Nutritional supplements are used in the management of diseases. The composition and form of nutrients in the supplements depend on the altered needs and gastrointestinal function of patients. The nutrition label of the supplements lacks information on the in vitro digestibility of nutrients which ultimately decides the availability of nutrients for absorption. Various factors that influence digestibility and availability include source of the nutrient, gut functionality, processing, physicochemical properties in the food matrix and inter-nutrient interactions. This study aimed at analyzing the in vitro starch and protein digestibility of six commercially available food formulations used in various disease conditions. The starch digestibility index (SDI) ranged from 64.47 ± 1.92 to 82.35 ± 2.71 and rapidly available glucose (RAG) from 26.06 ± 0.61 to 44.23 ± 0.38g per 100g. Dietary fiber content was inversely related with SDI (r - 0.845). The protein digestibility ranged from 41.42 ± 2.67 - 96.84 ± 1.01%. Formulations containing isolated forms of whey, casein and soy protein had a higher digestibility than in combinations. Besides information on the nutrient content, specifying the digestion characteristics of disease specific commercial formulations will help in their judicious use in meeting the nutritional requirements in various disease conditions.

Context: The use of predictive equations in hospital settings lacking access to dual energy X-ray absorptiometry or bioelectrical impedance analysis (BIA) can be beneficial because cachexia, muscle wasting and related increase in morbidity and mortality are known occurrences in patients with chronic obstructive pulmonary disease (COPD). Aim: To analyse the predictive performance of reported equations for fat-free mass (FFM) prediction against BIA-derived FFM in patients with stable COPD. Design/Materials and Methods: Seventy-two male patients aged between 40 and 75 years with stable COPD were evaluated for FFM by BIA. FFM was also predicted for the same patients using equations reported by Kulkarni et al., Solomon Yu et al., Janmahasatian et al., as well as Deurenberg equation. Statistical Analysis Used: The predictive performance was assessed by Bland-Altman plots, with subsequent calculations of bias, precision and accuracy. The performance was also analysed across the age and body mass index sub-groups. Results: The mean differences (95% confidence intervals) in the prediction of FFM using equations by Kulkarni et al.