Genetic model of MS severity predicts future accumalation of disability
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PROVIDER: PRJNA545549 | ENA |
REPOSITORIES: ENA
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Project description:Genetic model of MS severity predicts future accumalation of disability
| PRJNA545548 | ENA
Project description:Genetic model of MS severity predicts future accumalation of disability
| PRJNA545549 | ENA
Project description:No genetic modifiers of multiple sclerosis (MS) severity have been independently validated, leading to a lack of insight into genetic determinants of the rate of disability progression. We investigated genetic modifiers of MS severity in prospectively acquired training (N = 205) and validation (N = 94) cohorts, using the following advances: (1) We focused on 113 genetic variants previously identified as related to MS severity; (2) We used a novel, sensitive outcome: MS Disease Severity Scale (MS-DSS); (3) Instead of validating individual alleles, we used a machine learning technique (random forest) that captures linear and complex nonlinear effects between alleles to derive a single Genetic Model of MS Severity (GeM-MSS). The GeM-MSS consists of 19 variants located in vicinity of 12 genes implicated in regulating cytotoxicity of immune cells, complement activation, neuronal functions, and fibrosis. GeM-MSS correlates with MS-DSS (r = 0.214; p = 0.043) in a validation cohort that was not used in the modeling steps. The recognized biology identifies novel therapeutic targets for inhibiting MS disability progression.
| S-EPMC6898742 | biostudies-literature
Project description:A Gene Expression Signature that Predicts the Future Onset of Drug-Induced Renal Tubular Toxicity These data support the publication titled "A Gene Expression Signature that Predicts the Future Onset of Drug-Induced Renal Tubular Toxicity" Copyright (c) 2005 by Iconix Pharmaceuticals, Inc. Guidelines for commercial use: http://www.iconixbiosciences.com/guidelineCommUse.pdf replicated drug treatments with controls
2005-11-30 | E-GEOD-3210 | biostudies-arrayexpress
Project description:<p>The goal of "Comprehensive Multimodal Analysis of Neuroimmunological Diseases of the CNS" is to define the pathophysiological mechanisms underlying the development of disability in immune-mediated disorders of the central nervous system (CNS) and to distinguish these from physiological (and often beneficial) responses of the human immune system to CNS injury. The long-term objective of the trial is to acquire knowledge that would allow us to therapeutically inhibit the pathogenic mechanisms and enhance repair mechanisms in immune-mediated CNS diseases, thereby minimizing the extent of CNS tissue damage and promoting recovery.</p> <p>To date, 460 patients with a confirmed diagnosis of multiple sclerosis (MS) have been enrolled into the natural history clinical trial. In addition to standardized clinical, functional, neuroimaging and molecular/immunological data, blood samples were also collected for genetic research. However, only 299 study participants with confirmed MS currently have whole genome sequencing data available.</p> <p>In addition to the genome-wide data available for the 299 MS patients, this dbGaP submission provides demographic and phenotypic information for each subject collected at various points throughout the trial. We include race and family history of MS collected at the baseline visit as well as age and measures of disease severity collected at the most recent visit. As these data were randomized into discovery and validation cohorts, we also indicate the assigned group in the phenotypic data.</p> <p>It is hoped that these data may be applied to the development of clinically-useful tools such as diagnostic tests and new, sensitive scales of neurological disability, disease severity and CNS tissue destruction.</p>
| phs001833 | dbGaP
Project description:A Gene Expression Signature that Predicts the Future Onset of Drug-Induced Renal Tubular Toxicity These data support the publication titled "A Gene Expression Signature that Predicts the Future Onset of Drug-Induced Renal Tubular Toxicity" Copyright (c) 2005 by Iconix Pharmaceuticals, Inc. Guidelines for commercial use: http://www.iconixbiosciences.com/guidelineCommUse.pdf Keywords: time course
2005-12-01 | GSE3210 | GEO
Project description:The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n?=?133) and validation (n?=?68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r?=?0.5448, p?=?1.56e-06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions.
| S-EPMC5686060 | biostudies-literature
Project description:We investigated whether circulating microRNAs (miRNAs) are associated with residual insulin secretion at diagnosis and predict the severity of its future decline. We studied 53 newly diagnosed subjects enrolled in placebo groups of TrialNet clinical trials. We measured serum levels of 2,083 miRNAs using RNAseq technology, in fasting samples from the baseline visit (<100 days from diagnosis), during which residual insulin secretion was measured with a mixed meal tolerance test (MMTT). Area under the curve (AUC) C-peptide and peak C-peptide were stratified by quartiles of expression of 31 miRNAs. After adjustment for baseline C-peptide, age, BMI and sex, baseline levels of miR-3187-3p, miR-4302, and the miRNA combination of miR-3187-3p/miR-103a-3p predicted differences in MMTT C-peptide AUC/peak levels at the 12-month visit; the combination miR-3187-3p/miR-4723-5p predicted proportions of subjects above/below the 200 pmol/L clinical trial eligibility threshold at the 12-month visit. Thus, miRNA assessment at baseline identifies associations with C-peptide and stratifies subjects for future severity of C-peptide loss after 1 year. We suggest that miRNAs may be useful in predicting future C-peptide decline for improved subject stratification in clinical trials.
2020-09-01 | GSE157177 | GEO
Project description:A non-synonymous SNP in SIRT6 predicts neurological severity in Friedreich ataxia
| PRJNA827169 | ENA
Project description:To elucidate host response elements that define impending decompensation during SARS-CoV-2 infection, we enrolled subjects hospitalized with COVID-19 who were matched for disease severity and comorbidities at the time of admission and initial sampling. We then performed combined single-cell RNA sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) on peripheral blood mononuclear cells (PBMCs) from the time of admission and compared subjects who improved from their initial low-level oxygen requirement (no change from initial WHO Ordinal category 4/5) to those who later clinically decompensated and required invasive mechanical ventilation or died (WHO Ordinal score 7/8). Chromatin accessibility and transcriptomic immune profiles were markedly altered at admission in patients who will go on to develop critical illness. The greatest immunologic signals were seen in CD4+ T cells, inflammatory T cells, dendritic cells, and NK cell subsets, where an aggregate multiomic signature score calculated at admission offered strong prediction of future clinical deterioration (area under the receiver operating characteristic curve (auROC) 0.91). Epigenetic and transcriptional changes in PBMCs reveal unique, early, and conserved aspects of the immune response before typical clinical signals of decompensation are apparent and thus provide novel biomarkers that can predict future disease severity.
2023-10-17 | GSE215915 | GEO