权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Circadian analysis of peripheral and brain samples in epilepsy patients

癫痫患者外周血和脑样本的昼夜节律分析

基本信息

批准号：
10596644
负责人：
Taylor John Abel
金额：
$ 19.17万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10596644
关键词：
Adolescent and Young Adult Adverse event Amygdaloid structure Attention Autopsy Behavioral Biological Markers Blood Blood specimen Brain Brain Diseases Brain region Cessation of life Chronotherapy Circadian Rhythms Clinic Clinical Cognition Corpus striatum structure Data Development Disease Environmental Risk Factor Epilepsy Evaluation Event Excision Female Adolescents Future Gene Expression Genes Health Hippocampus Hour Human Individual Male Adolescents Measures Mental disorders Metabolic Molecular Myocardial Infarction Neocortex Neurons Nucleus Accumbens Operative Surgical Procedures Pathology Patients Pattern Periodicity Peripheral Pharmaceutical Preparations Phase Pilot Projects Predictive Factor Process Proxy Publishing Research Personnel Resected Resistance Sampling Schedule Seizures Sleep Stroke Therapeutic Time Tissues Transcript Urine aged brain tissue circadian cognitive testing cohort differential expression experience insight machine learning algorithm metabolomics neocortical nervous system disorder novel personalized approach prediction algorithm predictive marker prevent putamen sex side effect statistical and machine learning suprachiasmatic nucleus transcriptome sequencing treatment response

项目摘要

PROJECT SUMMARY Nearly every process that occurs in the brain and the body follows a 24 hour cycle. Moreover, adverse health events such as heart attacks, stroke and seizures tend to occur at particular times of day. Seizures often follow a rhythmic pattern, but this pattern can be very different from patient to patient. If one can determine the circadian factors that predict that a seizure is likely to occur, then this allows proper timing of treatments to provide the most therapeutic impact with fewest side effects. Indeed, several groups are starting to apply this chronotherapy as a personalized approach to predict and treat a number of disorders including seizures. While sleep/wake activity measures are a gross predictor of the optimal time of day for medication, they are confounded by a variety of environmental factors and do not always align with endogenous rhythms. The use of peripheral markers in blood or urine could serve to fine tune chronotherapeutic approaches as they are easily obtained in the clinic. Indeed, researchers have now been able to accurately predict the phase of the master clock in the suprachiasmatic nucleus (SCN) using as little as 1-2 blood samples. Moreover, better understanding of phase relationships between peripheral markers, SCN phase, and molecular rhythms in parts of the brain that experience seizures, would help not only in the administration of chronotherapy, but also in our understanding of why the brain might experience a seizure at a particular time of day. In this study, we have the rare opportunity to obtain human brain tissue, blood, and urine samples at the same time of day from male and female adolescents and young adults (~25 subjects) undergoing surgical resection for the treatment of seizures. In addition, prior to surgery, we will collect sleep/wake data, behavioral data, cognitive assessments, assessments of seizure patterns and psychiatric evaluations. This will allow us for the first time to measure transcripts and metabolites with known rhythms across multiple peripheral and brain samples at the same time of day from the same individuals, giving us precise data regarding how markers of phase in the peripheral samples align with markers in brain regions in which seizures occur. We will also be able to determine how these molecular and metabolic measures align with sleep/wake measures and various clinical features, seizure patterns, and differences between healthy and disease tissue, paying particular attention to any sex and developmental differences. This data will inform future mechanistic studies of why seizures occur at particular times of day, and optimize the use of peripheral samples to serve as biomarkers for the most appropriate timing for treatment.

项目总结