Novel biomarkers for prediabetes, diabetes, and associated complications

Vitamin D, Calcium, Magnesium, and Potassium Consumption and Markers of Glucose Metabolism in the Hispanic Community Health Study/Study of Latinos

OBJECTIVE

This was a cross-sectional study associating vitamin D, calcium, magnesium, and potassium intakes with markers of glucose metabolism in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

METHODS

HCHS/SOL is a multicenter, prospective, population-based cohort study on Hispanics/Latinos aged 18-74 years in the US. For this analysis, we included 10,609 participants who were free of diabetes. Analysis of covariance was used to assess associations of a range of micronutrient intake on the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), Hemoglobin A1c (HbA1c), and 2-hour post-oral glucose tolerance test (2h-plasma glucose) separately for normoglycemic and with pre-diabetes, after controlling for important confounders. All analyses accounted for the complex sample design and sampling weights.

RESULTS

HOMA-IR levels were significantly lower among adults with intakes in the highest quartile for vitamin D, magnesium, and potassium compared to the respective lowest quartiles, for those with normoglycemia and pre-diabetes, even after adjusting for confounders, such as diet quality (p < 0.05). For those with pre-diabetes, HOMA-IR levels were also significantly lower for those in the highest quartile of calcium intake. However, 2h-plasma glucose was significantly higher in those with intakes higher than quartile 1 for vitamin D and calcium among those with normoglycemia and significantly higher in quartile 3 of potassium intake for those with pre-diabetes, p < 0.05. No significant associations were found for HbA1c in either group.

CONCLUSIONS

Higher consumption of vitamin D, magnesium, and potassium are associated with optimal levels of HOMA-IR among participants with normoglycemia and pre-diabetes.

Identification of Pre-Diabetic Biomarkers in the Progression of Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is a major global health issue. The development of T2DM is gradual and preceded by the pre-diabetes mellitus (pre-DM) stage, which often remains undiagnosed. This study aimed to identify novel pre-DM biomarkers in a high-fat diet (HFD)-induced pre-DM mouse model. Male C57BL/6J mice were fed either a chow diet or HFD for 12 weeks. Serum and liver samples were isolated in a time-dependent manner. Semi-quantitative assessment of secretory cytokines was performed by cytokine array analysis, and 13 cytokines were selected for further analysis based on the changes in expression levels in the pre-DM and T2DM stages. HFD-fed mice gained body weight and exhibited high serum lipid, liver enzyme, glucose, and insulin levels during the progression of pre-DM to T2DM. The mRNA expression of inflammatory and lipogenic genes was elevated in HFD-fed mice The mRNA expression of Fc receptor, IgG, low affinity Iib, lectin, galactose binding, soluble 1, vascular cell adhesion molecule 1, insulin-like growth factor binding protein 5, and growth arrest specific 6 was elevated in the pre-DM, which was confirmed by measuring protein levels. Our study identified novel pre-DM biomarkers that may help to delay or prevent the progression of T2DM.

Current Challenges and Future Trends of Enzymatic Paper-Based Point-of-Care Testing for Diabetes Mellitus Type 2

A point-of-care (POC) can be defined as an in vitro diagnostic test that can provide results within minutes. It has gained enormous attention as a promising tool for biomarkers detection and diagnosis, as well as for screening of chronic noncommunicable diseases such as diabetes mellitus. Diabetes mellitus type 2 is one of the metabolic disorders that has grown exponentially in recent years, becoming one of the greatest challenges to health systems. Early detection and accurate diagnosis of this disorder are essential to provide adequate treatments. However, efforts to reduce incidence should remain not only in these stages but in developing continuous monitoring strategies. Diabetes-monitoring tools must be accessible and affordable; thus, POC platforms are attractive, especially paper-based ones. Paper-based POCs are simple and portable, can use different matrixes, do not require highly trained staff, and are less expensive than other platforms. These advantages enhance the viability of its application in low-income countries and hard-to-reach zones. This review aims to present a critical summary of the main components required to create a sensitive and affordable enzymatic paper-based POC, as well as an oriented analysis to highlight the main limitations and challenges of current POC devices for diabetes type 2 monitoring and future research opportunities in the field.

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.

Metabolic Dysfunction Biomarkers as Predictors of Early Diabetes

During the pathophysiological course of type 2 diabetes (T2D), several metabolic imbalances occur. There is increasing evidence that metabolic dysfunction far precedes clinical manifestations. Thus, knowing and understanding metabolic imbalances is crucial to unraveling new strategies and molecules (biomarkers) for the early-stage prediction of the disease's non-clinical phase. Lifestyle interventions must be made with considerable involvement of clinicians, and it should be considered that not all patients will respond in the same manner. Individuals with a high risk of diabetic progression will present compensatory metabolic mechanisms, translated into metabolic biomarkers that will therefore show potential predictive value to differentiate between progressors/non-progressors in T2D. Specific novel biomarkers are being proposed to entrap prediabetes and target progressors to achieve better outcomes. This study provides a review of the latest relevant biomarkers in prediabetes. A search for articles published between 2011 and 2021 was conducted; duplicates were removed, and inclusion criteria were applied. From the 29 studies considered, a survey of the most cited (relevant) biomarkers was conducted and further discussed in the two main identified fields: metabolomics, and miRNA studies.

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.

Severe respiratory failure in a patient with COVID-19 and acromegaly: rapid improvement after adding octreotide

Acromegaly is a rare disorder of unregulated hypersecretion of growth hormone that causes cardiovascular, metabolic and respiratory complications. Herein, we describe the case of a middle-aged man admitted to the hospital with severe COVID-19 found to have clinical and biochemical evidence of acromegaly. His respiratory status declined despite initiation of standard treatments, prompting initiation of subcutaneous octreotide. Following initiation of this therapy, he rapidly improved and was discharged from the hospital 2 days later. Subsequent workup revealed a pituitary macroadenoma that was surgically removed, with improvement in his acromegaly symptoms. COVID-19 disease severity is increased by pre-existing diabetes, lung disease and immunosuppression. Although this patient had obstructive sleep apnoea and pre-diabetes, we hypothesise that our patient's acromegaly contributed to his severe course, as reflected by his rapid improvement after starting treatment with subcutaneous octreotide. Acromegaly may predispose to more severe outcomes in patients with COVID-19.

Glycosylated Hemoglobin as a Predictor of Sepsis and All-Cause Mortality in Trauma Patients

BACKGROUND AND PURPOSE

Infection is a common comorbidity and cause of death in emergency trauma patients, especially in diabetic patients. Once the patients are admitted, they are more susceptible to further complications like sepsis and resultant increase in in-hospital mortality. Therefore, it is necessary to evaluate risk factors associated with sepsis after trauma and death in trauma patients.

METHODS

A total of 397 trauma patients were divided into 2 groups according to HbA1c level, HbA1c: <6.5% (n = 259), HbA1c: >6.5% (n = 138), and baseline clinical characteristics were collected. The independent risk factors of sepsis associated with trauma were screened using univariate and multivariate logistic regression analysis. Cox proportional hazards regression analysis was used to investigate risk factors for 30-day all-cause mortality.

RESULTS

The sepsis incidence (76.1% vs 35.9%, P<0.001) and mortality rate (29.7% vs 7.3%, P<0.001) were significantly higher in HbA1c>6.5% group. Multivariate logistic regression analysis revealed that the independent risk factors of sepsis after trauma were diabetes (OR: 3.1, 95% CI: 1.41-6.79), hypertension (OR: 2.55, 95% CI: 1.35-4.82), coagulation disorder (OR: 3.45, 95% CI: 1.23-9.67), creatinine (OR: 3.71, 95% CI: 1.66-8.31), urea nitrogen (OR: 0.96, 95% CI: 0.92-0.99), HbA1c%>6.5 (OR: 2.05, 95% CI: 1.65-2.54), increase in body mass index (OR: 1.08, 95% CI: 1.03-1.13) and lower initial GCS score (OR: 0.93, 95% CI: 0.88-0.99). Multivariable Cox proportional hazard analysis revealed that male (HR: 1.94, 95% CI: 1.21-3.12), HbA1c >6.5% (HR: 1.45, 95% CI: 1.32-1.6), albumin (HR: 0.54, 95% CI: 0.34-0.86), creatinine (HR: 1.02, 95% CI: 1.01-1.03), APTT (HR: 1.02, 95% CI: 1.01-1.03), SOFA score (HR: 1.2, 95% CI: 1.1-1.31), age >65 years (HR: 3.21, 95% CI: 1.95-5.3) were independent risk factor for trauma patients' mortality.

CONCLUSION

The prevalence of sepsis and mortality was higher in trauma patients with HbA1c >6.5%. HbA1c was independent risk factor for sepsis and all cases of mortality in trauma patients.

Experts' opinion on the detection and management of prediabetes in Lebanon

Given that the complications of type 2 diabetes can start at an early stage, early detection and appropriate management of prediabetes are essential. We aimed to develop an expert opinion on prediabetes in Lebanon to pave the way for national guidelines tailored for the Lebanese population in the near future. A panel of seven diabetes experts conducted a thorough literature review and discussed their opinions and experiences before coming up with a set of preliminary recommendations for the detection and management of prediabetes in Lebanon. Lebanese physicians employ multiple tests for the diagnosis of prediabetes and no national cut-off values exist. The panel agreed that prediabetes screening should be focused on patients exceeding 45 years of age with otherwise no risk factors and on adults with risk factors. The panel reached that fasting plasma glucose (FPG) and HbA1c should be used for prediabetes diagnosis in Lebanon. FPG values of 100-125 mg/dL or HbA1c values of 5.7%-6.4% were agreed upon as indicative of prediabetes. For the management of prediabetes, a three-step approach constituting lifestyle modifications, pharmacological treatment and bariatric surgery is recommended. There should be more focus on research on prediabetes in Lebanon. This preliminary report will be further discussed with the Lebanese Society of Endocrinology, Diabetes and Lipids in 2021 in order to come up with the first Lebanese national guidelines for the detection and management of prediabetes in Lebanon.

A Preliminary Study Showing the Impact of Genetic and Dietary Factors on GC-MS-Based Plasma Metabolome of Patients with and without PROX1-Genetic Predisposition to T2DM up to 5 Years Prior to Prediabetes Appearance

Risk factors for type 2 diabetes mellitus (T2DM) consist of a combination of an unhealthy, imbalanced diet and genetic factors that may interact with each other. Single nucleotide polymorphism (SNP) in the prospero homeobox 1 (PROX1) gene is a strong genetic susceptibility factor for this metabolic disorder and impaired β-cell function. As the role of this gene in T2DM development remains unclear, novel approaches are needed to advance the understanding of the mechanisms of T2DM development. Therefore, in this study, for the first time, postprandial changes in plasma metabolites were analysed by GC–MS in nondiabetic men with different PROX1 genotypes up to 5 years prior to prediabetes appearance. Eighteen contestants (12 with high risk (HR) and 6 with low risk (LR) genotype) participated in high-carbohydrate (HC) and normo-carbohydrate (NC) meal-challenge tests. Our study concluded that both meal-challenge tests provoked changes in 15 plasma metabolites (amino acids, carbohydrates, fatty acids and others) in HR, but not LR genotype carriers. Postprandial changes in the levels of some of the detected metabolites may be a source of potential specific early disturbances possibly associated with the future development of T2DM. Thus, accurate determination of these metabolites can be important for the early diagnosis of this metabolic disease.

Luteolin, an aryl hydrocarbon receptor antagonist, alleviates diabetic retinopathy by regulating the NLRP/NOX4 signalling pathway: Experimental and molecular docking study

OBJECTIVE

The present report evaluates the protective effects of luteolin against diabetic retinopathy (DR).

MATERIALS AND METHODS

Diabetes was induced in rats by i.p. administration of 60 mg/kg of streptozotocin (STZ), followed by treatment with luteolin for 4 weeks. The effects of luteolin were determined based on the blood glucose and cytokine levels, and parameters of oxidative stress in retinal tissue of DR rats. The diameter of retinal vessels was estimated by fundus photography. A Western blot assay was used to determine the expression of apoptotic proteins and Nod-like receptor 3 (NLRP3) pathway proteins in the retina of DR rats. A molecular docking study was performed to evaluate the interaction between luteolin and NLRP3.

RESULTS

The level of blood glucose was reduced in the luteolin-treated group compared with the DR group. Reductions in cytokines and oxidative stress were observed in the retinal tissues of the luteolin-treated group relative to the DR group. Moreover, treatment with luteolin reduced the expression of NLRP1, NOX4, TXNIP, and NLRP3 proteins, and ameliorated the altered expression of apoptotic proteins in the retina of DR rats.

CONCLUSION

In conclusion, luteolin prevents retinal apoptosis in DR rats by regulating the NLRP/NOX4 signalling pathway.

Association of Protein Z with Prediabetes and Type 2 Diabetes

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a progressive metabolic disease. Early detection of prediabetes is important to reduce the risk of T2DM. Some cytokines are known to be associated with T2DM. Therefore, we aimed to identify cytokines as novel biomarkers of glucose dysmetabolism.

METHODS

The first stage of the study included 43 subjects (13 subjects with newly diagnosed T2DM, 13 with prediabetes, and 16 with normoglycemia) for cytokine microarray analysis. Blood samples of the subjects were assessed for 310 cytokines to identify potential indicators of prediabetes. The second stage included 142 subjects (36 subjects with T2DM, 35 with prediabetes, and 71 with normoglycemia) to validate the potential cytokines associated with prediabetes.

RESULTS

We identified 41 cytokines that differed by 1.5-fold or more in at least one out of the three comparisons (normoglycemia vs. prediabetes, normoglycemia vs. T2DM, and prediabetes vs. T2DM) among 310 cytokines. Finally, we selected protein Z (PROZ) and validated this finding to determine its association with prediabetes. Plasma PROZ levels were found to be decreased in patients with prediabetes (1,490.32±367.19 pg/mL) and T2DM (1,583.34±465.43 pg/mL) compared to those in subjects with normoglycemia (1,864.07±450.83 pg/mL) (P<0.001). There were significantly negative correlations between PROZ and fasting plasma glucose (P=0.001) and hemoglobin A1c (P=0.010).

CONCLUSION

PROZ levels were associated with prediabetes and T2DM. We suggest that PROZ may be a promising biomarker for the early detection of prediabetes. Further large-scale studies are needed to evaluate the relationship and mechanism between PROZ and prediabetes and T2DM.

Potential clinical biomarkers in rheumatoid arthritis with an omic approach

Objective

To aid in the selection of the most suitable therapeutic option in patients with diagnosis of rheumatoid arthritis according to the phase of disease, through the review of articles that identify omics biological markers.

Methods

A systematic review in PubMed/Medline databases was performed. We searched articles from August 2014 to September 2019, in English and Spanish, filtered by title and full text; and using the terms "Biomarkers" AND “Rheumatoid arthritis".

Results

This article supplies an exhaustive review from research of objective measurement, omics biomarkers and how disease activity appraise decrease unpredictability in treatment determinations, and finally, economic, and clinical outcomes of treatment options by biomarkers’ potential influence. A total of 122 articles were included. Only 92 met the established criteria for review purposes and 17 relevant references about the topic were included as well. Therefore, it was possible to identify 196 potential clinical biomarkers: 22 non-omics, 20 epigenomics, 33 genomics, 21 transcriptomics, 78 proteomics, 4 glycomics, 1 lipidomics and 17 metabolomics.

Conclusion

A biomarker is a measurable indicator of some, biochemical, physiological, or morphological condition; evaluable at a molecular, biochemical, or cellular level. Biomarkers work as indicators of physiological or pathological processes, or as a result of a therapeutic management. In the last five years, new biomarkers have been identified, especially the omics, which are those that proceed from the investigation of genes (genomics), metabolites (metabolomics), and proteins (proteomics). These biomarkers contribute to the physician choosing the best therapeutic option in patients with rheumatoid arthritis.

CDATA[The Effect of Oxidative Stress and Antioxidant Therapies on Pancreatic β-cell Dysfunction: Results from in Vitro and in Vivo Studies

BACKGROUND

Oxidative stress is a hallmark of many diseases. A growing body of evidence suggests that hyperglycemia-induced oxidative stress plays an important role in pancreatic β-cells dysfunction and apoptosis, as well as in the development and progression of diabetic complications. Considering the vulnerability of pancreatic β-cells to oxidative damage, the induction of endogenous antioxidant enzymes or exogenous antioxidant administration has been proposed to protect pancreatic β-cells from damage.

OBJECTIVES

The present review aims to provide evidence of the effect of oxidative stress and antioxidant therapies on pancreatic β-cell function, based on in vitro and in vivo studies.

METHODS

The MEDLINE and EMBASE databases were searched to retrieve available data.

RESULTS

Due to poor endogenous antioxidant mechanisms, pancreatic β-cells are extremely sensitive to Reactive Oxygen Species (ROS). Many natural extracts have been tested in vitro in pancreatic β-cell lines in terms of their antioxidant and diabetes mellitus ameliorating effects, and the majority of them have shown a dose-dependent protective role. On the other hand, there is relatively limited evidence regarding the in vitro antioxidant effects of antidiabetic drugs on pancreatic β -cells. Concerning in vivo studies, several natural extracts have shown beneficial effects in the setting of diabetes by decreasing blood glucose and lipid levels, increasing insulin sensitivity, and by up-regulating intrinsic antioxidant enzyme activity. However, there is limited evidence obtained from in vivo studies regarding antidiabetic drugs.

CONCLUSION

Antioxidants hold promise for developing strategies aimed at the prevention or treatment of diabetes mellitus associated with pancreatic β-cells dysfunction, as supported by in vitro and in vivo studies. However, more in vitro studies are required for drugs.

Identification of Novel Biomarkers for Pre-diabetic Diagnosis Using a Combinational Approach

Reliable protein markers for pre-diabetes in humans are not clinically available. In order to identify novel and reliable protein markers for pre-diabetes in humans, healthy volunteers and patients diagnosed with pre-diabetes and stroke were recruited for blood collection. Blood samples were collected from healthy and pre-diabetic subjects 12 h after fasting. BMI was calculated from body weight and height. Fasting blood glucose (FBG), glycated hemoglobin (HbA1C), triglyceride (TG), total cholesterol, high-density lipoprotein, low-density lipoprotein (LDL), insulin and albumin were assayed by automated clinical laboratory methods. We used a quantitative proteomics approach to identify 1074 proteins from the sera of pre-diabetic and healthy subjects. Among them, 500 proteins were then selected using Mascot analysis scores. Further, 70 out of 500 proteins were selected via volcano plot analysis according to their statistical significance and average relative protein ratio. Eventually, 7 serum proteins were singled out as candidate markers for pre-diabetes due to their diabetic relevance and statistical significance. Immunoblotting data demonstrated that laminin subunit alpha 2 (LAMA2), mixed-lineage leukemia 4 (MLL4), and plexin domain containing 2 (PLXDC2) were expressed in pre-diabetic patients but not healthy volunteers. Receiver operating characteristic curve analysis indicated that the combination of the three proteins has greater diagnostic efficacy than any individual protein. Thus, LAMA2, MLL4 and PLXDC2 are novel and reliable serum protein markers for pre-diabetic diagnosis in humans.

Sleeping Duration, Napping and Snoring in Association with Diabetes Control among Patients with Diabetes in Qatar

BACKGROUND

Poor glycemic control is associated with chronic life-threatening complications. Several studies have revealed that sleep status is associated with glycemic control.

AIM

to examine the association between sleep duration, quality and glycemic control among adults with diabetes.

METHODS

Data on 2500 participants aged 18-60 years were collected from the Qatar Biobank (QBB). Sleep duration and quality were assessed by a self-completed health and lifestyle questionnaire, and glycemic control was assessed using HbA1c. Logistic regression was used to assess the association between sleep duration, napping, snoring and poor glycemic control.

RESULTS

After adjusting for age and gender, sleep duration was not associated with poor glycemic control. Lack of association persisted after controlling for smoking, physical activity, education, BMI, fruit and vegetable intake, insulin and medication use. However, sleeping for long hours at night (≥8 h) had a trend in increasing the risk of poor glycemic control (OR = 1.28; 95% CI: 0.94-1.74). Napping was positively associated with poor glycemic control. After adjusting for age and gender, patients who reported "sometimes, frequently, or always" napping had more than 30% increased risk of poor control as compared to patients who reported "never/rarely" napping. Snoring was not associated with poor glycemic control among the study sample when adjusted for age and gender (p = 0.61). Other factors were found to be associated with a better glycemic control such as female, high educational and high physical activity level.

CONCLUSIONS

our results suggest that napping may be an independent risk factor for a poor glycemic control in diabetes; further investigations are required.

Type 2 Diabetes as a Determinant of Parkinson's Disease Risk and Progression

Background:

Type 2 diabetes (T2DM) and Parkinson’s disease (PD) are prevalent diseases that affect an aging population. Previous systematic reviews and meta-analyses have explored the relationship between diabetes and the risk of PD, but the results have been conflicting.

Objective:

The objective was to investigate T2DM as a determinant of PD through a meta-analysis of observational and genetic summary data.

Methods:

A systematic review and meta-analysis of observational studies was undertaken by searching 6 databases. We selected the highest-quality studies investigating the association of T2DM with PD risk and progression. We then used Mendelian randomization (MR) to investigate the causal effects of genetic liability toward T2DM on PD risk and progression, using summary data derived from genome-wide association studies.

Results:

In the observational part of the study, pooled effect estimates showed that T2DM was associated with an increased risk of PD (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.07–1.36), and there was some evidence that T2DM was associated with faster progression of motor symptoms (standardized mean difference [SMD] 0.55, 95% CI 0.39–0.72) and cognitive decline (SMD −0.92, 95% CI −1.50 to −0.34). Using MR, we found supportive evidence for a causal effect of diabetes on PD risk (inverse-variance weighted method [IVW] OR 1.08, 95% CI 1.02–1.14; P = 0.010) and some evidence of an effect on motor progression (IVW OR 1.10, 95% CI 1.01–1.20; P = 0.032) but not on cognitive progression.

Conclusions:

Using meta-analyses of traditional observational studies and genetic data, we observed convincing evidence for an effect of T2DM on PD risk and new evidence to support a role in PD progression.

Novel Biomarkers in the Diagnosis of Benign and Malignant Gastrointestinal Diseases

BACKGROUND

Various noninvasive biomarkers have been used in the diagnosis, prognosis, and treatment of different gastrointestinal (GI) diseases for years. Novel technological developments and profound perception of molecular processes related to GI diseases over the last decade have allowed researchers to evaluate genetic, epigenetic, and many other potential molecular biomarkers in different diseases and clinical settings. Here, we present a review of recent and most relevant articles in order to summarize major findings on novel biomarkers in the diagnosis of benign and malignant GI diseases.

SUMMARY

Genetic variations, noncoding RNAs (ncRNAs), cell-free DNA (cfDNA), and microbiome-based biomarkers have been extensively analyzed as potential biomarkers in benign and malignant GI diseases. Multiple single-nucleotide polymorphisms have been linked with a number of GI diseases, and these observations are further being used to build up disease-specific genetic risk scores. Micro-RNAs and long ncRNAs have a large potential as noninvasive biomarkers in the management of inflammatory bowel diseases and GI tumors. Altered microbiome profiles were observed in multiple GI diseases, but most of the findings still lack translational clinical application. As of today, cfDNA appears to be the most potent biomarker for early detection and screening of GI cancers. Key Messages: Novel noninvasive molecular biomarkers show huge potential as useful tools in the diagnostics and management of different GI diseases. However, the use of these biomarkers in real-life clinical practice still remains limited, and further large studies are needed to elucidate the ultimate role of these potential noninvasive clinical tools.

A combined strategy of feature selection and machine learning to identify predictors of prediabetes

OBJECTIVE

To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population.

MATERIALS AND METHODS

We analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011-2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance.

RESULTS

Prediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05).

DISCUSSION

Combined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified.

CONCLUSION

This work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making.

Metabolomics Identifies Biomarker Signatures to Differentiate Pancreatic Cancer from Type 2 Diabetes Mellitus in Early Diagnosis

METHODS

Plasma metabolic profiles in 26 PC patients, 27 DM patients, and 23 healthy volunteers were examined using an ultraperformance liquid chromatography coupled with tandem mass spectrometry platform. Differential metabolite ions were then identified using the principal component analysis (PCA) model and the orthogonal partial least-squares discrimination analysis (OPLS-DA) model. The diagnosis performance of metabolite biomarkers was validated by logistic regression models.

RESULTS

We established a PCA model (R2X = 23.5%, Q2 = 8.21%) and an OPLS-DA model (R2X = 70.0%, R2Y = 84.9%, Q2 = 69.7%). LysoPC (16 : 0), catelaidic acid, cerebronic acid, nonadecanetriol, and asparaginyl-histidine were found to identify PC, with a sensitivity of 89% and a specificity of 91%. Besides, lysoPC (16 : 0), lysoPC (16 : 1), lysoPC (22 : 6), and lysoPC (20 : 3) were found to differentiate PC from DM, with higher accuracy (68% versus 55%) and higher AUC values (72% versus 63%) than those of CA19-9. The diagnostic performance of metabolite biomarkers was finally validated by logistic regression models.

CONCLUSION

We succeeded in screening differential metabolite ions among PC and DM patients and healthy individuals, thus providing a preliminary basis for screening the biomarkers for the early diagnosis of PC.

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.

Identification of metabolic markers predictive of prediabetes in a Korean population

Prediabetes (PD) is a high-risk state of developing type 2 diabetes, and cardiovascular and metabolic diseases. Metabolomics-based biomarker studies can provide advanced opportunities for prediction of PD over the conventional methods. Here, we aimed to identify metabolic markers and verify their abilities to predict PD, as compared to the performance of the traditional clinical risk factor (CRF) and previously reported metabolites in other population-based studies. Targeted metabolites quantification was performed in 1723 participants in the Korea Association REsource (KARE) cohort, from which 500 normal individuals were followed up for 6 years. We selected 12 significant metabolic markers, including five amino acids, four glycerophospholipids, two sphingolipids, and one acylcarnitine, at baseline, resulting in a predicted incidence of PD with an area under the curve (AUC) of 0.71 during follow-up. The performance of these metabolic markers compared to that of fasting glucose was significantly higher in obese patients (body mass index: BMI ≥ 25 kg/m², 0.79 vs. 0.58, P < 0.001). The combination with metabolic markers, CRF, and fasting glucose yielded the best prediction performance (AUC = 0.86). Our results revealed that metabolic markers were not only associated with the risk of PD, but also improved the prediction performance in combination with conventional approaches.

Evaluation of MASP1, CMPF and U.A serum levels in pre-diabetic subjects in comparison to Normal individuals for early diagnosis of subjects with pre-diabetes, a case-control study

Background

The present study designed to evaluate the Serum levels of CMPF, MASP1 and UA in pre-diabetic subjects was compared to normal subjects.

Methods

This research is a case-control study. We studied 44 pre-diabetics with 44 normal subjects and were evaluated serum concentration of CMPF, Masp1 and U.Ain both groups andfurthermore serum concentration FPG, BUN, Cr, Cho, TG, HDLc, LDLc, AST, ALT, ALP, HbA1c was examined and correlation between of CMPF, Masp1 and U.Aand other parameters was statistically analyzed.

Results

Serum levels of MASP1, CMPF, fasting plasma glucose (P < 0.001)and uric acid (P < 0.002) were significantly increased in pre-diabetic subjects. In this study, a significant difference was found between MASP1 and CMPF in pre-diabetic subjects compared to normal subjects (P=0.005, r=0.291). There was also a significant difference between serum levels of MASP1 with HbA1C (P=0.01, r=0.269).

Conclusion

Serum levels of CMPF, MASP1 and uric acid were increased in pre-diabetic subjects. These parameters can be used as a biomarker for the diagnosis of pre-diabetes.

Multiplatform Metabolomics Reveals Novel Serum Metabolite Biomarkers in Diabetic Retinopathy Subjects

Diabetic retinopathy (DR) is the main cause of vision loss or blindness in working age adults worldwide. The lack of effective diagnostic biomarkers for DR leads to unsatisfactory curative treatments. To define potential metabolite biomarkers for DR diagnosis, a multiplatform-based metabolomics study is performed. In this study, a total of 905 subjects with diabetes without DR (NDR) and with DR at different clinical stages are recruited. Multiplatform metabolomics methods are used to characterize the serum metabolic profiles and to screen and validate the DR biomarkers. Based on the criteria p < 0.05 and false-discovery rate < 0.05, 348 and 290 metabolites are significantly associated with the pathogenesis of DR and early-stage DR, respectively. The biomarker panel consisting of 12-hydroxyeicosatetraenoic acid (12-HETE) and 2-piperidone exhibited better diagnostic performance than hemoglobin A1c (HbA1c) in differentiating DR from diabetes, with AUCs of 0.946 versus 0.691 and 0.928 versus 0.648 in the discovery and validation sets, respectively. In addition, this panel showed higher sensitivity in early-stage DR detection than HbA1c. In conclusion, this multiplatform-based metabolomics study comprehensively revealed the metabolic dysregulation associated with DR onset and progression. The defined biomarker panel can be used for detection of DR and early-stage DR.

Urban-rural distinction of potential determinants for prediabetes in Indonesian population aged ≥15 years: a cross-sectional analysis of Indonesian Basic Health Research 2018 among normoglycemic and prediabetic individuals

BACKGROUND

Based on Basic Health Research (RISKESDAS) conducted by Ministry of Health, Indonesia, prediabetes prevalence tends to increase from 2007 until 2018. The numbers are relatively higher in rural than those in urban area despite of small discrepancies between the two (~ 2-4%). The purpose of this study was to identify urban-rural differences in potential determinants for prediabetes in Indonesia.

METHODS

This analysis used secondary data collected from nationwide Health Survey in 2018. Respondents were aged ≥15 years who met inclusion criteria of analysis with no history of diabetes mellitus. Prediabetes criteria followed American Diabetes Association 2019. Multiple logistic regression was also employed to assess the transition probability of potential determinants for prediabetes in urban and rural Indonesia.

RESULTS

Up to 44.8% of rural respondents were prediabetics versus their urban counterparts at 34.9%, yet non-response bias was observed in the two. Young adults aged 30 years were already at risk of prediabetes. Urban-rural distinction for marital status and triglyceride level was observed while other determinants tended to overlap across residence. Several modifiable factors might contribute differently in both population with careful interpretation.

CONCLUSIONS

The minimum age limit for early prediabetes screening may start from 30 years old in Indonesia. Urban-rural distinction for marital status and triglyceride level was observed, yet non-response bias between the two groups could not be excluded. A proper model for early prediabetes screening need to be developed from a cohort study with adequate sample size.

Urban-rural distinction of potential determinants for prediabetes in Indonesian population aged ≥15 years: a cross-sectional analysis of Indonesian Basic Health Research 2018 among normoglycemic and prediabetic individuals

BACKGROUND

Based on Basic Health Research (RISKESDAS) conducted by Ministry of Health, Indonesia, prediabetes prevalence tends to increase from 2007 until 2018. The numbers are relatively higher in rural than those in urban area despite of small discrepancies between the two (~ 2-4%). The purpose of this study was to identify urban-rural differences in potential determinants for prediabetes in Indonesia.

METHODS

This analysis used secondary data collected from nationwide Health Survey in 2018. Respondents were aged ≥15 years who met inclusion criteria of analysis with no history of diabetes mellitus. Prediabetes criteria followed American Diabetes Association 2019. Multiple logistic regression was also employed to assess the transition probability of potential determinants for prediabetes in urban and rural Indonesia.

RESULTS

Up to 44.8% of rural respondents were prediabetics versus their urban counterparts at 34.9%, yet non-response bias was observed in the two. Young adults aged 30 years were already at risk of prediabetes. Urban-rural distinction for marital status and triglyceride level was observed while other determinants tended to overlap across residence. Several modifiable factors might contribute differently in both population with careful interpretation.

CONCLUSIONS

The minimum age limit for early prediabetes screening may start from 30 years old in Indonesia. Urban-rural distinction for marital status and triglyceride level was observed, yet non-response bias between the two groups could not be excluded. A proper model for early prediabetes screening need to be developed from a cohort study with adequate sample size.

A proposal for score assignment to characterize biological processes from mass spectral analysis of serum

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Biological process-associated scores generated from mass spectrometry of serum.

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Scores demonstrated acceptable levels of reproducibility.

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Scores associated with biological processes and clinical outcome in cancer patients.

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Possible application to biomarker studies for treatment or monitoring of disease.

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Multiple biological processes assessed from 3 µL of serum.

Introduction

Most diseases involve a complex interplay between multiple biological processes at the cellular, tissue, organ, and systemic levels. Clinical tests and biomarkers based on the measurement of a single or few analytes may not be able to capture the complexity of a patient’s disease. Novel approaches for comprehensively assessing biological processes from easily obtained samples could help in the monitoring, treatment, and understanding of many conditions.

Objectives

We propose a method of creating scores associated with specific biological processes from mass spectral analysis of serum samples.

Methods

A score for a process of interest is created by: (i) identifying mass spectral features associated with the process using set enrichment analysis methods, and (ii) combining these features into a score using a principal component analysis-based approach. We investigate the creation of scores using cohorts of patients with non-small cell lung cancer, melanoma, and ovarian cancer. Since the circulating proteome is amenable to the study of immune responses, which play a critical role in cancer development and progression, we focus on functions related to the host response to disease.

Results

We demonstrate the feasibility of generating scores, their reproducibility, and their associations with clinical outcomes. Once the scores are constructed, only 3 µL of serum is required for the assessment of multiple biological functions from the circulating proteome.

Conclusion

These mass spectrometry-based scores could be useful for future multivariate biomarker or test development studies for informing treatment, disease monitoring and improving understanding of the roles of various biological functions in multiple disease settings.

LC-MS Lipidomics: Exploiting a Simple High-Throughput Method for the Comprehensive Extraction of Lipids in a Ruminant Fat Dose-Response Study

Typical lipidomics methods incorporate a liquid-liquid extraction with LC-MS quantitation; however, the classic sample extraction methods are not high-throughput and do not perform well at extracting the full range of lipids especially, the relatively polar species (e.g., acyl-carnitines and glycosphingolipids). In this manuscript, we present a novel sample extraction protocol, which produces a single phase supernatant suitable for high-throughput applications that offers greater performance in extracting lipids across the full spectrum of species. We applied this lipidomics pipeline to a ruminant fat dose-response study to initially compare and validate the different extraction protocols but also to investigate complex lipid biomarkers of ruminant fat intake (adjoining onto simple odd chain fatty acid correlations). We have found 100 lipids species with a strong correlation with ruminant fat intake. This novel sample extraction along with the LC-MS pipeline have shown to be sensitive, robust and hugely informative (>450 lipids species semi-quantified): with a sample preparation throughput of over 100 tissue samples per day and an estimated ~1000 biological fluid samples per day. Thus, this work facilitating both the epidemiological involvement of ruminant fat, research into odd chain lipids and also streamlining the field of lipidomics (both by sample preparation methods and data presentation).

Circulating miRNAs as a Predictive Biomarker of the Progression from Prediabetes to Diabetes: Outcomes of a 5-Year Prospective Observational Study

Due to a global increase in the prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need for early identification of prediabetes, as these people have the highest risk of developing diabetes. Circulating miRNAs have shown potential as progression biomarkers in other diseases. This study aimed to conduct a baseline comparison of serum-circulating miRNAs in prediabetic individuals, with the distinction between those who later progressed to T2DM and those who did not. The expression levels of 798 miRNAs using NanoString technology were examined. Spearman correlation, receiver operating characteristic (ROC) curve analysis, and logistic regression modeling were performed. Gene ontology (GO) and canonical pathway analysis were used to explore the biological functions of the miRNA target genes. The study revealed that three miRNAs were upregulated in the serum samples of patients who later progressed to T2DM. Pathway analysis showed that the miRNA target genes were mainly significantly enriched in neuronal NO synthase (nNOS) signaling in neurons, amyloid processing, and hepatic cholestasis. ROC analysis demonstrated that miR-491-5p, miR-1307-3p, and miR-298 can be introduced as a diagnostic tool for the prediction of T2DM (area under the curve (AUC) = 94.0%, 88.0%, and 84.0%, respectively). Validation by real-time quantitative polymerase chain reaction (qRT-PCR) confirmed our findings. The results suggest that circulating miRNAs can potentially be used as predictive biomarkers of T2DM in prediabetic patients.

Targeting Netrin-1 and -4 as a Novel Diagnostic Parameter and Treatment Option for Diabetic Retinopathy

Diabetic retinopathy (DR) is a retinal vascular disorder associated with both type 1 and type 2 diabetes mellitus (DM). It is characterized by specific loss of pericytes, which leads to an augmented blood vessel permeability, and development of new blood vessels (retinal neovascularization). Moreover, stiffening of eye membrane, inflammation, and apoptosis of endothelial cells also lead to damage of the blood-retinal barrier and blindness in most cases unless it's detected and managed early. Hence, this review was intended to assess the potential roles of Netrin-1 and -4 as new/alternative biomarkers and therapeutic options for DR. Netrin-1 and -4 have been the most known ligands and are well known for their role in neural guidance. DR has both neural and vascular components; therefore, biomarkers used for both neural and vascular retinal tissues are potentially important. According to different experimental and clinical studies, as compared to the normal groups, there was a significant increment in both retinal Netrin-1 and -4 mRNA and protein levels in the retinopathy groups. In addition, exogenous supplementation of these proteins is also used as a therapeutic agent for DR.

Diabetic‑induced alterations in hepatic glucose and lipid metabolism: The role of type 1 and type 2 diabetes mellitus (Review)

Diabetes mellitus (DM) is a growing health concern in society. Type 1 and type 2 DM are the two main types of diabetes; both types are chronic diseases that affect glucose metabolism in the body and the impaired regulation of glucose and lipid metabolism promotes the development and progression of DM. During the physiological metabolism process, the liver serves a unique role in glucose and lipid metabolism. The present article aimed to review the association between DM and glucose metabolism in the liver and discuss the changes of the following hepatic glucose fluxes: Gluconeogenesis, glucose/glucose 6-phosphate cycling, glycogenolysis, glycogenesis and the pentose phosphate pathway. Moreover, the incidence of fatty liver in DM was also investigated.

Potential Markers of Dietary Glycemic Exposures for Sustained Dietary Interventions in Populations without Diabetes

There is considerable interest in dietary and other approaches to maintaining blood glucose concentrations within the normal range and minimizing exposure to postprandial hyperglycemic excursions. The accepted marker to evaluate the sustained maintenance of normal blood glucose concentrations is glycated hemoglobin A1c (HbA1c). However, although this is used in clinical practice to monitor glycemic control in patients with diabetes, it has a number of drawbacks as a marker of efficacy of dietary interventions that might beneficially affect glycemic control in people without diabetes. Other markers that reflect shorter-term glycemic exposures have been studied and proposed, but consensus on the use and relevance of these markers is lacking. We have carried out a systematic search for studies that have tested the responsiveness of 6 possible alternatives to HbA1c as markers of sustained variation in glycemic exposures and thus their potential applicability for use in dietary intervention trials in subjects without diabetes: 1,5-anhydroglucitol (1,5-AG), dicarbonyl stress, fructosamine, glycated albumin (GA), advanced glycated end products (AGEs), and metabolomic profiles. The results suggest that GA may be the most promising for this purpose, but values may be confounded by effects of fat mass. 1,5-AG and fructosamine are probably not sensitive enough to the range of variation in glycemic exposures observed in healthy individuals. Use of measures based on dicarbonyls, AGEs, or metabolomic profiles would require further research into possible specific molecular species of interest. At present, none of the markers considered here is sufficiently validated and sensitive for routine use in substantiating the effects of sustained variation in dietary glycemic exposures in people without diabetes.

HbA1C as a marker of retrograde glycaemic control in diabetes patient with co-existed beta-thalassaemia: A case report and a literature review

WHAT IS KNOWN AND OBJECTIVE

The HbA1C marker used in assessing diabetes control quality is not sufficient in diabetes patients with thalassaemia.

CASE DESCRIPTION

A male diabetic patient with thalassaemia was hospitalized due to distal neuropathic pain, right toe trophic ulcer, unacceptable five-point glycaemic profile and recommended HbA1C value. After simultaneously initiated insulin therapy and management of ulcer by hyperbaric oxygen, the patient showed improved glycaemic control and ulcer healing, which led to the patient's discharge.

WHAT IS NEW AND CONCLUSION

In thalassaemia and haemoglobinopathies, due to discrepancies in the five-point glycaemic profile and HbA1C values, it is necessary to measure HbA1C with a different method or to determine HbA1C and fructosamine simultaneously.

The impact of phenotype, ethnicity and genotype on progression of type 2 diabetes mellitus

AIM

To conduct a comprehensive review of studies of glycaemic deterioration in type 2 diabetes and identify the major factors influencing progression.

METHODS

We conducted a systematic literature search with terms linked to type 2 diabetes progression. All the included studies were summarized based upon the factors associated with diabetes progression and how the diabetes progression was defined.

RESULTS

Our search yielded 2785 articles; based on title, abstract and full-text review, we included 61 studies in the review. We identified seven criteria for diabetes progression: 'Initiation of insulin', 'Initiation of oral antidiabetic drug', 'treatment intensification', 'antidiabetic therapy failure', 'glycaemic deterioration', 'decline in beta-cell function' and 'change in insulin dose'. The determinants of diabetes progression were grouped into phenotypic, ethnicity and genotypic factors. Younger age, poorer glycaemia and higher body mass index at diabetes diagnosis were the main phenotypic factors associated with rapid progression. The effect of genotypic factors on progression was assessed using polygenic risk scores (PRS); a PRS constructed from the genetic variants linked to insulin resistance was associated with rapid glycaemic deterioration. The evidence of impact of ethnicity on progression was inconclusive due to the small number of multi-ethnic studies.

CONCLUSION

We have identified the major determinants of diabetes progression-younger age, higher BMI, higher HbA1c and genetic insulin resistance. The impact of ethnicity is uncertain; there is a clear need for more large-scale studies of diabetes progression in different ethnic groups.

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular markers of pre-disease stages. A promising source of new molecular markers are metabolomics standing out the research of biomarkers in NMR approaches. An increasing number of nutritionists integrate metabolomics into their study design, making nutrimetabolomics one of the most promising avenues for improving personalized nutrition. This review highlight the major five risk factors associated with metabolic syndrome and related diseases including carbohydrate dysfunction, dyslipidemia, oxidative stress, inflammation, and gut microbiota dysbiosis. Together, it is proposed a profile of metabolites of each risk factor obtained from NMR approaches to target them using personalized nutrition, which will improve the quality of life for these patients.

Conceptualization of Heterogeneity of Chronic Diseases and Atherosclerosis as a Pathway to Precision Medicine: Endophenotype, Endotype, and Residual Cardiovascular Risk

The article discusses modern approaches to the conceptualization of pathogenetic heterogeneity in various branches of medical science. The concepts of endophenotype, endotype, and residual cardiovascular risk and the scope of their application in internal medicine and cardiology are considered. Based on the latest results of studies of the genetic architecture of atherosclerosis, five endotypes of atherosclerosis have been proposed. Each of the presented endotypes represents one or another pathophysiological mechanism of atherogenesis, having an established genetic substrate, a characteristic panel of biomarkers, and a number of clinical features. Clinical implications and perspectives for the study of endotypes of atherosclerosis are briefly reviewed.

Pancreatic extracellular volume fraction using T1 mapping in patients with impaired glucose intolerance

PURPOSE

To evaluate pancreatic T1 mapping and extracellular volume (ECV) fraction's feasibility to assess impaired glucose tolerance (IGT) patients.

METHODS

A total of 45 consecutive patients with known or suspected pancreatic disease underwent contrast-enhanced magnetic resonance (MR) imaging, including T1 mapping, using saturation recovery sequence. Patients were classified into three groups based on the American Diabetes Association criteria: no-diabetes subjects, HbA1c < 5.7%; pre-diabetes, 5.7% ≤ HbA1c < 6.5%; and type 2 diabetes mellitus (T2DM), HbA1c ≥ 6.5%. Pre-contrast pancreatic T1 value and ECV of the pancreas were computed, and then pre-contrast pancreatic T1 value, ECV and HbA1c values were compared. The present prospective study was approved by our institutional review board. Written informed consent was obtained from all patients.

RESULTS

A positive correlation between HbA1c values and both pre-contrast pancreatic T1 value and ECV (r = 0.79, P < 0.001 and r = 0.60, P < 0.001, respectively) were observed. The pre-contrast pancreatic T1 value and ECV were significantly higher in T2DM vs. no-diabetes subjects and pre-diabetes (P < 0.001). No significant difference between two qualitative values (P = 0.14) was found, however, the sensitivity, specificity, and area under the receiver-operating-characteristic curve differentiating no-diabetes subjects and pre-diabetes from T2DM were superior in ECV (100%, 93.5%, and 0.990) vs. pre-contrast pancreatic T1 values (84.6%, 96.8%, and 0.906).

CONCLUSIONS

The ECV of the pancreas could serve as a potential imaging biomarker for the assessment of pancreatic fibrosis leading to IGT.

Ultrasensitive nano-aptasensor for monitoring retinol binding protein 4 as a biomarker for diabetes prognosis at early stages

Prognosis of diabetes risk at early stages has become an important challenge due to the prevalence of this disease. Retinol binding protein 4 (RBP4), a recently identified adipokine, has been introduced as a predictor for the onset of diabetes type 2 in coming future. In the present report a sensitive aptasensor for detection of RBP4 is introduced. The immune sandwich was prepared by immobilizing biotinylated RBP4 aptamers on streptavidin coated polystyrene micro-wells and then incubation of RBP4 as target and finally addition of luminol-antibody bearing intercross-linked gold nanoparticles as reporter. The chemiluminescence intensity was recorded in the presence of hydrogen peroxide as oxidant agent and Au³⁺ as an efficient catalyst for luminol oxidation. The aptasensor responded to RBP4 in the linear concentration range from 0.001 to 2 ng/mL and detection limit was slightly less than 1 pg/mL. The proposed method has successfully applied to determine the RBP4 in patient real serums. By using the intercross-linked gold nanoparticles, it is possible to provide more accessible surface for immobilizing luminol and enhance the chemiluminescence signal. Therefore, the analytical parameters such as sensitivity, specificity, detection limit and linear range were improved in compare to the biosensors reported in the literature.

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.

Minor C allele of the SNP rs7873784 associated with rheumatoid arthritis and type-2 diabetes mellitus binds PU.1 and enhances TLR4 expression

Toll-like receptor 4 (TLR4) is an innate immunity receptor predominantly expressed on myeloid cells and involved in the development of various diseases, many of them with complex genetics. Here we present data on functionality of single nucleotide polymorphism rs7873784 located in the 3'-untranslated region (3'-UTR) of TLR4 gene and associated with various pathologies involving chronic inflammation. We demonstrate that TLR4 3'-UTR strongly enhanced the activity of TLR4 promoter in U937 human monocytic cell line while minor rs7873784(C) allele created a binding site for transcription factor PU.1 (encoded by SPI1 gene), a known regulator of TLR4 expression. Increased binding of PU.1 further augmented the TLR4 transcription while PU.1 knockdown or complete disruption of the PU.1 binding site abrogated the effect. We hypothesize that additional functional PU.1 site may increase TLR4 expression in individuals carrying minor C variant of rs7873784 and modulate the development of certain pathologies, such as rheumatoid arthritis and type-2 diabetes mellitus.

A Preliminary Cohort Study Assessing Routine Blood Analyte Levels and Neurological Outcome after Spinal Cord Injury

There is increasing interest in the identification of biomarkers that could predict neurological outcome following a spinal cord injury (SCI). Although initial American Spinal Injury Association (ASIA) Impairment Scale (AIS) grade is a good indicator of neurological outcome, for the patient and clinicians, an element of uncertainty remains. This preliminary study aimed to assess the additive potential of routine blood analytes following principal component analysis (PCA) to develop prognostic models for neurological outcome following SCI. Routine blood and clinical data were collected from SCI patients (n = 82) and PCA used to reduce the number of blood analytes into related factors. Outcome neurology was obtained from AIS scores at 3 and 12 months post-injury, with motor (AIS and total including all myotomes) and sensory (AIS, touch and pain) abilities being assessed individually. Multiple regression models were created for all outcome measures. Blood analytes relating to “liver function” and “acute inflammation and liver function” factors were found to significantly increase prediction of neurological outcome at both 3 months (touch, pain, and AIS sensory) and at 1 year (pain, R² increased by 0.025 and total motor, R² increased by 0.016). For some models “liver function” and “acute inflammation and liver function” factors were both significantly predictive, with the greatest combined R² improvement of 0.043 occurring for 3 month pain prediction. These preliminary findings support ongoing research into the use of routine blood analytes in the prediction of neurological outcome in SCI patients.

Glycation of glucose sensitive lysine residues K36, K438 and K549 of albumin is associated with prediabetes

Prediabetes is a risk factor for the development of diabetes. Early diagnosis of prediabetes may prevent the onset and progression of diabetes and its associated complications. Therefore, this study aimed at the identification of novel markers for efficient prediction of prediabetes. In this pursuit, we have evaluated the ability of glycated peptides of albumin in predicting prediabetes. Glycated peptides of in vitro glycated albumin were characterized by data dependent acquisition and parallel reaction monitoring using LC-HRMS. Amongst 14 glycated peptides characterized in vitro, four peptides, particularly, FK(CML)DLGEENFK, K(AML)VPQVSTPTLVEVSR, K(CML)VPQVSTPTLVEVSR, and K(AML)QTALVELVK, corresponding to 3 glucose sensitive lysine residues K36, K438, and K549, respectively showed significantly higher abundance in prediabetes than control. Additionally, the abundance of three of these peptides, namely K(AML)QTALVELVK, K(CML)VPQVSTPTLVEVSR and FK(CML)DLGEENFK was >1.8-fold in prediabetes, which was significantly higher than the differences observed for FBG, PPG, and HbA1c. Further, the four glycated peptides showed a significant correlation with FBG, PPG, HbA1c, triglycerides, VLDL, and HDL. This study supports that glycated peptides of glucose sensitive lysine residues K36, K438 and K549 of albumin could be potentially useful markers for prediction of prediabetes. SIGNIFICANCE: Undiagnosed prediabetes may lead to diabetes and associated complications. This study reports targeted quantification of four glycated peptides particulary FK(CML)DLGEENFK, K(AML)VPQVSTPTLVEVSR, K(CML)VPQVSTPTLVEVSR, and K(AML)QTALVELVK, corresponding to 3 glucose sensitive lysine residues K36, K438 and K549 respectively by parallel reaction monitoring in healthy and prediabetic subjects. These peptides showed significantly higher abundance in prediabetes than healthy subjects, and showed significant correlation with various clinical parameters including FBG, PPG, HbA1c, and altered lipid profile. Therefore, together these four peptides constitute a panel of markers that can be useful for prediction of prediabetes.

TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records

BACKGROUND

Insulin resistance is an early-stage deterioration of Type 2 diabetes. Identification and quantification of insulin resistance requires specific blood tests; however, the triglyceride-glucose (TyG) index can provide a surrogate assessment from routine Electronic Health Record (EHR) data. Since insulin resistance is a multi-factorial condition, to improve its characterisation, this study aims to discover non-trivial clinical factors in EHR data to determine where the insulin-resistance condition is encoded.

METHODS

We proposed a high-interpretable Machine Learning approach (i.e., ensemble Regression Forest combined with data imputation strategies), named TyG-er. We applied three different experimental procedures to test TyG-er reliability on the Italian Federation of General Practitioners dataset, named FIMMG_obs dataset, which is publicly available and reflects the clinical use-case (i.e., not all laboratory exams are prescribed on a regular basis over time).

RESULTS

Results detected non-conventional clinical factors (i.e., uricemia, leukocytes, gamma-glutamyltransferase and protein profile) and provided novel insight into the best combination of clinical factors for detecting early glucose tolerance deterioration. The robustness of these extracted clinical factors was confirmed by the high agreement (from 0.664 to 0.911 of Lin's correlation coefficient (r_c)) of the TyG-er approach among different experimental procedures. Moreover, the results of the three experimental procedures outlined the predictive power of the TyG-er approach (up to a mean absolute error of 5.68% and r_c=0.666,p<.05).

CONCLUSIONS

The TyG-er approach is able to carry information about the identification of the TyG index, strictly correlated with the insulin-resistance condition, while extracting the most relevant non-glycemic features from routine data.

The current status of blood epigenetic biomarkers for dementia

Dementia is an overarching term which describes a group of symptoms that result in long-term decline in cognitive functioning that is significant enough to affect daily function. It is caused by a number of different diseases, the most common of which is Alzheimer's disease. Currently, there are no definitive biomarkers for preclinical or diagnostic use, or which differentiate between underlying disease types. The purpose of this review is to highlight several important areas of research on blood-based biomarkers of dementia, with a specific focus on epigenetic biomarkers. A systematic search of the literature identified 77 studies that compared blood DNA methylation between individuals with dementia and controls and 45 studies that measured microRNA. Very few studies were identified that focused on histone modifications. There were many promising findings from studies in the field of blood-based epigenetic biomarkers of dementia, however, a lack of consistency in study design, technologies, and platforms used for the biomarker measurement, as well as statistical analysis methods, have hampered progress. To date, there are very few findings that have been independently replicated across more than one study, indicating a preponderance of false-positive findings and the field has likely been plagued by positive publication bias. Here, we highlight and discuss several of the limitations of existing studies and provide recommendations for how these could be overcome in future research. A robust framework should be followed to enable development of the most valid and reproducible biomarkers with the strongest clinical utility. Defining a series of biomarkers that may be complimentary to each other could permit a stronger multifactorial biomarker to be developed that would allow for not only accurate dementia diagnosis but preclinical detection.