ArticleEClinicalMedicine2020
Linguistic markers predict onset of Alzheimer's disease.
Article in EClinicalMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06081569 (Multimodal Deep Learning for the Diagnosis and Assessment of Alzheimer's Disease), which is not on this map. Cited by 93 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Multimodal Deep Learning for the Diagnosis and Assessment of Alzheimer's Disease
Who cites it
93 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Cognitive markers of resilience to dementia in mild Neurocognitive Impairment: a meta- analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Cognitive Functioning of Unaffected First-degree Relatives of Individuals With Late-onset Alzheimer's Disease: A Systematic Literature Review and Meta-analysis.Neuropsychology review · 2023Pooled it
- Speech changes in old age: Methodological considerations for speech-based discrimination of healthy ageing and Alzheimer's disease.International journal of language & communication disordersPooled it
- AI-Based Measurement Tools for Intrinsic Capacity in Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- Linguistic features from paragraph recall are markers of cognitive impairment.Journal of the International Neuropsychological Society : JINS · 2026Article
- Multimodal Dementia Prediction With Large Language Models: Cross-Attention Over Text, Audio, and Image.JMIR aging · 2026Article
- Machine Learning and Multimodal Biomarker Discovery in Alzheimer's Disease.Brain sciences · 2026Review
- Article
- A 60-second interpretable voice model for early dementia screening.PLOS digital health · 2026Article
- Multidimensional Prosodic and Semantic Coherence Modeling for Mandarin Mild Cognitive Impairment Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Digital speech-based markers to advance prognosis in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Design of an artificial intelligence model to screen spontaneous speech to detect Alzheimer's Disease.PLOS digital health · 2026Article
- Early Detection of Alzheimer's Disease and Related Dementias From Spontaneous Speech Using Foundation Speech and Language Models: Comparative Evaluation.JMIR formative research · 2026Article
- Linguistic Markers in At-Risk Mental States Using Natural Language Processing: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Benchmarking speech biomarkers of Alzheimer's against cognitive and neural measures.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Associations between ventricular boundary shift integral and composite cognitive domains across the alzheimer's disease continuum.Scientific reports · 2026Article
- The landscape of dementia research, diagnosis, treatment, and care in Latin America.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Language deficits across PET-based Braak stages of tau accumulation in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- Recent Advances in Nanoparticle-Based Drug Delivery Strategies to Cross the Blood-Brain Barrier in Targeted Treatment of Alzheimer's Disease.Pharmaceutics · 2026Review
33 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsTo 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.
findingsSignificant 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.
interpretationThe results suggest that language performance in naturalistic probes expose subtle early signs of progression to AD in advance of clinical diagnosis of impairment.
fundingPfizer, 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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.