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

Vibrational spectroscopy as a powerful tool for stratifying patients using minimal amounts of blood

In the current aging society, more and more people will suffer from age-related diseases such as cardiovascular diseases or diabetes mellitus. To better diagnose and treat these diseases, individual characterisation of the patients unique condition is needed. Vibrational spectroscopy, including Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR), is one of the favourite techniques being developed to enable personalized medicine. Vibrational spectroscopic techniques have the advantages of being non-destructive, rapid, and label-free, can be ultimately performed in high-throughput and provide biochemical fingerprint information on molecular level. Among the samples measured by vibrational spectroscopy, blood is a very popular and important specimen for personalized medicine

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

Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic

Glycated haemoglobin (HbA1c) is now more commonly used as an alternative test to the fasting plasma glucose and oral glucose tolerance tests for the identification of Type 2 Diabetes Mellitus (T2DM) because it is easily obtained using the point-of-care technology and represents long-term blood sugar levels. According to WHO guidelines, HbA1c values of 6.5% or above are required for a diagnosis of T2DM. However outcomes of a large number of trials with HbA1c have been inconsistent across the clinical spectrum and further research is required to determine the efficacy of HbA1c testing in identification of T2DM. Medical records from a diabetes screening program in Australia illustrate that many patients could be classified as diabetics if other clinical indicators are included, even though the HbA1c result does not exceed 6.5%. This suggests that a cutoff for the general population of 6.5% may be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied to identify markers that can be used with HbA1c. The results indicate that T2DM is best classified by HbA1c at 6.2% - a cutoff level lower than the currently recommended one, which can be even less, having assumed the threshold flexibility, if additionally to HbA1c being high the rule is conditioned on oxidative stress or inflammation being present, atherogenicity or adiposity being high, or hypertension being diagnosed, etc

Diabetic nephropathy: early detection and therapeutic strategies

The increasing global prevalence of diabetes poses a huge challenge to health services. The diagnosis is accompanied by a reduction in life expectancy, primarily due to cardiovascular disease which is inextricably linked to microvascular complications such as diabetic nephropathy (DN). Microalbuminuria (MA) is generally accepted as the primary clinical hallmark of DN, but despite widespread prescribing of agents blocking the renin angiotensin aldosterone system (RAAS) in these patients many continue to progress towards end-stage renal disease (ESRD). Clinical trials evaluating early initiation of RAAS blocking agents in untargeted, nonalbuminuric diabetic patients have shown potential for delaying disease progression but these effects are generally counterbalanced by side effects and adverse events associated with these therapies. Discovery of novel biomarkers to identify individuals at highest risk of DN who would stand to benefit most from targeted preclinical intervention would be a significant step towards implementation of personalised medicine in this population. One technique which shows promise is proteomics, based on the concept of separation and quantification of peptides in a biological sample to produce a disease-specific pattern. A panel of 273 urinary peptides (CKD273) has been shown to have potential for identification of nonalbuminuric diabetic patients who are at risk of progression to overt DN. However, many such novel biomarkers are described in the literature and to date none have successfully made the transition from research studies to routine clinical practice. In order to be considered for clinical implementation novel biomarkers are required to be subject to a rigorous evaluation process. In brief there are several key steps beginning with proof-of-concept studies; progressing through validation in independent populations to demonstration of incremental value beyond the current guideline-endorsed tests; thereafter proof of clinical applicability in determining treatment strategies and cost-effectiveness are required. The work contained within this thesis is designed to address each of these aspects with regard to use of the CKD273 proteomic panel as a biomarker for early detection of DN

The Impact of Big Data on Chronic Disease Management

Introduction: Population health management – and specifically chronic disease management – depend on the ability of providers to identify patients at high risk of developing costly and harmful conditions such as diabetes, heart failure, and chronic kidney disease (CKD). The advent of big data analytics could help identify high-risk patients which is really beneficial to healthcare practitioners and patients to make informed decisions in a timelier manner with much more evidence in hand. It would allow doctors to extend effective treatment but also reduces the costs of extending improved care to patients. Purpose: The purpose of this study was to identify current applications of big data analytics in healthcare for chronic disease management and to determine its real-world effectiveness in improving patient outcomes and lessening financial burdens. Methodology: The methodology for this study was a literature review. Six electronic databases were utilized and a total of 49 articles were referenced for this research. Results: Improvement in diagnostic accuracy and risk prediction and reduction of hospital readmissions has resulted in significant decrease in health care cost. Big data analytic studies regarding care management and wellness programs have been largely positive. Also, Big data analytics guided better treatment leading to improved patient outcomes. Discussion/Conclusion: Big data analytics shows initial positive impact on quality of care, patient outcomes and finances, and could be successfully implemented in chronic disease management

Impact of type 2 diabetes and periodontal disease on oral status of Sudanese adults. Clinical, microbial and immune-inflammatory aspects

Diabetes is a major global public health challenge, afflicting 380 million people worldwide. The onset is insidious and progression is associated with irreversible medical complications, including oral diseases. Little is known about the oral health of patients with diabetes in developing countries such as The Sudan, which is currently experiencing an alarming increase in diabetes cases. The overall aim of this thesis was to evaluate oral health indicators in Sudanese adults with type 2 diabetes (T2D) and to investigate the impact of T2D and chronic periodontitis on biomarkers of inflammation and glucose regulation in gingival crevicular fluid (GCF) and saliva. The subjects comprised 157 T2D cases and 304 controls without diabetes, 461 in all. Participants were interviewed using a structured questionnaire on socio-demographics, lifestyle and oral health related quality of life (OHRQoL). The clinical examination comprised full mouth probing depths, bleeding on probing, dental plaque index, tooth mobility index, furcation involvement and coronal and root caries. In GCF samples, the levels of 10 glucoregulatory molecules and 27 inflammatory molecules were measured by bead-based multiplex assays. MMP-8, MMP-9, OPG and RANKL in whole saliva samples were quantified by ELISA. Subgingival plaque samples were analysed by conventional polymerase chain reaction (PCR), to assess the prevalence of six periodontal pathogens. T2D patients had poorer periodontal parameters, more missing teeth and poorer OHRQoL than individuals without diabetes. Chronic periodontitis was associated with disturbed GCF levels of biomarkers related to the onset and medical complications of T2D. On the other hand, T2D was associated with a high Th-2/Th-1 cytokines ratio and disturbed levels of molecules involved in the anti-inflammatory and healing processes. T2D had no significant effect on either the prevalence of the investigated periodontal pathogens or the levels of salivary MMP-8, MMP-9 and OPG. Further research is warranted to identify disease markers which could form the basis of a test to alert the dentist to patients with undiagnosed T2D, or those at risk of developing the disease

Machine-learning to Stratify Diabetic Patients Using Novel Cardiac Biomarkers and Integrative Genomics

Background: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary diagnostic for diabetics, its ability to predict long-term, health outcomes across diverse demographics, ethnic groups, and at a personalized level are limited. The purpose of this study was to provide a model for precision medicine through the implementation of machine-learning algorithms using multiple cardiac biomarkers as a means for predicting diabetes mellitus development. Methods: Right atrial appendages from 50 patients, 30 non-diabetic and 20 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine-learning was applied to physiological, biochemical, and sequencing data for each patient. Supervised learning implementing SHapley Additive exPlanations (SHAP) allowed binary (no diabetes or type 2 diabetes) and multiple classifcation (no diabetes, prediabetes, and type 2 diabetes) of the patient cohort with and without the inclusion of HbA1c levels. Findings were validated through Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Classifcation and Regression Tree (CART) models with tenfold cross validation. Results: Total nuclear methylation and hydroxymethylation were highly correlated to diabetic status, with nuclear methylation and mitochondrial electron transport chain (ETC) activities achieving superior testing accuracies in the predictive model (~84% testing, binary). Mitochondrial DNA SNPs found in the D-Loop region (SNP-73G, -16126C, and -16362C) were highly associated with diabetes mellitus. The CpG island of transcription factor A, mitochondrial (TFAM) revealed CpG24 (chr10:58385262, P=0.003) and CpG29 (chr10:58385324, P=0.001) as markers correlating with diabetic progression. When combining the most predictive factors from each set, total nuclear methylation and CpG24 methylation were the best diagnostic measures in both binary and multiple classifcation sets. Conclusions: Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discover

Computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus

BACKGROUND: Diabetes is one of the commonest chronic medical conditions, affecting around 347 million adults worldwide. Structured patient education programmes reduce the risk of diabetes-related complications four-fold. Internet-based self-management programmes have been shown to be effective for a number of long-term conditions, but it is unclear what are the essential or effective components of such programmes. If computer-based self-management interventions improve outcomes in type 2 diabetes, they could potentially provide a cost-effective option for reducing the burdens placed on patients and healthcare systems by this long-term condition. OBJECTIVES: To assess the effects on health status and health-related quality of life of computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus. SEARCH METHODS: We searched six electronic bibliographic databases for published articles and conference proceedings and three online databases for theses (all up to November 2011). Reference lists of relevant reports and reviews were also screened. SELECTION CRITERIA: Randomised controlled trials of computer-based self-management interventions for adults with type 2 diabetes, i.e. computer-based software applications that respond to user input and aim to generate tailored content to improve one or more self-management domains through feedback, tailored advice, reinforcement and rewards, patient decision support, goal setting or reminders. DATA COLLECTION AND ANALYSIS: Two review authors independently screened the abstracts and extracted data. A taxonomy for behaviour change techniques was used to describe the active ingredients of the intervention. MAIN RESULTS: We identified 16 randomised controlled trials with 3578 participants that fitted our inclusion criteria. These studies included a wide spectrum of interventions covering clinic-based brief interventions, Internet-based interventions that could be used from home and mobile phone-based interventions. The mean age of participants was between 46 to 67 years old and mean time since diagnosis was 6 to 13 years. The duration of the interventions varied between 1 to 12 months. There were three reported deaths out of 3578 participants.Computer-based diabetes self-management interventions currently have limited effectiveness. They appear to have small benefits on glycaemic control (pooled effect on glycosylated haemoglobin A1c (HbA1c): -2.3 mmol/mol or -0.2% (95% confidence interval (CI) -0.4 to -0.1; P = 0.009; 2637 participants; 11 trials). The effect size on HbA1c was larger in the mobile phone subgroup (subgroup analysis: mean difference in HbA1c -5.5 mmol/mol or -0.5% (95% CI -0.7 to -0.3); P < 0.00001; 280 participants; three trials). Current interventions do not show adequate evidence for improving depression, health-related quality of life or weight. Four (out of 10) interventions showed beneficial effects on lipid profile.One participant withdrew because of anxiety but there were no other documented adverse effects. Two studies provided limited cost-effectiveness data - with one study suggesting costs per patient of less than $140 (in 1997) or 105 EURO and another study showed no change in health behaviour and resource utilisation. AUTHORS' CONCLUSIONS: Computer-based diabetes self-management interventions to manage type 2 diabetes appear to have a small beneficial effect on blood glucose control and the effect was larger in the mobile phone subgroup. There is no evidence to show benefits in other biological outcomes or any cognitive, behavioural or emotional outcomes