concentrations were significantly lower in diabetic subjects and correlated negatively with HbA1C. PAPP-A concentration was lower in patients with HbA1C > 8.2% (0.35 mUI/l [0.07–0.43]) compared with that in patients with HbA1C < 5.9% (0.72 mUI/l [0.2–0.92], P < 0.03). However, PAPP-A levels were not changed in hypercholesteraemic subjects when compared with normal cholesterolaemia subjects. It is also reported that genetic deletion of PAPP-A is associated with resistance to atherosclerotic lesion development in apolipoprotein E-deficient mice fed with a high-fat diet by decreasing bioavailability of IGF-1. This study indicates that PAPP-A is essential to promote lesion formation through regulation of IGF-1 action [94]. Serum PAPP-A and IGF-1 do not appear to be useful serum biomarkers for carotid atherosclerosis in type 2 diabetic patients with stable glycemic control, despite scientific evidence of their local role in atherosclerosis. [87]. However, Hjortebjerg et al. have reported that PAPP-A is a prognostic marker for acute coronary syndrome [96]. Recently Conover et al. reported that targeted inhibition of PAPP-A reduces atherosclerotic plaque burden in mice. This study is giving evidence that inhibition of PAPP-A can be used as therapeutic strategy in atherosclerosis [97]. However, further studies need to be conducted to find its role in diabetes and associated cardiovascular complication.


5. Conclusion

Cardiovascular complication is the major cause of the death of diabetes worldwide. At present, all standard available markers are useful to detect diabetes and cardiovascular disease separately but not suitable for identifying the cardiovascular complication at early and late stages of the diseases progression. There is an urgent need to identify novel biomarkers by using different omics approaches using large number of patients having desired phenotype. Identified markers can also able to assist in clinical decision making such as interventions and medications. Recently all new markers such as vitamin D, GDF-15, galectin-3, and cyclophilin-A identified have a strong association with type 2 diabetes and cardiovascular disease. However, these yet have not been implemented in the clinical practice. Before accepting any new markers as clinical biomarkers, the following questions need to be answered: whether new identified biomarkers can be used to take clinical decision for any particular diseases, whether it can be useful in therapeutic management and provide any diagnostic and prognostic information, and whether identified biomarkers can be used as a single marker or in a combination with other biomarkers. Identifying new biomarker may also help to understand the affected signalling pathways related to the disease and discover novel therapy against diabetes and cardiovascular complications.

Future biomarker discovery is showing excitement and raising many challenges. One of the major challenges in biomarker discovery is to develop biomarkers for personalised medicine. Biomarkers can play a critical role in classifying patients into subpopulations. In the present days, predicting the therapeutic strategy through personalised medicine is more familiar. However, more research needs to be done to develop specific biomarker to make personalised medicine successful. Personalised medicine is developing tremendously in cancer treatment. However, researchers should focus more on diabetes and cardiovascular disease to initiate personalised medicine in metabolic diseases. Other challenges in biomarker discovery include active collaboration between basic scientists and clinicians. Formation of different societies and organisations need to be established like HUPO (Human Proteome Organization) organisation for the proteomics and to prepare biomarkers databases for free access. Scientific communities need to debate with the issue whether individual diagnostic and prognostic biomarker or combined panel of biomarkers are more useful to predict the cardiovascular outcome among diabetic patients.


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Written By

Ramu Adela and Sanjay K. Banerjee

Submitted: October 26th, 2015 Reviewed: February 18th, 2016 Published: August 17th, 2016