Multiscale networks in multiple sclerosis.

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.

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.

Retinal Changes in Double-Antibody Seronegative Neuromyelitis Optica Spectrum Disorders.

BACKGROUND AND OBJECTIVES: To systematically describe the clinical picture of double-antibody seronegative neuromyelitis optica spectrum disorders (DN-NMOSD) with specific emphasis on retinal involvement. METHODS: Cross-sectional data of 25 people with DN-NMOSD (48 eyes) with and without a history of optic neuritis (ON) were included in this study along with data from 25 people with aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorder (AQP4-NMOSD, 46 eyes) and from 25 healthy controls (HCs, 49 eyes) for comparison. All groups were matched for age and sex and included from the collaborative retrospective study of retinal optical coherence tomography (OCT) in neuromyelitis optica (CROCTINO). Participants underwent OCT with central postprocessing and local neurologic examination and antibody testing. Retinal neurodegeneration was quantified as peripapillary retinal nerve fiber layer thickness (pRNFL) and combined ganglion cell and inner plexiform layer thickness (GCIPL). RESULTS: This DN-NMOSD cohort had a history of [median (inter-quartile range)] 6 (5; 9) attacks within their 5 ± 4 years since onset. Myelitis and ON were the most common attack types. In DN-NMOSD eyes after ON, pRNFL (p < 0.001) and GCIPL (p = 0.023) were thinner compared with eyes of HCs. Even after only one ON episode, DN-NMOSD eyes already had considerable neuroaxonal loss compared with HCs. In DN-NMOSD eyes without a history of ON, pRNFL (p = 0.027) and GCIPL (p = 0.022) were also reduced compared with eyes of HCs. However, there was no difference in pRNFL and GCIPL between DN-NMOSD and AQP4-NMOSD for the whole group and for subsets with a history of ON and without a history of ON-as well as between variances of retinal layer thicknesses. DISCUSSION: DN-NMOSD is characterized by severe retinal damage after ON and attack-independent retinal neurodegeneration. Most of the damage occurs during the first ON episode, which highlights the need for better diagnostic markers in DN-NMOSD to facilitate an earlier diagnosis as well as for effective and early treatments. In this study, people with DN-NMOSD presented with homogeneous clinical and imaging findings potentially suggesting a common retinal pathology in these patients.

Consumo de ácidos grasos e incidencia de cataratas: estudio de la cohorte Seguimiento Universidad de Navarra / Dietary fat intake and incidence of cataracts: The SUN Prospective study in the cohort of Navarra, Spain

Fundamento y objetivo: Examinar la asociación entre la ingestión de distintos tipos de ácidos grasos y la incidencia de cataratas en los participantes de la cohorte Seguimiento Universidad de Navarra.acientes y método Entre 12.308 participantes de la cohorte, inicialmente libres de cataratas y seguidos prospectivamente hasta un máximo de 6 años, identificamos 182 casos nuevos de cataratas. La ingestión de ácidos grasos se evaluó mediante un cuestionario validado y semicuantitativo de frecuencia de consumo de alimentos (136 ítems). Se incluyeron variables sociodemográficas, antropométricas, referidas al estilo de vida y hábitos saludables, así como condicionantes médicos. La incidencia declarada de cataratas se recogió a partir de cuestionarios bianuales. Resultados: Los participantes pertenecientes a la categoría de mayor consumo de ácidos grasos omega-6 presentaron una reducción no significativa de riesgo de cataratas en el modelo ajustado por posibles confusores, pero no por otros ácidos grasos (odds ratio [OR] ajustada: 0,58; intervalo de confianza [IC] del 95%: 0,33–1,03). Cuando todos los tipos de ácidos (saturados, monoinsaturados, poliinsaturados omega-6 y poliinsaturados omega-3) se incluyeron simultáneamente en el mismo modelo multivariable, dicha asociación alcanzó significación estadística (OR: 0,54; IC del 95%: 0,29–0,99). Ningún otro tipo de ácido graso mostró asociación independiente significativa con el desarrollo de cataratas. Conclusiones: El elevado consumo de ácidos grasos poliinsaturados omega-6 puede tener una asociación modesta, pero significativa, con la reducción del riesgo de desarrollo de cataratas (AU)

Background and objective: To evaluate prospectively the association between dietary fat intake and the incidence of cataracts in participants from the SUN cohort. Patients and methods: Among 12308 men and women, initially free of cataracts and followed-up for up to 6 years, we identified 182 incident cases of cataracts. Fatty acids intake was assessed by a validated 136-item semiquantitative food frequency questionnaire. Socio-demographic and anthropometric characteristics, lifestyle, health-related habits and information about medical conditions were also collected. Incident cases of cataract were ascertained by self-reports using a biennale questionnaire. Results: Participants belonging to the highest category of omega-6 fatty acids intake had a non-significantly reduced risk of cataracts in the model adjusted for potential confounders but not for other fatty acids (adjusted OR: 0.58; 95% CI: 0.33, 1.03). When all types of fatty acids (saturated, monounsaturated, polyunsaturated omega-6 and polyunsaturated omega-3) were included simultaneously in the same multivariable model, this inverse association became significant (adjusted OR: 0.54 95%CI: 0.29, 0.99). There were no significant associations between other dietary fats and the risk of cataracts. Conclusion:High intake of omega-6 fatty acids showed a modest but significant inverse association with the development of cataracts (AU)