Dataset Information

Multivariate clustering of progression profiles reveals different depression patterns in prodromal Huntington disease.

ABSTRACT: Although Huntington disease (HD) is caused by an autosomal dominant mutation, its phenotypic presentation differs widely. Variability in clinical phenotypes of HD may reflect the existence of disease subtypes. This hypothesis was tested in prodromal participants from the longitudinal Neurobiological Predictors of Huntington Disease (PREDICT-HD) study.We performed clustering using longitudinal data assessing motor, cognitive, and depression symptoms. Using data from 521 participants with 2,716 data points, we fit growth mixture models (GMM) that identify groups based on multivariate trajectories.In various GMM, different phases of disease progression were partitioned by progression trajectories of motor and cognitive signs, and by overall level of depression symptoms. More progressed motor signs were accompanied by more progressed cognitive signs, but not always by higher levels of depressive symptoms. In several models, there were at least 2 groups with similar trajectories for motor and cognitive signs that showed different levels for depression symptoms-one with a very low level of depression and the other with a higher level of depression.Findings indicate that at least intermediate HD progression might be associated with different levels of depression. Depression is one of the few symptoms that is treatable in HD and has implications for clinical care. Identification of potential depression subtypes may also help to select appropriate patients for clinical trials.

SUBMITTER: Kim JI

PROVIDER: S-EPMC4640959 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Multivariate clustering of progression profiles reveals different depression patterns in prodromal Huntington disease.

Kim Ji-in JI Long Jeffrey D JD Mills James A JA McCusker Elizabeth E Paulsen Jane S JS

Neuropsychology 20150525 6

<h4>Objective</h4>Although Huntington disease (HD) is caused by an autosomal dominant mutation, its phenotypic presentation differs widely. Variability in clinical phenotypes of HD may reflect the existence of disease subtypes. This hypothesis was tested in prodromal participants from the longitudinal Neurobiological Predictors of Huntington Disease (PREDICT-HD) study.<h4>Method</h4>We performed clustering using longitudinal data assessing motor, cognitive, and depression symptoms. Using data fr ...[more]

PMID: 26011117

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Project description:Cognitive changes in the prodromal phase of Huntington disease (prHD) are found in multiple domains, yet their neural bases are not well understood. One component process that supports cognition is inhibitory control. In the present fMRI study, we examined brain circuits involved in response inhibition in 65 prHD participants and 36 gene-negative (NEG) controls using the stop signal task (SST). PrHD participants were subdivided into three groups (LOW, MEDIUM, HIGH) based on their CAG-Age Product (CAP) score, an index of genetic exposure and a proxy for expected time to diagnosis. Poorer response inhibition (stop signal duration) correlated with CAP scores. When response inhibition was successful, activation of the classic frontal inhibitory-network was normal in prHD, yet stepwise reductions in activation with proximity to diagnosis were found in the posterior ventral attention network (inferior parietal and temporal cortices). Failures in response inhibition in prHD were related to changes in inhibition centers (supplementary motor area (SMA)/anterior cingulate and inferior frontal cortex/insula) and ventral attention networks, where activation decreased with proximity to diagnosis. The LOW group showed evidence of early compensatory activation (hyperactivation) of right-hemisphere inhibition and attention reorienting centers, despite an absence of cortical atrophy or deficits on tests of executive functioning. Moreover, greater activation for failed than successful inhibitions in an ipsilateral motor-control network was found in the control group, whereas such differences were markedly attenuated in all prHD groups. The results were not related to changes in cortical volume and thickness, which did not differ among the groups. However, greater hypoactivation of classic right-hemisphere inhibition centers [inferior frontal gyrus (IFG)/insula, SMA/anterior cingulate cortex (ACC)] during inhibition failures correlated with greater globus pallidus atrophy. These results are the first to demonstrate that response inhibition in prHD is associated with altered functioning in brain networks that govern inhibition, attention, and motor control.

Project description:This paper describes an ensemble cluster analysis of bivariate profiles of HIV biomarkers, viral load and CD4 cell counts, which jointly measure disease progression. Data are from a prevalent cohort of HIV positive participants in a clinical trial of vitamin supplementation in Botswana. These individuals were HIV positive upon enrollment, but with unknown times of infection. To categorize groups of participants based on their patterns of progression of HIV infection using both biomarkers, we combine univariate shape-based cluster results for multiple biomarkers through the use of ensemble clustering methods. We first describe univariate clustering for each of the individual biomarker profiles, and make use of shape-respecting distances for clustering the longitudinal profile data. In our data, profiles are subject to either missing or irregular measurements as well as unobserved initiation times of the process of interest. Shape-respecting distances that can handle such data issues, preserve time-ordering, and identify similar profile shapes are useful in identifying patterns of disease progression from longitudinal biomarker data. However, their performance with regard to clustering differs by severity of the data issues mentioned above. We provide an empirical investigation of shape-respecting distances (Fréchet and dynamic time warping (DTW)) on benchmark shape data, and use DTW in cluster analysis of biomarker profile observations. These reveal a primary group of 'typical progressors,' as well as a smaller group that shows relatively rapid progression. We then refine the analysis using ensemble clustering for both markers to obtain a single classification. The information from joint evaluation of the two biomarkers combined with ensemble clustering reveals subgroups of patients not identifiable through univariate analyses; noteworthy subgroups are those that appear to represent recently and chronically infected subsets.Supplementary informationThe online version contains supplementary material available at 10.1007/s41060-022-00323-2.

Dataset Information

Multivariate clustering of progression profiles reveals different depression patterns in prodromal Huntington disease.

Publications

Multivariate clustering of progression profiles reveals different depression patterns in prodromal Huntington disease.

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