Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer's disease

• Background In Alzheimer's disease, amyloid- beta (A beta) 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 beta pathology. Methods A cohort of n=2293 participants, of whom n=749 were A beta positive, was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A beta 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 beta pathology, models fit only to A beta-positive subjects were compared to models fit to an extended cohort including subjects without established A beta pathology, adjusting for covariate differences between the cohorts. Results A beta pathology status was determined based on the A beta(42)/A beta(40) ratio. The best predictive model of change in cognitive test scores for A beta-positive subjects at the 2-year follow-up achieved an R-2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F-1 score of 0.791. A beta-positive subjects declined faster on average than those without A beta pathology, but the specific level of CSF A beta was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R-2 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 R-2 score of 0.228. Conclusion Our analysis shows that CSF levels of A beta are not strong predictors of the rate of cognitive decline in A beta-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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Neurosciences (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Geriatrik (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Geriatrics (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Annan medicin och hälsovetenskap -- Gerontologi, medicinsk/hälsovetenskaplig inriktning (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Other Medical and Health Sciences -- Gerontology, specialising in Medical and Health Sciences (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Neurologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Neurology (hsv//eng)

Keyword

Alzheimer's disease

Amyloid-beta

Progression

Prediction

Machine

learning

mini-mental-state

combined cerebrospinal-fluid

association workgroups

diagnostic guidelines

national institute

dementia

risk

recommendations

framework

mri

Neurosciences & Neurology

Prediction

Publication and Content Type

ref (subject category)

art (subject category)