Cardiovascular risk factors in secondary progressive multiple sclerosis: A cross-sectional analysis from the MS-STAT2 randomized controlled trial.
Humans
Middle Aged
Multiple Sclerosis
/ pathology
Multiple Sclerosis, Chronic Progressive
/ diagnostic imaging
Cardiovascular Diseases
/ diagnostic imaging
Cross-Sectional Studies
Risk Factors
Brain
/ diagnostic imaging
Magnetic Resonance Imaging
/ methods
Memory, Short-Term
Heart Disease Risk Factors
Atrophy
/ pathology
Disability Evaluation
Disease Progression
STAT2 Transcription Factor
cardiovascular risk
comorbidity
multiple sclerosis
progressive multiple sclerosis
secondary progressive multiple sclerosis
Journal
European journal of neurology
ISSN: 1468-1331
Titre abrégé: Eur J Neurol
Pays: England
ID NLM: 9506311
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
revised:
31
05
2023
received:
29
03
2023
accepted:
05
06
2023
medline:
8
8
2023
pubmed:
15
6
2023
entrez:
15
6
2023
Statut:
ppublish
Résumé
There is increasing evidence that cardiovascular risk (CVR) contributes to disability progression in multiple sclerosis (MS). CVR is particularly prevalent in secondary progressive MS (SPMS) and can be quantified through validated composite CVR scores. The aim was to examine the cross-sectional relationships between excess modifiable CVR, whole and regional brain atrophy on magnetic resonance imaging, and disability in patients with SPMS. Participants had SPMS, and data were collected at enrolment into the MS-STAT2 trial. Composite CVR scores were calculated using the QRISK3 software. Prematurely achieved CVR due to modifiable risk factors was expressed as QRISK3 premature CVR, derived through reference to the normative QRISK3 dataset and expressed in years. Associations were determined with multiple linear regressions. For the 218 participants, mean age was 54 years and median Expanded Disability Status Scale was 6.0. Each additional year of prematurely achieved CVR was associated with a 2.7 mL (beta coefficient; 95% confidence interval 0.8-4.7; p = 0.006) smaller normalized whole brain volume. The strongest relationship was seen for the cortical grey matter (beta coefficient 1.6 mL per year; 95% confidence interval 0.5-2.7; p = 0.003), and associations were also found with poorer verbal working memory performance. Body mass index demonstrated the strongest relationships with normalized brain volumes, whilst serum lipid ratios demonstrated strong relationships with verbal and visuospatial working memory performance. Prematurely achieved CVR is associated with lower normalized brain volumes in SPMS. Future longitudinal analyses of this clinical trial dataset will be important to determine whether CVR predicts future disease worsening.
Sections du résumé
BACKGROUND AND PURPOSE
There is increasing evidence that cardiovascular risk (CVR) contributes to disability progression in multiple sclerosis (MS). CVR is particularly prevalent in secondary progressive MS (SPMS) and can be quantified through validated composite CVR scores. The aim was to examine the cross-sectional relationships between excess modifiable CVR, whole and regional brain atrophy on magnetic resonance imaging, and disability in patients with SPMS.
METHODS
Participants had SPMS, and data were collected at enrolment into the MS-STAT2 trial. Composite CVR scores were calculated using the QRISK3 software. Prematurely achieved CVR due to modifiable risk factors was expressed as QRISK3 premature CVR, derived through reference to the normative QRISK3 dataset and expressed in years. Associations were determined with multiple linear regressions.
RESULTS
For the 218 participants, mean age was 54 years and median Expanded Disability Status Scale was 6.0. Each additional year of prematurely achieved CVR was associated with a 2.7 mL (beta coefficient; 95% confidence interval 0.8-4.7; p = 0.006) smaller normalized whole brain volume. The strongest relationship was seen for the cortical grey matter (beta coefficient 1.6 mL per year; 95% confidence interval 0.5-2.7; p = 0.003), and associations were also found with poorer verbal working memory performance. Body mass index demonstrated the strongest relationships with normalized brain volumes, whilst serum lipid ratios demonstrated strong relationships with verbal and visuospatial working memory performance.
CONCLUSIONS
Prematurely achieved CVR is associated with lower normalized brain volumes in SPMS. Future longitudinal analyses of this clinical trial dataset will be important to determine whether CVR predicts future disease worsening.
Substances chimiques
STAT2 protein, human
0
STAT2 Transcription Factor
0
Banques de données
ClinicalTrials.gov
['NCT03387670']
Types de publication
Randomized Controlled Trial
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2769-2780Subventions
Organisme : Multiple Sclerosis Society
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom
Investigateurs
Wallace Brownlee
(W)
Megan Wynne
(M)
Leanne Hockey
(L)
Josephine Parker
(J)
Jennifer Flight
(J)
Chris Frost
(C)
Jennifer Nicholas
(J)
Stuart Nixon
(S)
Judy Beveridge
(J)
Siddharthan Chandran
(S)
Peter Connick
(P)
Dawn Lyle
(D)
Ian Galea
(I)
Elisabeth Jarman
(E)
Helen Ford
(H)
Linford Fernandes
(L)
Maruthi Vinjam
(M)
Sue Pavitt
(S)
Basil Sharrack
(B)
David Paling
(D)
Abdullah Shehu
(A)
Tarunya Arun
(T)
Mohamed Belhag
(M)
Owen Pearson
(O)
Gillian Ingram
(G)
Christopher Rickards
(C)
Gavin McDonnell
(G)
Stella Hughes
(S)
Cord Spilker
(C)
Leonora Fisniku
(L)
Julia Aram
(J)
Claire Rice
(C)
Stefano Pluchino
(S)
Luca Peruzzotti-Jametti
(L)
Sreedharan Harikrishnan
(S)
Nikki Guck
(N)
Neil Robertson
(N)
Emma Tallantyre
(E)
Timothy Harrower
(T)
Paul Gallagher
(P)
Fayyaz Ahmed
(F)
Carolyn Young
(C)
Heike Arndt
(H)
Eli Silber
(E)
Richard Nicholas
(R)
Martin Duddy
(M)
Martin Lee
(M)
Nikos Evangelou
(N)
Christopher Allen
(C)
Matthew Craner
(M)
Ruth Geraldes
(R)
Jeremy Hobart
(J)
Charles Hillier
(C)
Suresh Chhetri
(S)
Miriam Mattoscio
(M)
Abhijit Chaudhuri
(A)
Seema Kalra
(S)
Agne Straukiene
(A)
David Rog
(D)
Informations de copyright
© 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.
Références
Palladino R, Marrie RA, Majeed A, Chataway J. Evaluating the risk of macrovascular events and mortality among people with multiple sclerosis in England. JAMA Neurol. 2020;77(7):820-828.
Marrie RA, Rudick R, Horwitz R, et al. Vascular comorbidity is associated with more rapid disability progression in multiple sclerosis. Neurology. 2010;74(13):1041-1047.
Jakimovski D, Gandhi S, Paunkoski I, et al. Hypertension and heart disease are associated with development of brain atrophy in multiple sclerosis: a 5-year longitudinal study. Eur J Neurol. 2019;26(1):87-e8.
Moccia M, Lanzillo R, Palladino R, et al. The Framingham cardiovascular risk score in multiple sclerosis. Eur J Neurol. 2015;22(8):1176-1183.
Kowalec K, McKay KA, Patten SB, et al. Comorbidity increases the risk of relapse in multiple sclerosis. Neurology. 2017;89(24):2455-2461.
Marrie RA. Comorbidity in multiple sclerosis: implications for patient care. Nat Rev Neurol. 2017;13(6):375-382. doi:10.1038/nrneurol.2017.33
Rodgers J, Friede T, Vonberg FW, et al. The impact of smoking cessation on multiple sclerosis disease progression. Brain. 2021;2021:2-29. doi:10.1093/brain/awab385/6384574
Zhao D, Liu J, Xie W, Qi Y. Cardiovascular risk assessment: a global perspective. Nat Rev Cardiol. 2015;12(5):301-311.
D'Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation. 2008;117(6):743-753.
Petruzzo M, Reia A, Maniscalco GT, et al. The Framingham cardiovascular risk score and 5-year progression of multiple sclerosis. Eur J Neurol. 2021;28(3):893-900.
Marrie RA, Patel R, Figley CR, et al. Higher Framingham risk scores are associated with greater loss of brain volume over time in multiple sclerosis. Mult Scler Relat Disord. 2021;54:103088.
ClinRisk. QRISK3 [Internet]. 2018 [cited 2022 Jan 1]. Available from: https://qrisk.org/three/
NICE. CVD risk assessment and management. Clinical Knowledge Summaries. 2020. Available at: https://www.nice.org.uk/guidance/cg181
Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017;357:1-21. doi:10.1136/bmj.j2099
World Medical Association. Declaration of Helsinki: ethical principles for medical research involving human subjects. http://www.wma.net/en/30publications/10policies/b3/17c.pdf
Nordestgaard BG, Langsted A, Mora S, et al. Fasting is not routinely required for determination of a lipid profile: clinical and laboratory implications including flagging at desirable concentration cut-points-a joint consensus statement from the European Atherosclerosis Society and European Federation of Clinical Chemistry and Laboratory Medicine. Eur Heart J. 2016;37(25):1944-1958.
Valverde S, Salem M, Cabezas M, et al. One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks. NeuroImage Clin. 2019;21:101638.
Prados F, Cardoso MJ, Kanber B, et al. A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis. Neuroimage. 2016;139:376-384.
Cardoso MJ, Modat M, Wolz R, et al. Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans Med Imaging. 2015;34(9):1976-1988.
Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage. 2002;17(1):479-489.
Johnston R, Jones K, Manley D. Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour. Qual Quant. 2018;52(4):1957-1976.
Tranmer M, Murphy J, Elliot M, Pampaka M. Multiple linear regression. 2nd ed. Cathie Marsh Inst Work Pap [Internet]; 2020 (01):59. Available from: https://hummedia.manchester.ac.uk/institutes/cmist/archive-publications/working-papers/2020/2020-1-multiple-linear-regression.pdf
Langdon DW, Amato MP, Boringa J, et al. Recommendations for a brief international cognitive assessment for multiple sclerosis (BICAMS). Mult Scler J. 2012;18(6):891-898.
Benedict RHB, Drake AS, Irwin LN, et al. Benchmarks of meaningful impairment on the MSFC and BICAMS. Mult Scler. 2016;22(14):1874-1882.
Parker RA, Weir CJ. Multiple secondary outcome analyses: precise interpretation is important. Trials. 2022;23(1):21-24.
Aguinis H, Gottfredson RK, Joo H. Best-practice recommendations for defining, identifying, and handling outliers. Organ Res Methods. 2013;16(2):270-301.
Williams T, Alexander S, Blackstone J, et al. Optimising recruitment in clinical trials for progressive multiple sclerosis: observational analysis from the MS-SMART and MS-STAT2 randomised controlled trials. Trials. 2022;23(1):644. doi:10.1186/s13063-022-06588-z
Chard DT, Brex PA, Ciccarelli O, et al. The longitudinal relation between brain lesion load and atrophy in multiple sclerosis: a 14 year follow up study. J Neurol Neurosurg Psychiatry. 2003;74(11):1551-1554.
Williams TE, Holdsworth KP, Nicholas JM, et al. Assessing neurofilaments as biomarkers of neuroprotection in progressive multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. 2022;9(2):e1130. doi:10.1212/NXI.0000000000001130
Kappus N, Weinstock-Guttman B, Hagemeier J, et al. Cardiovascular risk factors are associated with increased lesion burden and brain atrophy in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2016;87(2):181-187.
Weinstock-Guttman B, Zivadinov R, Horakova D, et al. Lipid profiles are associated with lesion formation over 24 months in interferon-β treated patients following the first demyelinating event. J Neurol Neurosurg Psychiatry. 2013;84(11):1186-1191.
Cox SR, Lyall DM, Ritchie SJ, et al. Associations between vascular risk factors and brain MRI indices in UK biobank. Eur Heart J. 2019;40(28):2290-2299.
Frischer JM, Weigand SD, Guo Y, et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann Neurol. 2015;78(5):710-721.
Fitzgerald KC, Salter A, Tyry T, Fox RJ, Cutter G, Marrie RA. Measures of general and abdominal obesity and disability severity in a large population of people with multiple sclerosis. Mult Scler J. 2020;26(8):976-986.
Bove R, Secor E, Healy BC, et al. Evaluation of an online platform for multiple sclerosis research: patient description, validation of severity scale, and exploration of BMI effects on disease course. PLoS One. 2013;8(3):e59707.
Manuel Escobar J, Cortese M, Edan G, et al. Body mass index as a predictor of MS activity and progression among participants in BENEFIT. Mult Scler J. 2021;28:1277-1285.
Mowry EM, Azevedo CJ, Mcculloch CE, et al. Body mass index, but not vitamin D status, is associated with brain volume change in MS. Neurology. 2018;91(24):E2256-E2264.
Ben-Zacharia AB, Janal MN, Brody AA, Wolinsky J, Lublin F, Cutter G. The effect of body mass index on brain volume and cognitive function in relapsing-remitting multiple sclerosis: a CombiRx secondary analysis. J Cent Nerv Syst Dis. 2021;13:117957352110421.
Filippatou AG, Lambe J, Sotirchos ES, et al. Association of body mass index with longitudinal rates of retinal atrophy in multiple sclerosis. Mult Scler J. 2020;26(7):843-854.
Janowitz D, Wittfeld K, Terock J, et al. Association between waist circumference and gray matter volume in 2344 individuals from two adult community-based samples. Neuroimage. 2015;122:149-157. doi:10.1016/j.neuroimage.2015.07.086
Pannacciulli N, Del Parigi A, Chen K, Le DSNT, Reiman EM, Tataranni PA. Brain abnormalities in human obesity: a voxel-based morphometric study. Neuroimage. 2006;31(4):1419-1425.
Raji CA, Ho AJ, Parikshak NN, et al. Brain structure and obesity. Hum Brain Mapp. 2010;31(3):353-364.
Taki Y, Kinomura S, Sato K, et al. Relationship between body mass index and gray matter volume in 1,428 healthy individuals. Obesity. 2008;16(1):119-124.
Ward MA, Carlsson CM, Trivedi MA, Sager MA, Johnson SC. The effect of body mass index on global brain volume in middle-aged adults: a cross sectional study. BMC Neurol. 2005;5:1-7.
Kurth F, Levitt JG, Phillips OR, et al. Relationships between gray matter, body mass index, and waist circumference in healthy adults. Hum Brain Mapp. 2013;34(7):1737-1746.
Yokum S, Ng J, Stice E. Relation of regional gray and white matter volumes to current BMI and future increases in BMI: a prospective MRI study. Int J Obes (Lond). 2012;36(5):656-664.
Bobb JF, Schwartz BS, Davatzikos C, Caffo B. Cross-sectional and longitudinal association of body mass index and brain volume. Hum Brain Mapp. 2014;35(1):75-88.
Tüngler A, Van der Auwera S, Wittfeld K, et al. Body mass index but not genetic risk is longitudinally associated with altered structural brain parameters. Sci Rep. 2021;11(1):24246. doi:10.1038/s41598-021-03343-3
Reia A, Petruzzo M, Falco F, et al. A retrospective exploratory analysis on cardiovascular risk and cognitive dysfunction in multiple sclerosis. Brain Sci. 2021;11(4):502.
Qiu C, Fratiglioni L. A major role for cardiovascular burden in age-related cognitive decline. Nat Rev Cardiol. 2015;12(5):267-277.
Gottesman RF, Schneider ALC, Zhou Y, et al. Association between midlife vascular risk factors and estimated brain amyloid deposition. JAMA. 2017;317(14):1443-1450.
Geraldes R, Esiri MM, Perera R, et al. Vascular disease and multiple sclerosis: a post-mortem study exploring their relationships. Brain. 2020;143(10):2998-3012.
Oesterle A, Laufs U, Liao JK. Pleiotropic effects of statins on the cardiovascular system. Circ Res. 2017;120(1):229-243.