Identifying subtypes of mild cognitive impairment in Parkinson’s disease using cluster analysis

Pourzinal, Dana; Yang, Jihyun; Byrne, Gerard; O’Sullivan, John D.; Mitchell, Leander K.; McMahon, Katie L.; Copland, David A.; Dissanayaka, N.

doi:10.1007/s00415-020-09977-z

Cited by 20 publications

(33 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We did not have this information readily available, and not applying this broadly used MDS criteria limits the comparability of our findings to other studies that use it. Even so, there is precedence for not including subjective cognitive complaints within an actuarial version of the PD-MCI criteria [ 7 , 74 , 75 , 76 ], and evidence for the usefulness of this criteria is mixed [ 77 , 78 ]. Finally, clinicians often make a diagnosis of MCI in situations where there has been a performance drop from an estimated premorbid baseline (e.g., a, drop from superior to low average ranges).…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, recent studies have found that cognitive phenotypes derived from cluster analyses are more strongly correlated with AD biomarkers and are more strongly linked to dementia progression than traditional a priori classification methods [ 4 , 5 ]. To date, few studies have compared these two approaches in individuals with Parkinson disease or addressed some of the psychometric issues inherent when comparing these approaches to each other [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, results remain mixed over which cutoff criteria best identifies who is at greatest risk for impending dementia [ 8 , 24 , 27 ]. Previous work comparing empirical approaches to a PD-MCI classification, based on a prespecified impairment cutoff (−1.5 SD), found greater portions of PD-MCI participants in the more broadly impaired or amnestic phenotypes [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes

et al. 2021

View full text Add to dashboard Cite

Prevalence rates for mild cognitive impairment in Parkinson’s disease (PD-MCI) remain variable, obscuring the diagnosis’ predictive utility of greater dementia risk. A primary factor of this variability is inconsistent operationalization of normative cutoffs for cognitive impairment. We aimed to determine which cutoff was optimal for classifying individuals as PD-MCI by comparing classifications against data-driven PD cognitive phenotypes. Participants with idiopathic PD (n = 494; mean age 64.7 ± 9) completed comprehensive neuropsychological testing. Cluster analyses (K-means, Hierarchical) identified cognitive phenotypes using domain-specific composites. PD-MCI criteria were assessed using separate cutoffs (−1, −1.5, −2 SD) on ≥2 tests in a domain. Cutoffs were compared using PD-MCI prevalence rates, MCI subtype frequencies (single/multi-domain, executive function (EF)/non-EF impairment), and validity against the cluster-derived cognitive phenotypes (using chi-square tests/binary logistic regressions). Cluster analyses resulted in similar three-cluster solutions: Cognitively Average (n = 154), Low EF (n = 227), and Prominent EF/Memory Impairment (n = 113). The −1.5 SD cutoff produced the best model of cluster membership (PD-MCI classification accuracy = 87.9%) and resulted in the best alignment between PD-MCI classification and the empirical cognitive profile containing impairments associated with greater dementia risk. Similar to previous Alzheimer’s work, these findings highlight the utility of comparing empirical and actuarial approaches to establish concurrent validity of cognitive impairment in PD.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Another commonality across domain-based studies was the global subtype, which was present in all cluster structures except for two [26,31]. Below-average performance across most or all of the cluster variables defined this subtype, and it was associated with older age (mean age range = 66-75) [22,24,27,34], lower education [13,24,27], later age at onset [34], and greater motor symptoms [18,24,27],…”

Section: Globally Impaired Phenotypementioning

confidence: 99%

“…memory dysfunction [22,31], Pattern 1 [32], and posterior-cortical [27] subtypes were all defined by impairments in memory recall. The prevalence ranged from 12% [24] to 42% [27].…”

Section: Amnestic Phenotypementioning

confidence: 99%

Systematic review of data‐driven cognitive subtypes in Parkinson disease

Pourzinal

Yang

Lawson

et al. 2022

Euro J of Neurology

Self Cite

View full text Add to dashboard Cite

Background and purpose Recent application of the mild cognitive impairment concept to Parkinson disease (PD) has proven valuable in identifying patients at risk of dementia. However, it has sparked controversy regarding the existence of cognitive subtypes. The present review evaluates the current literature pertaining to data‐driven subtypes of cognition in PD. Methods Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) guidelines, systematic literature searches for peer‐reviewed articles on the topic of cognitive subtyping in PD were performed. Results Twenty‐two relevant articles were identified in the systematic search. Subtype structures showed either a spectrum of severity or specific domains of impairment. Domain‐specific subtypes included amnestic/nonamnestic, memory/executive, and frontal/posterior dichotomies, as well as more complex structures with less definitive groupings. Preliminary longitudinal evidence showed some differences in cognitive progression among subtypes. Neuroimaging evidence provided insight into distinct patterns of brain alterations among subtypes. Conclusions Recurring phenotypes in the literature suggest strong clinical relevance of certain cognitive subtypes in PD. Although the current literature is limited, it raises critical questions about the utility of data‐driven methods in cognitive research. The results encourage further integration of neuroimaging research to define the latent neural mechanisms behind divergent subtypes. Although there is no consensus, there appears to be growing consistency and inherent value in identifying cognitive subtypes in PD.

show abstract

Prediction of Cognitive Heterogeneity in Parkinson's Disease: A 4‐Year Longitudinal Study Using Clinical, Neuroimaging, Biological and Electrophysiological Biomarkers

Puig‐Davi,

Martinez‐Horta,

Pérez‐Carasol

et al. 2024

Annals of Neurology

View full text Add to dashboard Cite

ObjectiveCognitive impairment in Parkinson's disease (PD) can show a very heterogeneous trajectory among patients. Here, we explored the mechanisms involved in the expression and prediction of different cognitive phenotypes over 4 years.MethodsIn 2 independent cohorts (total n = 475), we performed a cluster analysis to identify trajectories of cognitive progression. Baseline and longitudinal level II neuropsychological assessments were conducted, and baseline structural magnetic resonance imaging, resting electroencephalogram and neurofilament light chain plasma quantification were carried out. Linear mixed‐effects models were used to study longitudinal changes. Risk of mild cognitive impairment and dementia were estimated using multivariable hazard regression. Spectral power density from the electroencephalogram at baseline and source localization were computed.ResultsTwo cognitive trajectories were identified. Cluster 1 presented stability (PD‐Stable) over time, whereas cluster 2 showed progressive cognitive decline (PD‐Progressors). The PD‐Progressors group showed an increased risk for evolving to PD mild cognitive impairment (HR 2.09; 95% CI 1.11–3.95) and a marked risk for dementia (HR 4.87; 95% CI 1.34–17.76), associated with progressive worsening in posterior‐cortical‐dependent cognitive processes. Both clusters showed equivalent clinical and sociodemographic characteristics, structural magnetic resonance imaging, and neurofilament light chain levels at baseline. Conversely, the PD‐Progressors group showed a fronto‐temporo‐occipital and parietal slow‐wave power density increase, that was in turn related to worsening at 2 and 4 years of follow‐up in different cognitive measures.InterpretationIn the absence of differences in baseline cognitive function and typical markers of neurodegeneration, the further development of an aggressive cognitive decline in PD is associated with increased slow‐wave power density and with a different profile of worsening in several posterior‐cortical‐dependent tasks. ANN NEUROL 2024

show abstract

Identifying subtypes of mild cognitive impairment in Parkinson’s disease using cluster analysis

Cited by 20 publications

References 30 publications

Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes

Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes

Systematic review of data‐driven cognitive subtypes in Parkinson disease

Prediction of Cognitive Heterogeneity in Parkinson's Disease: A 4‐Year Longitudinal Study Using Clinical, Neuroimaging, Biological and Electrophysiological Biomarkers

Contact Info

Product

Resources

About