Providing a common language for obesity: the European Association for the Study of Obesity obesity taxonomy.

BACKGROUND: The basis for a high-performing and resilient healthcare system is having a common, precise, and scientifically accurate language used across all stakeholder groups. However, such a common language is lacking for obesity. Therefore, the European Association for the Study of Obesity undertook a taxonomy initiative to provide standardised language for obesity as commonly used from policy to practice for other major policy-prioritised non-communicable diseases (NCDs). METHODS: An online Delphi consensus study was conducted, involving a panel of experts representing stakeholder groups of policymakers, healthcare professionals, people with lived experience, and researchers. Based on the understanding of obesity as an adiposity-based chronic disease, 54 statements demarcated into definition, scope and contextual usage were developed across six themes: Definition of obesity, Causes, onset and progression, Obesity prevention, Screening and early diagnosis, Treatment and management, Obesity consequences. RESULTS: Of the 194 invited experts, 70 (36%), 63 (33%), and 58 (30%) experts participated in rounds one, two, and three, respectively. Consensus was achieved on 70% of the proposed definitions, scope, and contextual usage after round one, 94% after round two and 100% after round three. The Definition of Obesity theme included distinctions between population-level indicators and individual-level signs of obesity, and how pre-obesity was defined. The Causes, Onset and Progression theme characterised the timing of obesity development. The Obesity Prevention theme explicitly differentiated between health promotion and primary prevention. Both the Screening and Early Diagnosis, and the Treatment and Management themes defined concepts supporting a continuum of care model. The Consequences of Obesity theme encompassed health and socio-economic outcomes. CONCLUSION: The taxonomy provides a contemporary evidence-based language about obesity that aligns with language used for policy-prioritised NCDs. The taxonomy is useful for education, advocacy, and communication and can be used by policymakers, healthcare professionals, people living with obesity, researchers, and health system users.

Outcome of Patients with Pregnancy-Associated Breast Cancer Who Have Subsequent Pregnancies.

BACKGROUND: After treatment of pregnancy-associated breast cancer (PABC), some women desire future pregnancy. While safety of pregnancy after breast cancer has been demonstrated, the same cannot be said about women with PABC. OBJECTIVE: The aim of this study was to describe the incidence and outcomes of patients with PABC with subsequent pregnancies compared with those without another pregnancy. METHODS: A retrospective chart review identified patients diagnosed with breast cancer during pregnancy or within 5 years postpartum between 2011 and 2023. Patients were then screened for further pregnancy. Clinicopathologic variables, oncologic outcomes, and pregnancy outcomes were recorded. The Chi-square test and t-test were used to compare patients with subsequent pregnancy with those without. Kaplan-Meier method and log-rank test were used to estimate 5-year disease-free survival (DFS). RESULTS: Overall, 75 patients with PABC were identified, 58 of whom had PABC and no further pregnancies (NSP-PABC) and 17 with subsequent pregnancy (SP-PABC). Compared with patients with NSP-PABC, patients with SP-PABC were significantly younger (p = 0.015) and less likely to have prior pregnancies (p < 0.001). Overall median follow-up was 4.3 years. Calculated 5-year DFS rates were 86.2% and 89.0% for the SP-PABC and NSP-PABC groups, respectively (p = 0.76). Calculated 5-year overall survival was 100% and 90.7% for the SP-PABC and NSP-PABC groups, respectively (p = 0.22). Within the SP-PABC group, 14/17 patients had successful deliveries. CONCLUSIONS: This study provides the first descriptions of patients with PABC and subsequent pregnancy. Additional investigation, likely with pooled analysis from multiple institutions, is necessary to determine the oncologic and obstetric safety of pregnancy following PABC.

Successful Use of a Cadaver Model to Teach Ultrasound-Guided Breast Procedures to Surgical Trainees.

INTRODUCTION: In academic breast surgery, ultrasound use tends to be limited to radiology departments, thus formal surgical resident training in breast ultrasound is sparse. Building on residents' ultrasound skills in our general surgery training program, we developed a novel curriculum to teach ultrasound-guided breast procedures (UGBPs), including core needle biopsy (CNB) and wire localization (WL). We hypothesized that learning UGBPs on cadavers would be preferred to learning with a breast phantom model using chicken breasts. METHODS: Residents received a 1-h lecture on breast CNB and WL followed by a 1-h hands-on laboratory session. Olives stuffed with red pimentos were used to replicate breast masses and implanted in chicken breasts and the breasts of lightly embalmed and unembalmed female cadavers. All residents practiced UGBPs with a course instructor on both models. Residents completed anonymous prelaboratory and postlaboratory surveys utilizing five-point Likert scales. RESULTS: A total of 35 trainees participated in the didactics; all completed the prelaboratory survey and 28 completed the postlaboratory survey. Participant clinical year ranged from 1 to 6. Residents' confidence in describing and performing CNBs and WLs increased significantly on postlaboratory surveys, controlling for clinical year (P < 0.001). Eighty-point seven percent preferred learning UGBPs on cadavers over phantoms most commonly citing that the cadaver was more realistic. CONCLUSIONS: Following a novel 2-h UGBP training curriculum using phantom and cadaveric models, resident confidence in describing and performing UGBPs significantly improved. Most favored the cadaveric model and reported that the course prepared them for real-life procedures.

Information on Measles, Mumps and Rubella Vaccine in the Copenhagen School Health Records Register.

BACKGROUND: The Copenhagen School Health Record Register (CSHRR) includes health information from school examinations and is now updated with information on measles, mumps and rubella vaccines for the cohorts born from 1977 to 1994. AIM: The aim of this study is to provide a comprehensive description and validation the newly digitised vaccine information in the CSHRR. METHODS: We describe the data collection and the newly digitalised information in the CSHRR. We investigate the extent to which the full CSHRR population is representative of Copenhagen and the entire Danish population. Furthermore, we explore how the registry information on vaccination uptake based on reimbursement data matches the vaccine information obtained from CSHRR for the period during which both data are available. RESULTS: The CSHRR population matches closely the complete population of all schoolchildren in Copenhagen, and information on vaccine uptake in CSHRR matches with vaccine registry data for later cohorts. However, a sizable proportion of the immigrant children in the CSHRR have missing information on vaccination. Removing children who have had no additional immunisations enhances data quality. CONCLUSIONS: The CSHRR covers a large share of the Danish population and includes detailed vaccine information. By linking the data to other registry data, the updated CSHRR is valuable resource for future research.

Early life body size and puberty markers as predictors of breast cancer risk later in life: A neural network analysis.

BACKGROUND: The early life factors of birthweight, child weight, height, body mass index (BMI) and pubertal timing are associated with risks of breast cancer. However, the predictive value of these factors in relation to breast cancer is largely unknown. Therefore, using a machine learning approach, we examined whether birthweight, childhood weights, heights, BMIs, and pubertal timing individually and in combination were predictive of breast cancer. METHODS: We used information on birthweight, childhood height and weight, and pubertal timing assessed by the onset of the growth spurt (OGS) from 164,216 girls born 1930-1996 from the Copenhagen School Health Records Register. Of these, 10,002 women were diagnosed with breast cancer during 1977-2019 according to a nationwide breast cancer database. We developed a feed-forward neural network, which was trained and tested on early life body size measures individually and in various combinations. Evaluation metrics were examined to identify the best performing model. RESULTS: The highest area under the receiver operating curve (AUC) was achieved in a model that included birthweight, childhood heights, weights and age at OGS (AUC = 0.600). A model based on childhood heights and weights had a comparable AUC value (AUC = 0.598), whereas a model including only childhood heights had the lowest AUC value (AUC = 0.572). The sensitivity of the models ranged from 0.698 to 0.760 while the precision ranged from 0.071 to 0.076. CONCLUSION: We found that the best performing network was based on birthweight, childhood weights, heights and age at OGS as the input features. Nonetheless, this performance was only slightly better than the model including childhood heights and weights. Further, although the performance of our networks was relatively low, it was similar to those from previous studies including well-established risk factors. As such, our results suggest that childhood body size may add additional value to breast cancer prediction models.