Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (<age 18) and adult-onset depression. The most significant variant (p = 8.77 × 10⁻⁸) localized to an intron of SAMD3. In silico functional annotation of top signals (p < 1 × 10⁻⁵) demonstrated gene expression enrichment in the brain and during embryonic development. Network analysis identified differential associations between four variants (in/near INTU, FAT1, CNTN6, and TM9SF2) and plasma metabolites (phosphatidylcholines, carnitines, biogenic amines, and amino acids) in early- compared with adult-onset MDD. Multi-omics integration identified differential biosignatures of early- and adult-onset MDD. These biosignatures call for future studies to follow participants from childhood through adulthood and collect repeated -omics and neuroimaging measures to validate and deeply characterize the biomarkers of susceptibility and/or resistance to MDD development.

Metabolomic and lipidomic analyses provide an opportunity for novel biological insights. Cholangiocarcinoma (CCA) remains a highly lethal cancer with limited response to systemic, targeted, and immunotherapeutic approaches. Using a global metabolomics and lipidomics platform, this study aimed to discover and characterize metabolomic variations and associated pathway derangements in patients with CCA. Leveraging a biospecimen collection, including samples from patients with digestive diseases and normal controls, global serum metabolomic and lipidomic profiling was performed on 213 patients with CCA and 98 healthy controls. The CCA cohort of patients included representation of intrahepatic, perihilar, and distal CCA tumours. Metabolome-wide association studies utilizing multivariable linear regression were used to perform case-control comparisons, followed by pathway enrichment analysis, CCA subtype analysis, and disease stage analysis. The impact of biliary obstruction was evaluated by repeating analyses in subsets of patients only with normal bilirubin levels. Of the 420 metabolites that discriminated patients with CCA from controls, decreased abundance of cysteine-glutathione disulfide was most closely associated with CCA. Additional conjugated bile acid species were found in increased abundance even in the absence of clinically relevant biliary obstruction denoted by elevated serum bilirubin levels. Pathway enrichment analysis also revealed alterations in caffeine metabolism and mitochondrial redox-associated pathways in the serum of patients with CCA. The presented metabolomic and lipidomic profiling demonstrated multiple alterations in the serum of patients with CCA. These exploratory data highlight novel metabolic pathways in CCA and support future work in therapeutic targeting of these pathways and the development of a precision biomarker panel for diagnosis. Cholangiocarcinoma (CCA) is a highly lethal hepatobiliary cancer with limited treatment response, highlighting the need for a better understanding of the disease biology. Using a global metabolomics and lipidomics platform, we characterized distinct changes in the serum of 213 patients with CCA compared with healthy controls. The results of this study elucidate novel metabolic pathways in CCA. These findings benefit stakeholders in both the clinical and research realms by providing a foundation for improved disease diagnostics and identifying novel targets for therapeutic design.

Pharmacogenetics 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.

Pharmacogenomic (PGx) biomarkers integrated using machine learning can be embedded within the electronic health record (EHR) to provide clinicians with individualized predictions of drug treatment outcomes. Currently, however, drug alerts in the EHR are largely generic (not patient‐specific) and contribute to increased clinician stress and burnout. Improving the usability of PGx alerts is an urgent need. Therefore, this work aimed to identify principles for optimal PGx alert design through a health‐system‐wide, mixed‐methods study. Clinicians representing multiple practices and care settings (N = 1062) in urban, rural, and underserved regions were invited to complete an electronic survey comparing the usability of three drug alerts for citalopram, as a case study. Alert 1 contained a generic warning of pharmacogenomic effects on citalopram metabolism. Alerts 2 and 3 provided patient‐specific predictions of citalopram efficacy with varying depth of information. Primary outcomes included the System's Usability Scale score (0–100 points) of each alert, the perceived impact of each alert on stress and decision‐making, and clinicians' suggestions for alert improvement. Secondary outcomes included the assessment of alert preference by clinician age, practice type, and geographic setting. Qualitative information was captured to provide context to quantitative information. The final cohort comprised 305 geographically and clinically diverse clinicians. A simplified, individualized alert (Alert 2) was perceived as beneficial for decision‐making and stress compared with a more detailed version (Alert 3) and the generic alert (Alert 1) regardless of age, practice type, or geographic setting. Findings emphasize the need for clinician‐guided design of PGx alerts in the era of digital medicine.

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.

This report sought to employ multi-modal integration of pre-treatment brain (electroencephalogram, resting-state functional magnetic resonance imaging) and blood (immune and metabolic) biomarkers to facilitate causal inference-based treatment selection by virtue of establishing predictability of remission to multi-stage antidepressant treatment. Data from two stages of pharmacotherapy in the ‘Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression’ (EMBARC) study from participants with both brain and blood biomarkers were included (N = 197). Participants were initially randomized to sertraline or placebo (Stage 1), and depending on clinical response at week-8, their therapy in Stage 2 was either maintained or switched (to sertraline, if a non-responder to placebo, or to bupropion, if a non-responder to sertraline). Three readily accessible clinical features combined with 15 multi-modal features associated with baseline depression severity predicted stage 2 remission with an AUC of 0.74, 0.71, and 0.73 for sertraline, bupropion, and placebo treatment respectively. Propensity score-matching (causal inference) was conducted across Stage 2 treatment arms, and the same features were used to build an unsupervised model to produce the probability of remission to the given Stage 2 treatment (as factual outcome), as well as the alternative treatment not given (as counter factual). While the accuracy of observed outcomes across treatment arms was 82%, the accuracies of predicted counterfactual (unobserved) outcomes warrant future prospective studies. 16 weeks and associated biomarker-based prediction of counterfactuals suggest that the selected markers are highly sensitive features for guiding antidepressant treatment selection.