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Aims and Objectives: This study aimed to investigate the current status of the core competencies and self‐directed learning ability of diabetes specialist nurses and to explore the relationship between these core competencies and their self‐directed learning ability. Design: A cross‐sectional survey design was used. Methods: This cross‐sectional study was conducted via a web‐based questionnaire platform in China from January 14 to April 24, 2023. The survey included a general information questionnaire, a diabetes specialist nurses' core competencies self‐assessment scale, and a nursing staff's self‐directed learning ability evaluation scale. The data was collected online. Descriptive, correlation and multiple linear regression analyses were conducted using SPSS 26.0 software. Results: 118 diabetes specialist nurses from 11 cities participated in this study. A positive correlation was observed between the core competencies of diabetes specialist nurses and their self‐directed learning ability. The characteristics affecting the core competencies of diabetes specialist nurses included age, participation in external learning and communication and self‐directed learning. Conclusions: The training of diabetes specialist nurses can focus on core competencies, and the ability to self‐direct learning can be used as an entry point to customize feasible theoretical and practical courses. The training system can further improve diabetes specialist nurses' core competencies and self‐directed learning abilities. Relevance to Clinical Practice: A reference can be established that nursing managers and nursing educators can use to develop training programs for specialist nurses by validating the link between their core competencies and self‐directed learning skills. Patient or Public Contribution: Participants were involved solely in the data collection process. No participant contributions were required for the study's design, outcome measurement or implementation. [ABSTRACT FROM AUTHOR]

Objective: In this study, we employed a Mendelian randomization (MR) approach to investigate the independent causal associations of six body composition traits with gestational diabetes mellitus (GDM)., Methods: Genome-wide significant levels (P < 10 × 5 -8 ) of single nucleotide polymorphisms associated with body water mass, total protein, whole body fat-free mass, weight, whole body fat mass, and body fat percentage were used as instrumental variables. Data on GDM were obtained from the FinnGen Consortium, and both univariable and multivariable Mendelian randomization were performed. We utilized five different analytical methods including inverse-variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode to assess the robustness of the results., Results: With univariable Mendelian randomization, the risk of GDM increased per 1-standard deviation (SD) increase in weight (OR weight  = 1.297, P = 3.06 × 10 -5 ), whole body fat mass (OR whole body fat mass  = 1.408, P = 1.32 × 10 -6 ), and the risk of GDM increased per percent increase in body fat percentage (OR body fat percentage  = 1.661, P = 1.01 × 10 -8 ). Total protein had a protective effect on the risk of GDM (OR total protein  = 0.880, P = 0.048). However, there was no significant causal association between increases in body water mass and whole body fat-free mass per SD and the risk of GDM. Causal associations between weight, whole body fat mass, body fat percentage, and total protein with GDM were reduced to null in multivariable Mendelian randomization., Conclusion: The present study furnishes genetic evidence to elucidate the causal relationship between body composition traits and GDM. Additionally, further studies are imperative to establish a causal connection between body composition traits and gestational diabetes mellitus., (© 2023 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.)