Delaney, K

ABSTRACT Major depressive disorder (MDD) is a substantial public health challenge. Pharmacogenomics (PGx), which identifies genetic variations that predict drug treatment outcomes, may have utility for clinical practice, but adequate representation of all populations is needed. As precision medicine in psychiatry moves towards the use of Artificial Intelligence (AI) and Machine Learning (ML) to predict treatment outcomes using PGx data, representation of diverse populations will be especially important in order to mitigate algorithmic bias and achieve equitable and generalizable findings. This work sought to quantify population diversity in pharmacogenomic studies of MDD through a systematic review. Data from 390 MDD antidepressant PGx studies were extracted from 5739 articles screened. Studies summarized were predominantly conducted in Europe, East Asia, and North America. Across all global studies, the study population comprised 57.3% White, 36.4% Asian, 1.7% Black, 3.5% Hispanic/Latino, and 0.1% Native American or Indigenous participants. Only sixty‐three (16.2%) studies included Black or Latino/Hispanic patients. Additionally, Black, Asian, Hispanic/Latino, and American Indian/Alaska Native populations were statistically underrepresented in U.S. study populations when compared to national census data, while Asian and Black populations were underrepresented in the United Kingdom. The overrepresentation of participants from a limited number of countries combined with the underrepresentation of Black and Hispanic/Latino populations could impact the extent to which pharmacogenomic testing and associated AI/ML‐based PGx tools could individualize antidepressant medication regimens for treating MDD.

ABSTRACTPharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. The primary aim of this systematic review was to summarize antidepressant pharmacogenetic studies to enhance understanding of the genes, variants, datatypes/methodologies, and outcomes investigated in the context of MDD. The secondary aim was to identify clinical genetic panels indicated for antidepressant prescribing and summarize their genes and variants. Screening of N = 5793 articles yielded N = 390 for inclusion, largely comprising adult (≥ 18 years) populations. Top‐studied variants identified in the search were discussed and compared with those represented on the N = 34 clinical genetic panels that were identified. Summarization of articles revealed sources of heterogeneity across studies and low rates of replicability of pharmacogenetic associations. Heterogeneity was present in outcome definitions, treatment regimens, and differential inclusion of mediating variables in analyses. Efficacy outcomes (i.e., response, remission) were studied at greater frequency than adverse‐event outcomes. Studies that used advanced analytical approaches, such as machine learning, to integrate variants with complimentary biological datatypes were fewer in number but achieved higher rates of significant associations with treatment outcomes than candidate variant approaches. As large biological datasets become more prevalent, machine learning will be an increasingly valuable tool for parsing the complexity of antidepressant response. This review provides valuable context and considerations surrounding pharmacogenetic associations in MDD which will help inform future research and translation efforts for guiding antidepressant care.

AbstractBackgroundThe reproducibility of gastric emptying (GE) measured with scintigraphy in patients is poorly understood. Our aims were to assess the intra and inter‐individual reproducibility of these parameters in patients with upper gastrointestinal symptoms.MethodsSixty patients (21 diabetics, 39 non‐diabetics) with upper gastrointestinal symptoms underwent scintigraphic‐assessment of GE of a solid meal (296 kcal, 30% fat) over 4 hours on two occasions at an average interval of 15 days. The concordance correlation coefficient (CCC), intra and inter‐individual coefficients of variation (COV) of GE endpoints were analyzed.ResultsThe GE t1/2 was 134 ± 8 minutes (mean ± SEM) for the first and 128 ± 6 minutes for the second study. The mean (95% CI) CCC between the two studies was 0.79 (0.67, 0.87) for GE at 1 hour, 0.83 (0.75, 0.9) for GE at 2 hours, 0.54 (0.34, 0.7) for GE at 4 hours, and 0.79 (0.68, 0.86) for GE t1/2. However, in 18 of 60 patients (30%), the characterization of GE as normal, delayed, or rapid differed between the first and second studies. For gastric empting t1/2, the inter‐individual coefficients of variation was 40%; the intra‐individual COV was 20%, comparable in diabetics and non‐diabetics, and greater in patients with rapid (28%) than delayed (18%) or normal GE (12%).Conclusions & InferencesAmong patients with upper gastrointestinal symptoms, GE measured with scintigraphy is relatively reproducible. In 30% of cases, the interpretation was different between the two assessments. Hence, a diagnosis of gastroparesis based on a single study may occasionally be inaccurate.