ArticleScientific reports2025
A data-driven cluster analysis to explore cognitive reserve and modifiable risk factors in early phases of cognitive decline.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Data-driven subtyping of Alzheimer's emergency presentations using unsupervised machine learning.IBRO neuroscience reports · 2026Article
- Article
- Detecting multimorbidity patterns in Alzheimer's disease using unsupervised machine learning: A nationwide emergency department study (2007-2022).Journal of Alzheimer's disease : JAD · 2026Article
- The Modulating Effect of Cognitive Reserve on Dysfunctional Beliefs in Aging.Europe's journal of psychology · 2026Article
- Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder.Brain informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study was aimed to cluster sub-groups of patients with varying degrees of cognitive impairment (Subjective Cognitive Decline, mild or Major Neurocognitive Disorder) based on their modifiable risk factors and cognitive reserve with k-means analysis. As a secondary analysis, we described the identified clusters from different perspectives, i.e., socio-demographic characteristics, cognitive functioning, and mental health. The analysis revealed two clusters, which were composed by 27 and 43 patients characterized by protective (Cluster 1) and unprotective (Cluster 2) everyday life habits, respectively. The two groups showed significant differences across all examined dimensions, with Cluster 1 demonstrating a more favourable profile compared to Cluster 2. Specifically, Cluster 1 exhibited advantages in: (1) sociodemographic (education, technological skills, and occupation), (2) cognitive (global cognitive functioning, executive functioning, and working memory), and (3) mental health (mood state and quality of life) characteristics. Such a finding is representative of a more positive individual wellbeing for people who adopt protective behaviours. In the field of dementia prevention, these results support the importance to intervene proactively and simultaneously in the management of multiple risk factors during the entire lifespan.
Indexed as
Identifiers
What OpenQuestion holds
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.