Literature DB >> 29979093

Five years before multiple sclerosis onset: Phenotyping the prodrome.

José Ma Wijnands1, Feng Zhu1, Elaine Kingwell1, Yinshan Zhao1, Okechukwu Ekuma2, Xinya Lu3, Charity Evans4, John D Fisk5, Ruth Ann Marrie6, Helen Tremlett1.   

Abstract

BACKGROUND: The multiple sclerosis (MS) prodrome is poorly characterized.
OBJECTIVE: To phenotype the MS prodrome via health care encounters.
METHODS: Using data from a population-based cohort study linking administrative and clinical data in four Canadian provinces, we compared physician and hospital encounters and prescriptions filled (via International Classification of Diseases chapters, physician specialty or drug classes) for MS subjects in the 5 years before the first demyelinating claim in an administrative cohort or the clinical symptom onset in an MS clinic-derived cohort, to age-, sex- and geographically matched controls. Rate ratios (RRs), 95% confidence intervals (95% CIs) and proportions were estimated.
RESULTS: The administrative and clinical cohorts included 13,951/66,940 and 3202/16,006 people with and without MS (cases/controls). Compared to controls, in the 5 years before the first demyelinating claim or symptom onset, cases had more physician and hospital encounters for the nervous (RR (range) = 2.31; 95% CI: 1.05-5.10 to 4.75; 95% CI: 3.11-7.25), sensory (RR (range) = 1.40; 95% CI: 1.34-1.46 to 2.28; 95% CI: 1.72-3.02), musculoskeletal (RR (range) = 1.19; 95% CI: 1.07-1.33 to 1.70; 95% CI: 1.57-1.85) and genito-urinary systems (RR (range) = 1.17; 95% CI: 1.05-1.30 to 1.59; 95% CI: 1.48-1.70). Cases had more psychiatrist and urologist encounters (RR (range) = 1.48; 95% CI: 1.36-1.62 to 1.80; 95% CI: 1.61-2.01), and higher proportions of musculoskeletal, genito-urinary or hormonal-related prescriptions (1.1-1.5 times higher, all p < 0.02). However, cases had fewer pregnancy-related encounters than controls (RR = 0.78; 95% CI: 0.71-0.86 to 0.88; 95% CI: 0.84-0.92).
CONCLUSION: Phenotyping the prodrome 5 years before clinical recognition of MS is feasible.

Entities:  

Keywords:  Multiple sclerosis; health care utilization; multi-centre; population-based data; prodrome

Mesh:

Year:  2018        PMID: 29979093     DOI: 10.1177/1352458518783662

Source DB:  PubMed          Journal:  Mult Scler        ISSN: 1352-4585            Impact factor:   6.312


  16 in total

1.  Predicting onset of secondary-progressive multiple sclerosis using genetic and non-genetic factors.

Authors:  Elina Misicka; Corriene Sept; Farren B S Briggs
Journal:  J Neurol       Date:  2020-04-24       Impact factor: 4.849

Review 2.  From the prodromal stage of multiple sclerosis to disease prevention.

Authors:  Ruth Ann Marrie; Mark Allegretta; Lisa F Barcellos; Bruce Bebo; Peter A Calabresi; Jorge Correale; Benjamin Davis; Philip L De Jager; Christiane Gasperi; Carla Greenbaum; Anne Helme; Bernhard Hemmer; Pamela Kanellis; Walter Kostich; Douglas Landsman; Christine Lebrun-Frenay; Naila Makhani; Kassandra L Munger; Darin T Okuda; Daniel Ontaneda; Ronald B Postuma; Jacqueline A Quandt; Sharon Roman; Shiv Saidha; Maria Pia Sormani; Jon Strum; Pamela Valentine; Clare Walton; Kathleen M Zackowski; Yinshan Zhao; Helen Tremlett
Journal:  Nat Rev Neurol       Date:  2022-07-15       Impact factor: 44.711

3.  Comorbid disease burden among MS patients 1968-2012: A Swedish register-based cohort study.

Authors:  Kelsi A Smith; Sarah Burkill; Ayako Hiyoshi; Tomas Olsson; Shahram Bahmanyar; David Wormser; Yvonne Geissbühler; Alan Moore; Vineetkumar Kharat; Scott Montgomery
Journal:  Mult Scler       Date:  2020-03-12       Impact factor: 6.312

4.  Hospital diagnosed pneumonia before age 20 years and multiple sclerosis risk.

Authors:  Kelsi A Smith; Ayako Hiyoshi; Sarah Burkill; Shahram Bahmanyar; Johan Öckinger; Lars Alfredsson; Tomas Olsson; Scott Montgomery
Journal:  BMJ Neurol Open       Date:  2020-06-16

5.  Embedding electronic health records onto a knowledge network recognizes prodromal features of multiple sclerosis and predicts diagnosis.

Authors:  Charlotte A Nelson; Riley Bove; Atul J Butte; Sergio E Baranzini
Journal:  J Am Med Inform Assoc       Date:  2022-01-29       Impact factor: 4.497

Review 6.  The multiple sclerosis prodrome.

Authors:  Naila Makhani; Helen Tremlett
Journal:  Nat Rev Neurol       Date:  2021-06-21       Impact factor: 42.937

7.  Cost-of-Illness Progression Before and After Diagnosis of Multiple Sclerosis: A Nationwide Register-Based Cohort Study in Sweden of People Newly Diagnosed with Multiple Sclerosis and a Population-Based Matched Reference Group.

Authors:  Chantelle Murley; Petter Tinghög; Kristina Alexanderson; Jan Hillert; Emilie Friberg; Korinna Karampampa
Journal:  Pharmacoeconomics       Date:  2021-05-10       Impact factor: 4.981

8.  Stressful life events are associated with the risk of multiple sclerosis.

Authors:  X Jiang; T Olsson; J Hillert; I Kockum; L Alfredsson
Journal:  Eur J Neurol       Date:  2020-08-23       Impact factor: 6.089

9.  Perinatal Depression and Anxiety in Women With Multiple Sclerosis: A Population-Based Cohort Study.

Authors:  Karine Eid; Øivind Fredvik Torkildsen; Jan Aarseth; Heidi Øyen Flemmen; Trygve Holmøy; Åslaug Rudjord Lorentzen; Kjell-Morten Myhr; Trond Riise; Cecilia Simonsen; Cecilie Fredvik Torkildsen; Stig Wergeland; Johannes Sverre Willumsen; Nina Øksendal; Nils Erik Gilhus; Marte-Helene Bjørk
Journal:  Neurology       Date:  2021-04-21       Impact factor: 9.910

10.  Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches.

Authors:  Anna Onisiforou; George M Spyrou
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

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