Penalized nonlinear mixed effects model to identify biomarkers that predict disease progression.

TitlePenalized nonlinear mixed effects model to identify biomarkers that predict disease progression.
Publication TypeJournal Article
Year of Publication2017
AuthorsChen, Huaihou, Donglin Zeng, and Yuanjia Wang
JournalBiometrics
Volume73
Issue4
Pagination1343-1354
Date Published2017 12
ISSN1541-0420
KeywordsAlgorithms, Biomarkers, Brain, Disease Progression, Humans, Huntington Disease, Neuroimaging, Nonlinear Dynamics, Organ Size, Prognosis
Abstract

Precise modeling of disease progression in neurodegenerative disorders may enable early intervention before clinical manifestation of a disease, which is crucial since early intervention at the premanifest stage is expected to be more effective. Neuroimaging biomarkers are indicative of the underlying disease pathology and may be used to predict future disease occurrence at the premanifest stage. As observed in many pivotal studies, longitudinal measurements of clinical outcomes, such as motor or cognitive symptoms, often present nonlinear sigmoid shapes over time, where the inflection points of the trajectories mark a meaningful time in disease progression. Therefore, to identify neuroimaging biomarkers predicting disease progression, we propose a nonlinear mixed effects model based on a sigmoid function to predict longitudinal clinical outcomes, and associate a linear combination of neuroimaging biomarkers with subject-specific inflection points. Based on an expectation-maximization (EM) algorithm, we propose a method that can fit a nonlinear model with many potentially correlated biomarkers for random inflection points while achieving computational stability. Variable selection is introduced in the algorithm in order to identify important biomarkers of disease progression and to reduce prediction variability. We apply the proposed method to the data from the Predictors of Huntington's Disease study to select brain subcortical regional volumes predictive of the inflection points of the motor and cognitive function trajectories. Our results reveal that brain atrophy in the striatum and expansion of the ventricular system are highly predictive of the inflection points. Furthermore, these inflection points may precede clinically defined disease onset by as early as a decade and thus may be useful biomarkers as early signs of Huntington's Disease onset.

DOI10.1111/biom.12663
Alternate JournalBiometrics
Original PublicationPenalized nonlinear mixed effects model to identify biomarkers that predict disease progression.
PubMed ID28182831
PubMed Central IDPMC5790569
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
R01 NS073671 / NS / NINDS NIH HHS / United States
U01 NS082062 / NS / NINDS NIH HHS / United States