Detection Of Diabetes Mellitus Research Articles

Survey on electrocardiography signal analysis and diabetes mellitus: unraveling the complexities and complications

Electrocardiography (ECG) is crucial in the medical field to assess cardiovascular diseases. ECG signal generates information, i.e., QRS complexes that imply the cardiac health of the human body. It is depicted in the form of a graph with voltage versus time interval. A distorted, inverted, lagged, small waveform implies an abnormality in a cardiac system. This study highlights the generation of an ECG signal, QRS complexes undertoned towards different diseases, event detection, and signal processing methods. It has become crucial to highlight the possibilities and advances that can be derived from an ECG signal. Throughout this study, an instance of diabetes mellitus (DM) is considered for creating concrete awareness and understanding of an ECG signal in DM. This study focuses on finding the correlation between ECG and DM. Detection of DM from ECG signal is also studied. The findings of this survey paper conclude that the correlation between DM individuals with cardiovascular complications has autonomic neuropathy, which may lead to myocardial infarction. It is also found that the QRS complex and its abnormalities are not specific to complications in DM. However, non-invasive detection of diabetes through ECG signals demonstrates future research potential.

An optimized diabetes mellitus detection model for improved prediction of accuracy and clinical decision-making

Diabetes Mellitus (DM) is an enduring metabolic illness that disturbs many individuals globally. This study addresses the global impact of Diabetes Mellitus (DM) and emphasizes the critical role of accurate DM detection in early diagnosis, effective treatment, and prevention of complications. The research introduces an optimized DM detection model, the GBM-DRU (Gradient Boosting Machine - Data Reduction Unit) network, which integrates feature engineering and ensemble learning techniques to enhance prediction accuracy and support clinical decision-making. The GBM-DRU network combines the powerful gradient boosting machine algorithm with a data reduction unit (DRU) for efficient feature selection, reducing dimensionality and improving computational efficiency. Feature engineering enhances discriminatory power, while ensemble learning methods, including bagging and boosting, improve overall model performance. Rigorous experiments on a comprehensive dataset of DM patients demonstrate that the proposed approach outperforms existing models in terms of accuracy, sensitivity, specificity, and AUC-ROC. The optimized model provides valuable insights into feature importance, aiding clinical decision-making and deepening the understanding of DM risk factors. Therefore, the GBM-DRU network, utilizing feature engineering and ensemble learning, presents a viable approach to precise diagnosis of diabetes mellitus, with favorable implications for patient outcomes, disease control, and public health campaigns. The improved prediction accuracy, feature interpretability, and clinical decision support capabilities of the model may have a beneficial effect on public health campaigns, disease management, and patient outcomes.

Prediction model for diabetes mellitus using machine learning algorithms for enhanced diagnosis and prognosis in healthcare

<p>Diabetes mellitus (DM) affects the hormone insulin, which causes improper glucose metabolism and raises the body’s blood sugar levels. With 4.2 million fatalities in 2019, DM is one of the top 10 global causes of mortality. Early detection of DM will aid in its treatment and avert complications. There must be a quick and simple technique to diagnose it. Such diseases can be managed and human lives can be saved with early diagnosis. Smart prediction techniques like Machine Learning (ML) have produced encouraging outcomes in predictive classifications. There has been a lot of interest in ML-based decision-support platforms for the prediction of chronic illnesses to provide improved diagnosis and prognosis help to medical professionals and the general population. By building predictive models using diagnostic medical datasets gathered from DM patients, ML algorithms efficiently extract knowledge that helps predict diabetic individuals. The association between DM and a healthy lifestyle is used in the model. In this study, the NHANES (National Health and Nutrition Examination Survey) data set is utilized, and five ML methods such as Artificial Neural Networks (ANN), CATBoost, XGBoost, XGBoost-histogram, and Light GBM to predict DM. The results of the experiment demonstrate that the XGB-h model outperformed other ML methods regarding area under the receiver operating characteristic curve (AUC-ROC), and accuracy. The most effective XGB-h framework can be used in a mobile app and a website to rapidly forecast DM. Real-time prediction using details delivered by the model at runtime can be developed as a whole bundle as a product. Clinicians can quickly determine who is likely to get diabetes using the proposed strategy, which will facilitate prompt intervention and caring.</p>

Health system costs of providing outpatient care for diabetes in people with TB in the Philippines

<sec><title>BACKGROUND</title>Diabetes mellitus (DM) is a known risk factor for active TB. A key activity in the Philippines is to integrate TB services with other disease programmes, with a target of DM screening in 90% of TB cases. However, costs of providing DM outpatient services for TB patients are not well known.</sec><sec><title>METHODS</title>We estimated the costs of providing integrated DM outpatient services within TB services from the health system perspective. Resources for outpatient DM services were valued using the bottom-up approach for capital goods, staff time and consumables. Resource quantities were obtained by interviewing 60 healthcare professionals in 11 health facilities in the Philippines.</sec><sec><title>RESULTS</title>The mean cost per service ranged from USD0.53 for DM risk assessment to USD23.72 for oral glucose tolerance test. The cost per case detected for different algorithms varied from USD17.43 to USD80.81. The monthly cost per patient was estimated at USD8.95 to USD12.36.</sec><sec><title>CONCLUSION</title>Our study provides the first estimates of costs for providing integrated DM outpatient services and TB care in a low- and middle-income country. The costs of DM detection in TB patients suggests that it may be useful to further investigate the cost-effectiveness and affordability of service delivery.</sec>

The combination between praziquantel treatment and fatty liver disease in relation to diabetes mellitus in Northeast of Thailand

Diabetes mellitus (DM) is a growing global public health concern, including Thailand. Prior research has established an association between fatty liver disease (FLD) and DM. Moreover, the distribution of DM was also found in liver fluke endemic areas where praziquantel (PZQ) was used prevalently. However, limited information exists regarding factors associated with DM, specifically in FLD patients undergoing PZQ treatment. Our study aimed to evaluate the association between the combination of PZQ treatment and FLD diagnosis, and DM in Northeast Thailand. We conducted the study among participants in northeastern Thailand with self-reported DM diagnosis and PZQ treatment history using health questionnaires. Additionally, FLD diagnosis was confirmed through ultrasonography from the Cholangiocarcinoma Screening Program data collected from 2013-2022. We employed multilevel mixed-effect logistic regression to assess the association between the combination of PZQ treatments and FLD diagnosis, and DM, represented as adjusted odds ratios (AOR) and their 95% confidence intervals (CI). Of 756,431 participants, the overall DM prevalence was 7%. Notably, 10.45% of individuals with FLD+ and 8.23% with PZQ+ were found to have DM. Specifically, the prevalence of DM in individuals with PZQ+ and FLD-, PZQ- and FLD+, and PZQ+ and FLD+ combinations were 7.14%, 10%, and 11.81%, respectively. In comparison to PZQ- and FLD-, participants with PZQ+ and FLD-, PZQ- and FLD+, and PZQ+ and FLD+ were associated with DM (AOR = 1.27; 95% CI: 1.24-1.30, AOR = 1.8; 95% CI: 1.76-1.85, and AOR = 2.19; 95% CI: 2.12-2.27, respectively). In conclusion, the combination of PZQ and FLD plays a crucial role in the association with DM, particularly in individuals with PZQ+ and FLD+. Our findings emphasize the importance of DM screening, with special attention to individuals with a combination of PZQ treatment and FLD diagnosis, in order to promote early detection and management of DM in Northeast Thailand.

Serum-based ATR-FTIR spectroscopy combined with multivariate analysis for the diagnosis of pre-diabetes and diabetes.

Diabetes mellitus (DM) is a metabolic disease with an increasing prevalence that is causing worldwide concern. The pre-diabetes stage is the only reversible stage in the patho-physiological process towards DM. Due to the limitations of traditional methods, the diagnosis and detection of DM and pre-diabetes are complicated, expensive, and time-consuming. Therefore, it would be of great benefit to develop a simple, rapid and inexpensive diagnostic test. Herein, the infrared (IR) spectra of serum samples from 111 DM patients, 111 pre-diabetes patients and 333 healthy volunteers were collected using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy and this was combined with the multivariate analysis of principal component analysis linear discriminant analysis (PCA-LDA) to develop a discriminant model to verify the diagnostic potential of this approach. The study found that the accuracy of the test model established by ATR-FTIR spectroscopy combined with PCA-LDA was 97%, and the sensitivity and specificity were 100% and 100% in the control group, 94% and 98% in the pre-diabetes group, and 91% and 98% in the DM group, respectively. This indicates that this method can effectively diagnose DM and pre-diabetes, which has far-reaching clinical significance.

EFEKTIFAS EDUKASI DALAM PENCEGAHAN DIABETES MELITUS DI DESA GENENG POLOKARTO SUKOHARJO

Background: Diabetes Mellitus (DM) is a metabolic syndrome due to impaired quantity and function of insulin which causes high blood glucose levels (hyperglycemia). Early prevention needs to be done for early detection of chronic diseases such as DM which have good risk factors due to unhealthy lifestyles and heredity. Early detection of DM can be done through screening by checking blood sugar levels at the time. In addition, success in preventing the onset of DM and controlling blood sugar levels in people with DM depends on people's behavior. Changes in behavior towards a healthy lifestyle in the context of correct DM prevention and control will be realized if the community has sufficient knowledge about DM and its prevention. The purpose of this activity is to add health information to the public about education on the prevention of Diabetes Mellitus. The method is carried out by lectures, discussions and questions and answers while the media uses leaflets. The education delivered increases public knowledge about diabetes risk prevention education, so that people can independently prevent the risk of diabetes mellitus which is a degenerative disease and is predicted to increase every year.

Prevalence and risk factors of diabetes foot ulcers in Kano, northwestern Nigeria

BackgroundFoot complications account for more hospital admissions than any other diabetes mellitus (DM) complications with adverse outcomes being foot ulcers and amputation.ObjectiveTo determine the prevalence and risk factors of diabetic foot ulcers in Kano, Northwestern Nigeria.MethodsA descriptive cross-sectional study was conducted in the diabetes outpatient clinics and medical and surgical wards of two hospitals in Kano, Nigeria. Data were collected on socio-demographic characteristics, type, and duration of DM. The study subjects were assessed for the presence of and risk factors for foot ulcers.ResultsWe recruited 394 patients with DM (163 males and 231 females) with a mean (SD) age and duration of DM of 50.8 ± 12.5 years and 7.72 ± 6.65 years respectively. Type 2 DM was present in 95% of the study subjects. Diabetic foot ulcer (DFU) was present in 57 (14.5%) of the patients. Risk factors associated with DFU assessed using univariate analysis were older age, longer duration of DM, presence of peripheral neuropathy (PN), peripheral arterial disease (PAD), diabetic retinopathy, nephropathy, foot deformities, previous DFU, and poor glycemic control. The independent determinants of DFU were previous DFU, foot deformities, retinopathy, PN, PAD, and poor glycemic control.ConclusionDFU can be found in our setting and the predominant risk factors for DFU are common and remain unchanged in our environment. This study, therefore, buttresses the effect of early detection and treatment of DM in preventing the complications that arise from the disease.

Presence of hypertension might pose a potential pitfall in detection of diabetes mellitus non-invasively using the second derivative of photoplethysmography

ABSTRACT Indices derived from photoplethysmography (PPG) have shown promising results as non-invasive digital biomarkers for the detection of diabetes mellitus (DM). Considering the mutual endothelial insult leading to similar undesirable peripheral hemodynamic perturbations, hypertension (HT) may blunt this classification performance. Second derivative PPG (SD-PPG) indices were derived from the second derivative of the PPG signal. The variables of interest were the previously described peaks of the initial positive (a), early negative (b), re-increasing (c), late re-decreasing (d), diastolic positive (e) and negative (f) waves and the ratios between them. Patients were classified according to their type 2 DM and hypertension phenotypes. SD-PPG indices were compared between diseased subgroups, healthy controls and also dichotomous classification performance was evaluated. Two SDPPG indices, b/a ratio and the vascular ageing index (VAI = (b-c-d-e)/a) responded to isolated DM type 2 (n = 29) amongst healthy subjects (n = 106) (area under the curve (AUC) = 0.629 p = 0.034 and 0.631 p = 0.031 20 respectively). However, the classification performance became insignificant with the inclusion of HT patients (n=30). (p = 0.839 vs. p = 0.656). These results suggest that the coexistence of HT and DM may hinder the use of SD-PPG for noninvasive DM detection.

Awareness of diabectic retinopathy among diabetics in urban population of Hyderabad, South India

Diabetes Mellitus (DM) is a significant public health burden in India. Focus on Diabetes and its complications is important to prevent mortality and morbidity due to this disease. Lack of awareness is one of the main barrier for detection of DM and its complications. Diabetic retinopathy (DR) is one of the most common complication of DM which causes of blindness and visual impairment. The purpose of this study was to assess knowledge and attitude of diabetic patients towards eye care and DR in urban south Indian population.Cross-sectional study was done on 537 patients with Type 2 Diabetes Mellitus. All patients underwent complete ocular examination including visual acuity, anterior segment and fundus examination using slit-lamp biomicroscope by attending Ophthalmologist. They were administered questionnaire to test their knowledge and attitude. Their answers were documented. Statistical analysis was done using SPSS software (IBM).Out of 537 patients (50.9%) patients knew that there was relationship between Diabetes and Retinopathy while (49.1%) patients were not aware of. 60.4% of patients were of the opinion that there was no need of regular screening if vision in both eyes are good and 39.6% of patients were of the opinion that they should undergo a routine ophthalmology screening every year despite of good vision in both the eyes. 69.6% of patients said that good control of DM might prevent DR. The level of knowledge of DR was found to be significantly associated with patients’ educational level (p=0.038). Patients with relatively higher educational level were more aware of DR occurring as a consequence of diabetes. Patients with longer duration of Diabetes had more knowledge on DR (p=0.001).In our study there was an association between educational qualification and knowledge regarding Diabetic retinopathy. (p value=0.04). This stresses on the importance of health education amongst rural and illiterate population.

Prevalence of Diabetes Mellitus among Patients with Tuberculosis and Its Associated Factors in Sana'a, Yemen, 2021.

Diabetes mellitus (DM) is one of tuberculosis' (TB) ending barriers. TB patients with DM are at a higher risk than non-diabetes patients to develop complication, relapse and die. In Yemen, information on TB-DM comorbidity is lacking. This study aimed to determine the prevalence and associated factors of diabetes among TB patients at the National Tuberculosis Center (NTC) in Sana'a. A facility-based cross-sectional study was conducted. All TB patients aged >15 years who attended the NTC from July to November 2021 were screened for DM. Socio-demographic and behavioral information were collected through face-to-face interviews using questionnaires. A total of 331 TB patients were enrolled, 53% were males, 58% aged <40 years, and 74% were newly diagnosed with TB. Overall, DM prevalence was 18%. Higher rates of DM were found among TB patients that were male (OR = 3.0; 95% CI; 1.4-6.7), ≥50 years of age (OR = 10.8; 95% CI; 4.3-27.3), and those with a family history of diabetes (OR = 3.4; 95% CI; 1.6-6.9). Almost one fifth of TB patients had DM. The early detection of DM through immediate screening after a TB diagnosis and periodically during the course of treatment is crucial for TB patients' optimal care. Dual diagnostics for reducing the dual burden of TB-DM comorbidity is recommended.

Prevalence and predictors of undiagnosed type 2 diabetes and pre-diabetes among adult Egyptians: a community-based survey

BackgroundThe global prevalence of abnormal glycemic level comprising diabetes mellitus (DM) and pre-diabetes (PDM) is rapidly increasing with special concern for the entity silent or undiagnosed diabetes; those unaware of their condition. Identification of people at risk became much easier with the use of risk charts than the traditional methods. The current study aimed to conduct a community-based screening for T2DM to estimate the prevalence of undiagnosed DM and to assess the AUSDRISK Arabic version as a predictive tool in an Egyptian context.MethodsA cross-sectional study was conducted among 719 Adults aging 18 years or more and not known to be diabetics through a population-based household survey. Each participant was interviewed to fill demographic and medical data as well as the AUSDRISK Arabic version risk score and undergo testing for fasting plasma glucose (FPG) and oral glucose tolerance test (OGTT).ResultsThe prevalence of DM and PDM were 5% and 21.7% respectively. The multivariate analysis revealed that age, being physically inactive, history of previous abnormal glycemic level and waist circumference were the predictors for having abnormal glycemic level among the studied participants. At cut off points ≥ 13 and ≥ 9, the AUSDRISK respectively discriminated DM [sensitivity (86.11%), specificity (73.35%), and area under the curve (AUC): 0.887, 95% CI: 0.824–0.950] and abnormal glycemic level [sensitivity (80.73%), specificity (58.06%), and AUC: 0.767, 95% CI: 0.727–0.807], p < 0.001.ConclusionsOvert DM just occupies the top of an iceberg, its unseen big population have undiagnosed DM, PDM or been at risk of T2DM because of sustained exposure to the influential risk factors. The AUSDRISK Arabic version was proved to be sensitive and specific tool to be used among Egyptians as a screening tool for the detection of DM or abnormal glycemic level. A prominent association has been demonstrated between AUSDRISK Arabic version score and the diabetic status.

Edukasi Deteksi Dini Diabetes Melitus Kader Puskesmas Pondok Kacang Timur Tangerang Selatan

The number of Diabetes Mellitus (DM) patients in the world is continuing to increase. Initially, in 1980, there were only 108 million patients, which increased to 422 million individuals in 2014. The number of incidences is increasing disproportionately in countries with middle and low incomes compared to high-income countries. Mortality caused by diabetes mellitus occurs in 20.9 people per 100,000 of the country's population. Several causes of the high incidence of mortality caused by DM, include delays in determining a diagnosis and non-adherence to treatment. Health center cadres are an extension of the puskesmas in reaching out to the community in the working area of each public health center. Based on this context, this activity aimed to educate health center cadres in regard to early detection of DM and complications related to DM in order that they can educate the public and motivate them to cooperate in the treatment of DM. After the education program was implemented, an increase in knowledge about DM was found among health center cadres. This finding was determined by looking at the mean score, which increased from 74.8 during the pretest to 84.4 during the post-test.

ExpACVO-Hybrid Deep learning: Exponential Anti Corona Virus Optimization enabled Hybrid Deep learning for tongue image segmentation towards diabetes mellitus detection

A metabolic disease known as diabetes mellitus (DM) is primarily brought on by an increase in blood sugar levels. On the other hand, DM and the complications it causes, such as diabetic Retinopathy (DR), will quickly emerge as one of the major health challenges of the twenty-first century. This indicates a huge economic burden on health-related authorities and governments. The detection of DM in the earlier stage can lead to early diagnosis and a considerable drop in mortality. Therefore, in order to detect DM at an early stage, an efficient detection system having the ability to detect DM is required. An effective classification method, named Exponential Anti Corona Virus Optimization (ExpACVO) is devised in this research work for Diabetes Mellitus (DM) detection using tongue images. Here, the UNet-Conditional Random Field-Recurrent Neural Network (UNet-CRF-RNN) is used to segment the images, and the proposed ExpACVO algorithm is used to train the UNet-CRF-RNN. Deep Q Network (DQN) classifier is used for DM detection, and the proposed ExpACVO is used for DQN training. The proposed ExpACVO algorithm is a newly created formula that combines Anti Corona Virus Optimization(ACVO) with Exponential Weighted Moving Average (EWMA). With maximum testing accuracy, sensitivity, and specificity values of 0.932, 0.950, and 0.914, respectively, the developed technique thus achieved improved performance.

Incidence and predictors of diabetes mellitus among severe COVID-19 patients in western Ethiopia: a retrospective cohort study

Background: Evidence reported a high occurrence of diabetes mellitus (DM) during the time of COVID-19. This study aimed to assess the incidence of DM and its predictors among severe COVID-19 patients admitted to the treatment centre of Wollega University Referral Hospital (WURH), western Ethiopia.Methods: A facility-based retrospective cohort study was conducted among severe COVID-19 patients diagnosed using the rRT-PCR from September 30, 2020, to June 10, 2021. EpiData version 3.2 was used for data entry, and STATA version 14 for analysis. A Cox proportional hazard regression analysis was used to determine factors associated with DM. A multivariable Cox regression model with 95% CI and adjusted hazard ratio (AHR) was used to identify a significant predictor of the incidence of DM at p-value < 0.05.Results: A total of 304 patient cards with complete data were included in the final analysis. The mean age of the participants was 43.3 (SD ± 16.9) years. Of the total 304 patients admitted with severe COVID-19, 14.5% were newly diagnosed with DM with an overall incidence rate of 13.7 per 1 000 person days’ observation (PDO). The median time to occurrence of DM was 11 days (95% CI 7, 13) days. Age > 41 years (AHR = 2.54, 95% CI 1.15, 5.57), living in urban (AHR = 2.49, 95% CI 1.12, 5.52) and loss of appetite (AHR = 2.24, 95% CI 1.16, 4.34) increased the hazard of DM incidence, while presenting to the health facility after two days of clinical manifestation (AHR = 0.49, 95% CI 0.23, 0.96) decreased the risk of developing DM.Conclusions: The incidence rate of DM among patients admitted with severe COVID-19 in the study area was found to be 13.7 per 1 000 person days’ observation. Higher age, urban residence, early presentation to a health facility and loss of appetite were independent predictors of DM incidence. Therefore, we recommend early detection of DM and frequent monitoring of blood glucose for patients diagnosed with COVID-19.

Multi-Feature Complementary Learning for Diabetes Mellitus Detection Using Pulse Signals.

Computational pulse diagnosis is a convenient, non-invasive, and effective Diabetes Mellitus (DM) detection technique. Generally, diverse pulse features are extracted from different views to represent pulse signals and then used for achieving the pulse diagnosis. However, current pulse-based DM detection methods only used one pulse feature for detection, ignoring the fact that diverse pulse features can be combined together to boost the diagnosis performance. To this end, we propose a novel Multi-Feature Complementary Learning (MFCL) model for DM detection. By designing feature-specific projections, multiple features are separately projected into a shared observation space and effectively fused into one vector. Besides, a mapping function is built to correlate the fused vectors to category labels to make the fused vectors suitable for classification. Inspired by the graph Laplacian matrix, which effectively preserves the correlations among samples from different categories, we integrate it in MFCL and design a discriminative prior to make the fused vectors sufficiently discriminative. Finally, an optimization algorithm is proposed to alternatively optimize the projection variables and then generate fused feature vectors. The proposed method reaches an accuracy of 92.85% in DM detection, outperforming state-of-the-art methods.

Magnitude of impaired fasting glucose and undiagnosed diabetic mellitus and associated risk factors among adults living in Woreta town, northwest Ethiopia: a community-based cross-sectional study, 2021

BackgroundImpaired fasting glucose (IFG) is an early warning system that provides prior information to prevent the future development of DM and diabetes-related problems, but early detection of DM is not practically applicable in Ethiopia. This study was aimed to assess the magnitude of impaired fasting glucose and undiagnosed diabetes mellitus (DM) and associated factors.MethodsA community-based, cross-sectional study was conducted from May to June 30, 2021. A structured interviewer-administered questionnaire was used to collect data. Anthropometric measurements were also recorded. A fasting blood sugar (FBS) test was assessed by samples taken early in the morning. Epi-Info 7.2.5.0 was used to enter data, which was then exported to SPSS 25 for analysis. To identify factors associated with IFG, logistics regression was used. The level of statistical significance was declared at p 0.05.ResultThree hundred and twenty-four (324) participants with a mean age of 43.76 ± 17.29 years were enrolled. The overall magnitude of impaired fasting glucose (IFG) and undiagnosed diabetes mellitus (DM) were 43.2% and 10.0%, respectively. Waist circumference (AOR: 1.72, 95% CI 1.23–3.14), hypertension (AOR: 3.48, 95% CI 1.35–8.89), family history of Diabetic mellitus (AOR: 2.34, 95% CI 1.37–5.79) and hypertriglyceridemia (AOR: 2.35, 95% CI 1.41–5.43) were found to be independently associated with impaired fasting glucose.ConclusionIndividuals who are overweight, hypertriglyceridemia, and are hypertensive should have regular checkups and community-based screening.

Clinical routines for diabetes screening and treatment in cardiac rehabilitation improves detection and treatment of diabetes mellitus in patients with myocardial infarction

Abstract Background Patients with myocardial infarction (MI) have a high prevalence of diabetes mellitus (DM). Prognosis in patients with MI and DM is substantially worse than for those without DM. An unacceptably high proportion of patients with DM remain both undiagnosed and undertreated despite follow-up at cardiac rehabilitation (CR) centres. Purpose Using nationwide survey and registry data to investigate associations of clinical routines for DM screening and treatment at CR centres in Sweden with detection and treatment of DM at one-year post-MI. Methods Data on CR clinical routines were derived from the Perfect-CR survey, evaluating structures and follow-up processes at CR centres in Sweden (n=78). The response rate was 100% and missing data was minimal. Clinical routines for DM screening and treatment during CR (exposures) included the following: 1) laboratory assessments of fasting glucose and/or HbA1c as a part of initial patient assessment by a nurse, 2) routine use of oral glucose tolerance test (OGTT), 3) joint case rounds with diabetologists, and 4) whether diabetes medication is adjusted by cardiologists. Patient baseline and outcome data was derived from the national quality registry SWEDEHEART (n=7549). Primary outcome was DM incidence at one-year post-MI. Secondary outcome was the proportion of patients receiving diabetes medication other than insulin (secondary outcome). The association between exposures (for each clinical routine and cumulatively [0–4 work routines]) and outcomes was estimated using unadjusted and adjusted logistic regression, adjusting for relevant covariates. Results Number (%) of CR centres applying each of the clinical routines is shown in Table 1. The most common routine applied was fasting glucose and/or HbA1c being routinely evaluated at initial patient assessment (n=48 (62%)), while the least common was CR centres having joint case rounds with diabetologists (n=7 (9%)). Twenty (26%) CR centres did not apply any of the clinical routines while 7 (9%) centres applied 3 or 4 routines. Unadjusted and adjusted odds ratios (OR) with 95% confidence intervals (CI) for incident DM are shown in Figure 1. Compared to not applying any routines, 1) applying one or more routines was positively associated higher DM incidence at one-year post-MI (p for trend in unadjusted and adjusted models &amp;lt;0.001. Figure 1), and 2) at centres where all four working routines were applied, the odds for patients being treated with diabetes medication was significantly higher (crude OR 2.37 [1.80–3.13], adjusted OR 1.78 [1.19–2.66]). Conclusion Applying structured clinical routines for DM screening and treatment within CR can improve detection and treatment of DM in patients with MI Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): The current study was supported by The Swedish Research Council for Health, Working Life and Welfare (FORTE, grant number 2019-00365); The Swedish Heart and Lung Association (grant number 20190431); The Swedish Heart and Lung Patient Organization; The Swedish Cardiology Society; The faculty of Medicine, Lund University, Sweden; Astra Zeneca; and Amgen.

Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer's disease.

BackgroundRecent research has investigated the connection between Diabetes Mellitus (DM) and Alzheimer’s Disease (AD). Insulin resistance plays a crucial role in this interaction. Studies have focused on dysregulated proteins to disrupt this connection. Non-coding RNAs (ncRNAs), on the other hand, play an important role in the development of many diseases. They encode the majority of the human genome and regulate gene expression through a variety of mechanisms. Consequently, identifying significant ncRNAs and utilizing them as biomarkers could facilitate the early detection of this cross-talk. On the other hand, computational-based methods may help to understand the possible relationships between different molecules and conduct future wet laboratory experiments.Materials and methodsIn this study, we retrieved Genome-Wide Association Study (GWAS, 2008) results from the United Kingdom Biobank database using the keywords “Alzheimer’s” and “Diabetes Mellitus.” After excluding low confidence variants, statistical analysis was performed, and adjusted p-values were determined. Using the Linkage Disequilibrium method, 127 significant shared Single Nucleotide Polymorphism (SNP) were chosen and the SNP-SNP interaction network was built. From this network, dense subgraphs were extracted as signatures. By mapping each signature to the reference genome, genes associated with the selected SNPs were retrieved. Then, protein-microRNA (miRNA) and miRNA-long non-coding RNA (lncRNA) bipartite networks were built and significant ncRNAs were extracted. After the validation process, by applying the scoring function, the final protein-miRNA-lncRNA tripartite network was constructed, and significant miRNAs and lncRNAs were identified.ResultsHsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-423-5p, and hsa-miR-3184-5p, the four most significant miRNAs, as well as NEAT1, XIST, and KCNQ1OT1, the three most important lncRNAs, and their interacting proteins in the final tripartite network, have been proposed as new candidate biomarkers in the cross-talk between DM and AD. The literature review also validates the obtained ncRNAs. In addition, miRNA/lncRNA pairs; hsa-miR-124-3p/KCNQ1OT1, hsa-miR-124-3p/NEAT1, and hsa-miR-124-3p/XIST, all expressed in the brain, and their interacting proteins in our final network are suggested for future research investigation.ConclusionThis study identified 127 shared SNPs, 7 proteins, 15 miRNAs, and 11 lncRNAs involved in the cross-talk between DM and AD. Different network analysis and scoring function suggested the most significant miRNAs and lncRNAs as potential candidate biomarkers for wet laboratory experiments. Considering these candidate biomarkers may help in the early detection of DM and AD co-occurrence.

FTIR spectroscopy as a point of care diagnostic tool for diabetes and periodontitis: A saliva analysis approach

Diabetes mellitus (DM) is a general term for heterogeneous metabolic disorders whose main characteristic is chronic hyperglycemia. Considering that conventional diagnostic methods are currently unable of early DM detection and the number of diabetic patients has been increasing worldwide, there is a clinical need for novel diagnostic approaches. Fourier-transform infrared (FT-IR) spectroscopy technique an alternative non-invasive diagnostic method for real-time evaluation of biofluids such as saliva. This study aims evaluate the feasibility of diagnosing diabetes and periodontitis through saliva samples based on their FT-IR spectra. Our first collection and spectral analysis of samples was a pilot study and comprised a total of 23 patients, 2 healthy, 9 with diabetes and 12 with diabetes and periodontitis. By using weighted KNN as classifier, we have obtained an area under the Receiver Operating Characteristic curve of 0.92 and 0.95 when considering the diabetes or diabetes + periodontitis groups as positive groups, respectively.