Managing the multisystemic symptoms of type I Gaucher Disease (GD) requires a multidisciplinary team approach that includes disease-specific treatments, as well as supportive care. This involves a range of medical specialists, general practitioners, supportive care providers, and patients. Phenotype classification and the setting of treatment goals are important for optimizing the management of type I GD, and providing personalized care. The ability to classify disease severity using validated measurement tools allows the standardization of patient monitoring, and the measurement of disease progression and treatment response. Defining treatment goals is useful to provide a benchmark for assessing treatment response, and managing the expectations of patients and their families. Although treatment goals will vary depending on disease severity, they include the stabilization, improvement or reversal (if possible) of clinical manifestations. Enzyme replacement therapy (ERT) is the standard care for patients with type I GD, but a novel substrate reduction therapy (SRT), Eliglustat has demonstrated safety and efficacy in selected patients. To ensure that treatment goals are being achieved, regular, comprehensive follow up is necessary.

Obesity is a worldwide epidemic; hence it is crucial to understand how it can be effectively prevented/treated. Thus, obesity should be tackled considering that it is a multifactorial condition. This work examines a wide range of factors and attempts to understand which contribute most to variability in overweight/obesity. Eighty-four subjects with overweight/obesity were recruited. Dietary baseline information was obtained by analyzing three 24-hour recalls. Resting metabolic rate was measured using indirect calorimetry, physical activity through accelerometry, cardiometabolic parameters were determined in blood samples and body composition by anthropometry and bioimpedance. Factor analysis of principal components (PCA) was carried out (SPSS v22.0). Large interindividual variability was observed in dietetic, biochemical, and physical activity measurements (coefficient of variation ≥30%), but body composition was homogeneous. Volunteers had an unbalanced diet and low physical activity. PCA reduced the 26 analyzed variables into 7 factors, accounting for 81,5% of total data variability. The main factor was the ‘‘caloric and lipid factor’’ related to diet, responsible for 25.9% of total variability, followed by ‘‘adiposity factor’’ (16,9%) and ‘‘cardiometabolic risk factor’’ (13,2%). Diet seems to be the factor that contributes the most to data variability in people with overweight/obesity, especially the intake of lipids, energy and proteins.