Editorial: Novel Biomarkers for Type 2 Diabetes

Emerging biomarkers in type 2 diabetes mellitus

Diabetes mellitus is a chronic condition caused by high blood glucose resulting from insufficient insulin production or cellular resistance to insulin action or both. It is one of the fastest-growing public health concerns worldwide. Development of long-term nephropathy, retinopathy, neuropathy, and cardiovascular disease are some of the complications commonly associated with poor blood glycemic control. Type 2 diabetes mellitus (T2DM), the most prevalent type of diabetes, accounts for around 95 % of all cases globally. Although middle-aged or older adults are more likely to develop T2DM, its prevalence has grown in children and young people due to increased obesity, sedentary lifestyle and poor nutrition. Furthermore, it is believed that more than 50 % of cases go undiagnosed annually. Routine screening is essential to ensure early detection and reduce risk of life-threatening complications. Herein, we review traditional biomarkers and highlight the ongoing pursuit of novel and efficacious biomarkers driven by the objective of achieving early, precise and prompt diagnoses. It is widely acknowledged that individual biomarkers will inevitably have certain limitations necessitating the need for integrating multiple markers in screening.

Metabolomics of Type 2 Diabetes Mellitus in Sprague Dawley Rats-In Search of Potential Metabolic Biomarkers

Type 2 diabetes mellitus (T2DM) is an expanding global health concern, closely associated with the epidemic of obesity. Individuals with diabetes are at high risk for microvascular and macrovascular complications, which include retinopathy, neuropathy, and cardiovascular comorbidities. Despite the availability of diagnostic tools for T2DM, approximately 30-60% of people with T2DM in developed countries are never diagnosed or detected. Therefore, there is a strong need for a simpler and more reliable technique for the early detection of T2DM. This study aimed to use a non-targeted metabolomic approach to systematically identify novel biomarkers from the serum samples of T2DM-induced Sprague Dawley (SD) rats using a comprehensive two-dimensional gas chromatography coupled with a time-of-flight mass spectrometry (GCxGC-TOF/MS). Fifty-four male Sprague Dawley rats weighing between 160-180 g were randomly assigned into two experimental groups, namely the type 2 diabetes mellitus group (T2DM) (n = 36) and the non-diabetic control group (n = 18). Results from this study showed that the metabolite signature of the diabetic rats was different from that of the non-diabetic control group. The most significantly upregulated metabolic pathway was aminoacyl-t-RNA biosynthesis. Metabolite changes observed between the diabetic and non-diabetic control group was attributed to the increase in amino acids, such as glycine, L-asparagine, and L-serine. Aromatic amino acids, including L-tyrosine, were associated with the risk of future hyperglycemia and overt diabetes. The identified potential biomarkers depicted a good predictive value of more than 0.8. It was concluded from the results that amino acids that were associated with impaired insulin secretion were prospectively related to an increase in glucose levels. Moreover, amino acids that were associated with impaired insulin secretion were prospectively related to an increase in glucose levels.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

THE INCREASED PLASMA LEVELS OF INTERMEDIN IN PATIENTS WITH TYPE 2 DIABETES MELLITUS

Context

Intermedin (IMD) is the member of calcitonin gene-related peptide family, and tightly associated with type 2 diabetes mellitus (T2DM). The change of plasma IMD levels in T2DM is still unknown.

Objective

We aimed to investigate the plasma levels of IMD in patients with T2DM.

Design

Fortyone patients with T2DM who were hospitalized in the endocrinology department of Civil Aviation General Hospital from January 2012 to June 2015 were enrolled, and 44 volunteers were selected as the control group.

Subjects and Methods

Plasma level of IMD was detected by ELISA. Diagnostic value of IMD was analyzed by area under the receiver operating characteristic (ROC) curve (AUC).

Results

The plasma level of IMD in T2DM group was higher than that in the healthy control group, whereas smoking or cardiovascular complications did no influence the IMD levels. IMD levels were correlated with BMI, DBP, triglyceride, uric acid, urea nitrogen, fasting and 2 hours postprandial blood glucose, and HbA1C. The greatest value of AUC for IMD was only 58.73%.

Conclusions

Although plasma levels of IMD were increased in patients with T2DM, the very low diagnostic value of IMD for T2DM might not be used for the disease diagnosis.

Changes of Cytokines in Children With Tic Disorder

Tic disorder (TD) is a common childhood-onset disease associated with abnormal development of brain networks involved in the motor and sensory processing. The underlying pathophysiological mechanisms in TD are still unclear. An involvement of immune mechanisms in its pathophysiology has been proposed. This study investigates the association between the changes of cytokines and the etiology and development of TD. Different expressions of cytokines in a larger number of samples in our study may provide new insights to the field. The levels of cytokines (IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ) were evaluated in 1,724 patients who were clinically diagnosed with TD from 1 to 17.5 years old and 550 were from 6 months to 14.5 years old in the control group. We assessed the levels of cytokines according to the patient's medication status and the severity of the disease. Of the cytokines we investigated, the serum IL-6 concentration of children with TD was significantly higher than that of the control group, while the levels of other cytokines were lower in TD patients. In the patient group whose YTGSS score ranged from 1 to 9, the IL-4, IL-10, and IFN-γ levels increased in medication group compared to unmedication group. Our data suggested that the cytokines (IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ) may play an important role in the etiology and the severity in TD. Whether drug intervention in the early stage of tic disorder has a better effect on children needs further research.

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning

In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.

DNA Methylation and Type 2 Diabetes: Novel Biomarkers for Risk Assessment?

Diabetes is a severe threat to global health. Almost 500 million people live with diabetes worldwide. Most of them have type 2 diabetes (T2D). T2D patients are at risk of developing severe and life-threatening complications, leading to an increased need for medical care and reduced quality of life. Improved care for people with T2D is essential. Actions aiming at identifying undiagnosed diabetes and at preventing diabetes in those at high risk are needed as well. To this end, biomarker discovery and validation of risk assessment for T2D are critical. Alterations of DNA methylation have recently helped to better understand T2D pathophysiology by explaining differences among endophenotypes of diabetic patients in tissues. Recent evidence further suggests that variations of DNA methylation might contribute to the risk of T2D even more significantly than genetic variability and might represent a valuable tool to predict T2D risk. In this review, we focus on recent information on the contribution of DNA methylation to the risk and the pathogenesis of T2D. We discuss the limitations of these studies and provide evidence supporting the potential for clinical application of DNA methylation marks to predict the risk and progression of T2D.

Analysis of an Indian diabetes prevention programme on association of adipokines and a hepatokine with incident diabetes

To study the association and possible predictive role of visfatin, resistin, fetuin-A and chemerin with incident type 2 diabetes (T2DM) among Asian Indians with prediabetes. Their association with insulin resistance, β-cell function, glycaemia and anthropometry were also studied. This is a nested case-control study of a large 2-year prospective prevention trial in persons at high risk of developing T2DM. Baseline HbA1c values between 6.0% (42 mmol/mol) and 6.2% (44 mmol/mol) were chosen for this analysis (n = 144). At follow-up, persons with incident T2DM (HbA1c ≥ 6.5%, 48 mmol/mol) were grouped as cases (n = 72) and those reverted to normoglycaemia, (HbA1c < 5.7% (39 mmol/mol) as controls (n = 72). Insulin resistance showed the strongest association with incident T2DM ((Odds Ratio (OR): 23.22 [95%CI 6.36-84.77]; p < 0.0001). Baseline visfatin (OR: 6.56 [95%CI 2.21-19.5]; p < 0.001) and fetuin-A (OR: 1.01 [95%CI (1.01-1.04)]; p < 0.0001) independently contributed to the conversion of prediabetes to T2DM. The contribution was significantly higher when their elevated levels coexisted (OR: 12.63 [95%CI 3.57-44.63]; p < 0.0001). The area under the curve was 0.77 ± SE 0.4 (95%CI 0.69-0.85) and 0.80 ± SE 0.04 (95%CI 0.73-0.88) for visfatin (median 17.7 ng/ml, sensitivity and specificity: 75%, p < 0.0001) and fetuin-A (mean 236.2 µg/ml, sensitivity: 71%, specificity: 75%, p < 0.0001) respectively. Higher baseline visfatin and fetuin-A concentrations are strongly associated with incident T2DM and are predictive of future diabetes.

The susceptibility of SERPINE1 rs1799889 SNP in diabetic vascular complications: a meta-analysis of fifty-one case-control studies

BACKGROUND

The serine protease inhibitor-1 (SERPINE1) rs1799889 single nucleotide polymorphism (SNP) has been constantly associated with diabetes mellitus (DM) and its vascular complications. The aim of this meta-analysis was to evaluate this association with combined evidences.

METHODS

The systematic search was performed for studies published up to March 2021 which assess the associations between SERPINE1 rs1799889 SNP and the risks of DM, diabetic retinopathy (DR), diabetic cardiovascular disease (CVD) and diabetic nephropathy (DN). Only case-control studies were identified, and the linkage between SERPINE1 rs1799889 polymorphism and diabetic vascular risks were evaluated using genetic models.

RESULTS

51 comparisons were enrolled. The results revealed a significant association with diabetes risk in overall population (allelic: OR = 1.34, 95 % CI = 1.14-1.57, homozygous: OR = 1.66, 95 % CI = 1.23-2.14, heterozygous: OR = 1.35, 95 % CI = 1.08-1.69, dominant: OR = 1.49, 95 % CI = 1.18-1.88, recessive: OR = 1.30, 95 % CI = 1.06-1.59) as well as in Asian descents (allelic: OR = 1.45, 95 % CI = 1.16-1.82, homozygous: OR = 1.88, 95 % CI = 1.29-2.75, heterozygous: OR = 1.47, 95 % CI = 1.08-2.00, dominant: OR = 1.64, 95 % CI = 1.21-2.24, recessive: OR = 1.46, 95 % CI = 1.09-1.96). A significant association was observed with DR risk (homozygous: OR = 1.25, 95 % CI = 1.01-1.56, recessive: OR = 1.20, 95 % CI = 1.01-1.43) for overall population, as for the European subgroup (homozygous: OR = 1.32, 95 % CI = 1.02-1.72, recessive: OR = 1.38, 95 % CI = 1.11-1.71). A significant association were shown with DN risk for overall population (allelic: OR = 1.48, 95 % CI = 1.15-1.90, homozygous: OR = 1.92, 95 % CI = 1.26-2.95, dominant: OR = 1.41, 95 % CI = 1.01-1.97, recessive: OR = 1.78, 95 % CI = 1.27-2.51) and for Asian subgroup (allelic: OR = 1.70, 95 % CI = 1.17-2.47, homozygous: OR = 2.46, 95 % CI = 1.30-4.66, recessive: OR = 2.24, 95 % CI = 1.40-3.59) after ethnicity stratification. No obvious association was implied with overall diabetic CVD risk in any genetic models, or after ethnicity stratification.

CONCLUSIONS

SERPINE1 rs1799889 4G polymorphism may outstand for serving as a genetic synergistic factor in overall DM and DN populations, positively for individuals with Asian descent. The association of SERPINE1 rs1799889 SNP and DR or diabetic CVD risks was not revealed.

Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India

BACKGROUND

Non-communicable diseases (NCDs) have become a major cause of morbidity and mortality in India. Perturbation of host-microbiome interactions may be a key mechanism by which lifestyle-related risk factors such as tobacco use, alcohol consumption, and physical inactivity may influence metabolic health. There is an urgent need to identify relevant dysmetabolic traits for predicting risk of metabolic disorders, such as diabetes, among susceptible Asian Indians where NCDs are a growing epidemic.

METHODS

Here, we report the first in-depth phenotypic study in which we prospectively enrolled 218 adults from urban and rural areas of Central India and used multiomic profiling to identify relationships between microbial taxa and circulating biomarkers of cardiometabolic risk. Assays included fecal microbiota analysis by 16S ribosomal RNA gene amplicon sequencing, quantification of serum short chain fatty acids by gas chromatography-mass spectrometry, and multiplex assaying of serum diabetic proteins, cytokines, chemokines, and multi-isotype antibodies. Sera was also analysed for N-glycans and immunoglobulin G Fc N-glycopeptides.

RESULTS

Multiple hallmarks of dysmetabolism were identified in urbanites and young overweight adults, the majority of whom did not have a known diagnosis of diabetes. Association analyses revealed several host-microbe and metabolic associations.

CONCLUSIONS

Host-microbe and metabolic interactions are differentially shaped by body weight and geographic status in Central Indians. Further exploration of these links may help create a molecular-level map for estimating risk of developing metabolic disorders and designing early interventions.

Circulating proteins protect against renal decline and progression to end-stage renal disease in patients with diabetes

Diabetic kidney disease (DKD) and its major clinical manifestation, progressive renal decline that leads to end-stage renal disease (ESRD), are a major health burden for individuals with diabetes. The disease process that underlies progressive renal decline comprises factors that increase risk as well as factors that protect against this outcome. Using untargeted proteomic profiling of circulating proteins from individuals in two independent cohorts with type 1 and type 2 diabetes and varying stages of DKD followed for 7 to 15 years, we identified three elevated plasma proteins-fibroblast growth factor 20 (OR, 0.69; 95% CI, 0.54 to 0.88), angiopoietin-1 (OR, 0.72; 95% CI, 0.57 to 0.91), and tumor necrosis factor ligand superfamily member 12 (OR, 0.75; 95% CI, 0.59 to 0.95)-that were associated with protection against progressive renal decline and progression to ESRD. The combined effect of these three protective proteins was demonstrated by very low cumulative risk of ESRD in those who had baseline concentrations above median for all three proteins, whereas the cumulative risk of ESRD was high in those with concentrations below median for these proteins at the beginning of follow-up. This protective effect was shown to be independent from circulating inflammatory proteins and clinical covariates and was confirmed in a third cohort of diabetic individuals with normal renal function. These three protective proteins may serve as biomarkers to stratify diabetic individuals according to risk of progression to ESRD and might also be investigated as potential therapeutics to delay or prevent the onset of ESRD.

Genome-wide association study of circulating interleukin 6 levels identifies novel loci

Interleukin 6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been associated with an increased risk of complex disease pathogenesis. We conducted a two-staged, discovery and replication meta genome-wide association study (GWAS) of circulating serum IL-6 levels comprising up to 67 428 (ndiscovery = 52 654 and nreplication = 14 774) individuals of European ancestry. The inverse variance fixed effects based discovery meta-analysis, followed by replication led to the identification of two independent loci, IL1F10/IL1RN rs6734238 on chromosome (Chr) 2q14, (Pcombined = 1.8 × 10-11), HLA-DRB1/DRB5 rs660895 on Chr6p21 (Pcombined = 1.5 × 10-10) in the combined meta-analyses of all samples. We also replicated the IL6R rs4537545 locus on Chr1q21 (Pcombined = 1.2 × 10-122). Our study identifies novel loci for circulating IL-6 levels uncovering new immunological and inflammatory pathways that may influence IL-6 pathobiology.

Genomics, Proteomics and Metabolomics Approaches for Predicting Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients

BACKGROUND

There is a continuous rise in the prevalence of type 2 diabetes mellitus (T2DM) worldwide and most patients are unaware of the presence of this chronic disease at the early stages. T2DM is associated with complications related to long-term damage and failure of multiple organ systems caused by vascular changes associated with glycated end products, oxidative stress, mild inflammation, and neovascularization. Among the most frequent complications of T2DM observed in about 20-40% of T2DM patients is diabetes nephropathy (DN).

METHODS

A literature search was made in view of highlighting the novel applications of genomics, proteomics and metabolomics, as the new prospective strategy for predicting DN in T2DM patients.

RESULTS

The complexity of DN requires a comprehensive and unbiased approach to investigate the main causes of disease and identify the most important mechanisms underlying its development. With the help of evolving throughput technology, rapidly evolving information can now be applied to clinical practice.

DISCUSSION

DN is also the leading cause of end-stage renal disease and comorbidity independent of T2DM. In terms of the comorbidity level, DN has many phenotypes; therefore, timely diagnosis is required to prevent these complications. Currently, urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) are gold standards for assessing glomerular damage and changes in renal function. However, GFR estimation based on creatinine is limited to hyperfiltration status; therefore, this makes albuminuria and eGFR indicators less reliable for early-stage diagnosis of DN.

CONCLUSION

The combination of genomics, proteomics, and metabolomics assays as suitable biological systems can provide new and deeper insights into the pathogenesis of diabetes, as well as discover prospects for developing suitable and targeted interventions.

A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes

BACKGROUND

Individuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict.

METHODS

We designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which "omics" studies will be built. Cases (n = 535) and controls (n = 895) were defined as having an annual eGFR decline of ≥3 and <1 mL/min/1.73 m2, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques.

RESULTS

At baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m2, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was -5.7 and 0.6 mL/min/1.73 m2, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68-0.79; p < 0.001).

CONCLUSION

Known risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.

Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

CONTEXT

There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease.

OBJECTIVE

To investigate associations between plasma metabolites and kidney function in people with type 2 diabetes (T2D).

DESIGN

3089 samples from individuals with T2D, collected between 1999 and 2015, from 5 independent Dutch cohort studies were included. Up to 7 years follow-up was available in 1100 individuals from 2 of the cohorts.

MAIN OUTCOME MEASURES

Plasma metabolites (n = 149) were measured by nuclear magnetic resonance spectroscopy. Associations between metabolites and estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and eGFR slopes were investigated in each study followed by random effect meta-analysis. Adjustments included traditional cardiovascular risk factors and correction for multiple testing.

RESULTS

In total, 125 metabolites were significantly associated (PFDR = 1.5×10-32 - 0.046; β = -11.98-2.17) with eGFR. Inverse associations with eGFR were demonstrated for branched-chain and aromatic amino acids (AAAs), glycoprotein acetyls, triglycerides (TGs), lipids in very low-density lipoproteins (VLDL) subclasses, and fatty acids (PFDR < 0.03). We observed positive associations with cholesterol and phospholipids in high-density lipoproteins (HDL) and apolipoprotein A1 (PFDR < 0.05). Albeit some metabolites were associated with UACR levels (P < 0.05), significance was lost after correction for multiple testing. Tyrosine and HDL-related metabolites were positively associated with eGFR slopes before adjustment for multiple testing (PTyr = 0.003; PHDLrelated < 0.05), but not after.

CONCLUSIONS

This study identified metabolites associated with impaired kidney function in T2D, implying involvement of lipid and amino acid metabolism in the pathogenesis. Whether these processes precede or are consequences of renal impairment needs further investigation.

The Clinical Utility of Salivary Biomarkers in the Identification of Type 2 Diabetes Risk and Metabolic Syndrome

Type 2 diabetes is traditionally diagnosed by the use of an oral glucose tolerance test and/or HbA1c, both of which require serum collection. Various biomarkers, which are measurable biological substances that provide clinical insight on disease state, have also been effective in the early identification and risk prediction of inflammatory diseases. Measuring biomarker concentrations has traditionally been obtained through serum collection as well. However, numerous biomarkers are detectable in saliva. Salivary analysis has more recently been introduced into research as a potential non-invasive, cost-effective diagnostic for the early identification of type 2 diabetes risk in adults and youth. Therefore, the purpose of this review was to compare 6 established inflammatory biomarkers of type 2 diabetes, in serum and saliva, and determine if similar diagnostic effectiveness is seen in saliva. A lack of standardized salivary analysis, processing, and collection accounts for errors and inconsistencies in conclusive data amongst studies. Proposing a national standardization in salivary analysis, coupled with increased data and research on the utility of saliva as a diagnostic, poses the potential for salivary analysis to be used in diagnostic settings.