Bhalala Oneil G, Beamish Jessica, Eratne Dhamidhu, Summerell Patrick, Porter Tenielle, Laws Simon M, Kang Matthew Jy, Huq Aamira J, Chiu Wei-Hsuan, Cadwallader Claire, Walterfang Mark, Farrand Sarah, Evans Andrew H, Kelso Wendy, Churilov Leonid, Watson Rosie, Yassi Nawaf, Velakoulis Dennis, Loi Samantha M
Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia.
Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Aust N Z J Psychiatry. 2025 Apr;59(4):378-388. doi: 10.1177/00048674241312805. Epub 2025 Jan 17.
Young-onset neurocognitive symptoms result from a heterogeneous group of neurological and psychiatric disorders which present a diagnostic challenge. To identify such factors, we analysed the Biomarkers in Younger-Onset Neurocognitive Disorders cohort, a study of individuals <65 years old presenting with neurocognitive symptoms for a diagnosis and who have undergone cognitive and biomarker analyses.
Sixty-five participants (median age at assessment of 56 years, 45% female) were recruited during their index presentation to the Royal Melbourne Hospital Neuropsychiatry Centre, a tertiary specialist service in Melbourne, Australia, and categorized as either early-onset Alzheimer's disease ( = 18), non-Alzheimer's disease neurodegeneration ( = 23) or primary psychiatric disorders ( = 24). Levels of neurofilament light chain, glial fibrillary acidic protein and phosphorylated-tau 181, apolipoprotein E genotype and late-onset Alzheimer's disease polygenic risk scores were determined. Information-theoretic model selection identified discriminatory factors.
Neurofilament light chain, glial fibrillary acidic protein and phosphorylated-tau 181 levels were elevated in early-onset Alzheimer's disease compared with other diagnostic categories. A multi-omic model selection identified that a combination of cognitive and blood biomarkers, but not the polygenic risk score, discriminated between early-onset Alzheimer's disease and primary psychiatric disorders (area under the curve ⩾ 0.975, 95% confidence interval: 0.825-1.000). Phosphorylated-tau 181 alone significantly discriminated between early-onset Alzheimer's disease and non-Alzheimer's disease neurodegeneration causes (area under the curve = 0.950, 95% confidence interval: 0.877-1.00).
Discriminating between early-onset Alzheimer's disease, non-Alzheimer's disease neurodegeneration and primary psychiatric disorders causes of young-onset neurocognitive symptoms is possible by combining cognitive profiles with blood biomarkers. These results support utilizing blood biomarkers for the work-up of young-onset neurocognitive symptoms and highlight the need for the development of a young-onset Alzheimer's disease-specific polygenic risk score.
早发性神经认知症状由多种神经和精神疾病引起，这给诊断带来了挑战。为了确定这些因素，我们分析了早发性神经认知障碍队列中的生物标志物，该研究针对年龄小于65岁且出现神经认知症状以进行诊断并已接受认知和生物标志物分析的个体。
65名参与者（评估时的中位年龄为56岁，45%为女性）在首次就诊于澳大利亚墨尔本一家三级专科服务机构皇家墨尔本医院神经精神病中心时被招募，并被分类为早发性阿尔茨海默病（n = 18）、非阿尔茨海默病神经退行性变（n = 23）或原发性精神障碍（n = 24）。测定了神经丝轻链、胶质纤维酸性蛋白和磷酸化tau 181的水平、载脂蛋白E基因型以及晚发性阿尔茨海默病多基因风险评分。信息理论模型选择确定了鉴别因素。
与其他诊断类别相比，早发性阿尔茨海默病患者的神经丝轻链、胶质纤维酸性蛋白和磷酸化tau 181水平升高。多组学模型选择确定，认知和血液生物标志物的组合而非多基因风险评分能够区分早发性阿尔茨海默病和原发性精神障碍（曲线下面积⩾0.975，95%置信区间：0.825 - 1.000）。仅磷酸化tau 181就能显著区分早发性阿尔茨海默病和非阿尔茨海默病神经退行性变病因（曲线下面积 = 0.950，95%置信区间：0.877 - 1.00）。
通过将认知特征与血液生物标志物相结合，可以区分早发性阿尔茨海默病、非阿尔茨海默病神经退行性变和早发性神经认知症状的原发性精神障碍病因。这些结果支持将血液生物标志物用于早发性神经认知症状的检查，并强调了开发早发性阿尔茨海默病特异性多基因风险评分的必要性。
