Genomics Biomarkers for Type 2 Diabetes

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Luis Felipe Padilla-Martinez	+ 2478 word(s)	2478	2022-01-05 04:13:01	\|
2	Format change	Jessie Wu	Meta information modification	2478	2022-01-18 02:26:24	\|

This entry is adapted from the peer-reviewed paper 10.3390/ijms23010295

Type 2 diabetes (T2D) is a deficiency in how the body regulates glucose. Uncontrolled T2D will result in chronic high blood sugar levels, eventually resulting in T2D complications. These complications, such as kidney, eye, and nerve damage, are even harder to treat.

genomics type 2 diabetes biomarkers

1. Introduction

Type 2 diabetes (T2D) remains a significant clinical burden worldwide. Approximately 462 million individuals (6.28% of the world’s population) lived with T2D in 2017, and the prevalence continues to rise ^[1]. Better screening, diagnosis, and treatment approaches are needed to combat T2D. Early screening would ensure the timely implementation of lifestyle interventions for those at risk of developing T2D ^[2]. Early diagnosis is substantially beneficial for T2D patients. Even though T2D incidence peaks at age 55–59, many develop the disease at earlier ages and are often undiagnosed for years ^[1]^[3]. Early diagnosis would allow early treatment and prevent T2D complications, which are more challenging to treat ^[4]. Personalized T2D management, where drug choice is based on each patient’s characteristics, would maximize glycemic control efficiency and minimize side effects ^[5].

These goals can be achieved by identifying T2D biomarkers for each purpose. A biomarker is a biological observation that predicts an important endpoint or intermediate outcome in the clinical diagnosis ^[6]. The Biomarkers, EndpointS, and other Tools (BEST) glossary from the US National Institutes of Health (NIH) ^[7] defines seven biomarker categories: susceptibility/risk ^[8], diagnostic ^[9], monitoring ^[10], prognostic ^[11], predictive ^[12], pharmacodynamic/response ^[13], and safety ^[14] (Table 1).

Table 1. Definitions of biomarkers and potential applications.

Type of Biomarker	BEST Definition	Application/Example
Susceptibility/risk	A biomarker that indicates the potential for developing a disease or medical condition in an individual who does not currently have clinically apparent disease or medical condition.	BRCA1/2 mutations can be used to identify individuals with a predisposition to develop breast cancer
Diagnostic	A biomarker to detect or confirm the presence of a disease or condition of interest or to identify individuals with a subtype of the disease.	HbA1c can be used to identify patients with T2DM
Monitoring	A biomarker measured repeatedly for assessing disease status or medical condition or for evidence of exposure to (or effect of) a medical product or an environmental agent.	Hepatitis C virus or HIV RNA may be measured repeatedly to monitor treatment response
Prognostic	A biomarker to identify likelihood of a clinical event, disease recurrence, or progression in patients who have the disease or medical condition of interest	BRCA1/2 mutations can evaluate the likelihood of second breast cancer.
Predictive	A biomarker used to identify individuals who are more likely than similar individuals without the biomarker to experience a favorable or unfavorable effect from exposure to a medical product or an environmental agent.	BRCA1/2 mutations can identify ovarian cancer patients likely to respond to PARP inhibitors.
Pharmacodynamic/response	A biomarker used to show that a biological response has occurred in an individual who has been exposed to a medical product or an environmental agent.	HbA1c may be used to assess diabetes control after treatment
Safety	A biomarker measured before or after an exposure to a medical product or an environmental agent to indicate the likelihood, presence, or extent of toxicity as an adverse effect.	Neutrophil count can be used to adjust dose for patients on cytotoxic chemotherapy.

2. Genomics Biomarkers for T2D

The science of genomes, or “genomics”, studies the structure, expression, and function of the whole DNA sequences of an organism. It has rapidly expanded towards a more functional level, studying the evolution, mapping, and editing of genomes ^[15]. Genomics analyzes genetic variants, such as single nucleotide polymorphisms (SNPs) and chromosomal abnormalities related to medical conditions ^[16]. T2D genomics studies used TaqMan qPCR assays, next-generation sequencing, and DNA microarray technologies.

2.1. Blood

Among all sample types, blood biomarkers represent the most accessible and most studied, given their properties and ease of collection in clinical practice ^[17]. A wide variety of T2D-related biomarkers in blood using genomic data has been reported (Table 2).

Heritability is a strong predictor of T2D (26–69% depending on the age of onset), thus motivating the search for genetic predictors of T2D ^[18]. One way to translate genetic data to a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score (PRS) ^[19]^[20]. Prediction accuracy of a PRS is often assessed by measuring the area under the receiver operating characteristic curve (AUC). The AUC compares the rates of true positives and false positives, accounting for the overall performance of predictive models ^[21]. The first PRS for T2D was developed in 2006 using three genetic variants: KCNJ11, PPARG, and TCF7L2 ^[22]. Two years later, three PRSs were developed using more SNPs. Two of them had 18 SNPs, and the third one had 11. Their AUC was 78%, 90%, and 74%, respectively ^[23]^[24]^[25]. In 2014, a 62-SNP PRS was developed using the DIAGRAMv3 Panel of Genes ^[26], with an AUC of 90% ^[27]. In 2016, Chikowore analyzed a South African population; a PRS using only four SNPs was created, taking into account sex, age, BMI, and systolic blood pressure as clinical risk factors, with an AUC of 66.5%. This research was the first to develop PRS in non-caucasian ethnic group with a high sensitivity and specificity ^[28]. Recent PRS models include many more SNPs, thanks to the larger genome-wide association studies (GWAS) in recent years. Furthermore, in 2016, Lall and colleagues published a PRS using 1000 SNPs from the DIAGRAM Panel of Genes, with an AUC of 77% ^[29].

Besides using SNPs in a PRS, some studies demonstrate other ways of utilizing SNPs as risk biomarkers. Ding and colleagues demonstrate that a low circulating level of sex hormone-binding globulin is a potential risk biomarker of T2D. Nevertheless, the clinical use of SHBG needs further examination ^[30]. In 2015, a study showed that endogenous bilirubin and the associated SNP, rs6742078 in the UGT1A1 locus, are associated with the risk of T2D, making it a candidate as a risk biomarker ^[31]. Two years later, Wheeler identified 60 common genetic variants associated with HbA1c using genome-wide association meta-analyses from 82 European, African, East Asian, and South Asian ancestry cohorts. They found out that a G6PD deficiency can be clinically silent until illness strikes. Therefore, screening with direct glucose measurements in people with G6PD deficiency may be helpful as a risk predictor of T2D ^[32].

One of the first studies researching predictor biomarkers of T2D was done in 2010. In the study, the levels of plasma GAPDH, representing total cell-free DNA, were measured. The levels of cell-free GAPDH were significantly higher in the plasma samples of T2D diabetic patients, becoming a biomarker candidate ^[33]. The most recent study was done in 2019, where they assessed the potential role of mitochondria in T2D by analyzing blood-based indices of mitochondrial DNA copy number (mtDNACN) and cell-free mitochondrial DNA (CFmtDNA). As a result, they found a significant locus in the LRRK2/MUC19 region, indicating that mitochondrial dysfunction is intimately linked to T2D prediction, therefore being a candidate biomarker ^[34].

Table 2. Genomics biomarkers from blood samples for T2D.

Sample Type	Profiling Method	Sample Size (Controls, T2D, Other)	Biomarker	Ref
Blood	Microarray	178, 178	FTO, PSMD6, SLC44A3, C2CD4B	^[28]
Blood	GWAS, microarray	33,241 ^	G6PD	^[32]
Blood	qRT-PCR	3669, 2409	KCNK11, PPARG, TCF7L2	^[22]
Blood	qRT-PCR	23, 23, 6 **	LRRK2/MUC19 (mtDNACN and CFmtDNA)	^[34]
Blood	Microarray	2776 ^	NOTCH2, BCL11A, THADA, IGF2BP2, PPARG, ADAMTS9, CDKAL1, VEGFA, JAZF1, SLC30A8, CDKNA/2B, HHEX, CDC123, TCF7L2, KCNJ11, INS, DCD, TSPAN8	^[24]
Blood	Microarray	9092, 1181	Panel of Genes DIAGRAM	^[29]
Blood	qRT-PCR, DNA Sequencing	3471 ^	Panel of Genes DIAGRAMv3	^[27]
Blood	qRT-PCR	2598, 2309	TCF7L2, KCNJ11, CDKN2A, PPARG, ADAM30, CDKN2B, IGF2BP2, FTO, CDKAL1, SLC30A8, TSPAN8, CDC123, WFS1, TCF2, ADAMTS9, HHEX-IDE, THADA, JAZF1	^[23]
Blood	qRT-PCR	18,831 ^	TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX	^[25]
Blood	Microarray	3171, 210	UGT1A1	^[31]
Blood Plasma	qRT-PCR	20, 25, 25 **	GAPDH	^[33]
Blood Plasma	qRT-PCR	359, 359	SHBG	^[30]

** T2D patients with complications or comorbidities; ^ cohort study.

2.2. Urine

Urine samples have several advantages above blood: the collection is easy and non-invasive, and the samples are available in large volumes. Anyone can collect urine samples, unlike blood samples which require clinical personnel ^[35]. Urine is not generally used for DNA analysis because the extracted DNA is less stable than blood. So, most T2D genomics studies collect blood and urine samples to study the associations between blood genetic variants and urine proteins (Table 3). However, recent studies have improved DNA extraction from urine ^[36]. One recent T2D study reported associations between mitochondrial DNA (mtDNA) from urine samples with T2D, demonstrating the possibility of urine-extracted DNA as biomarkers for T2D ^[37].

Table 3. Genomics biomarkers from urine samples for T2D.

Sample Type	Profiling Method	Sample Size (Controls, T2D, Other)	Biomarker	Ref
Blood (plasma or serum) and urine	qRT-PCR	4668, 0, 2290 **	Urine albumin-to-creatinine ratio and WFS1 (rs10010131)	^[38]
Blood (plasma) and urine	qRT-PCR	0, 290, 285 **	Urine albumin and PGC-1α (rs8192678)	^[39]
Blood and urine	qRT-PCR	35, 0, 42 **	Urine creatinine ratio with mtDNA	^[37]

** T2D patients with complications or comorbidities.

All T2D genomics urine studies looked into diabetic kidney disease (DKD), including diabetic nephropathy (DN) (Table 3). DKD is one of the significant complications of T2D and is the leading cause of end-stage renal failure. About one-third of diabetes patients will develop kidney disease, so identifying DKD biomarkers is essential for early prevention ^[40]. T2D studies on urinary biomarkers usually include patients with normo-, micro-, and macroalbuminuria.

Two studies found associations between SNPs measured from blood samples with urine proteins. A prospective study reported that rs10010131 in the WFS1 gene is associated with a higher estimated glomerular filtration rate (eGFR) in T2D patients with increased albuminuria. This result suggests a potential role of WFS1 as a risk biomarker of T2D and its kidney complications ^[38]. The second study examined the association between urine albumin and rs8192678 in the PGC-1α gene, a genotype previously related to nephropathy in T2D patients. The genotype was found to be associated with a 70% increase in urine albumin excretion in T2D patients with proteinuria compared to T2D patients with normoalbuminuria, making PGC-1α a candidate prognostic biomarker ^[39].

The only T2D study on urine-extracted DNA looked into mtDNA from healthy controls and T2D patients with and without proteinuria. Plasma and urine mtDNA content significantly differed between T2D patients and controls, where a reduction in plasma mtDNA content and increased urinary mtDNA/creatinine ratio were observed. The study did not report significant differences between T2D patients with and without proteinuria. So, the findings suggest that mtDNA could be a diagnostic biomarker of T2D ^[37].

2.3. Other Non-Invasive Biomarkers and the Use of Metagenomics

Saliva is another non-invasive sample to research biomarkers using genomic technology. Saliva-extracted DNA has comparable quantity and quality with blood, making saliva a suitable source of DNA for genetic studies. So, unlike urine, saliva has been used in large genetic epidemiological and metabolic studies ^[41]. However, there are no T2D studies that profile human DNA from saliva samples. One study profiled polymorphisms for CHGA from peripheral blood leukocytes and measured CHGA concentration in saliva (Table 4). The study found associations between two polymorphisms (rs9658635 in the promoter region and Glu264Asp in exon 6) with higher salivary CHGA production ^[42]. It remains to be seen whether these DNA biomarkers can also be detected in saliva samples.

In addition to human DNA, microbial DNA can also be extracted from saliva. Metagenomics studies the DNA sequences of an entire community of microorganisms ^[43]. The metagenomics studies aim to find associations between microbial profiles and human phenotypes. The technologies most frequently used in this area are 16S rRNA sequencing and whole-genome sequencing ^[16]. A recent study reported a higher relative abundance of Bulleidia, Ruminococcaceae, and Helicobacter pylori in T2D patients compared to healthy controls. However, the study was done in only nine T2D patients, so future studies are needed to confirm the findings ^[44] (Table 4).

Table 4. Genomics and metagenomics biomarkers from saliva and fecal samples for T2D.

Sample Type	Profiling Method	Sample Size (Controls, T2D, Other)	Biomarker	Ref
Fecal	16S rRNA sequencing	20, 20	Gut microbiome (Ruminococcaceae, Lachnospiraceae, and Enterobacteriaceae)	^[45]
Fecal	16S rRNA sequencing	10, 10	Gut microbiome (Akkermansia muciniphila)	^[46]
Fecal	16S rRNA sequencing	1427, 122, 1305 #	Gut microbiome (Bacterial sepecies with enriched ARG)	^[47]
Fecal	16S rRNA sequencing	214, 48, 17 $, 151 *	Gut microbiome (Escherichia, Veillonella, Blautia and Anaerostipes)	^[48]
Fecal	16S rRNA sequencing	55, 0, 71 #, 38 **	Gut microbiome (Ruminococcus torques)	^[49]
Saliva	16S rRNA sequencing	27, 9, 31 #, 20 $, 46 **	Oral Microbiome (Bulleidia, Ruminococcaceae, and Helicobacter pylori)	^[44]

* Pre-diabetes; ** T2D patients with complications or comorbidities; $ T2D patients with treatment; # non-T2D subjects with comorbidities.

T2D genetic studies have also been done using fecal samples. The genetic material from fecal samples is mostly bacterial, although human DNA can be detected in small amounts. There are efforts to improve human DNA extraction from stool samples, but significant challenges still exist for its use in population studies ^[50]. Therefore, all T2D studies with fecal samples are metagenomics studies (Table 4).

In 2016, a study showed evidence that the inflammation of the gut increased the values of biomarkers related to T2D. Microbial signatures of Akkermansia muciniphila demonstrate the existence of inflammation, increasing the risk of T2D before they are reflected by clinical markers ^[46]. Other metagenomics studies identified Akkermansia muciniphila, Ruminococcus torques, Ruminococcaceae, Lachnospiraceae, and Enterobacteriaceae to be associated with T2D ^[51]^[52]^[53]^[54]. In a critically high-risk population, a recent study demonstrated associations between Escherichia, Veillonella, Blautia and Anaerostipes with T2D ^[48]. Another study looked into the abundance of antibiotic resistance genes (ARG) in fecal microbiome profiles. The study found ARG enrichment in T2D patients, which could be potential biomarkers of T2D ^[47]. It remains to be seen if these T2D-associated changes in the gut contribute to T2D pathogenesis.

3. Conclusions and Future Directions

Table 5 summarizes and classifies candidate omics biomarkers reported in at least two studies. Most of these biomarkers are risk, diagnostic, prognostic, and response biomarkers profiled from blood samples. To the best of our knowledge, no monitoring DNA or RNA biomarkers have been reported for T2D. So, more longitudinal studies are needed to identify monitoring biomarkers and novel risk biomarkers. Despite many biomarkers reported, there is little overlap between studies due to study methodology differences. For genomic studies, the PRSs can potentially improve biomedical outcomes via precision medicine on T2D. However, large-scale biobanks with diverse ethnic populations need to be considered in more studies to address the need for a broad genomic approach to this global disease. Lastly, more research on non-invasive biomarkers is needed, especially on T2D patients with no complications. Non-invasive biomarkers would be better for screening and diagnosing T2D in many people. Validation studies are also necessary to confirm the utility of these biomarkers. Nevertheless, these studies have identified potential T2D biomarkers that could help guide future T2D studies.

Table 5. Classification of potential genomics biomarkers of T2D

Biomarker type	Definition	Genomics
Risk	Risk for developing T2D in those who appear healthy	PPARG, FTO, CDC123, TCF7L2, CDKAL1, WFS1, KCNJ11, SLC30A8, ADAMTS9, IGF2BP2, TSPAN8, JAZF1
Diagnostic	Confirming the presence of T2D or identifying a subset of T2D	Ruminococcaceae (Gut microbiome)

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Subjects: Endocrinology & Metabolism; Genetics & Heredity

Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : Luis Felipe Padilla-Martinez

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Update Date: 29 Mar 2022

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