F31 Understanding cognitive heterogeneity beyond disease burden in huntington's disease. (September 2018)
- Record Type:
- Journal Article
- Title:
- F31 Understanding cognitive heterogeneity beyond disease burden in huntington's disease. (September 2018)
- Main Title:
- F31 Understanding cognitive heterogeneity beyond disease burden in huntington's disease
- Authors:
- White, Alice
Li, Yiming
Kuan, Wei-Li
Mason, Sarah - Abstract:
- Abstract : Background: Despite its monogenic cause, the cognitive problems in Huntington's Disease are heterogeneous. This is particularly pronounced in the premanifest (preHD) stage of the disease. The existing tools cannot explain all of the heterogeneity observed nor can they accurately inform the progression of these aspects of the disease at an individual level. Method: We have developed a data driven mathematical model using the cross-sectional data from the first ENROLL-HD data cut to classify preHD participants into two groups. Machine learning methods were used to identify variables of interest that were inputted into a support vector machine which was used to classify participants. The model was validated in a local subset of the PREDICT-HD cohort. Results: The support vector machine used performance on four cognitive tests to classify participants into two distinct groups: group one who perform worse on cognitive measures and group two who perform better. Importantly, the two groups did not differ on measure of age, CAG repeat number and disease burden measures. We also found a significant genetic association between membership of the cognitively better group and the minor alleles of NCOR1 and ADORA2B through supplementary genotyping analysis. Conclusion: This work suggests that participants can be classified into distinct groups on the basis of their cognitive performance alone, even in the premanifest stage of HD. There may be genetic interactions within the HDAbstract : Background: Despite its monogenic cause, the cognitive problems in Huntington's Disease are heterogeneous. This is particularly pronounced in the premanifest (preHD) stage of the disease. The existing tools cannot explain all of the heterogeneity observed nor can they accurately inform the progression of these aspects of the disease at an individual level. Method: We have developed a data driven mathematical model using the cross-sectional data from the first ENROLL-HD data cut to classify preHD participants into two groups. Machine learning methods were used to identify variables of interest that were inputted into a support vector machine which was used to classify participants. The model was validated in a local subset of the PREDICT-HD cohort. Results: The support vector machine used performance on four cognitive tests to classify participants into two distinct groups: group one who perform worse on cognitive measures and group two who perform better. Importantly, the two groups did not differ on measure of age, CAG repeat number and disease burden measures. We also found a significant genetic association between membership of the cognitively better group and the minor alleles of NCOR1 and ADORA2B through supplementary genotyping analysis. Conclusion: This work suggests that participants can be classified into distinct groups on the basis of their cognitive performance alone, even in the premanifest stage of HD. There may be genetic interactions within the HD gene pathway that explain some of the cognitive heterogeneity that we see in preHD patients. … (more)
- Is Part Of:
- Journal of neurology, neurosurgery and psychiatry. Volume 89(2018)Supplement 1
- Journal:
- Journal of neurology, neurosurgery and psychiatry
- Issue:
- Volume 89(2018)Supplement 1
- Issue Display:
- Volume 89, Issue 1 (2018)
- Year:
- 2018
- Volume:
- 89
- Issue:
- 1
- Issue Sort Value:
- 2018-0089-0001-0000
- Page Start:
- A50
- Page End:
- A51
- Publication Date:
- 2018-09
- Subjects:
- premanifest -- machine learning -- cognition -- classification -- HD gene pathway
Neurology -- Periodicals
Nervous system -- Surgery -- Periodicals
Psychiatry -- Periodicals
616.8 - Journal URLs:
- http://jnnp.bmjjournals.com/ ↗
http://www.pubmedcentral.nih.gov/tocrender.fcgi?action=archive&journal=192 ↗
http://www.bmj.com/archive ↗ - DOI:
- 10.1136/jnnp-2018-EHDN.135 ↗
- Languages:
- English
- ISSNs:
- 0022-3050
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
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