Serum neurofilament light chain concentration predicts disease worsening in multiple sclerosis

Serum neurofilament light chain associates with symptom burden in Lyme neuroborreliosis patients: a longitudinal cohort study from Norway

OBJECTIVES

Serum neurofilament light chain (sNfL), an indicator of neuronal damage, is increasingly recognized as a potential biomarker for disease activity in neurodegenerative disorders. In this study, we wanted to investigate sNfL as a prognostic marker in a large, well-defined population of 90 patients with Lyme neuroborreliosis (LNB). In addition, we sought to explore associations between symptoms and sNfL levels during the acute phase of LNB.

MATERIALS AND METHODS

Patients diagnosed with definite or possible LNB were recruited from a double-blinded, placebo-controlled, multi-center trial, in which the participants were randomly assigned to 2 or 6 weeks of oral doxycycline treatment. The sNfL levels were measured using a single molecule array assay at both diagnosis and 6-month follow-up, and analysed against clinical parameters, variations in symptom burden and long-term complaints as assessed by a composite clinical score.

RESULTS

At the time of diagnosis, approximately 60% of the patients had elevated sNfL levels adjusted for age. Notably, mean sNfL levels were significantly higher at diagnosis (52 pg/ml) compared to 6 months after treatment (12 pg/ml, p < 0.001), when sNfL levels had normalized in the majority of patients. Patients with objective signs of spinal radiculitis had significantly higher baseline sNfL levels compared to patients without spinal radiculitis (p = 0.033).

CONCLUSION

Our findings suggest that sNfL can serve as a biomarker for peripheral nerve tissue involvement in the acute phase of LNB. As found in an earlier study, we confirm normalization of sNfL levels in blood after treatment. We found no prognostic value of acute-phase sNfL levels on patient outcome.

Predicting disease severity in multiple sclerosis using multimodal data and machine learning

BACKGROUND

Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.

METHODS

We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre.

RESULTS

We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts.

CONCLUSION

Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

Neurofilament Light Chain in Adult and Pediatric Multiple Sclerosis: A Promising Biomarker to Better Characterize Disease Activity and Personalize MS Treatment

Many biological markers have been explored in multiple sclerosis (MS) to better quantify disease burden and better evaluate response to treatments, beyond clinical and MRI data. Among these, neurofilament light chain (Nf-L), although non-specific for this disease and found to be increased in other neurological conditions, has been shown to be the most promising biomarker for assessing axonal damage in MS, with a definite role in predicting the development of MS in patients at the first neurological episode suggestive of MS, and also in a preclinical phase. There is strong evidence that Nf-L levels are increased more in relapsing versus stable MS patients, and that they predict future disease evolution (relapses, progression, MRI measures of activity/progression) in MS patients, providing information on response to therapy, helping to anticipate clinical decisions in patients with an apparently stable evolution, and identifying patient non-responders to disease-modifying treatments. Moreover, Nf-L can contribute to the better understanding of the mechanisms of demyelination and axonal damage in adult and pediatric MS. A fundamental requirement for its clinical use is the accurate standardization of normal values, corrected for confounding factors, in particular age, sex, body mass index, and presence of comorbidities. In this review, a guide is provided to update clinicians on the use of Nf-L in clinical activity.

Association between serum multi-protein biomarker profile and real-world disability in multiple sclerosis

Few studies examined blood biomarkers informative of patient-reported outcome (PRO) of disability in people with multiple sclerosis (MS). We examined the associations between serum multi-protein biomarker profiles and patient-reported MS disability. In this cross-sectional study (2017-2020), adults with diagnosis of MS (or precursors) from two independent clinic-based cohorts were divided into a training and test set. For predictors, we examined seven clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying therapy [DMT], and time interval between sample collection and closest PRO assessment) and 19 serum protein biomarkers potentially associated with MS disease activity endpoints identified from prior studies. We trained machine learning (ML) models (Least Absolute Shrinkage and Selection Operator regression [LASSO], Random Forest, Extreme Gradient Boosting, Support Vector Machines, stacking ensemble learning, and stacking classification) for predicting Patient Determined Disease Steps (PDDS) score as the primary endpoint and reported model performance using the held-out test set. The study included 431 participants (mean age 49 years, 81% women, 94% non-Hispanic White). For binary PDDS score, combined feature input of routine clinical factors and the 19 proteins consistently outperformed base models (comprising clinical features alone or clinical features plus one single protein at a time) in predicting severe (PDDS ≥ 4) versus mild/moderate (PDDS < 4) disability across multiple machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS = 0.91) and other metrics. For ordinal PDDS score, LASSO model comprising combined clinical factors and 19 proteins as feature input (R2PDDS = 0.31) again outperformed base models. The two best-performing LASSO models (i.e., binary and ordinal PDDS score) shared six clinical features (age, sex, race/ethnicity, disease subtype, disease duration, DMT efficacy) and nine proteins (cluster of differentiation 6, CUB-domain-containing protein 1, contactin-2, interleukin-12 subunit-beta, neurofilament light chain [NfL], protogenin, serpin family A member 9, tumor necrosis factor superfamily member 13B, versican). By comparison, LASSO models with clinical features plus one single protein at a time as feature input did not select either NfL or glial fibrillary acidic protein (GFAP) as a final feature. Forcing either NfL or GFAP as a single protein feature into models did not improve performance beyond clinical features alone. Stacking classification model using five functional pathways to represent multiple proteins as meta-features implicated those involved in neuroaxonal integrity as significant contributors to predictive performance. Thus, serum multi-protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus single protein biomarker, reaching clinically actionable performance.

Immune cell subpopulations and serum neurofilament light chain are associated with increased risk of disease worsening in multiple sclerosis

Changes is lymphocyte subpopulations in peripheral blood have been proposed as biomarkers for evaluation of disease activity in multiple sclerosis (MS). Serum neurofilament light chain (sNfL) is a biomarker reflecting neuro-axonal injury in MS that could be used to monitor disease activity, response to drugs and to prognosticate disease course. Here we show a moderate correlation between sNfL and lymphocyte cell subpopulations, and our data furthermore suggest that sNfL and specific immune cell subpopulations together could predict future disease worsening in MS.

Multiple sclerosis and circadian rhythms: Can diet act as a treatment?

Multiple sclerosis (MS) is an autoimmune inflammatory and neurodegenerative disease of the central nervous system (CNS) with increasing incidence and prevalence. MS is associated with inflammatory and metabolic disturbances that, as preliminary human and animal data suggest, might be mediated by disruption of circadian rhythmicity. Nutrition habits can influence the risk for MS, and dietary interventions may be effective in modulating MS disease course. Chronotherapeutic approaches such as time-restricted eating (TRE) may benefit people with MS by stabilizing the circadian clock and restoring immunological and metabolic rhythms, thus potentially counteracting disease progression. This review provides a summary of selected studies on dietary intervention in MS, circadian rhythms, and their disruption in MS, including clock gene variations, circadian hormones, and retino-hypothalamic tract changes. Furthermore, we present studies that reported diurnal variations in MS, which might result from circadian disruption. And lastly, we suggest how chrononutritive approaches like TRE might counteract MS disease activity.

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.

Serum neurofilament light chain, brain infarcts, and the risk of stroke: a prospective population-based cohort study

Neurofilament light chain (NfL), a neuron-specific protein, has been related to several neurodegenerative diseases. In addition, elevated levels of NfL have also been observed in patients admitted to the hospital for stroke, suggesting that NfL as a biomarker may extend well beyond neurodegenerative diseases. Therefore, using data from the Chicago Health and Aging Project (CHAP), a population-based cohort study, we prospectively investigated the association of serum NfL levels with incident stroke and brain infarcts. During a follow-up of 3603 person-years, 133 (16.3%) individuals developed incident stroke, including ischemic and hemorrhagic. The HR (95%CI) of incident stroke was 1.28 (95%CI 1.10-1.50) per 1 standard deviation (SD) increase of log10 NfL serum levels. Compared to participants in the first tertile of NfL (i.e., lower levels), the risk of stroke was 1.68 times higher (95%CI 1.07-2.65) in those in the second tertile and 2.35 times higher (95%CI 1.45-3.81) in those in the third tertile of NfL. NfL levels were also positively associated with brain infarcts; 1-SD in log10 NfL levels was associated with 1.32 (95%CI 1.06-1.66) higher odds of one or more brain infarcts. These results suggest that NfL may serve as a biomarker of stroke in older adults.

Molecular biomarkers and cognitive impairment in multiple sclerosis: State of the field, limitations, and future direction - A systematic review and meta-analysis

OBJECTIVE

Multiple sclerosis (MS) is associated with cognitive impairment (CI) such as slowed information processing speed (IPS). Currently, no immunocellular or molecular markers have been established in cerebrospinal fluid and serum analysis as surrogate biomarkers with diagnostic or predictive value for the development of CI. This systematic review and meta-analysis aims to sum up the evidence regarding currently discussed markers for CI in MS.

METHODS

A literature search was conducted on molecular biomarkers of CI in MS, such as neurofilament light chain, chitinases, and vitamin D.

RESULTS

5543 publications were screened, of which 77 entered the systematic review. 13 studies were included in the meta-analysis. Neurofilament light chain (CSF: r_p = -0.294, p = 0.003; serum: r_p = -0.137, p = 0.001) and serum levels of vitamin D (r_p = 0.190, p = 0.014) were associated with IPS outcomes.

CONCLUSIONS

Neurofilament light chain and vitamin D are promising biomarkers to track impairments in IPS in MS. Further longitudinal research is needed to establish the use of molecular biomarkers to monitor cognitive decline.

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.

Brain disconnectome mapping derived from white matter lesions and serum neurofilament light levels in multiple sclerosis: A longitudinal multicenter study

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Individual disconnectome maps generated using a template of 7T MRI data.

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Disconnectome maps conceptualize distal brain network aberrations.

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Using lesions maps from our MS cohort, we produced individual disconnectome maps.

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Serum neurofilament light levels were associated with disconnectome maps.

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Voxel-wise analyses revealed interesting association with serum neurofilament light levels.

Background and Objectives

Connectivity-based approaches incorporating the distribution and magnitude of the extended brain network aberrations caused by lesions may offer higher sensitivity for axonal damage in patients with multiple sclerosis (MS) than conventional lesion characteristics. Using individual brain disconnectome mapping, we tested the longitudinal associations between putative imaging-based brain network aberrations and levels of serum neurofilament light chain (NfL) as a neuroaxonal injury biomarker.

Methods

MS patients (n = 312, mean age 42.9 years, 71 % female) and healthy controls (HC) (n = 59, mean age 39.9 years, 78 % female) were prospectively enrolled at four European MS centres, and reassessed after two years (MS, n = 242; HC, n = 30). Post-processing of 3 Tesla (3 T) MRI data was performed at one centre using a harmonized pipeline, and disconnectome maps were calculated using BCBtoolkit based on individual lesion maps. Global disconnectivity (GD) was defined as the average disconnectome probability in each patient’s white matter. Serum NfL concentrations were measured by single molecule array (Simoa). Robust linear mixed models (rLMM) with GD or T2-lesion volume (T2LV) as dependent variables, patient as a random factor, serum NfL, age, sex, timepoint for visit, diagnosis, treatment, and center as fixed factors were run.

Results

rLMM revealed significant associations between GD and serum NfL (t = 2.94, p = 0.003), age (t = 4.21, p = 2.5 × 10⁻⁵), and longitudinal changes in NfL (t = -2.29, p = 0.02), but not for sex (t = 0.63, p = 0.53) or treatments (t = 0.80–0.83, p = 0.41–0.42). Voxel-wise analyses revealed significant associations between dysconnectivity in cerebellar and brainstem regions and serum NfL (t = 7.03, p < 0.001).

Discussion

In our prospective multi-site MS cohort, rLMMs demonstrated that the extent of global and regional brain disconnectivity is sensitive to a systemic biomarker of axonal damage, serum NfL, in patients with MS. These findings provide a neuroaxonal correlate of advanced disconnectome mapping and provide a platform for further investigations of the functional and potential clinical relevance of brain disconnectome mapping in patients with brain disorders.