From scientific sessions to topical issues, ECTRIMS addressed the most important questions facing MS and neurological experts today. The program included inspiring keynote speeches from renowned international MS experts as well as the latest findings from research and practice. The focus was also on aspects that are essential for patients, such as early diagnosis and predicting the course of the disease.
Predicting disease progression in multiple sclerosis (MS) is challenging due to the heterogeneity of clinical presentation and disease progression within traditional phenotype groups. Identifying homogeneous subgroups with common patterns of clinical symptom progression using unsupervised machine learning could improve predictive models. Therefore, unsupervised machine learning was applied to clinical data from a large cohort of MS patients with mixed phenotype [1]. The aim was to identify subgroups with common clinical characteristics, determine whether subgroups differ in terms of demographics, MRI characteristics, current DMT status and DMT type, and determine whether assignment to a subgroup predicts disability progression differently depending on age and months on DMT. In the final sample (n=6362), subtypes were identified based on the first clinical symptom(s) that occurred in the temporal staging model: Cognition first (subtype 1, 38% of the sample), Motor first (subtype 2, 31%), Fatigue-anxiety-depression first (subtype 3, 31%). The subtypes differed in terms of age, gender, whole brain atrophy, T2-LV, DMT status and DMT type. Cox regression revealed a slower decline in subtypes 1 and 2 compared to 3 based on age alone; subtype 2 declined more slowly than 1 and 3 based on months on DMT.
Focus on early diagnostics
The diagnosis of MS is made on the basis of a combination of clinical, imaging and laboratory findings, with evidence of temporal and spatial spread required according to the 2017 McDonald criteria. But how should a delay in diagnosis be classified in terms of potential impact and what factors could influence such a delay? These questions were investigated in a retrospective cross-sectional study [2]. Patients with a delayed diagnosis of isolated clinical syndrome (CIS) or MS who were observed in the neurology outpatient clinic for demyelinating diseases between 2018 and 2023 were selected. Delayed diagnosis was defined as a period of more than 12 weeks between the start of treatment in the clinic and diagnosis. Sociodemographic and clinical data were extracted from medical records. 137 patients were identified in a population of more than 600 patients. Patients were divided into two categories based on the first documented clinical manifestation: motor (25.5%) and non-motor (74.5%). Almost all patients had supratentorial lesions on magnetic resonance imaging (93.4%), while 53.3% had infratentorial lesions and 69.3% had spinal cord lesions. In addition, 40.1% showed lesions at all three sites. The median Expanded Disability Scale Score (EDSS) of the sample was 1.5.
In summary, delay in the diagnosis of CIS or MS not only limits therapeutic options for patients and the potential for early intervention, but can also lead to irreversible consequences. Furthermore, given the rising costs associated with disability in MS worldwide, early diagnosis could help to reduce this economic burden and improve patients’ quality of life. Therefore, monitoring time to diagnosis and identifying the factors that contribute to delays are essential aspects of effective treatment.
Focus on gender-specific differences
Women have a significantly higher risk of developing MS than men, although men often have a more severe disease course. However, it is unclear whether there are gender differences in established disease, e.g. differences in the distribution of white matter (WM) pathobiology between female (F) and male (M) MS patients. Therefore, differences in WM tract integrity between the sexes in early MS have been investigated [3]. Using transverse T2-weighted and diffusion-weighted magnetic resonance imaging (DW-MRI), T2-hyperintense lesions and tract-based spatial statistics (TBSS) were extracted from 1:1 sex-matched MS patients for age, disease duration (DD) and clinical disability (as measured by the Expanded Disability Status Scale, EDSS). Probabilities of WM disconnection were calculated based on manually segmented, binarized T2 hyperintense lesion masks using the Tractotron tool of the BCBToolKit. Mean fractional anisotropy (FA) of WM tracts was extracted from DW-MRI using FSL and the JHU ICBM-DTI-81 atlas. ANCOVAs adjusted for age were used to assess sex-specific differences in the probabilities of WM disconnection and mean FA measurements of WM tracts.
The study included 50 women and 50 men. Despite a lower likelihood of WM disconnection due to T2 hyperintense lesions, female MS patients consistently showed lower FA levels in regions associated with motor function than corresponding male patients. Male MS patients were more likely to have WM disconnection in multiple pathways due to T2 hyperintense lesions located primarily in the left hemisphere. The results suggest early WM disruption in female MS patients without lesional impairment.
Congress: ECTRIMS 2024
Literature:
- Leavitt V, et al: New clinical subtypes of MS identified in big data predict disability progression and response to DMT treatment. P001/213 ECTRIMS 2024 Multiple Sclerosis Journal Volume 30, Issue 3_suppl, September 2024, Pages 125-680.
- Medeiros B, et al: Unveiling the Silent Struggle: Exploring the Global Impact of Delayed Diagnosis in Multiple Sclerosis, P005/1034. ECTRIMS 2024. Multiple Sclerosis Journal Volume 30, Issue 3_suppl, September 2024, Pages 125-680.
- Siddarth P, et al: Sex Differences in White Matter Tract Integrity in Early Relapsing-Remitting Multiple Sclerosis. P088/1718 ECTRIMS 2024 Multiple Sclerosis Journal Volume 30, Issue 3_suppl, September 2024, Pages 125-680.
InFo NEUROLOGIE & PSYCHIATRIE 2024; 22(6): 22 (published on 2.12.24, ahead of print)
