Association of major depression with blood pressure and vascular complications of type 2 diabetes mellitus

Background: The WHO estimates the diabetic population to increase to 366 million by 2030 worldwide, with maximum 79.4 million Indians. Depression is an undiagnosed co-morbidity leading to significant disability, non-compliance and postulated to cause poorer glycemic control leading to early disease complications. We aimed to detect depression and study its correlation with vascular complications among type 2 diabetes mellitus (T2DM) patients.Methods: In an observational study, 312 randomly selected T2DM patients were evaluated at tertiary care center in Northern India. Socio-demographic, clinical and laboratory data was collected. Montgomery Asberg depression rating scale (MADRS) was used to detect depression. Groups with and without major depression were compared for various diabetes variables. Statistical analysis was carried out using the SPSS version 14.0.Results: One third T2DM patients (32.05%) suffered from major depression. Depression was significantly associated with diabetic patients having cardiac (p 0.01), ophthalmic (p 0.04), nephropathy (p 0.01), cerebrovascular (p 0.001) complications and diabetic foot (p 0.04). However, depression showed no significant association with systolic blood pressure, neuropathic and infectious complications.Conclusions: Identification of depression and its appropriate management may go a long way in delaying diabetic vascular complications by improving treatment adherence and subsequently glycemic control.

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Association of major depression with blood pressure and vascular complications of type 2 diabetes mellitus

Background: The WHO estimates the diabetic population to increase to 366 million by 2030 worldwide, with maximum 79.4 million Indians. Depression is an undiagnosed co-morbidity leading to significant disability, non-compliance and postulated to cause poorer glycemic control leading to early disease complications. We aimed to detect depression and study its correlation with vascular complications among type 2 diabetes mellitus (T2DM) patients. Methods: In an observational study, 312 randomly selected T2DM patients were evaluated at tertiary care center in Northern India. Socio-demographic, clinical and laboratory data was collected. Montgomery Asberg depression rating scale (MADRS) was used to detect depression. Groups with and without major depression were compared for various diabetes variables. Statistical analysis was carried out using the SPSS version 14.0. Results: One third T2DM patients (32.05%) suffered from major depression. Depression was significantly associated with diabetic patients having cardiac (p 0.01), ophthalmic (p 0.04), nephropathy (p 0.01), cerebrovascular (p 0.001) complications and diabetic foot (p 0.04). However, depression showed no significant association with systolic blood pressure, neuropathic and infectious complications. Conclusions: Identification of depression and its appropriate management may go a long way in delaying diabetic vascular complications by improving treatment adherence and subsequently glycemic control. [Int J Res Med Sci 2016; 4(3.000): 926-930

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

BACKGROUND: Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient\u27s primary cancer remains fundamental to their treatment, CUP patients are significantly disadvantaged and most have a poor survival outcome. Developing robust and accessible diagnostic methods for resolving cancer tissue of origin, therefore, has significant value for CUP patients. METHODS: We developed an RNA-based classifier called CUP-AI-Dx that utilizes a 1D Inception convolutional neural network (1D-Inception) model to infer a tumor\u27s primary tissue of origin. CUP-AI-Dx was trained using the transcriptional profiles of 18,217 primary tumours representing 32 cancer types from The Cancer Genome Atlas project (TCGA) and International Cancer Genome Consortium (ICGC). Gene expression data was ordered by gene chromosomal coordinates as input to the 1D-CNN model, and the model utilizes multiple convolutional kernels with different configurations simultaneously to improve generality. The model was optimized through extensive hyperparameter tuning, including different max-pooling layers and dropout settings. For 11 tumour types, we also developed a random forest model that can classify the tumour\u27s molecular subtype according to prior TCGA studies. The optimised CUP-AI-Dx tissue of origin classifier was tested on 394 metastatic samples from 11 tumour types from TCGA and 92 formalin-fixed paraffin-embedded (FFPE) samples representing 18 cancer types from two clinical laboratories. The CUP-AI-Dx molecular subtype was also independently tested on independent ovarian and breast cancer microarray datasets FINDINGS: CUP-AI-Dx identifies the primary site with an overall top-1-accuracy of 98.54% in cross-validation and 96.70% on a test dataset. When applied to two independent clinical-grade RNA-seq datasets generated from two different institutes from the US and Australia, our model predicted the primary site with a top-1-accuracy of 86.96% and 72.46% respectively. INTERPRETATION: The CUP-AI-Dx predicts tumour primary site and molecular subtype with high accuracy and therefore can be used to assist the diagnostic work-up of cancers of unknown primary or uncertain origin using a common and accessible genomics platform. FUNDING: NIH R35 GM133562, NCI P30 CA034196, Victorian Cancer Agency Australia