Hughes, R. A. | Search bioRxiv

bioRxiv2018

Targeting TrkB with a brain-derived neurotrophic factor mimetic promotes myelin repair in the brain

Methods to promote myelin regeneration in response to central myelin loss are essential to prevent the progression of clinical disability in demyelinating diseases. The neurotrophin brain-derived neurotrophic factor (BDNF) is known to promote myelination during development via oligodendrocyte expressed TrkB receptors. Here, we use a structural mimetic of BDNF to promote myelin regeneration in a preclinical mouse model of central demyelination. We show that selective targeting of TrkB with the BDNF-mimetic enhances remyelination, increasing oligodendrocyte differentiation, the frequency of myelinated axons, and myelin sheath thickness after a demyelinating insult. Treatment with exogenous BDNF exerted an attenuated effect, increasing myelin sheath thickness only. Further, following conditional deletion of TrkB from pre-myelinating oligodendrocytes, we show the effects of the BDNF-mimetic on oligodendrocyte differentiation and remyelination are lost, indicating these are dependent on oligodendrocyte expression of TrkB. Overall, these studies demonstrate that targeting oligodendrocyte TrkB promotes in vivo remyelination in the brain.

neuroscience↗

bioRxiv2017

Selection bias in instrumental variable analyses

Participants in epidemiological and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). The circumstances in which selection causes bias in an instrumental variable (IV) analysis are not widely understood by practitioners of IV analyses. We use directed acyclic graphs (DAGs) to depict assumptions about the selection mechanism (factors affecting selection) and show how DAGs can be used to determine when a two-stage least squares (2SLS) IV analysis is biased by different selection mechanisms. Via simulations, we show that selection can result in a biased IV estimate with substantial confidence interval undercoverage, and the level of bias can differ between instrument strengths, a linear and nonlinear exposure-instrument association, and a causal and noncausal exposure effect. We present an application from the UK Biobank study, which is known to be a selected sample of the general population. Of interest was the causal effect of education on the decision to smoke. The 2SLS exposure estimates were very different between the IV analysis ignoring selection and the IV analysis which adjusted for selection (e.g., 1.8 [95% confidence interval -1.5, 5.0] and -4.5 [-6.6, -2.4], respectively). We conclude that selection bias can have a major effect on an IV analysis and that statistical methods for estimating causal effects using data from nonrandom samples are needed.

epidemiology↗