Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany.
Department of Neurology, Ulm University, Ulm, Germany.
Am J Med Genet B Neuropsychiatr Genet. 2019 Apr;180(3):232-245. doi: 10.1002/ajmg.b.32719. Epub 2019 Feb 20.
Studying individuals with extreme phenotypes could facilitate the understanding of disease modification by genetic or environmental factors. Our aim was to identify Huntington's disease (HD) patients with extreme symbol digit modality test (SDMT) scores. We first examined in HD the contribution of cognitive measures of the Unified Huntington's Disease Rating Scale (UHDRS) in predicting clinical endpoints. The language-independent SDMT was used to identify patients performing very well or very poorly relative to their CAG and age cohort. We used data from REGISTRY and COHORT observational study participants (5,603 HD participants with CAG repeats above 39 with 13,868 visits) and of 1,006 healthy volunteers (with 2,241 visits), included to identify natural aging and education effects on cognitive measures. Separate Cox proportional hazards models with CAG, age at study entry, education, sex, UHDRS total motor score and cognitive (SDMT, verbal fluency, Stroop tests) scores as covariates were used to predict clinical endpoints. Quantile regression for longitudinal language-independent SDMT data was used for boundary (2.5% and 97.5% quantiles) estimation and extreme score analyses stratified by age, education, and CAG repeat length. Ten percent of HD participants had an extreme SDMT phenotype for at least one visit. In contrast, only about 3% of participants were consistent SDMT extremes at two or more visits. The thresholds for the one-visit and two-visit extremes can be used to classify existing and new individuals. The identification of these phenotype extremes can be useful in the search for disease modifiers.
研究具有极端表型的个体可以帮助我们理解遗传或环境因素对疾病的影响。我们的目的是确定亨廷顿病（HD）患者中具有极端符号数字模态测试（SDMT）评分的个体。我们首先在 HD 中检查了统一亨廷顿病评定量表（UHDRS）认知测量对预测临床终点的贡献。使用独立于语言的 SDMT 来识别相对于其 CAG 和年龄队列表现非常好或非常差的患者。我们使用 REGISTRY 和 COHORT 观察性研究参与者的数据（5603 名 CAG 重复次数大于 39 的 HD 参与者，有 13868 次就诊）和 1006 名健康志愿者的数据（有 2241 次就诊），以确定自然衰老和教育对认知测量的影响。使用 Cox 比例风险模型，将 CAG、研究开始时的年龄、教育、性别、UHDRS 总运动评分和认知（SDMT、语言流畅性、Stroop 测试）评分作为协变量，以预测临床终点。使用纵向独立语言的 SDMT 数据的分位数回归进行边界（2.5%和 97.5%分位数）估计和极端评分分析，按年龄、教育和 CAG 重复长度分层。10%的 HD 参与者在至少一次就诊时表现出极端 SDMT 表型。相比之下，只有约 3%的参与者在两次或更多次就诊时表现出一致的 SDMT 极端情况。单次就诊和两次就诊极端值的阈值可用于对现有和新个体进行分类。这些表型极端值的识别在寻找疾病修饰因子方面可能很有用。
