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Recent advances in molecular biomarkers for diabetes mellitus: A

systematic review

Ty Lees1, 2, 3, Najah Nassif2, 3, Ann Simpson2, 3, Fatima Shad-Kaneez 2, 3, Rose

Martiniello-Wilks2, 3, Yiguang Lin2, Allan Jones2, Xianqin Qu2, 3, Sara Lal1, 2, 3, 4

1 Neuroscience Research Unit, School of Life Sciences, University of Technology Sydney, PO

Box 123, Broadway NSW 2007, Australia

Ty.Lees@uts.edu.au; Sara.Lal@uts.edu.au

2 Chronic Disease Solutions Team, School of Life Sciences, University of Technology Sydney,

PO Box 123, Broadway NSW 2007, Australia

Najah.Nassif@uts.edu.au; Ann.Simpson@uts.edu.au; Kaneez.FatimaShad@uts.edu.au;

Allan.Jones@uts.edu.au; Rosetta.Martiniello-Wilks@uts.edu.au; Yiguang.Lin@uts.edu.au;

Xianqin.Qu@uts.edu.au

3Centre for Health Technologies, University of Technology Sydney, PO Box 123, Broadway,

NSW, 2007, Australia

4 Correspondence:

Associate Professor Sara Lal

Neuroscience Research Unit, School of Life Sciences, University of Technology Sydney,

New South Wales, Australia

Email: Sara.Lal@uts.edu.au

Phone: +61 2 9514 1592

Fax: +61 2 9514 8206

Mailing address: PO Box 123, Broadway NSW 2007, Australia

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Recent advances in molecular biomarkers for diabetes mellitus: A

systematic review

Context: Diabetes is a growing global metabolic epidemic. Current research is

focusing on exploring how the biological processes and clinical outcomes of diabetes

are related and developing novel biomarkers to measure these relationships, as this can

subsequently improve diagnostic, therapeutic and management capacity.

Objective: To identify the most recent advances in molecular biomarkers of diabetes

and directions that warrant further research.

Methods: Using a systematic search strategy, the MEDLINE, CINAHL and OVID

MEDLINE databases were canvassed for articles that investigated molecular

biomarkers for diabetes. Initial selections were made based on article title, whilst final

inclusion was informed by a critical appraisal of the full text of each article.

Results: The systematic search returned 246 records, of which 113 were unique.

Following screening, 29 records were included in the final review. Three main research

strategies (the development of novel technologies, broad biomarker panels, and

targeted approaches) identified a number of potential biomarkers for diabetes including

miR-126, C-reactive protein, 2-aminoadipic acid and betatrophin.

Conclusion: The most promising research avenue identified is the detection and

quantification of micro RNA. Further, the utilisation of functionalised electrodes as a

means to detect biomarker compounds also warrants attention.

Keywords: Diabetes; Molecular; Biomarker, diabetes mellitus

1. Introduction:

Diabetes mellitus is a growing metabolic epidemic that affects at least 5.6% of the global

population (International Diabetes Federation, 2015 (IDF), World Health Organisation, 2016

(WHO)), and it is predicated that by 2040, 642 million people will be living with diabetes

(IDF, 2015). In terms of mortality, the WHO has reported that in 2012 directly caused 1.5

million deaths, and an additional 2.2 million deaths were attributed to high blood glucose

concentrations and the associated increased risk of disease (The Emerging Risk Factors

Collaboration, 2010, Baker IDI Heart and Diabetes Institute et al., 2012, Yau et al., 2012,

Bourne et al., 2013, WHO, 2016). Furthermore, diabetes also has a vast economic impact,

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currently accounting for 12% of current global health expenditure (IDF, 2015).

The aforementioned health and economic burden attributable to diabetes mellitus has

seen an increasing global demand for research. One research area that is experiencing an

exponential surge of interest is the discovery and development of biomarkers that can be used

in the diagnosis, prognosis, and treatment of diabetes. To improve medical biomarker

development, the Federal Drug Admistration (FDA) and National Institute of Health (NIH)

recently developed the Biomarkers, EndpointS & other Tools (BEST) Resource, which

comprises a glossary that clarifies important biomarker definitions and their roles in

biomedical research, clinical practice and the development of medical products. BEST

defines biomarkers as measurable indicators to evaluate normal biological processes,

pathogenic processes, or responses to an intervention, including therapeutics (FDA-NIH

Biomarker Working Group, 2016). Indeed, diabetes biomarker research has seen a number of

success stories; however, these are far from perfect and require further research (Caveney and

Cohen, 2011).

Given the enormity of the problem of diabetes, developing means to evaluate the

relationship between the biological processes and clinical outcomes of diabetes is vital for

deepening our knowledge of the disease (Caveney and Cohen, 2011), and may improve our

diagnostic, therapeutic and management capabilities (Strimbu and Tavel, 2010). This review

will consider the importance of biomarkers for diabetes and their role in facilitating our

understanding of diabetes and its management.

1.1 Objective

The objective of the present review was to identify the most recent advances (from 2011 to

the present (September 2016)) in the knowledge base regarding molecular biomarkers of

diabetes with the aim to identify the most promising recent advances and directions for

further research.

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2. Methods

2.1 Selection criteria for the present review

2.1.1 Review period

This systematic review was confined to the relevant articles published between January 2011

and September 2016, with the aim of reviewing the most recent advances in diabetes

biomarker research.

The systematic search for this review paper was initially conducted on the 28th of July,

2016, and updated on the 14th of September, 2016.

2.1.2 Types of studies and study design

All research studies published during the specific period in English as full peer-reviewed

journal articles, were included in the current review.

2.1.3 Categories of effects

All studies were included based on the primary search measures stipulated in Section 2.1.3.1.

Studies were not excluded based on the field of research or method of investigation used.

2.1.3.1 Primary effects

The identification or evaluation of molecular biomarkers for diabetes, were selected as

primary effects. These criteria were not confined to any major research area, as a broad

evaluation of the knowledge base was intended for the present review. The selection criteria

were guided by the collective expertise of the authors.

2.2 Literature search methods

Only full peer-review journal publications, irrespective of country of origin or design, were

utilised for this review. Studies published in a language other than English, or papers

published as Review articles, Letters to the editor, News releases, Editorials, and Research

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highlights were excluded.

2.3 Electronic database search

The databases selected for the systematic search were MEDLINE, CINAHL and OVID

MEDLINE (Ovid MEDLINE (R) In-Process & Other Non-Indexed Citations, Ovid

MEDLINE (R) Daily, Ovid MEDLINE (R) and Ovid OLDMEDLINE (R) 1946 to Present).

The primary search terms were confined to the title field and included: Diabetes, and

Biomarker. The descriptors and synonyms were modified as per the specific requirements for

each database. The search structure for the included databases is described below. The

MEDLINE and CINAHL databases were searched together using the same inputs, and hence

their results were pooled.

The specific search syntax for the OVID MEDLINE databases was as follows:

No. of Results

Diabetes.ti. 180,291

Biomarker.ti. 15,836

1 AND 2 145

Limit 3 to yr=”2011 – Current" 115

The specific search syntax for the MEDLINE and CINAHL databases was as follows:

No. of Results

TI Diabetes 224,642

TI Biomarker 17,155

S1 AND S2 166

Limited to 2011-2016 131

It is important to note that of the 131 results retuned from the MEDLINE and

CINAHL databases, 114 were attributed to MEDLINE and the remaining 17 to CINAHL.

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2.4 Selection of studies:

Following the database searches, duplicate records were removed, and two authors (TL and

SL) subsequently evaluated the titles returned. No disagreement was identified between the

two reviewers. Following this visual inspection, other rejection criteria, as previously

mentioned, were applied, after which full texts for these studies were subsequently sourced

and critically appraised for inclusion in the final review.

3. Results

The systematic search returned a total of 246 results of which 124 were duplicate results,

providing a total of 113 unique papers for potential inclusion in the current review; after

exclusion processes, a total of 50 papers were identified for inclusion. Of these 50 papers, 29

were included in the final review, and are subsequently discussed. A flow chart for the

systematic review search and selection procedure is presented in Figure 1.

4. Discussion:

Before beginning, it is important to note that biomarker research for diabetes can be separated

into two broad categories: the first focuses on the development and identification of

biomarkers for comorbid conditions or complications of diabetes, for example diabetic

kidney disease; the second focuses on the identification and development of biomarkers for

diabetes mellitus; this review will focus on the latter.

4.1 Development and validation of new methods/approaches

Three of the 29 papers, not only investigated one or more biomarkers for diabetes, but also

presented new investigative methods.

Based on increasing evidence linking the complement system to the pathogenesis of

vascular diabetes complications, Ghosh et al. (2013) hypothesised that serum concentration

of glycated complementary regulatory protein CD59 (GCD59) may prove to be a biomarker

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of diabetes, and subsequently developed, optimised and validated an ELISA for GCD59. In

preliminary testing, the non-diabetic (NDM) samples had significantly lower mean random

plasma glucose and HbA1c values than the diabetes mellitus (DM) samples. Most

importantly, it was found that GCD59 values were strongly positively associated with HbA1c

values. Utilising a 1 SPU as a cut-off value (with no other information e.g. duration or

treatment of diabetes included), GCD59 concentration showed a 93% sensitivity and 100%

specificity for the discrimination of DM from NDM samples, and generated a Receiver

Operating Characteristics (ROC) curve with an area under the curve (AUC) of 0.98. A larger

validation experiment concurred and demonstrated that the GCD59 values were significantly

higher in DM samples. Further, adjusted linear regression models independently and

positively associated GCD59 with HbA1c in the entire study cohort; an association that

persisted across both the DM, and NDM subgroups. Ghosh et al. (2013) concluded that blood

GCD59 levels can significantly discriminate between DM and NDM individuals with high

sensitivity and specificity, and also suggest that the described assay may functionally

complement and possibly replace existing methods. However, it should be noted that the

developed assay currently does not offer any significant advantage over existing methods.

The second paper was a technical report from Zhang et al. (2014), who developed a

plasmonic gold substrate platform for near infrared (NIR) fluorescence enhanced detection of

islet cell antigen-specific autoantibodies. Testing serum samples from 39 patients with new

onset diabetes (26 were later diagnosed with Type 1 diabetes mellitus [T1DM], and 13 with

Type 2 diabetes mellitus [T2DM]) and 5 NDM controls the authors found that both the

plasmonic gold chip and laboratory RIA had 100% sensitivity and 85% specificity.

Furthermore, utilising multiple fluorescence enhanced NIR dyes, Zhang et al. (2014)

managed to simultaneously detect IgG, IgM and IgA subclass autoantibodies against each

islet antigen (insulin, GAD65, and IA2). These results demonstrate that the plasmonic gold

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platform was able to simultaneously detect and quantify autoantibodies to 3 islet antigens and

their individual isotypes in ultralow volumes of whole-blood; possibly enabling a sample to

be obtained from a finger prick and potentially enabling clinic based T1DM diagnosis.

Overall, the proposed platform provides a relatively simplistic method to detect the auto-

antibodies used to diagnose T1DM; whether or not this extends to non-diagnosed individuals

remains to be determined. Further, whilst the specificity and sensitivity of the plasmonic

platform is equivalent to the currently accepted method, it does rely on specialised equipment

to nanofabricate the microarray, and takes 1-2 hours, all of which arguably decrease

accessibility and increase cost. However, if additional research improves specificity, reduces

processing time, and allows the platform to be produced at the right economic price point, it

could well become clinically deployable.

The final paper comes from Bishnoi et al. (2014), who made use of an edge plane

pyrolytic graphite electrode modified with gold nanoparticles and attached to single walled

carbon nanotubes to quantify the urinary concentration and biomarker potential of 8-

hydroxyguanine (8-OH-Gua); a oxidative metabolite that is regularly excreted into the urine

of patients with diabetes. Utilising urine samples from 3 male controls and 3 male diabetics,

the concentration of 8-OH-Gua in patients with diabetes was determined to be almost 3-fold

higher than the control subjects; however as the authors do not report any statistical analysis

the significance of these findings cannot be further discussed. Nonetheless, the clinical

relevance of the variation and possibly oxidative DNA damage should still be noted.

Furthermore, these results demonstrate a number of positives regarding this novel sensor.

Firstly, 8-OH-GUA was successfully measured in a complex mixture of uric and ascorbic

acid, indicating target specificity and applicability for urine analysis. Secondly, the sensor

requires no complex sample pre-treatment, a point that is particularly important as this could

theoretically reduce analysis time and expenditure, as intermediate preparatory steps and

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reagents are not required. Overall, the paper suggests the novel sensor provides an efficient

and seemingly effective means of quantifying 8-OH-Gua. Additionally, urinary 8-OH-Gua

concentration may function as a means of evaluating oxidative DNA damage; however as

many factors can lead to increased oxidative damage, the specificity for diabetes diagnosis is

questionable.

In summary, a number of research groups are focusing on not only biomarker

discovery, but also developing novel, sensitive and specific means of their detection. Whilst,

these novel biomarkers and biosensors are under translational development, additional

research regarding their clinical utility is still warranted.

4.2 Broad biomarker approaches

Of the remaining 26 papers, 3 utilised broad approaches for the discovery of diagnostic

biomarkers for diabetes.

The first paper from Varvel et al. (2014), retrospectively assessed 1,687 (aged 53 ± 15

years, 42% male) patients using a panel of 19 blood-based biomarkers and glycaemia derived

factors, as well as markers of insulin resistance and beta cell function. Based on fasting

plasma glucose and HbA1c measures, 415 patients had glycaemic control values consistent

with prediabetes, and 343 with diabetes. The remainder were considered normoglycaemic

and subcategorised as either high-normal or normal. In terms of the panel results, 84.5% of

the total cohort had at least one feature of insulin resistance, 63.9% had at least two features

of insulin resistance, 57.3% had at least one feature of beta cell dysfunction, and 33.3% had

at least two features of beta cell dysfunction. Additionally, 45% of patients who returned one

or more biomarkers of insulin resistance or beta cell dysfunction in the high range had not

been classified “at risk” by fasting glucose and HbA1c measures. A further 21 patients were

classified pre-diabetic or diabetic by fasting glucose and HbA1c measures but were normal

on all other markers. Finally, 82% of the normoglycaemic patients showed at least one

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biomarker in the high range. At follow-up, significantly more pre-diabetic patients reverted to

normal than developed diabetes; a similar result was found for patients initially classified as

high normal. Overall, the comprehensive multi-marker panel proved to be effective in the

detection of early insulin resistance/metabolic disease; however, it did rely on completing the

whole panel. Importantly Varvel et al. (2014) noted that no individual biomarker or small

subset of biomarkers was responsible for the improvement in sensitivity; a finding which

combined with the varied methodologies (and associated cost) used in the panel reduces the

likelihood that such a broad panel would be implemented.

In fact, an earlier study from Borges et al. (2011) took a similar multidimensional

panel approach to characterise protein microheterogeneity, and assess whether or not it could

inform biomarkers related to pathobiologies of T2DM, and CVD comorbidities. Using

standardised mass spectrometric immunoassays protein microheterogeneity was characterised

and quantified in blood samples from 37 control individuals, and a varying number of age

and sex matched patients categorised into 5 disease subgroups: T2DM (n=50), T2DM with

congestive heart failure (CHF, n =25), T2DM/CHF and previous myocardial infarction (MI, n

= 17), non-diabetes with CHF/MI (n = 25), and non-diabetes with CHF (n= 29). Borges et al.

(2011) utilised principal component analysis (PCA) to evaluate markers within a common

pathobiology and find the lowest number of markers that provided the greatest degree of

separation between disease subgroups. In this manner, glycation markers were reduced to

albumin, β2-microglobulin, cystatin C, vitamin D binding protein, and C-reactive protein;

oxidation markers to sulfoxidised apolipoprotein A-1, and apolipoprotein C-I (apoCI), and

truncation markers to N-terminal di-peptide truncations on RANTES and apoCI. PCA was

again used to condense the multi-marker data into a single metric for each of the 3 categories

(glycation, oxidation, and truncation) which then informed ROC AUCs. Interestingly, the

resultant ROCs determined that markers of protein oxidation and truncation, not glycation are

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able to distinguish subjects with uncomplicated diabetes, from those with complicated

diabetes. Most importantly Borges et al. (2011) were not able to distinguish any DM

subgroup from controls in their work. From this analysis, it would seem that biomarkers

based on protein modification hold limited promise in the diagnosis of T2DM, however their

applicability to sub-typing patients based on comorbidities holds greater prospects.

Finally, Knebel et al. (2016) collected blood and plasma samples from two proband

groups, the first was comprised of clinically healthy offspring with at least one T2DM parent

(OSP) and the second was comprised of clinically healthy offspring without a familial history

of T2DM (CON). They utilised an untargeted mass spectrometric approach to examine

whether lipid induced alterations in plasma metabolite concentration can serve as disease-

related biomarkers. In general, OSP participants reported higher BMI, waist circumference,

waist to hip ratio and diastolic blood pressure than CON participants. Additionally, HbA1c

was significantly higher in the OSP group, but was still in a healthy range. Since there were a

near equal number of OSP patients displaying either an increase or decrease in blood glucose

concentration at 2 hours following an oral glucose load, the OSP group was further

subdivided for analysis. With respect to the metabolomics analysis, 1249 of the initial 2498

variables (buckets) passed quality control; however, none of the investigated metabolites

exclusively determined either OSP subgroup or time point. However, partial least squares

discriminant analyses did separate the two OSP subgroups in the fasting and postprandial

states, as well as in the intragroup variation from 0 to 4 h; suggesting metabolite abundance

differs in the affected and proband subgroups. Subsequent correlation analysis between the

buckets and diabetes related traits (e.g. blood glucose concentration) or surrogate parameters

(e.g. insulin resistance) revealed differing metabolomics profiles. Indeed, in the fasting state,

the abundance of 23 buckets differentiated the proband subgroups. Ten of these buckets were

identified as either peptide ligands, receptor antagonists or signalling mediators and modified

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fatty acids. Similarly, the postprandial state identified 24 differing buckets, of which 12 were

determined to be mainly signalling mediators. In summary, Knebel et al. (2016) suggest there

are significant metabolic differences in at risk individuals prior to the development of

hyperglycaemia and that metabolite composition can differentiate at-risk individuals into

subgroups. However, the predictive value of the identified metabolites requires additional

research. Further, the ability for metabolic patterns to differentiate between at risk and control

individuals was not reported on, and is an endeavour that may shed more insight on the

results of Knebel et al. (2016).

Despite a number of factors, first and foremost of which is often cost, limiting the

direct translation of these types of studies to the bedside, broad spectrum approaches for the

identification of novel biomarkers will continue to remain a staple due to the speed at which

targets may be identified, and the information that they provide for more targeted

examinations.

4.3 Targeted Biomarker approaches

The remaining 23 papers, all utilised targeted investigations by examining a single or a

limited number of compound/s and outlining the usefulness of the selected target/s as

biomarkers of diabetes.

4.3.1 Circulating microRNA

Interestingly, of these 23 papers, 8 focused on identifying various microRNAs (miR) and

considered their potential as biomarkers for diabetes. miR belong to a class of small

noncoding RNA that function as translation repressors by partially pairing with the 3’

untranslated region of target messenger RNA (Zhang et al., 2013). Utilising circulating miR

concentration as a biomarker has been previously explored in cancer (Kosaka et al., 2010,

Bonci et al., 2016), cardiovascular (Min and Chan, 2015) and neurological diseases (Blennow

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et al., 2015, Denk et al., 2015). Further support for their utility as biomarkers is derived from

their stability and resistance to ribonucleases, freeze/thaw cycles, and other experimental

conditions (Kroh et al., 2010); which makes it possible for serum and/or plasma samples to

be frozen for several months without notable degradation of miR (Mraz et al., 2009).

The first of the included studies was from Erener et al. (2013) who explored islet-

enriched miR-375 as a potential marker of beta cell death and predictor of diabetes in mice.

In a preliminary experiment, Erener et al. (2013) determined that miR-375 levels were

elevated in circulation upon acute beta cell death and chronic hyperglycaemia. Following

this, quantitative RT-PCR (RT-qPCR) was utilised to measure miR-375 levels in plasma

samples of streptozotocin (STZ) treated C57BL/6 and non-obese diabetic (NOD) mice; miR-

375 concentration was also quantified in media samples of cytokine- or STZ-treated islets in

the presence/absence of cell-death inhibitors. The RT-qPCR analysis indicated that following

STZ administration plasma miR-375 concentration increased considerably prior to any blood

glucose elevation. miR-375 levels gradually decreased over time but were 2.8 fold higher a

week after STZ administration. Further RT-qPCR analysis, this time using a NOD mouse

model, indicated that miR-375 levels similarly increased prior to the onset of diabetes and

hyperglycaemia. The miR-375 concentration remained high until onset of diabetes, after

which the level decreased. The authors concluded that miR-375 can serve as a biomarker of

beta cell death and potential predictor of diabetes in mice, and that miR profiling could be a

viable diagnostic option.

Yang et al. (2014), employed high-throughput Solexa sequencing and RT-qPCR to

extensively determine the serum miR expression profiles of T2DM and pre-diabetes, and

assessed the viability of using these profiles as non-invasive biomarkers. They found eight

miR (miR-23a, let-7i, miR-486, miR-96, miR-186, miR-191. miR-192, and miR-146a) were

significantly decreased in T2D relative to controls. However, only miR-23a concentration

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was significantly lower in T2DM and prediabetes compared to controls; it also significantly

declined in T2DM compared to prediabetes. ROC analysis for miR-23a yielded an AUC of

0.835 (95% CI 0.717-0.954); using a cut-off value of 1.645, the sensitivity was 79.2% and

specificity was 75% in discriminating T2DM from controls. Similar analysis differentiating

prediabetes from controls returned an AUC of 0.69 (95% CI 0.525-0.855); using a cut off

value of 1.320, sensitivity was 70% and specificity was 60%. The authors concluded that

serum miR-23a concentration was significantly lower in T2DM and prediabetes than

controls, and was also able to distinguish T2DM from prediabetes, and prediabetes from

controls. In the same year, Liu et al. (2014), used RT-qPCR direct serum assay to examine

serum miR-126 concentration., and similar found serum miR-126 concentration was

significantly lower in T2DM compared to prediabetic states and in T2DM and prediabetic

individuals compared to healthy controls. Results associated miR-126 and T2DM, as well as

prediabetes; suggesting that circulating miR-126 could be used to distinguish T2DM patients,

as well as pre-diabetic subjects from healthy subjects. ROC analysis accounting for age,

gender, BMI, blood glucose, HbA1c, and miR-126 provided an AUC of 0.893 (95% CI

0.838-0.947). Furthermore, miR-126 expression was significantly associated with treatment,

suggesting that miR-126 could be useful not only as a diagnostic biomarker, but also in

treatment management.

Previous to this, Zhang et al. (2013) had examined the biomarker potential of plasma

miR-126. Using RT-qPCR they evaluated the expression of five DM-associated miR (29b,

15a, 28-3p, 223, and 126) in three study groups: normal controls, T2DM susceptible

individuals, and patients with T2DM. Their analysis determined that only miR-126 was

differentially expressed between the sample groups, with a reduction being identified in

susceptible individuals and T2DM patients when compared with normal controls. More

recently, Zhang et al. (2015) performed a retrospective longitudinal analysis of the predictive

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significance of plasma miR-126 concentration. With the same group of participants, it was

found that baseline miR-126 concentration was significantly higher in individuals who did

not develop T2DM. Importantly, these results showed miR-126 expression was altered prior

to the manifestation of T2DM. Furthermore, miR-126 concentration and T2DM were

significantly correlated, and subsequent ROC curve analysis showed that miR-126

distinguishes patients with T2DM from NDM patients with an AUC of 0.806. Expanding

upon this analysis, and using a practical cut-off point of 35, Zhang et al. (2015) suggested

that an individual with plasma miR-126 levels below this are more likely to develop diabetes

in the following 2 years, and initial testing reported 77.78% sensitivity and 66.67%

specificity for this threshold. Overall, their second study further supports the suggestion that

miR-126 could function as a predictor of the onset of T2DM, and may be able to be

developed into a mostly non-invasive and rapid diagnostic tool.

Most recently, Al-Kafaji et al. (2016) investigated circulating miR-126 as a potential

biomarker for predicting coronary artery disease (CAD) in T2DM. Importantly their RT-

qPCR analysis also provides further insight into the predictive capability of miR-126 for non-

complicated T2DM. It was found that circulating miR-126 was significantly lower in T2DM

patients (both with and without CAD) than healthy controls. Odds ratio analysis indicated a

strong and moderate association between miR-126 concentration and non-complicated

T2DM, and T2DM with coronary artery disease. Importantly, ROC analysis demonstrated a

significant ability to separate both non-complicated T2DM (AUC: 0.932, CI: 0.858-1.000),

and T2DM with CAD (AUC: 0.948, CI: 0.894-1.000) from controls, as well as non-

complicated T2DM from T2DM with CAD (AUC: 0.807, CI: 0.714-0.900). From these ROC

analyses Al-Kafaji et al. (2016) concluded that serum miR-126 concentration may function as

a diagnostic biomarker for not only T2D but also CAD. In an earlier study, Al-Kafaji et al.

(2015) investigated the biomarker potential of peripheral blood miR-15a levels, and found

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strong associations between lower miR-15a expression and T2DM, as well as prediabetes.

Additionally, miR-15a was significantly lower in T2DM and pre-diabetic individuals

compared with healthy controls. Subsequent ROC AUC analysis indicated that serum miR-

15a expression could distinguish T2DM patients from healthy controls (AUC 0.864, CI

0.751-0.977), as well as individuals with pre-diabetes from healthy controls (AUC 0.852, CI

0.752-0.953). Thus, serum miR-15a concentration may effectively function as a diagnostic

biomarker as it can distinguish T2DM and prediabetes individuals from healthy controls.

Finally, using a combination of quantitative stem-loop RT-qPCR, RT-qPCR, ELISA

and a luciferase reporter gene assay, Luo et al. (2015) determined that miR-103b expression

was lower, and SFRP4 gene expression was higher in all DM subgroups (prediabetes, non-

complicated diabetes, T2DM with coronary heart disease) compared to a control group.

Further, miR-103b over-expression was found to significantly inhibit SFRP4 mRNA or

protein expression, whilst downregulation of miR-103b had the opposite effect. Overall, it

was suggested that platelet-derived miR-103b could negatively regulate the expression of

SFRP4 mRNA/protein in prediabetes, and because of this interaction miR-103b could

function as a novel biomarker for the early diagnosis of T2DM.

4.3.2 Other targeted approaches

The remaining 15 papers investigated a single or limited number of compounds for use as a

biomarker. In a large multi-centre study Thanabalasingham et al. (2011) assessed the clinical

validity of high-sensitivity C-reactive protein (hsCRP) in the diagnosis of diabetes subtypes,

specifically maturity-onset diabetes of the young (MODY) associated with heterozygous

HNF1A gene mutations. It was found that hsCRP concentration was significantly lower in

individuals with HNF1A-MODY when compared to individuals with other diabetes

aetiologies. Subsequent summary ROC analysis determined that hsCRP was able to reliably

distinguish HNF1A-MODY from other diabetes aetiologies with 78% sensitivity and 80%

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specificity, and as such suggest that hsCRP possesses sufficient sensitivity and specificity to

enable precise molecular diagnosis of monogenic diabetes, particularly HNF1A-MODY.

However, as it has been previously associated with other metabolic measures, such as

HbA1c, hsCRP may have broader diagnostic and prognostic capabilities that could be

explored.

In 2012, a short report from Il'yasova et al. (2012) furthered a previous pilot study

that reported an inverse association between F2-isoprostane concentration and T2DM risk. In

this larger study, spectrometric analysis determined that baseline levels of three F2-

isoprostanes (iPF2α-III, 2, 3-dinor-iPF2a-III, ipF2a-VI) were significantly lower among

T2DM subjects and inversely associated with incident T2DM. Il'yasova et al. (2012)

concluded that F2-isoprostanes may be metabolically protective, and that their urinary

concentration could function as a biomarker for T2DM risk. However, whether or not

translates directly to any kind of diagnostic capability requires further research. In the same

year, Liu et al. (2012) used two rat models (LEW.1WR1 and BBDP), to investigate the role

and biomarker potential of an allele of the β-chain variable region gene Tcrb-V13 (Vβ13) in

T1DM. In their model, treatment with the 17D5 mouse anti-rat Vβ13 monoclonal antibody

significantly lowered insulitis in pancreata, as well as the incidence of autoimmune and viral

induced diabetes. Further experimentation revealed that constant anti-Vβ13 treatment

completely prevented diabetes through 115 days of age. Liu et al. (2012) concluded that

therapeutic depletion Vβ13a T cells can prevent autoimmune diabetes in multiple rat models;

and that abundant Vβ13a+ T cells in prediabetic islets suggests that Vβ13a is a marker of the

disease. That said, the Tcrb-V13 gene and Vβ13a are mouse/rat specific; and despite the

existence of human homologs, it is not known if the therapeutic and diagnostic potential of

Vβ13a will translate to humans. More recently, Wang et al. (2014) used male Wistar rats to

investigate arginase 1 activity in diabetes, and subsequently assessed its biomarker potential.

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Wang et al. (2014) found serum arginase 1 activity was elevated in diabetic animals when

compared to controls and subsequent kappa analysis revealed a concordance value between

arginase 1 activity and clinical diabetes diagnosis of 0.876. These results indicate that

arginase 1 may have useful prognostic and diagnostic utility in diabetes; however, whether or

not these results will translate to humans remains unknown.

In early 2013, Takada et al. (2013) investigated circulating low-molecular weight

proteins, and attempted to identify a distinguishable expression profile in diabetes. SDS

PAGE fractionation of serum samples from diabetes patients and healthy controls identified a

prominent and consistently upregulated protein band of approximately 180 kDa. Subsequent

mass spectrometric analysis identified 20 deduced amino acid sequences identical to α2-

macroglobulin. Moreover, quantitative analysis revealed a diabetes specific elevation in

monomeric and tetrameric α2-macroglobulin, and additional correlation analysis positively

correlated monomeric α2-macroglobulin with HbA1c. As such, it follows that serum analysis

of monomeric α2-macroglobulin may be useful in diabetes diagnosis. Taking a similarly

expansive approach, Wang et al. (2013) utilised liquid chromatography-tandem mass

spectrometry to profile 70 plasma intermediary metabolites to identify new biomarkers of

diabetes risk. Of the 70 screened metabolites, 2-aminoadipic acid (2-AAA) was most strongly

associated with future development of diabetes, with plasma 2-AAA concentration leading to

greater odds of developing diabetes in the follow-up period. Interestingly, 2-AAA was not

significantly correlated with HbA1c, but was significantly correlated with fasting insulin, and

a number of insulin resistance variables. In summary, plasma 2-AAA concentration appears

to be effective in predicting the development of diabetes, even over a lengthy follow-up

period of 12 years. However, prior to clinical deployment sensitivity and specificity

calculations, as well as the validation of a targeted mass spectrometry assay is required. Late

2013 saw Heraclides et al. (2013) use ELISA to determine if soluble urokinase plasminogen

19

activator receptor (suPAR) was associated with incident T2DM in high risk individuals and if

body weight and smoking status modified this relationship. At a 3 year follow up, they

determined that per twofold increase in baseline suPAR the odds of developing T2DM

increased by 48%. Importantly, this relationship was strongly modified by body weight

status, and after adjustment remained true for only overweight individuals and non-smokers.

Whilst not offering the broad utility of some of the other previously discussed targets, it is

possible that suPAR concentration may be useful in monitoring the development of T2DM,

particularly in overweight individuals.

The following year, Ma et al. (2014) explored plasma apelin concentration as a

biomarker for T2DM. Baseline plasma apelin concentration was higher in women than in

men; however, Kaplan-Meier analysis associated higher plasma apelin concentration with an

increased diabetes risk in men but not women. Additionally, adjusted cox proportional hazard

modelling determined that plasma apelin could significantly predict the development of

diabetes in men, but not women. Overall, plasma apelin looks to be a useful biomarker for the

risk of incident diabetes, and may improve prediction ability over traditional risk factors;

however this is only applicable to male patients, limiting its clinical reach.

In early 2015, Ghosh et al. (2015) investigated the influence of prediabetic states and

T2DM on erythrocyte carbonic anhydrase (CA) activity, and whether this influences oxygen-

isotope fractionations of exhaled CO2. Their analysis found markedly enhanced post glucose

dose CA activity and isotopic enrichment of O18 in exhaled CO2 in T2DM, whilst healthy

controls again exhibited marked depletions. Subsequent ROC analysis determined optimal

diagnostic cut-off points for the change in O18 change over baseline, and change in CA

activity, which returned a diagnostic sensitivity and specificity of ~95%, and ~91%, and

91.7% and 94.9%, respectively. This suggests that O18 isotope inclusion in CO2 has the

potential to identify the metabolic transformation from NDM to prediabetes, and then onto

20

T2DM. Furthermore, CA itself may also function as a biomarker of T2DM, and, as it is a

circulating serum enzyme, it may prove to be a clinically accessible target for future

biomarker development. Soon after, Cooper et al. (2015) continued down a less traditional

route, and examined the association between a composite biomarker score (CB score)

comprising plasma vitamin C, beta-carotene and lutein and risk of incident diabetes. Odds

ratio analysis found an inverse association between CB score and the odds of diabetes when

comparing the highest and lowest quartiles; a result that remained largely unmodified, even

after adjustment for demographic and lifestyle factors (age, smoking status, etc.). Hence, the

combined measurement of plasma vitamin C, beta-carotene and lutein may function as a

biomarker for mechanisms that contribute to diabetes, and as such may function as a

prognostic biomarker.

Around the same time, Akinkuolie et al. (2015) examined the relation of baseline

GlycA (a novel protein glycan side-chain biomarker) with incident T2DM in healthy women,

and compared this biomarker to hsCRP. In their examination, GlycA was significantly

correlated with hsCRP, as well as a number of other metabolic risk indicators including

HbA1c, interleukin-6, BMI, fibrinogen and triglycerides. Most importantly, adjusted Cox

hazard models demonstrated that the highest incidence rate of diabetes was found in the

highest quartile, indicating increased GlycA and hsCRP was associated with an increased risk

of developing T2DM. In terms of prediction, Akinkuolie et al. (2015) provide evidence that

GlycA may act as a biomarker of T2DM, and could possess greater efficacy than HbA1c.

However, it should be noted that the study utilised healthy female participants, and whilst

there should not be a sex-based difference in acute-phase inflammatory glycoproteins, further

confirmation in male participants is warranted. The last study of 2015, saw Yi et al. (2015)

assess serum betatrophin concentration as a diagnostic biomarker for T2DM. Their analysis

determined that serum betatrophin concentration was approximately 1.8 times higher in

21

T2DM patients than in NDM individuals. Furthermore, stratifying for BMI indicated that

obese T2DM subjects had an approximately 6.5 times higher betatrophin concentration than

controls. Subsequent ROC analysis for T2DM returned an AUC of 0.824, and a sensitivity

and specificity of 83.56% and 72.41% respectively, suggesting that betatrophin may function

as a diagnostic biomarker for T2DM particularly in obese individuals; however additional

sensitivity and specificity improvements are required. Additionally, repeating this experiment

in T1DM could be an interesting future direction.

In 2016, Olsen et al. (2016) used methylation sensitive RT-qPCR to examine the

biomarker potential of demethylated circulating free amylin DNA (cfDNA). Their analysis

yielded a strong signal for demethylated amylin DNA from both murine and human islets, as

well as enriched human beta cells. Most importantly, a statistically significant increase in

demethylated DNA was found in recent onset T1DM, when compared to healthy controls,

indicating demethylated amylin cfDNA may be a useful means of determining beta cell death

and subsequently diagnosing T1DM. Second for 2016, Anand et al. (2016) investigated the

association of secreted frizzled-related protein 4 (SFRP4) with insulin resistance and β-cell

dysfunction, as well as its biomarker potential. Their ELISA protocol determined that

circulating SFRP4 levels were highest in T2D individuals, followed by individuals with

prediabetes, and was lowest in NDM. Furthermore, standardised controlled (for age, sex,

waist circumference, glycated haemoglobin, etc.) polytomous regression models indicated

that a higher SFRP4 concentration was independently associated with T2DM and prediabetes

suggesting that elevated SFRP4 may be a good marker of beta cell dysfunction and insulin

resistance, and subsequently may not only be useful diagnostically, but also hold some

prognostic value. Most recently, Haseda et al. (2016) utilised seromic analysis and ELISA to

identify a biomarker for fulminant type 1 diabetes (FT1D). Their seromic analysis found 9

antibodies with high signals in FT1D, most important of which was anti-CD300e as it was

22

significantly higher in FT1D, when compared to type 1a diabetes (T1AD), T2DM, auto-

immune thyroid disease and healthy controls. Subsequent ROC analysis for FT1D and HC

returned an AUC of 0.849, with a sensitivity and specificity of 73.1% and 87.1%

respectively. As such, anti-CD300e may function as a biomarker for FT1D, although

additional testing is required. Additionally, the results of Haseda et al. (2016) demonstrates

that anti-CD300e distinguished patients with T1AD or T2DM from healthy controls, further

expanding the potential utility of the antibody.

Overall, recent biomarker research utilising targeted approaches has identified a

plethora of possible targets and a multitude of directions for future research. Importantly, a

number of the included studies identified compounds including miR-126, and SFRP4 that

may not only be useful diagnostic biomarkers, but could also have prognostic and therapeutic

uses.

5. Conclusions

Biomarker research for diabetes has shown good progress with the last 5 years heading in

exciting directions. Broadly, it seems apparent that molecular biomarkers of diabetes offers

promise for the clinical settings, and future research would do well to further examine,

validate and compare the novel discoveries covered in this review to existing gold standard

markers, for example, HbA1c, as this may further develop these biomarkers for clinical use.

Furthermore, although not covered in this review, it is quite possible that with improved

sequencing technology, genetic biomarkers will offer far greater prognostic capabilities

particularly for those already at risk of developing diabetes. Additionally, genetic biomarker

research may also provide important information regarding new molecular targets, as proxy

measures of gene expression can often be just as revealing.

Delving into more specific conclusions from the reviewed articles, the most promising

avenue for long term research is the detection and quantification of miR, most importantly

23

miR-126, miR-15a, and miR-375 as they currently exhibit promising specificity and

sensitivity for the detection of diabetes. However, further validation of the diagnostic

capability of miR for diabetes is still required. Furthermore, as miR is largely detected

utilising RT-qPCR, a complementary research pathway would be the development, and

validation of multiplex assays, as such assays may lead to an improvement in diagnostic

capability. Additionally, serum factors such as 2-aminoadipic acid, C-reactive protein, and

betatrophin also offer potential to function as effective biomarkers for diagnosis of diabetes,

although in their current state, a number of confounders need to be accounted for with more

research.

A second future research avenue that warrants attention is the utilisation of

functionalised electrodes such as that developed by Bishnoi et al. (2014). Indeed, a number of

research groups are currently developing the technology and application of these electrodes.

Based on some early promising results, it appears as though the research may be reaching a

precipice where the application of such technologies becomes the primary focus rather than

the technology itself, and it could well be time to encourage this research path. Finally, it is

anticipated that the use of biomarker combinations (or signatures) for diabetes may improve

specificity and provide valuable clinical information for health professionals leading to better

personalise treatments for patients with diabetes.

Acknowledgements

The authors would like to acknowledge the School of Life Sciences, UTS for providing the funding

that enabled the Chronic Disease Solutions team to collaboratively complete this review.

Additionally, we would also like to acknowledge the first author for conducting and completing the

review, and each co-author for their contributions, suggestions and input into the review.

Disclosure of Interest

Ty Lees, the first author, was paid by the School of Life Sciences, UTS, to conduct and draft the

current review as part of a collaborative research effort of the Chronic Disease Solutions team. All

other authors report no conflicts of interest.

24

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Figure 1 –Systematic review search method

Figure Captions

Figure 1 (adapted from Moher et al. (2009)) depicts the flow of information through the

different phases of the systematic review. The initial search identified 246 records, from

which 133 duplicates were removed. Of the remaining 113 records, 50 were selected for full

appraisal and 29 were included in the final review.