Exploring the causal relationships between type 2 diabetes and neurological disorders using a Mendelian randomization strategy

Medicine, Journal Year: 2024, Volume and Issue: 103(46), P. e40412 - e40412

Published: Nov. 15, 2024

While there is ample evidence indicating an increased occurrence of general neurological conditions among individuals with diabetes, has been limited exploration into the cause-and-effect connection between type 2 diabetes (T2D) and specific disorders, including like carpal tunnel syndrome Bell’s palsy. We used Mendelian randomization (MR) approach to investigate causal effects T2D on 67 diseases. primarily utilized inverse-variance weighted method for analysis, also employed median MR-Egger methods in our study. To detect correct potential outliers, MR-PRESSO analysis was used. Heterogeneity assessed using Cochrane Q-values. The MR analyses found a possible relationship risk increase 8 diseases at suggestive level ( P < .05). Notably, positive findings that met false discovery rate threshold, nerve, nerve root, plexus disorders (odds ratio [OR] = 1.11; 95% confidence interval [CI] 1.08–1.15); (OR 1.05; CI 1.03–1.07) 1.10; 1.05–1.16) were identified. Our affirm association certain disorders.

Language: Английский

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Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Health Information Science and Systems, Journal Year: 2022, Volume and Issue: 10(1)

Published: Feb. 9, 2022

Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic production. The challenge of this work identify T2D-associated features that can distinguish T2D sub-types for prognosis treatment purposes. We thus employed machine learning (ML) techniques categorize patients using data from the Pima Indian Dataset Kaggle ML repository. After preprocessing, several feature selection were used extract subsets, range classification analyze these. then compared derived results best classifiers considering accuracy, kappa statistics, area under receiver operating characteristic (AUROC), sensitivity, specificity, logarithmic loss (logloss). To evaluate performance different classifiers, we investigated their outcomes summary statistics with resampling distribution. Therefore, Generalized Boosted Regression modeling showed highest accuracy (90.91%), followed (78.77%) specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Additive Model LOESS Models also gave maximum sensitivity (100%), AUROC (95.26%) lowest (30.98%) respectively. Notably, was top-ranked algorithm according non-parametric Friedman testing. Of identified these models, levels, body mass index, diabetes pedigree function, age consistently as most frequently accurate outcome predictors. These indicate utility methods in constructing improved prediction models successfully predictors population.The online version contains supplementary material available at 10.1007/s13755-021-00168-2.

Language: Английский

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In silico molecular docking and ADME/T analysis of Quercetin compound with its evaluation of broad-spectrum therapeutic potential against particular diseases

Informatics in Medicine Unlocked, Journal Year: 2022, Volume and Issue: 29, P. 100894 - 100894

Published: Jan. 1, 2022

Progression in computational research has made it possible for the silico methods to offer epochal benefits both regulatory needs and pharmaceutical industry assess safety profile. Myriad amounts of flavonoids are present human diet. They showed potential therapeutic effects against a wide range illnesses. One most ubiquitously distributed extensively studied is flavonol Quercetin (Quercetin). The current study aspires reveal as potent inhibitor Tuberculosis, Malaria, Inflammatory diseases, Breast cancer, Obesity, Alzheimer's disease analogy standard drugs each disease. A molecular docking with specific proteins associated diseases was done using Schrodinger Maestro (v11.1) software. QikProp module used ADME prediction, admetSAR online database evaluated toxicity ligand. Molecular results also higher scores than commercially available drugs. Moreover, properties delineated no carcinogenicity mutagenicity along lower rat acute & acceptable oral level. possessed (−9.00, −6.36, −8.53, −7.28, −7.89, −6.68 kcal/mol) Anti-tuberculosis, Anti-malarial, Anti-inflammatory, Antineoplastic (Breast –cancer), Anti-obesity Anti-Alzheimers drugs, respectively when compared Therefore, from score, we can conclude that be more inhibitory agent selected market. However, congenial clinical empirical studies required explicit an effectual candidate drug equitable treatment above-referred diseases.

Language: Английский

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Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus

Clinical Medicine Insights Endocrinology and Diabetes, Journal Year: 2023, Volume and Issue: 16

Published: Jan. 1, 2023

Type 2 diabetes mellitus (T2DM) is the most common metabolic disorder. The aim of present investigation was to identify gene signature specific T2DM.

Language: Английский

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Development of chitosan lipid nanoparticles to alleviate the pharmacological activity of piperine in the management of cognitive deficit in diabetic rats

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 8, 2024

The aim of the present study was to prepare and evaluate Piperine (PP) loaded chitosan lipid nanoparticles (PP-CLNPs) its biological activity alone or in combination with antidiabetic drug Metformin (MET) management cognitive deficit diabetic rats. successfully on CLNPs prepared using chitosan, stearic acid, Tween 80 Tripolyphosphate (TPP) at different concentrations. developed exhibited high entrapment efficiency that ranged from 85.12 97.41%, a particle size range 59.56-414 nm negatively charged zeta potential values (- 20.1 - 43.9 mV). In vitro release revealed enhanced PP compared free suspensions for up 24 h. vivo studies treatment optimized PP-CLNPs formulation (F2) exerted enhancing effect ameliorated oxidative stress associated diabetes. acted as an effective bio-enhancer which increased potency metformin protecting brain tissue diabetes-induced neuroinflammation memory deterioration. These results suggested could be promising delivery system encapsulating thus can used adjuvant therapy high-risk impairment conditions.

Language: Английский

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Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression

Briefings in Bioinformatics, Journal Year: 2020, Volume and Issue: 22(5)

Published: Nov. 13, 2020

Abstract Glioblastoma (GBM) is a common malignant brain tumor which often presents as comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate show an abnormality, but differ in cellular behavior. So, their etiology not well understood, nor it clear how influence behavior or growth. This led us to employ quantitative analytical framework unravel shared differentially expressed genes (DEGs) cell signaling pathways that could link using datasets acquired from the Gene Expression Omnibus database (GEO) The Cancer Genome Atlas (TCGA) where normal tissue disease-affected were examined. After identifying DEGs, we identified disease-gene association networks performed gene ontology (GO) analyses hub protein identifications predict roles of these DEGs. We expanded our study determine significant may play role progression survival patients by exploiting clinical genetic factors Cox Proportional Hazard Model Kaplan–Meier estimator. In this study, 177 DEGs 129 upregulated 48 downregulated identified. Our findings indicate new ways incidence progression, growth establishment also function biomarkers for prognosis potential targets therapies. comparison gold standard databases provides further proof support connection pathology underlying progression.

Language: Английский

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Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Informatics in Medicine Unlocked, Journal Year: 2021, Volume and Issue: 28, P. 100840 - 100840

Published: Dec. 30, 2021

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious ailment known as new coronavirus disease (COVID-19). Despite fact that prevalence COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and patients so unwell. Detecting biomarkers for using peripheral blood mononuclear cells (PBMCs) may aid drug treatment. This research aimed find cell transcripts represent levels gene expression associated progression. Through bioinformatics pipeline, two RNA-Seq transcriptomic datasets one microarray dataset were studied discovered 102 significant differentially expressed genes (DEGs) shared by three derived from PBMCs. To identify roles these DEGs, we disease-gene association networks signaling pathways, well performed ontology (GO) studies identified hub protein. Identified molecular pathways improved our understanding pathophysiology COVID-19, blood-based proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, TOP2A could be used therapeutic intervention. In subjects, effective putative connections between pathological processes cells, suggesting diagnose monitor disease's initiation progression developing therapeutics.

Language: Английский

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Bioinformatics and system biology approaches to identify pathophysiological impact of COVID-19 to the progression and severity of neurological diseases

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 138, P. 104859 - 104859

Published: Sept. 23, 2021

Language: Английский

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Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: June 16, 2022

Pulmonary arterial hypertension (PAH) is a chronic cardiopulmonary syndrome with high pulmonary vascular load and eventually causing RV heart failure even death. However, the mechanism of remains unclear. The purpose this research to detect underlying key genes potential PAH using several bioinformatic methods. microarrays GSE22356, GSE131793 GSE168905 were acquired from GEO. Subsequently, host bioinformatics techniques such as DAVID, STRING, R language Cytoscape utilized investigate DEGs between healthy controls conduct GO annotation, KEGG enrichment analysis PPI network construction etc. Additionally, we predicted transcription factors regulating through iRegulon plugin CIBERSORT was used immune infiltration analysis. One thousand two hundred seventy-seven (403 up-regulated 874 down-regulated) identified peripheral blood samples 32 patients 29 controls, among which SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 ITGA2B detected hub genes. functional changes mainly enriched in protein binding, extracellular exosome, space, region integral component plasma membrane. are chiefly at hemoglobin complex, microparticle, oxygen transporter activity. Among TF-DEGs network, 42 target 6 TFs an NES > 4 (TEAD4, TGIF2LY, GATA5, GATA1, GATA2, FOS). Immune showed that monocytes occupied largest proportion cells. trend results cells illustrated had higher NK cell activation, monocyte, T CD4 memory mast than lower naive. We most significant markers PAH. These these probably have precise regulatory relationships development

Language: Английский

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Shared genetics of psychiatric disorders and type 2 diabetes:a large-scale genome-wide cross-trait analysis

Journal of Psychiatric Research, Journal Year: 2023, Volume and Issue: 159, P. 185 - 195

Published: Jan. 28, 2023

Language: Английский

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