Muscular Dystrophy To Deplete Lean Body Muscle Mass And Destroy Motor Function
Overview
Appendicular lean mass index (ALMI) is noted to be associated with motor function in individuals with Duchenne muscular dystrophy (DMD). However, there is a need to quantify the relationship between ALMI and clinical outcomes specific to Duchenne muscular dystrophy. This study aimed to explore the associations between dual-energy x-ray absorptiometry (DXA)-derived ALMI estimates and motor function in ambulatory Duchenne muscular dystrophy patients.
A retrospective analysis was conducted using longitudinal clinical data from 137 glucocorticoid-treated Duchenne muscular dystrophy patients. Motor function was assessed using the North Star Ambulatory Assessment (NSAA) and the 10 Meter Walk/Run Test (10MWT). Body composition was evaluated using DXA, with ALMI calculated by dividing the lean mass of the arms and legs by height squared (m²), and fat mass index (FMI) calculated by dividing total body fat mass by height squared. Linear mixed-effects models were employed to assess the associations between ALMI and motor function, while controlling for age and FMI.
The comprehensive prediction model, which included age, age squared, ALMI, and FMI, accounted for 57% of the variance in NSAA scores and 63% of the variance in 10MWT speed. An increase of 1 kg/m² in ALMI was associated with a 5.4-point higher NSAA score (p < .001) and a 0.45 m/s faster 10MWT speed (p < .001). Conversely, a 1 kg/m² increase in FMI was linked to a 1.5-point decrease in NSAA score (p < .001) and a 0.14 m/s slower 10MWT speed (p < .001).
Estimates of ALMI and FMI derived from DXA are correlated with motor function in Duchenne muscular dystrophy patients and may contribute to understanding the variations in disease progression in Duchenne muscular dystrophy.
Introduction
Long-term glucocorticoid treatment in Duchenne muscular dystrophy (DMD) helps to slow disease progression but is associated with significant side effects, including stunted height growth, excessive weight gain, and increased fat accumulation. The combination of muscle loss and fat mass gain in patients undergoing glucocorticoid therapy is believed to contribute to the worsening of motor function as they age. The variability in how quickly patients with Duchenne muscular dystrophy experience functional decline complicates the detection of meaningful changes in clinical trials. The U.S. Food and Drug Administration (FDA) has emphasized the need for biomarkers that accurately reflect the condition of skeletal muscle at various biological levels to aid in drug development.
Accurately assessing skeletal muscle mass in patients with Duchenne muscular dystrophy, who often experience restricted height growth and increased fat accumulation, requires considering both height and fat mass. Dual-energy X-ray absorptiometry (DXA) is a practical, low-burden imaging method for estimating body composition. The Appendicular Lean Mass Index (ALMI) and Fat Mass Index (FMI) are height-normalized indicators of muscle and fat mass, respectively. These indices provide a comprehensive estimate of body composition in individuals with DMD, helping to analyze the relationship between muscle and fat mass and motor function.
Patients with Duchenne muscular dystrophy exhibit lower ALMI compared to their unaffected peers, and this disparity becomes more pronounced during adolescence as muscle wasting progresses. Higher ALMI levels have been linked to better motor function, as measured by the Functional Mobility Scale. However, previous research has not thoroughly examined the relationship between ALMI and established Duchenne muscular dystrophy motor function assessments, considering key patient factors such as age and fat mass. This study aimed to explore the associations between DXA-derived estimates of appendicular lean muscle mass and two validated motor function assessments, the 10 Meter Walk/Run Test (10MWT) and the North Star Ambulatory Assessment (NSAA) in ambulatory patients with DMD.
Method
The UMass Chan Medical School Institutional Review Board (IRB) granted approval for the use of clinical data in this study, waiving the requirement for informed consent (IRB H00024125). The clinical registry of the UMass Medical School Duchenne Program was queried to gather records of patients who attended clinical visits between January 30, 2019, and February 1, 2023. The study included patients who met the following criteria: (1) a clinical diagnosis of Duchenne muscular dystrophy (DMD) confirmed by a pathogenic variant in the dystrophin gene and/or muscle biopsy evidence of dystrophin deficiency; (2) age between 4 and 18 years at the time of the visit; (3) ambulatory status at the time of the visit, confirmed by completing the 10-Meter Walk Test (10MWT) barefoot without assistance; and (4) glucocorticoid treatment aligned with current care guidelines for at least six months prior to the visit. Patients were excluded if they had a documented Becker muscular dystrophy phenotype, independent ambulation beyond 18 years of age, or a North Star Ambulatory Assessment (NSAA) score of 16 or higher after age 16.
Motor function assessments, including the NSAA and 10MWT, were conducted as part of interdisciplinary clinical evaluations every 6 to 12 months. The NSAA was administered according to established procedures by one of three allied health professionals (two physical therapists and one occupational therapist) with expertise in standardized assessments for Duchenne muscular dystrophy patients. The 10MWT was conducted by the clinic’s neurologist or nurse practitioner. Dual-energy X-ray absorptiometry (DXA) scans were performed annually as part of routine care to monitor bone health, focusing on the lumbar spine, distal femur, and whole-body composition. Assessments completed within 30 days of each other were considered as a single visit time point. Standard in-clinic visits occurred every 6 to 12 months.
Body composition was evaluated using whole-body DXA scans (Hologic, Marlborough, MA), providing measurements of lean mass, fat mass, and bone mass. Lean mass calculations excluded bone mineral content, focusing solely on soft tissue. The Appendicular Lean Mass Index (ALMI) was determined by dividing the total lean mass of the arms and legs (in kilograms) by the height squared (in meters). The Fat Mass Index (FMI) was calculated by dividing total body fat mass (in kilograms) by height squared (in meters). Age was calculated in decimal years based on the participant’s date of birth and the date of the DXA assessment, while height was measured without shoes using a calibrated stadiometer.
Motor function was assessed through two methods: (1) The NSAA, a 0–34 point scale measuring ambulatory function in Duchenne muscular dystrophy patients, with higher scores indicating better motor function, and (2) The 10MWT, which measures the time taken to walk 10 meters, with results converted to speed in meters per second for statistical analysis.
Statistical Analysis
Statistical and descriptive analyses were conducted using R version 4.1.1 (2021-08-10). Linear mixed-effects models, estimated using maximum likelihood and the lmer function in the lme4 package, were employed. A significance level of p < .05 was applied to all statistical tests. To assess the relationship between ALMI and the motor outcomes (NSAA and 10MWT speed), linear random-intercept models were used, accounting for repeated measures and adjusting for age and FMI. A series of four models were tested, progressively adding variables such as age, age squared, ALMI, and FMI. The proportion of variance explained by each model, also known as the coefficient of determination (R-squared), was calculated to evaluate the reduction in prediction error variance compared to the null model.
Result
Participant characteristics are summarized in Table 1. From the initial 365 clinical registry records screened, 288 observations from 137 participants met the inclusion criteria. Out of these, four observations were missing NSAA data, resulting in 284 observations for NSAA analysis and 288 for 10MWT analysis. The sample included a broad age range and varied levels of motor function ability. The majority of observations (94%) involved participants who were taking daily deflazacort at the time of the visit. Additionally, seven participants (accounting for nine observations) were undergoing treatment with FDA-approved exon-skipping therapies, such as casimersen, eteplirsen, golodirsen, or viltolarsen.
Across all models, a higher ALMI was consistently associated with better motor function on both the NSAA and 10MWT. Conversely, a higher FMI (Fat Mass Index) correlated with worse motor function on these tests. Without adjusting for other variables, ALMI accounted for 16% of the variance in both NSAA scores and 10MWT speed. FMI, on the other hand, explained 18% of the variance in NSAA scores and 23% in 10MWT speed.
Including ALMI in the models significantly improved the explained variance in NSAA scores by 14% compared to models that only controlled for age. In the most comprehensive model (NSAA Model 3), which included all covariates (age, age², ALMI, and FMI), 57% of the variance in NSAA scores was explained. Specifically, a 1 kg/m² increase in ALMI predicted a 5.4-point higher NSAA score, while a 1 kg/m² increase in FMI predicted a 1.5-point lower NSAA score, controlling for age, age², and FMI/ALMI respectively.
Similarly, the inclusion of ALMI in the 10MWT speed model increased the explained variance by 14% compared to the age-only model. The full model (10MWT Model 3), incorporating all covariates, explained 63% of the variance in 10MWT speed. A 1 kg/m² increase in ALMI was associated with a 0.45 m/s faster 10MWT speed, while a 1 kg/m² increase in FMI was associated with a 0.14 m/s slower 10MWT speed, after adjusting for age and other factors.
The participant demographics are detailed in Table 1, with a median age of 10.1 years and a range of 4.1 to 17.9 years. The median NSAA score was 21.0, and the median 10MWT speed was 1.9 m/s. The median ALMI and FMI were 4.6 kg/m² and 5.8 kg/m², respectively. The majority of participants (94%) were on daily glucocorticoid therapy, primarily deflazacort, with most having undergone multiple DXA scans.
These findings underscore the importance of ALMI in predicting motor function in participants with Duchenne muscular dystrophy, suggesting that lean mass is a significant factor in motor performance, while fat mass may negatively impact motor outcomes.
Conclusion
This study highlights that a higher Appendicular Lean Mass Index (ALMI), which estimates skeletal muscle mass relative to height, is linked to better motor function as measured by the North Star Ambulatory Assessment (NSAA) and the 10-Meter Walk Test (10MWT). In contrast, a higher Fat Mass Index (FMI), another height-normalized estimate that reflects body fat, is associated with poorer motor function. Notably, both ALMI and FMI contribute uniquely to explaining variations in motor function, beyond what is accounted for by age alone. These findings align with earlier research suggesting that dual-energy X-ray absorptiometry (DXA)-derived estimates of muscle and fat mass could be crucial in understanding the variability in motor impairment observed in Duchenne Muscular Dystrophy (DMD) patients.
Previous studies, such as the work by Goemans et al., identified multiple predictors, including anthropometric measures and baseline motor function, which together explained a significant portion of the variance in motor outcomes like the six-minute walk distance over a year. However, the current study demonstrates that using only four predictors, a comparable or even greater proportion of variance (57%–63%) can be explained for motor function measures like the NSAA and 10MWT. This approach, which estimates that ALMI alone accounts for about 14%–16% of the variance in these motor outcomes, offers advantages such as reducing the risk of overfitting models and enhancing statistical power. Importantly, this study focuses on predicting concurrent motor function rather than changes over a year, as reported in earlier research. The evidence suggests that ALMI is a significant indicator of motor function in Duchenne muscular dystrophy, paving the way for future studies to explore the prognostic value of ALMI and FMI in predicting changes over time, potentially aiding clinical trial design and patient management.
Interestingly, while it was hypothesized that ALMI would be the primary predictor of motor function in Duchenne muscular dystrophy patients, FMI explained a larger portion of the variance when the two were analyzed separately. This was unexpected, given that ALMI represents muscle mass, the primary pathology in Duchenne muscular dystrophy. In contrast, FMI reflects fat accumulation, which can be influenced by factors like glucocorticoid therapy, diet, and inactivity. One possible reason for this is that DXA might overestimate muscle mass by including fibrotic tissue, especially in advanced stages of the disease. Nonetheless, both ALMI and FMI were significant predictors of motor function in the combined model, suggesting that both muscle and fat mass contribute to motor function variance.
Without adjusting for age or FMI, a 1 kg/m² increase in ALMI predicted a 4.1-point higher NSAA score, surpassing the minimal clinically important difference (MCID) for NSAA scores. This change in ALMI, which exceeds the standard deviation in the sample, indicates a substantial impact on motor function. Further research is needed to quantify the change in ALMI that predicts a meaningful improvement in motor outcomes.
It is important to note that these analyses did not account for several factors influencing motor function, such as genetic variability, glucocorticoid treatment, or FDA-approved exon-skipping therapies. These factors likely impact motor function by increasing or preserving skeletal muscle mass, which ALMI measures. Recent research supports this, showing that patients with specific dystrophin gene deletions associated with milder Duchenne muscular dystrophy phenotypes have higher ALMI values. Future studies with larger cohorts could explore how consistent the relationships between ALMI, FMI, and motor function are across different ages, functional levels, and treatment variations.
In conclusion, this study finds that DXA-derived estimates of both lean and fat mass predict unique variance in motor function as measured by NSAA scores and 10MWT speed in Duchenne muscular dystrophy patients. Further research into the prognostic value of ALMI and FMI could enhance their utility in clinical trial design and patient management.
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