Dienst van SURF
© 2025 SURF
Purpose of reviewTo help guide metabolic support in critical care, an understanding of patients’ nutritional status and risk is important. Several methods to monitor lean body mass are increasingly used in the ICU and knowledge about their advantages and limitations is essential.Recent findingsComputed tomography scan analysis, musculoskeletal ultrasound, and bioelectrical impedance analysis are emerging as powerful clinical tools to monitor lean body mass during ICU stay. Accuracy, expertise, ease of use at the bedside, and costs are important factors, which play a role in determining, which method is most suitable. Exciting new research provides an insight into not only quantitative measurements, but also qualitative measurements of lean body mass, such as infiltration of adipose tissue and intramuscular glycogen storage.SummaryMethods to monitor lean body mass in the ICU are under constant development, improving upon bedside usability and offering new modalities to measure. This provides clinicians with valuable markers with which to identify patients at high nutritional risk and to evaluate metabolic support during critical illness.
Rationale: Lean body mass, including muscle, is known to decrease with age, which may contribute to loss of physical function, an indicator of frailty. Moreover, low muscle thickness is considered an indicator of frailty in critically ill patients. However, little is known about the relationship between muscle thickness and frailty in community dwelling adults. Therefore, we studied the association between frailty and whole body lean body mass index (LBMi) and muscle thickness of the rectus femoris (RF) in community dwelling older adults. Methods: In older adults aged ≥55y, who participated in the Hanze Health and Ageing Study, frailty status was assessed with a multidimensional instrument, measuring frailty on a cognitive, psychosocial en physical level, i.e., the Groningen Frailty Indicator (GFI), using ≥4 as cut-off score for frailty. LBMi (kg/m2) was estimated with BIA (Quadscan 4000©, Bodystat), using the build-in equation. Muscle thickness (mm) of the RF was measured with ultrasound, using the Bodymetrix© (Intelametrix). Univariate and multivariate binary logistic regression analyses were performed for LBMi and for RF thickness. Multivariate analysis corrected for age, sex, body mass index (kg/m2), and handgrip strength (handgrip dynamometer; kg). A p-level of <0.05 was considered significant and Odds Ratios (OR; [95% CI]) were presented. Results: 93 participants (age 65.2±7.7 years; male 46 %; LBMi 17.2±2.6 kg/m2; RF 14.6±4.4 mm; median GFI =1 (interquartile range=0-3; frail: n=18) were included in the analysis. In both the univariate and multivariate analysis, LBMi (p=0.082, OR=0.82 [0.66-1.03]; p=0.077, OR=0.55 [0.28-1.07] respectively) and muscle thickness of RF (p=0.436, OR=0.95 [0.84-1.08]; p=0.796, OR= 1.02 [0.88-1.18] respectively) were not significantly associated with frailty. None of the co-variables were significantly associated with frailty either. Conclusion: In this sample of older adults aged ≥55 years, LBMi and RF thickness are not associated with frailty. However, frail participants scored at cut-off or just above, and measurements in a population with higher scores for frailty may provide further insight in the association between lean body mass and muscle thickness and frailty.
Background: The diagnosis of sarcopenia is essential for early treatment of sarcopenia in older adults, for which assessment of appendicular lean mass (ALM) is needed. Multi-frequency bio-electrical impedance analysis (MF-BIA) may be a valid assessment tool to assess ALM in older adults, but the evidences are limited. Therefore, we validated the BIA to diagnose low ALM in older adults.Methods: ALM was assessed by a standing-posture 8 electrode MF-BIA (Tanita MC-780) in 202 community-dwelling older adults (age ≥ 55 years), and compared with dual-energy X-ray absorptiometry (DXA) (Hologic Inc., Marlborough, MA, United States; DXA). The validity for assessing the absolute values of ALM was evaluated by: (1) bias (mean difference), (2) percentage of accurate predictions (within 5% of DXA values), (3) the mean absolute error (MAE), and (4) limits of agreement (Bland-Altman analysis). The lowest quintile of ALM by DXA was used as proxy for low ALM (< 22.8 kg for men, < 16.1 kg for women). Sensitivity and specificity of diagnosing low ALM by BIA were assessed.Results: The mean age of the subjects was 72.1 ± 6.4 years, with a BMI of 25.4 ± 3.6 kg/m2, and 71% were women. BIA slightly underestimated ALM compared to DXA with a mean bias of -0.6 ± 1.2 kg. The percentage of accurate predictions was 54% with a MAE of 1.1 kg, and limits of agreement were -3.0 to + 1.8 kg. The sensitivity for ALM was 80%, indicating that 80% of subjects who were diagnosed as low ALM according to DXA were also diagnosed low ALM by BIA. The specificity was 90%, indicating that 90% of subjects who were diagnosed as normal ALM by DXA were also diagnosed as normal ALM by the BIA.Conclusion: This comparison showed a poor validity of MF-BIA to assess the absolute values of ALM, but a reasonable sensitivity and specificity to recognize the community-dwelling older adults with the lowest muscle mass.