Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia

Abstract

We 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.

Comparing machine learning‐derived MRI‐based and blood‐based neurodegeneration biomarkers in predicting syndromal conversion in early AD

“…This probably explains why previous study reported a lower prevalence but a greater conversion rate among CU subjects than us. On the other hand, the proportion and progression of A+T+ among MCI subjects are in line with previous reports, which showed a prevalence varying from 28% to 60% among MCI subjects with about 40%–50% progressed to AD dementia within a relatively short follow‐up period 4,46,47 . These findings highlighted the need to not only use biomarkers for Aβ and tauopathy to define AD, but also consider utilizing neurodegeneration biomarkers to predict the speed of cognitive decline.…”

“…Moreover, knowledge of the rate of cognitive decline may affect the design and interpretation of the results of clinical trials for early AD. 4 For example, a plausible reason explaining the statistically significant benefit of high-dose aducanumab observed only in EMERGE and not in ENGAGE could be related to the rapid cognitive decline observed in the placebo group of EMERGE. 5 Future trials should consider recruiting subjects with similar risks of syndromal conversion.…”

“…Therefore, subjects with a low risk of conversion may require only regular monitoring, whereas those with high risks may be prioritized for more aggressive interventions. Moreover, knowledge of the rate of cognitive decline may affect the design and interpretation of the results of clinical trials for early AD 4 . For example, a plausible reason explaining the statistically significant benefit of high‐dose aducanumab observed only in EMERGE and not in ENGAGE could be related to the rapid cognitive decline observed in the placebo group of EMERGE 5 .…”

Hemispheric Cortical, Cerebellar and Caudate Atrophy Associated to Cognitive Impairment in Metropolitan Mexico City Young Adults Exposed to Fine Particulate Matter Air Pollution

“…The current preclinical criteria aimed at elderly populations that is being used in the current literature [118][119][120][121][122][123] is certainly not ideal. We must agree with Dubois et al, [124] that the NIA-AA [119] that relies on biomarkers has limitations, and that those limitations certainly apply to young cohorts with no comorbidities, but with cognition deficits associated with extensive MRI changes.…”

“…We must agree with Dubois et al, [124] that the NIA-AA [119] that relies on biomarkers has limitations, and that those limitations certainly apply to young cohorts with no comorbidities, but with cognition deficits associated with extensive MRI changes. Specifically, we know that 55% of the MMC population (average age 21.6 ± 5.8 years, with 13.7 ± 1.3 formal education years) is already cognitively impaired, but we are very aware it will be extremely difficult to apply the ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia [118][119][120]. Moreover, of deep concern is the fact that in Aβ-positive elderly [121], 16% of variance in cross-sectional cognitive impairment was accounted for by Aβ, 46-47% by tau, and 25-29% by atrophy, and the Aβ-tau-atrophy pathway accounted for 50% to 56% of variance in longitudinal cognitive decline [121].…”