Diabetes Biomarkers Research Articles

Membraneless Ionic Liquid Droplet Nanoprobe for Vapor Sensing and Gas Phase Scanning Electrochemical Microscopy

Gas phase sensing of volatile chemicals is a critical area of research relevant to biomedical diagnosis and environmental hazard assessment. Among various types of sensors, electrochemical sensors garnered attention as cost-effective and portable methods. Most electrochemical sensors needed the membrane to prevent solvent evaporation for detecting the gaseous species. However, the presence of the membrane induced slow response time due to the penetration of gas molecules through the membrane. To improve the systems, a membraneless ionic liquid droplet nanoprobe was developed. Gaseous species can permeate the thin ionic liquid layer, which was also used as the electrolytes, and react on the electrode in extremely fast response. Furthermore, nano-sized electrode makes available to detect sensitively with low charging capacitance. Using the probe type form factor, application to gas phase scanning electrochemical microscopy (SECM) was attempted with high spatial resolution. Oxygen detection, the most commonly studied, was implemented for the first application of the ionic liquid nanoprobe. The limit of detection of this probe was 13 ppm, which is extremely good for electrochemical oxygen sensors. Also, the fast response (< 0.1 s) made the mapping of oxygen concentration using SECM. The nanoprobe showed more than a hundred times better spatial resolution (several micrometer scale) than the previous gas phase SECM study mapping the oxygen. As the application of this oxygen probe, Li-air battery analysis can be conducted. Oxygen was used in the reaction at the cathode of the Li-air battery. As mapping the oxygen concentration above the cathode, It is expected that structural causes for the irreversibility at the cathode can also be found. As oxygen sensing, other electrochemical active gaseous species can be detected with the electrochemical sensor. Trinitrotoluene is a well-known explosive compound, and it can be detected in electrochemistry. Due to its low vapor pressure, di-nitroaromatic compounds, like dinitrotoluene (DNT), dinitrobenzene (DNB), and dinitrophenol (DNP), were used as explosive footprints, which have higher vapor pressure and have often been identified as marker molecules. Using our ionic liquid nanoprobe, we developed the nitroaromatic compounds (NACs) sensor for real-time detection of trace vapors, without any sample pre-treatment and at room temperature. Furthermore, in a simulated airport carousel, real-time and preemptive NACs detection was demonstrated with fast response time. As oxygen detection, gas phase SECM was conducted and 3D mapping capability of the differential vapor pressure was demonstrated, exercising the potential of the NACs nanoprobe in locating the source of an explosive risk. With the sensitive sensing performance of nanoprobe, it can be used as breath analysis. There are several biomarkers to indicate certain diseases. For example, acetone is a representative biomarker of diabetes. Using this sensing platform, acetone sensing was implemented, and it may be used for the initial detection of diabetes.

Transdermal Blood Sampling for C-Peptide Is a Minimally Invasive, Reliable Alternative to Venous Sampling in Children and Adults With Type 1 Diabetes.

C-peptide and islet autoantibodies are key type 1 diabetes biomarkers, typically requiring venous sampling, which limits their utility. We assessed transdermal capillary blood (TCB) collection as a practical alternative. Ninety-one individuals (71 with type 1 diabetes, 20 control; individuals with type 1 diabetes: aged median 14.8 years [interquartile range (IQR) 9.1-17.1], diabetes duration 4.0 years [1.5-7.7]; control individuals: 42.2 years [38.0-52.1]) underwent contemporaneous venous and TCB sampling for measurement of plasma C-peptide. Participants with type 1 diabetes also provided venous serum and plasma, and TCB plasma for measurement of autoantibodies to glutamate decarboxylase, islet antigen-2, and zinc transporter 8. The ability of TCB plasma to detect significant endogenous insulin secretion (venous C-peptide ≥200 pmol/L) was compared along with agreement in levels, using Bland-Altman. Venous serum was compared with venous and TCB plasma for detection of autoantibodies, using established thresholds. Acceptability was assessed by age-appropriate questionnaire. Transdermal sampling took a mean of 2.35 min (SD 1.49). Median sample volume was 50 µL (IQR 40-50) with 3 of 91 (3.3%) failures, and 13 of 88 (14.7%) <35 µL. TCB C-peptide showed good agreement with venous plasma (mean venous ln[C-peptide] - TCB ln[C-peptide] = 0.008, 95% CI [-0.23, 0.29], with 100% [36 of 36] sensitivity/100% [50 of 50] specificity to detect venous C-peptide ≥200 pmol/L). Where venous serum in multiple autoantibody positive TCB plasma agreed in 22 of 32 (sensitivity 69%), comparative specificity was 35 of 36 (97%). TCB was preferred to venous sampling (type 1 diabetes: 63% vs. 7%; 30% undecided). Transdermal capillary testing for C-peptide is a sensitive, specific, and acceptable alternative to venous sampling; TCB sampling for islet autoantibodies needs further assessment.

Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage

Skin microangiopathy has been associated with diabetes. Here we show that skin-microangiopathy phenotypes in humans can be correlated with diabetes stage via morphophysiological cutaneous features extracted from raster-scan optoacoustic mesoscopy (RSOM) images of skin on the leg. We obtained 199 RSOM images from 115 participants (40 healthy and 75 with diabetes), and used machine learning to segment skin layers and microvasculature to identify clinically explainable features pertaining to different depths and scales of detail that provided the highest predictive power. Features in the dermal layer at the scale of detail of 0.1–1 mm (such as the number of junction-to-junction branches) were highly sensitive to diabetes stage. A ‘microangiopathy score’ compiling the 32 most-relevant features predicted the presence of diabetes with an area under the receiver operating characteristic curve of 0.84. The analysis of morphophysiological cutaneous features via RSOM may allow for the discovery of diabetes biomarkers in the skin and for the monitoring of diabetes status.

Biomarkers and Inflammatory Pathways in Diabetes Mellitus Progression

With the rapidly increasing prevalence of diabetes in the global population, an early diagnosis of both type 1 diabetes mellitus and type 2 diabetes mellitus is critical to preventing long-term complications. Two potential components of unlocking a consistent early diagnosis of diabetes are understanding biological inflammatory pathways and identifying more sensitive biomarkers of diabetes. Novel biomarkers, such as interleukin, assist in monitoring the destruction of pancreatic beta cells, while inflammatory pathways can help explain the underpinnings of the insulin resistance found in both types of diabetes. In addition, research into 5-methoxytryptamine and 20-Hydroxy-leukotriene B4 would prove useful as a result of their heightened levels in patients with normal glucose levels.

Identifying the mitochondrial metabolism network by integration of machine learning and explainable artificial intelligence in skeletal muscle in type 2 diabetes

Imbalance in glucose metabolism and insulin resistance are two primary features of type 2 diabetes/diabetes mellitus. Its etiology is linked to mitochondrial dysfunction in skeletal muscle tissue. The mitochondria are vital organelles involved in ATP synthesis and metabolism. The underlying biological pathways leading to mitochondrial dysfunction in type 2 diabetes can help us understand the pathophysiology of the disease. In this study, the mitochondrial gene expression dataset were retrieved from the GSE22309, GSE25462, and GSE18732 using Mitocarta 3.0, focusing specifically on genes that are associated with mitochondrial function in type 2 disease. Feature selection on the expression dataset of skeletal muscle tissue from 107 control patients and 70 type 2 diabetes patients using the XGBoost algorithm having the highest accuracy. For interpretation and analysis of results linked to the disease by examining the feature importance deduced from the model was done using SHAP (SHapley Additive exPlanations). Next, to comprehend the biological connections, study of protein-protien and mRNA-miRNA networks was conducted using String and Mienturnet respectively. The analysis revealed BDH1, YARS2, AKAP10, RARS2, MRPS31, were potential mitochondrial target genes among the other twenty genes. These genes are mainly involved in the transport and organization of mitochondria, regulation of its membrane potential, and intrinsic apoptotic signaling etc. mRNA-miRNA interaction network revealed a significant role of miR-375; miR-30a-5p; miR-16-5p; miR-129-5p; miR-1229-3p; and miR-1224-3p; in the regulation of mitochondrial function exhibited strong associations with type 2 diabetes. These results might aid in the creation of novel targets for therapy and type 2 diabetes biomarkers.

A lighting-up fluorescent chemoprobe for nanomolar recognition of diabetic ketoacidosis biomarker D-3-hydroxybutyrate: DFT study and its applications in urine and water samples

As an important biomarker, D-3-hydroxybutyrate (D-3-HB) is closely associated to the occurrence and development for diabetes and diabetic ketoacidosis (DKA). Herein, a novel fluorescent probe Eu(flu)2Cl (flu = (E)-3-(2-hydroxyphenyl)acrylic acid) was synthesized for monitoring D-3-HB by a simple coordination. The experimental results showed that Eu(flu)2Cl had high selectivity and sensitivity for D-3-HB in DMSO solution with a detection limit of 78 nM. Meanwhile, the recognition was not disturbed by the tested anions, cations and some amino acids. More importantly, the possible binding mechanism between Eu(flu)2Cl and D-3-HB was proposed and verified by the Job's plot titration experiments, density functional theory (DFT) and mass spectra experiment. Furthermore, the fluorescence sensing performance of Eu(flu)2Cl makes it perfect for practical applications in drinkable water and urine samples with satisfactory recovery values (93.75%–111.04 %). This would provide an efficient detection method for the early detection of DKA.

Machine learning for predicting diabetic metabolism in the Indian population using polar metabolomic and lipidomic features.

To identify metabolite and lipid biomarkers of diabetes in the Indian subpopulation in newly diagnosed diabetic and long-term diabetic individuals. To utilize the global polar metabolomic and lipidomic profiles to predict the susceptibility of an individual to diabetes using machine learning algorithms. 87 individuals, including healthy, newly diabetic, and long-term diabetics on medication, were included in the study. Post consent, their serum was used to isolate polar metabolome and lipidome. NMR and LCMS were used to identify the polar metabolites and lipids, respectively. Statistical analysis was done to determine significantly altered molecules. NMR and LCMS comprehensive data were utilized to generate diabetic models using machine learning algorithms. 10 more individuals (pre-diabetic) were recruited, and their polar metabolomic and lipidomic profiles were generated. Pre-diabetic metabolic profiles were then utilized to predict the diabetic status of the metabolome and lipidome beyond glucose levels. Mannose, Betaine, Xanthine, Triglyceride (38:1), Sphingomyelin (d63:7), and Phosphatidic acid (37:2) are some of the top key biomarkers of diabetes. The predictive model generated showed the receiver operating characteristic area under the curve (ROC-AUC) as 1 on both test and validation data indicating excellent accuracy. This model then predicted the diabetic closeness of the metabolism of pre-diabetic individuals based on probability scores. Polar metabolic and lipid profile of diabetic individuals is very different from that of healthy individuals. Lipid profile alters before the polar metabolic profile in diabetes-susceptible individuals. Without regard to glucose, the diabetic closeness of the metabolism of any individual can be determined.

A proteomic meta-analysis refinement of plasma extracellular vesicles

Extracellular vesicles play major roles in cell-to-cell communication and are excellent biomarker candidates. However, studying plasma extracellular vesicles is challenging due to contaminants. Here, we performed a proteomics meta-analysis of public data to refine the plasma EV composition by separating EV proteins and contaminants into different clusters. We obtained two clusters with a total of 1717 proteins that were depleted of known contaminants and enriched in EV markers with independently validated 71% true-positive. These clusters had 133 clusters of differentiation (CD) antigens and were enriched with proteins from cell-to-cell communication and signaling. We compared our data with the proteins deposited in PeptideAtlas, making our refined EV protein list a resource for mechanistic and biomarker studies. As a use case example for this resource, we validated the type 1 diabetes biomarker proplatelet basic protein in EVs and showed that it regulates apoptosis of β cells and macrophages, two key players in the disease development. Our approach provides a refinement of the EV composition and a resource for the scientific community.

NanoLC-timsTOF-Assisted Analysis of Glycated Albumin in Diabetes-Affected Plasma and Tears.

Glycation is a spontaneous and nonenzymatic glycosylation. Glycated albumin (GA), which serves as an important biomarker in plasma in the diagnosis and characterization of diabetes, can be passively filtered from the plasma to tears. Tears are important targets for research in clinical diagnostics due to the ability to collect this biofluid noninvasively and repeatably. Therefore, the analysis of GA in tear film provides information for monitoring diabetes progression independent of blood pathologies. Due to the limited volume (1-5 μL) of natural tear film, we developed a small volume assay using a nano liquid chromatography-trapped ion mobility spectrometry-time-of-flight MS (nanoLC-timsTOF) platform for the analysis of glycated albumin in human plasma and tear films affected by diabetes. The peptides containing lysine 525, which is the main glycation site in GA, were relatively quantified and represented as the GA level. The results of the measurements showed that GA levels were significantly higher in diabetes-affected plasma and tears compared to controls with a p-value < 0.01. A strong correlation of glycated albumin levels was observed for the plasma and tear film in diabetes samples (Pearson coefficient 0.92 with a p-value 0.0012). Moreover, the number of GA glycation sites was significantly higher in diabetes-affected plasma and tear comparatively to controls. Among all the glycation sites in plasma albumin, the GA level quantified by lysine 136/137 had a strong correlation with more commonly used lysine 525, suggesting that lysine 136 /137 is an alternative diabetes biomarker in plasma. Overall, our findings demonstrate GA in tears as a biomarker for monitoring diabetes progression, highlighting new possibilities for quick and noninvasive diabetes detection and monitoring.

Assessing miR-146a-5P with IL-6 and TLR-4 serum levels in diabetic mellitus patients with or without urinary tract infection

Introduction and Aim: Diabetes is a chronic metabolic disease that affects glucose levels. This condition increases blood sugar and weakens immunity, damaging neutrophil function and urine antibacterial activity. IL-6 and TLR-4 are involved in inflammation and immune system control, while miR-146-5p may be a biomarker for diabetes. This study examines IL-6, TLR-4, and miR-146-5p in diabetics with and without UT infection. Materials and Methods: In this study, blood samples were collected from 100 diabetic mellitus patients suffering from UTI in addition to 100 individuals apparently healthy as control. The serum levels of IL-6 and TLR-4 were detected using commercial BTlab brand ELISA kits. The plasma RNA expression level of the miR-146a-5p was determined by real time-PCR using a specific primer for miR-146a-5p with the U6 gene utilized as a reference gene. Results: The results showed that diabetic patient’s group had a significantly higher response of IL-6 than controls (75.6 vs. 27.6). TLR-4 levels showed high levels in diabetic patients’ group (1.3) in comparison with the control group (0.441). Both IL-6 and TLR-4 levels showed a significant difference between the diabetic patients with UTI compared to control. The results revealed no-significant differences in the levels among both diabetic patients with UTI and patients without UTI. TLR-4 and IL-6 showed a significant inverse relationship while the relation between TLR-4 level and miR-146a-5p expression level showed a negative relationship. Conclusion: The levels of IL-6, TLR-4, and miR-146-5p expression were found to be considerably greater in individuals with diabetes having UTIs compared to patients with diabetes alone. An inverse relationship existed between IL-6 and TLR-4 levels as well as between TLR-4 and miR-146a-5p. The expression levels of IL-6 and miR-146-5p were nearly equal.

Differences in left anterior descending coronary artery disease in participants without standard modifiable cardiovascular risk factors: the Multi-Ethnic Study of Atherosclerosis (MESA)

Abstract Background/Introduction The development of coronary artery disease (CAD) and myocardial infarction (MI) in the absence of standard modifiable cardiovascular risk factors (SMuRFs) has been highlighted as an unmet clinical need, accounting for between 10-27% of ST elevation MI and associated with higher 30-day mortality. One potential contributor to the early mortality is the higher observed rates of LAD territory culprit lesions in SMuRFless patients compared to those with at least one SMuRF. Purpose To characterise the differential proportion of LAD disease in an asymptomatic population with no prior cardiovascular disease (CVD) history, comparing individuals with CAD despite no SMuRFs to those with at least one SMuRF in the Multi-Ethnic Study of Atherosclerosis (MESA). Methods A MESA sub-cohort with a baseline coronary artery calcium score (CACS) &amp;gt;0 Agatston units (AU) was prepared. SMuRF status was assigned by self-report, treatment, or biomarkers for hypertension, hypercholesterolaemia, diabetes, and smoking. Proportion of disease in the left main, LAD, circumflex, and right coronary arteries were calculated by the vessel CACS divided by the total CACS. Standard descriptive statistics were used to characterise baseline differences. Group comparisons were completed with Wilcoxon rank sum for continuous measures and Chi-square for categorical. Results A CAC positive cohort of 3,417 participants was prepared from participants with SMuRF and CT imaging data, 14.3% of whom were considered SMuRFless. Total CACS was lower in SMuRFless participants compared to those with ≥1 SMuRF (58 AU vs. 102 AU, p&amp;lt;0.0001). However, proportion of calcium burden in the LAD was significantly higher in SMuRFless participants with CAD (74.2% vs. 56.8%, p&amp;lt;0.0001). When considering clinically significant CACS strata, proportion of LAD calcium burden was not significantly different between SMuRFless vs. ≥1 SMuRF participants with a total CACS of 1-99 AU (93.7% vs. 80.0%). However, statistically significant differences in LAD calcium proportion were observed between SMuRFless vs. ≥1 SMuRF participants with a total CACS 100-400 AU (69.5% vs. 57.8%) and CACS&amp;gt;400AU (45.2% vs. 40.5%). This difference remained significant during multivariate modelling, including consideration of absolute CACS. Conclusion Of patients with evidence of CAD (CACS&amp;gt;0) in the MESA cohort, SMuRFless status was associated with a higher proportion of LAD disease compared to participants with ≥1 SMuRF. This adds to previous observations in STEMI cohorts suggesting a heightened susceptibility to atherosclerosis development in the LAD territory against a lower traditional risk burden, and highlights the need for consideration of distinct biology and clinical significance in future studies.Fig. 1Fig. 2

Development of highly sensitive and selective trace acetone sensor using perovskite yttrium ferrite: Mechanism, kinetics and phase dependence study

Detection and monitoring of exhale breath acetone, bio-marker of diabetes is the need of the day to save millions human life. Trace acetone sensor having capability to detect breath acetone has been developed using perovskite YFeO3 powder (YFO) synthesized through a facile solution combustion technique. It is observed that YFO started to form hexagonal phase at 700 °C (YF-7) and ultimately converted to orthorhombic phase at 1000 °C (YF-10). Amongst others, YF-10 delivered best trace acetone sensing performance including a eight-fold response (S= Rg/Ra∼10.25 folds) to 1ppm acetone with ultrafast response-recovery time (∼0.6s-2s). Further, YF-10 sensor shows excellent selectivity, long-term stability more than 180 days, almost insensitive towards moisture and lower detection limit as low as 100 ppb. X-ray photoelectron spectroscopy (XPS) reveals that along with Fe3+ there exist Fe2+ ions which become maximum at 1000 °C. Formation of Fe2+ gives rise to oxygen vacancies which is maximum for YF-10 which is confirmed by PL and EPR studies. Enhanced acetone sensing performance of orthorhombic YFO is explained using quantitative phase analysis, honey-comb morphology, regulation of oxygen vacancies and bivalency of iron in different phases of yttrium ferrite. Eley−Rideal model has been invoked to explain the affinity towards acetone in comparison with other interfering gases. DFT calculation demonstrate that adsorption energy, a major parameter for gas sensing performance of any materials is lower for orthorhombic phase. In totality, it is observed that YFeO3 powder based sensor have huge potential for exhale breath analysis to monitor diabetes.

Does Ramadan Intermittent Fasting Affect the Fasting Blood Glucose Level among Type II Diabetic Patients?

The level of fasting blood glucose (FBG) is influenced by several factors, including health status, genetics, and diet. Some studies have reported a beneficial effect of Ramadan Intermittent Fasting (RIF) on diabetic patients. However, clinical observations have shown that diabetes is exacerbated in some patients. This study aims to investigate the influence of RIF on the FBG level, a biomarker of hyperglycemia and diabetes, and to identify factors associated with variations in FBG levels during RIF among diabetic patients. This study is a cross-sectional study. We monitored the FBG levels of 181 type II diabetic patients over a two-month period, from 20 February to 20 April 2023, which represents the Islamic lunar months of Shaban (8th month) and Ramadan (9th month). Ramadan provides a prominent month of intermittent fasting practice for studying its physiological effects on diabetes. We collected clinical data from each participant, including demographic information, co-morbidities, and medications used during this period. Based on our findings, diabetic patients were classified into three groups depending on the influence of RIF on FBG levels: the positively affected group (44%), whose average FBG levels were reduced; the neutrally affected group (24%), whose average FBG levels did not change; and the negatively affected group (32%), whose average FBG levels increased during the fasting month of Ramadan compared to the previous month. Furthermore, we found that the positive effect of RIF was more frequent among obese, non-geriatric, and male diabetic patients, while the negative effect of RIF was more frequent among patients who were not adhering to the medication. This study concludes that RIF affects FBG levels differently among diabetic patients. These findings should be taken into consideration when treating diabetic patients during the fasting month of Ramadan, and further studies are needed to identify (1) factors associated with inter-individual variation in the response to RIF and (2) those who are great candidates for RIF.

A spectroscopy based prototype for the noninvasive detection of diabetes from human saliva using nanohybrids acting as nanozyme

The recent prediction of diabetes to be a global pandemic invites a detection strategy preferably non-invasive, and bloodless to manage the disease and the associated complications. Here, we have synthesized chitosan polymer functionalized, organic–inorganic bio-compatible nano-hybrids of Mn3O4 nanoparticles, and characterized it by utilizing several optical methodologies for the structural characterization which shows the Michaelis Menten (MM) kinetics for glucose and alpha-amylase protein (well-known diabetes biomarkers). We have also studied the potentiality for the detection of alpha-amylase in human salivary secretion which is reported to be strongly correlated with uncontrolled hyperglycemia. Finally, we have developed a prototype for the measurement of glucose (LOD of 0.38 mg/dL, LOQ of 1.15 mg/dL) and HbA1c (LOD of 0.15% and LOQ of 0.45%) utilizing the basic knowledge in the study for the detection of uncontrolled hyperglycemia at the point-of-care. With the limited number of clinical trials, we have explored the potential of our work in combating the diabetic pandemic across the globe in near future.

Anthropometry and blood biomarkers of diabetes and their possible association with obesity and metabolic syndrome.

Diabetes, a rapidly increasing heterogeneous disorder, is closely linked to the epidemic of obesity and metabolic syndrome (MetS). At present, we do not understand completely the blood biomarkers, molecular aetiology, and role of lifestyle modification and interventions to combat diabetes hand in hand with obesity and the MetS epidemic. To measure different anthropometric and blood biomarkers in pre-diabetic and diabetic patients, we collected data and blood samples from patients in a hospital OPD. This was a cross-sectional study that included the identification of possible relationships between different parameters to predict early diagnostic markers of diabetes. We found increased body mass index (BMI), fasting blood glucose, neck, waist, and hip circumference, sagittal abdominal diameter, and skin fold thickness in the diabetic as compared to the pre-diabetic group. Also, serum uric acid and insulin resistance (HOMA-IR) values were significantly increased in diabetic individuals. We found a significant positive correlation between serum uric acid and BMI, fasting blood glucose, serum insulin, and HOMA-IR values. Here, we found that pre-diabetic and diabetic patients have increased fasting glucose levels while we did not find any difference in insulin levels. Both pre-diabetic and diabetic patients show high serum uric acid, positively associated with a higher prevalence of diabetes and HOMA-IR. Uric acid may hence be an important parameter for early diagnostics. These findings may be used as a basis for future studies that aim to identify the mechanistic details of the association of uric acid with insulin signaling and hence better understanding of the phenomenon associated with diabetes. The online version contains supplementary material available at 10.1007/s40200-023-01276-4.

Non-Invasive Method of Human Exhaled Breath Analysis for Diabetes Detection Using Bidirectional Long-Short-Term Memory Algorithm

Volatile organic compounds (VOCs) have the potential to be used as biomarkers for pathophysiological and physical abnormalities associated with several disorders. A promising non-invasive metabolic monitoring method is the Analysis of VOCs in exhaled breath. It may also be used to monitor the development of certain diseases and their early detection. Diabetes is a metabolic disease and a complicated syndrome. The relationship between oxidative stress, inflammatory syndrome, hypertension, and diabetes is complicated. This study describes the creation of an Internet of Things (IoT) based breath analyzer to identify and track diseases using exhaled breath. Diabetic breath biomarkers and breath analysis are the main topics of discussion. A group of 25 diabetic patients and 15 non-diabetic individuals were tested using this system. Data is initially gathered using the wired module and the Cool Term software. The system is created for both wired and wireless devices. A deep learning algorithm analyses the disease characteristics after data collection. It clearly distinguishes between samples with diabetes and those without with 84% accuracy. This technology could detect a non-transmissible or transmissible disease early, preventing infection to others.

Oxidative Stress Biomarkers in Cystic Fibrosis and Cystic Fibrosis-Related Diabetes in Children: A Literature Review.

The most common inherited condition that results in death, particularly in those of Caucasian heritage, is cystic fibrosis (CF). Of all the young adults diagnosed with cystic fibrosis, 20% will develop hyperglycemia as a complication, later classified as a disease associated with cystic fibrosis. Impaired insulin secretion and glucose intolerance represent the primary mechanisms associated with diabetes (type 1 or type 2) and cystic fibrosis. Oxidative stress represents the imbalance between oxygen-reactive species and antioxidant defense mechanisms. This pathogenic mechanism is vital in triggering other chronic diseases, including cystic fibrosis-related diabetes. It is essential to understand oxidative stress and the significant impact it has on CFRD. This way, therapies can be individually adjusted and tailored to each patient's needs. This review aims to understand the connection between CFRD and oxidative stress. As a subsidiary element, we analyzed the effects of glycemic balance on complications and their evolution over time, providing insights into their potential benefits in mitigating oxidative stress-associated complications.

Alignment Between 24-h Light-Dark and Activity-Rest Rhythms Is Associated With Diabetes and Glucose Metabolism in a Nationally Representative Sample of American Adults.

The alignment between environmental stimuli (e.g., dark, light) and behavior cycles (e.g., rest, activity) is an essential feature of the circadian timing system, a key contributor to metabolic health. However, no previous studies have investigated light-activity alignment in relation to glycemic control in human populations. The analysis included ∼7,000 adults (aged 20-80 years) from the National Health and Nutrition Examination Survey (NHANES) (2011-2014) with actigraphy-measured, multiday, 24-h activity and light data. We used phasor analysis to derive phasor magnitude and phasor angle, which measures coupling strength and phase difference between the activity-rest and light-dark cycles, respectively. We used multinomial logistic regression and multiple linear regression to study phasor magnitude and phasor angle in relation to diabetes (primary outcome) and multiple secondary biomarkers of glycemic control. Lower alignment strength (i.e., a shorter phasor magnitude) and more delayed activity relative to the light cycle (i.e., a larger phasor angle) were both associated with diabetes. Specifically, compared with individuals in the quintiles indicating the most proper alignment (Q5 for phasor magnitude and Q1 for phasor angle), those in the quintiles with the most impaired alignment had a >70% increase in the odds of diabetes for phasor magnitude (odds ratio 1.76 [95% CI 1.39, 2.24]) and for phasor angle (1.73 [1.34, 2.25]). Similar associations were observed for biomarkers for glucose metabolism. The results were generally consistent across diverse sociodemographic and obesity groups. The alignment pattern between 24-h activity-rest and light-dark cycles may be a critical factor in metabolic health.

A new turn-on fluorescent probe for fast detection of diabetic biomarker beta-hydroxybutyrate in vitro

Beta-hydroxybutyrate (β-HB) serve as a valuable diagnostic biomarker for Diabetic Ketoacidosis (DKA). Here, a new Schiff base fluorescent probe T was designed and synthesized to detect β-HB level in aqueous solution in vitro. The probe T can detect β-HB sensitively and selectively in DMF solution (5.0 × 10−5 M) among other interfering species (cations, anions, amino acids, biomarkers). The detection limit of probe T for β-HB was calculated to be 0.154 μM. These results demonstrate that the probe T may provide a convenient method for rapid detection of β-HB to diagnose diabetic ketoacidosis.

Phloretin as an add-on therapy to losartan attenuates diabetes-induced AKI in rats: A potential therapeutic approach targeting TLR4-induced inflammation

AimTargeting Toll-like receptor 4 (TLR4) and Angiotensin II type 1 receptor (AT1R) could provide renoprotection during acute kidney injury (AKI) mainly by regulating inflammation, oxidative stress, mitochondrial dysfunction, and apoptosis. Phloretin (TLR4 inhibitor) as an add-on therapy to losartan (AT1R inhibitor) could provide more therapeutic benefits against AKI under diabetic condition. We aimed to study the effect of phloretin as an add-on therapy to losartan against AKI under diabetic condition. Main methodsTo mimic diabetic AKI condition, bilateral ischemia-reperfusion injury (BIRI) was done in diabetic male Wistar rats, and sodium azide treatment was given to high glucose NRK52E cells to mimic hypoxia-reperfusion injury. In diabetic rats, phloretin (50 mg/kg/per os (p.o.)) and losartan (10 mg/kg/p.o.) treatment was given for 4 days and 1 h prior to surgery while in NRK52E cells, both drugs (phloretin 50 μM and losartan 10 μM) were given 24 h prior to the hypoxia condition. The in vivo and in vitro samples were further used for different experiments. Key findingsTreatment with phloretin and losartan decreased diabetic and AKI biomarkers such as plasma creatinine, blood urea nitrogen (BUN), and kidney injury molecular 1 (KIM1). Moreover, a combination of phloretin and losartan significantly preserved ΔΨm and kidney morphology potentially by inhibiting TLR4-associated inflammation and AT1R-associated mitochondrial dysfunction, thereby oxidative stress. SignificanceCombination therapy of phloretin and losartan was more effective than monotherapies. Both drugs target TLR4/MyD88/NF-κB pathway and reduce inflammation and mitochondrial dysfunction in AKI under diabetic condition.