Estimating the causal effects of modifiable, non-genetic factors on Huntington disease progression using propensity score weighting.

Title
Estimating the causal effects of modifiable, non-genetic factors on Huntington disease progression using propensity score weighting.
Publication type
Journal Article
Year of Publication
2021
Journal
Parkinsonism & Related Disorders
Volume
83
Pagination
56-62
Date published
2021 Jan 13
ISSN
1873-5126
Abstract

INTRODUCTION: Despite being genetically inherited, it is unclear how non-genetic factors (e.g., substance use, employment) might contribute to the progression and severity of Huntington's disease (HD).

METHODS: We used propensity score (PS) weighting in a large (n = 2914) longitudinal dataset (Enroll-HD) to examine the impact of education, employment status, and use of tobacco, alcohol, and recreational and therapeutic drugs on HD progression. Each factor was investigated in isolation while controlling for 19 other factors to ensure that groups were balanced at baseline on potential confounders using PS weights. Outcomes were compared several years later using doubly robust models.

RESULTS: Our results highlighted cases where modifiable (non-genetic) factors - namely light and moderate alcohol use and employment - would have been associated with HD progression in models that did not use PS weights to control for baseline imbalances. These associations did not hold once we applied PS weights to balance baseline groups. We also found potential evidence of a protective effect of substance use (primarily marijuana use), and that those who needed antidepressant treatment were likely to progress faster than non-users.

CONCLUSIONS: Our study is the first to examine the effect of non-genetic factors on HD using a novel application of PS weighting. We show that previously-reported associated factors - including light and moderate alcohol use - are reduced and no longer significantly linked to HD progression after PS weighting. This indicates the potential value of PS weighting in examining non-genetic factors contributing to HD as well as in addressing the known biases that occur with observational data.