Reversibility and therapeutic development for neurodevelopmental disorders, insights from genetic animal models

Neurodevelopmental Disorders (NDDs) encompass a broad spectrum of conditions resulting from atypical brain development. Over the past decades, we have had the fortune to witness enormous progress in diagnosis, etiology discovery, modeling, and mechanistic understanding of NDDs from both fundamental and clinical research. Here, we review recent neurobiological advances from experimental models of NDDs. We introduce several examples and highlight breakthroughs in reversal studies of phenotypes using genetically engineered models of NDDs. The in-depth understanding of brain pathophysiology underlying NDDs and evaluations of reversibility in animal models paves the foundation for discovering novel treatment options. We discuss how the expanding property of cutting-edge technologies, such as gene editing and AAV-mediated gene delivery, are leveraged in animal models for the therapeutic development of NDDs. We envision opportunities and challenges toward faithful modeling and fruitful clinical translation.

Callosal Angle and Evans Index predict beta amyloid and tau protein in patients with dementia

INTRODUCTION

Aβ and tau protein have been widely investigated for the diagnosis of dementia entities, most commonly Alzheimer's disease (AD). However, their measurement is an interventional, time-consuming procedure, while it requires specialized personnel, unlike the evaluation of radiological markers, such as Evans index (EI) and Callosal angle (CA). This study aims to investigate the correlation between EI, CA, Aβ and total tau in order to outline a basis of diagnostic evaluation for the patient's CSF biomarker profile.

METHODS

Sixty-two (62) patients who presented with dementia symptoms participated in this study. Aβ and total tau levels in their CSF as well as CA and EI values from their MRIs were measured. Multiple regression was employed to predict Aβ and total tau values from EI and CA. From the Durbin-Watson analysis (d1=2.057, d2=1.881) we can assume that there is no first order linear auto-correlation in our multiple linear regression data.

RESULTS

The variables statistically significantly predicted both Aβ and total tau (F1=8.720, R1=0.484, p1=0.001 and F2=4.110, R2=0.355, p2=0.022). Out of all the variables, it was shown that CA, EI (p<0.05) and CA (p<0.05) added statistically significantly to the prediction of Aβ and total tau.

CONCLUSION

Collectively, these results support a significant correlation between EI and Aβ and CA, Aβ and total tau. This highlights the potential role of radiological markers as rough estimates of biomarker levels in everyday clinical practice, assisting to a robust and inexpensive diagnosis.

The correlation of Evans index with CSF protein levels and ventriculomegaly

OBJECTIVE

The role of ventriculomegaly and its interplay between various dementia entities and their pathogenesis, as well as its correlation to the levels of CSF protein markers, remains to be elucidated. Evans index (EI) is a well-established radiological marker in the diagnosis of idiopathic Normal Pressure Hydrocephalus (iNPH), assessing ventricular enlargement. However, there are no previous studies investigating the correlation between EI, age, beta amyloid (Aβ), tau and phosphorylated-tau (p-tau) proteins in the CSF. The aim of this study was to examine the correlation of the above CSF proteins, EI and age in patients with dementia.

METHODS

Sixty two (62) patients, with dementia and levels of Aβ, Tau and p-Tau in their CSF as well as age and the EI participated in this study. Multiple regression analysis was performed to investigate possible correlations between the aforementioned factors. A multiple regression was employed to predict Aβ values from age, tau and p-tau values in the CSF. The Durbin-Watson d was 1.942 so we can assume that there is no first order linear auto-correlation in our multiple linear regression data.

RESULTS

The variables statistically significantly predicted Aβ, F-value=4.429, P=0.011, R2=0.483. Specifically, out of all the variables, it was shown that only EI and age added statistically significantly to the prediction, P<.05. Specifically, these results are interpreted as: for every 0.01 unit increase in EI, it was predicted a -38.698 decrease in the Αβ value and for every 1 year increase in age, it was predicted a -17 decrease in the Αβ value.

CONCLUSION

There seems to be a significant correlation between EI value, age and Aβ reduction in the CSF. No correlation was found between EI and tau and p-tau proteins in the CSF. This may indicate that Aβ levels in the CSF are affected significantly by ventriculomegaly and not as much by pathophysiological pathways characteristic for each dementia entity. On the other hand, tau and p-tau proteins could possibly play a crucial role in the differential diagnosis of iNPH and other dementia entities, as these proteins cannot be predicted by ventricular enlargement.