Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SEARCH for Diabetes in Youth Study - Genetic Risk Score

ABSTRACT: SEARCH for Diabetes in Youth Study - Genetic Risk Score

PROVIDER: PRJNA782026 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

SEARCH for Diabetes in Youth Study - Genetic Risk Score

Project description:SEARCH for Diabetes in Youth Study - Genetic Risk Score

| PRJNA782027 | ENA

Predictive computational obesity risk framework through integration of gene expression profiles and genetic risk score.

Project description:We aimed to predict obesity risk with genetic data, specifically, obesity-associated gene expression profiles. Genetic risk score was computed. The genetic risk score was significantly correlated with BMI when an optimization algorithm was used. Linear regression and built support vector machine models predicted obesity risk using gene expression profiles and the genetic risk score with a new mathematical method.

2018-01-25 | GSE109597 | GEO

D-score - D-score: a search engine independent MD-score

Project description:Processing of the dataset of synthetic phosphopeptides by Savitzki et al. (MCP, 2011) using multiple search engines. Establishment of the D-score: a search engine independent MD-score.

2013-01-23 | PXD000021 | Pride

Perfluorinated compounds and high fat diet in relation to CVD-relevant metabolomic pathways in the SEARCH for Diabetes in Youth study

Project description:This project aims to evaluate the association between environmental exposure to perfluorinated alkyl substances (PFCs) and the development of risk factors for cardiometabolic disease in youth diagnosed with diabetes in the SEARCH Cohort Study. The longitudinal study of newly diagnosed cases of type 1 and type 2 diabetes examines serum metabolome changes at baseline and follow-up at approximately 5 years (all >3 years from baseline). Exposures to PFCs and biological effects characterized by serum metabolome changes will be associated with known cardiometabolic risk factors in youth diagnosed with diabetes.

2021-10-06 | ST001932 | MetabolomicsWorkbench

Serum Protein Risk Stratification Score for Diagnostic Evaluation of Metabolic Dysfunction-Associated Steatohepatitis (MASH)

Project description:Reliable non-invasive tools to diagnose at risk metabolic dysfunction-associated steatohepatitis (MASH) are urgently needed to improve management. We developed a risk stratification score incorporating proteomics-derived serum markers with clinical variables to identify high risk MASH patients (NAFLD Activity Score (NAS) >4 and fibrosis score >2). In this three-phase proteomic study of biopsy-proven metabolic dysfunction-associated steatotic fatty liver disease (MASLD), we first developed a multi-protein predictor for discriminating NAS>4 based on SOMAscan proteomics quantifying 1,305 serum proteins from 57 US patients. Four key predictor proteins were verified by ELISA in the expanded US cohort (N=168), and enhanced by adding clinical variables to create the 9-feature MASH Dx Score which predicted MASH and also high risk MASH (F2+). The MASH Dx Score was validated in two independent, external cohorts from Germany (N=139) and Brazil (N=177). The discovery phase identified a 6-protein classifier that achieved an AUC of 0.93 for identifying MASH. Significant elevation of four proteins (THBS2, GDF15, SELE, IGFBP7) was verified by ELISA in the expanded discovery and independently in the two external cohorts. MASH Dx Score incorporated these proteins with established MASH risk factors (age, BMI, ALT, diabetes, hypertension) to achieve good discrimination between MASH and MASLD without MASH (AUC:0.87- discovery; 0.83- pooled external validation cohorts), with similar performance when evaluating high risk MASH F2-4 (vs. MASH F0-1 and MASLD without MASH). The MASH Dx Score offers the first reliable non-invasive approach combining novel, biologically plausible ELISA-based fibrosis markers and clinical parameters to detect high risk MASH in patient cohorts from the US, Brasil and Europe.

2023-12-22 | GSE251855 | GEO

Cardiovascular risk in patients with type 2 diabetes is associated with alteration of blood monocyte phenotype and plasma metabolome

Project description:To gain insight into the relationship between circulating monocytes and cardiovascular risk (CV) progression in patients with type 2 diabetes (T2D), we collected blood monocytes (CD14 positive selection) from 92 people with type 2 diabetes and coronary artery calcium score (CAC-score). Gene expression profiles of circulating monocytes were assessed by RNA sequencing.

2024-01-08 | GSE201087 | GEO

Predictive computational obesity risk framework through integration of gene expression profiles and genetic risk score.

Project description:Predictive computational obesity risk framework through integration of gene expression profiles and genetic risk score.

| PRJNA431452 | ENA

Mortality risk score in postsurgical patients based on gene expression

Project description:Nowadays, there are different ICU scoring systems to predict the likelihood of mortality, such as Acute Physiology And Chronic Health Condition (APACHE), Sequential Organ Failure Assessment (SOFA), and SAPS (Simplified Acute Physiology Score). Theses risk scores are based on the use of physiologic and other clinical data. However, the use of these score systems depend on the clinical trust in the reliability and predictions by physicians. In this work, we have evaluated the expression profile by microarray analysis from postsurgical patients with the aim of proposing a candidate set genes as a mortality risk score.

2020-05-05 | GSE132897 | GEO

Mortality risk score in postsurgical patients based on gene expression

Project description:Mortality risk score in postsurgical patients based on gene expression

| PRJNA549420 | ENA

Gene expression signature-based novel prognostic risk score in gastric cancer

Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis. 65 primary gastric adenocarcinoma, 6 GIST and 19 surrounding normal fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at Yonsei cancer center(Seoul, Korea). Microarray experiment and data analysis were done at Dept. of systems biology, MDACC DNA microarray (Illumina human V3)

2011-07-18 | E-GEOD-13861 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data