Objective: To develop a mobile application for assessing the likelihood of developing depression in patients with Parkinson’s disease (PwPD), taking into account the number of CAG repeats of the HTT gene.

Background: Parkinson’s disease (PD) is often complicated by depression, since it’s known that almost 50% of PwPD have depression [Jennifer G Goldman, 2020]. Although the extent to which common HTT CAG repeat size variations in the normal range affect the risk of depression in the general population, including PwPD is still unknown [Sarah L. Gardiner 2017].

Method: 270 PwPD were tested by neurological, neuropsychological (HADS, BDI-II, MMSE-test) and genetic examinations (the number of CAG repeats of the HTT gene).

Results: The program is designed for neurologists to assess the likelihood of developing a depressive state in PwPD based on genetic data. The program input parameters are the number of CAG-repeats of the HTT gene. The program is based on a random forest machine learning model, pre-trained on clinical data. The program is implemented in Java. Program size is about 12 kb.

Conclusion: Our study provides important evidence that repetitive polymorphisms might act as hitherto underappreciated but complex genetic modifiers of depression. A mobile application for assessing the likelihood of developing depression in PwPD, as well as taking into account the genetic predisposition, helps doctors to have a clinical alertness to this category of patients and timely adjust therapy.

References: 1. Jennifer G Goldman, 2020 2. Sarah L. Gardiner, 2017

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MDS Abstracts - https://www.mdsabstracts.org/abstract/mobile-application-for-assessing-the-likelihood-of-developing-depression-in-patients-with-parkinsons-disease/