Respiratory Impairment in Aging Populations

描述（由申请人提供）： 呼吸功能障碍最常见的定义为：1）基于FEV 1/FVC降低的气流受限（例如COPD），严重程度仅由FEV 1定义;或2）基于FVC降低但FEV 1/FVC正常的限制性模式（例如间质性肺病）。FEV 1/FVC以及FEV 1和FVC单独的诊断阈值通常由阻塞性肺病全球倡议（GOLD）或美国胸科学会（ATS）确定。5 -7然而，这些阈值具有严重的年龄相关局限性，并且经常会错误识别老年人群中的呼吸功能障碍。8 -22例如，GOLD使用0.70的FEV 1/FVC阈值，但FEV 1/FVC <0.70经常发生在年龄>50.6-15岁的健康、无症状的从不吸烟者中。GOLD还使用FEV 1和FVC的百分比预测（%Pred）阈值，但由于这些阈值不能解释肺功能的年龄相关变异性，因此给定的%Pred阈值并不等同于所有人。9，16，17相反，ATS将FEV 1/FVC和FVC的阈值设置为正常下限（ATS-LLN 5）.5-7这种方法也有严重的局限性，因为ATS-LLN 5通常是根据回归方程计算的，这些回归方程错误地假设了预测因子和肺功能测量之间的线性关系，和/或错误地假设参考值在整个生命周期内具有正态分布和恒定变异性。9否则，ATS还使用FEV 1的%Pred阈值。 因此，我们提出了一种替代方法来定义呼吸功能障碍，使用Lambda-Mu-Sigma（LMS）计算的肺活量Z评分。1，9，10 LMS计算的Z评分解释了年龄相关的变化，包括肺活量表现的变异性和参考数据的偏度。9使用这种方法，我们已经证明LMS定义的呼吸功能障碍与多种健康结果相关，15，18-23，但我们以前的工作没有解决知识的几个差距。首先，我们只评估了年龄<80岁的白人，因为LMS计算的Z分数以前不适用于非白人和年龄>80岁的人。这项任务现在是可能的，因为新的LMS方程已经发布了多个种族和年龄高达95.10，24其次，作为健康结果的兴趣，我们没有评估运动能力，呼吸药物的使用，或住院治疗以外的COPD。第三，我们没有评估“肺表型”，特别是那些由生理学或CT成像定义的表型。最后，我们没有评估肺功能随时间的变化。因此，本提案的目标是填补这些知识空白。 使用新的LMS方程和来自多个老龄化人群的数据库，包括美国退伍军人和广泛的健康结果和肺表型，我们提出了以下具体目标：目标1。评估呼吸损害与健康结果之间的关系。我们假设：1）与GOLD或ATS相比，LMS方法将显示呼吸损害与健康结果之间的更大关联; 2）呼吸损害的不一致名称（GOLD和ATS分别异常，但LMS正常）将与健康结果无关。 目标2.评估呼吸损害与肺表型之间的关系。我们假设：1）与GOLD或ATS相比，LMS方法将显示呼吸损害与各种肺表型之间的更大关联; 2）呼吸损害的不一致命名（如目标1中定义）将与肺表型无关。 目标3。评价肺功能随时间的变化，FEV 1和FVC表示为LMS计算的Z评分、%Pred和绝对容量（mL）。这些分析将包括基于组的轨迹建模和可能影响肺活量轨迹的关键时变风险因素。我们假设：1）年龄与肺活量测量的下降轨迹相关; 2）LMS计算的Z评分，而不是%Pred和绝对体积（mL），将显示与随时间变化的风险因素更大的相关性。

DESCRIPTION (provided by applicant): Respiratory impairment is most often defined spirometrically as: 1) airflow limitation (e.g. COPD), based on a reduced FEV1/FVC, with severity then defined by FEV1 alone; or 2) restrictive-pattern (e.g. interstitial lung disease), based on a reduced FVC but normal FEV1/FVC. The diagnostic thresholds for FEV1/FVC and for FEV1 and FVC alone are usually established by the Global Initiative for Obstructive Lung Disease (GOLD) or the American Thoracic Society (ATS).5-7 These thresholds have serious age-related limitations, however, and will frequently misidentify respiratory impairment in aging populations.8-22 GOLD, for example, uses a threshold of 0.70 for FEV1/FVC, but an FEV1/FVC <0.70 frequently occurs in healthy, asymptomatic never-smokers, aged >50.6-15 GOLD additionally uses percent predicted (%Pred) thresholds for FEV1 and FVC but, because these do not account for the age-related variability in spirometric performance, a given %Pred threshold is not equivalent for all persons.9,16,17In contrast, the ATS sets thresholds for FEV1/FVC and FVC at the lower limit of normal (ATS-LLN5).5-7 This approach also has serious limitations, because the ATS-LLN5 is frequently calculated from regression equations that incorrectly assume a linear relationship between predictors and spirometric measures, and/or incorrectly assume that reference values have a normal distribution and constant variability across the lifespan.9 Otherwise, the ATS also uses %Pred thresholds for FEV1. Consequently, we propose an alternative approach for defining respiratory impairment, using spirometric Z- scores as calculated by Lambda-Mu-Sigma (LMS).1,9,10 The LMS-calculated Z-scores account for age-related changes, including variability in spirometric performance and skewness of reference data.9 Using this approach, we have shown that LMS-defined respiratory impairment is associated with multiple health outcomes,15,18-23 but our prior work did not address several gaps in knowledge. First, we evaluated only whites aged <80, because LMS-calculated Z-scores were not previously available for non-whites and those aged >80. This task is now possible because new LMS equations have been published for multiple ethnicities and age up to 95.10,24 Second, as health outcomes of interest, we did not evaluate exercise capacity, use of respiratory medications, or hospitalizations other than for COPD. Third, we did not evaluate "pulmonary phenotypes," specifically those defined by physiology or CT imaging. Lastly, we did not evaluate spirometric change over time. Accordingly, the objective of this proposal is to address these gaps in knowledge. Using new LMS equations and databases from multiple aging populations, including U.S. Veterans and a broad array of health outcomes and pulmonary phenotypes, we propose the following specific aims: Aim 1. To evaluate the association between respiratory impairment and health outcomes. We hypothesize that: 1) the LMS approach, compared to GOLD or ATS, will show a greater association between respiratory impairment and health outcomes; and 2) discordant designations of respiratory impairment (GOLD and ATS abnormal, respectively, but LMS normal) will not be associated with health outcomes. Aim 2. To evaluate the association between respiratory impairment and pulmonary phenotypes. We hypothesize that: 1) the LMS approach, compared to GOLD or ATS, will show a greater association between respiratory impairment and various pulmonary phenotypes; and 2) discordant designations of respiratory impairment (as defined in Aim 1) will not be associated with a pulmonary phenotype. Aim 3. To evaluate spirometric change over time, with FEV1 and FVC expressed as LMS-calculated Z- scores, %Pred, and absolute volumes (mL). These analyses will include group-based trajectory modeling and key time-varying risk factors that may affect spirometric trajectories. We hypothesize that: 1) aging is associated with declining trajectories of spirometri measures; and 2) LMS-calculated Z-scores, rather than %Pred and absolute volumes (mL), will show a greater association with time-varying risk factors.