Dementia and geriatric cognitive disorders extra · 2025
The Utility of CSF Biomarkers in Diagnosing Alzheimer's Disease: A Thai Cohort Study.
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Nanthasi Witsarut, Rattanabannakit Chatchawan, Wongkom Natthamon, Dujada Pathitta, Raksthaput Atthapon, Chaichanettee Sunisa, Phoyoo Paphawadee, Wachirutmanggur Leatchai, An Seong Soo A, Senanarong Vorapun
Аннотация
INTRODUCTION: Cutoff values for cerebrospinal fluid biomarkers vary by analytic technique and population, which complicates the differentiation of Alzheimer's disease (AD) from non-AD dementias. We aimed to establish local cerebrospinal fluid biomarker cutoffs within a Thai cohort.
MATERIALS AND METHODS: We recruited 68 patients with various forms of dementia from the Memory Clinic at Siriraj Hospital, Thailand. Each patient underwent clinical subtyping for dementia, and their cerebrospinal fluid levels of Aβ42, p-tau181, and t-tau were quantified using the Fujirebio INNOTEST ELISA. We then employed a data-driven approach, specifically a Z-score-based Gaussian Mixture Model, to define intersection cutoffs for Aβ42, p-tau181, t-tau, and the p-tau181/Aβ42 ratio. These established biomarker cutoffs were subsequently incorporated with clinical manifestations to refine the clinicobiological diagnoses.
RESULTS: Our study included 67 patients (mean age 65.5 ± 7.4 years, 61.2% female). Using a data-driven approach, we established the following CSF biomarker cutoffs for identifying AD in this Thai cohort: Aβ42 at 492.67 pg/mL, p-tau181 at 44.00 pg/mL, t-tau at 545.97 pg/mL, and the p-tau181/Aβ42 ratio at 0.057. After incorporating these CSF biomarker results with clinical profiles, the diagnoses changed in 17.9% of the patients.
CONCLUSIONS: In this study, CSF cutoffs for differentiating AD from non-AD dementia were established through a data-driven approach, which has been demonstrated as a valid alternative methodology. The integration of clinical and biological profiles is paramount in achieving accurate dementia diagnoses.
MATERIALS AND METHODS: We recruited 68 patients with various forms of dementia from the Memory Clinic at Siriraj Hospital, Thailand. Each patient underwent clinical subtyping for dementia, and their cerebrospinal fluid levels of Aβ42, p-tau181, and t-tau were quantified using the Fujirebio INNOTEST ELISA. We then employed a data-driven approach, specifically a Z-score-based Gaussian Mixture Model, to define intersection cutoffs for Aβ42, p-tau181, t-tau, and the p-tau181/Aβ42 ratio. These established biomarker cutoffs were subsequently incorporated with clinical manifestations to refine the clinicobiological diagnoses.
RESULTS: Our study included 67 patients (mean age 65.5 ± 7.4 years, 61.2% female). Using a data-driven approach, we established the following CSF biomarker cutoffs for identifying AD in this Thai cohort: Aβ42 at 492.67 pg/mL, p-tau181 at 44.00 pg/mL, t-tau at 545.97 pg/mL, and the p-tau181/Aβ42 ratio at 0.057. After incorporating these CSF biomarker results with clinical profiles, the diagnoses changed in 17.9% of the patients.
CONCLUSIONS: In this study, CSF cutoffs for differentiating AD from non-AD dementia were established through a data-driven approach, which has been demonstrated as a valid alternative methodology. The integration of clinical and biological profiles is paramount in achieving accurate dementia diagnoses.