Excess fat within bone marrow is associated with lower bone density. Metabolic stressors such as chronic caloric restriction (CR) can exacerbate marrow adiposity, and increased glucocorticoid signaling and adrenergic signaling are implicated in this phenotype. The current study tested the role of glucocorticoid signaling in CR-induced stress by conditionally deleting the glucocorticoid receptor (Nr3c1; hereafter abbreviated as GR) in bone marrow osteoprogenitors (Osx1-Cre) of mice subjected to CR and ad libitum diets. Conditional knockout of the GR (GR-CKO) reduced cortical and trabecular bone mass as compared to WT mice under both ad libitum feeding and CR conditions. No interaction was detected between genotype and diet, suggesting that the GR is not required for CR-induced skeletal changes. The lower bone mass in GR-CKO mice, and the further decrease in bone by CR, resulted from suppressed bone formation. Interestingly, treatment with the β-adrenergic receptor antagonist propranolol mildly but selectively improved metrics of cortical bone mass in GR-CKO mice during CR, suggesting interaction between adrenergic and glucocorticoid signaling pathways that affects cortical bone. GR-CKO mice dramatically increased marrow fat under both ad libitum and CR-fed conditions, and surprisingly propranolol treatment was unable to rescue CR-induced marrow fat in either WT or GR-CKO mice. Additionally, serum corticosterone levels were selectively elevated in GR-CKO mice with CR, suggesting the possibility of bone–hypothalamus–pituitary–adrenal crosstalk during metabolic stress. This work highlights the complexities of glucocorticoid and β-adrenergic signaling in stress-induced changes in bone mass, and the importance of GR function in suppressing marrow adipogenesis while maintaining healthy bone mass.

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Autism Spectrum Disorders, hereafter referred to as autism, emerge early and persist throughout life, contributing significantly to global years lived with disability. Typically, an autism diagnosis depends on clinical assessments by highly trained professionals. This high resource demand poses a challenge in resource-limited areas where skilled personnel are scarce and awareness of neurodevelopmental disorder symptoms is low. We have developed and tested a novel app, START, that can be administered by non-specialists to assess several domains of the autistic phenotype (social, sensory, motor functioning) through direct observation and parent report. N=131 children (2-7 years old; 48 autistic, 43 intellectually disabled, and 40 typically developing) from low-resource settings in the Delhi-NCR region, India were assessed using START in home settings by non-specialist health workers. We observed a consistent pattern of differences between typically and atypically developing children in all three domains assessed. The two groups of children with neurodevelopmental disorders manifested lower social preference, higher sensory sensitivity, and lower fine-motor accuracy compared to their typically developing counterparts. Parent-report further distinguished autistic from non-autistic children. Machine-learning analysis combining all START-derived measures demonstrated 78% classification accuracy for the three groups (ASD, ID, TD). Qualitative analysis of the interviews with health workers and families (N= 15) of the participants suggest high acceptability and feasibility of the app. These results provide proof of principle for START, and demonstrate the potential of a scalable, mobile tool for assessing neurodevelopmental disorders in low-resource settings.

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