Predicting Cardiovascular Outcomes Using Diabetes-Induced Transcriptomic Networks
使用糖尿病诱导的转录组网络预测心血管结果
基本信息
- 批准号:10679593
- 负责人:
- 金额:$ 4.55万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-06-01 至 2027-05-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAffectAlgorithmsAtherosclerosisBiologicalBiologyBlood GlucoseCardiovascular DiseasesCardiovascular systemCholesterolChronicChronic DiseaseClinicalClinical DataComputer ModelsConnecticutDataData SetDiabetes MellitusDiseaseDisease OutcomeEnvironmentEquationEventFoam CellsFutureGenesGeneticGenetic TranscriptionGoalsImmunologicsIndividualInflammationInflammatoryInterventionKnowledgeLeukocytesLipidsMetabolic DiseasesModelingModernizationMolecular ProfilingNCOR2 geneNoiseNon-Insulin-Dependent Diabetes MellitusOutcomeOutputPathogenesisPathogenicityPathologyPathway AnalysisPathway interactionsPatientsPerformancePersonsPhysiciansPlayProteinsPublicationsRiskRisk AssessmentRisk FactorsRoleSNW1 GeneSamplingScientistSki-interacting proteinTestingTrainingUniversitiesVirulence Factorsblood glucose regulationcardiovascular disorder riskcardiovascular risk factorcareerclinical predictive modelcohortdesigndifferential expressionefficacy evaluationfeature selectionfollow-upgene networkgenetic signatureglycemic controlhigh riskimprovedinnovationmachine learning algorithmmedical schoolsmodel developmentmonocytemulti-ethnicnext generationnon-diabeticnovelpredict clinical outcomepredictive modelingpreventskillsstandard of caresupervised learningtooltranscriptomics
项目摘要
ABSTRACT
Type 2 diabetes mellitus (T2DM) is an increasingly prevalent chronic disease that affects more than 400 million
people worldwide. One of the major complications of T2DM is exacerbated atherosclerotic cardiovascular
disease (CVD). Even when modern lipid and glucose control strategies are applied, T2DM is associated with a
two- to four-fold increase in CVD risk, suggesting the effect of additional pathologies, such as inflammation.
However, current tools to predict CVD outcomes for T2DM patients incorporate only clinical and demographic
variables into their models, and they thus attain only a moderate ability to discriminate the highest-risk patients
in need of targeted clinical intervention. Our lab recently discovered that monocyte-derived foam cells, which
are well-known to play a central role in atherosclerotic CVD, can undergo both homeostatic (non-inflammatory)
and pathogenic (inflammatory) foaming. Using a transcriptomic signature from pathogenic foam cells, our lab
developed a CVD prediction model called CR30 which outperformed existing tools. To address the critical
knowledge gap of identifying CVD risk specifically in T2DM patients, I analyzed monocyte transcriptomic data
from the Multi-Ethnic Study on Atherosclerosis (MESA). From this preliminary analysis, I identified a
transcriptomic signature unique to T2DM patients with CVD, containing a super-network downstream of the co-
regulator proteins SNW1, NCOR2, and CITED2. We hypothesize that this transcriptomic super-network
represents a unique molecular signature which can be used to improve prediction of atherosclerotic
cardiovascular events in individuals with T2DM. In this proposal, I will test this hypothesis by applying two
different strategies to develop predictive models. In Aim 1, I will apply supervised machine learning
approaches to select a set of genes from my preliminary analysis which are predictive of T2DM-CVD
outcomes. I will then test several modeling strategies in training and building a T2DM-CVD prediction model
incorporating this gene set combined with clinical data. In Aim 2, I will use another approach to incorporate
T2DM-CVD molecular signature into modeling by focusing on the transcriptomic super-network. I will generate
enrichment scores for the super-network, then incorporate the scores as variables into model development.
The long-term goal of this project is to identify biological risk factors for CVD in patients with T2DM. The
anticipated impacts are the identification of novel targets for mechanistic studies and the advancement of
biology-informed approaches to clinical outcomes prediction. The training goals of this proposal will provide
me with biologically-informed quantitative skills. This interdisciplinary, highly translational project will leverage
the innovative environment and unique opportunities in the sponsor’s lab and the University of Connecticut
School of Medicine. The expected outcomes from this project will promote my career goals of becoming a
next-generation physician-scientist capable of integrating biological knowledge and quantitative skills to solve
clinical problems for patients with chronic disease.
摘要
项目成果
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