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OBJECTIVE: The European Reference Network on Rare Endocrine Conditions (Endo-ERN) aims to organize high-quality healthcare throughout Europe, including care for pituitary adenoma patients. As surgery is the mainstay of treatment, we aimed to describe the current surgical practice and published surgical outcomes of pituitary adenoma within Endo-ERN. DESIGN AND METHODS: Systematic review and meta-analysis of studies reporting surgical outcomes of pituitary adenoma patients within Endo-ERN MTG6 pituitary reference centers between 2010 and 2019. A survey was completed by reference centers on their current surgical practice. RESULTS: A total of 18 out of 43 (42%) reference centers located in 7 of the 20 (35%) MTG6-represented countries published 48 articles. Remission rates were 50% (95% CI: 42-59) for patients with acromegaly, 68% (95% CI: 60-75) for Cushing's disease, and 53% (95% CI: 39-66%) for prolactinoma. Gross total resection was achieved in 49% (95% CI: 37-61%) of patients and visual improvement in 78% (95% CI: 68-87). Mortality, hemorrhage, and carotid injury occurred in less than 1% of patients. New-onset hypopituitarism occurred in 16% (95% CI: 11-23), transient diabetes insipidus in 12% (95% CI: 6-21), permanent diabetes insipidus in 4% (95% CI: 3-6), syndrome of inappropriate secretion of antidiuretic hormone (SIADH) in 9% (95% CI: 5-14), severe epistaxis in 2% (95% CI: 0-4), and cerebrospinal fluid leak in 4% (95% CI: 2-6). Thirty-five (81%) centers completed the survey: 54% were operated endoscopically and 57% were together with an ENT surgeon. CONCLUSION: The results of this study could be used as a first benchmark for the outcomes of pituitary adenoma surgery within Endo-ERN. However, the heterogeneity between studies in the reporting of outcomes hampers comparability and warrants outcome collection through registries.

This International Consensus Guideline was developed by experts in the field of small for gestational age (SGA) of 10 pediatric endocrine societies worldwide. A consensus meeting was held and 1300 articles formed the basis for discussions. All experts voted about the strengths of the recommendations. The guideline gives new and clinically relevant insights into the etiology of short stature after SGA birth, including novel knowledge about (epi)genetic causes. Further, it presents long-term consequences of SGA birth and also reviews new treatment options, including treatment with gonadotropin-releasing hormone agonist (GnRHa) in addition to growth hormone (GH) treatment, as well as the metabolic and cardiovascular health of young adults born SGA after cessation of childhood GH treatment in comparison with appropriate control groups. To diagnose SGA, accurate anthropometry and use of national growth charts are recommended. Follow-up in early life is warranted and neurodevelopment evaluation in those at risk. Excessive postnatal weight gain should be avoided, as this is associated with an unfavorable cardiometabolic health profile in adulthood. Children born SGA with persistent short stature < −2.5 SDS at age 2 years or < −2 SDS at 3 to 4 years of age, should be referred for diagnostic workup. In case of dysmorphic features, major malformations, microcephaly, developmental delay, intellectual disability, and/or signs of skeletal dysplasia, genetic testing should be considered. Treatment with 0.033 to 0.067 mg GH/kg/ day is recommended in case of persistent short stature at age of 3 to 4 years. Adding GnRHa treatment could be considered when short adult height is expected at pubertal onset. All young adults born SGA require counseling to adopt a healthy lifestyle.

Objective: The European Reference Network on Rare Endocrine Conditions (Endo-ERN) aims to organize high-quality healthcare throughout Europe, including care for pituitary adenoma patients. As surgery is the mainstay of treatment, we aimed to describe the current surgical practice and published surgical outcomes of pituitary adenoma within Endo-ERN. Design and Methods: Systematic review and meta-analysis of studies reporting surgical outcomes of pituitary adenoma patients within Endo-ERN MTG6 pituitary reference centers between 2010 and 2019. A survey was completed by reference centers on their current surgical practice. Results: A total of 18 out of 43 (42%) reference centers located in 7 of the 20 (35%) MTG6- represented countries published 48 articles. Remission rates were 50% (95% CI: 42–59) for patients with acromegaly, 68% (95% CI: 60–75) for Cushing’s disease, and 53% (95% CI: 39–66%) for prolactinoma. Gross total resection was achieved in 49% (95% CI: 37–61%) of patients and visual improvement in 78% (95% CI: 68–87). Mortality, hemorrhage, and carotid injury occurred in less than 1% of patients. New-onset hypopituitarism occurred in 16% (95% CI: 11–23), transient diabetes insipidus in 12% (95% CI: 6–21), permanent diabetes insipidus in 4% (95% CI: 3–6), syndrome of inappropriate secretion of antidiuretic hormone (SIADH) in 9% (95% CI: 5–14), severe epistaxis in 2% (95% CI: 0–4), and cerebrospinal fluid leak in 4% (95% CI: 2–6). Thirty-five (81%) centers completed the survey: 54% were operated endoscopically and 57% were together with an ENT surgeon. Conclusion: The results of this study could be used as a first benchmark for the outcomes of pituitary adenoma surgery within Endo-ERN. However, the heterogeneity between studies in the reporting of outcomes hampers comparability and warrants outcome collection through registries.

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project “BigO: Big data against childhood obesity” used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.

This article aims to provide guidance on prevention and treatment of COVID-19 in patients with genetic obesity. Key principals of the management of patients with genetic obesity during COVID-19 pandemic for patients that have contracted COVID-19 are to be aware of: possible adrenal insufficiency (e.g., POMC deficiency, PWS); a more severe course in patients with concomitant immunodeficiency (e.g., LEP and LEPR deficiency), although defective leptin signalling could also be protective against the pro-inflammatory phenotype of COVID-19; disease severity being masked by insufficient awareness of symptoms in syndromic obesity patients with intellectual deficit (in particular PWS); to adjust medication dose to increased body size, preferably use dosing in m2; the high risk of malnutrition in patients with Sars-Cov2 infection, even in case of obesity. Key principals of the obesity management during the pandemic are to strive for optimal obesity management and a healthy lifestyle within the possibilities of the regulations to prevent weight (re)gain and to address anxiety within consultations, since prevalence of anxiety for COVID-19 is underestimated.

Obesity is currently affecting very large portions of the global population. Effective prevention and treatment starts at the early age and requires objective knowledge of population-level behavior on the region/neighborhood scale. To this end, we present a system for extracting and collecting behavioral information on the individual-level objectively and automatically. The behavioral information is related to physical activity, types of visited places, and transportation mode used between them. The system employs indicator-extraction algorithms from the literature which we evaluate on publicly available datasets. The system has been developed and integrated in the context of the EU-funded BigO project that aims at preventing obesity in young populations., Comment: Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canada

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants., Comment: Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canada