Exome Sequencing Identifies Carriers of the Autosomal Dominant Cancer Predisposition Disorders Beyond Current Practice Guideline Recommendations.

PURPOSE: The autosomal dominant cancer predisposition disorders hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS) are genetic conditions for which early identification and intervention have a positive effect on the individual and public health. The goals of this study were to determine whether germline genetic screening using exome sequencing could be used to efficiently identify carriers of HBOC and LS. METHODS: Participants were recruited from three geographically and racially diverse sites in the United States (Rochester, MN; Phoenix, AZ; Jacksonville, FL). Participants underwent Exome+ sequencing (Helix Inc, San Mateo, CA) and return of results for specific genetic findings: HBOC (BRCA1 and BRCA1) and LS (MLH1, MSH2, MSH6, PMS2, and EPCAM). Chart review was performed to collect demographics and personal and family cancer history. RESULTS: To date, 44,306 participants have enrolled in Tapestry. Annotation and interpretation of all variants in genes for HBOC and LS resulted in the identification of 550 carriers (prevalence, 1.24%), which included 387 with HBOC (27.2% BRCA1, 42.8% BRCA2) and 163 with LS (12.3% MSH6, 8.8% PMS2, 4.5% MLH1, 3.8% MSH2, and 0.2% EPCAM). More than half of these participants (52.1%) were newly diagnosed carriers with HBOC and LS. In all, 39.2% of HBOC/LS carriers did not satisfy National Comprehensive Cancer Network (NCCN) criteria for genetic evaluation. NCCN criteria were less commonly met in underrepresented minority populations versus self-reported White race (51.5% v 37.5%, P = .028). CONCLUSION: Our results emphasize the need for wider utilization of germline genetic sequencing for enhanced screening and detection of individuals who have LS and HBOC cancer predisposition syndromes.

Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review.

BACKGROUND: The occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers. OBJECTIVE: This study aims to identify physiological biomarkers of burnout, and establish what gaps are currently present in the use of wearable technologies for burnout prediction among health care professionals (HCPs). METHODS: A comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases were Ovid: MEDLINE(R), Embase, Healthstar, APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science Core Collection via Clarivate Analytics, Scopus via Elsevier, EBSCOhost: Academic Search Premier, CINAHL with Full Text, and Business Source Premier. Studies observing anxiety, burnout, stress, and depression using a wearable device worn by an HCP were included, with HCP defined as medical students, residents, physicians, and nurses. Bias was assessed using the Newcastle Ottawa Quality Assessment Form for Cohort Studies. RESULTS: The initial search yielded 505 papers, from which 10 (1.95%) studies were included in this review. The majority (n=9) used wrist-worn biosensors and described observational cohort studies (n=8), with a low risk of bias. While no physiological measures were reliably associated with burnout or anxiety, step count and time in bed were associated with depressive symptoms, and heart rate and heart rate variability were associated with acute stress. Studies were limited with long-term observations (eg, ≥12 months) and large sample sizes, with limited integration of wearable data with system-level information (eg, acuity) to predict burnout. Reporting standards were also insufficient, particularly in device adherence and sampling frequency used for physiological measurements. CONCLUSIONS: With wearables offering promise for digital health assessments of human functioning, it is possible to see wearables as a frontier for predicting burnout. Future digital health studies exploring the utility of wearable technologies for burnout prediction should address the limitations of data standardization and strategies to improve adherence and inclusivity in study participation.

Change in neurocognitive functioning in patients with treatment-resistant depression with serial intravenous ketamine infusions: The Bio-K multicenter trial.

This nonrandomized, multicenter, open-label clinical trial explored the impact of intravenous (IV) ketamine on cognitive function in adults (n = 74) with treatment-resistant depression (TRD). Patients received three IV ketamine infusions during the acute phase and, if remitted, four additional infusions in the continuation phase (Mayo site). Cognitive assessments using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) were conducted at baseline, end of the acute phase, and end of the continuation phase (Mayo site). Results showed a significant 53 % (39/74) remission rate in depression symptoms after the acute phase. In adjusted models, baseline language domain score was associated with a higher odd of remission (Odds Ratio, 1.09, 95 % CI = 1.03-1.17, p = 0.004) and greater improvement in MADRS at the end of the acute phase (ß =-0.97; 95 % CI, -1.74 to -0.20; P = 0.02). The likelihood of remission was not significantly associated with baseline immediate or delayed memory, visuospatial/constructional, or attention scores. In the continuation phase, improvements in immediate and delayed memory and attention persisted, with additional gains in visuospatial and language domains. Limitations included an open-label design, potential practice effects, and ongoing psychotropic medication use. Overall, the study suggests cognitive improvement, not deterioration, associated with serial IV ketamine administrations for TRD. These findings encourage future studies with larger sample sizes and longer follow-up periods to examine any potential for deleterious effect with recurrent ketamine use for TRD. Trial Registration: ClinicalTrials.gov: NCT03156504.

The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses.

BACKGROUND: When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data. METHODS: A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly. DISCUSSION: The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices. TRIAL REGISTRATION: NCT05481138.

Clinical outcomes in the biomarkers of ketamine (Bio-K) study of open-label IV ketamine for refractory depression.

OBJECTIVE: We conducted an open-label clinical trial ("Bio-K") using IV ketamine for treatment-resistant depression to identify biomarkers linked to remission. Here, we report the clinical efficacy and side effect outcomes of Bio-K. METHODS: Across 4 US sites, 75 patients ages 18-65 with treatment-refractory unipolar or bipolar depression received 3 IV ketamine infusions over an 11-day period. Key exclusion criteria were psychotic symptoms, significant substance abuse, unstable medical conditions, and any use of cannabis. Pre-existing antidepressant medication was maintained. Primary outcome was remission as measured by Montgomery-Asberg Depression Rating Scale (MADRS), with secondary outcome of 50 % reduction in Beck Suicide Scale score. Safety monitoring and varying durations of infusions were also key parameters. RESULTS: Using remission as MADRS score <10, after 3 infusions 52 % achieved remission, with 67 % achieving response. Of those achieving response after a single infusion, 66 % (22 of 33) reached remission after 3 infusions, while 40 % (16 of 40) non-responders after the first infusion went on to achieve remission after 3 infusions. Only 20 % of non-responders after 2 infusions achieved remission. Most (81 %) participants had significant suicidal ideation at baseline; of these, two-thirds (67 %) experienced at least a 50 % reduction in suicidality. Side effects were minimal. Uniquely, we had three different types of infusion categories, with individuals receiving: (1) slow (100-min) infusions only or (2) regular (40-min) infusions only or (3) a mix of infusion durations. These three infusion groups showed comparable safety and efficacy. Exploration of clinical factors revealed no link between BMI, age, or gender to remission. CONCLUSIONS: The consistency of outcomes across 4 clinical sites and across multiple instruments, suggests high acute efficacy and safety of IV ketamine for serious depressive episodes. Duration of infusion did not alter outcomes. Meaningfully, 40 % of non-responders after a single infusion did reach remission subsequently, while only 20 % of non-responders after 2 infusions achieved remission, suggesting early response is suggestive for eventual remission. Our data on varying ketamine infusion duration adds novel insights into the clinical administration of this new treatment for refractory and severe patients. Our limitations included a lack of a control group, necessitating caution about conclusions of efficacy, balanced by the utility of reporting "real-world" outcomes across multiple clinical sites. We could also not separately analyze results for bipolar disorder due to small numbers. Together, the Bio-K clinical results are promising and provide significant sample sizes for forthcoming biological markers analyses.

Pharmacomicrobiomics of Antidepressants in Depression: A Systematic Review.

This systematic review evaluated the animal and human evidence for pharmacomicrobiomics (PMx) interactions of antidepressant medications. Studies of gut microbiota effects on functional and behavioral effects of antidepressants in human and animal models were identified from PubMed up to December 2022. Risk of bias was assessed, and results are presented as a systematic review following PRISMA guidelines. A total of 28 (21 animal, 7 human) studies were included in the review. The reviewed papers converged on three themes: (1) Antidepressants can alter the composition and metabolites of gut microbiota, (2) gut microbiota can alter the bioavailability of certain antidepressants, and (3) gut microbiota may modulate the clinical or modeled mood modifying effects of antidepressants. The majority (n = 22) of studies had at least moderate levels of bias present. While strong evidence is still lacking to understand the clinical role of antidepressant PMx in human health, there is evidence for interactions among antidepressants, microbiota changes, microbiota metabolite changes, and behavior. Well-controlled studies of the mediating and moderating effects of baseline and treatment-emergent changes in microbiota on therapeutic and adverse responses to antidepressants are needed to better establish a potential role of PMx in personalizing antidepressant treatment selection and response prediction.

Poor Neonatal Adaptation After Antidepressant Exposure During the Third Trimester in a Geographically Defined Cohort.

Objective: To examine the associations between antidepressant exposure during the third trimester of pregnancy, including individual drugs, drug doses, and antidepressant combinations, and the risk of poor neonatal adaptation (PNA). Patients and Methods: The Rochester Epidemiology Project medical records-linkage system was used to study infants exposed to selective serotonin reuptake inhibitors (SSRIs; n=1014), bupropion, (n=118), serotonin-norepinephrine reuptake inhibitors (n=80), antidepressant combinations (n=20), or other antidepressants (n=22) during the third trimester (April 11, 2000-December 31, 2013). Poor neonatal adaptation was defined based on a review of medical records. Poisson regression was used to examine the risk of PNA with serotonergic antidepressant and drug combinations compared with that with bupropion monotherapy as well as with high- vs standard-dose antidepressants. When possible, analyses were performed using propensity score (PS) weighting. Results: Forty-four infants were confirmed cases of PNA. Serotonin-norepinephrine reuptake inhibitor monotherapy, antidepressant combinations, and paroxetine monotherapy were associated with a significantly higher risk of PNA than bupropion monotherapy in unweighted analyses. High-dose SSRI exposure was associated with a significantly increased risk of PNA in unadjusted (relative risk, 2.61; 95% confidence interval, 1.35-5.04) and PS-weighted models (relative risk, 2.29; 95% confidence interval, 1.17-4.48) compared with standard-dose SSRI exposure. The risk of PNA was significantly higher with high-dose paroxetine and sertraline than with standard doses in the PS-weighted analyses. The other risk factors for PNA included maternal anxiety disorders. Conclusion: Although the frequency of PNA in this cohort was low (3%-4%), the risk of PNA was increased in infants exposed to serotonergic antidepressants, particularly with SSRIs at higher doses, during the third trimester of pregnancy compared with that in infants exposed to standard doses. Potential risk factors for PNA also included third-trimester use of paroxetine (especially at higher doses) and maternal anxiety.

Convergence of four measures of multi-morbidity.

Objectives: To compare the agreement between percentile ranks from 4 multi-morbidity scores. Design: Population-based descriptive study. Setting: Olmsted County, Minnesota (USA). Participants: We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years). Methods: For each person, we calculated 4 multi-morbidity scores using readily available diagnostic code lists from the US Department of Health and Human Services, the Clinical Classifications Software, and the Elixhauser Comorbidity Index. We calculated scores using diagnostic codes received in the 5 years before the index birthday and fit quantile regression models across age and separately by sex to transform unweighted, simple counts of conditions into percentile ranks as compared to peers of same age and of same sex. We compared the percentile ranks of the 4 multi-morbidity scores using intra-class correlation coefficients (ICCs). Results: We assessed agreement in 181,553 persons who reached a total of 1,075,433 birthdays at ages 18 years through 85 years during the study period. In general, the percentile ranks of the 4 multi-morbidity scores exhibited high levels of agreement in 6 score-to-score pairwise comparisons. The agreement increased with older age for all pairwise comparisons, and ICCs were consistently greater than 0.65 at ages 50 years and older. Conclusions: The assignment of percentile ranks may be a simple and intuitive way to assess the underlying trait of multi-morbidity across studies that use different measures.

Examining the relationship between severe persistent mental illness and surgical outcomes in women undergoing mastectomy for breast cancer.

BACKGROUND: Severe persistent mental illness (SPMI) is associated with worse outcomes in cancer patients. Less is known about the relationship between SPMI and surgical outcomes after mastectomy for breast cancer. METHODS: We selected patients with breast cancer and SPMI from the National Inpatient Sample (2016-2018) and used propensity score matching. We then used multivariate analysis, Kruskal-Wallis tests, and conditional logistic regression to compare demographics and outcomes. RESULTS: The study sample consisted of 670 patients: 536 without SPMI and 134 with SPMI. SPMI was associated with bilateral mastectomy (bilateral: 53% vs. unilateral: 42.7%, p = 0.033) and decreased frequency of breast reconstruction (p < 0.001). SPMI was associated with more extended hospitalization (4 days vs. 2 days, p < 0.001) and increased risk of developing post-procedural infection and sepsis (OR 2.909). CONCLUSIONS: SPMI is associated with bilateral mastectomy, more extended hospitalization, and increased risk for post-procedural infection and sepsis - suggesting the need for increased use of standardized screening tools to identify SPMI in patients and inform perioperative management correctly.

Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder.

Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder.

INTRODUCTION: The efficacy of antidepressants for patients with major depressive disorder (MDD) varies from individual to individual, making the prediction of therapeutic outcomes difficult. Better methods for predicting antidepressant outcomes are needed. However, complex interactions between biological, psychological, and environmental factors affect outcomes, presenting immense computational challenges for prediction. Using machine learning (ML) techniques with pharmacogenomics data provides one pathway toward individualized prediction of therapeutic outcomes of antidepressants. AREAS COVERED: This report systematically reviews the methods, results, and limitations of individual studies of ML and pharmacogenomics for predicting response and/or remission with antidepressants in patients with MDD. Future directions for research and pragmatic considerations for the clinical implementation of ML-based pharmacogenomic algorithms are also discussed. EXPERT OPINION: ML methods utilizing pharmacogenomic and clinical data demonstrate promising results for predicting short-term antidepressant response. However, predictions of antidepressant treatment outcomes depend on contextual factors that ML algorithms may not be able to capture. As such, ML-driven prediction is best viewed as a companion to clinical judgment, not its replacement. Successful implementation and adoption of methods predicting antidepressant response warrants provider education about ML and close collaborations between computing scientists, pharmacogenomic experts, health system engineers, laboratory medicine experts, and clinicians.

Vilazodone for Major Depression in Adults: Pharmacological Profile and an Updated Review for Clinical Practice.

This article provides an updated review of the pharmacological profile and available efficacy and tolerability/safety data for vilazodone, one of the most recent antidepressant drugs to be approved in the USA for the treatment of major depressive disorder (MDD) in adults. The efficacy of vilazodone for MDD in adults is supported by four positive short-term (8-10 weeks), randomized, placebo-controlled trials. Beyond these pivotal trials, we review updated research findings pertaining to the clinical effects of vilazodone for MDD including the results of switch studies, small comparative efficacy trials, key pooled and secondary data analyses focused on important depressive subtypes (anxious depression) and predictors of treatment outcome, and safety studies including direct studies of sexual side-effects. Despite these additional research efforts and use for over a decade, important gaps in the clinical evidence base remain with vilazodone. Hypothesized differences in efficacy and adverse effects between other antidepressants and vilazodone based on its multimodal mechanism of action (combining serotonin reuptake inhibition with serotonin 5-HT1A partial agonist effects) have not been comprehensively demonstrated in clinical studies and its effectiveness as a continuation- or maintenance-phase therapeutic is not yet established. Questions remain regarding its reproductive and lactational safety profiles and its efficacy as a potential next-step therapeutic for patients with MDD who do not respond to first-line antidepressants such as selective serotonin reuptake inhibitors. Suggestions for clinical use of vilazodone and discussion of its place among the broad range of pharmacotherapies for adults with MDD are provided.

A Characterization of the Clinical Global Impression Scale Thresholds in the Treatment of Adolescent Depression Across Multiple Rating Scales.

Introduction: The Clinical Global Impressions-Improvement (CGI-I) scale is widely used in clinical research to assess symptoms and functioning in the context of treatment. The correlates of the CGI-I with efficacy scales for adolescent major depressive disorder are poorly understood. This study focused on benchmarking CGI-I scores with changes in the Children's Depression Rating Scale-Revised (CDRS-R) and the Quick Inventory of Depressive Symptomatology-Adolescent (17-item) Self-Report (QIDS-A17-SR). Methods: We examined three datasets with the clinician-rated CDRS-R to ascertain equivalent percent changes in total scores and CGI-I ratings. Exploratory analyses examined corresponding percentage changes in the QIDS-A17-SR and the CGI-I ratings. The CGI-I was the reference scale for nonparametric equipercentile linking with the Equate package in R. Results: CGI-I scores of 1 mapped to ≥78%-95% change in CDRS-R scores at 4-6 weeks across three datasets. CGI-I scores of 2 mapped to 56%-94% change in CDRS-R scores at 4-6 weeks across three studies. CGI-I scores of 3 mapped to 30%-68% changes in CDRS-R scores at 4-6 weeks across three studies. CGI-I scores of 4 mapped to a range of 29%-44% at 4-6 weeks across three studies. There was no significant difference (p ≥ 0.6) between treatment groups in both the Treatment of Adolescents with Depression and Treatment of Resistant Depression in Adolescents studies, for each CGI-I score ( = 1, or = 2 or = 3, or ≥4), associated mapping of total depression severity score, or associated percent change from baseline for corresponding follow-up visits. There was no significant sex difference (p > 0.2) in CGI-I linkages to CDRS-R total or percentage changes. Conclusions: These findings establish clear relationships among CGI-I scores and the CDRS-R and the QIDS-A17-SR. These benchmarks have utility for clinical trial study design, inter-rater reliability training, and clinical implementation.

Toward a Definition of "No Meaningful Benefit" From Antidepressant Treatment: An Equipercentile Analysis With Cross-Trial Validation Across Multiple Rating Scales.

Background: Many patients with major depressive disorder (MDD) who experience no meaningful benefit (NMB) from antidepressive treatment go undetected. However, there is a lack of consensus on the definition of NMB from antidepressants.Methods: Equipercentile linking was used to identify a threshold for percent change in 17-item Hamilton Depression Rating Scale (HDRS-17) scores that equated with a Clinical Global Impressions-Improvement (CGI-I) score of 3 (minimally improved), a proxy for NMB, after 4 and 8 weeks of citalopram or escitalopram treatment, using data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS). The NMB threshold for the HDRS-17 was validated by equating a CGI-I rating of 3 with percent change values from the clinician- and patient-rated versions of the Quick Inventory of Depressive Symptomatology (QIDS-C and QIDS-SR) using data from PGRN-AMPS and phase 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial. This study was conducted between June 2021 and September 2021.Results: In PGRN-AMPS, a 30% improvement in HDRS-17 score corresponded to a CGI-I rating of 3 at 4 and 8 weeks. The 30% improvement threshold was also observed for QIDS-C and QIDS-SR scores in both PGRN-AMPS and STAR*D. Similar results were observed for percent change in HDRS-17 and QIDS-based measures in lower- and higher-severity groups based on a median split of baseline total scores.Conclusions: Improvement in depressive severity of ≤ 30%, as assessed using the HDRS-17, QIDS-C, and QIDS-SR, may validly define NMB from antidepressants during short-term treatment.

Association of Depression and Anxiety With the Accumulation of Chronic Conditions.

Importance: Longitudinal associations between comorbid depression and anxiety with the accumulation of chronic illnesses are unclear, and questions remain about the contributions associated with each condition in the increasing prevalence of multimorbidity. Objective: To compare the risk and rate of accumulating chronic conditions in people with depression, anxiety, and comorbid depression and anxiety vs individuals with neither depression nor anxiety. Design, Setting, and Participants: This cohort study used the Rochester Epidemiology Project medical records-linkage system to identify residents of Olmsted County, Minnesota, from January 1, 2005, to December 31, 2014, with follow-up ending December 31, 2017. The sample was divided into cohorts anchored at birthday ages of 20, 40, and 60 years. Individuals were classified at anchoring birthday age as having depression alone, anxiety alone, comorbid depression and anxiety, or neither depression nor anxiety (reference group), using electronically extracted diagnosis codes from the International Classification of Diseases, Ninth Revision (ICD-9) in the 5 years before each anchoring birthday. Data were analyzed from August 2020 through November 2021. Exposures: Depression alone, anxiety alone, comorbid depression and anxiety, or neither depression nor anxiety (reference group). Main Outcomes and Measures: The main outcome was sex-specific risk, calculated as hazard ratios (HRs) and rates of accumulation, calculated as mean annual incidence rates per 100 person-years, of 15 common chronic conditions within each birthday age cohort through the end of study. Results: Among the 40 360 individuals included across all 3 age cohorts, 21 516 (53.3%) were women. After balancing cohorts on race, Hispanic ethnicity, education level, body mass index, smoking status, and calendar year at index birthday, the risk of accumulating chronic conditions was significantly increased among women with depression alone (cohort aged 20 years: HR, 1.20 [95% CI, 1.02-1.42]; cohort aged 40 years: HR, 1.20 [95% CI, 1.10-1.31]; cohort aged 60 years: HR, 1.09 [95% CI, 1.02-1.16]) and women with comorbid depression and anxiety (cohort aged 20 years: HR, 1.60 [95% CI, 1.28-1.99]; cohort aged 40 years: HR, 1.41 [95% CI, 1.21-1.65]; cohort aged 60 years: HR, 1.29 [95% CI, 1.15-1.44]) compared with referent women in the same birthday cohorts and in men with comorbid depression and anxiety compared with referent men in the cohort aged 20 years (HR, 1.77 [95% CI, 1.08-2.91]). For women, the rates of accumulation of conditions were significantly higher across birthday cohorts in the comorbid depression and anxiety group compared with the depression alone group (eg, cohort aged 20 years: difference, 1.2 [95% CI, 0.2-2.1] per 100 person-years) and reference group (eg, cohort aged 20 years: difference, 1.7 [95% CI, 0.9-2.6] per 100 person-years). For men, compared with the reference group, the rates of accumulation of conditions were significantly higher in men with comorbid depression and anxiety in the cohort aged 20 years (difference, 1.4 [95% CI, 0.1-2.6] per 100 person-years) and in men with depression in the cohort aged 40 years (difference, 2.0 [95% CI, 0.8-3.2] per 100 person-years). Conclusions and Relevance: In this cohort study, the risk of accumulating chronic conditions was increased with depression and comorbid depression and anxiety in women across the age span and in younger men with comorbid depression and anxiety. Compared with women without depression or anxiety, there was a more rapid rate of accumulation of chronic conditions in women with depression and anxiety individually and an even higher rate when depression and anxiety cooccurred.

Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder.

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 (

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.

BACKGROUND: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS: The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.

Positive Urine Drug Screens and External Mortality in Teenagers Who Present for Medical Care.

Objective: To examine whether a positive urine drug of abuse screen in youth who receive medical care is associated with subsequent risk of external mortality (eg, overdose, suicide, homicide, accident).Methods: This was a population-based retrospective cohort study of all Olmsted County (Minnesota) residents who were 13-18 years of age at the time of urine drug screen (UDS) testing (January 1, 1999, to November 28, 2012). Cox regression models were used to examine the relationships between having a positive UDS and external mortality, adjusted for sex, race, age, alcohol exposure, psychiatric diagnoses as defined by the International Classification of Diseases (ICD-9/ICD-10), and medical setting of UDS testing. Separate analyses were done for (1) overall UDS results, (2) tetrahydrocannabinol (THC), and (3) cocaine.Results: Of the 2,772 teenagers included in this study (47.2% male), a total of 26 died of external causes during a median follow-up period of 11.8 years. Testing positive for any illicit substance was not associated with significantly increased risk of external mortality (hazard ratio [HR] = 1.9; 95% CI, 0.9-4.2). Testing positive for cocaine was associated with significantly increased risk of external mortality (HR = 7.0; 95% CI, 1.9-25.0). Testing positive for THC was associated with a marginally significantly increased risk of external mortality (HR = 2.1; 95% CI, 1.0-4.7); however, when cocaine was added as a covariate in the analysis, the relationship between THC-positive UDS and mortality was still elevated but was no longer statistically significant (HR = 1.8; 95% CI, 0.8-4.1).Conclusions: History of cocaine-positive UDS may help identify a population of young people who are at high risk of premature death.