Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Background: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. Methods: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. Results: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. Conclusion: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Background: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. Methods: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. Results: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. Conclusion: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers

Deepr: A Convolutional Net for Medical Records

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space

Optimized stacking ensemble for early-stage diabetes mellitus prediction

This paper presents an optimized stacking-based hybrid machine learning approach for predicting early-stage diabetes mellitus (DM) using the PIMA Indian diabetes (PID) dataset and early-stage diabetes risk prediction (ESDRP) dataset. The methodology involves handling missing values through mean imputation, balancing the dataset using the synthetic minority over-sampling technique (SMOTE), normalizing features, and employing a stratified train-test split. Logistic regression (LR), naïve Bayes (NB), AdaBoost with support vector machines (AdaBoost+SVM), artificial neural network (ANN), and k-nearest neighbors (k-NN) are used as base learners (level 0), while random forest (RF) meta-classifier serves as the level 1 model to combine their predictions. The proposed model achieves impressive accuracy rates of 99.7222% for the ESDRP dataset and 94.2085% for the PID dataset, surpassing existing literature by absolute differences ranging from 10.2085% to 16.7222%. The stacking-based hybrid model offers advantages for early-stage DM prediction by leveraging multiple base learners and a meta-classifier. SMOTE addresses class imbalance, while feature normalization ensures fair treatment of features during training. The findings suggest that the proposed approach holds promise for early-stage DM prediction, enabling timely interventions and preventive measures

A comparative analysis on diagnosis of diabetes mellitus using different approaches: A survey

Diabetes Mellitus is commonly known as diabetes. It is one of the most chronic diseases as the World Health Organization (WHO) report shows that the number of diabetes patients has risen from 108 million to 422 million in 2014. Early diagnosis of diabetes is important because it can cause different diseases that include kidney failure, stroke, blindness, heart attacks, and lower limb amputation. Different diabetes diagnosis models are found in literature, but there is still a need to perform a survey to analyze which model is best. This paper performs a literature review for diabetes diagnosis approaches using Artificial Intelligence (neural networks, machine learning, deep learning, hybrid methods, and/or stacked-integrated use of different machine learning algorithms). More than thirty-five papers have been shortlisted that focus on diabetes diagnosis approaches. Different datasets are available online for the diagnosis of diabetes. Pima Indian Diabetes Dataset (PIDD) is the most commonly used for diabetes prediction. In contrast with other datasets, it has key factors which play an important role in diabetes diagnosis. This survey also throws light on the weaknesses of the existing approaches that make them less appropriate for a diabetes diagnosis. In artificial intelligence techniques, deep learning is widespread and in medical research, heart rate is getting more attention. Deep learning combined with other algorithms can give better results in diabetes diagnosis and heart rate should be used for other cardiac disease diagnoses

Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease

The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice

A NOVEL APPROACH FOR FINDING DIABETIC MELLITUS USING ENSEMBLE MODEL FOR AN OPTIMIZED CLASSIFICATION

  Diabetic mellitus is a chronic disease caused by hyperglycemia which should be treated with high care and medications. The objective of this work is to identify and classify the severity of the diabetic disease using the training data set. This is caused due to the defect in insulin secretion that may affect several organs in the body. Blood pressure and diabetic mellitus are the common twin diseases occurred in about 69.2 million people living in India around 8.7% of the population as per the data resealed in the year 2015. Correct diet, regular exercise will control disease to a great extent. In this research paper the applied methodology is a concurrent classifier for the diabetic mellitus and the results are analyzed with the supervised learning. From the University of California and Irvine repository related attributes for the diabetic mellitus are carefully measured through the ensemble classifier and the results are categorized in the dataset. This work results that boosting can be made to the dataset for obtaining accurate results and classifications. In the conclusion, ensemble methodology is the well proven methodology from the year 1993. For forecasting in N†number of domains, so for the ensemble classifier produces 93% of the accurate results are made. An audit can be made on the results and suggestions are given to the patients for taking medications with the help of medical practitioners

A Comparative Performance Analysis of Hybrid and Classical Machine Learning Method in Predicting Diabetes

Diabetes mellitus is one of medical science’s most important research topics because of the disease’s severe consequences. High blood glucose levels characterize it. Early detection of diabetes is made possible by machine learning techniques with their intelligent capabilities to accurately predict diabetes and prevent its complications. Therefore, this study aims to find a machine learning approach that can more accurately predict diabetes. This study compares the performance of various classical machine learning models with the hybrid machine learning approach. The hybrid model includes the homogenous model, which comprises Random Forest, AdaBoost, XGBoost, Extra Trees, Gradient Booster, and the heterogeneous model that uses stacking ensemble methods. The stacking ensemble or stacked generalization approach is a meta-classifier in which multiple learners collaborate for prediction. The performance of the homogeneous hybrid models, Stacked Generalization and the classic machine learning methods such as Naive Bayes and Multilayer Perceptron, k-Nearest Neighbour, and support vector machine are compared. The experimental analysis using Pima Indians and the early-stage diabetes dataset demonstrates that the hybrid models achieve higher accuracy in diagnosing diabetes than the classical models. In the comparison of all the hybrid models, the heterogeneous model using the Stacked Generalization approach outperformed other models by achieving 83.9% and 98.5%. Doi: 10.28991/ESJ-2023-07-01-08 Full Text: PD