The objective of this study was to evaluate the Diabetes Eating Problems Survey-Revised (DEPS-R) against the Eating Disorder Diagnostic Interview (EDDI). Specific aims were to (1) assess the ability of the DEPS-R to identify Diagnostic and Statistical Manual-5 (DSM-5) eating disorders, including sensitivity and specificity of the current DEPS-R cutoff ≥20 and (2) report the correlation of each item to the presence of any eating disorder. Baseline data from 293 women (14-35 years) with type 1 diabetes (T1D) and body image concerns enrolled in a multinational randomized controlled trial were examined. Receiver operating characteristic (ROC) analysis, univariate logistic regression and two-sample t-test were performed. The ROC analysis demonstrated good accuracy of the DEPS-R with an area under the curve (AUC) of 0.82 (95% CI 0.79-0.94). The cutoff of ≥20 yielded a sensitivity of 87.5% (95% CI 83.6%-91.3%) and a specificity of 60.4% (95% CI 54.8%-66.0%). Univariate logistic regression identified 12 items as significantly correlated with the presence of any eating disorder. The highest odds ratios (OR) were observed for items 9 (OR = 3.64), 8 (OR = 2.85), 13 (OR = 2.36), 14 (OR = 2.23), 15 (OR = 1.99) and 5 (OR = 1.99). This is the first study to investigate the ability of the DEPS-R to identify DSM-5 eating disorder diagnosis established via a diagnostic interview using a ROC-analysis. DEPS-R cutoff ≥20 correctly identified most cases with eating disorders but showed moderate specificity, considered acceptable as an initial screening tool for disordered eating. In clinical care, specific DEPS-R items may be emphasized to explore the presence of disordered eating behaviours and eating disorders.

Avoidant/restrictive food intake disorder (ARFID) involves restrictive eating driven by sensory sensitivity, low interest in food, or fear of negative consequences of eating. ARFID affects males and females equally and presents across the weight spectrum. Leptin, an anorexigenic hormone linked to adiposity, may contribute to low hunger and early satiety in ARFID. Prior findings indicate low leptin levels in low-weight females with ARFID; however, the role of leptin in ARFID across weight status and sex remains unknown. We hypothesized that youth with full/subthreshold ARFID, including a subgroup of low-weight males, would exhibit lower fasting leptin levels than healthy controls (HCs). We explored cross-sectional and longitudinal associations between leptin and clinical characteristics. Participants were youth aged 10-23 years (BMI percentile M [SD] = 41.4 (33.6)) with full/subthreshold ARFID (n = 95; 49.5% female) and HCs (n = 28; 50.0% female). At baseline, participants completed questionnaires, a fasted blood draw, and a diagnostic interview for ARFID, readministered at 1- and 2year follow-ups. Leptin levels did not differ between ARFID and HC groups. Low-weight ARFID males had significantly lower leptin than HC males (η 2 = 0.258); however, this difference was nonsignificant after adjusting for BMI percentile. Leptin showed medium-to-large positive associations with age, BMI, and anxiety but not appetite or ARFID severity. Baseline leptin did not predict symptom or weight changes longitudinally. Hypoleptinemia was only evident in low-weight ARIFD and strongly correlated with BMI percentile. However, relationships with anxiety may suggest additional roles of leptin in underlying ARFID psychopathology. Findings should be replicated in larger, more representative samples.

Climbing fibre (CF) inputs to Purkinje cells (PCs) instruct plasticity and learning in the cerebellum1-3. Paradoxically, CFs also excite molecular layer interneurons (MLIs)4,5, a cell type that inhibits PCs and can restrict plasticity and learning6,7. However, two types of MLI with opposing influences have recently been identified: MLI1s inhibit PCs, reduce dendritic calcium signals and suppress plasticity of granule cell to PC synapses2,6-9, whereas MLI2s inhibit MLI1s and disinhibit PCs8. To determine how CFs can activate MLIs without also suppressing the PC calcium signals necessary for plasticity and learning, we investigated the specificity of CF inputs onto MLIs. Serial electron microscopy reconstructions indicate that CFs contact both MLI subtypes without making conventional synapses, but more CFs contact each MLI2 through more sites with larger contact areas. Slice experiments indicate that CFs preferentially excite MLI2s through glutamate spillover4,5. In agreement with these anatomical and slice experiments, in vivo Neuropixels recordings show that spontaneous CF activity excites MLI2s, inhibits MLI1s and disinhibits PCs. By contrast, learning-related sensory stimulation produces more complex responses, driving convergent CF and granule cell inputs that could either activate or suppress MLI1s. This balance was robustly shifted towards MLI1 suppression when CFs were synchronously active, in turn elevating the PC dendritic calcium signals necessary for long-term depression. These data provide mechanistic insight into why CF synchrony can be highly effective at inducing cerebellar learning2,3 by revealing a critical disinhibitory circuit that allows CFs to act through MLIs to enhance PC dendritic calcium signals necessary for plasticity.

Categorical variants, or sets of genomic alterations constrained by shared properties, are pervasive across clinical, regulatory, and research domains in the biomedical ecosystem, yet their inconsistent and non-computable representation hinders data interoperability and clinical interpretation. We surveyed genomic knowledgebases spanning regulatory approvals and the biomedical literature and found that categorical variants underpin a substantial proportion of clinical genomics knowledge, but are largely described using incompatible bespoke models. To address this, we developed the GA4GH Categorical Variation Representation Specification (Cat-VRS), a constraint-based framework that provides a unified computable representation for both precise and intentionally broad categories across molecular and systemic variant domains. Cat-VRS enables harmonization of genomic knowledgebases, computable category-based search, and automated matching between assayed variants and categorical entities in clinical and research contexts. By providing a principled, extensible model for categorical variation, Cat-VRS enables computable reasoning over genomic variant categories and establishes a foundation for the standardized representation and exchange of genomic knowledge.