Skime, Michelle

This study uncovers a previously unrecognized role for NAV3 in TGF-β1-driven cardiac fibroblast activation. We show that NAV3 facilitates profibrotic remodeling through noncanonical signaling and cell cycle arrest, independently of SMAD2/3. These findings position NAV3 as a novel regulator of fibroblast phenotype and a potential modulator of cardiac fibrosis.

Importance The potential utility of real-time digital therapeutics that identify impending behavioral outbursts to proactively apply evidence-based interventions for managing children with disruptive behavior disorder has not been tested. Objectives To evaluate feasibility and adherence of a digitally augmented, real-time, proactive behavioral intervention (smartwatch) for children with disruptive behaviors. Design, Setting, and Participants This randomized clinical trial was conducted between March 1, 2022, and December 31, 2023, at Mayo Clinic in Rochester, Minnesota. The study enrolled children aged 3 to 7 years with externalizing behavior problems rated above the clinically significant range (I score ≥120; T score ≥60) on the Eyberg Child Behavior Inventory (ECBI). Interventions Twelve weekly sessions of parent-child interaction therapy (PCIT) (treatment as usual [TAU] arm) vs artificial intelligence (AI)–enhanced PCIT augmented with real-time behavioral outburst alerts (PCIT-AI arm). Main Outcomes and Measures The primary outcome was achieving the a priori feasibility benchmark set at greater than 70% for both completion of PCIT sessions and daily smartwatch wear. Parental reaction time to behavior prompts was measured in seconds. Secondary outcomes were percentage changes in ECBI and Pediatric Sleep Questionnaire scores. Behavioral outburst duration was examined based on parental logs of start and end times of observed tantrums using a smartphone application. Result This study enrolled 50 children (median [IQR] age, 5.0 [4.0-6.0] years; 34 [68%] male and 16 [32%] female; 28 randomized to the PCIT-AI arm and 22 to the PCIT-TAU arm). Of these children, 37 (74%; 21 in the PCIT-AI arm and 16 in the PCIT-TAU arm) completed PCIT, with children wearing the watch a median (IQR) 75.7% (67.8%-82.9%) of the time, thus achieving the feasibility benchmark (primary outcome). Median (IQR) response time to behavior prompts in the PCIT-AI arm was 3.65 (2.07-7.08) seconds. Assessing between-group differences, there was no statistically significantly greater numerical improvement in ECBI-intensity percentage change (13.86%; 95% CI, −1.41% to 29.14%), ECBI-problem percentage change subscale scores (38.59%; 95% CI, −1.22% to 78.40%), or absolute change in Pediatric Sleep Questionnaire scores (−0.16; 95% CI, −1.85 to 1.53) with the PCIT-AI arm than with the PCIT-TAU arm. Children in the PCIT-AI arm had significantly shorter mean (SD) tantrum durations (10.4 [20.8] minutes vs 22.1 [30.0] minutes; P < .001) and lower odds of tantrums lasting for 15 minutes or more (odds ratio, 3.66; 95% CI, 2.72-4.95). Conclusions and Relevance In this randomized clinical trial, both recruitment and technology feasibility benchmarks were achieved. The findings inform the design of fully powered efficacy study of wearable, digitally augmented PCIT for children with disruptive behaviors. Trial Registration ClinicalTrials.gov Identifier: NCT05077722

Abstract Background Acamprosate is an FDA-approved medication for the treatment of alcohol use disorder (AUD), however, only about 50% of patients will respond to acamprosate. Previously, we conducted an acamprosate trial (n=442), which is one of the largest acamprosate clinical studies ever conducted and currently the only study with multi-omics data. To date, no biological measures are utilized to predict response to acamprosate treatment. Aims & Objectives We set out to apply our established pharmaco-omics informed genomics strategy to identify potential biomarkers associated with acamprosate treatment response. Method Our open-label acamprosate clinical trial recruited 442 patients with AUD, all of whom were treated with acamprosate for three months. The primary outcomes were 1) relapse to alcohol use and 2) relapse to heavy drinking. Alcohol consumption was measured using the self-reported timeline follow back method. Plasma protein levels were measured using Olink proximity extension immunoassays. Results Our established “Pharmaco-Omics-informed genome-wide association study (GWAS)” research strategy identified 12 proteins, including interleukin-17 receptor B (IL17RB), that were associated with acamprosate treatment response. A GWAS for IL17RB concentrations identified several genome-wide significant signals. Specifically, the top hit single nucleotide polymorphism (SNP) rs6801605 with a minor allele frequency of 38% in the European American population mapped 4 kilobase (Kb) upstream of IL17RB, and intron 1 of the choline dehydrogenase (CHDH) gene on chromosome 3 (p: 4.8E-20). The variant genotype (AA) for the SNP rs6801605 was associated with lower IL17RB protein expression. In addition, we identified a series of genetic variants in IL17RB that were associated with acamprosate treatment outcomes. Furthermore, the variant genotypes for all of those IL17RB SNPs were protective against alcohol relapse. Finally, we demonstrated that the basal level of mRNA expression of IL17RB was inversely correlated with those of nuclear factor-κB (NF-κB) subunits, and a significantly higher expression of NF-κB subunits was observed in AUD patients who relapsed to alcohol use. Discussion & Conclusions This study illustrates that IL17RB genetic variants might contribute to acamprosate treatment outcomes. This series of studies represents an important step toward generating functional hypotheses that could be tested to gain insight into mechanisms underlying variation in acamprosate treatment response phenotypes.

ABSTRACTBackgroundInsomnia and substance use disorders (SUD) are common comorbidities of bipolar disorder (BD). Genome‐wide association studies (GWAS) have uncovered shared genetic contributions to insomnia and BD as well as SUDs and BD. Electronic health record (EHR) derived phenotypes (phecodes) and questionnaire data were used to examine the relationship between insomnia genetic liability and SUDs in BD.Methods40,839 participants from the Mayo Clinic Bipolar Disorder Biobank (BD Biobank; n = 774) and Mayo Clinic Biobank (n = 485 BD cases, n = 39,580 controls) were included in the analyses of diagnosis (phecode) outcomes (insomnia, SUD, alcohol use disorder [AUD] and tobacco use disorder [TUD]). Analyses of specific SUD outcomes obtained through the BD Biobank questionnaire included 1789 cases and considered BD subtype. Logistic regression was used to test for associations between insomnia polygenic risk scores (PRS) and insomnia and SUD outcomes in BD cases and controls.ResultsInsomnia PRS was associated with having an insomnia diagnosis (phecode) in the EHR in controls (OR = 1.19, p = 9.64e‐33) but not in BD cases (OR = 1, p = 0.95). Associations between insomnia PRS and SUD diagnoses were significant in BD cases and controls, with the association being stronger in BD cases (interaction p = 0.024). In the BD Biobank data, the insomnia PRS was associated with increased odds of AUD (OR = 1.19, p = 4.26e‐04), TUD (OR = 1.21, p = 1.25e‐05) and cannabis use disorder (OR = 1.16, p = 4.19e‐03).ConclusionThe effect of genetic predisposition to insomnia on SUD risk may be stronger in BD cases than in controls, which could have clinical care implications for individuals with BD and comorbid SUD.

Alcohol use disorder (AUD) is the most prevalent substance use disorder. Excessive alcohol consumption leads to a range of health issues. We set out to identify inflammatory markers linked to alcohol consumption, which might ultimately offer novel insight into genetic underpinnings and have implications for alcohol-associated disease. Alcohol consumption and blood-based multi-omics data were collected by The Mayo Clinic Center for Individualized Treatment of Alcohol Dependence study. Plasma samples from patients with AUD were used for proteomics analysis using the OLINK “Explore Inflammation” panel (n=410). Liver enzymes were also measured. A genome-wide association study (GWAS) was performed to explore the relationship between genetic variants and plasma TREM2 levels. Our findings show thatplasma triggering receptor expressed on myeloid cells 2 (TREM2), a key gene associated with neurodegenerative disease, was the most significant signal correlated with alcohol consumption, and has also been associated with liver enzyme levels in patients with AUD. We identified the rs7232 single nucleotide polymorphism (SNP) in MS4A6A as a key genetic variant associated with plasma TREM2 levels, with the minor allele (A) linked to higher TREM2 levels and increased alcohol consumption, particularly in men. Furthermore, MA4A6A is an ethanol-responsive gene in a SNP-dependent manner, and the variant genotype of the rs7232 SNP was associated with lower expression for MA4A6A due to proteasome-mediated protein degradation. In summary, this study provides insight into the relationship between plasma TREM2 levels, alcohol consumption, and liver function in AUD patients, shedding light on genetic factors underlying alcohol-related diseases.

Objective: Parents frequently purchase and inquire about smartwatch devices to monitor child behaviors and functioning. This pilot study examined the feasibility and accuracy of using smartwatch monitoring for the prediction of disruptive behaviors. Methods: The study enrolled children ( N = 10) aged 7–10 years hospitalized for the treatment of disruptive behaviors. The study team completed continuous behavioral phenotyping during study participation. The machine learning protocol examined severe behavioral outbursts (operationalized as episodes that preceded physical restraint) for preparing the training data. Supervised machine learning methods were trained with cross-validation to predict three behavior states—calm, playful, and disruptive. Results: The participants had a 90% adherence rate for per protocol smartwatch use. Decision trees derived conditional dependencies of heart rate, sleep, and motor activity to predict behavior. A cross-validation demonstrated 80.89% accuracy of predicting the child's behavior state using these conditional dependencies. Conclusion: This study demonstrated the feasibility of 7-day continuous smartwatch monitoring for children with severe disruptive behaviors. A machine learning approach characterized predictive biomarkers of impending disruptive behaviors. Future validation studies will examine smartwatch physiological biomarkers to enhance behavioral interventions, increase parental engagement in treatment, and demonstrate target engagement in clinical trials of pharmacological agents for young children.

BACKGROUND: Dilated cardiomyopathy (DCM) is a major cause of heart failure and carries a high mortality rate. Myocardial recovery in DCM-related heart failure patients is highly variable, with some patients having little or no response to standard drug therapy. A genome-wide association study may agnostically identify biomarkers and provide novel insight into the biology of myocardial recovery in DCM. METHODS: A genome-wide association study for change in left ventricular ejection fraction was performed in 686 White subjects with recent-onset DCM who received standard pharmacotherapy. Genome-wide association study signals were subsequently functionally validated and studied in relevant cellular models to understand molecular mechanisms that may have contributed to the change in left ventricular ejection fraction. RESULTS: The genome-wide association study identified a highly suggestive locus that mapped to the 5′-flanking region of the CDCP1 (CUB [complement C1r/C1s, Uegf, and Bmp1] domain containing protein 1) gene (rs6773435; P =7.12×10 −7 ). The variant allele was associated with improved cardiac function and decreased CDCP1 transcription. CDCP1 expression was significantly upregulated in human cardiac fibroblasts (HCFs) in response to the PDGF (platelet-derived growth factor) signaling, and knockdown of CDCP1 significantly repressed HCF proliferation and decreased AKT (protein kinase B) phosphorylation. Transcriptomic profiling after CDCP1 knockdown in HCFs supported the conclusion that CDCP1 regulates HCF proliferation and mitosis. In addition, CDCP1 knockdown in HCFs resulted in significantly decreased expression of soluble ST2 (suppression of tumorigenicity-2), a prognostic biomarker for heart failure and inductor of cardiac fibrosis. CONCLUSIONS: CDCP1 may play an important role in myocardial recovery in recent-onset DCM and mediates its effect primarily by attenuating cardiac fibrosis.

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.

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN‐AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.