D16 Cerebrospinal fluid biomarkers for assessing Huntington disease onset and severity

Caron, Nicholas S.; Haqqani, Arsalan S.; Sandhu, Ash; Aly, Amirah E-E; Black, Hailey Findlay; Bone, Jeffrey; McBride, Jodi L.; Abulrob, Abedelnasser; Stanimirovic, Danica; Leavitt, Blair R.; Hayden, Michael R.

doi:10.1136/jnnp-2022-ehdn.72

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“…Huntington's disease (HD) is a rare disease that causes progressive degeneration of the brain and is inherited in an autosomal dominant manner. On average, the age at onset (AAO) for HD is around 45 years [1]. HD causes involuntary movements, cognitive impairments, psychiatric and behavioural problems, and pro-gressive weight loss [2].…”

Section: Introductionmentioning

confidence: 99%

“…HD causes involuntary movements, cognitive impairments, psychiatric and behavioural problems, and pro-gressive weight loss [2]. The genetic cause of the disease is a CAG repeat expansion in the coding region of the huntingtin gene (HTT ;ENSG00000197386), which is translated into an expanded stretch of glutamine amino acids in the huntingtin protein (HTT) [1]. This mutant protein is the main cause of neuropathology in HD [3].…”

Section: Introductionmentioning

confidence: 99%

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Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis & driving capability prediction

Ouwerkerk

Feleus

Zwaan

et al. 2023

Preprint

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Background: In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington’s disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world’s largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments. Results: Simple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main. Conclusions: Our pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO predictions and allowed for the development of an advisory model that can assist clinicians and participants in estimating future driving capability.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis & driving capability prediction

Ouwerkerk

Feleus

Zwaan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

D16 Cerebrospinal fluid biomarkers for assessing Huntington disease onset and severity

Cited by 1 publication

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Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis & driving capability prediction

Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis & driving capability prediction

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D16 Cerebrospinal fluid biomarkers for assessing Huntington disease onset and severity

Cited by 1 publication

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Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis &amp; driving capability prediction

Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis &amp; driving capability prediction

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Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis & driving capability prediction

Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis & driving capability prediction