James MacKillop

Background Adults with cannabis hyperemesis syndrome (CHS) are increasingly presenting to the emergency department (ED), and this systematic review will evaluate the direct evidence on the effectiveness of capsaicin and dopamine antagonists in its clinical management. Methods A bibliographic search was conducted to address the following population–intervention–control–outcome (PICO) question: (P) adults >18 years old with a diagnosis of acute CHS presenting to the ED; (I) dopamine antagonists (e.g., haloperidol, droperidol) and topical capsaicin; (C) usual care or no active comparator; and (O) symptoms improvement/resolution in ED, ED length of stay, admission rate, ED recidivism, need for rescue medication, and adverse events. This systematic review was conducted in accordance with PRISMA reporting recommendations. Results From 53 potentially relevant articles, seven articles were included: five observational studies and two randomized controlled trials, including a total of 492 patients. Five of these studies evaluated the efficacy of capsaicin cream ( n = 386), and two examined dopamine antagonists (haloperidol, droperidol; n = 106). There was mixed evidence for the efficacy of capsaicin for reducing nausea and emesis. Both studies evaluating dopamine antagonists detected clinical benefit to usual care or no active comparator. Conclusions There is limited direct evidence on the efficacy of dopamine antagonists or capsaicin for treating CHS in the ED. Current evidence is mixed for capsaicin and potentially beneficial for dopamine antagonists. Because of the small number of studies, small number of participants, lack of standardization of treatment administration, and risk of bias of the included studies, methodologically rigorous trials on both types of intervention are needed to directly inform ED management of CHS.

Objective Obesity is a disorder of excessive adiposity, typically assessed via the anthropometric density measure of BMI. Numerous studies have implicated BMI with differences in brain structure, but with highly inconsistent findings. Methods Machine learning elastic net regression models with cross‐validation were conducted to characterize a neuroanatomical morphometry profile associated with BMI in 1100 participants (22% BMI > 30, n = 242) from the Human Connectome Project Young Adult project. Results Using five‐fold cross‐validation, the multiregion neuroanatomical profile substantively predicted BMI ( R 2 = 10.05%), and this was robust in a held‐out test set ( R 2 = 8.87%). In terms of specific regions, the neuroanatomical profile was enriched for nodes in the default mode, executive control, and salience networks. The relationship between the morphometry profile and BMI itself was partially mediated by impulsive delay discounting and general cognitive ability. Conclusions Taken together, these findings reveal a robust machine learning‐derived neuroanatomical profile of BMI, one that comprises nodes in motivational brain networks and suggests the functional links to obesity are via self‐regulatory capacity and cognitive function. image