Novel biomarkers for prediabetes, diabetes, and associated complications

Lyophilized ash gourd (Benincasa hispida (Thunb.) Cogn.) juice alleviates diet-induced prediabetes in a rat model

Prediabetes is characterized by elevated blood sugar levels, indicating an increased risk of developing diabetes. This study evaluated the effects of ash gourd (AG), a tropical fruit from the Cucurbitaceae family, on prediabetes, as well as its phytochemical composition. A prediabetic rat model was developed in Sprague-Dawley (SD) rats by administering a high fat diet (HFD) for 16 weeks. This model exhibited reduced pancreatic function, heightened insulin resistance, and decreased insulin sensitivity compared to a standard diet group, leading to hyperglycemia and dyslipidemia, hallmarks of prediabetic conditions. Histological analysis of hepatic tissue revealed macro- and microvesicular fat accumulation and inflammatory changes, supporting these findings. This study highlights the utility of HFD-induced SD rats as a model for prediabetic conditions. Following this, lyophilized ash gourd juice (LAGJ) powder was administered to the prediabetic rat model to assess its potential for reversing prediabetic conditions. LAGJ administration resulted in a significant reduction in fasting blood sugar (FBS) levels, glucose intolerance, and insulin resistance. Additionally, LAGJ significantly mitigated fatty liver changes compared to the prediabetic untreated control (PUC) group. Histological examination of liver tissue in the LAGJ treated group showed a typical architecture similar to that of the normal control group. These findings indicate that LAGJ could be a promising intervention for individuals with prediabetes who are at risk of developing type 2 diabetes and fatty liver disease. Phytochemical analysis of AG pulp revealed the presence of stigmasterol, stigmasteryl β-glucoside, and 6'-O-palmitoyl stigmasteryl β-glucoside.

Investigating the altered expression of miR-486-5p and miR-novel-chr1_40444 in dysglycemia in a South African population

AIMS

This study aims to investigate miR-486-5p and miR-novel-chr1_40444 expressions in dysglycemic individuals. Validating RNA-sequencing findings in a larger sample via reverse transcription qPCR (RT-qPCR), we aim to address global diagnostic and screening limitations, using an African cohort as an example.

MATERIALS AND METHODS

This cross-sectional study involved 1,271 individuals [normoglycemic (n = 974), prediabetic (n = 206), screen-detected type 2 diabetes (n = 91)] from the ongoing Vascular and Metabolic Health (VMH) study in Cape Town, South Africa. Whole blood miRNA expression was assessed using TaqMan-based RT-qPCR, with data normalized to an endogenous control (miR-16-5p).

RESULTS

Significant underexpression was observed in prediabetes vs normoglycemia for miR-486-5p (P = 0.038), whilst both miRNAs demonstrated significant upregulation in screen-detected type 2 diabetes vs normoglycemia (miR-486-5p, P = 0.009; miR-novel-chr1_40444, P < 0.001), and screen-detected type 2 diabetes in comparison with prediabetes (miR-486-5p, P < 0.001; miR-novel-chr1_40444, P < 0.001). Multivariable regression analyses revealed pronounced interrelations between miR-novel-chr1_40444 and screen-detected type 2 diabetes in unadjusted and adjusted models (Model 1: P < 0.001, Model 2: P < 0.001, Model 3: P = 0.030). Moreover, receiver operating characteristic (ROC) curves revealed significantly enhanced diagnostic capabilities for screen-detected type 2 diabetes vs either normoglycemia (AUC = 0.971, P < 0.001), non-diabetes (AUC = 0.959, P < 0.001), or prediabetes (AUC = 0.902, P < 0.001) when combining the miRNAs with 2 h postprandial glucose.

CONCLUSIONS

This study demonstrated the enhanced power of incorporating miRNAs with traditional markers in distinguishing screen-detected type 2 diabetes, warranting further investigations on their unique role in the development of type 2 diabetes.

Accuracy of novel anthropometric indices for assessing the risk for progression of prediabetes to diabetes; 13 years of results from Isfahan Cohort Study

Objective

We examined the accuracy of novel anthropometric indices in predicting the progression of prediabetes to diabetes.

Subjects and methods

This study was performed on the pre-diabetic sub-population from Isfahan Cohort Study (ICS). Participants were followed up from 2001 to 2013. During every 5-year follow-up survey, patients' data regarding the incidence and time of incidence of diabetes were recorded. We evaluated the association between the risk of developing diabetes and novel anthropometric indices including: visceral adiposity index (VAI), lipid accumulation products (LAP), deep abdominal adipose tissue (DAAT), abdominal volume index (AVI), A body shape index (ABSI), body roundness index (BRI) and weight-adjusted waist index (WWI). We categorized the indices into two groups according to the median value of each index in the population. We used Cox regression analysis to obtain hazard ratios (HR) using the first group as the reference category and used receiver operating characteristics (ROC) curve analysis for comparing the predictive performance of the indices.

Results

From 215 included subjects, 79 developed diabetes during the 13-year follow-up. AVI, LAP, BRI, and VAI indicated statistically significant HR in crude and adjusted regression models. LAP had the greatest association with the development of diabetes HR = 2.18 (1.36-3.50) in multivariable analysis. ROC curve analysis indicated that LAP has the greatest predictive performance among indices (area under the curve = 0.627).

Conclusion

Regardless of baseline confounding variables, prediabetic patients with a higher LAP index may be at significantly higher risk for developing diabetes.

Breakthroughs in road mapping IL-35 mediated immunotherapy for type-1 and autoimmune diabetes mellitus

IL-35 is a recently discovered protein made up of IL-12α and IL-27β chains. It is encoded by IL12A and EBI3 genes. Interest in researching IL-35 has significantly increased in recent years, as evidenced by numerous scientific publications. Diabetes is on the rise globally, causing more illness and death in developing countries. The International Diabetes Federation (IDF) reports that diabetes is increasingly affecting children and teenagers, with varying rates across different regions. Therefore, scientists seek new diabetes treatments despite the growth of drug research. Recent research aims to emphasize IL-35 as a critical regulator of diabetes, especially type 1 and autoimmune diabetes. This review provides an overview of recent research on IL-35 and its link to diabetes and its associated complications. Studies suggest that IL-35 can offer protection against type-1 diabetes and autoimmune diabetes by regulating macrophage polarization, T-cell-related cytokines, and regulatory B cells (Bregs). This review will hopefully assist biomedical scientists in exploring the potential role of IL-35-mediated immunotherapy in treating diabetes. However, further research is necessary to determine the exact mechanism and plan clinical trials.

A systematic review and meta-analysis of ischemia-modified albumin in diabetes mellitus

Aim

There is an ongoing search for novel biomarkers of diabetes. We conducted a systematic review and meta-analysis of the serum concentrations of ischemia-modified albumin (IMA), a candidate biomarker of oxidative stress, acidosis, and ischemia, in patients with pre-diabetes, different types of diabetes mellitus (type 1, T1DM, type 2, T2DM, and gestational, GDM), and healthy controls.

Methods

We searched for case-control studies published in PubMed, Web of Science, and Scopus from inception to December 31, 2023. The risk of bias and the certainty of evidence were assessed using the Joanna Briggs Institute Critical Appraisal Checklist and GRADE, respectively.

Results

In 29 studies, T2DM patients had significantly higher IMA concentrations when compared to controls (standard mean difference, SMD = 1.83, 95 % CI 1.46 to 2.21, p˂0.001; I2 = 95.7 %, p < 0.001; low certainty of evidence). Significant associations were observed between the SMD and glycated hemoglobin (p = 0.007), creatinine (p = 0.003), triglycerides (p = 0.029), and the presence of diabetes complications (p = 0.003). Similar trends, albeit in a smaller number of studies, were observed in T1DM (two studies; SMD = 1.59, 95 % CI -0.09 to 3.26, p˂0.063; I2 = 95.8 %, p < 0.001), GDM (three studies; SMD = 3.41, 95 % CI 1.14 to 5.67, p = 0.003; I2 = 97.0 %, p < 0.001) and pre-diabetes (three studies; SMD = 15.25, 95 % CI 9.86 to 20.65, p˂0.001; I2 = 99.3 %, p < 0.001).

Conclusion

Our study suggests that IMA is a promising biomarker for determining the presence of oxidative stress, acidosis, and ischemia in pre-diabetes and T1DM, T2DM, and GDM. However, the utility of measuring circulating IMA warrants confirmation in prospective studies investigating clinical endpoints in pre-diabetes and in different types of diabetes (PROSPERO registration number: CRD42024504690).

Advances in electrochemical biosensors employing carbon-based electrodes for detection of biomarkers in diabetes mellitus

Background and purpose

The increase in diabetes cases has become a major concern in the healthcare sector, necessitating the development of efficient and minimal diagnostic methods. This study aims to provide a comprehensive examination of electrochemical biosensors for detecting diabetes mellitus biomarkers, with a special focus on the utilization of carbon-based electrodes.

Review approach

A detailed analysis of electrochemical biosensors incorporating various carbon electrodes, including screen-printed carbon electrodes, glassy carbon electrodes, and carbon paste electrodes, is presented. The advantages of carbon-based electrodes in biosensor design are highlighted. The review covers the detection of several key diabetes biomarkers, such as glucose, glycated hemoglobin (HbA1c), glycated human serum albumin (GHSA), insulin, and novel biomarkers.

Key results

Recent developments in electrochemical biosensor technology over the last decade are summarized, emphasizing their potential in clinical applications, particularly in point-of-care settings. The utilization of carbon-based electrodes in biosensors is shown to offer significant advantages, including enhanced sensitivity, selectivity, and cost-effectiveness.

Conclusion

This review underscores the importance of carbon-based electrodes in the design of electrochemical biosensors and raises awareness for the detection of novel biomarkers for more specific and personalized diabetes mellitus cases. The advancements in this field highlight the potential of these biosensors in future clinical applications, especially in point-of-care diagnostics.

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers

Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.

Liposome Encapsulation Enhances the Antidiabetic Efficacy of Silibinin

Silibinin has considerable therapeutic potential for the treatment of diabetes through anti-inflammatory, antioxidant, and immunomodulatory properties. However, the therapeutic application of silibinin is quite limited due to its poor bioavailability. In the present study, an attempt was made to improve the antidiabetic efficacy of silibinin by its encapsulation in liposomal vesicles. The liposomes with a high encapsulation efficiency of silibinin (96%) and a zeta potential of -26.2 ± 0.6 mV were developed and studied using nicotinamide/streptozotocin-induced diabetic rats. Administration of silibinin-loaded liposomes to diabetic rats lowered glucose levels, increased insulin levels, and improved pancreatic islet architecture. The anti-inflammatory effect of silibinin-loaded liposomes was demonstrated by a decrease in serum C-reactive protein (CRP) levels and a reduced deposition of collagen fibers in the islets of diabetic rats. Furthermore, silibinin-loaded liposomes were more efficient in lowering glucose, alanine transaminase, triglyceride, and creatinine levels in diabetic rats than pure silibinin. In addition, silibinin-loaded liposomes had a significantly better effect on beta-cell mass and Glut2 glucose receptor distribution in diabetic islets than pure silibinin. The present results clearly show that liposome encapsulation of silibinin enhances its antidiabetic efficacy, which may contribute to the therapeutic benefit of silibinin in the treatment of diabetes and its complications.

Screening type 2 Diabetes mellitus among Indians using inflammatory biomarkers

Diabetes is a metabolic disorder associated with chronic inflammation; pre-diabetes phase promotes to inflammatory mechanism then finally progress to diabetes and its associated complications. Therefore, it is of interest to investigate the changes in inflammatory biomarkers Evidence that inflammatory markers play a role in the development as well as severity of Type 2 diabetes mellitus (T2DM). This study has been designed to decipher the involvement of Tumor Necrosis Factor (TNFα), Interleukin-6 (IL-6), Nesfatin-1 and Blood sugar in the etiopathogenesis of T2DM. This retrospective observational study analyzed patient records from our hospital, focusing on those with diabetes or pre-diabetes. Glycosylated hemoglobin, inflammatory biomarkers, Fasting Blood Glucose, and Post-Prandial Blood Glucose were assessed. SPSS 28 facilitated statistical analysis; utilizing Bivariate Correlation assessed the relationship between inflammatory biomarkers and diabetes status (glycosylated hemoglobin). In the pre-diabetic vs. diabetic groups, significant differences exist in IL-6 (p=0.0344), TNF-α (p=0.041), Nesfatin-1 (p=0.0485), fasting blood glucose (p=0.036), and 2h post-prandial blood glucose (p=0.048). IL6 (AUC=0.729, p<0.001), TNF (AUC=0.761, p<0.001), and Nesfatin1 (AUC=0.892, p<0.001) show moderate discriminative power. PP (AUC=0.992, p<0.001) and hbA1c (AUC=0.993, p<0.001) exhibit excellent discriminatory ability. Correlations: IL6 with TNF (r=0.672, p<0.001) and Nesfatin1 (r=0.542, p<0.001); TNF with Nesfatin1 (r=0.591, p<0.001), hbA1c (r=0.683, p<0.001), and PP (r=0.367, p<0.001); Nesfatin1 with PP (r=0.594, p<0.001) and hbA1c (r=0.800, p<0.001). Age has a negative correlation with hbA1c (r=-0.119, p=0.086). Thus, data shows a significant association between inflammatory markers, blood glucose levels, and the progression from pre-diabetes to diabetes.

International Diabetes Federation Position Statement on the 1-hour post-load plasma glucose for the diagnosis of intermediate hyperglycaemia and type 2 diabetes

Many individuals with intermediate hyperglycaemia (IH), including impaired fasting glycaemia (IFG) and impaired glucose tolerance (IGT), as presently defined, will progress to type 2 diabetes (T2D). There is confirmatory evidence that T2D can be prevented by lifestyle modification and/or medications, in people with IGT diagnosed by 2-h plasma glucose (PG) during a 75-gram oral glucose tolerance test (OGTT). Over the last 40 years, a wealth of epidemiological data has confirmed the superior value of 1-h plasma glucose (PG) over fasting PG (FPG), glycated haemoglobin (HbA1c) and 2-h PG in populations of different ethnicity, sex and age in predicting diabetes and associated complications including death. Given the relentlessly rising prevalence of diabetes, a more sensitive, practical method is needed to detect people with IH and T2D for early prevention or treatment in the often lengthy trajectory to T2D and its complications. The International Diabetes Federation (IDF) Position Statement reviews findings that the 1-h post-load PG ≥ 155 mg/dL (8.6 mmol/L) in people with normal glucose tolerance (NGT) during an OGTT is highly predictive for detecting progression to T2D, micro- and macrovascular complications, obstructive sleep apnoea, cystic fibrosis-related diabetes mellitus, metabolic dysfunction-associated steatotic liver disease, and mortality in individuals with risk factors. The 1-h PG of 209 mg/dL (11.6 mmol/L) is also diagnostic of T2D. Importantly, the 1-h PG cut points for diagnosing IH and T2D can be detected earlier than the recommended 2-h PG thresholds. Taken together, the 1-h PG provides an opportunity to avoid misclassification of glycaemic status if FPG or HbA1c alone are used. The 1-h PG also allows early detection of high-risk people for intervention to prevent progression to T2D which will benefit the sizeable and growing population of individuals at increased risk of T2D. Using a 1-h OGTT, subsequent to screening with a non-laboratory diabetes risk tool, and intervening early will favourably impact the global diabetes epidemic. Health services should consider developing a policy for screening for IH based on local human and technical resources. People with a 1-h PG ≥ 155 mg/dL (8.6 mmol/L) are considered to have IH and should be prescribed lifestyle intervention and referred to a diabetes prevention program. People with a 1-h PG ≥ 209 mg/dL (11.6 mmol/L) are considered to have T2D and should have a repeat test to confirm the diagnosis of T2D and then referred for further evaluation and treatment. The substantive data presented in the Position Statement provides strong evidence for redefining current diagnostic criteria for IH and T2D by adding the 1-h PG.

Liquid Biopsy: A Game Changer for Type 2 Diabetes

As the burden of type 2 diabetes (T2D) continues to escalate globally, there is a growing need for novel, less-invasive biomarkers capable of early diabetes detection and monitoring of disease progression. Liquid biopsy, recognized for its minimally invasive nature, is increasingly being applied beyond oncology, and nevertheless shows its potential when the collection of the tissue biopsy is not possible. This diagnostic approach involves utilizing liquid biopsy markers such as cell-free nucleic acids, extracellular vesicles, and diverse metabolites for the molecular diagnosis of T2D and its related complications. In this context, we thoroughly examine recent developments in T2D liquid biopsy research. Additionally, we discuss the primary challenges and future prospects of employing liquid biopsy in the management of T2D. Prognosis, diagnosis and monitoring of T2D through liquid biopsy could be a game-changing technique for personalized diabetes management.

Low-Density Neutrophils Contribute to Subclinical Inflammation in Patients with Type 2 Diabetes

Type 2 diabetes (T2D) is characterized by low-grade inflammation. Low-density neutrophils (LDNs) represent normally less than 2% of total neutrophils but increase in multiple pathologies, releasing inflammatory cytokines and neutrophil extracellular traps (NETs). We assessed the count and role of high-density neutrophils (HDNs), LDNs, and NET-related activities in patients with T2D. HDNs and LDNs were purified by fluorescence-activated cell sorting (FACS) and counted by flow cytometry. Circulating inflammatory and NETs biomarkers were measured by ELISA (Enzyme Linked Immunosorbent Assay). NET formation was quantified by confocal microscopy. Neutrophil adhesion onto a human extracellular matrix (hECM) was assessed by optical microscopy. We recruited 22 healthy volunteers (HVs) and 18 patients with T2D. LDN counts in patients with diabetes were significantly higher (160%), along with circulating NETs biomarkers (citrullinated H3 histone (H3Cit), myeloperoxidase (MPO), and MPO-DNA (137%, 175%, and 69%, respectively) versus HV. Circulating interleukins (IL-6 and IL-8) and C-Reactive Protein (CRP) were significantly increased by 117%, 171%, and 79%, respectively, in patients compared to HVs. Isolated LDNs from patients expressed more H3Cit, MPO, and NETs, formed more NETs, and adhered more on hECM compared to LDNs from HVs. Patients with T2D present higher levels of circulating LDN- and NET-related biomarkers and associated pro-inflammatory activities.

Recent Advances in Electrochemical Biosensors Targeting Stress Markers

INTRODUCTION

When the body experiences a change in its internal environment due to factors such as mood (euphoria, stress) and illness, it releases biomarkers in large quantities. These biomarkers are used for detecting a disease at its early stages. This involves the detection of insufficient quantities of biocomponents, which can be done by using nanomaterials, conventional materials, and biotechnology; thus, scientists can increase the sensitivity of electrochemical sensors. According to studies conducted in this area, electrochemical sensors have shown promise as a diagnostic tool due to their ability to identify and pinpoint illness biomarkers. The present review article was compiled to gather the latest information on electrochemical biosensors targeting stress markers.

MATERIALS AND METHODS

The authors searched scholarly databases like ScienceDirect, Pubmed, Medline, and Scopus for information on electrochemical biosensors targeting stress markers.

RESULTS

In this article, we looked at the recent developments in electrochemical sensors for stress monitoring. Because of advances in nanomaterial and biomolecule processes, electrochemical biosensors have been developed with the sensitivity to detect several biomarkers in real-time in therapeutically relevant materials.

CONCLUSION

This biomarker sensor strategy can analyze various biofluids (sweat, plasma, urine, and saliva).

MORTALITY AND CARDIOVASCULAR RISK REDUCTION AFTER REVERSION OF PREDIABETES TO NORMOGLYCEMIA: A SYSTEMATIC REVIEW

Introduction

It is unclear whether reversion to normoglycemia decreases overall cardiovascular events and all-cause mortality risk in the long term. We aim to investigate the magnitude of change in cardiovascular risk and mortality in patients who reverted from a prediabetes state.

Methods

Three electronic databases, including PubMed, Proquest, and EBSCOHost databases, were utilized. A manual hand search of articles was also done. We selected studies that measure cardiovascular risk and all-cause mortality risk after reversion from prediabetes to normoglycemia. The following terms and its variant were used in the search strategy: 'reversion,' 'prediabetes,' 'normoglycemia,' cardiovascular risk,' and 'mortality.'

Results

Seven studies with a total of 73,845 participants were obtained. Most studies suggest that reversion of prediabetes reduced the cardiovascular and all-cause mortality risk (RR: 0.50 - 0.78) compared to persistent prediabetes state or progression to diabetes with long-term follow-up ranging from 5 to 12 years, while two studies did not show significant association in CVD and all-cause morality risk.

Conclusion

Although there were mixed results regarding if prediabetes poses a higher risk than normoglycemia for cardiovascular events and all-cause mortality, measures to normalize blood glucose for prediabetes should still be advocated.

The Longevity Protein Klotho: A Promising Tool to Monitor Lifestyle Improvements

Aging is not a disease; it is a natural evolution of human physiology. Medical advances have extended our life expectancy, but chronic diseases and geriatric syndrome continue to affect the increasingly aging population. Yet modern medicine perpetuates an approach based on treatment rather than prevention and education. In order to help solve this ever-growing problem, a new discipline has emerged: lifestyle medicine. Nutrition, physical activity, stress management, restorative sleep, social connection, and avoidance of risky substances are the pillars on which lifestyle medicine is founded. The aim of this discipline is to increase healthspan and reduce the duration of morbidity by making changes to our lifestyle. In this review, we propose the use of klotho protein as a novel biomarker for lifestyle medicine in order to quantify and monitor the health status of individuals, as no integrative tool currently exists.

Endothelial progenitor cells as biomarkers of diabetes-related cardiovascular complications

Diabetes mellitus (DM) constitutes a chronic metabolic disease characterized by elevated levels of blood glucose which can also lead to the so-called diabetic vascular complications (DVCs), responsible for most of the morbidity, hospitalizations and death registered in these patients. Currently, different approaches to prevent or reduce DM and its DVCs have focused on reducing blood sugar levels, cholesterol management or even changes in lifestyle habits. However, even the strictest glycaemic control strategies are not always sufficient to prevent the development of DVCs, which reflects the need to identify reliable biomarkers capable of predicting further vascular complications in diabetic patients. Endothelial progenitor cells (EPCs), widely known for their potential applications in cell therapy due to their regenerative properties, may be used as differential markers in DVCs, considering that the number and functionality of these cells are affected under the pathological environments related to DM. Besides, drugs commonly used with DM patients may influence the level or behaviour of EPCs as a pleiotropic effect that could finally be decisive in the prognosis of the disease. In the current review, we have analysed the relationship between diabetes and DVCs, focusing on the potential use of EPCs as biomarkers of diabetes progression towards the development of major vascular complications. Moreover, the effects of different drugs on the number and function of EPCs have been also addressed.

The role of heat shock proteins (HSPs) in type 2 diabetes mellitus pathophysiology

Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by sustained hyperglycemia caused by impaired insulin signaling and secretion. Metabolic stress, caused by an inappropriate diet, is one of the major hallmarks provoking inflammation, endoplasmic reticulum (ER) stress, and mitochondrial dysfunction. Heat shock proteins (HSPs) are a group of highly conserved proteins that have a crucial role in chaperoning damaged and misfolded proteins to avoid disruption of cellular homeostasis under stress conditions. To do this, HSPs interact with diverse intra-and extracellular pathways among which are the insulin signaling, insulin secretion, and apoptosis pathways. Therefore, HSP dysfunction, e.g. HSP70, may lead to disruption of the pathways responsible for insulin secretion and uptake. Consistently, the altered expression of other HSPs and genetic polymorphisms in HSP-producing genes in diabetic subjects has made HSPs hot research in T2DM. This paper provides a comprehensive overview of the role of different HSPs in T2DM pathogenesis, affected cellular pathways, and the potential therapeutic strategies targeting HSPs in T2DM.

Smart Contact Lenses as Wearable Ophthalmic Devices for Disease Monitoring and Health Management

The eye contains a complex network of physiological information and biomarkers for monitoring disease and managing health, and ocular devices can be used to effectively perform point-of-care diagnosis and disease management. This comprehensive review describes the target biomarkers and various diseases, including ophthalmic diseases, metabolic diseases, and neurological diseases, based on the physiological and anatomical background of the eye. This review also includes the recent technologies utilized in eye-wearable medical devices and the latest trends in wearable ophthalmic devices, specifically smart contact lenses for the purpose of disease management. After introducing other ocular devices such as the retinal prosthesis, we further discuss the current challenges and potential possibilities of smart contact lenses.

Predisposition to Type 2 Diabetes in Aspects of the Glycemic Curve and Glycated Hemoglobin in Healthy, Young Adults: A Cross-sectional Study

OBJECTIVES

Our aim in this study was to identify predictors for diabetes among the characteristics of the glycemic curve and glycated hemoglobin (A1C) in healthy, young adults.

METHODS

We used a cross-sectional study to establish predictors for diabetes based on earlier studies and evaluated occurrence of the condition in 81 healthy, young adult subjects. These volunteers underwent analysis of fasting plasma glucose, oral glucose tolerance test plasma glucose, A1C, and inflammatory markers (leukocytes, monocytes, and C-reactive protein). The nonparametric Mann-Whitney U test, Fisher's exact test, chi-square test, Kruskal-Wallis test, and multiple-comparisons test were used to analyze the data.

RESULTS

We studied 2 age groups, homogeneous in terms of family history of diabetes: one group ranged in age from ≥18 to <28 years (median 20 years; body mass index [BMI] 24 kg/m2) and the other group ranged in age from ≥28 to <45 years (median 35 years; BMI 24 kg/m2). The older group had a higher incidence of predictors (p=0.0005) and was associated with the predictors 30-minute blood glucose ≥164 mg/dL (p=0.0190), 60-minute blood glucose ≥125 mg/dL (p=0.0346), and A1C ≥5.5% (p=0.0162), with a monophasic glycemic curve (p=0.007). The younger group was associated with the 2-hour plasma glucose predictor ≥140 mg/dL (p=0.014). All subjects had fasting glucose in the normal range.

CONCLUSIONS

Healthy, young adults may already have predictors of diabetes, identified mainly by aspects of the glycemic curve and A1C, but at more modest levels than those with prediabetes.

C-Reactive Protein: Pathophysiology, Diagnosis, False Test Results and a Novel Diagnostic Algorithm for Clinicians

The current literature provides a body of evidence on C-Reactive Protein (CRP) and its potential role in inflammation. However, most pieces of evidence are sparse and controversial. This critical state-of-the-art monography provides all the crucial data on the potential biochemical properties of the protein, along with further evidence on its potential pathobiology, both for its pentameric and monomeric forms, including information for its ligands as well as the possible function of autoantibodies against the protein. Furthermore, the current evidence on its potential utility as a biomarker of various diseases is presented, of all cardiovascular, respiratory, hepatobiliary, gastrointestinal, pancreatic, renal, gynecological, andrological, dental, oral, otorhinolaryngological, ophthalmological, dermatological, musculoskeletal, neurological, mental, splenic, thyroid conditions, as well as infections, autoimmune-supposed conditions and neoplasms, including other possible factors that have been linked with elevated concentrations of that protein. Moreover, data on molecular diagnostics on CRP are discussed, and possible etiologies of false test results are highlighted. Additionally, this review evaluates all current pieces of evidence on CRP and systemic inflammation, and highlights future goals. Finally, a novel diagnostic algorithm to carefully assess the CRP level for a precise diagnosis of a medical condition is illustrated.

Metabolomics in diabetes mellitus: clinical insight

INTRODUCTION

Diabetes Mellitus (DM) is a chronic heterogeneous metabolic disorder characterized by hyperglycemia due to the destruction of insulin-producing pancreatic β cells and/or insulin resistance. It is now considered a global epidemic disease associated with serious threats to a patient's life. Understanding the metabolic pathways involved in disease pathogenesis and progression is important and would improve prevention and management strategies. Metabolomics is an emerging field of research that offers valuable insights into the metabolic perturbation associated with metabolic diseases, including DM.

AREA COVERED

Herein, we discussed the metabolomics in type 1 and 2 DM research, including its contribution to understanding disease pathogenesis and identifying potential novel biomarkers clinically useful for disease screening, monitoring, and prognosis. In addition, we highlighted the metabolic changes associated with treatment effects, including insulin and different anti-diabetic medications.

EXPERT OPINION

By analyzing the metabolome, the metabolic disturbances involved in T1DM and T2DM can be explored, enhancing our understanding of the disease progression and potentially leading to novel clinical diagnostic and effective new therapeutic approaches. In addition, identifying specific metabolites would be potential clinical biomarkers for predicting the disease and thus preventing and managing hyperglycemia and its complications.

Physiopathological mechanisms related to inflammation in obesity and type 2 diabetes mellitus

Overweight, obesity, and type 2 diabetes mellitus pose global health problems that are ever-increasing. A chronic low-grade inflammatory status and the presence of various pro-inflammatory markers either in circulation or within dysfunctional metabolic tissues are well established. The presence of these factors can, to some extent, predict disease development and progression. A central role is played by the presence of dysfunctional adipose tissue, liver dysfunction, and skeletal muscle dysfunction, which collectively contribute to the increased circulatory levels of proinflammatory factors. Weight loss and classical metabolic interventions achieve a decrease in many of these factors' circulating levels, implying that a better understanding of the processes or even the modulation of inflammation may alleviate these diseases. This review suggests that inflammation plays a significant role in the development and progression of these conditions and that measuring inflammatory markers may be useful for assessing disease risk and development of future treatment methods.

Diet and Acanthosis Nigricans over a Two-Year Period in Children of the Pacific Region

BACKGROUND

The impact that dietary carbohydrates have on children developing type 2 diabetes remains controversial. Furthermore, there are limited pediatric longitudinal studies on changes in body mass index (BMI) and diet related to the development of acanthosis nigricans (AN), a risk factor associated with type 2 diabetes.

METHODS

Two 24 h dietary records were collected for 558 children, 2-8 years of age, at baseline and at a 2-year follow-up. Data on age, sex, BMI, and the presence of AN were also collected at each time point from the Children's Healthy Living Program. Logistic regression was used to determine factors associated with the presence of AN at follow-up. Multinominal regression was used to determine factors associated with changes in AN status. Linear regression was used to measure the associations between changes in dietary intake and in the Burke Score for AN.

RESULTS

AN was present in 28 children at baseline and 34 children at follow-up. Adjusting for the presence of AN at baseline, age, sex, study group, baseline BMI, change in BMI z-score, time between assessments, and baseline intake, an increase from baseline for each teaspoon of sugar and serving of carbohydrate-rich food increased the risk for having AN at follow-up by 9% and 8%, respectively (p ≤ 0.05). An increased intake of added sugar (teaspoons) increased the risk of developing AN by 13% (p ≤ 0.01) and an increase in servings of foods rich in starch increased the risk of developing AN by 12% (p ≤ 0.01) compared to children who never had AN. Increasing the intake of fruit was also associated with decreased Burke Scores using multiple regression. However, the intake of energy and macronutrients were not associated with AN.

CONCLUSIONS

Added sugar and foods rich in starch were independently associated with the occurrence of AN, suggesting the type of carbohydrates consumed is a factor in AN occurrence.

Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort

Introduction

The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host.

Methods

Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).

Results

By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter.

Discussion

Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine.

Biomarkers in diabetic neuropathy

CONTEXT

The prevalence of diabetic neuropathy is drastically increasing in the world. To halt the progression of diabetic neuropathy, there is an unmet need to have potential biomarkers for the diagnosis and new drug discovery.

OBJECTIVE

To study various biomarkers involved in the pathogenesis of diabetic neuropathy.

METHODS

The literature was searched with the help of various scientific databases and resources like PubMed, ProQuest, Scopus, and Google scholar from the year 1976 to 2020.

RESULTS

Biomarkers of diabetic neuropathy are categorised as inflammatory biomarkers such as MCP-1, VEGF, TRPV1, NF-κB; oxidative biomarkers such as adiponectin, NFE2L2; enzyme biomarkers like NADPH, ceruloplasmin, HO-1, DPP-4, PARP α; miscellaneous biomarkers such as SIRT1, caveolin 1, MALAT1, and microRNA. All biomarkers have a significant role in the pathogenesis of diabetic neuropathy.

CONCLUSION

These biomarkers have a potential role in the progression of diabetic neuropathy and can be considered as potential targets for new drug discovery.

Analysis of protein expression changes in patients with prediabetes using proteomics approaches

RATIONALE

Proteomics and metabolomics are widely used in the study of diabetes, but rarely in prediabetes research. This study aimed to explore the mechanisms of early-onset type 2 diabetes mellitus (T2DM) by analyzing proteomic changes at different stages of glucose metabolism.

METHODS

A total of 40 individuals undergoing routine physical health examinations between December 2016 and April 2017 were enrolled. Subjects were divided into four groups based on fasting blood glucose (FPG) levels: FPG < 5.6 mmol/L (group A); FPG ≥ 5.6 mmol/L and <6.1 mmol/L (group B); FPG ≥ 6.1 mmol/L and <7.0 mmol/L (group C); and FPG ≥ 7.0 mmol/L (group D). Each group had 10 cases. Sera from these 40 subjects were analyzed by label-free quantitative liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS). LC/MS/MS with selected reaction monitoring mode was also performed for qualitative and quantitative metabolomics analysis. Differentially expressed proteins were identified. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the differentially expressed metabolites.

RESULTS

A total of 202 differentially expressed proteins were screened and were identified as mainly secreted proteins. Comparing group A with group B, 32 proteins were up-regulated and 18 proteins were down-regulated. Comparing group A with group C, 24 proteins were up-regulated and 24 proteins were down-regulated. Comparing group A with group D, 19 proteins were up-regulated and 17 proteins were down-regulated. The fold change for up-regulated proteins was >1.2, p < 0.05, while the fold change for down-regulated proteins was <-1.2, p < 0.05. PLS-DA and OPLS-DA revealed 113 differentially expressed metabolites. Correlation analysis of differentially expressed metabolites of group A versus group B revealed that among the down-regulated differential proteins, transforming growth factor β-induced protein ig-h3 correlated negatively with metabolite L-saccharin, while among the up-regulated differential proteins, apolipoprotein C-IV correlated negatively with metabolite 3-methyloxindole. Among all differentially expressed proteins, 19 proteins were associated with early initiation of chronic inflammation, including CD14 and CSF-1R, which were newly identified in the early onset of T2DM.

CONCLUSIONS

Many proteins are differentially expressed between prediabetes and after T2DM diagnosis, although the specific mechanism remains unclear. The expression level of CD14 was significantly up-regulated and that of CSF-1R was significantly down-regulated when FPG was ≥5.6 mmol/L, suggesting that CD14 and CSF-1R may be important markers for early-onset T2DM and may serve as new targets for T2DM treatment.

High-Density Lipoprotein Alterations in Type 2 Diabetes and Obesity

Alterations affecting high-density lipoproteins (HDLs) are one of the various abnormalities observed in dyslipidemia in type 2 diabetes mellitus (T2DM) and obesity. Kinetic studies have demonstrated that the catabolism of HDL particles is accelerated. Both the size and the lipidome and proteome of HDL particles are significantly modified, which likely contributes to some of the functional defects of HDLs. Studies on cholesterol efflux capacity have yielded heterogeneous results, ranging from a defect to an improvement. Several studies indicate that HDLs are less able to inhibit the nuclear factor kappa-B (NF-κB) proinflammatory pathway, and subsequently, the adhesion of monocytes on endothelium and their recruitment into the subendothelial space. In addition, the antioxidative function of HDL particles is diminished, thus facilitating the deleterious effects of oxidized low-density lipoproteins on vasculature. Lastly, the HDL-induced activation of endothelial nitric oxide synthase is less effective in T2DM and metabolic syndrome, contributing to several HDL functional defects, such as an impaired capacity to promote vasodilatation and endothelium repair, and difficulty counteracting the production of reactive oxygen species and inflammation.

Curcuminoid supplementation in canine diabetic mellitus and its complications using proteomic analysis

Introduction

Inflammation and oxidative stress contribute to diabetes pathogenesis and consequences. Therapeutic approaches for canine diabetes remain a challenge. Curcumin has anti-inflammatory and anti-oxidative effects and is beneficial for humans with diabetes mellitus (DM); however, data on its impact on canine diabetes is limited. This study aimed to evaluate the potential for causing adverse effects, anti-inflammatory effects, anti-oxidative effects and proteomic patterns of curcuminoid supplementation on canine DM.

Methods

Altogether, 18 dogs were divided into two groups: DM (n = 6) and healthy (n = 12). Curcuminoid 250 mg was given to the DM group orally daily for 180 days. Blood and urine sample collection for hematological parameters, blood biochemistry, urinalysis, oxidative stress parameters, inflammatory markers and proteomics were performed every 6 weeks.

Results and discussion

Curcuminoid supplementation with standard therapy significantly decreased oxidative stress with the increased glutathione/oxidized glutathione ratio, but cytokine levels were unaffected. According to the proteomic analysis, curcuminoid altered the expression of alpha-2-HS-glycoprotein, transthyretin, apolipoprotein A-I and apolipoprotein A-IV, suggesting that curcuminoid improves insulin sensitivity and reduces cardiovascular complications. No negative impact on clinical symptoms, kidneys or liver markers was identified. This study proposed that curcuminoids might be used as a targeted antioxidant strategy as an adjunctive treatment to minimize diabetes complications in dogs.

The role of pathogens in diabetes pathogenesis and the potential of immunoproteomics as a diagnostic and prognostic tool

Diabetes mellitus (DM) is a group of metabolic diseases marked by hyperglycemia, which increases the risk of systemic infections. DM patients are at greater risk of hospitalization and mortality from bacterial, viral, and fungal infections. Poor glycemic control can result in skin, blood, bone, urinary, gastrointestinal, and respiratory tract infections and recurrent infections. Therefore, the evidence that infections play a critical role in DM progression and the hazard ratio for a person with DM dying from any infection is higher. Early diagnosis and better glycemic control can help prevent infections and improve treatment outcomes. Perhaps, half (49.7%) of the people living with DM are undiagnosed, resulting in a higher frequency of infections induced by the hyperglycemic milieu that favors immune dysfunction. Novel diagnostic and therapeutic markers for glycemic control and infection prevention are desirable. High-throughput blood-based immunoassays that screen infections and hyperglycemia are required to guide timely interventions and efficiently monitor treatment responses. The present review aims to collect information on the most common infections associated with DM, their origin, pathogenesis, and the potential of immunoproteomics assays in the early diagnosis of the infections. While infections are common in DM, their role in glycemic control and disease pathogenesis is poorly described. Nevertheless, more research is required to identify novel diagnostic and prognostic markers to understand DM pathogenesis and management of infections. Precise monitoring of diabetic infections by immunoproteomics may provide novel insights into disease pathogenesis and healthy prognosis.

Application of FTIR Spectroscopy to Detect Changes in Skeletal Muscle Composition Due to Obesity with Insulin Resistance and STZ-Induced Diabetes

Age, obesity, and diabetes mellitus are pathophysiologically interconnected factors that significantly contribute to the global burden of non-communicable diseases. These metabolic conditions are associated with impaired insulin function, which disrupts the metabolism of carbohydrates, lipids, and proteins and can lead to structural and functional changes in skeletal muscle. Therefore, the alterations in the macromolecular composition of skeletal muscle may provide an indication of the underlying mechanisms of insulin-related disorders. The aim of this study was to investigate the potential of Fourier transform infrared (FTIR) spectroscopy to reveal the changes in macromolecular composition in weight-bearing and non-weight-bearing muscles of old, obese, insulin-resistant, and young streptozotocin (STZ)-induced diabetic mice. The efficiency of FTIR spectroscopy was evaluated by comparison with the results of gold-standard histochemical techniques. The differences in biomolecular phenotypes and the alterations in muscle composition in relation to their functional properties observed from FTIR spectra suggest that FTIR spectroscopy can detect most of the changes observed in muscle tissue by histochemical analyses and more. Therefore, it could be used as an effective alternative because it allows for the complete characterization of macromolecular composition in a single, relatively simple experiment, avoiding some obvious drawbacks of histochemical methods.

Optimization of a GC-MS method for the profiling of microbiota-dependent metabolites in blood samples: An application to type 2 diabetes and prediabetes

Changes in serum or plasma metabolome may reflect gut microbiota dysbiosis, which is also known to occur in patients with prediabetes and type 2 diabetes (T2DM). Thus, developing a robust method for the analysis of microbiota-dependent metabolites (MDMs) is an important issue. Gas chromatography with mass spectrometry (GC–MS) is a powerful approach enabling detection of a wide range of MDMs in biofluid samples with good repeatability and reproducibility, but requires selection of a suitable solvents and conditions. For this reason, we conducted for the first time the study in which, we demonstrated an optimisation of samples preparation steps for the measurement of 75 MDMs in two matrices. Different solvents or mixtures of solvents for MDMs extraction, various concentrations and volumes of derivatizing reagents as well as temperature programs at methoxymation and silylation step, were tested. The stability, repeatability and reproducibility of the 75 MDMs measurement were assessed by determining the relative standard deviation (RSD). Finally, we used the developed method to analyse serum samples from 18 prediabetic (PreDiab group) and 24 T2DM patients (T2DM group) from our 1000PLUS cohort. The study groups were homogeneous and did not differ in age and body mass index. To select statistically significant metabolites, T2DM vs. PreDiab comparison was performed using multivariate statistics. Our experiment revealed changes in 18 MDMs belonging to different classes of compounds, and seven of them, based on the SVM classification model, were selected as a panel of potential biomarkers, able to distinguish between patients with T2DM and prediabetes.

Untargeted approach to investigating the metabolomics profile of type 2 diabetes emiratis

Type 2 Diabetes (T2D) is expected to be the seventh most significant cause of death worldwide by 2030. Although research into its mechanism has received the attention it deserves, our understanding of T2D is still limited. This case-control study employs untargeted metabolomics to explore novel T2D plasma biomarkers in the Emirati population. Ninety-two UAE nationals were included in the cohort, with fifty T2D and forty-two non-T2D profiles. Participants were then stratified into three groups based on metabolic profiles, clinically verified diabetic status, and current HbA1c values: namely controlled diabetics, uncontrolled diabetics and prediabetics, and non-diabetics. The study identified fifteen significant differentially abundant metabolites between the uncontrolled diabetics group and the prediabetics or controlled diabetics group. Interestingly, some metabolites essential for the corticosteroid and thyroid signaling pathways were found to be significantly elevated in poorly controlled T2D, including cortisol, glycocholic acid, bile acids, thyroxine, and the tryptophan metabolite, 5-hydroxyindoleacetic acid. These findings align with those from prior western cohorts and suggest an intriguing linkage between T2D glycemic control and thyroid and adrenal signaling that may provide new diagnostic and prognostic indicators. RESEARCH SIGNIFICANCE: This study investigates the underlooked metabolomic role and correlation with T2D in the UAE population. The report indicates fifteen significant differentially abundant metabolites between on diabetics, uncontrolled diabetics and or controlled diabetics or prediabetics. This panel of metabolites such as thyroxine and corticosteroids should be considered further as potential diagnostic or prognostic biomarkers for T2D in the region.

In-depth investigation of the hypoglycemic mechanism of Morchella importuna polysaccharide via metabonomics combined with 16S rRNA sequencing

Increasing evidence indicates that type 2 diabetes mellitus (T2DM) is closely related to intestinal bacteria disorders and abnormal hepatic metabolism. Morchella importuna polysaccharide (MIP) shows excellent hypoglycemic activity in vitro. However, the hypoglycemic effect and mechanism of MIP in vivo have yet to be investigated. In this study, the blood glucose, blood lipid and insulin resistance of diabetic mice after MIP intervention were measured to evaluate its hypoglycemic effect. Then, the microbiome and metabolomics were combined to explore the hypoglycemic mechanism of MIP. Results indicated that high dose MIP (400 mg/kg) had significant hypoglycemic effect. Furthermore, MIP could reverse diabetes-induced intestinal disorder by increasing the abundance of Akkermansia, Blautia, Dubosiella, and Lachnospiraceae, as well as decreasing the abundance of Helicobacteraceae. Besides, the hepatic metabolites and complex network systems formed by multiple metabolic pathways were regulated after MIP treatment. Notably, a new biomarker of diabetes (N-P-coumaroyl spermidine) was discovered in this study. Moreover, the significant association between intestinal bacteria and hepatic metabolites was determined by correlations analysis, which in turn confirmed MIP alleviated T2DM via the gut-liver axis. Therefore, these findings elucidated in-depth hypoglycemic mechanisms of MIP and provided a new biomarker for the prevention of diabetes.

Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods

Prediabetes and diabetes are becoming alarmingly prevalent among adolescents over the past decade. However, an effective screening tool that can assess diabetes risks smoothly is still in its infancy. In order to contribute to such significant gaps, this research proposes a machine learning-based predictive model to detect adolescent diabetes. The model applies supervised machine learning and a novel feature selection method to the National Health and Nutritional Examination Survey datasets after an exhaustive search to select reliable and accurate data. The best model achieved an area under the curve (AUC) score of 71%. This research proves that a screening tool based on supervised machine learning models can assist in the automated detection of youth diabetes. It also identifies some critical predictors to such detection using Lasso Regression, Random Forest Importance and Gradient Boosted Tree Importance feature selection methods. The most contributing features to Youth diabetes detection are physical characteristics (e.g., waist, leg length, gender), dietary information (e.g., water, protein, sodium) and demographics. These predictors can be further utilised in other areas of medical research, such as electronic medical history.

Sleep duration irregularity is associated with elevated blood pressure in young adults

Sleep irregularity (i.e., highly variable sleep patterns) is an emerging risk factor for cardiometabolic disease. Though irregular sleep patterns are common among young adults, the cardiometabolic health (CMH) repercussions of sleep irregularity in this population are unclear. We examined associations between sleep duration and irregularity with measures of CMH in 44 (24 M/20 F, 23 ± 5y, BMI 26 ± 4 kg/m², blood pressure (BP): 125/71 ± 14/9 mmHg) young adults. Participants wore actigraphy monitors for seven-days and sleep duration irregularity was operationalized as the standard deviation of nightly sleep duration (sleep SD). CMH variables of interest included brachial and aortic BP, arterial stiffness (cf-PWV), augmentation index (AIx75), and fasting blood glucose and lipids. Associations between sleep duration and sleep SD with CMH variables were assessed via correlations adjusted for sex and BMI. Sleep duration generally was not associated with CMH indices. However, sleep SD was associated with brachial systolic (r = 0.433, p = .027) and diastolic BP (r = 0.415, p = .035). Similarly, sleep duration SD was associated with aortic systolic BP (r = 0.447, p = .022). Our findings show that sleep irregularity, but not duration, is associated with higher brachial and central BP in young adults.Abbreviations: AIx75: augmentation index at a heart rate of 75 beats per minute; BP: blood pressure; CMH: cardiometabolic health; cf-PWV: carotid-femoral pulse wave velocity; DXA: dual x-ray absorptiometry; mg/dl: milligrams per deciliter; PWA: pulse wave analysis; PWV: pulse wave velocity; sleep duration SD: standard deviation of nightly sleep duration.

Towards Multiplexed and Multimodal Biosensor Platforms in Real-Time Monitoring of Metabolic Disorders

Metabolic syndrome (MS) is a cluster of conditions that increases the probability of heart disease, stroke, and diabetes, and is very common worldwide. While the exact cause of MS has yet to be understood, there is evidence indicating the relationship between MS and the dysregulation of the immune system. The resultant biomarkers that are expressed in the process are gaining relevance in the early detection of related MS. However, sensing only a single analyte has its limitations because one analyte can be involved with various conditions. Thus, for MS, which generally results from the co-existence of multiple complications, a multi-analyte sensing platform is necessary for precise diagnosis. In this review, we summarize various types of biomarkers related to MS and the non-invasively accessible biofluids that are available for sensing. Then two types of widely used sensing platform, the electrochemical and optical, are discussed in terms of multimodal biosensing, figure-of-merit (FOM), sensitivity, and specificity for early diagnosis of MS. This provides a thorough insight into the current status of the available platforms and how the electrochemical and optical modalities can complement each other for a more reliable sensing platform for MS.

Nutraceutical Eriocitrin (Eriomin) Reduces Hyperglycemia by Increasing Glucagon-Like Peptide 1 and Downregulates Systemic Inflammation: A Crossover-Randomized Clinical Trial

This double-blind, randomized, placebo/controlled, crossover study evaluated the efficacy of Eriomin^® in reducing hyperglycemia and improving diabetes-related biomarkers in individuals with hyperglycemia above 110 mg/dL (mean 123 ± 18 mg/dL). Subjects (n = 30), divided into two groups (Eriomin or Placebo), who received a dose of 200 mg/d of the designated supplement for 12 weeks and, after a washout period of 2 weeks, switched to the other supplement in the following 12 weeks. Assessments of biochemical, metabolic, inflammatory, blood pressure, anthropometry, and dietary parameters were performed at the beginning and end of each intervention. Treatment with 200 mg/d of Eriomin significantly decreased blood glucose (-5%), homeostasis model assessment of insulin resistance (-11%), glucagon (-13%), interleukin-6 (-14%), tumor necrosis factor alpha (-20%), and alkaline phosphatase (-13%); but increased glucagon-like peptide 1 (GLP-1) by (17%) (P ≤ .05). At the end of the placebo period, there was a 13% increase in triglycerides (P ≤ .05). Other parameters evaluated did not change with Eriomin or placebo. In conclusion, intervention with Eriomin benefited the glycemic control of prediabetic and diabetic patients, with higher blood glucose levels, by increasing GLP-1 and decreasing systemic inflammation.

Metabolic Effects of an Oral Glucose Tolerance Test Compared to the Mixed Meal Tolerance Tests: A Narrative Review

The oral glucose tolerance test (OGTT) is recommended for assessing abnormalities in glucose homeostasis. Recognised as the gold standard test for diagnosing diabetes, the OGTT provides useful information about glucose tolerance. However, it does not replicate the process of absorption and digestion of complex foods, such as that which occurs with a mixed meal tolerance test (MMTT), an alternative that is still not well explored in the diagnosis of metabolic alterations. The MMTT could be an asset in detecting glucose homeostasis disorders, including diabetes since it has more similarities to the common dietary pattern, allowing early detection of subtle changes in metabolic homeostasis in response to combined nutrients. This alternative has the advantage of being more tolerable and pleasant to patients since it induces a more gradual increase in blood glucose, thus reducing the risk of rebound hypoglycemia and other related complications. The present article reviewed the clinical data available regarding the possibility of screening or diagnosing altered glucose homeostasis, including type 2 diabetes mellitus, with the MMTT.

Prognostic significance of metabolomic biomarkers in patients with diabetes mellitus and coronary artery disease

Background

Diabetes mellitus (DM) and coronary artery disease (CAD) constitute inter-related clinical entities. Biomarker profiling emerges as a promising tool for the early diagnosis and risk stratification of either DM or CAD. However, studies assessing the predictive capacity of novel metabolomics biomarkers in coexistent CAD and DM are scarce.

Methods

This post-hoc analysis of the CorLipid trial (NCT04580173) included 316 patients with CAD and comorbid DM who underwent emergency or elective coronary angiography due to acute or chronic coronary syndrome. Cox regression analyses were performed to identify metabolomic predictors of the primary outcome, which was defined as the composite of major adverse cardiovascular or cerebrovascular events (MACCE: cardiovascular death, myocardial infarction, stroke, major bleeding), repeat unplanned revascularizations and cardiovascular hospitalizations. Linear regression analyses were also performed to detect significant predictors of CAD complexity, as assessed by the SYNTAX score.

Results

After a median 2-year follow up period (IQR = 0.7 years), the primary outcome occurred in 69 (21.8%) of patients. Acylcarnitine ratio C4/C18:2, apolipoprotein (apo) B, history of heart failure (HF), age > 65 years and presence of acute coronary syndrome were independent predictors of the primary outcome in diabetic patients with CAD (aHR = 1.89 [1.09, 3.29]; 1.02 [1.01, 1.04]; 1.28 [1.01, 1.41]; 1.04 [1.01, 1.05]; and 1.12 [1.05–1.21], respectively). Higher levels of ceramide ratio C24:1/C24:0, acylcarnitine ratio C4/C18:2, age > 65 and peripheral artery disease were independent predictors of higher CAD complexity (adjusted β = 7.36 [5.74, 20.47]; 3.02 [0.09 to 6.06]; 3.02 [0.09, 6.06], respectively), while higher levels of apoA1 were independent predictors of lower complexity (adjusted β= − 0.65 [− 1.31, − 0.02]).

Conclusions

In patients with comorbid DM and CAD, novel metabolomic biomarkers and metabolomics-based prediction models could be recruited to predict clinical outcomes and assess the complexity of CAD, thereby enabling the integration of personalized medicine into routine clinical practice. These associations should be interpreted taking into account the observational nature of this study, and thus, larger trials are needed to confirm its results and validate them in different and larger diabetic populations.

Diabetes mellitus and perioperative outcomes: a scoping review of the literature

BACKGROUND

Diabetes mellitus (DM) is frequently encountered in the perioperative period. DM may increase the risk of adverse perioperative outcomes owing to the potential vascular complications of DM. We conducted a scoping review to examine the association between DM and adverse perioperative outcomes.

METHODS

A systematic search strategy of the published literature was built and applied in multiple databases. Observational studies examining the association between DM and adverse perioperative outcomes were included. Abstract screening determined full texts suitable for inclusion. Core information was extracted from each of the included studies including study design, definition of DM, type of DM, surgical specialties, and outcomes. Only primary outcomes are reported in this review.

RESULTS

The search strategy identified 2363 records. Of those, 61 were included and 28 were excluded with justification. DM was mostly defined by either haemoglobin A1c (HbA1c) or blood glucose values (19 studies each). Other definitions included 'prior diagnosis' or use of medication. In 17 studies the definition was unclear. Type 2 DM was the most frequently studied subtype. Five of seven studies found DM was associated with mortality, 5/13 reported an association with 'complications' (as a composite measure), and 12/17 studies found DM was associated with 'infection'. Overall, 33/61 studies reported that DM was associated with the primary outcome measure.

CONCLUSION

Diabetes mellitus is inconsistently defined in the published literature, which limits the potential for pooled analysis. Further research is necessary to determine which cohort of patients with DM are most at risk of adverse postoperative outcomes, and how control influences this association.

Advanced Glycation End-Products (AGEs): Formation, Chemistry, Classification, Receptors, and Diseases Related to AGEs

Advanced glycation end-products (AGEs) constitute a non-homogenous, chemically diverse group of compounds formed either exogeneously or endogeneously on the course of various pathways in the human body. In general, they are formed non-enzymatically by condensation between carbonyl groups of reducing sugars and free amine groups of nucleic acids, proteins, or lipids, followed by further rearrangements yielding stable, irreversible end-products. In the last decades, AGEs have aroused the interest of the scientific community due to the increasing evidence of their involvement in many pathophysiological processes and diseases, such as diabetes, cancer, cardiovascular, neurodegenerative diseases, and even infection with the SARS-CoV-2 virus. They are recognized by several cellular receptors and trigger many signaling pathways related to inflammation and oxidative stress. Despite many experimental research outcomes published recently, the complexity of their engagement in human physiology and pathophysiological states requires further elucidation. This review focuses on the receptors of AGEs, especially on the structural aspects of receptor-ligand interaction, and the diseases in which AGEs are involved. It also aims to present AGE classification in subgroups and to describe the basic processes leading to both exogeneous and endogeneous AGE formation.

Recent Developments in Biomarkers for Diagnosis and Screening of Type 2 Diabetes Mellitus

PURPOSE OF REVIEW

Diabetes mellitus is a complex, chronic illness characterized by elevated blood glucose levels that occurs when there is cellular resistance to insulin action, pancreatic β-cells do not produce sufficient insulin, or both. Diabetes prevalence has greatly increased in recent decades; consequently, it is considered one of the fastest-growing public health emergencies globally. Poor blood glucose control can result in long-term micro- and macrovascular complications such as nephropathy, retinopathy, neuropathy, and cardiovascular disease. Individuals with diabetes require continuous medical care, including pharmacological intervention as well as lifestyle and dietary changes.

RECENT FINDINGS

The most common form of diabetes mellitus, type 2 diabetes (T2DM), represents approximately 90% of all cases worldwide. T2DM occurs more often in middle-aged and elderly adults, and its cause is multifactorial. However, its incidence has increased in children and young adults due to obesity, sedentary lifestyle, and inadequate nutrition. This high incidence is also accompanied by an estimated underdiagnosis prevalence of more than 50% worldwide. Implementing successful and cost-effective strategies for systematic screening of diabetes mellitus is imperative to ensure early detection, lowering patients' risk of developing life-threatening disease complications. Therefore, identifying new biomarkers and assay methods for diabetes mellitus to develop robust, non-invasive, painless, highly-sensitive, and precise screening techniques is essential. This review focuses on the recent development of new clinically validated and novel biomarkers as well as the methods for their determination that represent cost-effective alternatives for screening and early diagnosis of T2DM.

Biomarkers of non-communicable chronic disease: an update on contemporary methods

Chronic diseases constitute a major global burden with significant impact on health systems, economies, and quality of life. Chronic diseases include a broad range of diseases that can be communicable or non-communicable. Chronic diseases are often associated with modifications of normal physiological levels of various analytes that are routinely measured in serum and other body fluids, as well as pathological findings, such as chronic inflammation, oxidative stress, and mitochondrial dysfunction. Identification of at-risk populations, early diagnosis, and prediction of prognosis play a major role in preventing or reducing the burden of chronic diseases. Biomarkers are tools that are used by health professionals to aid in the identification and management of chronic diseases. Biomarkers can be diagnostic, predictive, or prognostic. Several individual or grouped biomarkers have been used successfully in the diagnosis and prediction of certain chronic diseases, however, it is generally accepted that a more sophisticated approach to link and interpret various biomarkers involved in chronic disease is necessary to improve our current procedures. In order to ensure a comprehensive and unbiased coverage of the literature, first a primary frame of the manuscript (title, headings and subheadings) was drafted by the authors working on this paper. Second, based on the components drafted in the preliminary skeleton a comprehensive search of the literature was performed using the PubMed and Google Scholar search engines. Multiple keywords related to the topic were used. Out of screened papers, only 190 papers, which are the most relevant, and recent articles were selected to cover the topic in relation to etiological mechanisms of different chronic diseases, the most recently used biomarkers of chronic diseases and finally the advances in the applications of multivariate biomarkers of chronic diseases as statistical and clinically applied tool for the early diagnosis of chronic diseases was discussed. Recently, multivariate biomarkers analysis approach has been employed with promising prospect. A brief discussion of the multivariate approach for the early diagnosis of the most common chronic diseases was highlighted in this review. The use of diagnostic algorithms might show the way for novel criteria and enhanced diagnostic effectiveness inpatients with one or numerous non-communicable chronic diseases. The search for new relevant biomarkers for the better diagnosis of patients with non-communicable chronic diseases according to the risk of progression, sickness, and fatality is ongoing. It is important to determine whether the newly identified biomarkers are purely associations or real biomarkers of underlying pathophysiological processes. Use of multivariate analysis could be of great importance in this regard.

Semi-Quantitative MALDI Measurements of Blood-Based Samples for Molecular Diagnostics

Accurate and precise measurement of the relative protein content of blood-based samples using mass spectrometry is challenging due to the large number of circulating proteins and the dynamic range of their abundances. Traditional spectral processing methods often struggle with accurately detecting overlapping peaks that are observed in these samples. In this work, we develop a novel spectral processing algorithm that effectively detects over 1650 peaks with over 3.5 orders of magnitude in intensity in the 3 to 30 kD m/z range. The algorithm utilizes a convolution of the peak shape to enhance peak detection, and accurate peak fitting to provide highly reproducible relative abundance estimates for both isolated peaks and overlapping peaks. We demonstrate a substantial increase in the reproducibility of the measurements of relative protein abundance when comparing this processing method to a traditional processing method for sample sets run on multiple matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) instruments. By utilizing protein set enrichment analysis, we find a sizable increase in the number of features associated with biological processes compared to previously reported results. The new processing method could be very beneficial when developing high-performance molecular diagnostic tests in disease indications.

Nanoporous Substrates with Molecular-Level Perfluoroalkyl/Alkylamide Surface for Laser Desorption/Ionization Mass Spectrometry of Small Proteins

The rapid detection of biomolecules greatly contributes to health management, clinical diagnosis, and prevention of diseases. Mass spectrometry (MS) is effective for detecting and analyzing various molecules at high throughput. However, there are problems with the MS analysis of biological samples, including complicated separation operations and essential pretreatments. In this study, a nanostructured organosilica substrate for laser desorption/ionization mass spectrometry (LDI-MS) is designed and synthesized to detect peptides and small proteins efficiently and rapidly. The surface functionality of the substrate is tuned by perfluoroalkyl/alkylamide groups mixed at a molecular level. This contributes to both lowering the surface free energy and introducing weak anchoring sites for peptides and proteins. Analyte molecules applied onto the substrate are homogeneously distributed and readily desorbed by the laser irradiation. The organosilica substrate enables the efficient LDI of various compounds, including peptides, small proteins, phospholipids, and drugs. An amyloid β protein fragment, which is known as a biomarker for Alzheimer's disease, is detectable at 0.05 fmol μL^-1. The detection of the amyloid β at 0.2 fmol μL^-1 is also confirmed in the presence of blood components. Nanostructured organosilica substrates incorporating a molecular-level surface design have the potential to enable easy detection of a wide range of biomolecules.