Ulf Kallweit

Background and Purpose: While the estimation of the climate change effects is dependent on the models used, there is a growing consensus that emissions of greenhouse gases (GHG) cause global warming, resulting in the acceleration of climate change. The degree to which climate change is going to affect the nervous system of healthy individuals and how it may lead to an increase in the prevalence and burden of neurological disorders is presently incompletely understood and studied. It is imperative that the neurological community increases its awareness about climate change and strives for a clear evidence‐based consensus on the relationship between climate change and the negative consequences on brain health and neurological disorders. Methods: Part I provides a narrative overview of climate change in neurology, whereas Part II focuses on climate change action in neurology. Results: We provide clinicians and neuroscience stakeholders with tools to understand key concepts at the interface between climate change, neurological disorders, and brain health. Moreover, this paper states priorities in this field for the European Academy of Neurology, through the work of the Environmental Influences in Neurology Task Force, presenting 10 operational areas adapted from the WHO operational framework for building climate‐resilient and low‐carbon health systems . Conclusion: In the frame of its Brain Health Mission, the European Academy of Neurology is committed to supporting a neurology that is able to understand the impact of climate change and become a climate‐resilient neurology to promote health in general and brain health specifically.

The international Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (iSPHYNCS) is a multicentre study aimed at identifying novel biomarkers for central disorders of hypersomnolence (CDH). We analysed questionnaires and metadata to uncover distinct clusters of participants and explore phenotypic variability within CDH. Data were collected from 227 patients with CDH and 33 healthy controls. Participants completed validated clinical questionnaires and study‐specific questions addressing CDH‐related symptoms such as excessive daytime sleepiness, fatigue, cataplexy, disrupted sleep, and sleep paralysis. Demographic metadata (age, gender, BMI) were included. After excluding participants with missing over 30% of data ( n = 40), missing values were imputed using a multiple random forest algorithm. A robust clustering pipeline was employed: (1) random sampling of 60% of the dataset, (2) dimensionality reduction via UMAP, (3) K‐means clustering, and (4) consensus clustering across 500 iterations. Post hoc analysis was performed to identify biomarkers in data not used for clustering. We identified four distinct clusters. One predominantly comprised healthy controls, while another primarily contained individuals with narcolepsy type 1 (NT1). Two clusters represented predominantly the narcolepsy borderland group (NBL), with one distinctly characterised by higher symptom severity and psychiatric comorbidities. The clustering pipeline produced reproducible results, with the NT1 and healthy control clusters serving as internal validation. The differentiation between the two NBL clusters aligns with prior studies, suggesting a possible NBL subtype marked by increased fatigue and psychiatric comorbidities. These findings emphasise the phenotypic heterogeneity of CDH and the potential for cluster‐based approaches in management. Trial Registration: ClinicalTrials.gov identifier: NCT04330963.

Objective: The international Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (iSPHYNCS) is a multicenter study aimed at identifying novel biomarkers for central disorders of hypersomnolence (CDH). We analyzed questionnaires and metadata to uncover distinct clusters of participants and explore phenotypic variability within CDH. Methods: Data were collected from 227 patients with CDH and 33 healthy controls. Participants completed validated clinical questionnaires and study-specific questions addressing CDH-related symptoms such as excessive daytime sleepiness, fatigue, cataplexy, disrupted sleep, and sleep paralysis. Demographic metadata (age, gender, BMI) were included. After excluding participants with missing over 30% of data (n = 40), missing values were imputed using a multiple random forest algorithm. A robust clustering pipeline was employed: (1) random sampling of 60% of the dataset, (2) dimensionality reduction via UMAP, (3) K-means clustering, and (4) consensus clustering across 500 iterations. Post hoc analysis was performed to identify biomarkers in data not used for clustering. Results: We identified four distinct clusters. One predominantly comprised healthy controls, while another primarily contained individuals with narcolepsy type 1 (NT1). Two clusters represented predominantly the narcolepsy borderland group (NBL), with one distinctly characterized by higher symptom severity and psychiatric comorbidities. Conclusions: The clustering pipeline produced reproducible results, with the NT1 and healthy control clusters serving as internal validation. The differentiation between the two NBL clusters aligns with prior studies, suggesting a possible NBL subtype marked by increased fatigue and psychiatric comorbidities. These findings emphasize the phenotypic heterogeneity of CDH and the potential for cluster-based approaches in management. Statement of Significance: Central disorders of hypersomnolence (CDH) are complex and heterogeneous, challenging conventional diagnostic frameworks. This study employed an innovative clustering pipeline to identify four distinct phenotypic clusters within a large, multicenter cohort. The identification of a subtype in the narcolepsy borderland group with a high psychiatric burden adds critical insights into the variability of CDH and underscores the need for personalized diagnostic and therapeutic strategies. The internal validation of clusters through healthy controls and NT1 phenotypes enhances confidence in the pipeline’s robustness. Furthermore, the study highlights the potential for integrating clinical data to define disease severity and developing therapeutic strategies. These findings pave the way for novel approaches to understanding and managing CDH.