Clinical Validation of a Plasma-Based Antibody-Free LC-MS Method for Identifying CSF Amyloid Positivity in Mild Cognitive Impairment

José Antonio Allué^1*Leticia Sarasa¹Noelia Fandos¹Ricardo Gonzalo²Rubén Sabido-Vera²Jorge Loscos¹Judith Romero¹Adrián Sánchez¹Jose Terencio^1,2Jordi A. Matias-Guiu³Gerard Piñol-Ripoll^4,5María Pascual-Lucas^1*

• ¹Araclon Biotech, Zaragoza, Spain
• ²Scientific Innovation Office, Grifols S.A., Sant Cugat del Vallès (Barcelona), Spain
• ³Hospital Clinico San Carlos Servicio de Neurologia, Madrid, Spain
• ⁴Hospital Universitari de Santa Maria, Lleida, Spain
• ⁵Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic - Institut d'Investigacions Biomèdiques August Pi i Sunyer., Barcelona, Spain

Background: The recent approval of monoclonal antibodies for the treatment of Alzheimer´s disease (AD) in several countries has accelerated the need for affordable, simple and scalable methods to identify patients who are eligible for treatment with the new disease-modifying therapies (DMT). Blood-based biomarkers offer less invasive alternatives to established gold standards. We have clinically validated a predictive model combining plasma Aβ42/Aβ40, apolipoprotein E (APOE) genotype and age, in two independent real-world cohorts to identify brain amyloid deposition. Methods: We conducted a clinical validation study involving 450 patients with mild cognitive impairment (MCI) from two real-world cohorts (HCSC, Madrid, Spain and HUSM, Lleida, Spain). Plasma Aβ42/Aβ40 was measured by ABtest-MS, an antibody-free liquid chromatography-mass spectrometry method. CSF Aβ42/Aβ40 and p-tau181/Aβ42 (gold standards) were quantified with the Lumipulse® platform. The model was trained in the HCSC cohort and validated in the HUSM cohort. Finally, an overall analysis in the combined population was performed. A dual cutoff approach was used to classify the patients as positive or negative. Statistical analysis included bootstrap resampling and model calibration. Results: In the HCSC, HUSM, external validation and combined analysis, AUCs were 0.89 (95% confidence intervals-CI: 0.84–0.93), 0.88 (0.84–0.93), 0.88 (0.83–0.92) and 0.88 (0.84–0.91), with corresponding accuracies of 82.3%, 81.6%, 82.3%, and 81.1%, respectively. After the combined analysis, positive and negative predictive values (PPV and NPV) were established at 87.5%, resulting in cutoff values of 0.30 and 0.67 for the likelihood of amyloid negativity and positivity, respectively, for a prevalence of 62%. Probability values lower than 0.30 indicate low probability of brain amyloid deposition, while values greater than 0.67 indicate high probability. Less than 28% of the participants fell into the intermediate zone. Additional cutoffs were derived for different prevalence values. Predictive model calibration showed excellent agreement with observed data, confirming accurate predictions (slope = 0.98, intercept = -0.01). Conclusions: This predictive model has demonstrated high accuracy for the identification of brain amyloid deposition in patients with MCI. Derived cutoffs enabled over 70% reduction in invasive testing, supporting efficient and cost-effective identification of candidates for DMTs.