Взаимосвязь между БМИ и когнитивом, инвалидностью и качеством жизненных показателей при множественном склерозе: вторичный анализ DISCOMS.

Introduction

Multiple sclerosis (MS) is a chronic autoimmune, inflammatory, and neurodegenerative disorder of the central nervous system that leads to disability, cognitive decline, and emotional challenges. 1 Cognitive dysfunction, fatigue, depression, anxiety, and motor impairments reduce quality of life (QoL) for many patients with chronic illnesses. 2 While both genetic and environmental factors contribute to MS, modifiable risks such as body mass index (BMI) are receiving increased attention. 3 , 4 The role of BMI remains debated: some studies suggest it influences disease progression, while others show no effect. Obesity—affecting up to 70% of MS patients in some populations—has been linked to systemic inflammation and metabolic dysfunction, which may worsen neurodegeneration and disease burden. 5 – 7

Findings on BMI and MS outcomes remain inconsistent. Some reviews and randomized controlled trials (RCTs) associate higher BMI with disability progression, lesion burden, and cognitive dysfunction, while central obesity may correlate more strongly with disability than BMI alone. 8 , 9 Weight loss interventions have shown benefits for fatigue and symptom management, 9 yet other studies report no significant association between BMI and fatigue when controlling for treatment and activity levels. 10 – 12 Emerging evidence suggests that higher BMI may also affect treatment response in MS 13 and greater disability worsening. 14 These mixed results underscore the need for further research to clarify BMI’s role in MS progression and treatment outcomes.

The revised Scaffolding Theory of Aging and Cognition (STAC-R) guided this study. 1 The STAC-R consists of a model linking lifestyle activities, biological factors, cognition, and brain volume, depicting life course experiences that may enrich or deplete neuronal functions. The scaffolding model suggests that individuals with MS who accrue multiple neural insults throughout the course of their illness will exhibit loss of brain volume and poor cognitive function. 15 Hence, we hypothesized that high BMI may accelerate cognitive dysfunction and negative QoL markers. 2 This secondary analysis study aimed to investigate the association between BMI and cognitive function, disability progression, and QoL markers, including fatigue, sleep, depression, anxiety, and upper and lower extremity function, in individuals with MS.

Methods

Design: Parent trial

This secondary analysis used data from the DISCOMS trial, 1 a phase III/IV multicenter randomized controlled, rater-blinded trial sponsored by PCORI. Individuals with MS of any subtype, aged ⩾55 years, relapse-free for 5 years, and without new MRI lesions in 3 years while on continuous disease-modifying therapy (DMT) were recruited at 19 US MS centers. 1 Participants were randomized 1:1 within site via interactive response technology to continue or discontinue therapy. Relapse assessors and MRI readers were blinded; patients and treating investigators were not. The primary outcome was the percentage with a new disease event (relapse and/or new or enlarging T2 MRI lesion) over 2 years. The trial tested whether discontinuation was noninferior to continuation using intention-to-treat analysis with an 8% noninferiority margin. 1 The trial was registered at ClinicalTrials.gov ( NCT03073603 ) and approved by all institutional review boards (IRB). Written informed consent was obtained within 30 days before randomization.

Design: Secondary analysis study

A cross-sectional design was selected over a longitudinal approach due to significant heterogeneity in treatment exposure within the study population. The hypothesis of this secondary analysis was that there was an unfavorable association between high BMI and cognitive function, disability, and QoL markers as measured by valid and reliable instruments. A total of 259 patients completed the 2 years of the parent study and 253 patients were included in this secondary analysis, as they had baseline BMI readings, cognitive testing, disability scores, and QoL markers. There were no exclusion criteria in this secondary data analysis. This manuscript followed the STROBE guidelines.

Ethical approval

This secondary analysis study received IRB approval through the City University of New York (CUNY), Hunter College, ID: 2025-0157. This secondary data analysis project was determined to be a project that did not meet the CUNY human research protection program definition of research with human subjects. The ethical approval for this secondary analysis data was provided on 3/4/2025.

Study measures

Body mass index

BMI was calculated as weight in kilograms divided by height in meters squared. It was analyzed as both a continuous variable and categorical groups: normal (<25 kg/m 2 ), overweight (25–29.99 kg/m 2 ), obese (30–34.99 kg/m 2 ), and very obese (⩾35 kg/m 2 ). Continuous analysis detects linear associations, while categories identify threshold effects, allow comparison with prior studies, and improve clinical interpretability through alignment with public health guidelines.

Symbol digit modalities test

The symbol digit modalities test (SDMT) measures information processing speed 3 and is a sensitive but nonspecific indicator of cognitive decline in MS. 4 Low scores show decline without defining the deficit. 5 Widely used for its sensitivity, simplicity, and patient-friendliness, the SDMT was analyzed as both a continuous and dichotomous variable with a cutoff of 40. Categorizing continuous variables enhances interpretation, supports diagnostic and therapeutic decisions, and aligns care with standardized protocols. 6

The Expanded Disability Status Scale and the Patient-Determined Disease Steps

The Expanded Disability Status Scale (EDSS) is a clinician-assessed measure and the patient-determined disease steps (PDDS) a patient-reported measure of disability, both commonly used in MS. They were employed in the parent DISCOMS trial and this secondary analysis to evaluate disability. The EDSS ranges from 0 (normal) to 10 (death due to MS), based on eight functional systems with emphasis on ambulation, though it is less sensitive to nonmotor symptoms and progresses nonlinearly. 7 , 8 The PDDS, ranging from 0 to 8, assesses walking and mobility aids and correlates strongly with EDSS for ambulatory function. 9 , 10 Together, these scales provide complementary clinician- and patient-centered perspectives on disability in MS.

Quality of life in neurological disorders

The Quality of Life in Neurological Disorders (Neuro-QoL) was used in the parent study to assess physical, emotional, cognitive, and social effects in adults with neurological conditions. 11 It is a self-report tool covering 17 domains and sub-domains. 12 QoL markers included fatigue, sleep, anxiety, depression, affect, general concern, emotional behavior, communication, social ability, extremity function, executive function, and satisfaction. 11 – 13 Higher scores in anxiety, depression, fatigue, dyscontrol, and sleep disturbance indicated worse outcomes, while higher scores in mobility, cognition, affect, social participation, satisfaction, and communication indicated better outcomes. This study assessed four domains adapted from NIH 14 : physical (upper/lower extremity function, fatigue, sleep), emotional (affect, anxiety, depression, dyscontrol), cognitive (executive function, applied cognition, communication), and social (participation and satisfaction with social roles).

Data collection

Data for this secondary analysis were derived from a parent RCT in which participants were enrolled and followed from May 22, 2017, and February 3, 2020. Data for this study included those collected from patients with MS with available parameters of BMI, cognitive function as measured by the SDMT, disability scores as measured by the EDSS and PDDS, and QoL markers from the parent study. No data collection was performed in this secondary analysis study.

The inclusion criteria of the parent trial were age ⩾55 years; MS confirmed by 2010 McDonald criteria; any subtype; relapse- and MRI lesion-free for ⩾3 years; and ⩾5 years of continuous FDA-approved DMT, with ⩾2 years on the current therapy. Exclusion criteria included use of non-FDA-approved MS therapies in the past 5 years (rituximab, cyclophosphamide, methotrexate, cyclosporine), any use of siponimod or cladribine, prior use of mitoxantrone or alemtuzumab, and systemic steroid use for MS or >1 course for any reason in the past 5 years.

Data analysis

Descriptive statistics were used to characterize the study sample and summarize participant demographic and clinical characteristics at baseline. Continuous variables were described using means and standard deviations, whereas categorical variables were summarized using frequencies and percentages. These descriptive analyses provided an overview of the sample characteristics, including age, sex, race/ethnicity, BMI, DMT type and duration, MS phenotype, disability measures, and cognitive function. Covariates (age, sex, and DMT duration) were selected a priori based on their established relevance to MS outcomes and were retained in all models to control for potential confounding while minimizing overfitting given the study sample size. Outcome measures, including cognitive function, disability, and QoL indicators, were also specified a priori based on the study objectives and hypotheses. Exploratory analyses suggested that the linear relationships between BMI and cognitive function, disability, and QoL measures were generally mild and not strictly linear across all outcomes. Nevertheless, the observed patterns were considered sufficient to support the planned different regression analyses. Statistical significance was defined as a two-sided p -value <0.05.

QoL analyses

Associations between BMI and QoL outcomes were examined using Multivariate General Linear Models (MGLMs). Separate MGLMs were conducted for the physical, emotional, cognitive, and social Neuro-QoL domains, with BMI modeled as both a continuous and categorical independent variable. Within each model, multiple correlated Neuro-QoL measures were entered simultaneously as dependent variables, and analyses were adjusted for age, sex, and DMT duration at baseline. The multivariate approach was selected to account for correlations among outcomes within each domain and to reduce Type I error associated with multiple univariate tests.

Cognitive function and disability analyses

To evaluate associations between BMI and individual clinical outcomes, separate multivariable regression models were performed with cognitive function and disability as dependent variables. Cognitive function was assessed using the SDMT, and disability was assessed using the EDSS and PDDS. BMI was modeled as both a continuous and categorical independent variable, and all models were adjusted for age, sex, and DMT duration. Multivariable regression was selected for these analyses because each outcome was examined individually rather than as part of a correlated set of outcomes. To further evaluate the association between BMI and cognitive impairment, a multivariable logistic regression model was conducted with dichotomized SDMT status (SDMT ⩽40 indicating cognitive impairment vs >40) as the dependent variable and BMI (continuous and categorical) as the independent variable, adjusting for age, sex, and DMT duration. Lastly, to further evaluate the association between BMI and EDSS (a disability marker used in most clinical trials), a multivariable logistic regression model was conducted with a dichotomized EDSS categorical variable (EDSS ⩽4 indicating mild disability vs EDSS >4 indicating moderate-severe disability), and BMI (continuous and categorical) as the independent variable, adjusting for age, sex, and DMT duration.

Missing data

Missing data were minimal in the primary study dataset, with nearly complete data available for the variables included in the analyses. Given the very low proportion of missing observations, the potential for bias due to missing data was considered negligible, and no imputation procedures were deemed necessary.

Results

This study included 253 participants, mostly female (83.8%) and white (89.3%). Their mean BMI was 27.27 (SD = 5.6), with a range of 16.74–53.69, and 62% were overweight, obese, or very obese. Other demographics are described in Table 1 . Descriptive statistics of the QoL variables are displayed in Table 2 .

BMI as a continuous variable was shown to be associated with the physical domains in the MGLM analysis; higher BMI was associated with higher fatigue and higher BMI was associated with worsening lower extremities function ( Table 3 ). BMI as a continuous variable was not associated with the cognitive, emotional, and social domains as measured by the Neuro-QoL instrument ( Table 3 ).

BMI as a continuous independent variable was not associated with cognition or disability markers in the multivariable regression analysis; BMI as a continuous variable was not associated with cognitive function as measured by the SDMT as a continuous variable ( p = 0.729) or SDMT as a categorical dichotomous variable ( p = 0.517). Additionally, BMI as a continuous variable was not associated with disability as measured by the EDSS ( p = 0.076), and by the PDDS ( p = 0.055). However, a logistic regression evaluating the association between dichotomized EDSS with a cutoff of 4 and BMI as a continuous independent variable showed statistical association between them; higher BMI was associated with greater disability (EDSS > 4). Specifically, each 1-unit increase in BMI was associated with a 5.7% increase in the odds of having an EDSS score greater than 4 (β = 0.055, p = 0.045).

MGLM analyses of associations of baseline cognitive and social QoL domains with BMI as a four categorical variable (normal/reference group, overweight, obese, and very obese groups) were performed controlling for age, gender, and DMT duration did not reveal any statistically significant results. MGLM analysis of the relationship between baseline physical domain QoL markers using BMI as a categorical variable demonstrated statistical findings. Normal BMI (reference group) as compared to very obese BMI was significantly associated with QoL physical domain symptoms including fatigue (beta coefficient −6.892), sleep disturbance (beta coefficient −4.530), and lower extremities dysfunction (beta coefficient 5.161); thus, the very obese group had higher fatigue, higher sleep disturbance, and reduced lower extremities function ( Table 4 ). In addition, overweight patients with MS as compared to those with normal BMI had higher fatigue (beta coefficient −2.800; Table 4 ).

MGLM analysis of the relationship between baseline emotional domain QoL markers using BMI as a categorical variable demonstrated statistical findings. Normal BMI (reference group) as compared to very obese BMI was associated with QoL emotional domain symptoms, depression, and anxiety; thus, the very obese group had higher risk of emotional domain symptoms, depression (beta coefficient −4.478), and anxiety (beta coefficient −4.097; Table 5 ).

Multivariable regression analysis of BMI as a categorical variable controlling for age, gender, and DMT duration did not show statistical findings for cognitive function as measured by the SDMT and disability as measured by the EDSS or PDDS. The overweight, obese, and very obese BMI categories versus normal BMI did not show any association with cognitive function as measured by the SDMT as a continuous in the multivariable regression analysis or as a dichotomous categorical variable in the logistic regression analysis controlling for age, gender, and DMT duration.

Discussion

Most participants in our study had a BMI classified as overweight or obese or very obese as demonstrated in previous studies. 3 , 4 Conway et al. 4 and Taylor et al. 3 reported that overweight and obesity are highly prevalent among people with MS, affecting more than half of this population nationally. Although numerous studies have investigated the association between BMI and cognitive function in MS, findings have been inconsistent. In our sample of patients aged 55 and older—a demographic known to be more vulnerable to cognitive decline—we found no association between BMI and cognitive function as measured by both the SDMT and the Neuro-QOL cognitive domain. This outcome is particularly noteworthy given the increased risk of cognitive impairment in later life and the conflicting evidence in prior literature. Our findings indicate that BMI does not seem to be associated with cognitive dysfunction in this DISCOMS cohort, consisted of older patients with stable MS disease.

Notably, despite SDMT being a widely used and sensitive tool for measuring information processing speed—a domain frequently impaired in MS—its lack of specificity for this population 5 , 6 may partly account for our results. Other studies have similarly reported no association between BMI and cognitive function, such as those conducted by Ben-Zacharia et al., 16 Galioto et al., 17 and Sandroff and Hubbard. 18 In particular, Galioto et al. 17 examined 8713 MS patients and found no association between BMI (treated as a continuous variable) and the processing speed test (PST), nor differences in PST scores across BMI categories. These large-scale studies reinforce our findings that processing speed cognitive impairments in MS patients are not necessarily attributable to BMI. Conversely, other investigations have found that higher BMI correlated with lower SDMT scores and poorer cognitive function, suggesting a possible deleterious effect of obesity on cognition in MS. 7 – 9 For example, Owji et al. 7 reported a significant negative relationship between BMI and cognitive performance as measured by the PASAT, while controlling for sex, age, disease duration, and EDSS, but no such association using SDMT. This inconsistency in findings might be due to variations in the cognitive tests applied, sample characteristics, or methodological designs. Our study’s lack of association may stem from the restricted age range (⩾55 years) or insufficient power to detect small effect sizes, reflecting a limitation that should be considered in interpreting the results.

Regarding disability, our study found no association between BMI and functional impairment, whether measured by clinician-rated EDSS scores or patient-reported PDDS scores in older patients with stable MS disease; however, when disability was examined using a clinically meaningful dichotomized EDSS cutoff, higher BMI was associated with greater odds of moderate-to-severe disability (EDSS >4), suggesting that the relationship between BMI and disability may be more apparent at higher levels of disability burden. These results highlight the complexity and controversy linked to BMI effects in MS. Multiple studies have identified a positive correlation between BMI and clinical disability scores, including Lutfullin et al., 19 Stampanoni Bassi et al., 20 and Van Hijfte et al. 21 In these studies, higher BMI was linked to elevated EDSS scores, increased relapse frequency, and greater systemic inflammation pointing to a biologically plausible pathway by which obesity could exacerbate MS pathology. Lutfullin et al. 19 further showed that obesity at disease onset was associated with higher disability at baseline and during follow-up up to 6 years, with a significantly increased risk of reaching EDSS score 3 in patients with BMI ⩾30 kg/m 2 . Other research corroborates our negative multivariable regression results, indicating no meaningful link between BMI and disability. Livne-Margolin et al. 22 found that disability severity did not correlate with obesity, disease duration, or lifestyle factors. Similarly, Giannopapas et al. 23 conducted a systematic review and meta-analysis of 16 studies and revealed a significant bidirectional association between waist circumference and EDSS score, but no statistical correlation between BMI and EDSS, reinforcing the notion that BMI may not adequately capture adiposity’s effects on neurological disability in MS.

A salient area where our study did show significant results was the association between BMI and physical and emotional domains. We identified BMI as being associated with fatigue, sleep disturbance, lower extremity dysfunction, anxiety, and depression—even when controlling for age, gender, and duration of DMT. These findings align with a growing body of literature indicating that excess body weight contributes to physical and psychological impairments in MS, most likely through mechanisms involving systemic inflammation, metabolic dysfunction, and heightened energy demands for movement. 10 – 13 Obesity has also been linked to reduced health-related QoL, accelerated disease progression, and cognitive deterioration. 11 , 14 , 15 , 24 , 25 Furthermore, obesity is a recognized risk factor for sleep disturbances, 12 , 19 which are common in MS and contribute to daytime fatigue and poorer health outcomes. However, these relationships are not uniformly reported; Yazdchi et al. 26 found no significant correlation between sleep quality and BMI emphasizing the need for further research.

Conclusively, our findings reinforce the complex and multifaceted role of BMI in MS. While we found no association between BMI as a solo measure of adiposity and cognition as measured by the SDMT or disability as measured by the EDSS and the PDDS, the physical and emotional consequences of higher BMI were clearly evident. These results underscore the clinical importance of integrating weight management strategies, early screening, and tailored interventions—such as resistance training and behavioral therapies—to support better physical functioning, emotional resilience, and overall well-being in individuals living with MS. Addressing modifiable risk factors such as obesity may offer substantial benefits for QoL, even when traditional cognitive and neurological metrics show no association among older patients with stable MS disease.

Strengths and limitations

A key strength of our secondary analysis lies in its foundation on the DISCOMS trial—a rigorously designed, multicenter, randomized, controlled, rater-blinded, phase III/IV noninferiority study conducted over 2 years across 19 MS centers in the United States. The parent trial’s robust methodology, including strict eligibility criteria (age ⩾55, stable disease activity, continuous DMT use), and masked outcome assessments, enhances the reliability and generalizability of our findings. Thus, the results of our study are most generalizable to patients with similar demographic and clinical profiles, particularly those with stable disease and characteristics consistent with the DISCOMS population. This well-characterized cohort allowed for a focused exploration of the relationship between BMI and cognitive function, disability, and QoL markers in older adults with MS.

This secondary data analysis has a few limitations that must be acknowledged. A cross-sectional design was selected over a longitudinal approach due to significant heterogeneity in treatment exposure within the study population. Therefore, a cross-sectional analysis was deemed the most appropriate design to preserve internal validity and avoid biased estimates of treatment effects. However, this approach represents a limitation, as the cross-sectional design precludes assessment of temporal relationships and limits the ability to evaluate changes over time or infer causality between treatment exposure and outcomes. In addition, we did not adjust for multiple testing, which is acknowledged as a limitation of this exploratory study. The primary consequence is an increased risk of Type I error, potentially leading to the identification of more statistically significant associations than would be observed with a multiplicity adjustment. Furthermore, the lack of an association between BMI and the outcome variables should not be interpreted as evidence that no association exists. Instead, the results indicate insufficient evidence to reject the null hypothesis. Although a small association may have gone undetected, the observed effect sizes suggest that any true association is likely to be minimal.

In addition, the MRI data used in the primary trial were binary in nature—indicating presence or absence of disease activity—without quantitative metrics such as brain volume, lesion burden, or spinal cord involvement, which could have provided deeper insights into neurodegenerative processes. Furthermore, while cognitive function was assessed using validated screening tools, a comprehensive neuropsychological battery such as the minimal assessment of cognitive function in MS would have offered a more robust evaluation across multiple cognitive domains. Notably, BMI is a practical surrogate marker, not a precise measure of metabolically relevant fat burden, as it does not account for variations in body composition such as fat distribution, or mobility-related changes that are highly relevant in this population. 27 – 30 Despite these constraints, the study contributes meaningful evidence to the ongoing discourse on BMI’s role in MS outcomes, particularly in an aging MS cohort with stable disease.

Conclusion

The relationship between BMI, cognition, disability, and QoL in MS remains complex and inconsistent. In this older, stable DISCOMS cohort, BMI was more closely linked to selected physical and emotional QoL outcomes than to the measured cognition or disability indices. The present findings contribute to the growing body of literature examining the role of BMI in MS and underscore the importance of considering QoL as a multidimensional outcome. While BMI remains a practical and widely used measure in clinical and research settings, it does not distinguish between fat mass, lean body mass, or body fat distribution, all of which may have distinct implications for neurological and functional outcomes. Thus, future studies should incorporate more precise measures of adiposity and body composition to better characterize the relationship between body composition and MS outcomes.

Furthermore, longitudinal investigations are needed to clarify the temporal and potentially bidirectional relationships among BMI and other variables.

Acknowledgments

The authors thank all patients with MS that participated in the primary DISCOMS study.

Footnotes

ORCID iDs: Aliza Bitton Ben-Zacharia https://orcid.org/0000-0002-3174-8244

Gary Cutter https://orcid.org/0000-0002-8455-980X

Contributor Information

Aliza Bitton Ben-Zacharia, Graduate Program, Hunter College Bellevue School of Nursing, 60 West Street, New York, NY 10006, USA; Mount Sinai Phillips School of Nursing, Mount Sinai Hospital, 148 East 126th Street, New York, NY 10035, USA.

Gary Cutter, UAB School of Public Health, Birmingham, AL, USA.

Alya Shor, Hunter College Bellevue School of Nursing, New York, NY, USA.

Charity Morgan, Department of Biostatistics, The University of Alabama at Birmingham, Birmingham, AL, USA.

Eric Engebretson, Department of Neurology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

John Corboy, Neurology, University of Colorado School of Medicine, Rocky Mountain MS Center at the University of Colorado, Aurora, CO, USA; Department of Neurology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Shiela M. Strauss, Hunter College Bellevue School of Nursing, New York, NY, USA

Declarations

Ethics approval and consent to participate: This secondary analysis study received Institutional Review Board (IRB) approval through the City University of New York (CUNY), Hunter College, ID: 2025-0157. This secondary data analysis project was determined to be a project that did not meet the CUNY human research protection program (HRPP) definition of research with human subjects. The ethical approval for this secondary analysis data was provided on 3/4/2025.

Consent to participate: This study involves the secondary analysis of existing data. All data obtained from the primary study/source has been thoroughly de-identified and stripped of all direct and indirect identifiers prior to receipt by the research team. The investigator cannot readily ascertain the identity of the individuals, and there is no plan to re-identify the subjects. Data will be stored on secure, password-protected servers in accordance with institutional data security policies.

Consent for publication: Not applicable.

Author contributions: Aliza Bitton Ben-Zacharia: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing.

Gary Cutter: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Resources; Software; Validation; Visualization; Writing – original draft; Writing – review & editing.

Alya Shor: Conceptualization; Data curation; Investigation; Methodology; Resources; Visualization; Writing – original draft; Writing – review & editing.

Charity Morgan: Conceptualization; Data curation; Investigation; Methodology; Resources; Validation; Visualization; Writing – original draft; Writing – review & editing.

Eric Engebretson: Conceptualization; Data curation; Investigation; Methodology; Resources; Visualization; Writing – original draft; Writing – review & editing.

John Corboy: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Resources; Visualization; Writing – original draft; Writing – review & editing.

Shiela M. Strauss: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Resources; Visualization; Writing – original draft; Writing – review & editing.

Funding: The authors received no financial support for the research, authorship, and/or publication of this article.

The authors declare that there is no conflict of interest.

Availability of data and materials: Data and materials are available upon request.

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