Diabetes Biomarkers Research Articles

A dual-signal triple-readout optical sensing platform for α-glucosidase and β-glucosidase activity monitoring and inhibitor screening based on luminescent covalent organic framework

BackgroundAs promising biomarkers of diabetes, α-glucosidase (α-Glu) and β-glucosidase (β-Glu) play a crucial role in the diagnosis and management of diseases. However, there is a scarcity of techniques available for simultaneously and sensitively detecting both enzymes. What's more, most of the approaches for detecting α-Glu and β-Glu rely on a single-mode readout, which can be affected by multiple factors leading to inaccurate results. Hence, the simultaneous detection of the activity levels of both enzymes in a single sample utilizing multiple-readout sensing approaches is highly attractive. ResultsIn this work, we constructed a facile sensing platform for the simultaneous determination of α-Glu and β-Glu by utilizing a luminescent covalent organic framework (COF) as a fluorescent indicator. The enzymatic hydrolysis product common to both enzymes, p-nitrophenol (PNP), was found to affect the fluorometric signal through an inner filter effect on COF, enhance the colorimetric response by intensifying the absorption peak at 400 nm, and induce changes in RGB values when analyzed using a smartphone-based color recognition application. By combining fluorometric/colorimetric measurements with smartphone-assisted RGB mode, we achieved sensitive and accurate quantification of α-Glu and β-Glu. The limits of detection for α-Glu were determined to be 0.8, 1.22, and 1.85 U/L, respectively. Similarly, the limits of detection for β-Glu were 0.16, 0.42, and 0.53 U/L, respectively. SignificanceApplication of the proposed sensing platform to clinical serum samples revealed significant differences in the two enzymes between healthy people and diabetic patients. Additionally, the proposed sensing method was successfully applied for the screening of α-Glu inhibitors and β-Glu inhibitors, demonstrating its viability and prospective applications in the clinical management of diabetes as well as the discovery of antidiabetic medications.

The effect of statin treatment on glucose homeostasis in prediabetic individuals: A prospective, randomized, controlled trial.

This study aimed to evaluate the effects of rosuvastatin and pravastatin on glucose homeostasis and other biomarkers in individuals at high risk of developing diabetes. This prospective, randomized, open-labeled, and controlled trial included prediabetic individuals with impaired fasting glucose and impaired glucose tolerance. The participants were randomized into three groups: rosuvastatin (10 mg), pravastatin (40 mg), or control. Biomarkers of diabetes and glucose and insulin responses to oral glucose tolerance tests were assessed at baseline and after 6 months of treatment. The primary outcomes were comparisons of glucose homeostasis and biomarkers of diabetes among groups at baseline and after 6 months of treatment. A total of 141 subjects with impaired fasting glucose (IFG) were screened and 41 participants were recruited. Twenty-two subjects were randomized to either the rosuvastatin or pravastatin group and 19 subjects were assigned to the control group. After 6 months of treatment, all groups had similar cholesterol and triglyceride levels. Likewise, HbA1c levels, glucose, and insulin excursions during oral glucose tolerance test, were similar among the three groups. However, compared to the other groups, the rosuvastatin group had higher homeostasis model assessment for insulin resistance (HOMA-IR) (insulin resistance) and a lower Matsuda index (insulin sensitivity). Among prediabetic individuals with IFG, rosuvastatin treatment was associated with increased insulin resistance and decreased insulin sensitivity compared to pravastatin and control groups. Further research is needed to elucidate the underlying mechanisms and clinical implications of these findings.

Express analysis of acetone in exhaled air for diagnosing diabetes melli-tus

Exhaled air analysis is an actively developing area of medical non-invasive diagnostics, as it does not involve invasive interventions and can be performed repeatedly. It is known that acetone in exhaled air correlates with blood glucose and is a biomarker of diabetes. The use of analytical systems to analyse acetone in exhaled air in medical institutions will allow for the timely diagnosis of pathological changes in blood glucose levels, the early diagnosis of diabetes, and the monitoring of the effectiveness of treatments. In this study, we described the express analysis of acetone in exhaled air using a mobile diagnostic complex based on microfluidic systems including a PIA gas microchromatograph with a thermochemical detector, a planar chromatographic column, a thermostatically controlled system for the sampling and selective capture of interfering components, operated by electric microvalves, and an automatic dosing system with adjustable blowdown from 0.1 to 5 sec and specialised software. We proposed using a diagnostically significant range of concentrations: from 0.5 ppm to 20 ppm of acetone in exhaled air in the diagnosis of diabetes. The total analysis time is 3 min, retention time for acetone is 60 sec. Testing of the complex confirmed that it allows for the direct determination of acetone in exhaled air. The use of the capture and drying system prevents the loss of the target substance and ensures good convergence of the results. The deviation from the reference value does not exceed 10%. Field testing confirmed the data obtained on model mixtures. The developed mobile diagnostic complex with automatic programmed dosing and the technique for the quantitative determination of acetone in exhaled air (0.5-20 ppm) can be recommended for use in clinical studies in medical institutions.

Micronutrient Patterns and Low Intake of Vitamin A, Vitamin D, Folate, Magnesium, and Potassium Among Prediabetes and Type 2 Diabetes Patients.

Background Assessing micronutrient intake is important in identifying deficiencies that may contribute to insulin resistance, poor glycemic control, and increased risk of diabetes-related complications. The study's objectives were to evaluate micronutrient intake in prediabetes (PD) and type 2 diabetes (T2DM) patients compared to recommended dietary intakes (RDI) and to determine the associations between the micronutrient patterns and both anthropometric measurements and biomarkers of diabetes. Methods This cross-sectional study was conducted on 349 patients with T2DM and 252 patients with PD. Micronutrient intake was evaluated using a validated food frequency questionnaire. Micronutrient patterns were extracted from factor analysis using principal component analysis with varimax rotation. Participants in the highest tertile were considered to have the highest adherence to the corresponding micronutrient pattern. Results T2DM patients had a significantly lower intake of vitamin E (9.4 ± 0.2 vs. 10.1 ± 0.3 mg; p = 0.048), vitamin D (44.3 ± 1.1 vs. 48.9 ± 1.7 IU; p = 0.020), and thiamin (1.3 ± 0.1 vs. 1.4 ± 0.1 mg; p = 0.013) compared to PD patients. All patients had a significantly lower intake of vitamin A, vitamin D, folate, magnesium, and potassium and a significantly higher intake of vitamin B12 and copper compared to RDI. Three distinct micronutrient patterns were identified within each group. In the PD group, the Fe-Mn-Se pattern correlated significantly with waist circumference (WC) and fasting plasma glucose (FPG). The Vit.C-K-Folate pattern showed significant associations with body fat (BF). The Vit.B2-P-Vit.B12 pattern was significantly linked to WC, body mass index (BMI), BF, FPG, and serum insulin (SI). For the T2DM patients, the K-Folate-Mg pattern displayed an inverse and significant association with weight and WC. The Iron-Se-Vit.B3 pattern showed a significant association with low-density lipoprotein (LDL) cholesterol, triglycerides, and total cholesterol. The Vit.B2-P-Ca pattern was significantly associated with fasting plasma glucose (FPG). Conclusion This study demonstrated that T2DM patients had significantly lower vitamin E, vitamin D, and thiaminintake than PD patients. Both T2DM and PD patients had a significantly lower intake of vitamin A, vitamin D, folate, magnesium, and potassium compared to the RDI. Among the identified micronutrient patterns, only the K-Folate-Mg pattern exhibited a significant association withreduced body weight and WC.

Elevated pretreatment lactate dehydrogenase and albumin-to-alkaline phosphatase ratio predict poor prognosis and early treatment discontinuation in head and neck cancer patients with preexistent diabetes mellitus.

Increased serum lactate dehydrogenase (LDH) activity is considered as amarker of cellular necrosis and serves as ametabolomic diagnostic marker in several types of cancer including head and neck squamous cell carcinoma (HNSCC). LDH, an enzyme involved in the glycolytic cycle, is correlated not only with the activation of oncogenes such as HIF-α and Myc, but also with effects such as tumor proliferation and metastasis. Serum alkaline phosphatase (ALP) is amarker of cell differentiation and tumor induction. Albumin-to-alkaline phosphatase ratio (AAPR) could be an advantageous biomarker due to its easily accessible dynamics and cost-effectiveness. Elevated values of AAPR could be associated with longer overall survival (OS) in cases with solid tumors. Diabetes mellitus (DM) could influence the outcome of patients with HNSCC by contributing to insulin resistance and chronic inflammation, and by being involved in various aspects of carcinogenesis, disease progression and metastasis. However, the use of antihyperglycemic medications (metformin) can have beneficial effects by inhibiting tumor metabolic pathways. The biomarker role of LDH and AAPR in HNSCC patients with DM has been less evaluated. The purpose of the study was to assess the prognostic value of pretreatment serum lactate dehydrogenase (LDH) and albumin-to-alkaline phosphatase ratio (AAPR) in predicting the duration of non-surgical oncological treatment and glycemic control in cases of head and neck cancers patients with DM, including cases selected from the database of the oncology clinic and oncology outpatient clinic of the Craiova County Hospital. Both LDH and AAPR can be used as pre-treatment biomarkers predictive of treatment response, or prognostic tools included in complex multi-parametric models in HNC associated with DM. However, given the impact of short-term glycemic control on the LDH level, it is necessary to evaluate these biomarkers after assessing and controlling for DM, and with the recommended cut-off value set around 0.5. Due to the limited number of cases, it is necessary to validate the results in multicentric trials with alarger number of patients (Tab. 5, Ref. 50). Keywords: diabetes mellitus, HNC, LDH, AAPR, biomarkers, predictive, head and neck cancers, lactate dehydrogenase, albumin-to-alkaline phosphatase ratio.

Obesity modifies the association between diabetes and iron biomarkers and red cell indices in reproductive-aged women in the United States.

Obesity and diabetes are associated with impaired iron metabolism. We aimed to examine the independent relationship between diabetes and iron after controlling for body weight (or obesity) in women aged 20-49 years. The National Health and Nutrition Examination Survey data from 2015 to 2018 were used in this investigation. Body composition data, HbAc1, iron biomarkers (serum ferritin (SF), soluble transferrin receptor (sTfR), and body iron index (BII)), mean corpuscular volume (MCV), mean hemoglobin concentration (MCH), red cell distribution width (RDW), and hemoglobin were used. Linear regression models were used to examine how and to what extent body mass index (BMI) modified the relationship between diabetes and iron status biomarkers. A total of 1834 women aged 20-49 were included in the analysis with a mean (SD) age of 32 .2 ± 6.1 years and BMI of 29.5 ± 6.9 kg/m2. The mean SF (p = 0.014) and BII (p < 0.001) were lower, while sTfR (p < 0.001) was higher in women with diabetes than those with no diabetes. Mean estimates for MCV and MCH were lower, while RDW (p = 0.001) was higher in diabetes patients (all p < 0.001). Women with diabetes were more likely to have iron deficiency, anemia, and iron deficiency anemia than those without diabetes (18.1% vs 8.6%, p < 0.001), (24.4% vs 8.4%, p < 0.001), and (14.8% vs 5.2%, p < 0.001), respectively. Among women with obesity, those with diabetes had lower predicted ferritin (β = -0.19, p = 0.016), BII (β = -0.99, p = 0.016), and hemoglobin (β = -0.27, p = 0.042) than those without diabetes. The study shows that diabetes is linked to lower iron stores; this is exacerbated in those with obesity.

Association of type 2 diabetes with family history of diabetes, diabetes biomarkers, mental and physical disorders in a Kenyan setting

This study aimed to determine the degree of family relations and associated socio-demographics characteristics, clinical/physical and mental disorders in type 2 diabetes mellitus in a Kenyan diabetes clinic. This study was part of a large multicentre study whose protocol and results had been published. It took place at the outpatient diabetes clinic at a County Teaching and Referral Hospital in South East Kenya involving 182 participants. We used a socio-demographic questionnaire, the Hamilton Depression (HAM-D) and PHQ-9 rating scales for depression, the MINI International Neuropsychiatric Interview (MINI; V5 or V6) for DSM-5 diagnoses, the WHO-5 Well-being scale and Problem Areas in Diabetes Scale (PAID). We extracted from the notes all physical conditions. We enquired about similar conditions in 1st and 2nd degree relatives. Descriptive, Chi-square test, Fisher’s exact test, one way ANOVA, and Multinomial logistic regression analysis were conducted to test achievements of our specific aims. Of the 182 patients who participated in the study, 45.1% (82/182) reported a family history of diabetes. Conditions significantly (p < 0.05) associated with a degree of family history of diabetes were retinopathy, duration of diabetes (years), hypertension, and depressive disorder. On average 11.5% (21/182) scored severe depression (≥ 10) on PHQ-9 and 85.2% (115/182) scored good well-being (≥ 13 points). All DSM-5 psychiatric conditions were found in the 182 patients in varying prevalence regardless of relations. In addition, amongst the 182 patients, the highest prevalence was poor well-being on the WHO quality of life tool. This was followed by post-traumatic disorders (current), suicidality, and psychotic lifetime on DSM-5. The least prevalent on DSM-5 was eating disorders. Some type 2 diabetes mellitus physical disorders and depression have increased incidence in closely related patients. Overall, for all the patients, the prevalence of all DSM-5 diagnoses varied from 0.5 to 9.9%.

Nonenzymatic dual glucose sensing on boronic acid modified zeolitic imidazolate framework-67 nanoparticles for diabetes management.

For early diabetes identification and management, the progression of an uncomplicated and exceedingly responsive glucose testing technology is crucial. In this study, we present a new sensor incorporating a composite of metal organic framework (MOF) based on cobalt, coated with boronic acid to facilitate selective glucose binding. Additionally, we successfully employed a highly sensitive electro-optical immunosensor for the detection of subtle changes in concentration of the diabetes biomarker glycated haemoglobin (HbA1c), using zeolitic imidazolate framework-67 (ZIF-67) coated with polydopamine which further modified with boronic acid. Utilizing the polymerization characteristics of dopamine and the NH2 groups, a bonding structure is formed between ZIF-67 and 4-carboxyphenylboronic acid. ZIF-67 composite served as an effective substrate for immobilising 4-carboxyphenylboronic acid binding agent, ensuring precise and highly selective glucose identification. The sensing response was evaluated through both electrochemical and optical methods, confirming its efficacy. Under optimized experimental condition, the ZIF-67 based sensor demonstrated a broad detection range of 50-500mg dL-1, a low limit of detection (LOD) of 9.87mg dL-1 and a high correlation coefficient of 0.98. Furthermore, the 4-carboxyphenylboronic acid-conjugated ZIF-67-based sensor platform exhibited remarkable sensitivity and selectivity in optical-based detection for glycated haemoglobin within the clinical range of 4.7-11.3%, achieving a LOD of 3.7%. These findings highlight the potential of the 4-carboxyphenylboronic acid-conjugated ZIF-67-based electro-optical sensor as a highly sensitive platform for diabetes detection.

*Re-Print: The NS-3 Mixture of δ-Tocotrienol, Vitamin D3 and Resveratrol Modulates Gene Expression of Several Novel MicroRNAs Identified by Transcriptomic Analyses in People with Type 2 Diabetes

Aims: Type 2 diabetes mellitus is due to hyperglycemia, therefore fasting glucose and glycosylated hemoglobin (HbA1c) levels are used as biomarkers to determine onset of diabetes. RT-PCR estimation of pooled total mRNAs of EDTA treated whole blood (plasma) obtained after the treatment of NS-3 mixture of d-tocotrienol, vitamin D3, resveratrol of people with type 2 diabetes mellitus (T2DM) for 24 weeks, showed significant down-regulation of gene expression of several diabetes biomarkers (IRS-1, SOD-2, GCKR, IGFBP-2) and cytokines (IL-4, IL-6) as compared to pre-treatment values. Present study investigates the effectiveness of NS-3 on gene expression of mRNAs, miRNAs, and paired mRNA-miRNA in people with T2DM. Methods: Present study is an extension of a randomized placebo controlled double-blinded clinical trial of T2DM (n = 56/group) given two capsules/d of cellulose/olive oil (placebo), or NS-3 for 24-weeks. Pure mRNAs and miRNAs of plasma of pre-dose versus post-dose of NS-3 treated samples were analyzed by next generation sequencing (NGS). Data was uploaded into “Ingenuity Pathways Analyses”. Results: A total of 4000 genes are considered significant, based on &gt; 2-fold gene expression changes. Out of which 1373 genes are significantly differentially expressed in pre-dose vs post-dose (P &lt; 0.02) samples, 20 are up-regulated and 27 are down-regulated of NS-3 treated RNAs of T2DM. Gene expression of up-regulated miR-29b-3p modulates (GLUT4, insulin resistance), miR-624-5p (nephropathy biomarker), miR-361-5p (chronic inflammation), miR-130a-3p (glucose metabolism, insulin secretion), miR-3912-3p (lipid metabolism), and miR-11401 (cellular transcription). The miR-374c-5p (insulin resistance), miR-4326 (HbA1c level)), miR-874-3p (b-cell function) are down-regulated of NS-3 treated people with T2DM. Whereas messengerR-ML-1621513 (oxidative/stress), mR-CTD-2349P217 (insulin-mediated glucose-uptake), are up-regulated, and mR-CTC-246B1810 (b-cell/biology) are down-regulated in T2DM after NS-3 treatment. Venn diagrams have established genetic regulatory network images and canonical signaling pathways for mRNA, miRNA, and paired mRNA-miRNA of gene expression profiles of pre-dose vs post-dose of NS-3 treatment group. Conclusions: The NS-3 treatment of people with T2DM indicates up- or down-regulation of several new miRNAs (miR-29b-3p, miR-624-5p, miR-361-5p, miR-130a-3p, miR-3912-3p, miR-374c-5p, miR-4326 [HbA1c], miR-1247-3p, miR-874-5p) which may be used to identify onset of T2DM. Overexpression of mRNA-AL1621513 indicates oxidative stress in people with T2DM, resulting in complications of diabetes (neuropathy, retinopathy, and stroke).

The role of vascular adhesion protein-1 in diabetes and diabetic complications.

Vascular adhesion protein-1 (VAP-1) plays a dual role with its adhesive and enzymatic properties, facilitating leukocyte migration to sites of inflammation and catalyzing the breakdown of primary amines into harmful by-products, which are linked to diabetic complications. Present in various tissues, VAP-1 also circulates in a soluble form in the bloodstream. Diabetes is associated with several complications such as cardiovascular disease, retinopathy, nephropathy, and neuropathy, significantly contributing to disability and mortality. These complications arise from hyperglycemia-induced oxidative stress, inflammation, and the formation of advanced glycation end-products (AGEs). Earlier research, including our own from the 1990s and early 2000s, has underscored the critical role of VAP-1 in these pathological processes, prompting extensive investigation into its contribution to diabetic complications. In this review, we examine the involvement of VAP-1 in diabetes and its complications, alongside its link to other conditions related to diabetes, such as cancer and metabolic dysfunction-associated fatty liver disease. We also explore the utility of soluble VAP-1 as a biomarker for diabetes, its complications, and other related conditions. Since the inhibition of VAP-1 to treat diabetic complications is a novel and promising treatment option, further studies are needed to translate the beneficial effect of VAP-1 inhibitors observed in animal studies to clinical trials recruiting human subjects. Besides, future studies should focus on using serum sVAP-1 levels for risk assessment in diabetic patients, identifying those who need intensive glycemic control, and determining the patient population that would benefit most from VAP-1 inhibitor therapies.

Reduced muscle expression heterogeneity by categorising diabetic subjects resulted in improved statistical significance for trascriptome comparison

The information gained through studies investigating muscle gene expression profiles (GEPs) for diabetes biomarkers identification, has resulted in the general belief that muscle transcriptome is relatively unaltered while the subject gradually progresses from a healthy to a diabetic condition. In this study, we focused on identifying the diabetes subgroup based on the subject's muscle gene expression profile to reduce the expression heterogeneity and regain statistical significance in transcriptome comparison. Given the muscle GEPs of type-II diabetic (T2D) and normal glucose tolerant (NGT) subjects, the R-package WGCNA was used to identify a set of correlated genes termed a module. The Random Forest classifier trained over expression profiles of genes belonging to respective modules was used to classify subjects into subgroups. The study was able to categorize early-stage diabetic (ESD) and advance stage diabetic (ASD) subjects based on muscle gene expression profiles. The ASD subjects differed significantly from ESD subjects with respect to diabetes-related clinical traits. Comparison between ASD and NGT subjects showed significant up-regulation of muscle atrophy marker atrogin-1 along with the FOXO signaling pathway. The expression of PDK4, an important inhibitor of glycolytic flux was up-regulated whereas, glucose oxidation biomarkers, HK2 and LDHB were down-regulated in ASD subjects. The result obtained from this study highlights muscle-associated gene expression changes that could be used to categorize diabetic subjects into biologically relevant subgroups.

Role of Beta Trace Protein and Cystatin C as a Potential Biomarker for Early Detection of Type 2 Diabetic Nephropathy

Background: One of the most well-known diabetic microvascular consequences is diabetic nephropathy, which affects 40 percent of people with diabetes mellitus type 2. It develops into end stage renal disease, and diabetes biomarkers can be used as a predictor. Objectives: This study aimed to determine the concentration levels of serum Beta Trace Protein and serum Cystatin C in all stages of diabetic nephropathy disease. Subjects And Methods: A Case- control study included 120 Persons (30-persons appeared healthy as control and 90 patients proved with T2DM of both gender (64 males and 56 females), split into four groups by using urinary micro-albumin and albumin to creatinine ratio (UACR) as following: group-I: includes 30persons as a healthy control (UACR &lt; 30 mg/g creatinine). Group-II: includes 30patients with type2 DM normoalbuminuria (UACR &lt; 30 mg/g creatinine) as a positive control. Group-III: includes 30 patients with type 2 DM microalbuminuria (UACR 30 – 300 mg/g creatinine). Group-IV: includes 30 patientsا with type 2 DM macroalbuminuria (UACR˃ 300 mg/g creatinine), all groups with ages ranges between (40-69) years. in allا groups, Beta Trace Protein and Cystatin C quantitative enzyme immunoassay (double-antibody sandwich), calculated in serum and both biomarkers using the same methods. Results: Patients with diabetes have statistically substantially increased blood levels of Beta trace protein and Cystatin C. with macro-albuminuria groups in comparison to micro-albuminuria, normo-albuminuria and healthy control in addition, the mean for the micro-albuminuria group was greater than for the healthy control and normo-albuminuria groups. Conclusion: A rise in blood Beta trace protein and Cystatin C levels in the early nephropathy group microalbuminuria, which may be used as a predictor for early diagnosis of diabetic nephropathy.

Optical Fiber Ball Resonator Biosensor as a Platform for Detection of Diabetic Retinopathy Biomarkers in Tears

Optical Fiber Ball Resonator Biosensor as a Platform for Detection of Diabetic Retinopathy Biomarkers in Tears

Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study with a Cohort Group in South Africa.

This study aims to explore the potential of Internet of Things (IoT) devices and explainable Artificial Intelligence (AI) techniques in predicting biomarker values associated with GDM when measured 13 - 16 weeks prior to diagnosis. We developed a system that forecasts biomarkers such as LDL, HDL, triglycerides, cholesterol, HbA1c, and results from the Oral Glucose Tolerance Test (OGTT) including fasting glucose, 1-hour, and 2-hour post-load glucose values. These biomarker values are predicted based on sensory measurements collected around week 12 of pregnancy, including continuous glucose levels, short physical movement recordings, and medical background information. To the best of our knowledge, this is the first study to forecast GDM-associated biomarker values 13 to 16 weeks prior to the GDM screening test, using continuous glucose monitoring devices, a wristband for activity detection, and medical background data. We applied machine learning models, specifically Decision Tree and Random Forest Regressors, along with Coupled-Matrix Tensor Factorisation (CMTF) and Elastic Net techniques, examining all possible combinations of these methods across different data modalities. The results demonstrated good performance for most biomarkers. On average, the models achieved Mean Squared Error (MSE) between 0.29 and 0.42 and Mean Absolute Error (MAE) between 0.23 and 0.45 for biomarkers like HDL, LDL, cholesterol, and HbA1c. For the OGTT glucose values, the average MSE ranged from 0.95 to 2.44, and the average MAE ranged from 0.72 to 0.91. Additionally, the utilisation of CMTF with Alternating Least Squares technique yielded slightly better results (0.16 MSE and 0.07 MAE on average) compared to the well-known Elastic Net feature selection technique. While our study was conducted with a limited cohort in South Africa, our findings offer promising indications regarding the potential for predicting biomarker values in pregnant women through the integration of wearable devices and medical background data in the analysis. Nevertheless, further validation on a larger, more diverse cohort is imperative to substantiate these encouraging results.

Multiple cardiovascular risk factor care in 55 low- and middle-income countries: A cross-sectional analysis of nationally-representative, individual-level data from 280,783 adults.

The prevalence of multiple age-related cardiovascular disease (CVD) risk factors is high among individuals living in low- and middle-income countries. We described receipt of healthcare services for and management of hypertension and diabetes among individuals living with these conditions using individual-level data from 55 nationally representative population-based surveys (2009-2019) with measured blood pressure (BP) and diabetes biomarker. We restricted our analysis to non-pregnant individuals aged 40-69 years and defined three mutually exclusive groups (i.e., hypertension only, diabetes only, and both hypertension-diabetes) to compare individuals living with concurrent hypertension and diabetes to individuals with each condition separately. We included 90,086 individuals who lived with hypertension only, 11,975 with diabetes only, and 16,228 with hypertension-diabetes. We estimated the percentage of individuals who were aware of their diagnosis, used pharmacological therapy, or achieved appropriate hypertension and diabetes management. A greater percentage of individuals with hypertension-diabetes were fully diagnosed (64.1% [95% CI: 61.8-66.4]) than those with hypertension only (47.4% [45.3-49.6]) or diabetes only (46.7% [44.1-49.2]). Among the hypertension-diabetes group, pharmacological treatment was higher for individual conditions (38.3% [95% CI: 34.8-41.8] using antihypertensive and 42.3% [95% CI: 39.4-45.2] using glucose-lowering medications) than for both conditions jointly (24.6% [95% CI: 22.1-27.2]).The percentage of individuals achieving appropriate management was highest in the hypertension group (17.6% [16.4-18.8]), followed by diabetes (13.3% [10.7-15.8]) and hypertension-diabetes (6.6% [5.4-7.8]) groups. Although health systems in LMICs are reaching a larger share of individuals living with both hypertension and diabetes than those living with just one of these conditions, only seven percent achieved both BP and blood glucose treatment targets. Implementation of cost-effective population-level interventions that shift clinical care paradigm from disease-specific to comprehensive CVD care are urgently needed for all three groups, especially for those with multiple CVD risk factors.

Boosting acetone response of p-type Co3O4 sensor via Sn and Ni co-doping for diabetes diagnosis

The hole accumulation layer (HAL) configuration of p-type oxides (including Co3O4) in ambient air causes intrinsically low gas response and hinders their promising applications in exhaled gas analysis. Herein, Sn and Ni co-doping has been proposed to trigger the response of chemiresistive Co3O4 sensor toward acetone (biomarker of diabetes). Via incorporating 1 at% Sn and 0.5 at% Ni doping (Co2.95Sn0.03Ni0.02O4), the response to 100 ppm acetone has been boosted ∼2 orders (from 1.24 to 125.6) at 70 °C, the limit of detection (LoD) has been reduced ∼4 times (from 47.9 to 12.4 ppb), the optimal operation temperature has been decreased from 130 °C to ∼70 °C. Various characterizations suggest that co-doping induced abundant surface asymmetric oxygen vacancy defects (Co-□-Ni, Sn-□-Ni), which facilitate the catalytic oxidation of acetone molecules at relatively low operation temperature. In addition to excellent reproducibility and long-term stability, Co2.95Sn0.03Ni0.02O4 sensor could also operate under highly humid air background and reliably detect acetone concentration in exhaled breath at 150 °C, opening the opportunity for the practical application of p-type oxide sensors for diabetes diagnosis.

IDF23-0471 Impact of onion-based interventions on biomarkers of diabetes and its complications in diabetic rats-A systematic review

IDF23-0471 Impact of onion-based interventions on biomarkers of diabetes and its complications in diabetic rats-A systematic review

Enhanced acetone detection for non-invasive diabetes monitoring by atomic layer deposited WO3 nanoparticle on hierarchical In2O3 particles

This study addressed the critical need for non-invasive monitoring of diabetes by proposing an acetone gas sensor based on hierarchical In2O3 with atomic layer deposition (ALD)-deposited WO3 nanoparticles. The sensor fabrication involved a carefully designed process, leveraging ALD to control WO3 deposition, ensuring uniform distribution, and mitigating agglomeration. The resulting composite exhibited enhanced sensitivity, making it promising for detecting acetone, a key biomarker for diabetes. Material synthesis, including hydrothermal formation of In2O3 hierarchy particles and ALD of WO3, was meticulously conducted. Comprehensive characterizations, involving SEM, TEM, EDX, XRD, XPS, and BET, validated the successful synthesis and deposition. The sensor’s response to varying acetone concentrations (50–2000 ppb) was systematically investigated, revealing a positive correlation. The In2O3/WO3–2 sensor exhibited the highest sensitivity, attributed to the catalytic properties of WO3. The proposed sensor presented a cost-effective, sensitive, and selective solution, paving the way for non-invasive diabetes monitoring.

A Novel Mixture of δ-Tocotrienol, Vitamin D3, Resveratrol (NS-3) Significantly Decreases Diabetes Biomarkers Including Inflammatory in People with Type 2 Diabetes

Aims: Diabetes mellitus is a metabolic disorder identified by hyperglycemia due to insulin resistance. Impaired serum/plasma fasting glucose, HbA1c, hs-CRP are biomarkers, normally used to determine onset of diabetes. d-Tocotrienol, vitamin D3, and resveratrol (nutritional supplement-NS-3) are potent anticholesterolemic, anti-oxidative and anti-inflammatory agents. We hypothesized that a mixture of d-tocotrienol, vitamin D3, resveratrol (NS-3) will be more effective treatment for reducing diabetes biomarkers as compared to its individual components, in people with type 2 diabetes mellitus (T2DM). Methods: To test our hypothesis, evaluation of NS-3 mixture and its individual components was carried out on diabetes inflammatory biomarkers using peripheral blood mononuclear cells (PBMC) obtained from healthy, normal and people with T2DM. A randomized placebo controlled double-blinded prospective trial of individual components (n = 30/component), and NS-3 trial of people with T2DM (n = 52/group), were given two capsules/d of cellulose/olive oil as placebo, individual components, or NS-3 mixture for 24-weeks. Results: Significant down-regulation (15 - 74; P &lt; 0.002) of gene expression was observed with individual components and NS-3 on diabetes biomarkers (IRS-1, SOD-2, GCKR, ICAM-1, VCAM-1, IL-6, IL-8) in PBMCs of T2DM, and in serum values of fasting glucose (11%), HbA1c (10%), hs-CRP (23%), fasting insulin (9%), HOMA-IR (20%), MDA (20%) of NS-3 treated people with T2DM after 24-weeks. Treatment with individual components showed significant decrease but were less effective than the mixture. RT-PCR analysis of blood RNAs obtained from NS-3 treated people with T2DM for 24-weeks resulted in significant (P &lt; 0.01) down-regulation of gene expression in diabetes biomarkers (IRS-1, SOD-2, GCKR, IGFPB-2) compared to pre-dose values. Conclusions: Present results of in vitro and in vivo studies support our hypothesis that NS-3 mixture is more effective in lowering serum levels of several diabetes biomarkers including inflammatory gene expression markers compared to its individual components in people with T2DM.

Homocysteine: A Modern Biomarker for Diabetes and Cardiovascular Disease

Homocysteine: A Modern Biomarker for Diabetes and Cardiovascular Disease