Exhaled breath signal analysis for diabetes detection: an optimized deep learning approach

In this study, a flexible deep learning system for breath analysis is created using an optimal hybrid deep learning model. To improve the quality of the gathered breath signals, the raw data are first pre-processed. Then, the most relevant features like Improved IMFCC, BFCC (bark frequency), DWT, peak detection, QT intervals, and PR intervals are extracted. Then, using these features the hybrid classifiers built into the diabetic's detection phase is trained. The diabetic detection phase is modeled with an optimized DBN and BI-GRU model. To enhance the detection accuracy of the proposed model, the weight function of DBN is fine-tuned with the newly projected Sine Customized by Marine Predators (SCMP) model that is modeled by conceptually blending the standard MPA and SCA models, respectively. The final outcome from optimized DBN and Bi-GRU is combined to acquire the ultimate detected outcome. Further, to validate the efficiency of the projected model, a comparative evaluation has been undergone. Accordingly, the accuracy of the proposed model is above 98%. The accuracy of the proposed model is 54.6%, 56.9%, 56.95, 44.55, 57%, 56.95, 18.2%, and 56.9% improved over the traditional models like CNN + LSTM, CNN + LSTM, CNN, LSTM, RNN, SVM, RF, and DBN, at 60th learning percentage.

Triglyceride-Glucose Index as a Predictor of Lower Extremity Arterial Disease in Patients with Diabetes: A Hospitalized Population Retrospective Study

BACKGROUND

The triglyceride-glucose (TyG) index is a new, simple, and inexpensive marker of insulin resistance that is becoming increasingly important as a promising predictor of diseases such as atherosclerosis. Atherosclerosis is the main cause of lower extremity arterial disease (LEAD). In this study, we investigated the relationship between TyG index values and LEAD risk in patients with diabetes.

METHODS

Patients with diabetes hospitalized at the Endocrinology Department of our hospital from June 1, 2021, to May 31, 2022, were retrospectively included. Baseline data, biochemical indicators, and ankle-brachial index values were collected. Statistical methods were used to assess the relationship between TyG index values and the risk of LEAD.

RESULTS

A total of 1,040 hospitalized patients with diabetes were included, they were divided into the LEAD group with 168 patients and the no LEAD group with 872 patients. TyG index values in the LEAD group were higher than those in the no LEAD group (9.94 ± 0.78 vs. 9.36 ± 0.70, P < 0.001). TyG index values were independently correlated with LEAD risk in patients with diabetes (odds ratio = 3.92, 95% confidence interval (CI): 2.92-5.26, P < 0.001) in multivariate logistic regression analysis after adjusting for different risk factors (all P < 0.05). The area under the receiver operating characteristic curve was 0.72 (95% CI: 0.68-0.76) when TyG index values were used to diagnose LEAD in patients with diabetes. When Youden's index reached the maximum value of 0.34, the optimal TyG index threshold for predicting the onset of diabetic LEAD was 9.56, sensitivity was 71.1%, and specificity was 63.0%.

CONCLUSIONS

Increases in TyG index values were associated with the risk of LEAD in patients with diabetes and, thus, may be used as a predictor of LEAD in this patient population, especially in primary care institutions with relatively few resources.

A novel multi-feature learning model for disease diagnosis using face skin images

BACKGROUND

Facial skin characteristics can provide valuable information about a patient's underlying health conditions.

OBJECTIVE

In practice, there are often samples with divergent characteristics (commonly known as divergent samples) that can be attributed to environmental factors, living conditions, or genetic elements. These divergent samples significantly degrade the accuracy of diagnoses.

METHODOLOGY

To tackle this problem, we propose a novel multi-feature learning method called Multi-Feature Learning with Centroid Matrix (MFLCM), which aims to mitigate the influence of divergent samples on the accurate classification of samples located on the boundary. In this approach, we introduce a novel discriminator that incorporates a centroid matrix strategy and simultaneously adapt it to a classifier in a unified model. We effectively apply the centroid matrix to the embedding feature spaces, which are transformed from the multi-feature observation space, by calculating a relaxed Hamming distance. The purpose of the centroid vectors for each category is to act as anchors, ensuring that samples from the same class are positioned close to their corresponding centroid vector while being pushed further away from the remaining centroids.

RESULTS

Validation of the proposed method with clinical facial skin dataset showed that the proposed method achieved F1 scores of 92.59%, 83.35%, 82.84% and 85.46%, respectively for the detection the Healthy, Diabetes Mellitus (DM), Fatty Liver (FL) and Chronic Renal Failure (CRF).

CONCLUSION

Experimental results demonstrate the superiority of the proposed method compared with typical classifiers single-view-based and state-of-the-art multi-feature approaches. To the best of our knowledge, this study represents the first to demonstrate concept of multi-feature learning using only facial skin images as an effective non-invasive approach for simultaneously identifying DM, FL and CRF in Han Chinese, the largest ethnic group in the world.

Economic Costs of Diabetes in the U.S. in 2022

OBJECTIVE

This study updates previous estimates of the economic burden of diagnosed diabetes, with calculation of the health resource use and indirect costs attributable to diabetes in 2022.

RESEARCH DESIGN AND METHODS

We combine the demographics of the U.S. population in 2022 with diabetes prevalence, from national survey data, epidemiological data, health care cost data, and economic data, into a Cost of Diabetes Economic Model to estimate the economic burden at the population and per capita levels. Health resource use and associated medical costs are analyzed by age, sex, race/ethnicity, comorbid condition, and health service category. Data sources include national surveys (2015-2020 or most recent available), Medicare standard analytic files (2020), and administrative claims data from 2018 to 2021 for a large commercially insured population in the U.S.

RESULTS

The total estimated cost of diagnosed diabetes in the U.S. in 2022 is $412.9 billion, including $306.6 billion in direct medical costs and $106.3 billion in indirect costs attributable to diabetes. For cost categories analyzed, care for people diagnosed with diabetes accounts for 1 in 4 health care dollars in the U.S., 61% of which are attributable to diabetes. On average people with diabetes incur annual medical expenditures of $19,736, of which approximately $12,022 is attributable to diabetes. People diagnosed with diabetes, on average, have medical expenditures 2.6 times higher than what would be expected without diabetes. Glucose-lowering medications and diabetes supplies account for ∼17% of the total direct medical costs attributable to diabetes. Major contributors to indirect costs are reduced employment due to disability ($28.3 billion), presenteeism ($35.8 billion), and lost productivity due to 338,526 premature deaths ($32.4 billion).

CONCLUSIONS

The inflation-adjusted direct medical costs of diabetes are estimated to rise 7% from 2017 and 35% from 2012 calculations (stated in 2022 dollars). Following decades of steadily increasing prevalence of diabetes, the overall estimated prevalence in 2022 remains relatively stable in comparison to 2017. However, the absolute number of people with diabetes has grown and contributes to increased health care expenditures, particularly per capita spending on inpatient hospital stays and prescription medications. The enormous economic toll of diabetes continues to burden society through direct medical and indirect costs.

The laboratory investigation of diabetes insipidus: A review

Diabetes insipidus (DI) is a group of disorders that lead to inappropriate production of large volumes of dilute urine. The three main forms are central DI (CDI), nephrogenic DI (NDI) and primary polydipsia (PP). Differentiating CDI/NDI from PP is important as patients with true DI are at risk of severe dehydration without treatment. Biochemical testing is key in the diagnosis of DI. The indirect water deprivation test (WDT) is commonly used in the investigation of DI but has drawbacks including being cumbersome and sometimes producing equivocal results. Direct measurement of AVP has theoretical advantages but has generally only been used in specialist centres. Disadvantages include the requirement to measure AVP under hypertonic stimulation and pre-analytical/analytical challenges. Copeptin (CT-proAVP) is a proxy marker for AVP that is more stable, easier to measure and has been studied more widely in recent years. Historically, the evidence supporting the diagnostic performance of these tests has been relatively poor, being based on a few small, usually single-centre studies. However more recent, well-designed prospective studies are improving the evidence base for investigation of DI. These studies have focused on the utility of copeptin measurements during stimulation tests. There is evidence that measurement of copeptin under stimulation offers improved diagnostic performance compared to the WDT. There is currently a lack of systematic, evidence-based guidelines on the diagnosis of DI, but as the quality of the evidence defining the diagnostic performance of tests for DI continues to improve, a clearer consensus on the optimal approach should become achievable.

CD163 as a Potential Biomarker-associated Immune Inflammation in Diabetes Mellitus: A Systematic Review and Bioinformatics Analysis

BACKGROUND

Several studies have identified CD163 as a potential mediator of diabetes mellitus through an immune-inflammation. Further study is necessary to identify its specific mechanism.

OBJECTIVES

In this study, we aimed to investigate CD163 as a potential biomarker associated with immune inflammation in diabetes mellitus through a systematic review and bioinformatics analysis.

METHODS

We searched PubMed, Web of Science, the Cochrane Library, and Embase databases with a time limit of September 2, 2022. Furthermore, we conducted a systematic search and review based on PRISMA guidelines. Additionally, diabetic gene expression microarray datasets GSE29221, GSE30528, GSE30529, and GSE20966 were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo) for bioinformatics analysis. The PROSPERO number for this study is CRD420222347160.

RESULTS

Following the inclusion and exclusion criteria, seven articles included 1607 patients, comprising 912 diabetic patients and 695 non-diabetic patients. This systematic review found significantly higher levels of CD163 in diabetic patients compared to non-diabetic patients. People with diabetes had higher levels of CRP expression compared to the control group. Similarly, two of the three papers that used TNF- α as an outcome indicator showed higher expression levels in diabetic patients. Furthermore, IL-6 expression levels were higher in diabetic patients than in the control group. A total of 62 samples were analyzed by bioinformatics (33 case controls and 29 experimental groups), and 85 differential genes were identified containing CD163. According to the immune cell correlation analysis, CD163 was associated with macrophage M2, γδ T lymphocytes, macrophage M1, and other immune cells. Furthermore, to evaluate the diagnostic performance of CD163, we validated it using the GSE20966 dataset. In the validation set, CD163 showed high diagnostic accuracy.

CONCLUSION

This study suggests CD163 participates in the inflammatory immune response associated with diabetes mellitus and its complications by involving several immune cells. Furthermore, the results suggest CD163 may be a potential biomarker reflecting immune inflammation in diabetic mellitus.

Implementing pharmacist-led diabetic digital retina scan services at a federally qualified health center

BACKGROUND

Due to low national and local rates for annual eye exams in patients with diabetes, a pharmacist-led chronic care clinic creates a unique opportunity for pharmacists to provide digital retina scans in the primary care setting.

OBJECTIVES

The primary objective is to assess the impact of a pharmacist-led digital retina scan service on rates of annual eye exams among patients with diabetes.

PRACTICE DESCRIPTION

KC CARE Health Center, a federally qualified health center in Kansas City, Missouri, offers healthcare services to any member of the community regardless of insurance status. Pharmacists work in this setting to assist with management of chronic disease under a collaborative practice agreement.

PRACTICE INNOVATION

A pharmacist developed a workflow process to provide digital retina scans in the primary care setting for patients with diabetes who had not had an eye exam in the past 12 months. Images of each eye are captured using a RetinaVue 700 Imager and these images are submitted to an ophthalmologist for review.

EVALUATION METHODS

Rates of annual eye exams among patients with diabetes seen in the primary care clinic before and after retina scan service implementation were analyzed using a chi-squared test with an a-priori alpha of 0.05. Survey data of patient-reported barriers to follow-up were reported using descriptive statistics.

RESULTS

There was a 5% increase in the annual eye exam rate of patients with diabetes seen at the clinic during the study period. The increase in rate of annual eye exam from before the intervention to after was statistically significant (P < 0.001).

CONCLUSION

Implementing a pharmacist-led retina scan program in the primary care setting improved the rate of annual diabetic eye exams and demonstrated the benefit that pharmacists can have in enhancing accessibility to preventative care services.

Binary fire hawks optimizer with deep learning driven non-invasive diabetes detection and classification

Non-invasive diabetes detection refers to the utilization and development of technologies and methods that can monitor and diagnose diabetes without requiring invasive procedures, namely invasive glucose monitoring or blood sampling. The objective is to provide a more convenient and less burdensome approach to screening and management of diabetes. It is noteworthy that while non-invasive method offers promising avenues for diabetes detection, they frequently require validation through clinical studies and might have limitation in terms of reliability and accuracy than classical invasive approaches. In recent times, deep learning (DL) and feature selection (FS) are used to monitor and diagnose diabetes accurately without requiring invasive procedures. This technique combines the FS method with the DL algorithm for making accurate predictions and extracting relevant features from non-invasive data. This article introduces a new Binary Fire Hawks Optimizer with Deep Learning-Driven Non-Invasive Diabetes Detection and Classification (BFHODL-NIDDC) technique. The major intention of the BFHODL-NIDDC technique focuses on the involvement of non-invasive procedures for the detection of diabetes. In the BFHODL-NIDDC technique, data preprocessing is initially performed to preprocess the input data. Next, the BFHO algorithm chooses an optimal subset of features and improves the classifier results. For the identification of diabetes, multichannel convolutional bidirectional long short-term memory (MC-BLSTM) model is used. At last, the beetle antenna search (BAS) algorithm is used for the hyperparameter selection of the MC-BLSTM method which in turn enhances the detection performance of the MC-BLSTM model. A series of simulations were conducted on the diabetes dataset to assess the diabetes detection performance of the BFHODL-NIDDC technique. The experimental outcomes illustrated better performance of the BFHODL-NIDDC method over other recent approaches in terms of different metrics (Tab. 4, Fig. 9, Ref. 23). Keywords: diabetes, non-invasive detection, binary fire hawks optimizer, deep learning, hyperparameter tuning.

Pericardial fat volume and coronary risk factors as predictors of non-calcified coronary plaque presence among patients with coronary calcium score = 0

INTRODUCTION

There is scarce data linking pericardial fat volume (PFV) and classical coronary risk factors with non-calcified plaque presence among patients with CAC = 0 in the literature.

MATERIAL AND METHOD

A total of 811 patients with chest pain suggestive of angina underwent CT coronary angiography for the assessment of coronary artery disease were collected. Of these, 417 with CAC = 0 were included in the analysis.

RESULT

Patients with non-calcified plaque were older (54 ± 9 versus 50 ± 10, P = 0.01) and had a higher prevalence of diabetes mellitus (31% versus 17%, P = 0.02), high BMI (29.9 versus 28.3, P = 0.04), and increased PFV (123 cm3 versus 99 cm3, P < 0.01) compared to patients without plaque. In multivariate regression analysis, high BMI[OR(CI) = 1.1(1-1.3), P = 0.02] was an independent predictor of non-calcified coronary plaque presence among patients with CAC = 0 after adjustment to variables with P < 0.05 in the univariate analysis.

CONCLUSION

In patients with a CAC score of 0, advanced age, diabetes mellitus, increased PFV, and high BMI were all associated with the presence of non-calcified plaque. After multivariate adjustment, increased BMI remained a significant independent predictor for non-calcified plaque presence.

Time to presentation and mortality outcomes among patients with diabetes and acute myocardial infarction

BACKGROUND/AIMS

Due to limited real-world evidence on the association between time to presentation (T2P) and outcomes following acute myocardial infarction and diabetes (AMI-DM), we investigated the characteristics of patients with AMI-DM and their outcomes based on their T2P.

METHODS

4,455 patients with AMI-DM from a Korean nationwide observational cohort (2011-2015) were divided into early and late presenters according to symptom-to-door time. The effects of T2P on three-year all-cause mortality were estimated using inverse probability of treatment weighting (IPTW) and survival analysis.

RESULTS

The incidence of all-cause mortality was consistently higher in late presenters than in early presenters (11.4 vs. 17.2%; p < 0.001). In the IPTW-adjusted dataset, the incidence of all-cause mortality was numerically higher in late presenters than in early presenters (9.1 vs. 12.4%; p = 0.072). In the survival analysis, the cumulative incidence of all-cause mortality was significantly higher in late presenters than in early presenters before and after IPTW. In the subgroup with ST-elevation myocardial infarction, late presenters had a higher incidence of cardiac death than early presenters before (4.8 vs. 10.5%; p < 0.001) and after IPTW (4.2 vs. 9.7%; p = 0.034). In the initial glycated hemoglobin (HbA1c)-stratified analysis, these effects were attenuated in patients with HbA1c ≥ 9.0% before (adjusted hazard ratio [HR]: 1.45, 95% confidence interval [CI]: 0.80-2.64) and after IPTW (adjusted HR: 0.82, 95% CI: 0.40-1.67).

CONCLUSION

Late presentation was associated with higher mortality in patients with AMI-DM; therefore, multifaceted and systematic interventions are needed to decrease pre-hospital delays.

Current advancements and prospects of enzymatic and non-enzymatic electrochemical glucose sensors

This review discusses the most current developments and future perspectives in enzymatic and non-enzymatic glucose sensors, which have notably evolved over the preceding quadrennial period. Furthermore, a thorough exploration encompassed the sensor's intricate fabrication processes, the diverse range of materials employed, the underlying principles of detection, and an in-depth assessment of the sensors' efficacy in detecting glucose levels within essential bodily fluids such as human blood serums, urine, saliva, and interstitial fluids. It is worth noting that the accurate quantification of glucose concentrations within human blood has been effectively achieved by utilizing classical enzymatic sensors harmoniously integrated with optical and electrochemical transduction mechanisms. Monitoring glucose levels in various mediums has attracted exceptional attention from industrial to academic researchers for diabetes management, food quality control, clinical medicine, and bioprocess inspection. There has been an enormous demand for the creation of novel glucose sensors over the past ten years. Research has primarily concentrated on succeeding biocompatible and enhanced sensing abilities related to the present technologies, offering innovative avenues for more effective glucose sensors. Recent developments in wearable optical and electrochemical sensors with low cost, high stability, point-of-care testing, and online tracking of glucose concentration levels in biological fluids can aid in managing and controlling diabetes globally. New nanomaterials and biomolecules that can be used in electrochemical sensor systems to identify glucose concentration levels are developed thanks to advances in nanoscience and nanotechnology. Both enzymatic and non-enzymatic glucose electrochemical sensors have garnered much interest recently and have made significant strides in detecting glucose levels. In this review, we summarise several categories of non-enzymatic glucose sensor materials, including composites, non-precious transition metals and their metal oxides, hydroxides, precious metals and their alloys, carbon-based materials, conducting polymers, metal-organic framework (MOF)-based electrocatalysts, and wearable device-based glucose sensors deeply.

[Status Quo and Research Progress in Diagnosis and Treatment of Patients With Diabetes Mellitus and Chronic Kidney Disease]

As the incidence of diabetes mellitus is rapidly increasing worldwide,that of related complications,such as diabetic kidney disease(DKD),also increases,conferring a heavy economic burden on the patients,families,society,and government.Diabetes mellitus complicated with chronic kidney disease(CKD)includes DKD and the CKD caused by other reasons.Because of the insufficient knowledge about CKD,the assessment of diabetes mellitus complicated with CKD remains to be improved.The therapies for diabetes mellitus complicated with CKD focus on reducing the risk factors.In clinical practice,DKD may not be the CKD caused by diabetes.According to clinical criteria,some non-diabetic kidney disease may be misdiagnosed as DKD and not be treated accurately.This review summarizes the status quo and research progress in the assessment,diagnosis,and treatment of diabetes mellitus complicated with CKD and predicts the directions of future research in this field.

Artificial intelligence model for early detection of diabetes

Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life and increase the costs of healthcare. In recent years, prevalence and mortality rates have increased worldwide. The development of models with high predictive performance can help in the early identification of the disease. Objective. To develope a model based on artificial intelligence to support clinical decisionmaking in the early detection of diabetes. Materials and methods. We conducted a cross-sectional study, using a dataset that contained age, signs, and symptoms of patients with diabetes and of healthy individuals. Pre-processing techniques were applied to the data. Subsequently, we built the model based on fuzzy cognitive maps. Performance was evaluated with three metrics: accuracy, specificity, and sensitivity. Results. The developed model obtained an excellent predictive performance with an accuracy of 95%. In addition, it allowed to identify the behavior of the variables involved using simulated iterations, which provided valuable information about the dynamics of the risk factors associated with diabetes. Conclusions. Fuzzy cognitive maps demonstrated a high value for the early identification of the disease and in clinical decision-making. The results suggest the potential of these approaches in clinical applications related to diabetes and support their usefulness in medical practice to improve patient outcomes.

Mapping evidence on barriers to and facilitators of diagnosing noncommunicable diseases (NCDs) among people living with human immunodeficiency virus (PLWH) in low- and middle-income countries (LMICs) in Africa: A scoping review protocol

BACKGROUND

Noncommunicable diseases (NCDs) represent a global public health challenge in all population groups, but the prevalence of major NCDs, such as depression, hypercholesterolemia, hypertension, obesity and diabetes, is increasing at a rapid rate among people living with human immunodeficiency virus (PLWH). Studies show that integrated NCDs and human immunodeficiency virus (HIV) services have improved the patient outcome of PLWH with comorbidities with NCDs. It requires a strengthened and sustainable way of diagnosing major NCDs early among PLWH. However, there is limited evidence regarding the barriers to and facilitators of early diagnosis of NCDs (depression, hypercholesterolemia, hypertension, obesity and diabetes) among PLWH in low- and middle-income countries (LMICs). This review will systematically map the literature and describe the barriers and facilitators of diagnosing NCDs (depression, hypercholesterolemia, hypertension, obesity and diabetes) among PLWH in LMICs in Africa.

METHODS

The methodology presented by Arksey and O'Malley and the recommendation set out by Levac and colleagues will be used. PubMed, MEDLINE with full text via the EBSCO host, Google Scholar, Science Direct and Scopus will be used for a comprehensive search for data extraction. The search will be conducted using keywords, Boolean terms, and Medical Subject Headings (MeSH). All studies with eligible titles will be exported to the EndNote citation manager for deduplication and imported to Rayyan software for screening. Two reviewers will independently screen abstracts, and the preferred reporting items for systematic review and meta-analysis extension to scoping review (PRISMA-Sc) guidelines will be used to guide the search in conducting the scoping review. We will perform descriptive data analysis for quantitative studies, NVivo software version 12 will be used for qualitative studies, and thematic content analysis will be carried out. This scoping review will include studies that included PLWH with those diagnosed with major NCDs (depression, hypercholesterolemia, hypertension, obesity, and diabetes) in LMICs in Africa.

DISCUSSION

This scoping review will highlight evidence mapping on barriers and facilitators of diagnosing NCDs (depression, hypercholesterolemia, hypertension, obesity, and diabetes) among PLWH LMICs in Africa. Scoping Review Registration: registered on Open Science Framework (https://osf.io/xvtwd/).

Acetone Detection and Classification as Biomarker of Diabetes Mellitus Using a Quartz Crystal Microbalance Gas Sensor Array

A gas sensor array was developed and evaluated using four high-frequency quartz crystal microbalance devices (with a 30 MHz resonant frequency in fundamental mode). The QCM devices were coated with ethyl cellulose (EC), polymethylmethacrylate (PMMA), Apiezon L (ApL), and Apiezon T (ApT) sensing films, and deposited by the ultrasonic atomization method. The objective of this research was to propose a non-invasive technique for acetone biomarker detection, which is associated with diabetes mellitus disease. The gas sensor array was exposed to methanol, ethanol, isopropanol, and acetone biomarkers in four different concentrations, corresponding to 1, 5, 10, and 15 µL, at temperature of 22 °C and relative humidity of 20%. These samples were used because human breath contains them and they are used for disease detection. Moreover, the gas sensor responses were analyzed using principal component analysis and discriminant analysis, achieving the classification of the acetone biomarker with a 100% membership percentage when its concentration varies from 327 to 4908 ppm, and its identification from methanol, ethanol, and isopropanol.

Discrepancy in diagnoses of diabetes and prediabetes using fasting plasma glucose and glycosylated hemoglobin and the underdiagnosis by ICD-10 coding: data from a tertiary hospital in Thailand

Background

Early detection of prediabetes and diabetes better prevents long-term health complications. FPG and HbA1c levels are some common laboratory tests utilized as tools to diagnose diabetes and prediabetes, but the agreement rate between these two diagnostic tests varies, which could lead to underdiagnosis and thus undertreatment. This study aimed to analyze the agreement rate between FPG and HbA1c, as well as the physicians' accuracy of using these results to make a prediabetes or diabetes diagnosis through ICD-10 coding at a tertiary care hospital in Bangkok, Thailand.

Methods

A cross-sectional descriptive study was conducted using secondary data collected in a tertiary hospital's check-up clinic from August 16, 2019 to June 30, 2022 to study the prevalence and diagnosis of diabetes and prediabetes, determined through FPG and HbA1c laboratory results. We analyzed the two laboratory tests' diagnosis agreement rate and the physicians' accuracy of diagnosing diabetes and prediabetes in ICD-10 coding using the FPG and HbA1c results.

Results

Among 8,024 asymptomatic participants, the period prevalence diagnosed through laboratory results was 5.8% for diabetes and 19.8% for prediabetes. Diabetes and prediabetes prevalence based on laboratory data differs from that of ICD-10 coding data. Specifically, 79.6% of diabetes patients and 32.3% of prediabetes patients were coded using the ICD-10 coding system. 4,094 individuals had both FPG and HbA1c data. The agreement rate for diagnosing diabetes and prediabetes between the two laboratory results is 89.5%, with Kappa statistics of 0.58. Using only one of the two laboratory results would have missed a substantial number of patients.

Conclusion

Our findings highlight screening test discrepancies and underdiagnosis issues that impede diagnostic accuracy enhancement and refined patient management strategies. Early diagnoses of prediabetes and diabetes, especially before symptoms arise, could increase health consciousness in individuals, thereby enabling the implementation of lifestyle modifications and prevention of serious health complications. We emphasize the importance of diagnosing these conditions using both FPG and HbA1c, along with subsequent accurate ICD-10 coding. Even though some hospitals lack certified HbA1c testing, we suggest enhancing the availability of HbA1c testing, which could benefit many people in Thailand.Clinical trial registration:https://www.thaiclinicaltrials.org, identifier [TCTR20230824003].

Obesity indices and the risk of total and cardiovascular mortality among people with diabetes: a long-term follow-up study in Taiwan

BACKGROUND

The association between obesity indicators and mortality in individuals with diabetes remains unclear, and data on cardiovascular mortality are scarce. Therefore, we investigated the associations between the five adiposity indices and both all-cause and cardiovascular mortality in patients with diabetes.

METHODS

This cohort study included 34,686 adults with diabetes who underwent a standard health-screening program between 1996 and 2017 in Taiwan. The dates and causes of death till January 2022 were retrieved from the National Death Registry. Cox proportional hazards models were used to calculate the hazard ratios (HR) and 95% confidence intervals (CI) for all-cause and cardiovascular mortality in relation to body mass index (BMI), waist circumference, waist-hip ratio (WHR), body fat percentage (BF%), and A Body Shape Index (ABSI), using the third quintile as the reference group.

RESULTS

During a median follow-up of 15 years, there were 8,324 deaths, of which 1,748 were attributed to cardiovascular disease. After adjusting for demographics, lifestyle factors and comorbidities, ABSI was associated with all-cause mortality in an exposure-response manner; the HR (95% CI) for first and fifth vs. third quintile was 0.78 (0.69-0.89) and 1.24 (1.14-1.35), respectively. A similar but weaker exposure-response relationship was found between WHR and mortality. People with a lower BMI and BF% had an increased risk of mortality (HR [95% CI] for the first vs. third quintiles, 1.33 [1.22, 1.44] and 1.42 [1.30, 1.56], respectively). No association was observed between waist circumference categories and risk of mortality. Similar results were observed for the association of BF%, waist circumference, and ABSI with cardiovascular mortality. However, no significant association was observed between BMI and cardiovascular mortality. The association between WHR and cardiovascular mortality was stronger than that between WHR and all-cause mortality.

CONCLUSIONS

ABSI demonstrated a consistent exposure-response relationship with both all-cause and cardiovascular mortality in this Asian cohort with diabetes. Our findings highlight the importance of monitoring ABSI, a surrogate index of central adiposity, in patients with diabetes.

An enhanced diabetes prediction amidst COVID-19 using ensemble models

In the contemporary landscape of healthcare, the early and accurate prediction of diabetes has garnered paramount importance, especially in the wake of the COVID-19 pandemic where individuals with diabetes exhibit increased vulnerability. This research embarked on a mission to enhance diabetes prediction by employing state-of-the-art machine learning techniques. Initial evaluations highlighted the Support Vector Machines (SVM) classifier as a promising candidate with an accuracy of 76.62%. To further optimize predictions, the study delved into advanced feature engineering techniques, generating interaction and polynomial features that unearthed hidden patterns in the data. Subsequent correlation analyses, visualized through heatmaps, revealed significant correlations, especially with attributes like Glucose. By integrating the strengths of Decision Trees, Gradient Boosting, and SVM in an ensemble model, we achieved an accuracy of 93.2%, showcasing the potential of harmonizing diverse algorithms. This research offers a robust blueprint for diabetes prediction, holding profound implications for early diagnosis, personalized treatments, and preventive care in the context of global health challenges and with the goal of increasing life expectancy.

Prevalence and risk factors associated with undiagnosed diabetes in India: Insights from NFHS-5 national survey

Background

Undiagnosed diabetes is a significant public health concern in India, considering the accumulative burden of diabetes and its long-term complications. We have estimated the prevalence and factors associated with undiagnosed diabetes in India.

Methods

We used data from the fifth round of the National Family Health Survey (NFHS-5, 2019-21) to estimate undiagnosed diabetes prevalence aged under 50 (15-49) years. A log-binomial model with survey-adjusted Poisson regression was used to estimate the prevalence risk ratio (PR) between undiagnosed diabetes and various factors. Multinomial logistic regression analysis was performed to examine the factors associated with diagnosed diabetes (vs. healthy) and undiagnosed diabetes (vs. healthy). All the analyses were survey-weighted and stratified by gender and reported with 95% confidence intervals.

Results

The prevalence of diabetes for individuals aged 15-49 years was found to be 4.90% (4.80 to 5.00%) from the NFHS-5. Among them, the proportion of individuals with undiagnosed diabetes was 24.82% (24.07 to 25.59%), with higher among males (28.82% (26.45 to 31.30%)) than females (24.22% (23.44 to 25.01%)). The overall prevalence of undiagnosed diabetes was 1.22% (1.18 to 1.26%), with a higher prevalence among males (1.60% (1.46 to 1.76%)) than females (1.17% (1.13 to 1.21%)). Individuals who are middle-aged (45-49), have a higher body mass index (BMI), and are in a lower wealth index group, or live in the southern regions of India are at a higher risk of being undiagnosed for diabetes.

Conclusion

One in every four having diabetes is undiagnosed. The study highlights the need for public health interventions to improve diabetes screening and access to health care, particularly among middle-aged individuals, and those with higher BMI, as well as addressing lifestyle and dietary factors. The findings also reveal disparities in diabetes burden among population subgroups in India, underscoring the need for targeted efforts.

Correlation between Mean Platelet Volume and Gensini Score in Patients with Coronary Heart Disease in Different Diabetic States

SUBJECT

To investigate the correlation between mean platelet volume (MPV) levels and Gensini scores in stable coronary heart disease (CHD) patients with or without diabetes.

METHODS

A retrospective analysis was conducted on 2525 patients with stable CHD in Zhongshan Hospital, Fudan University. There were 1274 in the low MPV group and 1251 in the high MPV group, divided by a median MPV level of 10.9 fL. In the total population, 1605 patients were non-diabetic and 920 were diabetic. The severity of coronary artery disease was quantified using the Gensini score.

RESULTS

The Gensini score was significantly higher in the high MPV group than in the low MPV group (p < 0.001). MPV levels increased significantly with the number of stenotic (>50%) coronary vessels (p < 0.001). The Spearman analysis showed a positive correlation between MPV and Gensini score (r = 0.189, p < 0.001), which was more significant in the diabetic subgroup (r = 0.232, p < 0.001). Receiver operating characteristic (ROC) curves were employed to assess the predictive value of MPV for high Gensini scores, using the median value of 32 points as the cutoff. MPV levels in the diabetes cohort exhibited a higher predictive value for high Gensini scores (area under the curve: 0.635 [0.614-0.657], p < 0.001). Multivariate linear regression analysis showed that diabetes and MPV were independently associated with Gensini scores.

CONCLUSION

MPV levels in stable CHD patients can predict the severity of coronary artery stenosis. This correlation is more significant in the presence of diabetes.

Water-stable hydrophilic metal organic framework composite for the recognition of N-glycopeptides during diabetes progression by mass spectrometry

A hydrophilic Al-MOFs composite was prepared using cheap and available reagents in water via a suitable large-scale production, an economical and environment-friendly method for capturing N-glycopeptides. The prepared Al-MOFs composite with high hydrolytically stable and hydrophilic 1D channels exhibits an ultralow detection limit (0.5 fmol/μL), and excellent reusability (at least 10 cycles) in the capture of N-glycopeptides from standard bio-samples. Interestingly, the Al-MOFs composite also shows remarkable performance in practical applications, where 300 N-glycopeptides ascribed to 124 glycoproteins were identified in 1 µL human serum and were successfully applied in profiling the differences of N-glycopeptides during diabetes progression. Moreover, 12 specific glycoproteins used as biomarkers to accurately distinguish the progression of diabetes are identified. The present work provides a potential commercial method for large-scale glycoproteomics research in complex clinical samples while offering new guidance for the precise diagnosis of diabetes progression.

Disability Status and Secondary Prevention Among Survivors of Stroke: A Cross-Sectional Analysis of the 2011 to 2018 National Health and Nutrition Examination Survey

BACKGROUND

Among survivors of stroke, adherence to secondary prevention care is associated with decreased risk of recurrent stroke. However, not all survivors of stroke use secondary stroke prevention treatment. We examined the association between the disability status of survivors of stroke and their treatment and control of diabetes, hyperlipidemia, and hypertension.

METHODS AND RESULTS

In a cross-sectional analysis of the 2011 to 2018 National Health and Nutrition Examination Survey, we compared diabetes, hyperlipidemia, and hypertension treatment and control rates among self-reported survivors of stroke age ≥20 years with and without disability. Disability was defined as self-reporting any of 5 physical or 4 functional domains assessed using a structured questionnaire. Logistic regression models adjusted for age, sex, race and ethnicity, and history of medical conditions were used to estimate associations between disability status and risk factor treatment and control. The mean age of survivors of stroke was 65.1 years, and 55.5% were female; 76% (95% CI, 72.7%-79.3%) self-reported at least 1 disability. Age-standardized treatment rates for diabetes, hyperlipidemia, and hypertension were 33.1% (95% CI, 26.9%-39.2%), 67.5% (95% CI, 62.6%-72.3%), and 78.4% (95% CI, 74.6%-82.2%), respectively. Age-standardized control rates for diabetes, hyperlipidemia, and hypertension were 86.8% (95% CI, 83.8%-89.8%), 20.5% (95% CI, 15.0%-25.9%), and 47.1% (95% CI, 42.6%-51.7%), respectively. In adjusted models, those with and without disabilities had similar odds of risk factor treatment and control.

CONCLUSIONS

In the United States, three-quarters of survivors of stroke self-reported a disability, and these patients had similar odds of diabetes, hyperlipidemia, and hypertension treatment and control compared with those without disability.

Improving Diabetes Screening in the Primary Care Clinic

BACKGROUND

In our suburban primary care clinic, the average rate of screening for diabetes among eligible patients was only 51%, similar to national screening data. We conducted a quality improvement project to increase this rate.

METHODS

During the 6-month preintervention phase, we collected baseline data on the percentage of eligible patients screened per week (percentage of patients with hemoglobin A1c checked in the prior 3 years out of patients eligible for screening who completed a visit during the week). We then implemented a two-phase intervention. In phase 1 (approximately 8 months), we generated an electronic health record (EHR) report to identify eligible patients and pended laboratory orders for physicians to sign. In phase 2 (approximately 3 months), we replaced the phase 1 intervention with an EHR clinical decision support tool that automatically identifies eligible patients. We compared screening rates in the preintervention vs. intervention period. For phase 1, we also assessed laboratory completion rates and the laboratory results. We surveyed physicians regarding intervention acceptability and satisfaction at 3, 6, 9, and 12 months during the intervention period.

RESULTS

The weekly percentage of patients screened increased from an average of 51% in the preintervention phase to 65% in the intervention phase (p < 0.001). During phase 1, most patients underwent laboratory blood testing as recommended (83% within 3 months), and results were consistent with prediabetes in 23% and with diabetes in 4%. Overall, most physicians believed that the intervention appropriately identified patients due for screening and was helpful (100% of respondents agreed at 9 months vs. 71% at 3 months).

CONCLUSION

We successfully implemented a systematic screening intervention involving a manual workflow and EHR tool and improved diabetes screening rates in our clinic.

The relationship between diabetes burden and health-related quality of life in elderly people with diabetes

BACKGROUND

One of the most common chronic diseases in the elderly is diabetes. When diabetes is not well controlled, it can cause complications and affect health-related quality of life. Determining the burden of diabetes in elderly patients can provide a good health-related quality of life.

AIM

The study was conducted to examine the relationship between diabetes burden and health-related quality of life in elderly patients with diabetes.

METHODS

This is a descriptive and correlational study of 207 patients who applied to the diabetes outpatient clinics of a private and a public hospital in Istanbul. Respondents were completed with "Personal Information Form for Elderly People", "Elderly Diabetes Burden Scale" and the "Quality of Life in the Elderly Scale". Data were analyzed by SPSS. The tests used are Kolmogorov-Smirnov, Mann-Whitney U, Kruskal Wallis and Spearman Correlation tests.

RESULTS

The mean total score from the Elderly Diabetes Burden Scale was 47.13 ± 11.95 (18-88), and the mean score from the Quality of Life in the Elderly Scale was 19.36 ± 7.00. In the study, as the total diabetes burden score of the elderly patients increased, the Quality of Life in the Elderly Scale score decreased. There was a difference between the mean total score of the Elderly Diabetes Burden Scale and gender, education, living alone and using oral antidiabetic (p < 0.05).

CONCLUSIONS

As a result it was determined, a negative correlation was found between the diabetes CASP-19 scale total score and the total EDBS. Determining the burden of diabetes and affecting factors in elderly is important in terms of increasing the health-related quality of life. It may be recommended to plan diabetes education programs that will reduce the burden of diabetes and increase the health-related quality of life in elderly patients.

Examining health care access disparities in Hispanic populations with peripheral artery disease and diabetes

INTRODUCTION

This study investigated disparities in health care access for Hispanic adults with diabetes and peripheral artery disease (PAD) who are at risk of lower-extremity amputation and other cardiovascular morbidities and mortalities.

METHODS

We utilized the health care access survey data from the All of Us research program to examine adults (⩾ 18 years) with either diabetes and/or PAD. The primary associations evaluated were: could not afford medical care and delayed getting medical care in the past 12 months. Multivariable logistic regression models were used to assess the association of Hispanic ethnicity and survey responses, adjusting for age, sex, income, health insurance, and employment status.

RESULTS

Among 24,104 participants, the mean age was 54.9 years and 67% were women. Of these, 8.2% were Hispanic adults. In multivariable analysis, Hispanic adults were more likely to be unable to afford seeing a health care provider, and receiving emergency care, follow-up care, and prescription medications (p < 0.05) than non-Hispanic adults. Furthermore, Hispanic adults were more likely to report being unable to afford medical care due to cost (odds ratios [OR] 1.72, 95% CI 1.50-1.99), more likely to purchase prescription drugs from another country (OR 2.20, 95% CI 1.69-2.86), and more likely to delay getting medical care due to work (OR 1.46, 95% CI 1.22-1.74) and child care (OR 1.80, 95% CI 1.35-2.39) issues than non-Hispanic White adults.

CONCLUSION

The Hispanic population with diabetes and PAD faces substantial barriers in health care access, including a higher likelihood of delaying medical care and being unable to afford it.

Perceptions of the Healthcare System Among Black Men with Previously Undiagnosed Diabetes and Prediabetes

OBJECTIVE

Given the significant disparities in diabetes burden and access to care, this study uses qualitative interviews of Black men having HbA1c levels consistent with previously undiagnosed diabetes or prediabetes to understand their perceptions of the healthcare system.

RESEARCH DESIGN AND METHODS

We recruited Black men from Black-owned barbershops in Brooklyn, NY, who were screened using point-of-care HbA1c tests. Among those with HbA1c levels within prediabetes or diabetes thresholds, qualitative interviews were conducted to uncover prevalent themes related to their overall health status, health behaviors, utilization of healthcare services, and experiences with the healthcare system. We used a theoretical framework from the William and Mohammed medical mistrust model to guide our qualitative analysis.

RESULTS

Fifty-two Black men without a prior history of diabetes and an HbA1c reading at or above 5.7% were interviewed. Many participants stated that their health was in good condition. Some participants expressed being surprised by their abnormal HbA1c reading because it was not previously mentioned by their healthcare providers. Furthermore, many of our participants shared recent examples of negative interactions with physicians when describing their experiences with the healthcare system. Finally, several participants cited a preference for incorporating non-pharmaceutical options in their diabetes management plans.

CONCLUSION

To help alleviate the disparity in diabetes burden among Black men, healthcare providers should take a more active role in recognizing and addressing their own implicit biases, engage in understanding the specific healthcare needs and expectations of each patient, and consider emphasizing non-medication approaches to improve glycemic control.

SALIVA-based diagnostic approach for diabetes mellitus: a step towards non-invasive detection - a scoping review

OBJECTIVE

Diabetes mellitus (DM) is a chronic autoimmune disease whose main feature is chronic hyperglycemia. The causes of DM are impaired insulin secretion, impaired insulin action, or both. Saliva is a biofluid that can be considered as a "mirror" reflecting our body's health status; with the rapid advancement in salivaomics, saliva, being a non-invasive and safe source, could be a substitute for blood in the diagnosis and prognosis of diseases. As there are no precise guidelines about the salivary biomarkers correlated with the diagnosis of diabetes, a review was conducted to verify whether saliva analysis can be feasible and which biomarkers are more reliable, for the diagnosis of this disease.

MATERIALS AND METHODS

A literature search was performed through PubMed, Medline, Scopus, Web of Science, LILACS, Open Grey, and Cochrane Library databases. The "PRISMA" guidelines were used for the following review, and thirty-three studies were analyzed.

RESULTS

Almost all salivary glucose studies suggest that the estimation of this biomarker can be used as a potential indicator. Furthermore, studies that considered other biomarkers such as 1,5-anhydroglucitol, alpha-amylase, N-acetyl-β-D-hexosaminidase, asprosin, resistin, and fructosamine reported that these biomarkers resulted to be potentially useful for diabetes screening and diagnosis, with the exception of the cystatin SA.

CONCLUSIONS

In conclusion, several salivary biomarkers could be useful for monitoring DM, but it would be necessary to further expand the research and define precise values for each marker in order to predict with reasonable confidence if an individual is healthy or suffering from diabetes. Finally, standardized saliva collection and processing techniques are key to minimizing interindividual variability in saliva composition.

Prevalence of pre-diabetes in adults aged 25 - 45 years in a Durban-based clinical setting, South Africa: A retrospective study

AIM

Due to pre-diabetes being underexplored, its prevalence was investigated in study participants aged 25-45 years in a Durban-based tertiary-level clinical setting in South Africa.

METHODS

The study was done using a retrospective study design. Fasting blood samples from consented patients with no previous diagnosis of diabetes and within the specified age range were collected from King Edward Hospital in Durban. The pre-diabetes diagnosis was confirmed in participants with fasting glucose concentrations between 5.6 and 6.9 mmol/L and glycated haemoglobin (HbA1c) levels between 5.7 % and 6.4 % using the American Diabetes Association (ADA) and World Health Organisation (WHO) diagnosis criteria. The study participants' characterisation was stratified according to the diagnosis criterion, age, gender and ethnicity.

RESULTS

An alarming 68 % average pre-diabetes prevalence across ADA and WHO criteria in the Durban, eThekwini district sample population. The highest prevalence was recorded using the IFG criterion (83%) and the lowest when using the HbA1c criterion (54 %). Between the White, Black and Indian ethnic groups, the Indian group were more predisposed to pre-diabetes onset, with a prevalence of 62.7 %.

CONCLUSION

If pre-diabetes management is unattended, an unprecedented increase in metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and all-cause mortality incidence can be expected. Therefore, the study reveals a window of opportunity to intensify preventative measures and mitigate the incidence of T2DM.

Screening for Prediabetes and Diabetes in a National Network of Federally Qualified Health Centers: An Observational Study

BACKGROUND

In 2021, the U.S. Preventive Services Task Force (USPSTF) recommended screening for prediabetes and diabetes among adults aged 35-70 years with overweight or obesity. Studying dysglycemia screening in federally qualified health centers (FQHCs) that serve vulnerable patient populations is needed to understand health equity implications of this recommendation.

OBJECTIVE

To investigate screening practices among FQHC patients who would be eligible according to the 2021 USPSTF recommendation.

DESIGN

Retrospective cohort study analyzing electronic health records from a national network of 282 FQHC sites.

PARTICIPANTS

We included 183,329 patients without prior evidence of prediabetes or diabetes, who had ≥ 1 office visit from 2018-2020.

MAIN MEASURES

Screening eligibility was based on age and measured body mass index (BMI). The primary outcome, screening completion, was ascertained using hemoglobin A1c or fasting plasma glucose results from 2018-2020.

KEY RESULTS

Among 89,543 patients who would be eligible according to the 2021 USPSTF recommendation, 53,263 (59.5%) were screened. Those who completed screening had higher BMI values than patients who did not (33.0 ± 6.7 kg/m2 vs. 31.9 ± 6.2 kg/m2, p < 0.001). Adults aged 50-64 years had greater odds of screening completion relative to younger patients (OR 1.13, 95% CI: 1.10-1.17). Patients from racial and ethnic minority groups, as well as those without health insurance, were more likely to complete screening than White patients and insured patients, respectively. Clinical risk factors for diabetes were also associated with dysglycemia screening. Among patients who completed screening, 23,588 (44.3%) had values consistent with prediabetes or diabetes.

CONCLUSIONS

Over half of FQHC patients who would be eligible according to the 2021 USPSTF recommendation were screened. Screening completion was higher among middle-aged patients, those with greater BMI values, as well as vulnerable groups with a high risk of developing diabetes. Future research should examine adoption of the 2021 USPSTF screening recommendation and its impact on health equity.

Mediation role of anxiety on social support and depression among diabetic patients in elderly caring social organizations in China during COVID-19 pandemic: a cross-sectional study

BACKGROUND

Diabetes has become a prominent global public health problem, which is an important cause of death, disease burden, and medical and health economic burden. Previous studies have reported that majority of persons diagnosed with diabetes later presented with psychological and mental health diseases. The study aimed to explore the mediation role of anxiety on social support and depression among diabetic patents in elderly caring social organizations (SOs).

METHODS

A multi-stage stratified cluster random sampling method was used in this cross-sectional study, and a questionnaire consisting of demographic questionnaire, MSPSS, GAD-7, and CES-D-10 was utilized to gather data. SPSS 22.0 and MPLUS 7.4 were used for statistical analysis. Spearman correlation analysis was employed to investigate correlations of key variables. A generalized linear model was used to exam factors associated with depression. Finally, the mediation effect among study variables was investigated by structural equation modeling (SEM).

RESULTS

The average scores of social support, anxiety, and depression were 58.41 ± 14.67, 2.95 ± 3.95, and 7.24 ± 5.53, respectively. The factors of gender, social support, and anxiety were identified as significantly influential factors related to depression among diabetic patients in elderly caring SOs. The effect of social support on depression was significantly mediated by anxiety (β = -0.467, 95%CI: -0.813 to -0.251). Furthermore, anxiety partially mediated the relationship between family support and depression (β = -0.112, 95%CI: -0.229 to -0.012), and anxiety functioned as a complete mediator in the effect of significant others' support and depression (β = -0.135, 95%CI: -0.282 to -0.024).

CONCLUSIONS

The indirect effect of social support on depression through anxiety among diabetic patients in elderly caring SOs was elucidated. Social support played a key role in maintaining and regulating their mental health, particularly from family and significant others. Social support provided by both family and significant others exerted an important influence on maintaining and regulating their mental health. In light of this pathway, the elderly caring SOs should enhance the magnitude of social support from these two sources, thereby diminishing the likelihood of experiencing anxiety and depression.

Diabetes noninvasive diagnostics and monitoring through volatile biomarkers analysis in the exhaled breath using optical absorption spectroscopy

The review is aimed on the analysis the abilities of noninvasive diagnostics and monitoring of diabetes mellitus (DM) and DM-associated complications through volatile molecular biomarkers detection in the exhaled breath. The specific biochemical reactions in the body of DM patients and their associations with volatile molecular biomarkers in the breath are considered. The applications of optical spectroscopy methods, including UV, IR, and terahertz spectroscopy for DM-associated volatile molecular biomarkers measurements, are described. The applications of similar technique combined with machine learning methods in DM diagnostics using the profile of DM-associated volatile molecular biomarkers in exhaled air or "pattern-recognition" approach are discussed.

Cardiometabolic risk profile of young and old adults with diabetes: Pooled, cross-sectional analysis of 42 national health surveys

AIMS

To compare levels of cardiometabolic risk factors in young and old adults with diabetes.

METHODS

Pooled analysis of 42 STEPS Surveys (n = 133,717). Diabetes was defined as self-reported diagnosis or fasting plasma glucose ≥ 126 mg/dl. There were two age groups: < 40 and ≥ 40 years. We summarized the mean levels of four cardiometabolic risk factors by country, sex and age group.

RESULTS

In 11 (men) and seven countries (women), the mean BMI seemed higher in young versus old adults; largest difference was found in men in Qatar (∼6 kg/m2). For waist circumference, such pattern was observed in two (men) and in three (women) countries; largest difference in men in Tuvalu (∼7 cm). Regarding systolic blood pressure, in one country (Myanmar) the mean was higher in young men with ∼8 mmHg difference. Women in the oldest group always had higher mean systolic blood pressure. For total cholesterol, in 13 (men) and five (women) countries the mean was higher in young adults (difference was always <1 mmol/l).

CONCLUSIONS

Levels of cardiometabolic risk factors in young versus old adults with diabetes were heterogenous across 42 countries and depended on the risk factor. This calls to monitor cardiometabolic risk factors in young adults with diabetes.

Decrypting the circular RNAs does a favor for us: Understanding, diagnosing and treating diabetes mellitus and its complications

Circular RNAs (circRNAs), a novel type of single-stranded noncoding RNAs with a covalently closed loop structure, are generated in a circular conformation via non-canonical splicing or back-splicing events. Functionally, circRNAs have been elucidated to soak up microRNAs (miRNAs) and RNA binding proteins (RBPs), serve as protein scaffolds, maintain mRNA stability, and regulate gene transcription and translation. Notably, circRNAs are strongly implicated in the regulation of β-cell functions, insulin resistance, adipocyte functions, inflammation as well as oxidative stress via acting as miRNA sponges and RBP sponges. Basic and clinical studies have demonstrated that aberrant alterations of circRNAs expressions are strongly associated with the initiation and progression of diabetes mellitus (DM) and its complications. Here in this review, we present a summary of the biogenesis, transportation, degradation and functions of circRNAs, and highlight the recent findings on circRNAs and their action mechanisms in DM and its complications. Overall, this review should contribute greatly to our understanding of circRNAs in DM pathogenesis, offering insights into the further perspectives of circRNAs for DM diagnosis and therapy.

Associations of blood pressure with cardiovascular and mortality outcomes in over 2 million older persons with or without diabetes mellitus: A systematic review and meta-analysis of 45 cohort studies

BACKGROUND

The impact of blood pressure on cardiovascular disease (CVD) and mortality outcomes in older people with diabetes mellitus (DM) is not well quantified. Using a systematic review and meta-analysis of observational cohort studies, we aimed to compare the associations of blood pressure levels with cardiovascular and mortality outcomes in older people aged ≥ 65 years with or without DM.

METHODS

Studies were identified from MEDLINE, Embase, Web of Science, and search of bibliographies to July 2022. Study-specific risk ratios (RRs) with 95% confidence intervals (CIs) were pooled.

RESULTS

Forty-five unique observational cohort studies (n = 2305,189 participants) assessing the associations of systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) levels with adverse cardiovascular outcomes were included. In the general population, the pooled RRs (95% CIs) of SBP ≥ 140 vs < 140 mmHg and per 10 mmHg increase for composite CVD/MACE were 1.26 (0.96-1.64) and 1.15 (1.08-1.23), respectively. The respective estimates were 1.56 (1.04-2.34) and 1.10 (1.04-1.18) for patients with DM. SBP ≥ 130 vs < 130 mmHg was not associated with an increased risk of adverse cardiovascular outcomes in both populations. SBP < 120 vs ≥ 120 mmHg was associated with an increased risk of all cause-mortality in the general population (n = 10 studies). DBP ≥ 90 mmHg was associated with an increased risk of some adverse cardiovascular outcomes in both populations. Interaction analyses suggested similar risk of outcomes in both populations.

CONCLUSIONS

Observational evidence suggests SBP and DBP confer similar cardiovascular and mortality risk in older adults in the general population and those with DM. A blood pressure target range of > 130/80 to < 140/90 mmHg may be optimal for patients ≥ 65 years with DM, but specific targets may need to be individualised based on patients' unique circumstances. Furthermore, findings do not support stringent blood pressure control in this population group. Definitive RCTs are needed to support these observational findings.

In-human testing of a non-invasive continuous low-energy microwave glucose sensor with advanced machine learning capabilities

Continuous glucose monitoring schemes that avoid finger pricking are of utmost importance to enhance the comfort and lifestyle of diabetic patients. To this aim, we propose a microwave planar sensing platform as a potent sensing technology that extends its applications to biomedical analytes. In this paper, a compact planar resonator-based sensor is introduced for noncontact sensing of glucose. Furthermore, in vivo and in-vitro tests using a microfluidic channel system and in clinical trial settings demonstrate its reliable operation. The proposed sensor offers real-time response and a high linear correlation (R2 ∼ 0.913) between the measured sensor response and the blood glucose level (GL). The sensor is also enhanced with machine learning to predict the variation of body glucose levels for non-diabetic and diabetic patients. This addition is instrumental in triggering preemptive measures in cases of unusual glucose level trends. In addition, it allows for the detection of common artifacts of the sensor as anomalies so that they can be removed from the measured data. The proposed system is designed to noninvasively monitor interstitial glucose levels in humans, introducing the opportunity to create a customized wearable apparatus with the ability to learn.

An Italian case series' description of thiamine responsive megaloblastic anemia syndrome: importance of early diagnosis and treatment

BACKGROUND

Individuals with thiamine-responsive megaloblastic anemia (TRMA) mainly manifest macrocytic anemia, sensorineural deafness, ocular complications, and nonautoimmune diabetes. Macrocytic anemia and diabetes may be responsive to high-dosage thiamine treatment, in contrast to sensorineural deafness. Little is known about the efficacy of thiamine treatment on ocular manifestations.

CASES PRESENTATION

Our objective is to report data from four Italian TRMA patients: in Cases 1, 2 and 3, the diagnosis of TRMA was made at 9, 14 and 27 months. In 3 out of 4 subjects, thiamine therapy allowed both normalization of hyperglycemia, with consequent insulin suspension, and macrocytic anemia. In all Cases, thiamine therapy did not resolve the clinical manifestation of deafness. In Cases 2 and 3, follow-up showed no blindness, unlike Case 4, in which treatment was started for megaloblastic anemia at age 7 but was increased to high doses only at age 25, when the genetic diagnosis of TRMA was performed.

CONCLUSIONS

Early institution of high-dose thiamine supplementation seems to prevent the development of retinal changes and optic atrophy in TRMA patients. The spectrum of clinical manifestations is broad, and it is important to describe known Cases to gain a better understanding of this rare disease.

The effect of statins on mortality and cardiovascular disease in primary care hypertensive patients without other cardiovascular disease or diabetes

AIMS

Studies in primary healthcare (PHC) assessing the effect of primary prevention with statins on mortality and cardiovascular disease (CVD) are scarce. This study aimed to estimate the effect of statins on all-cause mortality, cardiovascular mortality, myocardial infarction (MI), and stroke in individuals in PHC with hypertension without CVD or diabetes.

METHODS AND RESULTS

Using the Swedish PHC quality assurance register QregPV, the study included 13 193 individuals with hypertension without CVD or diabetes, who had filled a first statin prescription between 2010 and 2016, and 13 193 matched controls without a filled statin prescription at the index date. Controls were matched on sex and propensity score using clinical data and data from national registers on comorbidities, prescriptions, and socioeconomic status. The effect of statins was estimated in Cox regression models. During a median of 4.2 years of follow-up, 395 individuals in the statin group vs. 475 in the control group died, 197 vs. 232 died of cardiovascular disease, 171 vs. 191 had an MI, and 161 vs. 181 had a stroke. The treatment effect of statins was significant for all-cause mortality [hazard ratio (HR) 0.83, 95% confidence interval (CI) 0.74-0.93] and cardiovascular mortality (HR 0.85, 95% CI 0.72-0.998). Overall, no significant treatment effect of statins was seen for MI (HR 0.89, 95% CI 0.74-1.07), but there was a significant interaction with sex (P = 0.008) with decreased risk of MI for women but not for men (HR 0.66, 95% CI 0.49-0.88 vs. HR 1.09, 95% CI 0.86-1.38).

CONCLUSION

Primary prevention with statins in PHC was associated with reduced risk of all-cause mortality, cardiovascular mortality, and in women, lower risk of MI.

Relationship between obesity indicators and hypertension-diabetes comorbidity in an elderly population: a retrospective cohort study

BACKGROUND

The prevalence of obesity, hypertension and diabetes is increasing. Hypertension and diabetes are common complications. Additionally, obesity and hypertension-diabetes comorbidity (HDC) are both closely related to insulin resistance. The aim of this study was to determine the association of obesity indicators with HDC in elderly individuals.

METHODS

This retrospective cohort study included 74,955 subjects aged ≥ 60 years living in Xinzheng, Henan Province, from January 2011 to December 2019. The data were collected from the annual health examination dataset. Cox proportional hazard regression models and competing-risk survival regression models were used to examine the relationships between the three indicators and HDC risk.

RESULTS

After 346,504 person-years of follow-up, HDC developed in 9,647 subjects. After further adjustments for confounders and death competing risks, compared with a body mass index (BMI) of 18.5-23.9 kg/m2, the fully adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) of BMI < 18.5, 24-27.9 and ≥ 28 kg/m2 for HDC morbidity were 0.651(0.538,0.788),1.00,1.396(1.336,1.459) and 1.769(1.666,1.878), respectively. Moreover, participants with abdominal obesity measured via waist circumference (WC) or waist-to-height ratio (WtHR) had a higher risk of HDC (HR:1.513; 95% CI: 1.45,1.578 and HR:1.412;95% CI: 1.353,1.473), respectively, than participants with low WC or with low WtHR. In the joint analyses, the highest risk was observed in participants who were overweight and who had central obesity (HR: 1.721; 95% CI: 1.635, 1.811) compared with the nonoverweight and noncentral obesity groups.

CONCLUSIONS

Increased BMI, WC and WtHR were associated with an increased risk of HDC. There was an additive interaction between general body adiposity (as measured via BMI) and central obesity (as measured via WC and WtHR) for HDC. Therefore, reasonable control of BMI, WC and WtHR may be an effective measure to prevent HDC among elderly individuals.

Advancing diabetes prediction with a progressive self-transfer learning framework for discrete time series data

Although diabetes mellitus is a complex and pervasive disease, most studies to date have focused on individual features, rather than considering the complexities of multivariate, multi-instance, and time-series data. In this study, we developed a novel diabetes prediction model that incorporates these complex data types. We applied advanced techniques of data imputation (bidirectional recurrent imputation for time series; BRITS) and feature selection (the least absolute shrinkage and selection operator; LASSO). Additionally, we utilized self-supervised algorithms and transfer learning to address the common issues with medical datasets, such as irregular data collection and sparsity. We also proposed a novel approach for discrete time-series data preprocessing, utilizing both shifting and rolling time windows and modifying time resolution. Our study evaluated the performance of a progressive self-transfer network for predicting diabetes, which demonstrated a significant improvement in metrics compared to non-progressive and single self-transfer prediction tasks, particularly in AUC, recall, and F1 score. These findings suggest that the proposed approach can mitigate accumulated errors and reflect temporal information, making it an effective tool for accurate diagnosis and disease management. In summary, our study highlights the importance of considering the complexities of multivariate, multi-instance, and time-series data in diabetes prediction.

Effects of diabetes mellitus on left ventricular function and deformation in patients with restrictive cardiomyopathies: a 3.0T CMR feature tracking study

BACKGROUND

Diabetes mellitus (DM) is the most common metabolic disease worldwide and a major risk factor for adverse cardiovascular events, while the additive effects of DM on left ventricular (LV) deformation in the restrictive cardiomyopathy (RCM) cohort remain unclear. Accordingly, we aimed to investigate the additive effects of DM on LV deformation in patients with RCM.

MATERIALS AND METHODS

One hundred thirty-six RCM patients without DM [RCM(DM-)], 46 with DM [RCM (DM+)], and 66 age- and sex-matched control subjects who underwent cardiac magnetic resonance (CMR) scanning were included. LV function, late gadolinium enhancement (LGE) type, and LV global peak strains (including radial, circumferential, and longitudinal directions) were measured. The determinant of reduced LV global myocardial strain for all RCM patients was assessed using multivariable linear regression analyses. The receiver operating characteristic curve (ROC) was performed to illustrate the relationship between DM and decreased LV deformation.

RESULTS

Compared with the control group, RCM (DM-) and RCM(DM+) patients presented increased LV end-diastolic index and end-systolic volume index and decreased LV ejection fraction. LV GPS in all three directions and longitudinal PDSR progressively declined from the control group to the RCM(DM-) group to the RCM(DM+) group (all p < 0.05). DM was an independent determinant of impaired LV GPS in the radial, circumferential, and longitudinal directions and longitudinal PDSR (β =  - 0.217, 0.176, 0.253, and - 0.263, all p < 0.05) in RCM patients. The multiparameter combination, including DM, showed an AUC of 0.81(95% CI 0.75-0.87) to predict decreased LV GLPS and an AUC of 0.69 (95% CI 0.62-0.76) to predict decreased LV longitudinal PDSR.

CONCLUSIONS

DM may have an additive deleterious effect on LV dysfunction in patients with RCM, especially diastolic dysfunction in RCM patients, indicating the importance of early identification and initiation of treatment of DM in patients with RCM.

Advances in the treatment of hepatogenous diabetes: A review

Hepatogenous diabetes (HD) is a glycogen metabolism disorder that arises as a consequence of chronic liver disease. The condition is frequently detected in patients diagnosed with cirrhosis, which is a result of advanced liver disease. The prognosis for patients with HD is generally poor, and they are at a heightened risk for serious complications such as gastrointestinal bleeding, primary peritonitis, and hepatic encephalopathy. Hepatogenous diabetes progression is often associated with cirrhosis progression, which leads to the development of liver cancer and increased patient mortality. Despite the prevalence and severity of HD, no systematic treatment strategy for clinical management of the condition has been proposed by any research or institutions to date. This paper conducts an extensive review of recent advancements in HD treatment in the quest for an effective treatment approach that may improve the overall prognosis of HD.

What are the perceptions and concerns of people living with diabetes and National Health Service staff around the potential implementation of AI-assisted screening for diabetic eye disease? Development and validation of a survey for use in a secondary care screening setting

INTRODUCTION

The English National Health Service (NHS) Diabetic Eye Screening Programme (DESP) performs around 2.3 million eye screening appointments annually, generating approximately 13 million retinal images that are graded by humans for the presence or severity of diabetic retinopathy. Previous research has shown that automated retinal image analysis systems, including artificial intelligence (AI), can identify images with no disease from those with diabetic retinopathy as safely and effectively as human graders, and could significantly reduce the workload for human graders. Some algorithms can also determine the level of severity of the retinopathy with similar performance to humans. There is a need to examine perceptions and concerns surrounding AI-assisted eye-screening among people living with diabetes and NHS staff, if AI was to be introduced into the DESP, to identify factors that may influence acceptance of this technology.

METHODS AND ANALYSIS

People living with diabetes and staff from the North East London (NEL) NHS DESP were invited to participate in two respective focus groups to codesign two online surveys exploring their perceptions and concerns around the potential introduction of AI-assisted screening.Focus group participants were representative of the local population in terms of ages and ethnicity. Participants' feedback was taken into consideration to update surveys which were circulated for further feedback. Surveys will be piloted at the NEL DESP and followed by semistructured interviews to assess accessibility, usability and to validate the surveys.Validated surveys will be distributed by other NHS DESP sites, and also via patient groups on social media, relevant charities and the British Association of Retinal Screeners. Post-survey evaluative interviews will be undertaken among those who consent to participate in further research.

ETHICS AND DISSEMINATION

Ethical approval has been obtained by the NHS Research Ethics Committee (IRAS ID: 316631). Survey results will be shared and discussed with focus groups to facilitate preparation of findings for publication and to inform codesign of outreach activities to address concerns and perceptions identified.

Optimization of fluorescent substrates for ADAM17 and their utility in the detection of diabetes

ADAM17 (a disintegrin and metalloproteinase 17) is a sheddase that releases various types of membrane-associated proteins, including adhesive molecules, cytokines and their receptors, and inflammatory mediators. Evidence suggests that the enzyme is involved in the proteolytic cleavage of antiaging transmembrane protein Klotho (KL). What is more, reduced serum and urinary KL levels are observed in the early stages of chronic kidney disease. This study aimed to optimise the ADAM17 specific and selective fluorescent substrates. Then, the obtained substrate was used to detect the enzyme in urine samples of patients diagnosed with diabetes. It turned out that in all cases we were able to detect proteolytic activity, which was the opposite of the healthy samples.

Optimizing diabetes classification with a machine learning-based framework

BACKGROUND

Diabetes is a metabolic disorder usually caused by insufficient secretion of insulin from the pancreas or insensitivity of cells to insulin, resulting in long-term elevated blood sugar levels in patients. Patients usually present with frequent urination, thirst, and hunger. If left untreated, it can lead to various complications that can affect essential organs and even endanger life. Therefore, developing an intelligent diagnosis framework for diabetes is necessary.

RESULT

This paper proposes a machine learning-based diabetes classification framework machine learning optimized GAN. The framework encompasses several methodological approaches to address the diverse challenges encountered during the analysis. These approaches encompass the implementation of the mean and median joint filling method for handling missing values, the application of the cap method for outlier processing, and the utilization of SMOTEENN to mitigate sample imbalance. Additionally, the framework incorporates the employment of the proposed Diabetes Classification Model based on Generative Adversarial Network and employs logistic regression for detailed feature analysis. The effectiveness of the framework is evaluated using both the PIMA dataset and the diabetes dataset obtained from the GEO database. The experimental findings showcase our model achieved exceptional results, including a binary classification accuracy of 96.27%, tertiary classification accuracy of 99.31%, precision and f1 score of 0.9698, recall of 0.9698, and an AUC of 0.9702.

CONCLUSION

The experimental results show that the framework proposed in this paper can accurately classify diabetes and provide new ideas for intelligent diagnosis of diabetes.

Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians

Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field.

Prevalence, patterns and associated risk factors for dyslipidaemia among individuals attending the diabetes clinic at a tertiary hospital in Central Malawi

BACKGROUND

Dyslipidaemia among individuals with diabetes is a significant modifiable risk factor for atherosclerotic cardiovascular diseases (ASCVDs). ASCVDs are a major cause of mortality and morbidity globally, especially in people with diabetes. In Malawi, limited data exist on the prevalence and biochemical characteristics of diabetic dyslipidaemia. This study investigated the prevalence and biochemical characteristics of dyslipidaemia in individuals attending the diabetes clinic at Kamuzu Central Hospital, the largest tertiary referral hospital in Central Malawi.

METHODS

Using a cross-sectional design, sociodemographic, medical and anthropometric data were collected from 391 adult participants who were enrolled in the study. Blood samples were analysed for glycosylated haemoglobin (HBA1c) and fasting lipid profiles. The prevalence of dyslipidaemia was calculated, and the biochemical characteristics of the dyslipidaemia were defined. The associations between dyslipidaemia and risk factors such as sociodemographic characteristics, obesity, and HBA1c levels were evaluated using logistic regression analysis.

RESULTS

Prevalence of dyslipidaemia was observed in 71% of the participants, and elevated low-density lipoprotein cholesterol was the most frequent lipid abnormality among the study participants. None of the participants were receiving any lipid-lowering therapy. On bivariate analysis, dyslipidemia was positively associated with female sex [OR 1.65 (95% CI 1.05- 2.58); p = 0.09], age ≥ 30 years [OR 3.60 (95% CI 1.17-7.68); p = 0.001] and overweight and obesity [OR 2.11 (95% CI 1.33-3.34); p = 0.002]. On multivariate analysis, being overweight or obese was an independent predictor of dyslipidaemia [AOR 1.8;(95% CI 1.15- 3.37); p = 0.04].

CONCLUSION

Dyslipidaemia was highly prevalent among individuals with diabetes in this study, and elevated low-density lipoprotein cholesterol was the most frequent lipid abnormality. Overweight and obesity were also highly prevalent and positively predicted dyslipidaemia. This study highlights the importance of appropriately addressing dyslipidaemia, overweight and obesity among individuals with diabetes in Malawi and other similar settings in Africa as one of the significant ways of reducing the risk of ASCVDs among this population.

A deep transfer learning approach for COVID-19 detection and exploring a sense of belonging with Diabetes

COVID-19 is an epidemic disease that results in death and significantly affects the older adult and those afflicted with chronic medical conditions. Diabetes medication and high blood glucose levels are significant predictors of COVID-19-related death or disease severity. Diabetic individuals, particularly those with preexisting comorbidities or geriatric patients, are at a higher risk of COVID-19 infection, including hospitalization, ICU admission, and death, than those without Diabetes. Everyone's lives have been significantly changed due to the COVID-19 outbreak. Identifying patients infected with COVID-19 in a timely manner is critical to overcoming this challenge. The Real-Time Polymerase Chain Reaction (RT-PCR) diagnostic assay is currently the gold standard for COVID-19 detection. However, RT-PCR is a time-consuming and costly technique requiring a lab kit that is difficult to get in crises and epidemics. This work suggests the CIDICXR-Net50 model, a ResNet-50-based Transfer Learning (TL) method for COVID-19 detection via Chest X-ray (CXR) image classification. The presented model is developed by substituting the final ResNet-50 classifier layer with a new classification head. The model is trained on 3,923 chest X-ray images comprising a substantial dataset of 1,360 viral pneumonia, 1,363 normal, and 1,200 COVID-19 CXR images. The proposed model's performance is evaluated in contrast to the results of six other innovative pre-trained models. The proposed CIDICXR-Net50 model attained 99.11% accuracy on the provided dataset while maintaining 99.15% precision and recall. This study also explores potential relationships between COVID-19 and Diabetes.

Rapidly adapted community health strategies to prevent treatment interruption and improve COVID-19 detection for Syrian refugees and the host population with hypertension and diabetes in Jordan

BACKGROUND

We evaluated community health volunteer (CHV) strategies to prevent non-communicable disease (NCD) care disruption and promote coronavirus disease 2019 (COVID-19) detection among Syrian refugees and vulnerable Jordanians, as the pandemic started.

METHODS

Alongside medication delivery, CHVs called patients monthly to assess stockouts and adherence, provide self-management and psychosocial support, and screen and refer for complications and COVID-19 testing. Cohort analysis was undertaken of stockouts, adherence, complications and suspected COVID-19. Multivariable models of disease control assessed predictors and non-inferiority of the strategy pre-/post-initiation. Cost-efficiency and patient/staff interviews assessed implementation.

RESULTS

Overall, 1119 patients were monitored over 8 mo. The mean monthly proportion of stockouts was 4.9%. The monthly proportion non-adherent (past 5/30 d) remained below 5%; 204 (18.1%) patients had complications, with 63 requiring secondary care. Mean systolic blood pressure and random blood glucose remained stable. For hypertensive disease control, age 41-65 y (OR 0.46, 95% CI 0.2 to 0.78) and with diabetes (OR 0.73, 95% CI 0.54 to 0.98) had decreased odds, and with baseline control had increased odds (OR 3.08, 95% CI 2.31 to 4.13). Cumulative suspected COVID-19 incidence (2.3/1000 population) was suggestive of ongoing transmission. While cost-efficient (108 US${\$}$/patient/year), funding secondary care was challenging.

CONCLUSIONS

During multiple crises, CHVs prevented care disruption and reinforced COVID-19 detection.