Sreepada, Lasya P

AbstractWe aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge‐based cut‐offs for each component of ATN and compared it to two data‐driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data‐driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia‐like and normal groups. Both data‐driven classification methods performed better than the empirical cut‐offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data‐driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.

AbstractBackgroundThe NIA‐AA Research Framework on Alzheimer’s disease (AD) represents an important advance in the biological characterization of the AD spectrum. While this framework predicts disease progression, definitions of amyloid, tau, and neurodegeneration positivity based on cut‐scores may limit the performance of predictive models.Method252 cognitively normal (CN), 424 amnestic mild cognitive impairment (aMCI) and 128 AD participants were included in this study. aMCI participants at baseline were classified using 2 different methods: 1) Using conventional AT[N] criteria based on proposed cut‐offs in CSF or brain imaging measures: participants were classified on the AD continuum (A+T‐N‐, A+T+N‐, A+T‐N+, A+T+N+) and non‐AD continuum (all other subgroups); and 2) using a machine learning (ML) based method (ensemble linear discriminant model built using CN and AD participants) to classify MCI participants as CN‐like or AD‐like based on continuous CSF biomarkers, regional amyloid PET, FDG‐PET, and volumetric MRI (variables contributing to AT[N] classification; Table 1). Predictive performance of AT[N]‐based classification and ML‐based classification were assessed using clinical outcomes at 0.5, 1, 2, 3, 4, and 5 years of follow‐up.ResultA total of 414 aMCI participants had at least one wave of follow‐up (N of 397, 400, 346, 297, 238, and 93 at 0.5, 1, 2, 3, 4, and 5 years, respectively). The progression rate from aMCI to AD was 5.8% at 6m, 9.5% at 1y, 17.9% at 2y, 19.9% at 3y, 20.2% at 4y, and 15% at 5y of follow‐up. Presence of AD pathology at baseline (AT[N]‐based AD continuum) predicted progression to AD with high sensitivity but modest specificity during follow up (Table 2). ML‐based classification was less sensitive but substantially more specific. ML‐based classification models were significantly more accurate than AT[N]‐based classification at all follow‐up periods (McNemar test, p‐value<0.001 for all).ConclusionA single cut‐point approach used in AT[N] classification lacks accuracy when research questions require high diagnostic or prognostic certainty. A multivariate approach using ML methods can improve accuracy of prediction of disease progression. PPVs were higher at all follow‐up time points for ML‐based classification, indicating that progression to AD occurs at a higher rate in persons identified by ML.