Search

Background: Atabecestat, a potent brain penetrable BACE1 inhibitor that reduces CSF amyloid beta (Aβ), was developed as an oral treatment for Alzheimer's disease (AD). Elevated liver enzyme adverse events were reported in three studies although only one case met Hy's law criteria to predict serious hepatotoxicity. Method: We performed a case-control genome-wide association study (GWAS) to identify genetic risk variants associated with liver enzyme elevation using 42 cases with alanine transaminase (ALT) above three times the upper limit of normal (ULN) and 141 controls below ULN. Additionally, we performed a GWAS using continuous maximal ALT/ULN (expressed as times the ULN) upon exposure to atabecestat as the outcome measure (n = 285). Results: No variant passed the genome-wide significance threshold (p = 5 × 10− 8) in the case-control GWAS. We identified suggestive association signals in genes (NLRP1, SCIMP, and C1QBP) implicated in the inflammatory processes. Among the genes implicated by position mapping using variants suggestively associated (p < 1 × 10− 5) with ALT elevation case-control status, gene sets involved in innate immune response (adjusted p-value = 0.05) and regulation of cytokine production (adjusted p-value = 0.04) were enriched. One genomic region in the intronic region of GABRG3 passed the genome-wide significance threshold in the continuous max(ALT/ULN) GWAS, and this variant was nominally associated with ALT elevation case status (p = 0.009). Conclusion: The suggestive GWAS signals in the case-control GWAS analysis suggest the potential role of inflammation in atabecestat-induced liver enzyme elevation. [ABSTRACT FROM AUTHOR]

Epigenetic mechanisms have been hypothesized to play a role in the etiology of major depressive disorder (MDD). In this study, we performed a meta-analysis between two case–control MDD cohorts to identify differentially methylated positions (DMPs) and differentially methylated regions (DMRs) in MDD. Using samples from two Cohorts (a total of 298 MDD cases and 63 controls with repeated samples, on average ~ 1.8 samples/subject), we performed an EWAS meta-analysis. Multiple cytosine-phosphate-guanine sites annotated to TNNT3 were associated with MDD reaching study-wide significance, including cg08337959 (p = 2.3 × 10–11). Among DMPs with association p values less than 0.0001, pathways from REACTOME such as Ras activation upon Ca2+ influx through the NMDA receptor (p = 0.0001, p-adjusted = 0.05) and long-term potentiation (p = 0.0002, p-adjusted = 0.05) were enriched in this study. A total of 127 DMRs with Sidak-corrected p value < 0.05 were identified from the meta-analysis, including DMRs annotated to TNNT3 (chr11: 1948933 to 1949130 [6 probes], Sidak corrected P value = 4.32 × 10–41), S100A13 (chr1: 153599479 to 153600972 [22 probes], Sidak corrected P value = 5.32 × 10–18), NRXN1 (chr2: 50201413 to 50201505 [4 probes], Sidak corrected P value = 1.19 × 10–11), IL17RA (chr22: 17564750 to 17565149, Sidak corrected P value = 9.31 × 10–8), and NPFFR2 (chr4: 72897565 to 72898212, Sidak corrected P value = 8.19 × 10–7). Using 2 Cohorts of depression case–control samples, we identified DMPs and DMRs associated with MDD. The molecular pathways implicated by these data include mechanisms involved in neuronal synaptic plasticity, calcium signaling, and inflammation, consistent with reports from previous genetic and protein biomarker studies indicating that these mechanisms are involved in the neurobiology of depression. [ABSTRACT FROM AUTHOR]

Background: Identifying biomarkers associated with Alzheimer's disease (AD) progression may enable patient enrichment and improve clinical trial designs. Epigenome-wide association studies have revealed correlations between DNA methylation at cytosine-phosphate-guanine (CpG) sites and AD pathology and diagnosis. Here, we report relationships between peripheral blood DNA methylation profiles measured using Infinium® MethylationEPIC BeadChip and AD progression in participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Results: The rate of cognitive decline from initial DNA sampling visit to subsequent visits was estimated by the slopes of the modified Preclinical Alzheimer Cognitive Composite (mPACC; mPACCdigit and mPACCtrailsB) and Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) plots using robust linear regression in cognitively normal (CN) participants and patients with mild cognitive impairment (MCI), respectively. In addition, diagnosis conversion status was assessed using a dichotomized endpoint. Two CpG sites were significantly associated with the slope of mPACC in CN participants (P < 5.79 × 10−8 [Bonferroni correction threshold]); cg00386386 was associated with the slope of mPACCdigit, and cg09422696 annotated to RP11-661A12.5 was associated with the slope of CDR-SB. No significant CpG sites associated with diagnosis conversion status were identified. Genes involved in cognition and learning were enriched. A total of 19, 13, and 5 differentially methylated regions (DMRs) associated with the slopes of mPACCtrailsB, mPACCdigit, and CDR-SB, respectively, were identified by both comb-p and DMRcate algorithms; these included DMRs annotated to HOXA4. Furthermore, 5 and 19 DMRs were associated with conversion status in CN and MCI participants, respectively. The most significant DMR was annotated to the AD-associated gene PM20D1 (chr1: 205,818,956 to 205,820,014 [13 probes], Sidak-corrected P = 7.74 × 10−24), which was associated with both the slope of CDR-SB and the MCI conversion status. Conclusion: Candidate CpG sites and regions in peripheral blood were identified as associated with the rate of cognitive decline in participants in the ADNI cohort. While we did not identify a single CpG site with sufficient clinical utility to be used by itself due to the observed effect size, a biosignature composed of DNA methylation changes may have utility as a prognostic biomarker for AD progression. [ABSTRACT FROM AUTHOR]

To elucidate how variants in genetic risk loci previously implicated in Alzheimer's Disease (AD) and/or frontotemporal dementia (FTD) contribute to expression of disease phenotypes, a phenome-wide association study was performed in two waves. In the first wave, we explored clinical traits associated with thirteen genetic variants previously reported to be linked to disease risk using both the 23andMe and UKB cohorts. We tested 30 additional AD variants in UKB cohort only in the second wave. APOE variants defining ε2/ε3/ε4 alleles and rs646776 were identified to be significantly associated with metabolic/cardiovascular and longevity traits. APOE variants were also significantly associated with neurological traits. ABI3 variant rs28394864 was significantly associated with cardiovascular (e.g. (hypertension, ischemic heart disease, coronary atherosclerosis, angina) and immune-related trait asthma. Both APOE variants and CLU variant were significantly associated with nearsightedness. HLA- DRB1 variant was associated with diseases with immune-related traits. Additionally, variants from 10+ AD genes (BZRAP1-AS1, ADAMTS4, ADAM10, APH1B, SCIMP, ABI3, SPPL2A, ZNF232, GRN, CD2AP, and CD33) were associated with hematological measurements such as white blood cell (leukocyte) count, monocyte count, neutrophill count, platelet count, and/or mean platelet (thrombocyte) volume (an autoimmune disease biomarker). Many of these genes are expressed specifically in microglia. The associations of ABI3 variant with cardiovascular and immune-related traits are one of the novel findings from this study. Taken together, it is evidenced that at least some AD and FTD variants are associated with multiple clinical phenotypes and not just dementia. These findings were discussed in the context of causal relationship versus pleiotropy via Mendelian randomization analysis. [ABSTRACT FROM AUTHOR]

To elucidate the genetic underpinnings of the antidepressant efficacy of S-ketamine (esketamine) nasal spray in major depressive disorder (MDD), we performed a genome-wide association study (GWAS) in cohorts of European ancestry (n = 527). This analysis was followed by a polygenic risk score approach to test for associations between genetic loading for psychiatric conditions, symptom profiles and esketamine efficacy. We identified a genome-wide significant locus in IRAK3 (p = 3.57 × 10–8, rs11465988, β = − 51.6, SE = 9.2) and a genome-wide significant gene-level association in NME7 (p = 1.73 × 10–6) for esketamine efficacy (i.e. percentage change in symptom severity score compared to baseline). Additionally, the strongest association with esketamine efficacy identified in the polygenic score analysis was from the genetic loading for depressive symptoms (p = 0.001, standardized coefficient β = − 3.1, SE = 0.9), which did not reach study-wide significance. Pathways relevant to neuronal and synaptic function, immune signaling, and glucocorticoid receptor/stress response showed enrichment among the suggestive GWAS signals. [ABSTRACT FROM AUTHOR]

• The Antidepressant Efficacy Questionnaire (AEQ) identified traits for treatment-resistant depression (TRD). • Vulnerability characteristics distinguished TRD from non-TRD. • Individuals with TRD suffer greater disease burden than those with non-TRD. To improve understanding of treatment-resistant depression (TRD) in a large population of individuals with depression, a self-reported antidepressant efficacy survey was designed and administered to 23andMe research participants. Participants with a current depressive episode or with a depressive episode within the last 5 years were queried for the effect of pharmacotherapy during the episode. TRD was defined as non-response to at least two antidepressants taken for at least 5–6 weeks. Non-TRD (NTRD) was defined as responsive to either the first or second medication taken for at least 3–4 weeks. Participants who could not be classified as TRD or NTRD were excluded from the analysis. Approximately 56,000 participants completed the survey, among which approximately 33,000 took medication for a depressive episode. The 3409 participants with self-reported TRD tended to have younger age of onset, and a more persistent course prior to initiation of treatment (e.g., a longer prior average episode duration and residual symptoms between episodes) than the 18,511 participants classified as NTRD. This survey identified depression characteristics, comorbidities, trigger events, and early childhood trauma that distinguish TRD from NTRD. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a 'predict and preempt' paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for ≥ 1 year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of ≥ 71%, false alarm rate of ≤ 2.3 alarm/patient/year with a median relapse detection time of 2–3 weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden. [ABSTRACT FROM AUTHOR]

Proteolytic fragments of amyloid and post-translational modification of tau species in Cerebrospinal fluid (CSF) as well as cerebral amyloid deposition are important biomarkers for Alzheimer’s Disease. We conducted genome-wide association study to identify genetic factors influencing CSF biomarker level, cerebral amyloid deposition, and disease progression. The genome-wide association study was performed via a meta-analysis of two non-overlapping discovery sample sets to identify genetic variants other than APOE ε4 predictive of the CSF biomarker level (Aβ1–42, t-Tau, p-Tau181P, t-Tau:Aβ1–42 ratio, and p-Tau181P:Aβ1–42 ratio) in patients enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Loci passing a genome-wide significance threshold of P < 5 x 10−8 were followed-up for replication in an independent sample set. We also performed joint meta-analysis of both discovery sample sets together with the replication sample set. In the discovery phase, we identified variants in FRA10AC1 associated with CSF Aβ1–42 level passing the genome-wide significance threshold (directly genotyped SNV rs10509663 PFE = 1.1 x 10−9, imputed SNV rs116953792 PFE = 3.5 x 10−10), rs116953792 (Pone-sided = 0.04) achieved replication. This association became stronger in the joint meta-analysis (directly genotyped SNV rs10509663 PFE = 1.7 x 10−9, imputed SNV rs116953792 PFE = 7.6 x 10−11). Additionally, we identified locus 15q21 (imputed SNV rs1503351 PFE = 4.0 x 10−8) associated with CSF Aβ1–42 level. No other variants passed the genome-wide significance threshold for other CSF biomarkers in either the discovery sample sets or joint analysis. Gene set enrichment analyses suggested that targeted genes mediated by miR-33, miR-146, and miR-193 were enriched in various GWAS analyses. This finding is particularly important because CSF biomarkers confer disease susceptibility and may be predictive of the likelihood of disease progression in Alzheimer’s Disease. [ABSTRACT FROM AUTHOR]

A critical challenge in the study and management of major depressive disorder (MDD) is predicting relapse. We examined the temporal correlation/coupling between depression and anxiety (called Depression-Anxiety Coupling Strength, DACS) as a predictor of relapse in patients with MDD. We followed 97 patients with remitted MDD for an average of 394 days. Patients completed weekly self-ratings of depression and anxiety symptoms using the Quick Inventory of Depressive Symptoms (QIDS-SR) and the Generalized Anxiety Disorder 7-item scale (GAD-7). Using these longitudinal ratings we computed DACS as random slopes in a linear mixed effects model reflecting individual-specific degree of correlation between depression and anxiety across time points. We then tested DACS as an independent variable in a Cox proportional hazards model to predict relapse. A total of 28 patients (29 %) relapsed during the follow-up period. DACS significantly predicted confirmed relapse (hazard ratio [HR] 1.5, 95 % CI [1.01, 2.22], p = 0.043; Concordance 0.79 [SE 0.04]). This effect was independent of baseline depressive or anxiety symptoms or their average levels over the follow-up period, and was identifiable more than one month before relapse onset. Small sample size, in a single study. Narrow phenotype and comorbidity profiles. DACS may offer opportunities for developing novel strategies for personalized monitoring, early detection, and intervention. Future studies should replicate our findings in larger, diverse patient populations, develop individual patient prediction models, and explore the underlying mechanisms that govern the relationship of DACS and relapse. • Predicting relapse in major depressive disorder is challenging. • We followed 97 patients with remitted MDD for an average of 394 days. • Depression-anxiety correlation independently predicted relapse risk. [ABSTRACT FROM AUTHOR]

Background: Growing evidence shows that epigenetic modifications play a role in Alzheimer's disease (AD). We performed an epigenome-wide association study (EWAS) to evaluate the DNA methylation differences using postmortem superior temporal gyrus (STG) and inferior frontal gyrus (IFG) samples. Results: Samples from 72 AD patients and 62 age-matched cognitively normal controls were assayed using Illumina© Infinium MethylationEPIC BeadChip. Five and 14 differentially methylated positions (DMPs) associated with pathology (i.e., Braak stage) with p value less than Bonferroni correction threshold of 6.79 × 10–8 in the STG and IFG were identified, respectively. These cytosine–phosphate–guanine (CpG) sites included promoter associated cg26263477 annotated to ABCA7 in the STG (p = 1.21 × 10–11), and cg14058329 annotated to the HOXA5/HOXA3/HOXA-AS3 gene cluster (p = 1.62 × 10–9) and cg09448088 (p = 3.95 × 10–9) annotated to MCF2L in the IFG. These genes were previously reported to harbor DMPs and/or differentially methylated regions (DMRs). Previously reported DMPs annotated to RMGA, GNG7, HOXA3, GPR56, SPG7, PCNT, RP11-961A15.1, MCF2L, RHBDF2, ANK1, PCNT, TPRG1, and RASGEF1C were replicated (p < 0.0001). One hundred twenty-one and 173 DMRs associated with pathology in the STG and IFG, respectively, were additionally identified. Of these, DMRs annotated to 30 unique genes were also identified as significant DMRs in the same brain region in a recent meta-analysis, while additional DMRs annotated to 12 genes were reported as DMRs in a different brain region or in a cross-cortex meta-analysis. The significant DMRs were enriched in promoters, CpG islands, and exons in the genome. Gene set enrichment analysis of DMPs and DMRs showed that gene sets involved in neuroinflammation (e.g., microglia differentiation), neurogenesis, and cognition were enriched (false discovery rate (FDR) < 0.05). Conclusions: Twenty-two DMPs and 30 DMRs associated with pathology were replicated, and novel DMPs and DMRs were discovered. [ABSTRACT FROM AUTHOR]

This document explores the APOE gene and its connection to neurodegeneration and cardiovascular disease. Specifically, it focuses on the rare APOECh variant, which may offer protection against Alzheimer's disease. The study identified 37 individuals with this variant in the UK Biobank population and examined various traits and biomarkers associated with neurological and cardiovascular disorders. The findings suggest that APOECh carriers may have lower levels of apolipoprotein B, potentially providing protection against Alzheimer's disease and major adverse cardiovascular events. However, further research is needed to confirm these findings. The document also provides summary statistics on lipid biomarkers among APOECh carriers and noncarriers, highlighting the relationship between elevated apolipoprotein B levels and increased risk of Alzheimer's disease. The study acknowledges limitations, such as the small sample size, and emphasizes the need for larger cohorts to validate the findings. Overall, the document offers valuable insights into the role of APOECh in disease etiology, but more research is necessary to fully understand its impact on dementia, dyslipidemia, and cardiovascular disease. [Extracted from the article]

Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual's own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research. [ABSTRACT FROM AUTHOR]

• Depression is a leading cause of functional impairment, morbidity and mortality. • Most patients with moderate to severe depression to not get sufficient physical activity. • Higher daily step counts are associated with lower levels of depressive symptom severity. • Antidepressants with significant gene-drug interaction potential are more common in patients with prior antidepressant use. • Physical activity augmentations to pharmacotherapy could improve outcomes for patients with depression. Background: Although the benefits of exercise on Major Depressive Disorder (MDD) are well established, longitudinal studies of objectively measured activity in clinical populations are needed to establish specific guidelines for exercise by persons with moderate-to-severe depression. This study examines the association between objectively assessed daily step count and depressive symptoms over a 24-week follow- up period in outpatients receiving treatment for moderate-to-severe depression. Methods: Participants were US Veterans with MDD enrolled in the Precision Medicine in Mental Health Care study (PRIME Care), a pragmatic, multi-site, randomized, controlled trial that examines the utility of genetic testing in the context of pharmacotherapy for MDD. Participants were a subset (N = 66) enrolled in actigraphy (using GT9X ActiGraph) monitoring component of the trial. Daily steps were examined as a predictor of depressive symptoms over 4-, 8-, 12-, 18-, and 24-weeks. Results: On average, participants took 3,460 (±1,768) steps per day. In generalized linear mixed models, an increase in 1,000 steps per day was associated with a 0.6-point decrease in depressive symptom severity at the subsequent follow-up assessment. Limitations: Activity monitoring was observational and causal inferences cannot be made between daily steps and subsequent depressive symptom severity. Results may not generalize to non-treatment-seeking populations. Conclusions: Study findings provide an initial metric for persons with clinically significant MDD, of whom most do not get sufficient daily activity. The findings can inform future trials aimed at determining how much daily activity is needed to improve symptoms in individuals with MDD. [ABSTRACT FROM AUTHOR]

Background: Although the benefits of exercise on Major Depressive Disorder (MDD) are well established, longitudinal studies of objectively measured activity in clinical populations are needed to establish specific guidelines for exercise by persons with moderate-to-severe depression. This study examines the association between objectively assessed daily step count and depressive symptoms over a 24-week follow- up period in outpatients receiving treatment for moderate-to-severe depression.Methods: Participants were US Veterans with MDD enrolled in the Precision Medicine in Mental Health Care study (PRIME Care), a pragmatic, multi-site, randomized, controlled trial that examines the utility of genetic testing in the context of pharmacotherapy for MDD. Participants were a subset (N = 66) enrolled in actigraphy (using GT9X ActiGraph) monitoring component of the trial. Daily steps were examined as a predictor of depressive symptoms over 4-, 8-, 12-, 18-, and 24-weeks.Results: On average, participants took 3,460 (±1,768) steps per day. In generalized linear mixed models, an increase in 1,000 steps per day was associated with a 0.6-point decrease in depressive symptom severity at the subsequent follow-up assessment.Limitations: Activity monitoring was observational and causal inferences cannot be made between daily steps and subsequent depressive symptom severity. Results may not generalize to non-treatment-seeking populations.Conclusions: Study findings provide an initial metric for persons with clinically significant MDD, of whom most do not get sufficient daily activity. The findings can inform future trials aimed at determining how much daily activity is needed to improve symptoms in individuals with MDD. [ABSTRACT FROM AUTHOR]

While 17% of US adults use tobacco regularly, smoking rates among persons with schizophrenia are upwards of 60%. Research supports a shared etiological basis for smoking and schizophrenia, including findings from genome-wide association studies (GWAS). However, few studies have directly tested whether the same or distinct genetic variants also influence smoking behavior among schizophrenia cases. Using data from the Psychiatric Genomics Consortium (PGC) study of schizophrenia (35476 cases, 46839 controls), we estimated genetic correlations between these traits and tested whether polygenic risk scores (PRS) constructed from the results of smoking behaviors GWAS were associated with schizophrenia risk or smoking behaviors among schizophrenia cases. Results indicated significant genetic correlations of schizophrenia with smoking initiation (r g = 0.159; P = 5.05 × 10−10), cigarettes-smoked-per-day (r g = 0.094; P = 0.006), and age-of-onset of smoking (r g = 0.10; P = 0.009). Comparing smoking behaviors among schizophrenia cases to the general population, we observe positive genetic correlations for smoking initiation (r g = 0.624, P = 0.002) and cigarettes-smoked-per-day (r g = 0.689, P = 0.120). Similarly, TAG-based PRS for smoking initiation and cigarettes-smoked-per-day were significantly associated with smoking initiation (P = 3.49 × 10−5) and cigarettes-smoked-per-day (P = 0.007) among schizophrenia cases. We performed the first GWAS of smoking behavior among schizophrenia cases and identified a novel association with cigarettes-smoked-per-day upstream of the TMEM106B gene on chromosome 7p21.3 (rs148253479, P = 3.18 × 10−8, n = 3520). Results provide evidence of a partially shared genetic basis for schizophrenia and smoking behaviors. Additionally, genetic risk factors for smoking behaviors were largely shared across schizophrenia and non-schizophrenia populations. Future research should address mechanisms underlying these associations to aid both schizophrenia and smoking treatment and prevention efforts. [ABSTRACT FROM AUTHOR]

• Response to escitalopram was associated with higher baseline serotonin levels. • Response to escitalopram was associated with lower baseline Kyn/Trp ratio and quinolinic acid (QuinA)/tryptophan (Trp) ratio. • Remitters to escitalopram showed significant increases in KynA/3HK ratio. The response of patients with major depressive disorders (MDD) to antidepressant treatments have been shown to be affected by multiple factors, including disease severity and inflammation. Increasing evidence indicates that the kynurenine metabolic pathway is activated by inflammation in MDD patients and plays a role in the pathophysiology of depression. Antidepressant treatments have been reported to affect kynurenine pathway metabolite levels as well. This study investigates differential associations between the antidepressant treatment outcome to escitalopram versus desvenlafaxine with the pre-treatment and post-treatment-changes in serotonin and kynurenine pathway metabolite levels. The levels of serotonin and of kynurenine pathway metabolites were measured in plasma using liquid chromatography-mass spectrometry (LC-MS) in 161 currently depressed patients with MDD at baseline and after 8 weeks of treatment with either escitalopram or desvenlafaxine. Treatment response was defined conventionally by a reduction of at least 50% in the Hamilton Depression Rating Scale 21 item (HAMD-21) total score from baseline; remission was defined by reaching a post-treatment HAMD-21 score ≤7. Response to escitalopram treatment was associated with higher baseline serotonin levels (p = 0.022), lower baseline kynurenine (Kyn)/tryptophan (Trp) ratio (p = 0.008) and lower baseline quinolinic acid (QuinA)/tryptophan (Trp) ratio (p = 0.047), suggesting a lower inflammation state. Greater improvement in depression symptoms as measured by percent change of HAMD-21 score from baseline was also associated with higher baseline serotonin levels (p = 0.033) in escitalopram treatment arm. Furthermore, remitters to escitalopram treatment showed significant increases in the kynurenic acid (KynA)/3-hydroxykynurenine (3HK) ratio after treatment (p = 0.015). In contrast, response to desvenlafaxine treatment was not associated with any metabolite analyzed. We also confirmed a previous report that plasma serotonin levels are lower in MDD patients compared to healthy controls (p = 0.004) and that the kynurenine plasma level is negatively associated with depression symptom severity (p = 0.047). In MDD patients the antidepressant response to escitalopram was positively associated with baseline serotonin levels and inversely associated with activation of the kynurenine pathway. These results appear consistent with previous literature showing that biomarker evidence of inflammation is associated with lower response to antidepressants from the selective serotonin reuptake inhibitor class. Moreover, increases in the kynurenic acid (KynA)/3-hydroxykynurenine (3HK) ratio, which previously has been characterized as a neuroprotective index, were associated with full remission under escitalopram treatment. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease impacting an estimated 44 million adults worldwide. The causal pathology of AD (accumulation of amyloid-beta and tau), precedes hallmark symptoms of dementia by more than a decade, necessitating development of early diagnostic markers of disease onset, particularly for new drugs that aim to modify disease processes. To evaluate differentially methylated positions (DMPs) as novel blood-based biomarkers of AD, we used a subset of 653 individuals with peripheral blood (PB) samples in the Alzheimer's disease Neuroimaging Initiative (ADNI) consortium. The selected cohort of AD, mild cognitive impairment (MCI), and age-matched healthy controls (CN) all had imaging, genetics, transcriptomics, cerebrospinal protein markers, and comprehensive clinical records, providing a rich resource of concurrent multi-omics and phenotypic information on a well-phenotyped subset of ADNI participants. Results: In this manuscript, we report cross-diagnosis differential peripheral DNA methylation in a cohort of AD, MCI, and age-matched CN individuals with longitudinal DNA methylation measurements. Epigenome-wide association studies (EWAS) were performed using a mixed model with repeated measures over time with a P value cutoff of 1 × 10−5 to test contrasts of pairwise differential peripheral methylation in AD vs CN, AD vs MCI, and MCI vs CN. The most highly significant differentially methylated loci also tracked with Mini Mental State Examination (MMSE) scores. Differentially methylated loci were enriched near brain and neurodegeneration-related genes (e.g., BDNF, BIN1, APOC1) validated using the genotype tissue expression project portal (GTex). Conclusions: Our work shows that peripheral differential methylation between age-matched subjects with AD relative to healthy controls will provide opportunities to further investigate and validate differential methylation as a surrogate of disease. Given the inaccessibility of brain tissue, the PB-associated methylation marks may help identify the stage of disease and progression phenotype, information that would be central to bringing forward successful drugs for AD. [ABSTRACT FROM AUTHOR]

• Self-depressed associations (SDAs) are implicit associations between elements of depression and oneself. • Greater SDAs at baseline and upward fluctuations in SDAs over follow-up predict shorter time to MDD recurrence. • SDAs are a robust risk factor for MDD recurrence following successful treatment. • The Depression Implicit Association Test (DIAT) may be a feasible and low-cost clinical screening tool. • SDAs represent a promising treatment target for improving relapse prevention. Identifying clinically relevant predictors of depressive recurrence following treatment for Major Depressive Disorder (MDD) is critical for relapse prevention. Implicit self-depressed associations (SDAs), defined as implicit cognitive associations between elements of depression (e.g., sad, miserable) and oneself, often persist following depressive episodes and may represent a cognitive biomarker for future recurrences. Thus, we examined whether SDAs, and changes in SDAs over time, prospectively predict depressive recurrence among treatment responders in the CAN-BIND Wellness Monitoring for MDD Study, a prospective cohort study conducted across 5 clinical centres. A total of 96 patients with MDD responding to various treatments were followed an average of 1.01 years. Participants completed the Depression Implicit Association Test (DIAT) – a computer-based measure of SDAs – every 8 weeks on a tablet device. Survival analyses indicated that greater SDAs at baseline and increases in SDAs over time predicted shorter time to MDD recurrence, even after accounting for depressive symptom severity. The findings show that SDAs are a robust prognostic indicator of risk for MDD recurrence, and that the DIAT may be a feasible and low-cost clinical screening tool. SDAs also represent a potential mechanism underlying the course of recurrent depression and are a promising target for relapse prevention interventions. [ABSTRACT FROM AUTHOR]

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant depression (TRD) via partition of the data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort into a training and a testing dataset. We also included data from a small yet completely independent cohort RIS-INT-93 as an external test dataset. We used features from enrollment and level 1 treatment (up to week 2 response only) of STAR*D to explore the feature space comprehensively and applied machine learning methods to model TRD outcome at level 2. For TRD defined using QIDS-C16 remission criteria, multiple machine learning models were internally cross-validated in the STAR*D training dataset and externally validated in both the STAR*D testing dataset and RIS-INT-93 independent dataset with an area under the receiver operating characteristic curve (AUC) of 0.70–0.78 and 0.72–0.77, respectively. The upper bound for the AUC achievable with the full set of features could be as high as 0.78 in the STAR*D testing dataset. Model developed using top 30 features identified using feature selection technique (k-means clustering followed by χ2 test) achieved an AUC of 0.77 in the STAR*D testing dataset. In addition, the model developed using overlapping features between STAR*D and RIS-INT-93, achieved an AUC of > 0.70 in both the STAR*D testing and RIS-INT-93 datasets. Among all the features explored in STAR*D and RIS-INT-93 datasets, the most important feature was early or initial treatment response or symptom severity at week 2. These results indicate that prediction of TRD prior to undergoing a second round of antidepressant treatment could be feasible even in the absence of biomarker data. [ABSTRACT FROM AUTHOR]