Title | Penalized nonlinear mixed effects model to identify biomarkers that predict disease progression. |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | Chen, Huaihou, Donglin Zeng, and Yuanjia Wang |
Journal | Biometrics |
Volume | 73 |
Issue | 4 |
Pagination | 1343-1354 |
Date Published | 2017 Dec |
ISSN | 1541-0420 |
Keywords | Algorithms, 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. |
DOI | 10.1111/biom.12663 |
Alternate Journal | Biometrics |
Original Publication | Penalized nonlinear mixed effects model to identify biomarkers that predict disease progression. |
PubMed ID | 28182831 |
PubMed Central ID | PMC5790569 |
Grant List | P01 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 |