Alzheimer's & dementia (Amsterdam, Netherlands) · 2025
Amyloid-negative neuropsychological norms: Added value in the era of biomarkers and disease-modifying therapies.
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Rubio-Guerra Sara, Sala Isabel, Sánchez-Saudinós María Belén, Videla Laura, Bejanin Alexandre, Estanga Ainara, Ecay-Torres Mirian, de Luis Carolina Lopez, Rami Lorena, Tort-Merino Adrià, Castellví Magdalena, Pozueta Ana, García-Martínez María, Gómez-Andrés David, Lage Carmen, López-García Sara, Sánchez-Juan Pascual, Balasa Mircea, Lladó Albert, Altuna Miren, Tainta Mikel, Arranz Javier, Zhu Nuole, Alcolea Daniel, Lleó Alberto, Fortea Juan, Rodríguez Eloy Rodríguez, Sánchez-Valle Raquel, Martínez-Lage Pablo, Illán-Gala Ignacio
Аннотация
INTRODUCTION: We previously applied generalized additive models for location, scale, and shape to derive amyloid β-negative next-generation norms (NGN) for a comprehensive neuropsychological battery. Here, we evaluated the accuracy of NGN in detecting cognitive impairment compared to traditional norms (TN).
METHODS: This multicenter study included N = 2405 participants classified as cognitively normal (CN, n = 987) or with mild cognitive impairment (MCI, n = 1418) using conventional criteria. All participants underwent neuropsychological testing and cerebrospinal fluid Alzheimer's disease (AD) biomarker assessment. We used actuarial neuropsychological criteria to reclassify all participants using TN and NGN. Diagnostic groups were compared on cognitive performance, AD biomarker positivity, and longitudinal cognitive trajectories.
RESULTS: Nineteen percent of TN-classified CN participants were diagnosed with MCI by NGN, whereas 3% of TN-classified MCI were identified as CN by NGN. NGN demonstrated stronger associations with neuropsychological performance, AD biomarkers, and progression than TN.
DISCUSSION: NGN enhance the detection of objective cognitive impairment, with direct implications for clinical practice and research.
HIGHLIGHTS: Next-generation norms (NGN) reclassify one of every five cases from cognitively normal (CN) to mild cognitive impairment (MCI).This group shows poor cognitive performance and a high prevalence of amyloid β positivity.NGN-based diagnosis of MCI predicts cognitive progression on follow-up.Results indicate that NGN improve the detection of objective cognitive impairment.NGN can inform biomarker use, therapy indication, and clinical trial design.
METHODS: This multicenter study included N = 2405 participants classified as cognitively normal (CN, n = 987) or with mild cognitive impairment (MCI, n = 1418) using conventional criteria. All participants underwent neuropsychological testing and cerebrospinal fluid Alzheimer's disease (AD) biomarker assessment. We used actuarial neuropsychological criteria to reclassify all participants using TN and NGN. Diagnostic groups were compared on cognitive performance, AD biomarker positivity, and longitudinal cognitive trajectories.
RESULTS: Nineteen percent of TN-classified CN participants were diagnosed with MCI by NGN, whereas 3% of TN-classified MCI were identified as CN by NGN. NGN demonstrated stronger associations with neuropsychological performance, AD biomarkers, and progression than TN.
DISCUSSION: NGN enhance the detection of objective cognitive impairment, with direct implications for clinical practice and research.
HIGHLIGHTS: Next-generation norms (NGN) reclassify one of every five cases from cognitively normal (CN) to mild cognitive impairment (MCI).This group shows poor cognitive performance and a high prevalence of amyloid β positivity.NGN-based diagnosis of MCI predicts cognitive progression on follow-up.Results indicate that NGN improve the detection of objective cognitive impairment.NGN can inform biomarker use, therapy indication, and clinical trial design.