Estimation of appendicular muscle mass and fat mass by near infrared spectroscopy in older persons

Update on Lean Body Mass Diagnostic Assessment in Critical Illness

Acute critical illnesses can alter vital functions with profound biological, biochemical, metabolic, and functional modifications. Despite etiology, patient's nutritional status is pivotal to guide metabolic support. The assessment of nutritional status remains complex and not completely elucidated. Loss of lean body mass is a clear marker of malnutrition; however, the question of how to investigate it still remains unanswered. Several tools have been implemented to measure lean body mass, including a computed tomography scan, ultrasound, and bioelectrical impedance analysis, although such methods unfortunately require validation. A lack of uniform bedside measurement tools could impact the nutrition outcome. Metabolic assessment, nutritional status, and nutritional risk have a pivotal role in critical care. Therefore, knowledge about the methods used to assess lean body mass in critical illnesses is increasingly required. The aim of the present review is to update the scientific evidence regarding lean body mass diagnostic assessment in critical illness to provide the diagnostic key points for metabolic and nutritional support.

Body composition in older community-dwelling adults with hip fracture: portable field methods validated by dual-energy X-ray absorptiometry

Ageing is associated with weight loss and subsequently poor health outcomes. The present study assessed agreement between two field methods, bioelectrical impedance spectroscopy (BIS) and corrected arm muscle area (CAMA) for assessment of body composition against dual-energy X-ray absorptiometry (DXA), the reference technique. Agreement between two predictive equations estimating skeletal muscle mass (SMM) from BIS against SMM from DXA was also determined. Assessments occurred at baseline < 14 d post-surgery (n 79), and at 6 months (6M; n 75) and 12 months (12M; n 63) in community-living older adults after surgical treatment for hip fracture. The 95 % limits of agreement (LOA) between BIS and DXA, CAMA and DXA and the equations and DXA were assessed using Bland-Altman analyses. Mean bias and LOA for fat-free mass (FFM) between BIS and DXA were: baseline, 0.7 (-10.9, 12.4) kg; 6M, - 0.5 (-20.7, 19.8) kg; 12M, 0.1 (-8.7, 8.9) kg and for SMM between CAMA and DXA were: baseline, 0.3 (-11.7, 12.3) kg; 6M, 1.3 (-4.5, 7.1) kg; 12M, 0.9 (-5.4, 7.2) kg. Equivalent data for predictive equations against DXA were: equation 1: baseline, 15.1 (-9.5, 20.6) kg; 6M, 17.1 (-12.0, 22.2) kg; 12M, 17.5 (-13.0, 22.0) kg; equation 2: baseline, 12.6 (-7.3, 19.9) kg; 6M, 14.4 (-9.7, 19.1) kg; 12M, 14.8 (-10.7, 18.9) kg. Proportional bias (BIS: β = -0.337, P< 0.001; CAMA: β = -0.294, P< 0.001) was present at baseline but not at 6M or 12 M. Clinicians should be cautious in using these field methods to predict FFM and SMM, particularly in the acute care setting. New predictive equations would be beneficial.