Diagnosis Of Diabetic Disease Research Articles

Comparison of Diabetic Retinopathy Lesions Identified Using Ultrawide Field Imaging and Optical Coherence Tomography Angiography

Introduction: Optical coherence tomography (OCT) angiography (OCTA) has the potential to influence the diagnosis and management of diabetic eye disease. This study aims to determine the correlation between diabetic retinopathy (DR) findings on ultrawide field (UWF) color photography (UWF-CP), UWF fluorescein angiography (UWF-FA), and OCTA. Methods: This is a cross-sectional, prospective study. One hundred and fourteen eyes from 57 patients with diabetes underwent mydriatic UWF-CP, UWF-FA, and OCTA. DR severity was assessed. Ischemic areas were identified on UWF-FA using ImageJ and the nonperfusion index (NPI) was calculated. Diabetic macular edema (DME) was assessed using OCT. Superficial capillary plexus vessel density (VD), vessel perfusion (VP), and foveal avascular zone (FAZ) area were automatically measured on OCTA. Pearson correlation coefficient between the imaging modalities was determined. Results: Forty-five eyes were excluded due to non-DR findings or prior laser photocoagulation; 69 eyes were analyzed. DR severity was associated with larger NPI (r = 0.55944, p < 0.0001) even after distinguishing between cones (Cone Nonperfusion Index [CPI]: r = 0.55617, p < 0.0001) and rods (Rod Nonperfusion Index [RPI]: r = 0.55285, p < 0.0001). In eyes with nonproliferative DR (NPDR), NPI is correlated with DME (r = 0.51156, p = 0.0017) and central subfield thickness (CST) (r = 0.67496, p < 0.0001). UWF-FA macular nonperfusion correlated with NPI (r = 0.42899, p = 0.0101), CPI (r = 0.50028, p = 0.0022), and RPI (r = 0.49027, p = 0.0028). Central VD and VP correlated with the DME presence (r = 0.52456, p < 0.0001; r = 0.51952, p < 0.0001) and CST (r = 0.50133, p < 0.0001; r = 0.48731, p < 0.0001). Central VD and VP were correlated with macular nonperfusion (r = 0.44503, p = 0.0065; r = 0.44239, p = 0.0069) in eyes with NPDR. Larger FAZ was correlated with decreased central VD (r = −0.60089, p = 0.0001) and decreased central VP (r = −0.59224, p = 0.0001). Conclusion: UWF-CP, UWF-FA, and OCTA findings provide relevant clinical information on diabetic eyes. Nonperfusion on UWF-FA is correlated with DR severity and DME. OCTA metrics of the superficial capillary plexus correlate with the incidence of DME and macular ischemia.

Associations between Diet Quality and Allostatic Load in US Adults: Findings from the National Health and Nutrition Examination Survey, 2015-2018.

Allostatic load (AL), the concept of cumulative biological risk from chronic stressful exposures, may provide a framework with which to examine the links between diet, physiological stress, and disease. This study examined the associations between diet quality and AL. This cross-sectional study was conducted using the 2015 through 2018 cycles of the National Health and Nutrition Examination Survey. Dietary intake was assessed using two 24-hour diet recalls. Diet quality was measured using the Healthy Eating Index 2015. AL was measured by a composite score of nine biochemical markers, with three or more dysregulated values signaling elevated AL. All markers were collected during a physical examination. This study was conducted in a nationally representative population of 5,343 US adults aged 19 years or older who had no prior diagnosis of diabetes (except gestational diabetes), cancer (except skin cancer), or cardiovascular disease; were not pregnant; and had complete dietary intake and AL biomarker data. The outcome was odds of elevated AL. Accounting for the complex survey design of the National Health and Nutrition Examination Survey, multivariable logistic regression was used to examine the associations between Healthy Eating Index 2015 score and elevated AL, adjusting for sociodemographic variables. A significant inverse association between AL and diet quality was observed among adults aged 31 years and older but not among those aged 19 to 30 years. Among adults aged 31 years and older, those in higher quintiles of Healthy Eating Index 2015 score had significantly lower odds of elevated AL compared with the lowest quintile (P for trend < 0.05). These results suggest that, in adults older than age 30 years, consuming a more healthful diet is inversely associated with AL.

Oxidized Albumin: Evaluation of Oxidative Stress as a Marker for the Progression of Kidney Disease.

Oxidative stress has been reported to be associated with the progression of renal pathology as well as with the onset of complications associated with this condition. Bardoxolone methyl, a nuclear factor-erythroid 2-related factor 2 (Nrf2) activator with anti-oxidative and inflammatory modulation effects, has been reported to improve renal function in clinical trials. As of this writing, there have been no systems for the quantitative evaluation of oxidative stress that could be applied as a clinical test. We recently reported on post-translational modifications of albumin using electrospray-ionization time-of-flight mass spectrometry (ESI-TOF MS) and the results indicated that oxidized albumin (cysteinylated albumin: a molecule that was oxidatively cysteinylated at Cys34) was found to increase with the progression of renal pathology. A clinical study demonstrated that monitoring the levels of oxidized albumin could be useful for the early diagnosis of diabetic kidney disease and for predicting renal prognosis. Higher levels of serum oxidized albumin were also associated with cardiovascular complications or sarcopenia in patients who were undergoing hemodialysis. The overall findings indicate that oxidized albumin could also be an index of therapeutic efficacy against kidney disease. Oxidized albumin is not only a marker for kidney disease but also a nephrotoxic substance. This suggests that measuring the levels of oxidized albumin could be a biomarker for diagnosing the progression of and complications associated with renal disease in addition to evaluating the efficacy of therapeutic drugs.

Identification of Markers for Diagnosis and Treatment of Diabetic Kidney Disease Based on the Ferroptosis and Immune.

In advanced diabetic kidney disease (DKD), iron metabolism and immune dysregulation are abnormal, but the correlation is not clear. Therefore, we aim to explore the potential mechanism of ferroptosis-related genes in DKD and their relationship with immune inflammatory response and to identify new diagnostic biomarkers to help treat and diagnose DKD. Download data from gene expression omnibus (GEO) database and FerrDb database, and construct random forest tree (RF) and support vector machine (SVM) model to screen hub ferroptosis genes (DE-FRGs). We used consistent unsupervised consensus clustering to cluster DKD samples, and enrichment analysis was performed by Gene Set Variation Analysis (GSVA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) and then assessed immune cell infiltration abundance using the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms. Ferroptosis scoring system was established based on the Boruta algorithm, and then, core compounds were screened, and binding sites were predicted by Coremine Medical database. We finally established a 7-gene signature (DUSP1, PRDX6, PEBP1, ZFP36, GABARAPL1, TSC22D3, and RGS4) that exhibited good stability across different datasets. Consistent clustering analysis divided the DKD samples into two ferroptosis modification patterns. Meanwhile, autophagy and peroxisome pathways and immune-related pathways can participate in the regulation of ferroptosis modification patterns. The abundance of immune cell infiltration differs significantly across patterns. Further, molecular docking results showed that the core compound could bind to the protein encoded by the core gene. Our findings suggest that ferroptosis modification plays a crucial role in the diversity and complexity of the DKD immune microenvironment, and the ferroptosis score system can be used to effectively verify the relationship between ferroptosis and immune cell infiltration in DKD patients. Kaempferol and quercetin may be potential drugs to improve the immune and inflammatory mechanisms of DKD by affecting ferroptosis.

Extracellular vesicles metabolic changes reveals plasma signature in stage-dependent diabetic kidney disease

Objective Early diagnosis of diabetic kidney disease (DKD) has long been a complex problem. This study aimed to analyze the metabolomic characteristics of plasma extracellular vesicles (EVs) at different stages of DKD in order to evaluate the metabolites of plasma EVs and select new biomarkers for the early diagnosis of DKD. Patients and methods A total of 78 plasma samples were collected, including samples from 20 healthy controls, 20 patients with type 2 diabetes mellitus (T2DM), 18 patients with DKD stage III, and 20 patients with DKD stage IV. In addition, EVs were isolated for metabolomics analysis. Results The results identified differences in EV metabolomic characteristics in DKD patients at different stages, as well as significant differences in EV metabolomics between T2DM patients without DKD and patients with DKD. Ten Significantly differential metabolites were associated with the occurrence and progression of DKD. Uracil, LPC(O-18:1/0:0), sphingosine 1-phosphate, and 4-acetamidobutyric acid were identified as potential early biomarkers for DKD, showing excellent predictive performance. Conclusion Uracil, LPC(O-18:1/0:0), sphingosine 1-phosphate, and 4-acetamidobutyric acid exhibited potential as suitable biomarkers for early DKD diagnosis. Unexpectedly, combining these four candidate metabolites resulted in enhanced predictive ability for DKD.

Epidemiology, comorbidities at diagnosis and outcomes associated with HIV late diagnosis from 2010 to 2019 in a Belgian reference centre: A retrospective study.

Our objective was to investigate the demographic factors, comorbidities, and outcomes of patients with a late diagnosis (LD) of HIV in a Belgian HIV reference centre. All patients with HIV who presented for care between 2010 and 2019 were included. They were excluded if time between diagnosis and presentation or first CD4 count exceeded 6months or if they had previously received antiretroviral therapy (ART). LD was defined as a CD4 cell count ≤350/mm3 or an AIDS-defining event at diagnosis. Data were retrospectively collected and included data on demographic variables, cardiovascular risk factors, comorbidities at diagnosis, first prescribed ART, and outcomes. Logistic regression was used to determine factors associated with LD. Of 1078 patients, 427 (39.6%) were LD. In multivariable analysis, the following factors were associated with LD: non-homosexual transmission route, being born in Sub-Saharan Africa (SSA), and age ≥35 years. Prevalence at diagnosis of malignancies, diabetes, and cardiovascular diseases did not differ between non-LD and LD, whereas renal impairment was more frequent in LD. In univariable analysis, high-density lipoprotein (HDL) cholesterol <40 mg/dL and estimated glomerular filtration rate <60 ml/min were associated with LD; in multivariable analysis, only HDL cholesterol <40 mg/dL was associated. Patients with LD experienced more adverse events leading to a switch in ART, virological failure, and death during follow-up. LD remains common in our centre, especially in non-homosexual patients and those born in SSA. Although not associated with an important burden of comorbidities at diagnosis, it still results in poorer outcomes, emphasizing the need to expand coverage and access to HIV testing.

Novel Urinary Metabolite Biomarkers in Diagnosis of Diabetic Kidney Disease

Novel Urinary Metabolite Biomarkers in Diagnosis of Diabetic Kidney Disease

Ultrasonography Combined with Blood Biochemistry on the Early Diagnosis of Diabetic Kidney Disease.

Objective Diabetic kidney disease (DKD) has been well recognized as a microvascular complication of diabetes mellitus. Perfusion of intrarenal arteries is closely related with development of DKD. The aim of the present study was to investigate relation of ultrasonography performance of intrarenal arteries and grade of DKD. Methods From May to December at 2021, a total of 54 DKD patients and 36 non-DKD cases were recruited. Ultrasonography performance of intrarenal and arteries at lower extremity was examined by high-resolution ultrasound diagnostic equipment; maximum (Vsmax) as well as minimum (Vdmin) blood velocity of arteries were recorded, and resistance index (RI) of arteries were calculated. Blood routine and biochemical parameters were determined from clinical laboratory of our hospital. Results According to eGFR grading, 42.50% of the 54 DKD cases are at Grade 1, and 18.52%, 11.11%, 9.26%, and 18.52% cases were at Grade 2, 3a, 3b, and 4-5, respectively. Blood urea and creatinine were significantly positively related with progress of DKD, while level of Hb was negatively related with DKD. By ultrasonography; we found that Vsmax and Vdmin of main renal artery (MRA), segmental renal artery (SRA), and interlobular renal artery (IRA) were significantly reduced compared with healthy cases; IR of the above arteries was dramatically elevated, and changes of the above data were more obvious than that of lower extremity. Vdmin of MRA, SRA, and IRA was negatively related with grading of DKD, while RI was positively related with the grading. Converging from RI and level of Hb, we found that the level of Hb is positively related with healthy status of the kidney, while RI of the arteries is negatively with that. Conclusions Resistance index (RI) of intrarenal arteries, obtained from ultrasonography combining with level of hemoglobin (Hb), is the predictor of progress of DKD.

Interpretation of Urine Routine Report of a Diabetic Patient

Introduction: Diabetic patients are at an increased risk of multiple complications, among which one of the most dreaded complications is diabetic nephropathy. Early diagnosis and appropriate management will at least prolong the life of the kidney and prevent the development of end-stage renal failure. Materials and Methods: Simple urinalysis is a useful tool for early diagnosis of diabetic kidney disease and helps differentiate between diabetic and nondiabetic kidney diseases. In this review, we have discussed the usefulness of urinalysis for a diabetic patient. Results: Early detection of an abnormality can be an alarming sign, and we can diagnose treatable causes of renal failure in diabetic patients. Conclusions: History, examination, and urinalysis are sufficient in most of the cases for diagnosing diabetic kidney disease. Renal biopsy is indicated in special situations where diagnosis cannot be made even after appropriate non-invasive investigations. Few limitations of the study are resource settings and knowledge of this simple test to predict an alarming sign of the kidney disease.

Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture

Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e-Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real-time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI-ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F -measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT-fog-cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS-key is describing text of that key PACS-key describing text of that key.

Identification and validation of key biomarkers for the early diagnosis of diabetic kidney disease.

Background: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. This study explored the core genes and pathways associated with DKD to identify potential diagnostic and therapeutic targets. Methods: We downloaded microarray datasets GSE96804 and GSE104948 from the Gene Expression Omnibus (GEO) database. The dataset includes a total of 53 DKD samples and 41 normal samples. Differentially expressed genes (DEGs) were identified using the R package “limma”. The Metascape database was subjected to Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to identify the pathway and functional annotations of DEGs. A WGCAN network was constructed, the hub genes in the turquoise module were screened, and the core genes were selected using LASSO regression to construct a diagnostic model that was then validated in an independent dataset. The core genes were verified by in vitro and in vivo experiments. Results: A total of 430 DEGs were identified in the GSE96804 dataset, including 285 upregulated and 145 downregulated DEGs. WGCNA screened out 128 modeled candidate gene sets. A total of eight genes characteristic of DKD were identified by LASSO regression to build a prediction model. The results showed accuracies of 99.15% in the training set (GSE96804) and 94.44% and 100%, respectively, in the test (GSE104948-GPL22945 and GSE104948-GPL24120). Three core genes (OAS1, SECTM1, and SNW1) with high connectivity were selected among the modeled genes. In vitro and in vivo experiments confirmed the upregulation of these genes. Conclusion: Bioinformatics analysis combined with experimental validation identified three novel DKD-specific genes. These findings may advance our understanding of the molecular basis of DKD and provide potential therapeutic targets for its clinical management.

Temperature and non-communicable diseases: Evidence from Indonesia's primary health care system.

Climate change induced rising temperatures will pose a detrimental threat to decent health in the coming decades. Especially at risk are individuals with chronic diseases, since heat can exacerbate a variety of health conditions. In this article, I examine the heat-morbidity relationship in the context of Indonesia, focusing on chronic, non-communicable diseases, namely diabetes, cardiovascular and respiratory diseases. Using a novel dataset from the Indonesian national health insurance scheme Jaminan Kesehatan Nasional/Badan Penyelenggara Jaminan Sosial (BPJS) and linking it with meteorological data on the daily-district level, I estimate the causal effect of high temperatures on the daily number of primary health care visits. The results show that on a hot day all-cause visits and visits with a diagnosis of diabetes and cardiovascular diseases increase by 8%, 25% and 14%, respectively. These increases are permanent and not offset by visit displacement or 'harvesting'. Visits related to respiratory diseases seem not to be affected by high temperatures. I use several climate change scenarios to predict the increase in visits and costs by the end of the century, which all forecast a substantial financial burden for the health care system. These results might have relevance for other middle-income countries with similar climatic conditions.

Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets

Screening and diagnosis of diabetic retinopathy disease is a well known problem in the biomedical domain. The use of medical imagery from a patient’s eye for detecting the damage caused to blood vessels is a part of the computer-aided diagnosis that has immensely progressed over the past few years due to the advent and success of deep learning. The challenges related to imbalanced datasets, inconsistent annotations, less number of sample images and inappropriate performance evaluation metrics has caused an adverse impact on the performance of the deep learning models. In order to tackle the effect caused by class imbalance, we have done extensive comparative analysis between various state-of-the-art methods on three benchmark datasets of diabetic retinopathy: - Kaggle DR detection, IDRiD and DDR, for classification, object detection and segmentation tasks. This research could serve as a concrete baseline for future research in this field to find appropriate approaches and deep learning architectures for imbalanced datasets.

Blended Ensemble Learning Prediction Model for Strengthening Diagnosis and Treatment of Chronic Diabetes Disease.

Diabetes mellitus (DM), commonly known as diabetes, is a collection of metabolic illnesses characterized by persistently high blood sugar levels. The signs of elevated blood sugar include increased hunger, frequent urination, and increased thirst. If DM is not treated properly, it may lead to several complications. Diabetes is caused by either insufficient insulin production by the pancreas or an insufficient insulin response by the body's cells. Every year, 1.6 million individuals die from this disease. The objective of this research work is to use relevant features to construct a blended ensemble learning (EL)-based forecasting system and find the optimal classifier for comparing clinical outputs. The EL based on Bayesian networks and radial basis functions has been proposed in this article. The performances of five machine learning (ML) techniques, namely, logistic regression (LR), decision tree (DT) classifier, support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF), are compared with the proposed EL technique. Experiments reveal that the EL method performs better than the existing ML approaches in predicting diabetic illness, with the remarkable accuracy of 97.11%. The proposed ensemble learning could be useful in assisting specialists in accurately diagnosing diabetes and assisting patients in receiving the appropriate therapy.

Lack of Awareness of Own Hypercholesterolemia or Statin Medication among Adult Statin Users in the United States: Prevalence and Patient Characteristics in a Repeated Cross-Sectional Study.

Knowledge of a patient’s medication is important in treating hyperlipidemia; however, little is known about this in practice. We carried out a repeated cross-sectional study to analyze a nationally representative sample of US adult statin users from the National Health and Nutrition Examination Survey, 1999–2018. We used medication bottle checks and self-reported survey data to estimate the percentage of individuals who are unaware of their hypercholesterolemia, type of medication, or how to take their medication. We used logistic regression to examine their characteristics. We included 8798 statin users; however, 17.6% were unaware of their hypercholesterolemia or statin use. Being older, male, non-Hispanic Black, taking a wider range of prescription medications, and previous diabetes or cardiovascular disease diagnosis were associated with lack of awareness. Serum low-density lipoprotein cholesterol level was lower among those lacking awareness (85.5 vs. 100.7 mg/dL; p < 0.001). Many of those unaware of drug type had been given little information about statins; 34.0% had no diagnosis of diabetes or cardiovascular disease, and of these, 27.1% were >75 years old. Roughly one in six lacked awareness, but no association was found with hypercholesterolemia control. Healthcare providers should ascertain a patient’s understanding and consider the risks and benefits of statin medication.

Computer-controlled diabetes disease diagnosis technique based on fuzzy inference structure for insulin-dependent patients

Computer-controlled diabetes disease diagnosis technique based on fuzzy inference structure for insulin-dependent patients

MP48-19 RISK OF METABOLIC AND CARDIOVASCULAR ADVERSE EVENTS WITH ABIRATERONE OR ENZALUTAMIDE AMONG MEN WITH ADVANCED PROSTATE CANCER

You have accessJournal of UrologyCME1 May 2022MP48-19 RISK OF METABOLIC AND CARDIOVASCULAR ADVERSE EVENTS WITH ABIRATERONE OR ENZALUTAMIDE AMONG MEN WITH ADVANCED PROSTATE CANCER Lillian Lai, Mary Oerline, Megan Caram, Phoebe Tsao, Samuel Kaufman, Brent Hollenbeck, and Vahakn Shahinian Lillian LaiLillian Lai More articles by this author , Mary OerlineMary Oerline More articles by this author , Megan CaramMegan Caram More articles by this author , Phoebe TsaoPhoebe Tsao More articles by this author , Samuel KaufmanSamuel Kaufman More articles by this author , Brent HollenbeckBrent Hollenbeck More articles by this author , and Vahakn ShahinianVahakn Shahinian More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002619.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Abiraterone and enzalutamide, used with androgen deprivation, are the most common treatments for men with advanced prostate cancer. Both have anti-androgenic properties and abiraterone is prescribed with prednisone, which may contribute to metabolic or cardiovascular adverse events. To understand the safety of abiraterone and enzalutamide use in real-world settings, we examined the association between the use of these agents and the risk of metabolic or cardiovascular events while on treatment. METHODS: 56,230 men with advanced prostate cancer were identified in a 20% sample of the 2011-2017 national Medicare claims. Abiraterone or enzalutamide use was analyzed as a time-dependent covariate. The primary outcome was the occurrence of a major metabolic or cardiovascular event, defined as an emergency room visit or hospitalization associated with a primary diagnosis of diabetes, hypertension, or cardiovascular disease. The secondary outcome was the occurrence of a minor metabolic or cardiovascular event, defined as an outpatient visit associated with a primary diagnosis of the aforementioned conditions. Men who experienced an event of interest in the 12 months prior to the study start date were excluded. The risks of primary and secondary composite outcomes were assessed using Cox regression. The occurrence of adverse events by specific diagnoses was also examined individually. RESULTS: Per unadjusted incidence rates, men receiving abiraterone had 1 additional emergency room visit/hospitalization due to a cardiovascular event for every 29 men treated (median time on drug: 4.7 months). Compared to men not receiving abiraterone, men receiving the drug were at increased risk of both a major composite adverse event (HR 1.77 95% CI 1.53-2.05 p <0.01) and a minor composite adverse event (HR 1.24 95% CI 1.05-1.47 p=0.01) (Table). Compared to men not receiving enzalutamide, men receiving enzalutamide were at an increased risk of a major composite adverse event (HR 1.22 95% CI 1.01-1.48 p=0.04) but not a minor composite adverse event (HR 1.04 95% CI 0.83-1.30 p=0.75). CONCLUSIONS: Treatment with abiraterone or enzalutamide is associated with increased risk of major metabolic or cardiovascular events in real-world settings. Source of Funding: AHRQ R01HS027507NCI T32 CA180984 Prostate Cancer Foundation © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e828 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Lillian Lai More articles by this author Mary Oerline More articles by this author Megan Caram More articles by this author Phoebe Tsao More articles by this author Samuel Kaufman More articles by this author Brent Hollenbeck More articles by this author Vahakn Shahinian More articles by this author Expand All Advertisement PDF DownloadLoading ...

Risk of Metabolic and Cardiovascular Adverse Events With Abiraterone or Enzalutamide Among Men With Advanced Prostate Cancer.

Abiraterone and enzalutamide are the most common oral agents for the treatment of men with advanced prostate cancer. To understand their safety profiles in real-world settings, we examined the association between the use of abiraterone or enzalutamide and the risk of metabolic or cardiovascular adverse events while on treatment. Men with advanced prostate cancer and their use of abiraterone or enzalutamide were identified in a 20% sample of the 2010-2017 national Medicare claims. The primary composite outcome was the occurrence of a major metabolic or cardiovascular adverse event, defined as an emergency room visit or hospitalization associated with a primary diagnosis of diabetes, hypertension, or cardiovascular disease. The secondary composite outcome was the occurrence of a minor metabolic or cardiovascular adverse event, defined as an outpatient visit associated with a primary diagnosis of the aforementioned conditions. Risks were assessed separately for abiraterone and enzalutamide using Cox regression. All statistical tests were 2-sided. Compared with men not receiving abiraterone, men receiving abiraterone were at increased risk of both a major composite adverse event (hazard ratio [HR] = 1.77, 95% confidence interval [CI] = 1.53 to 2.05; P < .001) and a minor composite adverse event (HR = 1.24, 95% CI = 1.05 to 1.47; P = .01). Compared with men not receiving enzalutamide, men receiving enzalutamide were at an increased risk of a major composite adverse event (HR = 1.22, 95% CI = 1.01 to 1.48; P = .04) but not a minor composite adverse event (HR = 1.04, 95% CI = 0.83 to 1.30; P = .75). Careful monitoring and management of men on abiraterone or enzalutamide through team-based approaches are critical.

Diagnosis of diabetic nephropathy and chronic kidney disease in prepubertal children with type 1 diabetes mellitus

The state of renal function in children with type 1 diabetes mellitus and small, up to 5 years old, disease experience was studied. The aim of the study was to determine the indicators of the initial stages of the formation of diabetic nephropathy and chronic kidney disease. A prospective cohort study of a group of 46 prepubertal patients with type 1 diabetes mellitus and less than 5 years of illness was carried out. Diabetic nephropathy at the stage of microalbuminuria was detected already in 8% of children. Signs of the first, preclinical stage of chronic kidney disease, manifested by hyperfiltration, were found in 20% of patients, impaired tubular functions — in 28%. A moderate decrease in glomerular filtration rate corresponding to stage 2 of chronic kidney disease was observed in 4% of children with diabetes. An increase in the concentration of endothelial growth factor in the blood serum and the morning portion of urine, as well as monocyte chemoattractant protein-1 in the blood serum are sensitive highly specific markers of the formation of chronic kidney disease in patients of prepubertal age with a history of diabetes mellitus of less than 5 years. The relationship between the characteristics of the course of diabetes mellitus with endothelial growth factor and monocyte chemoattractant protein-1 was not revealed, which reflects the independent mechanism of the influence of neoangiogenesis and the reaction of non-infectious inflammation on the formation of diabetic nephropathy and chronic kidney disease. The relationship between vascular endothelial growth factor and monocyte chemoattractant protein-1 with the formation of early signs of chronic kidney disease and diabetic nephropathy has been revealed. The results obtained can be used for early diagnosis of diabetic nephropathy and the formation of a risk group for kidney damage in children with diabetes mellitus.

IDF21-0460 How many urine samples are required for diagnosis and monitoring of diabetic kidney disease?

Background: Measurement of the albumin/creatinine ratio (UACR) from a random spot urine sample is commonly used for diabetic kidney disease (DKD) assessment. High day-to-day variability presents a challenge for both monitoring and diagnosis. Guidelines recommend performing multiple UACR tests for diagnosis, but consensus on the number of tests required has not been reached. Aim: To determine the within-individual variability of random spot UACR in people with type 2 diabetes and its implications for the number of samples required for monitoring and diagnosis of DKD. Method: People with type 2 diabetes (n=383; 66±9 years, 69% male) participating in the Progression of Diabetic Complications (PREDICT) prospective cohort study provided four random spot urine samples within a four-week period. UACR was measured according to standard hospital pathology laboratory protocols. Participants were classified as having normo- (69%; UACR <3mg/mmol), micro- (26%; UACR 3-30mg/mmol) or macroalbuminuria (5%; UACR>30mg/mmol), per the geometric mean of four samples. Within-individual variability was characterised by 95% limits of agreement (range encompassing 95% of differences between one/two/three samples vs. the mean of four). Relative reliability (the degree to which an individual maintains their rank in a population over repeated measures) was based on the intraclass correlation coefficient (ICC). We also determined the diagnostic accuracy of classifying albuminuria (UACR ≥3mg/mmol) based on a randomly selected subset of one/two/three samples (vs. mean of four using area under receiver operating characteristic [AUROC] curve analyses). Results: The 95% limits of agreement were derived for log-transformed UACR data (due to skewness), and then back-transformed to produce 95% limits as a ratio. These 95% limits (Table 1) showed that a single sample could be expected to be between 0.44 and 2.34 times the four-sample mean. This variability narrowed to 0.64/1.57 for the two-sample mean, and 0.75/1.28 for the three-sample mean. Relative reliability was more favourable (ICC=0.88 [0.87–0.90]).Table 1Agreement between UACR assessed using one/two/three urine samples, compared with the mean of four samplesOne sampleTwo samplesThree samples95% limits of agreement of one/two/three sample mean (X), with the four-sample mean (Y)Upper limit of ratio (X/Y)2.341.571.28Lower limit of ratio (X/Y)0.440.640.75Diagnostic metrics using classification of four-sample mean as the reference standardAUROC0.9540.9890.995Sensitivity82.4%91.6%92.4%Specificity90.9%95.5%98.5%PPV80.3%90.1%96.5%NPV92.0%96.2%96.7%Correctly classified88.3%94.3%96.6% Open table in a new tab AUROC curve analyses and corresponding diagnostic test metrics were calculated (Table 1). The AUROC was 0.95 for a single sample compared with the four-sample classification, rising to 0.99 for when two/three samples were averaged, respectively. Similarly, sensitivity was >80% for one sample, rising to >90% for two-sample and three-sample means. Discussion: The wide limits of agreement between results from one/two/three urine samples compared with four, suggests multiple samples are necessary to monitor UACR on a continuous scale. However, the high ICC and AUROC values for even a single UACR measurement suggest that this variability has only modest impact on diagnosis/classification. A higher number of repeated UACR measurements may be required for DKD monitoring, than for diagnosis.