Accurate detection of body composition changes is essential for evaluating nutritional interventions. In a previous 12-wk randomized controlled trial, adults with excess body weight who consumed a powdered meal replacement (PMR) under weight-stable conditions increased lean soft tissue assessed by dual-energy x-ray absorptiometry. This secondary analysis evaluated whether comparable changes were detected using other body composition techniques and whether hydration-related variation impacted tracking of fat-free mass (FFM) and related compartments. We hypothesized that multiple techniques would detect increases in FFM and hydration markers. Participants (n = 63) were randomized to either a PMR group, which added PMR twice daily to their usual diet, or a control group, which maintained their usual diet. Body composition was assessed using dual-energy x-ray absorptiometry, bioelectrical impedance analysis, air-displacement plethysmography (ADP), and an adapted 4-compartment (4C) model. Changes (95% confidence intervals) in the PMR group from baseline to week 12, compared with the control group, included increases in ADP-derived FFM (1.31 [0.51; 2.12] kg, P < 0.001), 4C-derived FFM (1.01 [0.25; 1.78] kg, P = 0.002), and total body protein (TBpro: 0.60 [0.14; 1.07] kg, P = 0.007). No significant changes were observed in fat mass or hydration-related compartments. In sex-stratified analysis, the PMR group increased ADP-derived FFM among males (1.70 [0.28; 3.12] kg, P = 0.021) and females (1.12 [0.05; 2.18] kg, P = 0.031). Overall, ADP was the most sensitive method for detecting longitudinal changes and performed comparably to the 4C model. Increases in FFM and TBpro appeared independent of hydration changes, suggesting true accretion of metabolically active tissue, likely muscle mass.
Background Celiac disease (CD) is a chronic autoimmune condition requiring strict adherence to a gluten-free diet (GFD) to prevent adverse health outcomes; however, strict diet adherence is difficult to maintain. Low psychological resilience has been linked to symptom severity in patients with gastrointestinal disorders including CD; however, resilience-building interventions have a low success rate in general. Psychological resilience has been linked to food skills, suggesting the possibility of improving individual resilience via culinary training. This cross-sectional study was designed to examine the relationships between food skills, psychological resilience, and GFD adherence in adults with CD. Methods An anonymous online survey was used to collect basic demographic and health data as well as the food skills, resilience, and diet adherence characteristics of participants. Participants (n = 327; ≥18 y) were recruited using ads placed on the websites of national CD support groups. Food skills, resilience, and GFD adherence were quantified using validated questionnaires: the Food Skills Questionnaire, the Resilience Scale-14, and the Celiac Dietary Adherence Test. Results Correlational analysis identified significant associations between food skills (meal planning, food preparation, and food safety), resilience, and age. The multiple linear regression analyses identified meal planning and resilience as significant explanatory variables for GFD adherence, accounting for 12.3% of the variance in adherence scores. Conclusions The findings suggest that both food skills and psychological resilience significantly influence GFD adherence. These two areas could serve as targets for intervention programs that improve the wellbeing of individuals with CD.
Objectives Artificial intelligence–assisted systems increasingly produce personalized meal plans, but their value depends on whether clinicians judge those plans credible and usable. We evaluated complete plans from a constrained artificial intelligence–assisted pipeline under blinded professional review. Methods In a cross-sectional exploratory web study, 54 eligible anonymous evaluator records from Brazilian dietitians contributed 130 evaluations of five matched plan pairs, with one pipeline-generated and one dietitian-designed plan per persona. Evaluators rated nine items across three domains and were offered an optional pairwise plan preference task. The overall score required all nine items and was available for 124 evaluations. Results Pipeline-generated plans received a mean rating of 3.67 (standard deviation, 0.57) (n = 59) compared with 3.11 (standard deviation, 0.73) for comparator plans (n = 65) (d = 0.86). Among 38 evaluator records with complete scores for both origins, 31 favored pipeline-generated plans, 6 favored dietitian-designed plans, and 1 had equal mean scores for both origins (paired t[37] = 5.32, P < 0.001). Pipeline-generated plans received higher ratings in all five persona pairs. This pattern was descriptive because evaluator records overlapped across pairs. Domain differences were +0.76 for nutritional adequacy, +0.69 for technical quality, and +0.21 for practical applicability. In the optional task, 32 of 42 eligible pairwise choices selected the pipeline-generated plan as more appropriate. Conclusions Under blinded presentation, practicing dietitians gave plans from the constrained pipeline a mean rating above the neutral anchor of the 5-point scale. These findings support prospective evaluation of a structured first draft workflow, with required clinician edits and patient-relevant outcomes as endpoints.
Objective This study aimed to investigate the incidence and clinical impact of hospital-acquired malnutrition (HAM) and develop a machine learning model for early risk prediction. Methods This secondary analysis included prospectively collected data from a multicenter observational study involving 34 tertiary hospitals in China. Adult patients hospitalized for 7 to 30 days were enrolled. Patients without malnutrition at admission were evaluated for HAM at discharge according to the Global Leadership Initiative on Malnutrition (GLIM) criteria. Machine learning models were developed using clinical and nutritional variables available at admission, and model performance was evaluated using discrimination, calibration, and clinical utility analyses. Results Among 4746 hospitalized patients, 1364 (28.7%) had malnutrition at admission. Of the 3382 patients without baseline malnutrition, 731 (21.6%) developed HAM during hospitalization. Patients with HAM had significantly higher rates of hospital-acquired complications (9.0% versus 2.9%), longer hospital stays (14.93 versus 13.53 days), and higher healthcare costs (¥48 980 versus ¥20 640) compared with those without HAM (all P < 0.001). Male sex, older age, pre-admission weight loss, reduced calf circumference, decreased handgrip strength, reduced muscle mass phenotype according to GLIM, and malignant tumors were associated with HAM. The XGBoost model demonstrated the best predictive performance, achieving an area under the receiver operating characteristic curve of 0.882 (95% CI: 0.857–0.906) in the independent test set. Conclusions HAM is common among hospitalized patients without malnutrition at admission, and the developed machine learning model may enable early identification of high-risk patients for timely nutritional intervention.
Background Low skeletal muscle (SM) is common at ICU admission and is associated with worse prognosis. Early identification of at-risk patients is essential, but determinants of low SM in critically ill patients remains unclear. Objective To identify sociodemographic, nutritional, and clinical characteristics associated with low SM at ICU admission. Methods This cross-sectional analysis included critically ill patients ≥18 years admitted within 24 hours. Sociodemographic and nutritional data were obtained through anamnesis, while clinical data were extracted from records. Quadriceps muscle layer thickness (QMLT) was measured using a B-mode US. Lower QMLT was defined as values below the sex-specific 25th percentile of the sample. Binary logistic regression was performed. Results 399 patients were included (age 61.4 ± 14.9 years; 56.6% male). Older age (OR = 1.02; CI 95% 1.0–1.04), previous unintentional weight loss (OR = 1.64; CI 95% 1.03–2.61), reduced food intake before admission (OR = 2.32; CI 95% 1.44–3.74), and pressure injuries (OR = 1.83; CI 95% 1.05–3.20) were associated with low QMLT. However, in multivariate analysis, only older age (OR = 1.02; CI 95% 1.00–1.04) and reduced food intake (OR = 2.26; CI 95% 1.22–4.18) remained independent determinants. Conclusion Older age and reduced food intake were independently associated with lower QMLT at ICU admission. These findings highlight the need for early screening and targeted strategies to mitigate muscle loss and improve outcomes in critically ill patients.
