Early Diagnosis Of Diabetes Mellitus Research Articles

Nuclear Changes in Controlled and Uncontrolled Type-2 Diabetic Patients In Buccal Exfoliative Cytology Smears Using Pap And Giemsa Staining – A Comparative Study

Background: Diabetes is a third leading cause of mortality and morbidity in the world. Oral exfoliative cytology may be a more appropriate adjunctive diagnostic tool in conditions like diabetes mellitus, where the invasive techniques lose viability. Exfoliative cytology helps to detect cellular aging, cell atrophy, alterations in nucleus and cytoplasm, binucleation and occasional karyorrhexis in type-2diabetic patients which proves to be a valuable tool for early diagnosis of diabetes.  The purpose of this research was to develop a new method for an early diagnosis of diabetes mellitus by exfoliative cytology using PAP stain and GIEMSA stains which is a nuclear specific stain. The aim of the study was to compare the exfoliative cytology of oral mucosa in controlled and uncontrolled type-2 diabetic patients using PAP stain and GIEMSA stain detect the nuclear and cytoplasmic changes. Materials And Methods: The smears were obtained from buccal mucosa of the mouth in total of 25 individuals out of which 10 are controlled (<120 mg/dl) and 10 are uncontrolled (>130 md/dl) diabetic patients and 5 normal individuals. These smear samples was obtained from the clinical lab of Saveetha Dental College. Results: The nuclear changes include nuclear bud, binucleate/micronucleate, karyolysis, increased nuclear cytoplasmic ratio & hyperchromatism. The binucleated cells were found to have higher differences between controlled & uncontrolled whereas karyorrhexis showed no significant changes between controlled & uncontrolled diabetes. Conclusion: This is a preliminary study conducted to analyse the potential interaction between the nuclear changes and the amount of genetic damage caused in diabetes. However, our result clearly demonstrated that  uncontrolled diabetic patients have significantly more genetic damage (in terms of MN frequency) compared to controlled diabetic patients. This indicates than MN may be a useful constituent in a panel of biomarkers for the risk of diabetes.

HbA1c Levels in Diabetes Mellitus Screening and Diagnosis

Diabetes Mellitus is a metabolic disorder characterized by increased blood sugar due to decreased insulin secretion by pancreatic beta cells and impaired insulin function (insulin resistance). By measuring glycohemoglobin (HbA1c) you can find out what percentage of the red blood cell dye (hemoglobin) contains sugar. To analyze HbA1c levels in screening and early diagnosis of diabetes mellitus. Observational descriptive research design. The population in this study were all type 2 DM sufferers at Bangil Pasuruan Regional Hospital, totaling 79 respondents. The sample consisted of 47 respondents taken using a purposive sampling technique. The research instrument was an examination of HbA1c levels and an observation sheet. With controlled HbA1c levels, all of them have a normal BMI, eat a regular diet regularly take insulin medication, and adopt more regular exercise habits. Meanwhile, respondents with uncontrolled HbA1c had a BMI of underweight, normal, overweight, and most did not exercise regularly and did not regularly take antidiabetic drugs.

A robust activatable two-photon fluorescent probe for endogenous formaldehyde biomarker visualization diagnosis and evaluation of diabetes mellitus

Diabetes mellitus and its complications are one of the largest healthcare burdens in the world and are increasing every year. However, the lack of effective biomarkers and non-invasive real-time monitoring tools remains a great challenge for the early diagnosis of diabetes mellitus. Endogenous formaldehyde (FA) represents a key reactive carbonyl species in biological systems, and altered metabolism and functions of FA have been closely related to the pathogenesis and maintenance of diabetes. Among various noninvasive biomedical imaging techniques, the identification-responsive fluorescence (FL) imaging could greatly benefit the comprehensive multi-scale assessment of some diseases such as diabetes. Herein, we have designed a robust activatable two-photon probe DM-FA for the first highly selective monitoring of fluctuations in FA levels during diabetes mellitus. Through the density functional theory (DFT) theoretical calculations, we elucidated the rationality of the activatable fluorescent probe DM-FA turning on the FL before and after the reaction with FA. In addition, DM-FA has excellent high selectivity, high growth factor and good photostability in the process of recognizing FA. Due to the brilliant two-photon and one-photon FL imaging capabilities of DM-FA, it has been successfully used to visualize of exogenous and endogenous FA in cells and mice. Remarkably, as a powerful FL imaging visualization tool, DM-FA was introduced for the first time to visually diagnose and explore diabetes through the fluctuation of FA content. The successful application of DM-FA in two-photon and one-photon FL imaging experiments found elevated FA levels in high glucose-stimulated diabetic cell models. We successfully visualized upregulation of FA levels in diabetic mice and decreased of FA levels in diabetic mice scavenged by NaHSO3 from multiple perspectives using multiple imaging modalities. This work may provide a novel strategy for the initial diagnosis of diabetes mellitus and the evaluation of the efficacy of drug therapy for treating diabetes mellitus, which will likely have a positive impact on clinical medicine.

Early diagnosis of diabetes mellitus using data mining and machine learning techniques

The remarkable developments in biotechnology as well as the health sciences have resulted in the production of an enormous amount of data, including high-throughput screening genomics information and clinical information obtained through extensive electronic health records (EHRs). The application of data mining and machine learning techniques in the biosciences is today more vital than ever to achieving this objective as attempts are made to intelligently translate all readily available data into knowledge. Diabetes mellitus (DM), a group of metabolic disorders, is well known to have a serious detrimental effect on population lives all over the world. Large-scale research into all aspects of diabetic has resulted in the production of enormous amounts of data (detection, etiopathophysiology, therapy, etc.). The goal of the current study is to conduct a thorough examination of the use of machine learning, data mining methods and tools in the field of diabetes research, with the first classification making an appearance to be the most popular. These applications relate to a Statistical model and Diagnosis, b) Diabetic Complications, c) Multiple genes Background and Environment, and e) Free Healthcare and Management. Numerous machine learning algorithms were applied. 85% of the methods used were supervised learning approaches, whereas 15% were uncontrolled ones, including association rules. Developed on improved support vector machines, the most successful and widely used algorithm (SVM). Medical datasets were predominantly used in terms of data kind.

Diagnostic accuracy of circular RNA for diabetes Mellitus: a systematic review and diagnostic Meta-analysis

BackgroundThis study aimed to investigate the pooled diagnostic ability of circular RNA (circRNA) molecules for diabetes mellitus.MethodsWe searched PubMed, Scopus, and Web of Science for relevant studies. A total of 2070 participants, including 775 diabetic patients and 1295 healthy individuals, from five studies were included in this meta-analysis. True positive, true negative, false positive, and false negative data were extracted to calculate pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristics curve. The Deeks’ funnel plot was applied for publication bias assessment, Cochran’s Q test and I2 index were applied for inter-study heterogeneity assessment. Besides, a subgroup analysis was performed for determining the source of heterogeneity between studies. P value < 0.05 was considered significance. All analysis were done by STATA version 14.ResultsCircRNA presented a sensitivity of 76% (95% confidence interval [95%CI]: 66-84%), specificity of 77% (95%CI: 58-89%), positive LR of 3.25 (95%CI: 1.69–6.23), negative LR of 0.31 (95%CI: 0.21–0.46), DOR of 10.41 (95%CI: 4.26–25.41), and AUC of 0.82 (95%CI: 0.79–0.85) for diabetes mellitus detection. More specifically, hsa_circ_0054633 showed a sensitivity of 67% (95%CI: 53-81%) and a specificity of 82% (95%CI: 63-100%).ConclusionCircRNAs show highly accurate diagnostic capability for type 2 diabetes mellitus and gestational diabetes mellitus. High sensitivity of circRNAs introduces them as potential noninvasive biomarkers for early diagnosis of diabetes mellitus and their high specificity introduces them as potential therapeutic targets by regulation of their expression.

A hybrid super ensemble learning model for the early-stage prediction of diabetes risk

Diabetes mellitus has become a rapidly growing chronic health problem worldwide. There has been a noticeable increase in diabetes cases in the last two decades. Recent advances in ensemble machine learning methods play an important role in the early detection of diabetes mellitus. These methods are both faster and less costly than traditional methods. This study aims to propose a new super ensemble learning model to enable an early diagnosis of diabetes mellitus. Super learner is a cross-validation-based approach that makes better predictions by combining prediction results of more than one machine learning algorithm. The proposed super learner model was created with four base-learners (logistic regression, decision tree, random forest, gradient boosting) and a meta learner (support vector machines) as a result of a case study. Three different dataset were used to measure the robustness of the proposed model. Chi-square was determined as an optimal feature selection technique from five different techniques, and also hyper-parameter settings were made with GridSearch. Finally, the proposed new super learner model achieved to obtain the best accuracy results in the detection of Diabetes mellitus compared to the base-learners for the early-stage diabetes risk prediction (99.6%), PIMA (92%), and diabetes 130-US hospitals (98%) dataset, respectively. This study revealed that super learner algorithms can be effectively used in the detection of diabetes mellitus. Also, obtaining of the high and convincing statistical scores shows the robustness of the proposed super learner model.

Non-Invasive In Vivo Estimation of HbA1c Using Monte Carlo Photon Propagation Simulation: Application of Tissue-Segmented 3D MRI Stacks of the Fingertip and Wrist for Wearable Systems

The early diagnosis of diabetes mellitus in normal people or maintaining stable blood sugar concentrations in diabetic patients requires frequent monitoring of the blood sugar levels. However, regular monitoring of the sugar levels is problematic owing to the pain and inconvenience associated with pricking the fingertip or using minimally invasive patches. In this study, we devise a noninvasive method to estimate the percentage of the in vivo glycated hemoglobin (HbA1c) values from Monte Carlo photon propagation simulations, based on models of the wrist using 3D magnetic resonance (MR) image data. The MR image slices are first segmented for several different tissue types, and the proposed Monte Carlo photon propagation system with complex composite tissue support is then used to derive several models for the fingertip and wrist sections with different wavelengths of light sources and photodetector arrangements. The Pearson r values for the estimated percent HbA1c values are 0.94 and 0.96 for the fingertip transmission- and reflection-type measurements, respectively. This is found to be the best among the related studies. Furthermore, a single-detector multiple-source arrangement resulted in a Pearson r value of 0.97 for the wrist. The Bland-Altman bias values were found to be -0.003 ± 0.36, 0.01 ± 0.25, and 0.01 ± 0.21, for the two fingertip and wrist models, respectively, which conform to the standards of the current state-of-the-art invasive point-of-care devices. The implementation of these algorithms will be a suitable alternative to the invasive state-of-the-art methods.

Classification Model for Diabetes Mellitus Diagnosis based on K-Means Clustering Algorithm Optimized with Bat Algorithm

Diabetes mellitus is a disease characterized by abnormal glucose homeostasis resulting in an increase in blood sugar. According to data from the International Diabetes Federation (IDF), Indonesia ranks 7th out of 10 countries with the highest number of diabetes mellitus patients in the world. The prevalence of patients with diabetes mellitus in Indonesia reaches 11.3 percent or there are 10.7 million sufferers in 2019. Prevention, risk analysis and early diagnosis of diabetes mellitus are necessary to reduce the impact of diabetes mellitus and its complications. The clustering algorithm is one of methods that can be used to diagnose and analyze the risk of diabetes mellitus. The K-mean Clustering Algorithm is the most commonly used clustering algorithm because it is easy to implement and run, computation time is fast and easy to adapt. However, this method often gets to be stuck at the local optima. The problem of the K-means Clustering Algorithm can be solved by combining the K-means Clustering algorithm with the global optimization algorithm. This algorithm has the ability to find the global optimum from many local optimums, does not require derivatives, is robust, easy to implement. The Bat Algorithm (BA) is one of global optimization methods in swarm intelligence class. BA uses automated enlargement techniques into a solution and it’s accompanied by a shift from exploration mode to local intensive exploitation. Based on the background that has been explained, this article proposes the development of a classification model for diagnosing diabetes mellitus based on the K-means clustering algorithm optimized with BA. The experimental results show that the K-means clustering optimized by BA has better performance than K-means clustering in all metrics evaluations, but the computational time of the K-means clustering optimized by BA is higher than K-means clustering.

ОСОБЕННОСТИ ПРОФИЛАКТИКИ СТОМАТОЛОГИЧЕСКИХ ЗАБОЛЕВАНИЙ У ПАЦИЕНТОВ C САХАРНЫМ ДИАБЕТОМ

We consider interrelation of diabetes mellitus and dental health. Knowledge of dentists and dental patients about diabetes is insufficient. The role of a dentist in the early diagnosis of diabetes mellitus and the features of the treatment of dental diseases in diabetic patients are discussed. Diabetes mellitus is one of the diseases receiving much attention in modern scientific and medical societies. It is easy to explain such a high interest: diabetes mellitus ranks third among the direct causes of death after cardiovascular and oncological diseases, therefore, the solution of issues related to diabetes mellitus is put at the state level in many countries. According to various sources, there are from 120 to 180 million diabetic patients in the world, which is 2-3 % of the total population of the planet. The incidence of diabetes mellitus seemed to become endemic and a crisis in the health sector - according to forecasts of the World Health Organization, by 2025 the number of diabetic patients in the world will increase to 333 million people. Along with the increase in the number of patients with diabetes mellitus, the number of patients suffering from complications of this serious disease increases, which leads to rapid disability. The objective of this study was to increase the effectiveness of prevention and treatment of inflammatory periodontal diseases in patients with diabetes mellitus.

Dysfunctional pancreatic cells differentiated from induced pluripotent stem cells with mitochondrial DNA mutations

Diabetes mellitus (DM) is a serious disease in which blood sugar levels rise abnormally because of failed insulin production or decreased insulin sensitivity. Although many studies are being conducted for the treatment or early diagnosis of DM, it is not fully understood how mitochondrial genome (mtDNA) abnormalities appear in patients with DM. Here, we induced iPSCs from fibroblasts, PBMCs, or pancreatic cells of three patients with type 2 DM (T2D) and three patients with non-diabetes counterpart. The mtDNA mutations were detected randomly without any tendency among tissues or patients. In T2D patients, 62% (21/34) of iPSC clones harbored multiple mtDNA mutations, of which 37% were homoplasmy at the 100% mutation level compared to only 8% in non-diabetes. We next selected iPSC clones that were a wild type or carried mutations and differentiated into pancreatic cells. Oxygen consumption rates were significantly lower in cells carrying mutant mtDNA. Additionally, the mutant cells exhibited decreased production of insulin and reduced secretion of insulin in response to glucose. Overall, the results suggest that screening mtDNA mutations in iPSCs from patients with T2D is an essential step before pancreatic cell differentiation for disease modeling or autologous cell therapy.

Nanomaterial-based optical- and electrochemical-biosensors for urine glucose detection: A comprehensive review

Urine glucose detection is an important diagnostic tool for screening and early diagnosis of diabetes mellitus. Detection of urine glucose has many advantages over blood glucose, such as non-invasive, easy-to-detect, simple sampling and being well accepted by patients. Therefore, it is commonly used to monitor diabetes progression, assist in therapeutic intervention as well as in point-of-care testing (POCT). In recent years, with the development of material science, electrochemistry and miniaturization technology, novel applications of natural enzymes, nanozymes as well as nanomaterials, such as metal (Au, Pt, Ni, Co, etc.), alloy, grapheme, in the analysis of urine glucose level have been increasing sharply. In particular, different types of nanozymes-based biosensors, inspired by natural enzymes, have been developed with improved characteristics of being low-cost, stable, and mass-produced. On the other hand, making use of portable devices, such as smartphones and microfluidic paper-based analytical devices, has facilitated on site accurate urine glucose monitoring in real time. All these rapid advancements in nanotechnologies and devices have contributed greatly to the development of cost effective, highly sensitive, user friendly urine glucose biosensors. This review summarizes the most recent improvements in two major types of urine glucose biosensors: the optical- and electrochemical-biosensors. We also discuss the limitations, challenges and perspectives of these biosensors. Finally, we propose future research directions, development trends and potential clinical applications of nanomaterial-based biosensors developed for urine glucose detection.

Plazma Glukoz Konsantrasyonu, Serum Insülin Direnci ve Diastolik Kan Basıncı Göstergeleri ile Makine Öğrenme Yöntemleri Kullanılarak Diyabet Hastalığının Erken Tanısı

Aim: It is a known fact that diabetes mellitus is increasing frequently and triggering many different diseases. Therefore, early diagnosis of the disease is important. This study was trying to predict the early diagnosis of the disease, according to machine learning methods by measuring plasma glucose concentration, serum insulin resistance, and diastolic blood pressure.Material and Methods: In the study, the public dataset from a website consists of 768 samples and nine variables. Three different machine learning strategies were used in the early diagnosis of diabetes mellitus (Support Vector Machine, Multilayer Perceptron, and Stochastic Gradient Boosting). 3 repeats and 10 fold cross-validation method was used to optimize the hyperparameters. The model’s performance parameters were evaluated based on accuracy, specificity, sensitivity, confusion matrix, positive predictive value (precision), negative predictive value, and AUC (area under the ROC curve).Results: According to the experimental results (the criteria of accuracy (0.79), sensitivity (0.57), specificity (0.91), positive predictive value (0.79), negative predictive value (0.80), and AUC (0.74)) the Support Vector Machine was more successful than other methods.Conclusion: Plasma glucose concentration, serum insulin resistance, and diastolic blood pressure markers are important indicators in the early diagnosis of diabetes mellitus. In this study, it was seen that these markers make a significant contribution to the early diagnosis of diabetes mellitus. However, it has been observed that these indicators alone will not be sufficient in the early diagnosis of the disease, especially since age, body mass index and pregnancy contribute significantly. 

IDF21-0569 Periodontal disease is associated with increased risk of Diabetes and Pre-diabetes

Background: Early diagnosis of diabetes mellitus and pre-diabetes is essential for the timely management of diabetes and its complications as well as the prevention of diabetes in individuals with pre-diabetes. Periodontal disease has also been recognized as the sixth complication of diabetes although this is still under-appreciated by endocrinologists or physicians in general. Since it has been suggested that severe periodontitis might be an early complication of diabetes, therefore periodontal disease may be a useful indicator to screen for the risk of diabetes or pre-diabetes in dental patients who otherwise do not know of their glycemic status. Aim: In this study, HbA1c levels and subsequently the prevalence of diabetes and pre-diabetes in participants with and without periodontitis of various stages were investigated. Method: From 88 mild periodontitis patients, 132 moderate periodontitis patients, 210 severe periodontitis patients and 66 patients without periodontitis (control), HbA1c values were obtained by a finger-prick blood sample taken from each participant which was then tested in a point-of-care HbA1c analyzer. Differences in mean HbA1c values and prevalence of diabetes and pre-diabetes between the different periodontitis and control groups were analyzed. Multivariate analyses were performed, correcting for known risk indicators for diabetes screening. Results: In this cohort of a total of 496 participants, 29(5.8%) and 154 (31.0%) patients were identified with HbA1c levels corresponding to the ADA guideline levels for diabetes (HbA1c > 6.5%) and pre-diabetes (HbA1c 5.7–6.4%), respectively. In addition, the mean HbA1c levels increases as the stage of periodontitis increases from no periodontitis to severe periodontitis. Periodontal disease of all stages have higher proportion of hyperglycaemia as compared to non-periodontitis(control) patients. However, it is the severe periodontitis group that showed a significantly higher percentage of patients with HbA1c values in the diabetes range as compared with the mild/moderate periodontitis group (p < 0.001). The prevalence of pre-diabetes was significantly higher in patients with moderate periodontitis compared with patients without periodontitis or with patients having mild periodontitis. In the severe periodontitis group, 18 (8.6%) patients had HbA1c levels in the diabetes range and 51 (24.3%) patients could be diagnosed with prediabetes according to the ADA-guidelines, whereas in the moderate periodontitis group, 59 (44.7%) subjects had HbA1c levels in the pre-diabetes range and only 2 (1.5%) of them reached HbA1c values for diabetes diagnosis. Discussion: Periodontitis is associated with increased levels of HbA1c and subsequently with an increased prevalence of those at risk for diabetes and pre-diabetes. We suggest that severe periodontal disease may be a useful risk indicator for early diabetes or pre-diabetes. This study also demonstrates the important role that dentists can play in the early referral of their patients who may already have undiagnosed diabetes and pre-diabetes to endocrinologist so as to receive timely medical diagnosis, prevent or delay progression from pre-diabetes to overt diabetes mellitus and intervention to prevent potential diabetes complications which in turn reduces disease burden.

Biomarkers for early diagnosis of diabetic kidney disease: still a long way to go.

According to the International Diabetes Federation’s Diabetes Atlas1, the global burden of diabetes mellitus (DM) in 2021 was 537 million and prevalence is expected to increase by 46 per cent by the year 2045. It is alarming that three-fourth of the global burden of DM is in resource-constrained middle- and low-income countries. It would be no surprise that the resources required for the management of DM and its associated complications would fall woefully short of expected requirements. Therefore, the prevention and early diagnosis of DM or its complications are need of the hour. Diabetic kidney disease (DKD) is one of the dreaded complications in patients with DM. Approximately, 40 per cent of patients with type 2 DM will go on to develop DKD2. Conventionally, DKD is characterized by the appearance of albuminuria which is followed by progressive fall in estimated glomerular filtration rate (eGFR). However, it is also well known that up to one-third of patients with type 2 DM and DKD might have kidney involvement manifesting as fall in eGFR without any significant albuminuria2. Therefore, screening for DKD has relied on both measurement of albuminuria and estimation of GFR by creatinine-based eGFR equations. Once established, DKD usually progresses to end-stage kidney failure requiring dialysis or kidney transplantation. The classical description of stages of diabetic nephropathy in patients with type 1 DM given by Mogensen3 in 1983 identified a long period of hyperfiltration and structural changes in kidneys before the development of albuminuria. Identifying kidney involvement before the onset of albuminuria and fall in eGFR would offer an opportunity for earlier detection of DKD. With the availability of omics platforms and discovery of newer biomarkers of glomerular or tubulointerstitial injury, research to test the utility of these biomarkers in patients with DM is being done. It is important to note that early detection might become more meaningful when detection would offer an opportunity for early intervention to retard progression. Therefore, establishing such biomarkers could have both diagnostic and therapeutic potentials. In view of this, the study by Mahapatra et al4 in this issue assumes significance. The authors have cross sectionally studied and classified patients with type 2 DM into clinical categories of hyperfiltration (HF), normoalbuminuria (NA), microalbuminuria (MA) or controls based on albuminuria and eGFR. The former three categories constituted the cases. Urinary angiotensinogen (ng/ml) and angiotensinogen to creatinine ratio (mg/g) were reported to be significantly different between controls and individual case groups (HF, NA or MA). In the multivariate logistic regression analysis, urinary angiotensinogen to creatinine ratio was independently associated with the classification of patients as cases or controls. The authors also measured urinary excretion of interleukin-18, neutrophil gelatinase-associated lipocalin (NGAL) and cystatin C. However, these biomarkers failed to show any independent association with the classification of the study population as cases or controls4. Biomarkers in kidney disease can be related to structural or functional alterations in the kidney. These can be classified in various ways such as markers of glomerular or tubulointerstitial injury or markers of underlying processes such as inflammation, oxidative stress, fibrosis or repair5. In advanced stages of kidney disease due to any aetiology, almost all functional compartments would be affected, and hence, the specificity of biomarkers for one type of injury or process would be low. However, in the early phases of kidney disease due to a specific aetiology, it is likely that biomarkers related to specific, early mechanisms of injury may be altered before any overt clinical or biochemical manifestations. Although classically described as a glomerular disease, it is well known now that tubulointerstitial compartment may get simultaneously or independently affected during early DKD. The fact that up to one-third of patients with type 2 DM with DKD may not have significant proteinuria attests to this fact. Furthermore, not all patients with DKD and those with mildly increased albuminuria progress to develop overt nephropathy. Therefore, the assessment of only albuminuria or proteinuria, especially when it is done only once, has its own share of drawbacks with respect to the diagnosis of DKD. Circulating cystatin C and kidney injury molecule-1, urinary NGAL, epidermal growth factor (EGF)/monocyte chemotactic protein-1 (MCP-1), α1 microglobulin and retinol-binding protein 4 are some of the tubular biomarkers that have been investigated in patients with DM for predicting the development or progression of DKD5. It is important to note that urinary EGF/MCP-1 has been inversely associated with the presence of DKD6. Urinary type 4 collagen, copeptin and CKD 273 (complex of 273 proteins identified on proteomics) are some of the other promising biomarkers that have been shown to be associated with the development of chronic kidney disease (CKD) in patients with DM7. Angiotensinogen in urine is an indirect marker of intra-renal renin-angiotensin aldosterone system (RAAS) activation which is one of the early mechanisms of kidney injury in patients with DM8. Specifically, increased urine angiotensinogen excretion implies increased generation of intra-renal angiotensin II9. Furthermore, intra-renal RAAS activation may occur independent of changes in systemic circulation. It is also important to note that in the presence of conditions that disrupt glomerular permeability barrier, e.g. podocyte injury, circulating angiotensinogen also appears in urine9. Therefore, it is not surprising to see the association of urinary angiotensinogen excretion with albuminuria or proteinuria. Increased urinary angiotensinogen excretion is not specific to DKD and can be seen in other causes of CKD9. It has also been shown to correlate with prognosis after acute kidney injury. Urinary angiotensinogen excretion has been shown to increase in graded manner between healthy controls and progressively increasing clinically defined stages of DKD. In a comparison of urinary angiotensinogen to creatinine ratio between patients with type 1 DM without albuminuria and matched healthy controls, it was shown to be significantly higher in patients10. Importantly, circulating angiotensinogen levels did not differ between the groups10. In a study in normotensive patients with type 2 DM and almost similar design as the present study4, the authors reported progressively increasing urinary angiotensinogen excretion with increasing urine albumin excretion starting from normoalbuminuric phase11. This study11 included patients with eGFR between 30 and 60 ml/min/1.73 m2 or presence of overt albuminuria (urine albumin excretion >300 mg/g of creatinine) unlike the present study. The present study, together with these studies, reinforces the graded association between urinary angiotensinogen excretion and varying degrees of kidney involvement in patients with DM. Associations have also been reported between urinary angiotensinogen excretion and cardiovascular complications in patients with DM and albuminuria12. However, at present, there is no evidence to support any impact of urinary angiotensinogen levels on therapeutic decision-making. Furthermore, its role in non-albuminuric DKD is still to be explored. Despite these limitations, strong associations with progression in DKD warrant more studies on this biomarker. The ideal design to discover or validate biomarkers for the prediction or early diagnosis of complications in DM would be a well-phenotyped, prospective, long-term cohort study that enrols patients as soon as they are diagnosed with DM. Serial evaluation of biomarkers would allow the evaluation of association of change in biomarkers with clinical phenotype over time. However, these are not always possible as such studies would need enormous resources and time. Although urine albumin excretion and fall in eGFR are two criteria that are used to establish a clinical diagnosis of DKD in appropriate clinical circumstances, the gold standard for the diagnosis of DKD is kidney biopsy. There is increasing evidence that kidney involvement in patients with DM especially type 2 could be due to other causes as patients with type 2 DM often have other comorbidities such as obesity, hypertension, metabolic syndrome and infections2. Unfortunately, as of now, the kidney biopsy is very difficult to justify for establishing diagnosis of DKD in otherwise typical clinical picture of DKD. Kidney biopsy in a patient with DM and kidney involvement are only considered when the clinical course or investigations are atypical, e.g. rapid decline in kidney function, presence of massive proteinuria or active urinary sediment or hypocomplementemia, absence of proteinuria and extra-renal manifestations suggesting secondary kidney involvement. The study by Mahapatra et al4 was a cross-sectional study that defined patient groups by their clinical course over 12 months in the absence of kidney biopsy. Although clinically robust, these observations only suggest associations that would need to be tested and validated in properly designed studies. Conflicts of Interest: None.

A Clinical Decision Support System Based on Machine Learning For The Prediction of Diabetes Mellitus.

Aim: Early diagnosis of diabetes mellitus (DM), one of the most important health problems worldwide, and taking necessary steps are very important. Therefore, it has become very important to develop models for the prediction of the disease. The aim of this study is to create a clinical decision support model with Stochastic Gradient Boosting, a machine learning model for DM prediction. Material and Methods: In the study, modeling was done with the Stochastic Gradient Boosting method using an open access data set including the factors associated with DM. Model results were evaluated with accuracy, balanced accuracy, sensitivity, selectivity, positive predictive value, negative predictive value, and F1-score performance metrics. In addition, 5-fold cross-validation method was used in the modeling phase. Finally, variable importance values were obtained by modeling. Results: Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score from by Stochastic Gradient Boosting modeling were 93.6%, 92.8%, 91.7%, 93.9%, 73.3%, 98.4%, and 81.5%, respectively. According to the variable importance values obtained for the input variables in the data set examined in this study, the most important variables are glucose, age, systolic BP, cholesterol, chol/HDL, BMI, height, waist/hip, HDL, waist, weight, diastolic BP, hip, and gender: male. Conclusion: In the current study, it was seen that the ML model applied with the results obtained can predict diabetes. Addition, according to the results of the relevant model, the most important risk factors for DM were determined and given in degrees of importance of the risk factors. With these results, necessary precautions can be taken for the disease at early levels.

Machine learning algorithms for early diagnosis of diabetes mellitus: A comparative study

A huge majority of people all over the globe are coping with the devastating effects of diabetes, and many of them are not being identified early enough. Diabetes has become one of the most common diseases for cause of death, it develops whenever the person's body becomes unable to create sufficient insulin and the level of glucose is increased in the blood. Overall, Diabetes' global influence has exploded in current years and is expected to continue to do so in the near future. Diabetes affects around 463 million people worldwide; By 2045, this figure will have risen to 700 million. In many nations, the number of people with type 2 diabetes is increasing. Diabetes has become one of the most common diseases for the cause of death, leading to major health issues such as blindness, kidney disease, strokes,heart disease, etc. In this research, some Machine Learning Algorithms are utilized in the prediction ofDiabetes because they are effective at aiding and making predictions from big amounts of data. This research conducts a comparativeevaluation of diabetes diagnosis based on Machine LearningAlgorithms like Naïve Bayes (NB), Support Vector Machine (SVM), Neural Network, Adaboost, K-nearest neighbor (KNN), Linear Kernel SVM etc. In a result, Neural Network produced the best results with the highest accuracy rate.

Serotonin level as a potent diabetes biomarker based on electrochemical sensing: a new approach in a zebra fish model

Serotonin (5-HT) levels have been associated with several exclusively metabolic disorders. Herein, a new approach for 5-HT level as a novel biomarker of diabetes mellitus is considered using a simple nanocomposite and HPLC method. Reduced graphene oxide (rGO) comprising gold nanoparticles (AuNPs) was decorated with 18-crown-6 (18.Cr.6) to fabricate a simple nanocomposite (rGO-AuNPs-18.Cr.6). The nanocomposite was positioned on a glassy carbon electrode (GCE) to form an electrochemical sensor for the biomarker 5-HT in the presence of L-tryptophan (L-Trp), dopamine (DA), ascorbic acid (AA), urea, and glucose. The nanocomposite exhibited efficient catalytic activity for 5-HT detection by square-wave voltammetry (SWV). The proposed sensor displayed high selectivity, excellent reproducibility, notable anti-interference ability, and long-term stability even after 2months. SWV defined a linear range of 5-HT concentration from 0.4 to 10μgL-1. A diabetic animal model (diabetic zebrafish model) was then applied to investigate 5-HT as a novel biomarker of diabetes. A limit of detection (LOD) of about 0.33μgL-1 was found for the diabetic group and 0.15μgL-1 for the control group. The average levels of 5-HT obtained were 9 and 2 μgL-1 for control and diabetic groups, respectively. The recovery, relative standard deviation (RSD), and relative error (RE) were found to be about 97%, less than 2%, and around 3%, respectively. The significant reduction in 5-HT level in the diabetic group compared to the control group proved that the biomarker 5-HT can be applied for the early diagnosis of diabetes mellitus.

Application of Early Diagnosis of Diabetes Mellitus (DM) Equipped with Calorie Needs for DM Sufferers using the Fuzzy Mamdani Method

Diabetes Mellitus (DM) is one of the deadliest degenerative diseases in the world. The prevalence of DM in Indonesia from year to year shows asignificant increase. The high number of these causes the need for appropriate action and anticipation for health workers, DM families and DM people themselves. In this study, a system application model was created by using informatics techniques in health for early diagnosis of DM and what calorie needs needed for DM sufferers. This system was created using a GUI application and the Mamdani fuzzy method. The purpose of creating this system is to help in making an initial decision for DM diagnosis. The results obtained, first a DM diagnosis system with 6 input variables, 3 output variables, and 155 rules with MAPE achieved 29.48%. The second is the calorie requirements system with 2 input variables, 2 output variables namely BMI with MAPE 10.57% BMR with MAPE 9.7% and 9 rules with the results achieved by 99%.

Study on glutamic acid decarboxylase antibodies (Anti–GAD) in blood serum for early diagnosis of diabetes mellitus

Study on glutamic acid decarboxylase antibodies (Anti–GAD) in blood serum for early diagnosis of diabetes mellitus

Relationship of HbA1c with Fasting Blood Glucose on Diagnostic Values and Lifestyle in Type II Diabetes Mellitus Patients

Early diagnosis of DM (Diabetes Mellitus) is very important in reducing complications. HbA1c has been recommended as a diagnosis of diabetes in the guidelines for clinical practice as a determination of type 2 diabetes in 2011 by WHO, but is there a significant relationship between HBA1c and fasting blood glucose levels (GDP) in a person after being diagnosed with Diabetes Mellitus. In this study, the relationship between HbA1c and GDP levels was assessed through observational cross-sectional analytic based studies. The research method uses a large sample selected through the GDP test (&gt; 125 mg / dl) of 17 people (5 men and 12 women). Statistical analysis of the test of the relationship between the HbA1c results and GDP with the Pearson Correlation, Crosstabs, and independent T test to determine the relationship of sex with GDP and HBA1c. The results of the study, there was a significant relationship between levels of GDP with HBA1c with p = 0.002, where the incidence of GDP (&gt; 125 mg / dl) in men was 17.7% and women were 52.9%, while the results of HBA1c (&gt; 6.5 %) in men 23.5% and women 52.9%. In conclusion, the results of HBA1c (&gt; 6.5%) can be used for diagnosis of DM, whereas the level of GDP is only to know that people with diabetes have changed their lifestyle or not, and it is found that women are more easily change lifestyles than men, due to GDP results (&lt;125 mg / dl) of 11.8% higher than the HBA1c yield (&lt;6.5%) of 5.9%.