scholarly journals Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction

2020 ◽  
Vol 21 (12) ◽  
pp. 4236 ◽  
Author(s):  
Hee-Sung Ahn ◽  
Jong Ho Kim ◽  
Hwangkyo Jeong ◽  
Jiyoung Yu ◽  
Jeonghun Yeom ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Random Forest ◽  
Renal Dysfunction ◽  
Univariate Analysis ◽  
Urine Samples ◽  
Support Vector ◽  
P Value ◽  
Albumin Concentration ◽  
Urinary Proteome

Renal dysfunction, a major complication of type 2 diabetes, can be predicted from estimated glomerular filtration rate (eGFR) and protein markers such as albumin concentration. Urinary protein biomarkers may be used to monitor or predict patient status. Urine samples were selected from patients enrolled in the retrospective diabetic kidney disease (DKD) study, including 35 with good and 19 with poor prognosis. After removal of albumin and immunoglobulin, the remaining proteins were reduced, alkylated, digested, and analyzed qualitatively and quantitatively with a nano LC-MS platform. Each protein was identified, and its concentration normalized to that of creatinine. A prognostic model of DKD was formulated based on the adjusted quantities of each protein in the two groups. Of 1296 proteins identified in the 54 urine samples, 66 were differentially abundant in the two groups (area under the curve (AUC): p-value < 0.05), but none showed significantly better performance than albumin. To improve the predictive power by multivariate analysis, five proteins (ACP2, CTSA, GM2A, MUC1, and SPARCL1) were selected as significant by an AUC-based random forest method. The application of two classifiers—support vector machine and random forest—showed that the multivariate model performed better than univariate analysis of mucin-1 (AUC: 0.935 vs. 0.791) and albumin (AUC: 1.0 vs. 0.722). The urinary proteome can reflect kidney function directly and can predict the prognosis of patients with chronic kidney dysfunction. Classification based on five urinary proteins may better predict the prognosis of DKD patients than urinary albumin concentration or eGFR.

scholarly journals Urinary Lysosomal Enzyme Activities and Albuminuria in Ghanaian Patients with Type 2 Diabetes Mellitus

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Henry Asare-Anane ◽  
Felix Twum ◽  
Emmanuel Kwaku Ofori ◽  
Erving L. Torgbor ◽  
Seth D. Amanquah ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Lysosomal Enzyme ◽  
Enzyme Activities ◽  
Renal Involvement ◽  
Fasting Blood Glucose ◽  
Urine Samples ◽  
Albumin Concentration ◽  
Spot Urine ◽  
Alanine Aminopeptidase

Renal tubular lysosomal enzyme activities like alanine aminopeptidase (AAP) and N-acetyl-β-D-glucosaminidase (NAG) have been shown to increase in patients developing diabetic nephropathy and nephrosclerosis. This study aimed to determine the activities of N-acetyl-β-D-glucosaminidase and alanine aminopeptidase and albumin concentration in urine samples of patients with type 2 diabetes. One hundred and thirty (65 type 2 diabetic and 65 nondiabetic) subjects participated in this study. Blood samples were drawn for measurements of fasting blood glucose, albumin (Alb), lipids, and creatinine (Cr). Early morning spot urine samples were also collected for activities of alanine aminopeptidase (AAP), N-acetyl-β-D-glucosaminidase (NAG), and concentration of albumin (U-Alb) and creatinine (U-Cr). Both NAG/Cr and AAP/Cr were significantly increased in diabetic subjects compared to controls (p<0.001). There was positive correlation between NAG/Cr and Alb/Cr (r=0.49,p<0.001) and between NAG/Cr and serum creatinine (r=0.441,p<0.001). A negative correlation was found between NAG/Cr and eGFR (r=-0.432,p<0.05). 9.3% and 12% of diabetics with normoalbuminuria had elevated levels of AAP/Cr and NAG/Cr, respectively. We conclude that measuring the urinary enzymes activities (NAG/Cr and AAP/Cr) could be useful as a biomarker of early renal involvement in diabetic complications.

scholarly journals The Association Between Serum Magnesium Level and Microalbuminuria in Type 2 Diabetes Mellitus Patients

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Masoud Mohebbi ◽  
Katayoun Samadi ◽  
Nazafarin Navari ◽  
Melika Ziafati-fahmideh-sani ◽  
Golshid Nourihosseini ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Serum Magnesium ◽  
P Value ◽  
Albumin Concentration ◽  
Cross Sectional ◽  
Urine Albumin ◽  
Significant Difference

Background: Diabetic nephropathy occurs in 20 - 30% of diabetic cases globally, and microalbuminuria (MA) is the first symptom of this disorder. Some studies have suggested that there is an association between the serum magnesium (Mg) level and MA. Objectives: Therefore, we investigated the association between the serum Mg level and MA in type 2 diabetes mellitus (T2DM) patients. Methods: We conducted a cross-sectional study on 122 subjects with T2DM. We categorized them into two groups of microalbuminuria (MA) and non-microalbuminuria (NMA) according to their urine albumin-creatinine ratio (UACR). MA was considered as a UACR of 30 to 300 mg/g. Participants were excluded if they had the following conditions: The age of under 16 years, cardiac, renal, or hepatic disorders, using corticosteroids, diuretics, Mg /calcium (Ca) supplements, and antiepileptic drugs, heavy physical activity within 24 hours before the test, pregnant and breastfeeding women, febrile patients, and patients who were unwilling to participate in the study. The analysis was performed using SPSS version 15. A P-value < 0.05 was considered significant. Results: Among the patients, 50.81% were male. Also, the mean body mass index (BMI) of the NMA group was greater than the MA group (29.84 ± 5.64 vs. 27.31 ± 3.14, P-value = 0.003). Mg levels of the MA and NMA groups showed no significant differences (2.13 ± 0.42 and 2.10 ± 0.43, respectively; P-value = 0.67). Overall, data analysis provided no significant difference between Mg level and the urine albumin concentration between the MA and NMA groups (P-value = 0.21 and 0.81, respectively.). Conclusions: Serum Mg level and MA have no significant relationship. Further prospective studies are needed to assay this issue.

scholarly journals Assessment of Risk of Type 2 Diabetes Mellitus with Stress as a Risk Factor using Classification Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 11273-11277
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Risk Factor ◽  
Random Forest ◽  
Health Concern ◽  
Support Vector

Rising prevalence of type 2 diabetes mellitus is a vital health concern today, not only in India but across the world. Several factors including dietary habits, genetics, lack of physical exercise and stress are known to affect the risk of type 2 diabetes. Although awareness has increased to some extent, many people with diabetes have limited knowledge about the risk factors before the diagnosis of disease. For chronic disease prevention there is a necessity to find out such risk factors and manage them appropriately. Statistical techniques can be employed to understand the risk of type 2 diabetes in different age group of people. The objective of the research was to evaluate relationship among stress and type 2 diabetes in people with different age groups by a statistical tool. The proposed method uses three machine learning classifiers namely Support Vector Machine (SVM), Logistic Regression and Random Forest (RF) to detect type 2 diabetes at an early stage. To develop an adaptive model the preprocessing step has been applied. The accuracy of predicting diabetes using SVM, Random Forest and Logistic Regression was 80.17%, 79.37%, 78.67% respectively. The results suggest that as compared to Random Forest and Logistic Regression, SVM is better in predicting occurrence and progress of type 2 diabetes mellitus with stress as a risk factor.

scholarly journals Analysis of the Influence of Risk Factors of Type 2 DM on Outpatients at DKT Bandar Hospital Lampung, 2020

2021 ◽  
Vol 4 (2) ◽  
pp. 4-14
Author(s):  
Feby Esmiralda ◽  
Aila Karyus ◽  
Kodrat Pramudho
Keyword(s):  
Physical Activity ◽  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Univariate Analysis ◽  
Bivariate Analysis ◽  
P Value ◽  
History Of

DM is a chronic metabolic disease characterized by hyperglycemia and cause serious complications with an increasing prevalence rate. Control of risk factors that affect the incidence of DM is needed to prevent the emergence of DM and delay disease complications. The purpose of this study is to determine the risk factors that influence the incidence of type 2 diabetes outpatients at the DKT Bandar Lampung Hospital. This type of research is quantitative observational analytic with a case control approach. The population came from all patients undergoing outpatient treatment at the Internal Medicine Department of the DKT Hospital in Bandar Lampung with 44 case samples and 44 control samples. Data analysis used univariate analysis with percentages, bivariate analysis with Chi Square and multivariate analysis with multiple logistic regression. The results showed that there was a significant influence between the risk factors for age (p value 0.017), hereditary history of diabetes (p value 0.03), physical activity (p value 0.002) and obesity (p value 0.001) with the incidence of type 2 diabetes, while a history of hypertension has no effect on the incidence of type 2 diabetes mellitus (p value 0.135). Meanwhile, the most dominant variable influencing the incidence of outpatient type 2 diabetes mellitus at DKT Bandar Lampung Hospital is physical activity with OR 5.29. Maximum promotive, preventive, curative and rehabilitative efforts are needed to control risk factors for type 2 diabetes

Predicting Type 2 Diabetes: A Machine Learning Approach

2020 ◽  
Vol 9 (2) ◽  
pp. 603-608
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Random Forest ◽  
Performance Measures ◽  
The Body ◽  
Support Vector ◽  
Common Disease ◽  
Suitable Model ◽  
Classification Models

Diabetes is a well-known common disease among people around the world. Diabetes causes many anomalies in the body and results in the patients to become under a long term medication. Detecting diabetes has been done via hectic medical tests and causes a delay for the patients to get to know their test results. However, data mining and machine learning approaches are in the frontline supporting the health care domain to make effective predictions in this regard. This paper elaborates about predicting Type 2 Diabetes Mellitus using classification models. A suitable secondary dataset was used to build classification models and the more suitable model was selected via the valid performance measures. In this line, the Random Forest, Support Vector Machine, Naïve Bayes and Artificial Neural Network models were built. Based on the performance measures, Random Forest has been identified as the more suitable classifier with the accuracy of 90%, the recall and precision value of 0.90.

The Effect of Renal Dysfunction on Circulating Sclerostin Level in Patients with Type 2 Diabetes

2020 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Mahmoud El sebaie ◽  
mohamed arafat ◽  
mohamed fawzy ◽  
ibrahim salem
Keyword(s):  
Type 2 Diabetes ◽  
Renal Dysfunction ◽  
Sclerostin Level

Mechanisms of Protective Effects of SGLT2 Inhibitors in Cardiovascular Disease and Renal Dysfunction

2019 ◽  
Vol 19 (20) ◽  
pp. 1818-1849 ◽  
Author(s):  
Ban Liu ◽  
Yuliang Wang ◽  
Yangyang Zhang ◽  
Biao Yan
Keyword(s):  
Diabetes Mellitus ◽  
Heart Failure ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Renal Dysfunction ◽  
Sglt2 Inhibitors ◽  
Sodium Glucose Cotransporter ◽  
Glucose Reabsorption ◽  
Renal Glucose Reabsorption

: Type 2 diabetes mellitus is one of the most common forms of the disease worldwide. Hyperglycemia and insulin resistance play key roles in type 2 diabetes mellitus. Renal glucose reabsorption is an essential feature in glycaemic control. Kidneys filter 160 g of glucose daily in healthy subjects under euglycaemic conditions. The expanding epidemic of diabetes leads to a prevalence of diabetes-related cardiovascular disorders, in particular, heart failure and renal dysfunction. Cellular glucose uptake is a fundamental process for homeostasis, growth, and metabolism. In humans, three families of glucose transporters have been identified, including the glucose facilitators GLUTs, the sodium-glucose cotransporter SGLTs, and the recently identified SWEETs. Structures of the major isoforms of all three families were studied. Sodium-glucose cotransporter (SGLT2) provides most of the capacity for renal glucose reabsorption in the early proximal tubule. A number of cardiovascular outcome trials in patients with type 2 diabetes have been studied with SGLT2 inhibitors reducing cardiovascular morbidity and mortality. : The current review article summarises these aspects and discusses possible mechanisms with SGLT2 inhibitors in protecting heart failure and renal dysfunction in diabetic patients. Through glucosuria, SGLT2 inhibitors reduce body weight and body fat, and shift substrate utilisation from carbohydrates to lipids and, possibly, ketone bodies. These pleiotropic effects of SGLT2 inhibitors are likely to have contributed to the results of the EMPA-REG OUTCOME trial in which the SGLT2 inhibitor, empagliflozin, slowed down the progression of chronic kidney disease and reduced major adverse cardiovascular events in high-risk individuals with type 2 diabetes. This review discusses the role of SGLT2 in the physiology and pathophysiology of renal glucose reabsorption and outlines the unexpected logic of inhibiting SGLT2 in the diabetic kidney.

scholarly journals Vitamin D status and risk of type 2 diabetes in the Norwegian HUNT cohort study: does family history or genetic predisposition modify the association?

2021 ◽  
Vol 9 (1) ◽  
pp. e001948
Author(s):  
Marion Denos ◽  
Xiao-Mei Mai ◽  
Bjørn Olav Åsvold ◽  
Elin Pettersen Sørgjerd ◽  
Yue Chen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Family History ◽  
Genetic Predisposition ◽  
Effect Modification ◽  
P Value ◽  
Family History Of Diabetes ◽  
Increased Risk ◽  
History Of

IntroductionWe sought to investigate the relationship between serum 25-hydroxyvitamin D (25(OH)D) level and the risk of type 2 diabetes mellitus (T2DM) in adults who participated in the Trøndelag Health Study (HUNT), and the possible effect modification by family history and genetic predisposition.Research design and methodsThis prospective study included 3574 diabetes-free adults at baseline who participated in the HUNT2 (1995–1997) and HUNT3 (2006–2008) surveys. Serum 25(OH)D levels were determined at baseline and classified as <50 and ≥50 nmol/L. Family history of diabetes was defined as self-reported diabetes among parents and siblings. A Polygenic Risk Score (PRS) for T2DM based on 166 single-nucleotide polymorphisms was generated. Incident T2DM was defined by self-report and/or non-fasting glucose levels greater than 11 mmol/L and serum glutamic acid decarboxylase antibody level of <0.08 antibody index at the follow-up. Multivariable logistic regression models were applied to calculate adjusted ORs with 95% CIs. Effect modification by family history or PRS was assessed by likelihood ratio test (LRT).ResultsOver 11 years of follow-up, 92 (2.6%) participants developed T2DM. A higher risk of incident T2DM was observed in participants with serum 25(OH)D level of<50 nmol/L compared with those of ≥50 nmol/L (OR 1.72, 95% CI 1.03 to 2.86). Level of 25(OH)D<50 nmol/L was associated with an increased risk of T2DM in adults without family history of diabetes (OR 3.87, 95% CI 1.62 to 9.24) but not in those with a family history (OR 0.72, 95% CI 0.32 to 1.62, p value for LRT=0.003). There was no effect modification by PRS (p value for LRT>0.23).ConclusionSerum 25(OH)D<50 nmol/L was associated with an increased risk of T2DM in Norwegian adults. The inverse association was modified by family history of diabetes but not by genetic predisposition to T2DM.

scholarly journals The cross-sectional association of renal dysfunction with tests of cognition in middle-aged adults with early type 2 diabetes

2020 ◽  
pp. 107805
Author(s):  
Joshua I. Barzilay ◽  
Naji Younes ◽  
Rodica Pop-Busui ◽  
Hermes Florez ◽  
Elizabeth Seaquist ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Renal Dysfunction ◽  
Early Type ◽  
Middle Aged ◽  
Cross Sectional ◽  
The Cross ◽  
Middle Aged Adults
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