Farren B S Briggs1, Justin C Yu2, Mary F Davis3, Jinghong Jiangyang4, Shannon Fu5, Erica Parrotta6, Douglas D Gunzler7, Daniel Ontaneda8. 1. Neuroimmunological Disorders Gene-Environment Epidemiology Laboratory, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 1312 Wolstein Research Building, 2103 Cornell, Rd, Cleveland, OH 44106, USA. Electronic address: farren.briggs@case.edu. 2. Neuroimmunological Disorders Gene-Environment Epidemiology Laboratory, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 1312 Wolstein Research Building, 2103 Cornell, Rd, Cleveland, OH 44106, USA. Electronic address: justin.c.yu@case.edu. 3. Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT, USA. Electronic address: mary_davis@byu.edu. 4. Neuroimmunological Disorders Gene-Environment Epidemiology Laboratory, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 1312 Wolstein Research Building, 2103 Cornell, Rd, Cleveland, OH 44106, USA. Electronic address: jinghong.jiangyang@case.edu. 5. Neuroimmunological Disorders Gene-Environment Epidemiology Laboratory, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 1312 Wolstein Research Building, 2103 Cornell, Rd, Cleveland, OH 44106, USA. Electronic address: shannon.fu@case.edu. 6. Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic Foundation, Cleveland, OH, USA. Electronic address: parrote@ccf.org. 7. Center for Health Care Research and Policy, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA. Electronic address: dgunzler@metrohealth.org. 8. Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic Foundation, Cleveland, OH, USA. Electronic address: ontaned@ccf.org.
Abstract
BACKGROUND: The phenotypic presentation of multiple sclerosis (MS) may predict long-term outcomes and little is known about factors contributing to heterogeneity at MS onset. Given temporality, it is likely MS risk factors also influence presentation of the disease near onset. METHODS: Using a retrospective cross-sectional study of MS cases, we investigated: age of onset (AOO), number of impaired functional domains (NIFDs), time to second relapse (TT2R), and early relapse activity (ERA). Machine learning variable selection was applied to epidemiologic data for each outcome, followed by multivariable regression models. The models were further adjusted for HLA-DRB1*15:01 carrier status and a MS genetic risk score (GRS). The TT2R and ERA analyses were restricted to relapsing remitting MS cases. RESULTS: HLA-DRB1*15:01, GRS, and smoking were associated with earlier AOO. Cases who were male, obese, had lower education, or had primary progressive MS were older at onset. For NIFDs, those with relapsing remitting MS and of lower SES had increased NIFDs. Among relapsing remitting cases, those who were older at onset, obese, and had polyfocal presentation had shorter TT2R, while ERA was greater among those younger at onset and who were obese. CONCLUSION: Individual characteristics including age, genetic profiles, obesity, and smoking status contribute to heterogeneity in disease presentation and modulate early disease course evolution.
BACKGROUND: The phenotypic presentation of multiple sclerosis (MS) may predict long-term outcomes and little is known about factors contributing to heterogeneity at MS onset. Given temporality, it is likely MS risk factors also influence presentation of the disease near onset. METHODS: Using a retrospective cross-sectional study of MS cases, we investigated: age of onset (AOO), number of impaired functional domains (NIFDs), time to second relapse (TT2R), and early relapse activity (ERA). Machine learning variable selection was applied to epidemiologic data for each outcome, followed by multivariable regression models. The models were further adjusted for HLA-DRB1*15:01 carrier status and a MS genetic risk score (GRS). The TT2R and ERA analyses were restricted to relapsing remitting MS cases. RESULTS:HLA-DRB1*15:01, GRS, and smoking were associated with earlier AOO. Cases who were male, obese, had lower education, or had primary progressive MS were older at onset. For NIFDs, those with relapsing remitting MS and of lower SES had increased NIFDs. Among relapsing remitting cases, those who were older at onset, obese, and had polyfocal presentation had shorter TT2R, while ERA was greater among those younger at onset and who were obese. CONCLUSION: Individual characteristics including age, genetic profiles, obesity, and smoking status contribute to heterogeneity in disease presentation and modulate early disease course evolution.
Authors: Amnon A Berger; Emily R Sottosanti; Ariel Winnick; Jonathan Izygon; Kevin Berardino; Elyse M Cornett; Alan D Kaye; Giustino Varrassi; Omar Viswanath; Ivan Urits Journal: Neurol Int Date: 2021-05-19
Authors: Vladeta Ajdacic-Gross; Nina Steinemann; Gábor Horváth; Stephanie Rodgers; Marco Kaufmann; Yanhua Xu; Christian P Kamm; Jürg Kesselring; Zina-Mary Manjaly; Chiara Zecca; Pasquale Calabrese; Milo A Puhan; Viktor von Wyl Journal: Front Neurol Date: 2021-07-06 Impact factor: 4.003