The excessive caloric intake and micronutrient deficiencies related to obesity after a long-term interdisciplinary therapy.

OBJECTIVE: The aim of this study was to assess the effectiveness of a long-term interdisciplinary lifestyle modification therapy on food intake, body composition, and anthropometric measurements of obese women. METHODS: Seventy obese women (age 41 ± 5.9 y) attended the interdisciplinary therapy, with nutrition, physical exercise, physiotherapy, and psychological support during the course of 1 y. Usual food intake was estimated by 3-d dietary record. Dual-energy x-ray absorptiometry was performed to determine body fat and fat-free mass. Waist and hip circumferences also were measured. Student's t test and Wilcoxon test were used for comparisons among categories; P < 0.05 was considered significant. RESULTS: The assessment of dietary intake showed significant changes after interdisciplinary therapy. A reduction in intake of total calories (18.3%), carbohydrates (15.6%), and total fats (13.1%) was observed. Total micronutrient intake did not change positively after therapy, as the intake level of 16 micronutrients was still lower than recommended. The therapy was effective in reducing significantly body weight (5.9%), body mass index (6.1%), body fat (4.7%), and waist (5.2%) and hip (3.8%) circumferences. CONCLUSIONS: Long-term interdisciplinary therapy was effective in decreasing the intake of calories, carbohydrates, and fats. The therapy positively changed the body composition and reduced anthropometric measurements. However, the intake of some micronutrients after therapy was still significantly lower than recommended. These results demonstrated that the proposed interdisciplinary methodology can be effective in treating obesity; however, the present study reinforced the need to address the micronutrient deficiency in the target population.

Resting energy expenditure in obese women: comparison between measured and estimated values.

Assessing energy requirements is a fundamental activity in clinical dietetic practice. The aim of this study was to investigate which resting energy expenditure (REE) predictive equations are the best alternatives to indirect calorimetry before and after an interdisciplinary therapy in Brazilian obese women. In all, twelve equations based on weight, height, sex, age, fat-free mass and fat mass were tested. REE was measured by indirect calorimetry. The interdisciplinary therapy consisted of nutritional, physical exercise, psychological and physiotherapy support during the course of 1 year. The average differences between measured and predicted REE, as well as the accuracy at the ±10 % level, were evaluated. Statistical analysis included paired t tests, intraclass correlation coefficients and Bland-Altman plots. Validation was based on forty obese women (BMI 30-39·9 kg/m2). Our major findings demonstrated a wide variation in the accuracy of REE predictive equations before and after weight loss in non-morbid, obese women. The equations reported by Harris-Benedict and FAO/WHO/United Nations University (UNU) were the only ones that did not show significant differences compared with indirect calorimetry and presented a bias <5 %. The Harris-Benedict equation provided 40 and 47·5 % accurate predictions before and after therapy, respectively. The FAO equation provided 35 and 47·5 % accurate predictions. However, the Bland-Altman analysis did not show good agreement between these equations and indirect calorimetry. Therefore, the Harris-Benedict and FAO/WHO/UNU equations should be used with caution for obese women. The need to critically re-assess REE data and generate regional and more homogeneous REE databases for the target population is reinforced.

Human gut microbiome viewed across age and geography.

Gut microbial communities represent one source of human genetic and metabolic diversity. To examine how gut microbiomes differ among human populations, here we characterize bacterial species in fecal samples from 531 individuals, plus the gene content of 110 of them. The cohort encompassed healthy children and adults from the Amazonas of Venezuela, rural Malawi and US metropolitan areas and included mono- and dizygotic twins. Shared features of the functional maturation of the gut microbiome were identified during the first three years of life in all three populations, including age-associated changes in the genes involved in vitamin biosynthesis and metabolism. Pronounced differences in bacterial assemblages and functional gene repertoires were noted between US residents and those in the other two countries. These distinctive features are evident in early infancy as well as adulthood. Our findings underscore the need to consider the microbiome when evaluating human development, nutritional needs, physiological variations and the impact of westernization.

Computational discovery of transcriptional regulatory rules.

MOTIVATION: Even in a simple organism like yeast Saccharomyces cerevisiae, transcription is an extremely complex process. The expression of sets of genes can be turned on or off by the binding of specific transcription factors to the promoter regions of genes. Experimental and computational approaches have been proposed to establish mappings of DNA-binding locations of transcription factors. However, although location data obtained from experimental methods are noisy owing to imperfections in the measuring methods, computational approaches suffer from over-prediction problems owing to the short length of the sequence motifs bound by the transcription factors. Also, these interactions are usually environment-dependent: many regulators only bind to the promoter region of genes under specific environmental conditions. Even more, the presence of regulators at a promoter region indicates binding but not necessarily function: the regulator may act positively, negatively or not act at all. Therefore, identifying true and functional interactions between transcription factors and genes in specific environment conditions and describing the relationship between them are still open problems. RESULTS: We developed a method that combines expression data with genomic location information to discover (1) relevant transcription factors from the set of potential transcription factors of a target gene; and (2) the relationship between the expression behavior of a target gene and that of its relevant transcription factors. Our method is based on rule induction, a machine learning technique that can efficiently deal with noisy domains. When applied to genomic location data with a confidence criterion relaxed to P-value = 0.005, and three different expression datasets of yeast S.cerevisiae, we obtained a set of regulatory rules describing the relationship between the expression behavior of a specific target gene and that of its relevant transcription factors. The resulting rules provide strong evidence of true positive gene-regulator interactions, as well as of protein-protein interactions that could serve to identify transcription complexes. AVAILABILITY: Supplementary files are available from http://www.jaist.ac.jp/~h-pham/regulatory-rules