Descriptive statistics in TARCC.

ImportanceDementia is an “overdetermined” syndrome. Few individuals are demented by any single biomarker, while several may independently explain small fractions of dementia severity. It may be advantageous to identify individuals afflicted by a specific biomarker to guide individualized treatment.ObjectiveWe aim to validate a psychometric classifier to identify persons adversely impacted by inflammation and replicate it in a second cohort.DesignSecondary analyses of data collected by the Texas Alzheimer’s Research and Care Consortium (TARCC) (N = 3497) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (N = 1737).SettingTwo large, well-characterized multi-center convenience samples.ParticipantsVolunteers with normal cognition (NC), Mild Cognitive Impairment (MCI) or clinical “Alzheimer’s Disease (AD)”.ExposureParticipants were assigned to “Afflicted” or “Resilient” classes on the basis of a psychometric classifier derived by confirmatory factor analysis.Main outcome(s) and measure(s)The groups were contrasted on multiple assessments and biomarkers. The groups were also contrasted regarding 4-year prospective conversions to “AD” from non-demented baseline diagnoses (controls and MCI). The Afflicted groups were predicted to have adverse levels of inflammation-related blood-based biomarkers, greater dementia severity and greater risk of prospective conversion.ResultsIn ADNI /plasma, 47.1% of subjects were assigned to the Afflicted class. 44.6% of TARCC’s subjects were afflicted, 49.5% of non-Hispanic Whites (NHW) and 37.2% of Mexican Americans (MA). There was greater dementia severity in the Afflicted class [by ANOVA: ADNI /F(1) = 686.99, p Conclusions and relevanceOur inflammation-specific psychometric classifier selects individuals with pre-specified biomarker profiles and predicts conversion to “AD” across cohorts, biofluids, and ethnicities. This algorithm might be applied to any dementia-related biomarker making the psychometric estimation of individual biomarker effects feasible without biomarker assessment. Our approach also distinguishes individuals resilient to individual biomarker effects allowing for more accurate prediction and precision intervention.【研究背景】痴呆症是一种「过度决定」综合征。极少有个体仅因单一生物标志物出现痴呆症状，而多种生物标志物可各自独立解释痴呆严重程度的小幅变化。识别受特定生物标志物影响的个体以指导个体化治疗，具备重要临床价值。 研究目的：我们旨在验证一款心理测量分类器，以识别受炎症负面影响的人群，并在第二个独立队列中重复验证该分类器的效能。 研究设计：对德克萨斯阿尔茨海默病研究与护理联盟（Texas Alzheimer’s Research and Care Consortium, TARCC）（N=3497）以及阿尔茨海默病神经影像学倡议（Alzheimer’s Disease Neuroimaging Initiative, ADNI）（N=1737）收集的数据集进行二次分析。 研究场景：两个大型、特征明确的多中心便利样本。 研究对象：认知正常（normal cognition, NC）、轻度认知障碍（mild cognitive impairment, MCI）或临床确诊「阿尔茨海默病（Alzheimer’s Disease, AD）」的志愿者。 暴露因素：基于验证性因子分析得到的心理测量分类器，将研究对象划分为「受累组」与「耐受组」。 主要结局与测量指标：对比两组受试者的多项临床评估结果与生物标志物水平；同时对比基线非痴呆诊断（对照组与MCI患者）在4年内向「阿尔茨海默病」转化的情况。我们预测受累组会表现出炎症相关血液生物标志物水平异常、更严重的痴呆程度，以及更高的转化风险。 研究结果：在ADNI队列的血浆样本中，47.1%的受试者被划分为受累组；TARCC队列中44.6%的受试者受累，其中非西班牙裔白人（non-Hispanic Whites, NHW）占比49.5%，墨西哥裔美国人（Mexican Americans, MA）占比37.2%。受累组的痴呆严重程度更高[方差分析：ADNI/F(1)=686.99, p<0.001；TARCC/F(1)=232.57, p<0.001]，且在所有认知分组中均保持该差异；受累组的炎症相关血浆生物标志物水平也显著升高，包括C反应蛋白、白细胞介素-6、纤维蛋白原及白细胞计数（所有p<0.001）。在基线为非痴呆的受试者中，受累组的4年AD转化风险显著高于耐受组[ADNI：风险比（hazard ratio, HR）=2.57, 95%置信区间（confidence interval, CI）=1.73-3.81, p<0.001；TARCC：HR=1.86, 95%CI=1.33-2.61, p<0.001]。该分类效应在种族亚组中保持一致，且不受基线认知状态的影响。 结论与意义：本研究针对炎症的心理测量分类器可筛选出具有预设生物标志物特征的个体，并在不同队列、生物体液及种族中预测向阿尔茨海默病的转化风险。该算法可应用于任何与痴呆相关的生物标志物，使得无需进行生物标志物检测即可实现个体生物标志物效应的心理测量评估成为可能。此外，本方法还可区分出对个体生物标志物效应具有耐受性的人群，从而实现更精准的预测与精准干预。