Bobo, William V.

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.

Abstract Two inherited autosomal dominant cancer predisposition conditions - BRCA related hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS) - are termed Centers for Disease Control and Prevention Tier 1 (CDCT1) genetic conditions, for which early identification and intervention have a positive impact on public health. Selection of individuals for genetic testing for these conditions is based on personal and family history. The goal of this study was to evaluate whether screening in a tertiary medical center using exome sequencing could efficiently identify carriers of HBOC and LS and to determine the frequency of incremental carriers identified outside of traditional clinical practice guidelines. Participants from three geographically diverse Mayo Clinic practices in the USA (Rochester MN, Phoenix AZ, Jacksonville FL) consented to clinical “Exome+” sequencing (Helix, San Mateo, CA; Mayo GeneGuide, Rochester, MN) in the TAPESTRY study (NCT05212428), which links sequencing data with electronic health records and the return of CDCT1 genetic findings. For this study we focused on two inherited cancer predisposition conditions: BRCA-related HBOC (BRCA1 and BRCA2) and LS (MLH1, MSH2, MSH6, PMS2 and EPCAM). Detailed chart review to collect demographic information, personal and family history, and assessment of clinical practice guidelines for genetic evaluation (National Cancer Control Network 2021). To date, 44,306 patients have enrolled and sequenced in TAPESTRY. Annotation and interpretation of all variants in 7 genes for HBOC and LS resulted in 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). Demographics of the cohort included: 62.7% female, mean age 55.2 years, non-white race 9.6% and 3.8% Hispanic/Latino ethnicity. A personal history of cancer was present in 46.4% of test positive patients, including 22.5% of HBOC patients with breast or ovarian cancer and 20.9% of LS patients with colorectal or uterine cancer. More than half of the patients (52.1%) were newly diagnosed with HBOC and LS based on the results of this study. Overall, 39.2% of HBOC/LS carriers identified through Exome+ sequencing did not satisfy NCCN criteria for genetic evaluation, this was higher for LS (56.2%) compared to HBOC (32%). Amongst those newly diagnosed with HBOC/LS (n=286), NCCN criteria were not satisfied in 60% of cases (78% for LS and 51% for HBOC). Of the pathogenic germline variant carriers who met NCCN guidelines for testing, 34.2% were not aware of their diagnosis prior to participation in this study. Our results emphasize the need for wide genomic screening for CDCT1 cancer predisposition syndromes. Such screening could identify at-risk carriers of HBOC and LS, who would not otherwise be identified through clinical practice guidelines. Citation Format: Emily Gay, Niloy Jewel Samadder, Michelle L. Bublitz, Melanie M. Peterson, Tammy A. Wilson, Lorelei A. Bandel, Sebastian M. Armasu, Robert A. Vierkant, Matthew J. Ferber, Eric W. Klee, Nicholas B. Larson, Teresa M. Kruisselbrink, Timothy B. Curry, Jan B. Egan, Jennifer L. Kemppainen, Jessa S. Bidwell, Jennifer L. Anderson, Tammy M. McAllister, T'Nita S. Walker, Katie L. Kunze, Vanda Lindpere, Michael A. Golafshar, Margaret Klint, Richard J. Presutti, William V. Bobo, Aleksander Sekulic, Jolene M. Summer Bolster, Cheryl L. Willman, Konstantinos N. Lazaridis. Genetic screening in a tertiary medical center identifies carriers of cancer predisposition diseases that would be missed by clinical guidelines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5768.

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.

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.

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.

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.

AbstractBackgroundDue to invasive treatments and stressors related to heart health, adults with congenital heart defects (CHDs) may have an increased risk of post‐traumatic stress disorder (PTSD), anxiety, and/or depressive disorders. Our objectives were to estimate the prevalence of these disorders among individuals with CHDs.MethodsUsing IBM® MarketScan® Databases, we identified adults age 18–49 years with ≥2 outpatient anxiety/depressive disorder claims on separate dates or ≥1 inpatient anxiety/depressive disorder claim in 2017. CHDs were defined as ≥2 outpatient CHD claims ≥30 days apart or ≥1 inpatient CHD claim documented in 2007–2017. We used log‐binomial regression to estimate adjusted prevalence ratios (aPR) and 95% confidence intervals (CI) for associations between CHDs and anxiety/depressive disorders.ResultsOf 13,807 adults with CHDs, 12.4% were diagnosed with an anxiety or depressive disorder. Adults with CHDs, compared to the 5,408,094 without CHDs, had higher prevalence of PTSD (0.8% vs. 0.5%; aPR: 1.5 [CI: 1.2–1.8]), anxiety disorders (9.9% vs. 7.5%; aPR: 1.3 [CI: 1.3–1.4]), and depressive disorders (6.3% vs. 4.9%; aPR: 1.3 [CI: 1.2–1.4]). Among individuals with CHDs, female sex (aPR range: 1.6–3.3) and inpatient admission (aPR range 1.1–1.9) were associated with anxiety/depressive disorders.ConclusionOver 1 in 8 adults with CHDs had diagnosed PTSD and/or other anxiety/depressive disorders, 30–50% higher than adults without CHDs. PTSD was rare, but three times more common in women with CHDs than men. Screening and referral for services for these conditions in people with CHDs may be beneficial.