Clinical assessment underestimates fat mass and overestimates resting energy expenditure in children with neuromuscular diseases

A comparative analysis of body composition assessment by BIA and DXA in children with type II and III spinal muscular atrophy

BACKGROUND

Body composition analysis is a valuable tool for assessing and monitoring the nutritional status of children with spinal muscular atrophy (SMA). This study was designed to compare the consistency of bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA), as the gold standard method for assessing body composition in clinical practice when treating children with type II and III SMA.

METHODS

From 2019 to 2021, we performed a retrospective analysis of body composition by DXA and BIA measurement methods in patients with type II and III SMA treated at a Chinese tertiary children's hospital. Fat mass (FM), muscle mass (MM), bone mineral content (BMC), and visceral fat area (VFA) were compared using paired sample t-tests. We calculated Lin's concordance correlation coefficient (CCC) and Spearman correlation coefficient to verify the correlation between DXA and BIA measurements. Bland-Altman analysis was used to assess the consistency of the two methods.

RESULTS

Fifty-seven children with type II and III SMA were recruited. Compared with body composition measured by DXA, the average FM measured by BIA is significantly lower (P <0.001), whereas the average MM, BMC, and VFA measured by BIA are significantly higher (P < 0.001) in children with SMA. Overall, the difference between MM (Delta [BIA-DAX] = 1.6 kg) and FM (Delta [BIA-DAX] = -1.6 kg) measured by DXA and BIA was minor, whereas the difference of VFA (Delta [BIA-DAX] = -43.5 cm) was significantly large. Correlation analysis indicated a substantial correlation of MM (CCC = 0.96 [95% confidence interval (CI) = 0.93-0.98], r = 0.967 [P < 0.0001]) and FM (CCC = 0.95 [95% CI = 0.92-0.97], r = 0.953 [P < 0.0001]), and poor correlation of BMC (CCC = 0.61 [95% CI = 0.42-0.75], r = 0.612 [P < 0.0001]) and VFA (CCC = 0.54 [95% CI = 0.33-0.70], r = 0.689 [P < 0.0001]) measurements between the two methods. The Bland-Altman analysis suggests that the majority of participants were within LOA. In addition, differences in MM and VFA measurements between BIA and DAX increased according to patients' increasing height, whereas differences in FM and BMC did not differ with height.

CONCLUSION

BIA overestimates MM and underestimates the FM, BMC, and VFA in children with SMA compared with DXA measurements. Overall, the non-invasive, easy-to-use, and repeatable BIA measurements were found to be in good agreement with DXA measurements, especially for FM and MM, which are essential parameters for the nutritional evaluation of children with SMA.

Assessment of body composition, metabolism, and pulmonary function in patients with myotonic dystrophy type 1

Abnormal body composition in myotonic dystrophy type 1 (DM1) are affected by energy intake above resting energy expenditure (REE). We aim to investigate the characteristics and relationship between body composition, REE, and pulmonary function in patients with DM1, and to examine their changes in 1 year. The study design was a single-center, cross-sectional, and longitudinal study of body composition, REE characteristics, and pulmonary function. Twenty-one male patients with DM1 and 16 healthy volunteers were registered in the study. Body composition was measured using dual-energy X-ray absorptiometry (DEXA). Fat mass (FM) index (kg/m2), fat-FM index (kg/m2), and skeletal mass index (kg/m2) were calculated. The measurements were taken breath by breath with a portable indirect calorimeter. The REE was calculated using the oxygen intake (VO2) and carbon dioxide output (VCO2) in the Weir equation. Basal energy expenditure (BEE) was calculated by substituting height, weight, and age into the Harris-Benedict equation. The study enrolled male patients with DM1 (n = 12) and healthy male volunteers (n = 16). Patients with DM1 (n = 7) and healthy volunteers (n = 14) could be followed in 1 year. The body composition of patients with DM1 was significantly higher in the FM index and significantly lower in the fat-FM index and skeletal mass index. The REE of patients with DM1 was significantly lower and was not associated with body composition. Patients with DM1 had poor metabolism that was not related to body composition. FM was high and lean body mass was low.

Predictive energy equations for spinal muscular atrophy type I children

BACKGROUND

Knowledge on resting energy expenditure (REE) in spinal muscular atrophy type I (SMAI) is still limited. The lack of a population-specific REE equation has led to poor nutritional support and impairment of nutritional status.

OBJECTIVE

To identify the best predictors of measured REE (mREE) among simple bedside parameters, to include these predictors in population-specific equations, and to compare such models with the common predictive equations.

METHODS

Demographic, clinical, anthropometric, and treatment variables were examined as potential predictors of mREE by indirect calorimetry (IC) in 122 SMAI children consecutively enrolled in an ongoing longitudinal observational study. Parameters predicting REE were identified, and prespecified linear regression models adjusted for nusinersen treatment (discrete: 0 = no; 1 = yes) were used to develop predictive equations, separately in spontaneously breathing and mechanically ventilated patients.

RESULTS

In naïve patients, the median (25th, 75th percentile) mREE was 480 (412, 575) compared with 394 (281, 554) kcal/d in spontaneously breathing and mechanically ventilated patients, respectively (P = 0.009).In nusinersen-treated patients, the median (25th, 75th percentile) mREE was 609 (592, 702) compared with 639 (479, 723) kcal/d in spontaneously breathing and mechanically ventilated patients, respectively (P = 0.949).Both in spontaneously breathing and mechanically ventilated patients, the best prediction of REE was obtained from 3 models, all using as predictors: 1 body size related measurement and nusinersen treatment status. Nusinersen treatment was correlated with higher REE both in spontaneously breathing and mechanically ventilated patients. The population-specific equations showed a lower interindividual variability of the bias than the other equation tested, however, they showed a high root mean squared error.

CONCLUSIONS

We demonstrated that ventilatory status, nusinersen treatment, demographic, and anthropometric characteristics determine energy requirements in SMAI. Our SMAI-specific equations include variables available in clinical practice and were generally more accurate than previously published equations. At the individual level, however, IC is strongly recommended for assessing energy requirements. Further research is needed to externally validate these predictive equations.