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Filippatou AG, Lambe J, Sotirchos ES, Fitzgerald KC, Aston A, Murphy OC, Pellegrini N, Fioravante N, Risher H, Ogbuokiri E, Kwakyi O, Toliver B, Davis S, Luciano N, Crainiceanu C, Prince JL, Mowry EM, Calabresi PA, Saidha S. Association of body mass index with longitudinal rates of retinal atrophy in multiple sclerosis. Mult Scler 2020; 26:843-854. [PMID: 32297826 DOI: 10.1177/1352458519900942] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
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.
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Affiliation(s)
- Angeliki G Filippatou
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jeffrey Lambe
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elias S Sotirchos
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kathryn C Fitzgerald
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew Aston
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Olwen C Murphy
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nicole Pellegrini
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nicholas Fioravante
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hunter Risher
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Esther Ogbuokiri
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ohemaa Kwakyi
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Brandon Toliver
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Simidele Davis
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nicholas Luciano
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Ellen M Mowry
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peter A Calabresi
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Shiv Saidha
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Seccia R, Gammelli D, Dominici F, Romano S, Landi AC, Salvetti M, Tacchella A, Zaccaria A, Crisanti A, Grassi F, Palagi L. Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis. PLoS One 2020; 15:e0230219. [PMID: 32196512 PMCID: PMC7083323 DOI: 10.1371/journal.pone.0230219] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 02/24/2020] [Indexed: 12/27/2022] Open
Abstract
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.
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Affiliation(s)
- Ruggiero Seccia
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Daniele Gammelli
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Fabio Dominici
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Silvia Romano
- Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Anna Chiara Landi
- Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Marco Salvetti
- Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
- IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy
| | - Andrea Tacchella
- Dept. of Physics, Istituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy
| | - Andrea Zaccaria
- Dept. of Physics, Istituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy
| | | | - Francesca Grassi
- Dept. of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Laura Palagi
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
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Wang Y, Tian F, Fitzgerald KC, Bhattarai JJ, Naismith RT, Hyland M, Calabresi PA, Mowry EM. Socioeconomic status and race are correlated with affective symptoms in multiple sclerosis. Mult Scler Relat Disord 2020; 41:102010. [PMID: 32088654 DOI: 10.1016/j.msard.2020.102010] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 02/13/2020] [Indexed: 12/01/2022]
Abstract
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.
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Affiliation(s)
- Yujie Wang
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St.Pathology 627, Baltimore, MD 21287, USA.
| | - Fan Tian
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St.Pathology 627, Baltimore, MD 21287, USA
| | - Kathryn C Fitzgerald
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St.Pathology 627, Baltimore, MD 21287, USA
| | - Jagriti Jackie Bhattarai
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, USA
| | - Robert T Naismith
- Department of Neurology, Washington University in St. Louis School of Medicine, USA
| | - Megan Hyland
- Department of Neurology, University of Rochester School of Medicine, USA
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St.Pathology 627, Baltimore, MD 21287, USA
| | - Ellen M Mowry
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St.Pathology 627, Baltimore, MD 21287, USA
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Stampanoni Bassi M, Iezzi E, Buttari F, Gilio L, Simonelli I, Carbone F, Micillo T, De Rosa V, Sica F, Furlan R, Finardi A, Fantozzi R, Storto M, Bellantonio P, Pirollo P, Di Lemme S, Musella A, Mandolesi G, Centonze D, Matarese G. Obesity worsens central inflammation and disability in multiple sclerosis. Mult Scler 2019; 26:1237-1246. [PMID: 31161863 DOI: 10.1177/1352458519853473] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
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.
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Affiliation(s)
| | - Ennio Iezzi
- Unit of Neurology and Neurorehabilitation, IRCCS Neuromed, Pozzilli, Italy
| | - Fabio Buttari
- Unit of Neurology and Neurorehabilitation, IRCCS Neuromed, Pozzilli, Italy
| | - Luana Gilio
- Unit of Neurology and Neurorehabilitation, IRCCS Neuromed, Pozzilli, Italy
| | - Ilaria Simonelli
- Service of Medical Statistics & Information Technology, Fondazione Fatebenefratelli per la Ricerca e la Formazione Sanitaria e Sociale, Lungotevere de' Cenci 5, Rome, Italy
| | - Fortunata Carbone
- Istituto per l'Endocrinologia e l'Oncologia Sperimentale, Consiglio Nazionale delle Ricerche, Naples, Italy; Unità di Neuroimmunologia, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Teresa Micillo
- Dipartimento di Biologia, Università di Napoli Federico II, Naples, Italy
| | - Veronica De Rosa
- Istituto per l'Endocrinologia e l'Oncologia Sperimentale, Consiglio Nazionale delle Ricerche, Naples, Italy; Unità di Neuroimmunologia, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Francesco Sica
- Unit of Neurology and Neurorehabilitation, IRCCS Neuromed, Pozzilli, Italy
| | - Roberto Furlan
- Neuroimmunology Unit, Institute of Experimental Neurology (INSpe), Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Annamaria Finardi
- Neuroimmunology Unit, Institute of Experimental Neurology (INSpe), Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Roberta Fantozzi
- Unit of Neurology and Neurorehabilitation, IRCCS Neuromed, Pozzilli, Italy
| | - Marianna Storto
- Unit of Neurology and Neurorehabilitation, IRCCS Neuromed, Pozzilli, Italy
| | - Paolo Bellantonio
- Unit of Neurology and Neurorehabilitation, IRCCS Neuromed, Pozzilli, Italy
| | - Pamela Pirollo
- Unit of Neurology and Neurorehabilitation, IRCCS Neuromed, Pozzilli, Italy
| | - Sonia Di Lemme
- Unit of Neurology and Neurorehabilitation, IRCCS Neuromed, Pozzilli, Italy
| | - Alessandra Musella
- Laboratory of Neuroimmunology and Synaptic Plasticity, IRCCS San Raffaele Pisana, Rome, Italy
| | - Georgia Mandolesi
- Laboratory of Neuroimmunology and Synaptic Plasticity, IRCCS San Raffaele Pisana, Rome, Italy
| | - Diego Centonze
- Unit of Neurology & Neurorehabilitation, IRCCS Neuromed, Pozzilli, Italy; Laboratory of Synaptic Immunopathology, Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Giuseppe Matarese
- Istituto per l'Endocrinologia e l'Oncologia Sperimentale, Consiglio Nazionale delle Ricerche, Naples, Italy; Treg Cell Lab, Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli Federico II, Naples, Italy
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