Ruben L. Andriessen

Understanding the trajectory of Huntington's disease (HD) is critical for patient stratification and the development of targeted interventions. Traditionally, studies relied on age–CAG models to estimate disease onset and progression, based on the well-established relationship between CAG repeat length and age at onset. However, additional genetic, environmental, and clinical factors can cause substantial variability. Recent machine learning approaches integrate clinical, imaging, and molecular data for more precise prediction of disease progression. Following PRISMA guidelines, we systematically reviewed studies on HD onset and progression. Using Web of Science, PubMed, and IEEE Xplore, 20 studies published between 2003 and 2024 met the inclusion criteria. We analyzed the machine learning approaches and input features used, assessed methodological quality, and evaluated risk of bias using the PROBAST tool. Overall, machine learning models, particularly support vector machines and ensemble approaches, consistently outperformed traditional age–CAG models. Several studies predicted conversion from premanifest to manifest HD within 5–10 years with high accuracy (88–98%). Beyond predicting onset, machine learning models have also been used to model dis-ease progression using clinical scores assessing motor, cognitive, and functional impairment. Performance was higher in studies incorporating structural and functional MRI biomarkers, and improved further with longitudinal clinical integration, enabling pre-diction of decline years before symptoms onset. Overall, machine learning shows strong potential to improve prognostic modeling in HD, especially through multimodal and longitudinal data. However, common methodological weaknesses and bias highlight the need for larger, externally validated studies using objective biomarkers.

Background: Huntington's Disease (HD) is a hereditary neurodegenerative disorder caused by a cytosine-adenine-guanine (CAG) repeat expansion (CAG > 35) in the Huntingtin ( HTT ) gene. Intermediate alleles (IAs, CAG = 27–35) are generally not associated with HD. However, IA carriers with symptoms have been reported in literature. Objective: To review the existing literature on IAs, in order to provide an overview of the clinical phenotype of IA carriers. Methods: Peer-reviewed articles published between 1993 and July 2024 from three databases (Embase, PubMed, and Web of Science) were included. Results: In case reports, a high percentage (90%) of IA carriers was reported to have symptoms (HD-related and -unrelated), or abnormalities in neuroimaging. Cohort studies also reported evidence of symptoms in IA carriers, although most cohorts did not obtain significant differences compared to controls. Conclusion: Based on this review, we argue that there is not enough evidence to draw a clear conclusion on the clinical phenotype of individuals carrying an intermediate allele of the HTT gene. Literature reports symptomatic IA carriers, but reported symptoms are non-specific and common in the general population. Additionally, the quality of the data is suboptimal, due to lack of detailed symptom descriptions, the absence of differential diagnoses, a selection bias, and a considerable publication bias towards IA carriers with symptoms. More research is needed to provide a better insight into the clinical phenotype of IA carriers.

Background: Studies focusing on the treatment of neuropsychiatric symptoms (NPS) in Huntington's disease (HD) are scarce and show a wide variation in design, outcome measures and methodological quality. The effectiveness of pharmacological treatment of NPS in HD has not been systematically reviewed so far.Objective: To provide an overview of the available literature on the effectiveness of pharmacological treatment of NPS in HD.Methods: PubMed and the Cochrane library were systematically searched for studies assessing the effects of pharmacotherapy of NPS, both as a primary and as secondary outcome. A risk of bias assessment was performed for each article.Results: Fifteen articles qualified for critical evaluation: 10 randomized controlled trials (RCTs) (five placebo‐controlled and five cross‐over) and five open label studies. One RCT reported improvement of the overall NPS with nabilone treatment; another RCT reported that fluoxetine slightly improved irritability. Lower‐level evidence from open studies suggests that the atypical antipsychotics cariprazine, olanzapine and risperidone may improve overall NPS, and that cariprazine, venlafaxine XR and olanzapine may improve depression. In addition, olanzapine may improve obsessive thoughts, aggression, anxiety and irritability.Conclusions: We conclude that although NPS in HD are common, hardly any clinical trials have addressed their treatment. As a result, convincing evidence that could guide clinical practice is lacking. More focused, and larger, multicenter trials focusing on NPS are urgently needed to generate the knowledge necessary to support the development of evidence‐based clinical treatment guidelines.

Background: Growing up with a parent with Huntington's disease (HD) profoundly impacts children. However, this impact and children's needs are often misunderstood, even by professional services. Even when resources are available, children often feel that their needs are unmet, raising concerns about the adequacy of available guidance and support. Objective: This study aims to offer an in-depth understanding of the multifaceted impact of growing up with a parent with HD, examining the needs for professional guidance on emotional and social aspects, and identifying specific areas where support can be improved to better aid them. Methods: This retrospective study utilized an exploratory sequential mixed methods design, combining qualitative focus groups ( n  = 13) and a quantitative survey ( n  = 23). Qualitative data were analyzed using an inductive thematic analysis with a descriptive phenomenological approach. Quantitative data were analyzed using descriptive statistics. Results: The impact of HD on children extends across various domains, affecting self-development, social interactions, and family dynamics. Support received at home varied, with limited access to professional help. Support needs primarily revolved around emotional support and access to comprehensive information. Key support providers, such as parents, peers, mentors, healthcare providers and coaches with expertise in HD, play crucial roles in addressing these needs. Conclusions: The study underscores challenges faced by children in HD families. By centering our efforts on the emotional well-being of these children, offering tailored information, involving their social network, providing community-based support, and strengthening parental support systems, we can improve the support required by children in these families.