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Background: We evaluated the performance of a clinical algorithm (Expali™), combining two or more positive answers to SCOFF questionnaire with Body Mass Index (BMI), to identify four Broad Categories of eating disorders (ED) derived from DSM-5., Methods: The clinical algorithm (Expali™) was developed from 104 combinations of BMI levels and answers to five SCOFF questions with at least two positive answers. Two senior ED physicians allocated each combination to one of the four Broad Categories of ED derived from DSM-5: restrictive disorder, bulimic disorder, hyperphagic disorder and other unspecified ED diagnosed by ED clinicians. The performance of Expali™ was evaluated on data from 206 patients with ED. Sensitivity, specificity values and Youden index were calculated for each category., Results: The 206 patients were diagnosed as follows: 31.5% restrictive disorder, 18.9% bulimic disorder, 40.8% hyperphagic disorder and 8.8% other ED. The sensitivity of Expali™ for restrictive, bulimic, hyperphagic and other unspecified ED were respectively: 76.9, 69.2, 79.7 and 16.7%. The Youden index was respectively 0.73, 0.57, 0.67 and 0.07., Conclusions: In a SCOFF-positive ED population (at least two positive answers), the clinical algorithm Expali™ demonstrated good suitability by correctly classifying three of the four Broad Categories of eating disorders (restrictive, bulimic and hyperphagic disorder). It could be useful both to healthcare professionals and the general population to enable earlier detection and treatment of ED and to improve patient outcomes.

Background & Aims: The resting energy expenditure (REE) predictive formulas are often used in clinical practice to adapt the nutritional intake of patients or to compare to REE measured by indirect calorimetry. We aimed to evaluate which predictive equations was the best alternative to REE measurements according to the BMI., Methods: 28 REE prediction equations were studied in a population of 1726 patients without acute or chronic high-grade inflammatory diseases followed in a Nutrition Unit for malnutrition, eating disorders or obesity. REE was measured by indirect calorimetry for 30 min after a fasting period of 12 h. Some formulas requiring fat mass and free-fat mass, body composition was measured by bioelectrical impedance analysis. The percentage of accurate prediction (±10%/REE measured) and Pearson r correlations were calculated., Results: Original Harris & Benedict equation provided 73.0% of accurate predictions in normal BMI group but only 39.3% and 62.4% in patients with BMI < 16 kg m(-2) and BMI ≥ 40 kg m(-2), respectively. In particularly, this equation overestimated the REE in 51.74% of patients with BMI < 16 kg m(-2). Huang equation involving body composition provided the highest percent of accurate prediction, 42.7% and 66.0% in patients with BMI < 16 and >40 kg m(-2), respectively., Conclusion: Usual predictive equations of REE are not suitable for predicting REE in patients with extreme BMI, in particularly in patients with BMI <16 kg m(-2). Indirect Calorimetry may still be recommended for an accurate assessment of REE in this population until the development of an adapted predictive equation., (Copyright © 2014 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

Background and aims: Body composition assessment is often used in clinical practice for nutritional evaluation and monitoring. The standard method, dual-energy X-ray absorptiometry (DXA), is hardly feasible in routine clinical practice contrary to Bioelectrical Impedance Analysis (BIA) method. We thus aimed to compare body composition assessment by DXA and BIA according to the body mass index (BMI) in a large cohort. Methods: Retrospectively, we analysed DXA and BIA measures in patients followed in a Nutrition Unit from 2010 to 2016. Body composition was assessed under standardized conditions in the morning, after a fasting period of 12 h, by DXA (Lunar Prodigy Advance) and BIA (Bodystat QuadScan 4000, Manufacturer’s equation). Bland-Altman test was performed for each class of BMI (kg/m2) and fat mass and fat free mass values were compared using Kruskal-Wallis test. Pearson correlations were also performed and the concordance coefficient of Lin was calculated. Results: Whatever the BMI, BIA and DXA methods reported higher concordance for values of FM than FFM. Body composition values were very closed for patients with BMI between 16 and 18,5 (difference < 1kg). For BMI > 18,5 and BMI < 40, BIA overestimated fat free mass from 3,38 to 8,28 kg, and underestimated fat mass from 2,51 to 5,67 kg compared with DXA method. For BMI ≥ 40, differences vary with BMI. For BMI < 16, BIA underestimated fat free mass by 2,25 kg, and overestimated fat mass by 2,57 kg. However, limits of agreement were very large either for FM and FFM values, irrespective of BMI. Conclusion: The small bias, particularly in patients with BMI between 16 and 18, suggests that BIA and DXA methods are interchangeable at a population level. However, concordance between BIA and DXA methods at the individual level is lacking, irrespective of BMI. [ABSTRACT FROM AUTHOR]

Background: Bioelectrical impedance analysis (BIA) is a simple and rapid technique to measure body composition (BC). Validity of BIA in patients with low body mass index (BMI) remains controversial. We assessed the validity of several BIA equations to evaluate fat-free mass (FFM), fat mass (FM) and muscle mass in patients with anorexia nervosa (AN) by using dual X ray absorptiometry (DXA) as reference. Methods: Sixteen BIA equations developed for FFM and appendicular lean mass (ALM) were applied on electrical data measured by BIA in AN patients with BMI <16 kg/m². BIA and DXA were done the same day after overnight fasting. Results were compared with the Bland–Altman method, Pearson correlation and a Lin concordance test. Results: Data from 115 female AN patients (14.6 ± 1.2 kg/m²; 32.3 ± 14.5 years) were included. FM and FFM assessed by DXA were, respectively, 4.2 ± 2.4 kg and 35.5 ± 3.8 kg. The best results were obtained with Sun's equation: respectively for FM and FFM, Bland Altman bias at 0.548 and 0.706 kg, Pearson correlation r at 0.86 and 0.86 and Lin concordance coefficient at 0.81 and 0.84. However, confidence intervals (CI) at 95% were high (−2.73–3.83 kg for FM; −4.55–3.13 kg for FFM). Other equations also showed high 95% CI. Accuracy was acceptable for Sun and Bedogni equations for FFM (approximately 66%) but very low for FM prediction considering all equations (<15%). Concerning ALM evaluated at 14.88 ± 2.04 kg by DXA, only Scafoglieri and Yoshida equations showed acceptable values: bias (−0.2 and 2.8%), Pearson r (0.89 and 0.86), Lin concordance coefficient (0.82 and 0.82) and accuracy (83.5 and 82.6%). Confidence intervals at 95% were high for both equations (−2.1–2.0 for Scafoglieri equation and −1.6–2.4 for Yoshida equation). Conclusion: In AN patients with BMI < 16 kg/m², no BIA equation tested was adapted to evaluate BC at the individual level. [ABSTRACT FROM AUTHOR]