Aqueous Humor Dynamic Components that Determine Intraocular Pressure Variance

决定眼压变化的房水动态成分

基本信息

批准号：
10004052
负责人：
Sayoko E Moroi
金额：
$ 54.65万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-03-03 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10004052
关键词：
Adrenergic beta-Antagonists Aftercare Age Age of Onset Aqueous Humor Behavioral Biological Markers Blindness Characteristics Clinical Clinical Sciences Cornea Data Set Databases Environmental Exposure Environmental Risk Factor Eye FDA approved Future Genetic Risk Genotype Glaucoma Goals Grant Health Services Healthcare Home environment Individual Knowledge Latanoprost Lead Linear Models Measures Mediating Medical Methods Newly Diagnosed Ocular Hypertension Office Visits Open-Angle Glaucoma Outcome Outcome Measure Patients Pattern Pharmaceutical Preparations Phenotype Physical activity Physiologic Intraocular Pressure Positioning Attribute Practice Guidelines Predictive Factor Prostaglandins Protocols documentation Randomized Randomized Clinical Trials Risk Risk Factors Safety Testing Timolol Treatment Efficacy Treatment Failure Variant Venous Pressure level Visual Fields aggressive therapy aqueous clinical decision-making clinical risk cohort cost design disorder risk experience follow-up improved improved outcome individual response innovation response risk variant secondary outcome sex trait treatment response

项目摘要

Abstract Randomized clinical trials (RCTs) show that reducing intraocular pressure (IOP) slows glaucoma progression. Despite the clinician’s use of these RCTs, practice guidelines, and experience, patients still progress to blindness. A gap in clinical science is our lack of knowledge on other risk factors that impact outcomes. Our long-term goal is to improve outcomes by identifying biomarkers, behavioral and environmental factors, that together profile a patient at risk for disease by age-of-onset, rate of progression, poor response to treatment, and large IOP fluctuation. We focus on two IOP patterns that continue to confound the clinician’s ability to provide consistent and effective IOP treatments: (1) IOP response to medications, ranging from non-responder to super responder, and (2) IOP fluctuation, ranging from small to large, with the latter leading to progressive visual field loss. Unfortunately, biomarkers that foretell these IOP patterns, which could improve clinical decision-making have yet to be identified -- a critical barrier to the clinician identifying patients for whom earlier or more aggressive treatment will mitigate glaucoma-related vision loss. The scientific premise is that these mechanisms (i.e., aqueous flow, outflow facility, episcleral venous pressure, and calculated uveoscleral flow) predict a patient’s IOP patterns. We will test the central hypothesis that variations in IOP response to drugs and IOP fluctuation can be predicted by the aqueous humor dynamic (AHD) factors that regulate IOP. We propose to test our hypothesis in 200 patients with ocular hypertension (OHT) or open-angle glaucoma (OAG), as both conditions are investigated in drug trials for IOP drug response. There are two aims: Aim 1. Test the hypothesis that AHD factors predict the IOP drug response. In Protocol 1, AHD factors will be measured under baseline without treatment, and after a randomized order of 1-week treatments with timolol 0.5% followed by a washout period and then latanoprost 0.005% or vice versa. Aim 2. Test the hypothesis that aqueous flow and outflow facility predict IOP fluctuation. In Protocol 2, IOP fluctuation will be measured in the non-clinic setting using the Icare® Home tonometer over multiple days at baseline and under monotherapy treatment during Protocol 1. Clinical Impact: Our approach to apply AHD methods to understand variation in drug response and IOP fluctuation is innovative. We predict that AHD factors will explain drug response and IOP fluctuation. Tying-down these relationships will provide new knowledge that will form the basis of future phenotype-genotype studies to identify genetic risk alleles of drug response and IOP fluctuation, resulting in an integrated risk score combining clinical and genetic risk profiles for drug response and IOP fluctuation. The ability to determine which patient needs earlier and more aggressive treatment will ultimately lead to more efficient medical management with fewer follow-up office visits to assess treatment efficacy, fewer treatment failures, and decreased glaucoma-related blindness.

抽象的随机临床试验（RCT）表明，降低眼压（IOP）可以减缓青光眼的进展。尽管临床医生使用了这些随机对照试验、实践指南和经验，但患者仍然进展到临床科学的一个空白是我们对影响结果的其他风险因素缺乏了解。长期目标是通过识别生物标志物、行为和环境因素来改善结果，通过发病年龄、进展速度、对治疗的不良反应来共同分析处于疾病风险的患者，我们重点关注两种 IOP 模式，它们继续困扰着临床医生的能力。提供一致且有效的 IOP 治疗：(1) IOP 对药物的反应，包括无反应到超级反应者，以及（2）眼压波动，范围从小到大，后者导致进行性不幸的是，预测这些眼压模式的生物标志物可以改善临床。决策尚未确定——这是临床医生确定患者的关键障碍早期或更积极的治疗可以减轻青光眼相关的视力丧失。这些机制（即房水流量、流出设施、巩膜外静脉压力和计算的葡萄膜巩膜压力）流量）预测患者的 IOP 模式我们将测试 IOP 反应变化的中心假设。药物和眼压波动可以通过房水动态（AHD）因素来预测我们建议在 200 名高眼压 (OHT) 或开角患者中检验我们的假设。青光眼 (OAG)，因为这两种情况都在药物试验中研究 IOP 药物反应，有两个目标：目标 1. 检验 AHD 因素预测 IOP 药物反应的假设在方案 1 中，AHD 因素。将在未经治疗的基线下进行测量，并在随机顺序进行 1 周治疗后进行测量 0.5% 噻吗洛尔，然后是冲洗期，然后是 0.005% 拉坦前列素，或反之亦然。目标 2。假设水流和流出设施预测 IOP 波动在方案 2 中，IOP 波动。将在非诊所环境中使用 Icare® Home 眼压计在多天内进行基线测量，在方案 1 期间接受单一疗法治疗。临床影响：我们将 AHD 方法应用于药物反应和眼压波动的变化是创新的，我们预测 AHD 因素将具有创新性。解释药物反应和眼压波动之间的关系将提供新的知识。形成未来表型-基因型研究的基础，以确定药物反应和眼压的遗传风险等位基因波动，从而产生结合药物反应的临床和遗传风险概况的综合风险评分确定哪些患者需要更早和更积极治疗的能力将得到改善。最终导致更有效的医疗管理，减少后续办公室访问以评估治疗疗效，减少治疗失败，并减少青光眼相关失明。