Social network structure is a novel protective factor related to cognitive reserve in MS
Author(s): ,
S. Levin
Affiliations:
Department of Neurology, Columbia University Medical Center, New York, NY
,
A. Dhand
Affiliations:
Department of Neurology, Brigham and Women`s Hospital, Boston, MA, United States
,
C. Riley
Affiliations:
Department of Neurology, Columbia University Medical Center, New York, NY
,
P. De Jager
Affiliations:
Department of Neurology, Columbia University Medical Center, New York, NY
V. Leavitt
Affiliations:
Department of Neurology, Columbia University Medical Center, New York, NY
ECTRIMS Online Library. Levin S. 10/11/18; 228547; P704
Seth Levin
Seth Levin
Contributions
Abstract

Abstract: P704

Type: Poster Sessions

Abstract Category: Clinical aspects of MS - Clinical assessment tools

Introduction: The social network (SN), a model of dynamic interrelationships among individuals, is an emerging determinant of health in neurologic disorders. Strong SN structures promote cognitive resilience in normal aging as well as dementia. Here, we present the first study exploring SN as a protective factor for cognition in multiple sclerosis (MS).
Methods: Fifty MS patients completed SN surveys. One patient was excluded for having a SN of one. Exploratory k-means cluster analysis (2 clusters pre-defined), performed by entering six SN variables (size, max degree, mean degree, density, effective size, constraint), yielded one cluster (n=35) reflecting a protective SN structure: larger SN size, higher effective size, lower constraint, lower density. These features are associated with better health outcomes in neurologic disease. The smaller cluster (n=14) was characterized by an at-risk SN structure: smaller SN size, smaller effective size, higher constraint, higher density. Group differences in the following variables were evaluated: age, sex, education, IQ, EDSS, T2 lesion volume, atrophy (whole brain and regional), cognition (memory, SDMT), fine motor function, depression, fatigue, and leisure activity participation across three domains: cognitive, social, exercise. Binomial regression to predict group membership was conducted. Finally, using principal components analysis, a SN factor was derived and its association to variables of interest was evaluated.
Results: The at-risk SN group had worse memory (p=.005), lower IQ (p< .001), higher EDSS (p< .001), and larger decline in exercise (p=.001) and social activity participation (p< .001). Predicting SN group using binomial regression and entering IQ in step 1, the independent contribution of memory remained significant at trend level (p =.072). Using SN factor, lower SN was associated with worse memory (p=.018), lower IQ (p< .001), and greater decrease in exercise (p=.037) and social activities (p=.002).
Conclusion: We have identified a pattern of protective SN characteristics in MS related to better memory function and maintenance of leisure activity participation. These results suggest an underutilized and potentially modifiable lifestyle factor contributing to cognitive reserve. SN is a promising protective factor that may be a useful outcome variable for future clinical trials of cognitive treatments in MS.
Disclosure: Seth Levin: nothing to disclose
Amar Dhand: nothing to disclose
Claire Riley: Advisory board compensation from Teva; paid editorial contributions from Elsevier
Philip De Jager: Member of advisory board at Celgene, Roche, Sanofi/Genzyme; sponsored research from Eisai, Roche, Biogen, and Lundbeck; Speaker honorarium from GlaxoSmithKline
Victoria Leavitt: Receives consulting fees from Healios, Inc.

Abstract: P704

Type: Poster Sessions

Abstract Category: Clinical aspects of MS - Clinical assessment tools

Introduction: The social network (SN), a model of dynamic interrelationships among individuals, is an emerging determinant of health in neurologic disorders. Strong SN structures promote cognitive resilience in normal aging as well as dementia. Here, we present the first study exploring SN as a protective factor for cognition in multiple sclerosis (MS).
Methods: Fifty MS patients completed SN surveys. One patient was excluded for having a SN of one. Exploratory k-means cluster analysis (2 clusters pre-defined), performed by entering six SN variables (size, max degree, mean degree, density, effective size, constraint), yielded one cluster (n=35) reflecting a protective SN structure: larger SN size, higher effective size, lower constraint, lower density. These features are associated with better health outcomes in neurologic disease. The smaller cluster (n=14) was characterized by an at-risk SN structure: smaller SN size, smaller effective size, higher constraint, higher density. Group differences in the following variables were evaluated: age, sex, education, IQ, EDSS, T2 lesion volume, atrophy (whole brain and regional), cognition (memory, SDMT), fine motor function, depression, fatigue, and leisure activity participation across three domains: cognitive, social, exercise. Binomial regression to predict group membership was conducted. Finally, using principal components analysis, a SN factor was derived and its association to variables of interest was evaluated.
Results: The at-risk SN group had worse memory (p=.005), lower IQ (p< .001), higher EDSS (p< .001), and larger decline in exercise (p=.001) and social activity participation (p< .001). Predicting SN group using binomial regression and entering IQ in step 1, the independent contribution of memory remained significant at trend level (p =.072). Using SN factor, lower SN was associated with worse memory (p=.018), lower IQ (p< .001), and greater decrease in exercise (p=.037) and social activities (p=.002).
Conclusion: We have identified a pattern of protective SN characteristics in MS related to better memory function and maintenance of leisure activity participation. These results suggest an underutilized and potentially modifiable lifestyle factor contributing to cognitive reserve. SN is a promising protective factor that may be a useful outcome variable for future clinical trials of cognitive treatments in MS.
Disclosure: Seth Levin: nothing to disclose
Amar Dhand: nothing to disclose
Claire Riley: Advisory board compensation from Teva; paid editorial contributions from Elsevier
Philip De Jager: Member of advisory board at Celgene, Roche, Sanofi/Genzyme; sponsored research from Eisai, Roche, Biogen, and Lundbeck; Speaker honorarium from GlaxoSmithKline
Victoria Leavitt: Receives consulting fees from Healios, Inc.

By clicking “Accept Terms & all Cookies” or by continuing to browse, you agree to the storing of third-party cookies on your device to enhance your user experience and agree to the user terms and conditions of this learning management system (LMS).

Cookie Settings
Accept Terms & all Cookies