Prognostic factors of disability in relapsing remitting multiple sclerosis

Association of body mass index with longitudinal rates of retinal atrophy in multiple sclerosis

BACKGROUND

Studies evaluating associations between body mass index (BMI) and optical coherence tomography (OCT) measures in multiple sclerosis (MS) are lacking.

OBJECTIVE

To assess whether elevated BMI is associated with accelerated retinal atrophy.

METHODS

In this observational study, 513 MS patients were followed with serial spectral-domain OCT for a median of 4.4 years. Participants were categorized as normal weight (BMI: 18.5-24.9 kg/m²), overweight (BMI: 25-29.9 kg/m²), and obese (BMI: ⩾30 kg/m²). Participants with diabetes mellitus or uncontrolled hypertension and eyes with optic neuritis (ON) ⩽6 months prior to baseline OCT or during follow-up were excluded. Statistical analyses were performed with mixed-effects linear regression.

RESULTS

Obese patients (n = 146) exhibited accelerated rates of ganglion cell + inner plexiform layer (GCIPL) atrophy relative to normal weight patients (n = 214; -0.57%/year (95% confidence interval (CI): -0.65% to -0.48%) versus -0.42%/year (95% CI: -0.49% to -0.35%); p = 0.012). GCIPL atrophy rate did not differ between overweight (n = 153) and normal weight patients (-0.47%/year vs -0.42%/year; p = 0.41). Each 1 kg/m² higher BMI was associated with accelerated GCIPL (-0.011%/year; 95% CI: -0.019% to -0.004%; p = 0.003) atrophy. Multivariable analyses accounting for age, sex, race, MS subtype, and ON history did not alter the above findings.

CONCLUSIONS

Elevated BMI, in the absence of overt metabolic comorbidities, may be associated with accelerated GCIPL atrophy. Obesity, a modifiable risk factor, may be associated with accelerated neurodegeneration in MS.

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only "real world" data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given "confidence threshold". For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how "real world" data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values.

Socioeconomic status and race are correlated with affective symptoms in multiple sclerosis

OBJECTIVE

Investigate the relationship between socioeconomic status (SES) and race with self-reported fatigue, depression, and anxiety levels in multiple sclerosis (MS).

METHODS

Cross-sectional review of the MS Partners Advancing Technology and Health Solutions (MS PATHS) database for adults with MS in the United States. We evaluated race and socioeconomic status (available markers: insurance, employment status, or level of education) as predictors of fatigue, depression, and anxiety sub-scores of the Neuro-QoL (Quality of life in neurological disorders), with particular interest between Caucasians/whites (CA) and African Americans/blacks (AA). Multivariate linear regression models included as covariates age, sex, disability status, smoking status, body mass index, and disease-modifying therapy.

RESULTS

7,430 individuals were included; compared to CA, AA tended to be younger, more female-predominant, and had a higher level of disability. AA had completed slightly less education, had a higher level of Medicaid coverage or uninsured status, and had higher rates of unemployed or disabled status. In the univariate model, markers of lower SES, by whichever definition we used, correlated with worse affective symptoms. In the multivariate model stratified by race, CA showed similar trends. In contrast, in AA, only lower SES by employment status was correlated with worse affective symptoms. In both CA and AA, moderate and severe level of disability correlated with worse affective symptoms.

CONCLUSION

SES and race may influence affective symptoms reported by individuals with MS. The reasons for the correlation are likely multifactorial. Longitudinal studies should strive to identify factors associated with risk of affective symptoms in MS that may be modifiable.

Obesity worsens central inflammation and disability in multiple sclerosis

BACKGROUND

Previous studies evidenced a link between metabolic dysregulation, inflammation, and neurodegeneration in multiple sclerosis (MS).

OBJECTIVES

To explore whether increased adipocyte mass expressed as body mass index (BMI) and increased serum lipids influence cerebrospinal fluid (CSF) inflammation and disease severity.

METHODS

In this cross-sectional study, 140 consecutive relapsing-remitting (RR)-MS patients underwent clinical assessment, BMI evaluation, magnetic resonance imaging scan, and blood and CSF collection before any specific drug treatment. The CSF levels of the following cytokines, adipocytokines, and inflammatory factors were measured: interleukin (IL)-6, IL-13, granulocyte macrophage colony-stimulating factor, leptin, ghrelin, osteoprotegerin, osteopontin, plasminogen activator inhibitor-1, resistin, and Annexin A1. Serum levels of triglycerides, total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) were assessed.

RESULTS

A positive correlation emerged between BMI and Expanded Disability Status Scale score. Obese RR-MS patients showed higher clinical disability, increased CSF levels of the proinflammatory molecules IL-6 and leptin, and reduced concentrations of the anti-inflammatory cytokine IL-13. Moreover, both the serum levels of triglycerides and TC/HDL-C ratio showed a positive correlation with IL-6 CSF concentrations.

CONCLUSION

Obesity and altered lipid profile are associated with exacerbated central inflammation and higher clinical disability in RR-MS at the time of diagnosis. Increased adipocytokines and lipids can mediate the negative impact of high adiposity on RR-MS course.