Linguistic markers predict onset of Alzheimer's disease

Abstract

Background: The aim of this study is to use classification methods to predict future onset of Alzheimer's disease in cognitively normal subjects through automated linguistic analysis.

Methods: To study linguistic performance as an early biomarker of AD, we performed predictive modeling of future diagnosis of AD from a cognitively normal baseline of Framingham Heart Study participants. The linguistic variables were derived from written responses to the cookie-theft picture-description task. We compared the predictive performance of linguistic variables with clinical and neuropsychological variables. The study included 703 samples from 270 participants out of which a dataset consisting of a single sample from 80 participants was held out for testing. Half of the participants in the test set developed AD symptoms before 85 years old, while the other half did not. All samples in the test set were collected during the cognitively normal period (before MCI). The mean time to diagnosis of mild AD was 7.59 years.

Findings: Significant predictive power was obtained, with AUC of 0.74 and accuracy of 0.70 when using linguistic variables. The linguistic variables most relevant for predicting onset of AD have been identified in the literature as associated with cognitive decline in dementia.

Interpretation: The results suggest that language performance in naturalistic probes expose subtle early signs of progression to AD in advance of clinical diagnosis of impairment.

Funding: Pfizer, Inc. provided funding to obtain data from the Framingham Heart Study Consortium, and to support the involvement of IBM Research in the initial phase of the study. The data used in this study was supported by Framingham Heart Study's National Heart, Lung, and Blood Institute contract (N01-HC-25195), and by grants from the National Institute on Aging grants (R01-AG016495, R01-AG008122) and the National Institute of Neurological Disorders and Stroke (R01-NS017950).

Conflict of interest statement

Elif Eyigoz and Guillermo Cecchi has worked as salaried employees of IBM Corp. for the full duration of this project. Melissa Naylor was a salaried employee of Pfizer, Inc. when assigned to this project, until October 2018, and since then has been a salaried employee of Takeda Pharmaceuticals. Sachin Mathur and Mar Santamaria have worked as salaried employees of Pfizer, Inc. for the full duration of this project. Guillermo Cecchi declares that IBM holds a patent (US-9508360-B2) for the extraction of one of the features used in the linguistic model.

© 2020 Published by Elsevier Ltd.

Figures

Fig. 1

The diagram depicts method for selection of cases vs controls and predictive model setting. Participants who developed MCI due to AD on or before age 85 were selected as cases, and participants remained dementia-free until age 85 were selected as controls. The three predictive models included only non-linguistic variables, only linguistic variables, or both (see Table 3), collected when participants were considered cognitively normal, and were trained to predict conversion status by age 85 vs. later or no conversion.

Fig. 2

Method of selection of participants for creating a test data set and a training data set from the FHS data. The available data consisted of 3113 samples from 1254 participants, 486 of which have been reviewed by a panel for dementia status. The participants who were reviewed by the panel were candidates for creating a test data set. Their samples were eliminated according to the inclusion criteria, and then the qualifying samples were passed through age, education and gender matching. This resulted in a test set of 80 samples. The participants who were not reviewed by the dementia review panel were used for creating a larger weakly-labeled data set, only for the purpose of machine learning training. Validation of predictive modeling consisted of the hold-out method (train on weak-labels, test on ground-truth), and cross-validation (train on ground-truth, test on ground-truth). .

Fig. 3

CTT examples from FHS, including an unimpaired sample (a), an impaired sample showing telegraphic speech and lack of punctuation (b), and an even more impaired sample showing in addition significant misspellings and minimal grammatic complexity, e.g. lack of subjects (c).

Fig. 4

The ROC curve of the test-set with the hold-out method for the linguistic-based model (see Table 3). This result was obtained by a Logistic Regression classifier.

Fig. 5

The results of the non-negative matrix factorization (NMF) analysis of the linguistic and the NP variables on longitudinal data. This plot demonstrates that the factorization of the variables without using time information temporal trend as well as a differentiation between cases and controls, which starts several years before cognitive impairment. The controls’ samples are projected onto the factorization learned from the cases’ samples and averaged over six-month intervals. Controls are shown in blue, and cases in red. The horizontal axis is years to/from cognitive impairment onset, where 0 stands for the date of cognitive impairment. .