The bioelectrical impedance analysis (BIA) international database: aims, scope, and call for data.

BACKGROUND: Bioelectrical impedance analysis (BIA) is a technique widely used for estimating body composition and health-related parameters. The technology is relatively simple, quick, and non-invasive, and is currently used globally in diverse settings, including private clinicians' offices, sports and health clubs, and hospitals, and across a spectrum of age, body weight, and disease states. BIA parameters can be used to estimate body composition (fat, fat-free mass, total-body water and its compartments). Moreover, raw measurements including resistance, reactance, phase angle, and impedance vector length can also be used to track health-related markers, including hydration and malnutrition, and disease-prognostic, athletic and general health status. Body composition shows profound variability in association with age, sex, race and ethnicity, geographic ancestry, lifestyle, and health status. To advance understanding of this variability, we propose to develop a large and diverse multi-country dataset of BIA raw measures and derived body components. The aim of this paper is to describe the 'BIA International Database' project and encourage researchers to join the consortium. METHODS: The Exercise and Health Laboratory of the Faculty of Human Kinetics, University of Lisbon has agreed to host the database using an online portal. At present, the database contains 277,922 measures from individuals ranging from 11 months to 102 years, along with additional data on these participants. CONCLUSION: The BIA International Database represents a key resource for research on body composition.

Prevalence of metabolic syndrome and its association with risk factors in patients with established atherosclerosis disease.

Risk factors can lead to clinical conditions, like metabolic syndrome, that predisposes the development of cardiovascular diseases. The aim of this study was to describe the prevalence and which risk factors cause more impact in metabolic syndrome in patients with established atherosclerosis disease. A cross-sectional study was performed as a subanalysis of Programa Alimentação Cardioprotetora Brasileira. Weight, height, waist circumference, blood pressure, lipid profile and fasting glucose were collected. Metabolic syndrome was defined according to the harmonized criteria. Linear regression was used to analyze the association between number of components of metabolic syndrome and risk factors. 82 patients were included and the prevalence of metabolic syndrome was 84.1%. Being overweight was associated with an increase by 0.55 point in diagnostic criteria of metabolic syndrome in crude analysis (95%CI 0.09-1.00) and 0.64 in adjusted analysis (95%CI 0.18-1.09), while former/current smoker status was responsible for raising by 0.48 the number of components of metabolic syndrome, only in adjusted analysis (95%CI 0.04-0.92). Overweight and former/current smoker status are associated with MS, increasing the probability of atherosclerotic events. A healthy lifestyle, that includes avoiding tobacco exposure and proper weight control, must be encouraged in this high-risk population.

Complementarity of NUTRIC score and Subjective Global Assessment for predicting 28-day mortality in critically ill patients.

AIMS: To compare the prognostic power of nutritional screening (NUTRIC score) and Subjective Global Assessment (SGA), executed alone or their complementarity, for predicting 28-day mortality risk in ICU patients. We also aimed to identify the cut-off point obtained in the NUTRIC that presented the best validity parameters for predicting mortality in this population. METHODS: A sample of 159 patients was evaluated in the first 24 hours of ICU admission. Modified NUTRIC score was performed (without interleucina-6). ROC curve and Youden criterion were used to identify the best cut-off point. Poisson regression and the number needed to screen (NNS) were used to test the complementarity between the tools and their ability to predict 28-day mortality. RESULTS: A sample of 159 patients was evaluated (51% male, 56.6 ± 20 years) and the APACHE II, SOFA and NUTRIC score medians were 22 (IQR:15;26), 6 (IQR:2;9) and 3 (IQR: 2;5), respectively. Almost 60% of the patients were malnourished (SGA B or C) and 32.7% died during 28-day follow-up. The area under ROC curve for NUTRIC score was 0.79. Using a new cutoff (NUTRIC score ≥ 4), patients with nutritional risk have a 28-day mortality risk almost 6 times higher than subjects without nutritional risk. Patients classified as SGA C showed a 28-day mortality risk 2.19 times higher compared to nourished ones. Evaluating the complementarity of the tools, patients classified as nutritional risk (NUTRIC score ≥ 4) and SGA C showed a 28-day mortality risk 7 times higher and a lower NNS when compared to those patients with a NUTRIC < 4 and any SGA category. CONCLUSIONS: A new cutoff value was identified for this population. Simultaneous SGA assessment in patients with nutritional risk may enhance the predictive power of 28-day mortality, providing better identification of higher risk patients who may benefit from a more aggressive nutritional therapy.

Obesity paradox in cancer: new insights provided by body composition.

BACKGROUND: Obesity, defined by body mass index (BMI), appears to have a paradoxical protective effect in several chronic diseases. OBJECTIVE: We investigated the obesity paradox in cancer patients by using body composition. DESIGN: The study was an observational study of 175 cancer patients assessed before chemotherapy. Obesity was defined as BMI (in kg/m(2)) ≥30 or fat mass index (FMI; fat mass divided by the square of height) ≥5.2 (men) and ≥8.2 (women) measured by using a bioelectrical impedance analysis. Low muscle mass (sarcopenia) was defined as fat-free mass index (fat-free mass divided by the square of height) <17.5 (men) and <15.1 (women). RESULTS: Most patients were women (65.7%) and had a mean (±SD) age of 56.9 ± 12.8 y. According to BMI criteria, 60% of patients were overweight or obese. The median survival time for overweight (2.64 y; range: 0.23-3.16 y) and obese (2.61 y; range: 0.26-3.20 y) patients was significantly higher than for patients with a normal (2.04 y; range: 0.06-3.05 y) or low (0.52 y; range: 0.19-0.98 y) BMI (P < 0.001). Sarcopenic patients had shorter survival, regardless of their FMI. Obesity predicted higher survival rates only when sarcopenia was absent. In a multivariate Cox regression model, sarcopenia was an independent predictor of higher mortality (HR: 5.19; 95% CI: 2.58, 10.43) after we controlled for BMI, age, and tumor stage. CONCLUSIONS: The obesity paradox is present in cancer patients only when obesity is defined by BMI. Patients with sarcopenic obesity had the poorest prognosis. Cancer patients with high mortality risk can be identified by a body-composition assessment.