Latent Class Analysis of MCI Using Longitudinal Clinical Features

 
   Lead Investigator:    John Hanfelt
   Institution      :    Dept. of Biostatistics, Emory University
   E-Mail           :    jhanfel@emory.edu
   Proposal ID      :    148


   Publications:


1  Hanfelt JJ
   Latent Class Methods to Explore the Heterogeneity of Neurodegenerative Diseases
   42809   Grant  NIH   R01 AG055634   , 

   FUNDED



2  Hanfelt JJ, Peng L, Goldstein FC, Lah JJ
   Latent classes of mild cognitive impairment are associated with clinical outcomes and neuropathology: Analysis of data from the National Alzheimer's Coordinating Center
   Journal Article  Neurobiol Dis,117:62-71,2018,PMC6093304

   PUBLISHED



3  Hart KR, Hanfelt JJ
   Robust Latent Class Analysis for Longitudinal Data
   2013   Abstract  Joint Statistical Meeting (JSM) Montreal, Canada , 

   ACCEPTED





   Proposal Description:
   The objective of this project is to explore empirically the heterogenous          structure of mild cognitive impairment (MCI), and in particular, to identify      subclassifications of MCI based on the longitudinal trajectories of relevant      clinical features.  The current method of clinically categorizing subtypes of     MCI, used by many research centers including the NIH funded Alzheimer's Disease                                                                                     Centers, is based on the differences in cognitive domains affected in the         individual that are hypothesized to relate to early manifestations of various     neurodegenerative dementias.  A growing literature indicates, however, that       additional clinical features apart from cognitive performance might be related    to both the rate of disease progression and the underlying disease etiologies.                                                                                      In a recently completed latent class analysis based on initial-visit data from    the NACC Uniform Data Set, we found a remarkable amount of heterogeneity in the                                                                                     clinical presentation of MCI (Hanfelt et al., American Journal of Geriatric       Psychiatry, 2011).  Findings suggested that there are several distinct            phenotypes of MCI characterized by either prominent cognitive features,           prominent functional and neuropsychiatric features, or a combination of all       three.  Two of these subgroups with functional and neuropsychiatric features      were significantly more likely to have cerebrovascular disease, which suggests    there might be distinct differences in disease etiology from the other            phenotypes.                                                                                                                                                         The above results were limited