Abstract: Introduction: We previously applied generalized additive models for location, scale, and shape to derive amyloid B-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.
