Wellness factors are related to cerebral grey matter and clinical outcomes in Multiple Sclerosis
ECTRIMS Online Library. Katz Sand I. 10/27/17; 200656; P1001
Dr. Ilana Katz Sand
Dr. Ilana Katz Sand
Contributions
Abstract

Abstract: P1001

Type: Poster

Abstract Category: Pathology and pathogenesis of MS - 17 Environmental factors

Background: Prior research suggests in addition to genetic influences, there is a strong environmental component mediating onset and disease course in MS. There has been increasing interest in potential modifiable risk factors related to health and wellness, particularly among MS patients desiring to increase their involvement in maximizing health outcomes. Here we examine the relationships between several general health and wellness factors and imaging and clinical measures related to disability in MS.
Methods: Persons with early MS (duration < 5 years) underwent 3.0T 3D T1 MRI. FreeSurfer derived mean cortical thickness (CT) and normalized cerebral grey matter volume (nGM). Wellness metrics included: blood pressure, resting heart rate (HR), body mass index (BMI), cholesterol (CHOL; total, HDL, LDL), vitamin D, hemoglobin A1C, and sleep (hours per night). We assessed disability objectively with the MS Functional Composite (MSFC) and subjectively with the MS Impact Scale (MSIS). Separate regressions predicted CT, nGM, MSFC, and MSIS, with age, sex, and education (proxy for socioeconomic status) entered in block one, and wellness metrics in block two (stepwise: entry p=.05, removal p=.10).
Results: 105 subjects (87 RRMS, 18 CIS; 68 women; median EDSS 1.0) completed the assessments. Greater CT was linked to more sleep (rp=.307, p=.002), lower HR (rp=-.249, p=.013), and lower CHOL (total, rp=-.211, p=.035). Greater nGM was predicted by lower HR (rp=-.229, p=.020), with trends for more sleep (rp=.185, p=.065) and lower CHOL (total, rp=-.173, p=.083). Better MSFC was predicted by lower HR (rp=-.268, p=.011) and more sleep (rp=.263, p=.042). Lower MSIS was predicted by lower CHOL (LDL, rp=.269, p=.006). Note that BMI did not independently predict any outcome, although higher BMI was marginally related to lower CT, and worse MSFC and MSIS (Ps< .10) when considered alone. In addition, higher BMI was related to less sleep (r=-.248, p=.011) and higher CHOL (r=.363, p< .001).
Conclusion: General wellness factors may impact disability in MS. Hours of sleep, resting HR (proxy for physical fitness), and cholesterol (partial proxy for diet), are related to important imaging and clinical metrics in MS. Links between sleep, cholesterol, and BMI may partially explain the association of BMI with negative outcomes in MS. These results need to be confirmed with larger and longitudinal data sets and exploration of clinical consequences.
Disclosure:
Katz Sand: nothing to disclose
Fabian: nothing to disclose
Pelle: nothing to disclose
Gallo: nothing to disclose
Sumowski: nothing to disclose
Lewis: nothing to disclose

Abstract: P1001

Type: Poster

Abstract Category: Pathology and pathogenesis of MS - 17 Environmental factors

Background: Prior research suggests in addition to genetic influences, there is a strong environmental component mediating onset and disease course in MS. There has been increasing interest in potential modifiable risk factors related to health and wellness, particularly among MS patients desiring to increase their involvement in maximizing health outcomes. Here we examine the relationships between several general health and wellness factors and imaging and clinical measures related to disability in MS.
Methods: Persons with early MS (duration < 5 years) underwent 3.0T 3D T1 MRI. FreeSurfer derived mean cortical thickness (CT) and normalized cerebral grey matter volume (nGM). Wellness metrics included: blood pressure, resting heart rate (HR), body mass index (BMI), cholesterol (CHOL; total, HDL, LDL), vitamin D, hemoglobin A1C, and sleep (hours per night). We assessed disability objectively with the MS Functional Composite (MSFC) and subjectively with the MS Impact Scale (MSIS). Separate regressions predicted CT, nGM, MSFC, and MSIS, with age, sex, and education (proxy for socioeconomic status) entered in block one, and wellness metrics in block two (stepwise: entry p=.05, removal p=.10).
Results: 105 subjects (87 RRMS, 18 CIS; 68 women; median EDSS 1.0) completed the assessments. Greater CT was linked to more sleep (rp=.307, p=.002), lower HR (rp=-.249, p=.013), and lower CHOL (total, rp=-.211, p=.035). Greater nGM was predicted by lower HR (rp=-.229, p=.020), with trends for more sleep (rp=.185, p=.065) and lower CHOL (total, rp=-.173, p=.083). Better MSFC was predicted by lower HR (rp=-.268, p=.011) and more sleep (rp=.263, p=.042). Lower MSIS was predicted by lower CHOL (LDL, rp=.269, p=.006). Note that BMI did not independently predict any outcome, although higher BMI was marginally related to lower CT, and worse MSFC and MSIS (Ps< .10) when considered alone. In addition, higher BMI was related to less sleep (r=-.248, p=.011) and higher CHOL (r=.363, p< .001).
Conclusion: General wellness factors may impact disability in MS. Hours of sleep, resting HR (proxy for physical fitness), and cholesterol (partial proxy for diet), are related to important imaging and clinical metrics in MS. Links between sleep, cholesterol, and BMI may partially explain the association of BMI with negative outcomes in MS. These results need to be confirmed with larger and longitudinal data sets and exploration of clinical consequences.
Disclosure:
Katz Sand: nothing to disclose
Fabian: nothing to disclose
Pelle: nothing to disclose
Gallo: nothing to disclose
Sumowski: nothing to disclose
Lewis: nothing to disclose

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