Multiple sclerosis (MS) is a progressive neurodegenerative autoimmune disease that affects nearly 1 million adults in the United States (1). The demyelination, inflammation, and neuronal loss of the condition leave adults with MS to face a myriad of physical and cognitive symptoms, including neuropathic and musculoskeletal pain, muscle weakness, fatigue, and depression (2, 3). Researchers have also found that approximately half of this population also has overweight or obesity (4), and this may speed MS-related disability progression.

Obesity may affect MS disease progression in two ways. First, it directly contributes to low-grade systemic inflammation and neuroinflammation through the release of pro-inflammatory cytokines and adipokines including TNF-α, IL-6, and leptin (5–8). This inflammation is associated with cognitive dysfunction and physical disability (9–11). Obesity also indirectly impacts MS by increasing the risks of other cardiometabolic conditions including hypertension and type 2 diabetes, which are also inflammatory and are associated with MS progression and disability.

Higher fat mass has been linked to lower brain volume in otherwise healthy adults at midlife (4). In adults with neurodegenerative diseases such as MS, Alzheimer’s disease (AD), and Parkinson’s disease (PD), increased release of pro-inflammatory cytokines and insulin resistance originating from excess adiposity have been correlated with an increased rate of disease progression and hospitalization (10, 11). Rather than a simple measure of total fat mass, specific depots appear to drive the deleterious influence of excess adiposity on health, with visceral and ectopic adipose deposition being particularly harmful for brain health.

Visceral adipose tissue (VAT) has been correlated with lower brain volume in adults at midlife, which puts those with neurodegenerative diseases at an increased risk of disease progression (12). One systematic review focused on associations between regional adiposity, cognitive function, and dementia-related brain changes showed links between abdominal fat depots and brain health (9). Results from 33 clinical trials based on adults from the general population showed that higher VAT and hepatic fat were associated with cognitive decline; however, to our knowledge, no research has been published on measuring VAT directly in adults with MS. This suggests that greater emphasis on measuring specific adipose tissue depots may need to be considered in research focused on disease management in adults with MS, who are already at greater risk of cognitive decline.

Despite a growing recognition of the impacts of excess adiposity on neurodegeneration, most research in MS does not include the rigorous measures of body composition needed to fully explore the mechanisms through which the amount and location of adipose tissue may lead to increased disease activity. The objectives of this mini review are to briefly discuss the measures of adiposity frequently used in current MS research, highlight key benefits and limitations of these methodologies, and present the benefits of combining indirect and direct techniques.

Anthropometric measures, such as body mass index (BMI), waist circumference, waist to height ratio (WHtR) and waist to hip ratio (WHR), are frequently used as measures of obesity and cardiovascular disease risk in current MS research. These can be helpful estimates of obesity when used in combination. For example, BMI and waist circumference have both been inversely associated with cognitive performance (11). BMI is the most widely used clinical assessment for obesity at a population level and classifies an individual as underweight (BMI < 18.5), normal weight (BMI = 18.5–24.9), overweight (BMI = 25.0–29.9), or one of three classes of obesity (BMI ≥ 30.0) (11). BMI relies on a weight to height ratio (weight (kg)/height(m)²) to estimate excess adiposity but lacks the ability to determine differences between fat mass and lean mass or account for fat distribution (11, 13, 14). Research has shown that adults with MS have more fat mass when compared to BMI-matched healthy controls. According to one cross-sectional study comparing BMI to dual-energy X-ray absorptiometry (DXA) measures of lean and fat mass in adults with MS and non-MS controls, BMI was associated with an underestimation of adiposity and overestimation of lean mass in adults with MS (15). Recent reviews of clinical research in MS suggest that fat distribution is linked with disability, but many studies limit measures of adiposity to BMI. Few clinical studies followed up with more extensive body composition techniques to distinguish between specific adipose tissue depots (16, 17).

Waist circumference, waist to height ratio, and waist to hip ratio are also used in clinical practice and research to assess cardiometabolic health risk. Similar to BMI, waist circumference and waist-to-hip ratio cannot distinguish between fat and lean mass but have the advantage of being directly associated with central obesity. For example, using waist circumference as a risk assessment for VAT has been well-documented in healthy populations, and researchers have highlighted it as a more effective tool for determining adipose-related disease risk than BMI alone (18). A recent systemic review and meta-analysis conducted by Giannopapas et al. (19) addressed the clinical use of waist circumference in combination with BMI to predict disability progression in MS. Outcomes from their research indicate that using these measures in combination creates a stronger predictor of cardiovascular risk in adults with MS than using either in isolation (19). As this research highlights, combining these two measurement tools could provide a more holistic estimation of how adiposity may be distributed to the visceral depot and lead to worsening health outcomes.

Using anthropometric measures may be a practical proxy measure for determining obesity, but incorporating body composition analyses is needed to understand the mechanisms through which excess adiposity impacts MS. Bioelectrical impedance (BIA) has been used in clinical research as an accessible technique for measuring fat mass in various populations with good correlation between it and DXA, but little research has addressed BIA in relation to nutritional status in adults with MS (20, 21). BIA provides a measurement of fat mass and fat free mass using low-volt electrical currents with monitored levels of impedance (22). Higher impedance reflects higher fat mass, and lower resistance suggests greater fat-free mass. However, this tool is heavily influenced by hydration status and could provide inaccurate measures due to factors such as age and water intake. While multi-frequency BIA devices offer more reliability than traditional, single-frequency BIA devices (23), hydration status may still skew interpretations of lean mass and fat mass in the general population (24–26). Research in MS has similar findings. For example, a recent study using BIA for fat mass evaluation in adults with MS noted that overestimations of fat free mas and underestimations of fat mass could occur due to changes in hydration status accompanying glucocorticoid therapy, a common treatment in MS (27). These medications cause water and sodium retention, which contributes to increased soft tissue hydration and potentially leads to misinterpretations of fat free mass and fat mass with BIA. Although typically non-invasive, cost-effective, and portable, this indirect technique may lack precision and accuracy due to measurement error and the inability to obtain tissue-specific measurements (15, 26). Studies in MS have used central adiposity estimates from BIA measures, but the application of BIA in evaluating VAT should be used with caution, considering BIA’s variable outcomes when compared against MRI and CT (26). In MS, there have been no known studies validating BIA algorithms of VAT estimation against a direct measure such as MRI or CT in populations with MS (Table 1).

Differentiating between subcutaneous, visceral, and intermuscular fat is necessary to understand how obesity affects MS disease progression. DXA, MRI, and computed tomography (CT) have been validated as the most reliable techniques for measuring adiposity in adult populations, but DXA and MRI have proven to be particularly helpful for adults with MS as these techniques detect specific locations of adiposity over time without the risk of high levels ionizing radiation (28). DXA estimates regional fat distribution, and MRI directly measures specific adipose tissue depots. Additionally, MRI can differentiate between subcutaneous, visceral, and intermuscular adipose tissues. This is a key strength of MRI and CT since DXA does not have this capability yet.

The role of obesity in brain health and MS progression is increasingly acknowledged, and more MS research now includes measures of body weight and estimates of obesity. Research shows a relationship between increased BMI and reduced physical mobility and cognitive function over time, with one case-controlled study reporting that steadily increasing BMI over a 15-year time-span was associated consistently with increased EDSS scores, reflecting reduced physical mobility, and a decline in symbol digit modality test (SDMT) scores, a commonly used measure of cognitive function in adults with MS (38). While these findings are important, research in the general population and in related neurodegenerative conditions demonstrates the need to shift focus from simple measures of body weight to the specific measures of location and tissue-specific adiposity (10).

Current MS research primarily uses BMI and body weight to measure obesity, which can further the understanding of the associations between excess adiposity and MS, but to truly understand mechanisms through which obesity leads to MS progression, it is necessary to use more specific measures of adiposity. Excess adiposity is known to contribute to the release of pro-inflammatory cytokines, worsened insulin sensitivity, and an increased risk of ectopic fat deposition, such as in the muscle, liver, and pancreas (39). Consequently, these may contribute to metabolic dysfunction, hormonal imbalances, disruption of the blood–brain barrier, and reduced brain health (40). More specifically, VAT has been linked to reduced brain health and a potentially heightened risk of developing AD (41). VAT contributes to increased circulating pro-inflammatory cytokines, such as TNF-a and IL-6, which can pass through the blood–brain barrier and play a role in neuroinflammation and increased oxidative stress. VAT has also been shown to contribute to insulin resistance, highlighting both the direct and indirect impacts of obesity on MS. Glucose is the brain’s primary energy source, and impairment in its uptake has been associated with a reduction in brain function and faster decline (42). These effects from VAT compounded with active symptoms of MS could place this population at higher risk for neurodegeneration and disease progression, but to date, most MS trials have not collected the types of measures necessary to conduct these analyses in adults with MS. While recent studies have reported associations with central adiposity and MS symptoms including pain, fatigue, and depression, this research used indirect methods to estimate VAT (e.g., waist circumference) (43). There have been no published reports of using MRI or CT to directly measure VAT in adults with MS. Additionally, while most existing data is cross sectional in nature, longitudinal studies that examine change in direct measures of VAT overtime are also needed.

Specific measures of obesity including DXA and MRI are also necessary to understand changes in body composition independent of weight loss. Lifestyle interventions including physical activity and diet interventions have demonstrated improved MS outcomes including fatigue and depression (34–36). While some of the benefits of these interventions may be due to weight loss, they also lead to preferential changes in body composition. For example, a 12-week exercise intervention in adults with excess adiposity showed that participants improved body composition after following high-intensity interval training (HIIT), resistance training, and combined training programs even without weight loss (44). When these changes in body composition occur without weight loss, they cannot be detected by BMI or body weight alone. Moreover, the metabolic and neurological impacts of these interventions cannot be fully understood unless studied within the context of body composition, rather than a simple obesity categorization.

It is important to acknowledge that adiposity is only one aspect of body composition. Lean mass is also important in the context of MS, since this population is at risk for muscle atrophy and dysfunction (45). In adults with MS and related neurodegenerative conditions, combined aerobic and anaerobic activity has shown to improve the quality of muscle tissue by increasing muscle mass and improving muscle quality through reductions in intermuscular adipose tissue (IMAT) (46, 47). Increasing muscle mass contributes to better overall physical function in adults with MS by improving walking, mobility, and strength (48, 49). Excess adiposity creates greater risk for fat infiltration into muscle tissue, such as IMAT, which is associated with functional decline in MS (16). Using IMAT as a measure of muscle health in MS research regularly could provide a better understanding of physical function over time. Using more advanced techniques that integrate measures of muscle mass and muscle health with adiposity could provide a more thorough view of participant health and risk of disease progression.

In conclusion, to fully understand the role of obesity in MS progression, multiple body composition measures should be incorporated into MS research. Anthropometric measurements, such as BMI and waist ratios, are cost-effective, easily implemented estimates of obesity but are not precise measures of adiposity. BIA has been used for distinguishing fat mass from fat free mass in MS studies with inconsistent outcomes. MRI, CT, and DXA are the best ways to measure adiposity and have been validated in this population, but they are not frequently used, due, in part, to the high cost and participant burden. Moving forward, researchers should consider adding multiple measures of adiposity to study protocols. For example, body mass may be used as a general descriptor of weight or weight change among study participants, while DXA can be added to estimate total and regional body fat, and MRI scans can be used to provide further distinction between specific adipose tissue depots.