Distilling Heterogeneity of Mild Cognitive Impairment in the National Alzheimer Coordinating Center Database Using Latent Profile Analysis

Blanken, Anna E.; Jang, Jung Yun; Ho, Jean K.; Edmonds, Emily C.; Han, S. Duke; Bangen, Katherine J.; Nation, Daniel A.

doi:10.1001/jamanetworkopen.2020.0413

Cited by 22 publications

(15 citation statements)

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“…In addition, LPA is based on specific mixture models 25 that analyze the joint distribution of a set of continuous observed variables (neuropsychological test z scores in this study) as a function of a finite and mutually exclusive and exhaustive number of unobserved components (mixtures) using a latent categorical variable or profile. 26,27 In this study, the latent variable was a profile of cognitive functioning in patients with MS. It should be noted that LPA does not necessitate any a priori categorization of the observed variables or indicators, thus facilitating a more granular examination of heterogeneity within and between latent-level groupings.…”

Section: Discussionmentioning

confidence: 99%

Identifying the Distinct Cognitive Phenotypes in Multiple Sclerosis

et al. 2021

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IMPORTANCECognitive impairment is a common and disabling feature of multiple sclerosis (MS), but a precise characterization of cognitive phenotypes in patients with MS is lacking.OBJECTIVES To identify cognitive phenotypes in a clinical cohort of patients with MS and to characterize their clinical and magnetic resonance imaging (MRI) features. DESIGN, SETTING, AND PARTICIPANTSThis multicenter cross-sectional study consecutively screened clinically stable patients with MS and healthy control individuals at 8 MS centers in Italy from January 1, 2010, to October 31, 2019. Patients with MS and healthy control individuals who were not using psychoactive drugs and had no history of other neurological or medical disorders, learning disability, severe head trauma, and alcohol or drug abuse were enrolled. MAIN OUTCOMES AND MEASURES Participants underwent a neurological examination and a cognitive evaluation with the Rao Brief Repeatable Battery and Stroop Color and Word Test. A subgroup of participants also underwent a brain MRI examination. Latent profile analysis was used on cognitive test z scores to identify cognitive phenotypes. Linear regression and mixed-effects models were used to define clinical and MRI features of each phenotype. RESULTS A total of 1212 patients with MS (mean [SD] age, 41.1 [11.1] years; 784 women [64.7%]) and 196 healthy control individuals (mean [SD] age, 40.4 [8.6] years; 130 women [66.3%]) were analyzed in this study. Five cognitive phenotypes were identified: preserved cognition (n = 235 patients [19.4%]), mild-verbal memory/semantic fluency (n = 362 patients [29.9%]), mild-multidomain (n = 236 patients [19.5%]), severe-executive/attention (n = 167 patients [13.8%]), and severe-multidomain (n = 212 patients [17.5%]) involvement. Patients with preserved cognition and mild-verbal memory/semantic fluency were younger (mean [SD] age, 36.5 [9.8] years and 38.2 [11.1] years) and had shorter disease duration (mean [SD] 8.0 [7.3] years and 8.3 [7.6] years) compared with patients with mild-multidomain (mean [SD] age, 42.6 [11.2] years; mean [SD] disease duration, 12.8 [9.6] years; P < .001), severe-executive/attention (mean [SD] age, 42.9 [11.7] years; mean [SD] disease duration, 12.2 [9.5] years; P < .001), and severe-multidomain (mean [SD] age, 44.0 [11.0] years; mean [SD] disease duration , 13.3 [10.2] years; P < .001) phenotypes. Severe cognitive phenotypes prevailed in patients with progressive MS. At MRI evaluation, compared with those with preserved cognition, patients with mild-verbal memory/semantic fluency exhibited decreased mean (SE) hippocampal volume (5.42 [0.68] mL vs 5.13 [0.68] mL; P = .04), patients with the mild-multidomain phenotype had decreased mean (SE) cortical gray matter volume (687.69 [35.40] mL vs 662.59 [35.48] mL; P = .02), patients with severe-executive/ attention had higher mean (SE) T2-hyperintense lesion volume (51.33 [31.15] mL vs 99.69 [34.07] mL; P = .04), and patients with the severe-multidomain phenotype had extensive brain damage, with decreased volume in a...

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Section: Discussionmentioning

confidence: 99%

Identifying the Distinct Cognitive Phenotypes in Multiple Sclerosis

et al. 2021

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“…To compute a personalized composite, the actual cognitive change rates with faster tau-PET-predicted decline were weighted higher than actual change rates with slower tau-PET-predicted cognitive decline. Such patient-specific cognitive composites take into account inter-individual variability to facilitate longitudinal assessment of heterogeneous cognitive trajectories [ 47 – 49 ] and may thus be applied as personalized endpoints in clinical trials [ 7 , 19 ]. Supporting this, we exploratorily performed simulated trials in which tau-PET-informed personalized cognitive composites increased the sensitivity to detect treatment effects compared to conventional composites, which have been previously used as endpoints in clinical trials [ 8 , 50 ].…”

Section: Discussionmentioning

confidence: 99%

Combining tau-PET and fMRI meta-analyses for patient-centered prediction of cognitive decline in Alzheimer’s disease

Biel

Luan²,

Brendel³

et al. 2022

Alz Res Therapy

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Background Tau-PET is a prognostic marker for cognitive decline in Alzheimer’s disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures. Methods We included Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer’s disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer’s disease-spectrum=46/65). All participants underwent baseline 18F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R2) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline. Results In both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort. Conclusion Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer’s disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.

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“…While a mixedeffect regression-the standard analysis of this type of data-would identify one single trajectory per test that represents the "average" pattern of decline (Salthouse 2019), model-based clustering discovers multiple patterns of decline that are statistically different. The use of this approach to discover patterns of cognitive decline is growing, and it is proving to be useful for stratification of risk of cognitive impairment and Alzheimer's (Blanken et al 2020;Lee et al 2018). To assess the clinical and biological value of the patterns of change discovered with this analysis, we annotated the patterns by their correlation with patients' medical history, medications, circulating biomarkers, genetic markers, and changes of other aging traits and clinical status over time.…”

Section: Introductionmentioning

confidence: 99%

Patterns of multi-domain cognitive aging in participants of the Long Life Family Study

et al. 2020

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Maintaining good cognitive function at older age is important, but our knowledge of patterns and predictors of cognitive aging is still limited. We used Bayesian model-based clustering to group 5064 participants of the Long Life Family Study (ages 49-110 years) into clusters characterized by distinct trajectories of cognitive change in the domains of episodic memory, attention, processing speed, and verbal

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Distilling Heterogeneity of Mild Cognitive Impairment in the National Alzheimer Coordinating Center Database Using Latent Profile Analysis

Abstract: Open Access. This is an open access article distributed under the terms of the CC-BY License.

Cited by 22 publications

References 8 publications

Identifying the Distinct Cognitive Phenotypes in Multiple Sclerosis

Identifying the Distinct Cognitive Phenotypes in Multiple Sclerosis

Combining tau-PET and fMRI meta-analyses for patient-centered prediction of cognitive decline in Alzheimer’s disease

Patterns of multi-domain cognitive aging in participants of the Long Life Family Study

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