Department of Communication Sciences & Disorders, University of Iowa, Iowa City, Iowa, USA.
Department of Integrative Structural and Computational Biology, Scripps Science Institute, La Jolla, California, USA.
Ear Hear. 2022 May/Jun;43(3):1023-1036. doi: 10.1097/AUD.0000000000001158.
About 15% of U.S. adults report speech perception difficulties despite showing normal audiograms. Recent research suggests that genetic factors might influence the phenotypic spectrum of speech perception difficulties. The primary objective of the present study was to describe a conceptual framework of a deep phenotyping method, referred to as AudioChipping, for deconstructing and quantifying complex audiometric phenotypes.
In a sample of 70 females 18 to 35 years of age with normal audiograms (from 250 to 8000 Hz), the study measured behavioral hearing thresholds (250 to 16,000 Hz), distortion product otoacoustic emissions (1000 to 16,000 Hz), click-evoked auditory brainstem responses (ABR), complex ABR (cABR), QuickSIN, dichotic digit test score, loudness discomfort level, and noise exposure background. The speech perception difficulties were evaluated using the Speech, Spatial, and Quality of Hearing Scale-12-item version (SSQ). A multiple linear regression model was used to determine the relationship between SSQ scores and audiometric measures. Participants were categorized into three groups (i.e., high, mid, and low) using the SSQ scores before performing the clustering analysis. Audiometric measures were normalized and standardized before performing unsupervised k-means clustering to generate AudioChip.
The results showed that SSQ and noise exposure background exhibited a significant negative correlation. ABR wave I amplitude, cABR offset latency, cABR response morphology, and loudness discomfort level were significant predictors for SSQ scores. These predictors explained about 18% of the variance in the SSQ score. The k-means clustering was used to split the participants into three major groups; one of these clusters revealed 53% of participants with low SSQ.
Our study highlighted the relationship between SSQ and auditory coding precision in the auditory brainstem in normal-hearing young females. AudioChip was useful in delineating and quantifying internal homogeneity and heterogeneity in audiometric measures among individuals with a range of SSQ scores. AudioChip could help identify the genotype-phenotype relationship, document longitudinal changes in auditory phenotypes, and pair individuals in case-control groups for the genetic association analysis.
尽管美国成年人中有 15%的人听力图正常，但仍有 15%的人报告存在言语感知障碍。最近的研究表明，遗传因素可能会影响言语感知障碍的表型谱。本研究的主要目的是描述一种深度表型分析方法的概念框架，称为 AudioChipping，用于解构和量化复杂的听力表型。
在一个年龄在 18 至 35 岁之间、听力图正常（250 至 8000Hz）的 70 名女性样本中，本研究测量了行为听力阈值（250 至 16000Hz）、畸变产物耳声发射（1000 至 16000Hz）、短声诱发听性脑干反应（ABR）、复合 ABR（cABR）、QuickSIN、利听数字测试评分、响度不适水平和噪声暴露背景。使用言语、空间和听力质量量表 12 项版（SSQ）评估言语感知障碍。使用多元线性回归模型确定 SSQ 评分与听力测量之间的关系。使用 SSQ 评分将参与者分为三组（高、中、低），然后进行聚类分析。在进行无监督 k-均值聚类生成 AudioChip 之前，对听力测量进行归一化和标准化。
结果显示，SSQ 与噪声暴露背景呈显著负相关。ABR 波 I 振幅、cABR 偏移潜伏期、cABR 反应形态和响度不适水平是 SSQ 评分的显著预测因子。这些预测因子解释了 SSQ 评分的约 18%。k-均值聚类用于将参与者分为三个主要组；其中一个组有 53%的参与者 SSQ 较低。
本研究强调了正常听力年轻女性 SSQ 与听觉脑干听觉编码精度之间的关系。AudioChip 有助于描述和量化个体间听力测量的内在同质性和异质性。AudioChip 可用于确定基因型-表型关系，记录听觉表型的纵向变化，并在遗传关联分析中为病例对照组配对个体。
