Serum neurofilament levels reflect outer retinal layer changes in multiple sclerosis

Artificial Intelligence and Multiple Sclerosis

In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.

Validity of serum neurofilament light chain as a prognostic biomarker of disease activity in multiple sclerosis

Multiple sclerosis (MS) is a chronic demyelinating and neuroinflammatory disease of the human central nervous system with complex pathoetiology, heterogeneous presentations and an unpredictable course of disease progression. There remains an urgent need to identify and validate a biomarker that can reliably predict the initiation and progression of MS as well as identify patient responses to disease-modifying treatments/therapies (DMTs). Studies exploring biomarkers in MS and other neurodegenerative diseases currently focus mainly on cerebrospinal fluid (CSF) analyses, which are invasive and impractical to perform on a repeated basis. Recent studies, replacing CSF with peripheral blood samples, have revealed that the elevation of serum neurofilament light chain (sNfL) in the clinical stages of MS is, potentially, an ideal prognostic biomarker for predicting disease progression and for possibly guiding treatment decisions. However, there are unresolved factors (the definition of abnormal values of sNfL concentration, the standardisation of measurement and the amount of change in sNfL concentration that is significant) that are preventing its use as a biomarker in routine clinical practice for MS. This updated review critiques these recent findings and highlights areas for focussed work to facilitate the use of sNfL as a prognostic biomarker in MS management.

The 2022 Lady Estelle Wolfson lectureship on neurofilaments

Neurofilament proteins (Nf) have been validated and established as a reliable body fluid biomarker for neurodegenerative pathology. This review covers seven Nf isoforms, Nf light (NfL), two splicing variants of Nf medium (NfM), two splicing variants of Nf heavy (NfH), α -internexin (INA) and peripherin (PRPH). The genetic and epigenetic aspects of Nf are discussed as relevant for neurodegenerative diseases and oncology. The comprehensive list of mutations for all Nf isoforms covers Amyotrophic Lateral Sclerosis, Charcot-Marie Tooth disease, Spinal muscular atrophy, Parkinson Disease and Lewy Body Dementia. Next, emphasis is given to the expanding field of post-translational modifications (PTM) of the Nf amino acid residues. Protein structural aspects are reviewed alongside PTMs causing neurodegenerative pathology and human autoimmunity. Molecular visualisations of NF PTMs, assembly and stoichiometry make use of Alphafold2 modelling. The implications for Nf function on the cellular level and axonal transport are discussed. Neurofilament aggregate formation and proteolytic breakdown are reviewed as relevant for biomarker tests and disease. Likewise, Nf stoichiometry is reviewed with regard to in vitro experiments and as a compensatory mechanism in neurodegeneration. The review of Nf across a spectrum of 87 diseases from all parts of medicine is followed by a critical appraisal of 33 meta-analyses on Nf body fluid levels. The review concludes with considerations for clinical trial design and an outlook for future research.

Association of Serum Neurofilament Light Chain With Inner Retinal Layer Thinning in Multiple Sclerosis

BACKGROUND AND OBJECTIVES

Serum neurofilament light chain (sNfL) and optical coherence tomography (OCT)-derived retinal measures (including peripapillary retinal nerve fiber layer [pRNFL] and macular ganglion cell layer/inner plexiform layer [GCIPL] thickness) have been proposed as biomarkers of neurodegeneration in multiple sclerosis (MS). However, studies evaluating the associations between sNfL and OCT-derived retinal measures in MS are limited.

METHODS

In this retrospective analysis of a longitudinal, observational, single-center cohort study, sNfL levels were measured in people with MS and healthy controls (HCs) using single molecule array. Participants with MS were followed with serial OCT for a median follow-up of 4.5 years. Eyes with optic neuritis (ON) within 6 months of baseline OCT or ON during follow-up were excluded. Age-normative cutoffs of sNfL were derived using the HC data, and MS participants with sNfL greater than the 97.5th percentile for age were classified as having elevated sNfL (sNfL-E). Analyses were performed with mixed-effects linear regression models and adjusted for age, sex, race, and history of ON.

RESULTS

A total of 130 HCs (age: 42.4 ± 14.2 years; 62% female) and 403 people with MS (age: 43.1 ± 12.0 years; 78% female) were included. Elevated sNfL levels were present at baseline in 80 participants with MS (19.9%). At baseline, sNfL-E participants had modestly lower pRNFL (-3.03 ± 1.50 μm; p = 0.044) and GCIPL thickness (-2.74 ± 1.02 μm; p = 0.007). As compared with those with sNfL within the reference range, eyes from NfL-E participants exhibited faster longitudinal thinning of the pRNFL (45% faster; -0.74 vs -0.51 μm/y; p = 0.015) and GCIPL (25% faster; -0.35 vs -0.28 μm/y; p = 0.021). Significant differences in rates of pRNFL and GCIPL thinning between sNfL groups were found only in those with relapsing-remitting MS but not progressive MS.

DISCUSSION

Elevated baseline sNfL is associated with accelerated rates of retinal neuroaxonal loss in relapsing-remitting MS, independent of overt ON, but may be less reflective of retinal neurodegeneration in progressive MS.

Retinal imaging with optical coherence tomography in multiple sclerosis: novel aspects

Optical coherence tomography (OCT) is of increasing interest in the clinical assessment of multiple sclerosis (MS) patients beyond the scope of clinical studies. In this narrative review, we discuss novel changes of OCT parameters during acute optic neuritis and the disease course of MS patients. OCT images document the changes of retinal layers during an episode of acute optic neuritis and can therefore provide valuable insights into the pathophysiology. Moreover, MS patients show progredient thinning of retinal layers throughout the disease. The thinning is accelerated through relapses as well as disease progression without relapse. The OCT parameters are also associated with clinical outcome parameters, including disability, cognitive function, and brain atrophy. The impact of disease-modifying therapies on OCT parameters is the subject of ongoing research and depends on the agent used. Additional data are still necessary before OCT parameters can be implemented in the clinical standard of care of MS patients.

COVID-19 outcomes of 10,881 patients: retrospective study of neurological symptoms and associated manifestations (Philippine CORONA Study)

Our study aimed to determine the effects of new-onset neurological symptoms (NNS) on clinically relevant outcomes in hospitalized patients with COVID-19 infection. We conducted a nationwide, comparative, retrospective, cohort study among adult, hospitalized COVID-19 patients involving 37 hospital sites from various regions in the Philippines. We included a total of 10,881 patients with confirmed COVID-19 infection (2008 had NNS while 8873 did not have NNS). The adjusted hazard ratios (aHRs) for mortality among the mild and severe cases were significantly higher by 1.660 (95% CI 1.132–2.435) and by 1.352 (95% CI 1.042–1.752), respectively, in the NNS group compared to those in the non-NNS group. The aHRs for respiratory failure in the NNS group were significantly increased by 1.914 (95% CI 1.346–2.722), by 1.614 (95% CI 1.260–2.068), and by 1.234 (95% CI 1.089–1.398) among the mild, severe, and critical cases, respectively. The aHRs for ICU admission in the NNS group were still significantly higher by 1.973 (95% CI 1.457–2.673) and by 1.831 (95% CI 1.506–2.226) among the mild and severe cases, respectively. Patients who had NNS were not significantly associated with a longer duration of ventilator dependence (adjusted odds ratio (aOR) 0.954, 95% CI 0.772–1.179), longer ICU stay (aOR 0.983, 95% CI 0.772–1.252) and longer hospital admission (aOR 1.045, 95% CI 0.947–1.153). The presence of NNS significantly increases the risk of mortality, respiratory failure and ICU admission among COVID-19 patients. Registration and associated protocol publication: ClinicalTrials.gov website (NCT04386083); Espiritu AI, Sy MCC, Anlacan VMM, Jamora RDG. The Philippine COVID-19 Outcomes: a Retrospective study Of Neurological manifestations and Associated symptoms (The Philippine CORONA study): a protocol study. BMJ Open. 2020;10:e040944.