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Journal of Alzheimer's disease : JAD

Combining a computerized cognitive test with serum biomarkers improves detection of early-onset neurodegenerative disorders.

BackgroundEarly diagnosis of early-onset dementia (EOD) is often challenging. Executive dysfunction is a common symptom in EOD, including Alzheimer's disease (AD). Currently, no specific tools for screening EOD at the primary health care exist. The availability of neuropsychological assessment and biomarker analyses is often limited to specialized memory clinics. However, recent advances in blood-biomarkers and better accessible computerized cognitive tests provide new opportunities.ObjectiveTo investigate the ability of serum biomarkers neurofilament light chain and glial fibrillary acidic protein, combined with a novel computerized Flexible Attention Test (FAT) to distinguish early-onset neurodegenerative disorders from other conditions resulting cognitive symptoms in a diagnostically challenging memory clinic population.MethodsCohort consisted of 206 participants with symptom onset ≤65 years and followed up to 24 months: EOD (n = 54, including 29 AD cases), neurological mild cognitive impairment (MCI-n, n = 34), MCI due to other causes (n = 104), and subjective cognitive decline (n = 14). Serum biomarkers were analyzed using single-molecule array, and discriminative accuracy of individual and combined tests was assessed using Receiver Operating Characteristic and discriminant analyses. FAT was compared to traditional neuropsychological tests.ResultsCombining serum biomarkers with cognitive tests were more accurate in detecting EOD and MCI-n from other conditions (area under the curve, AUC = 0.872) compared to each method individually (AUC = 0.633-0.783). Combination of the FAT and serum biomarkers reached 82.1% accuracy, comparable to traditional neuropsychological tests and biomarkers together (82.3%).ConclusionsIntegrating serum biomarkers with computerized FAT offers a promising strategy for screening EOD early and identifying patients for further evaluation.

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