Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease
Journal article, 2021

Background: In Alzheimer’s disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology. Methods: A cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts. Results: A β pathology status was determined based on the A β42/A β40 ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R2 score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R2 score of 0.228. Conclusion: Our analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.


Alzheimer’s disease



Machine learning


Hákon Valur Dansson

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Lena Stempfle

Data Science and AI

Hildur Egilsdóttir

Student at Chalmers

Alexander Schliep

University of Gothenburg

Erik Portelius

University of Gothenburg

Sahlgrenska University Hospital

Kaj Blennow

Sahlgrenska University Hospital

University of Gothenburg

Henrik Zetterberg

Sahlgrenska University Hospital

University of Gothenburg

University College London (UCL)

Fredrik Johansson

Data Science and AI

Alzheimers Research and Therapy

1758-9193 (ISSN)

Vol. 13 1 151

Subject Categories


Gerontology, specialising in Medical and Health Sciences






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