This study aims to prospectively collect harmonized, quantitative, and dimensional psychiatric phenotypes (suicidality, anhedonia, and obsessive-compulsive symptoms) and information on discrimination, stigma, and unfair treatment in up to 27,500 individuals across diverse ancestries and clinical populations for genetic analysis within the NIMH Ancestral Population Network (APN). Five APN projects are participating in the current study (PUMAS-Paisa, PUMAS-NeuroGAP, KOMOGEN-D, LATINO, and SAX-II), spanning recruitment over 15 countries across the Americas, Africa, and Asia. Participants are being enrolled via the recruitment protocols specific to each participating project. The harmonized clinical battery includes the Columbia-Suicide Severity Scale, Snaith-Hamilton Pleasure Scale, and Florida Obsessive-Compulsive Inventory-II to assess suicidality, anhedonia, and obsessive-compulsive symptoms, respectively. Additionally, we use the Discrimination and Stigma Scale-Ultra Short to evaluate discrimination and stigma across cultures. This project is a pioneering effort to prospectively assess transdiagnostic phenotypes in large-scale, diverse ancestral and clinical populations (across different diagnoses) using a harmonized clinical battery, advancing genetic research in mental health in underrepresented populations.

Most genome-wide association studies (GWAS) of serious mental illness (SMI) have been conducted for categorical diagnoses in samples of primarily European ancestry. The portability of findings to non-Europeans, and to SMI-related symptoms/dimensional traits remains uncertain. In a sample of 8,666 SMI cases and controls from the Paisa region of Colombia we show that a primarily European schizophrenia GWAS polygenic risk score (PRS) predicted all SMI diagnoses in this sample, as well as symptoms (assessed in cases only) and traits assessed agnostic to SMI diagnosis: a one SD unit (SDU) increase in this PRS was associated to decreased risk in cases of suicidal thoughts (OR=0.89, 95% confidence interval 0.84-0.94), depressed mood (OR=0.90, 95% confidence interval 0.85-0.95), and increased risk of delusions (OR=1.12, 95% confidence interval 1.06-1.18) and to decreased cognition (in cases and controls) across five distinct domains (average decrease in cognition of 0.065 SDU, p<7e-05). We show that a published European GWAS of cognition predicted levels of executive function (average decrease in cognition of 0.06 SDU per unit increase in PRS, p<2e-04), but not diagnosis or symptoms. Specific loci identified in the SMI GWAS also showed association to multiple diagnoses, symptoms, and cognitive traits in Paisa. The most noteworthy result was for a locus on chromosome 7p22.3, associated in multiple SMI GWAS, that showed association in Paisa to increased risk of bipolar disorder, and to reduced complex cognition and social cognition. Our findings demonstrate wide portability from European GWAS to an admixed American sample, with associations to multiple transdiagnostic phenotypes.

We studied cognitive dysfunction in Major Depressive Disorder (MDD) in a sample of healthy, unmedicated individuals without comorbidities. Additionally, we studied the association of cognitive function with response to selective serotonin reuptake inhibitors (SSRIs). Our sample consisted of 113 adult MDD participants and 88 matched healthy controls (HC). Sixty-nine of the MDD participants completed 8 weeks of SSRI treatment. All participants completed a cognitive battery assessing processing speed, executive function, and learning and memory at baseline. This was repeated at week 8 for MDD participants. MDD "Responders" were defined as having ≥50 % improvement on the Hamilton Depression Rating Scale score at week 8 compared to baseline. At baseline, MDD participants performed significantly worse than HC participants on the Symbol Digit Modalities Test (SDMT) (p < .001), Stroop color naming (p = .005) and color-word naming (p = .047), and Brief Visuospatial Memory Test-Revised (BVMT) total recall (p = .02), delayed recall (p < .001), and percent retention (p = .01). MDD participants improved significantly on 6 of the cognitive assessments over 8 weeks. However, there were no significant baseline differences between Responders and Non-responders. SSRI Response was associated with improvement only in the HVLT total recall (p = .02). Our results suggest: 1) a differentiated pattern of cognitive dysfunction exists in healthy, unmedicated MDD compared to HCs; 2) baseline cognition does not delineate an SSRI-responsive/-nonresponsive subgroup, and 3) SSRI response is not associated with broad cognitive improvement after 8 weeks when compared to Non-responders, emphasizing unmet therapeutic challenges.

BACKGROUND:Electronic health records (EHRs), increasingly available in low- and middle-income countries (LMICs), provide an opportunity to study transdiagnostic features of serious mental illness (SMI) and its trajectories. AIMS:Characterise transdiagnostic features and diagnostic trajectories of SMI using an EHR database in an LMIC institution. METHOD:We conducted a retrospective cohort study using EHRs from 2005-2022 at Clínica San Juan de Dios Manizales, a specialised mental health facility in Colombia, including 22 447 patients with schizophrenia (SCZ), bipolar disorder (BPD) or severe/recurrent major depressive disorder (MDD). Using diagnostic codes and clinical notes, we analysed the frequency of suicidality and psychosis across diagnoses, patterns of diagnostic switching and the accumulation of comorbidities. Mixed-effect logistic regression was used to identify factors influencing diagnostic stability. RESULTS:High frequencies of suicidality and psychosis were observed across diagnoses of SCZ, BPD and MDD. Most patients (64%) received multiple diagnoses over time, including switches between primary SMI diagnoses (19%), diagnostic comorbidities (30%) or both (15%). Predictors of diagnostic switching included mentions of delusions (odds ratio = 1.47, 95% CI 1.34-1.61), prior diagnostic switching (odds ratio = 4.01, 95% CI 3.7-4.34) and time in treatment, independent of age (log of visit number; odds ratio = 0.57, 95% CI 0.54-0.61). Over 80% of patients reached diagnostic stability within 6 years of their first record. CONCLUSIONS:Integrating structured and unstructured EHR data reveals transdiagnostic patterns in SMI and predictors of disease trajectories, highlighting the potential of EHR-based tools for research and precision psychiatry in LMICs.

Introduction:Clinical notes in electronic health records offer valuable insight into the symptom profiles and trajectories of patients with severe mental illness (SMI). However, systematically extracting symptoms at scale remains a challenge, especially in languages other than English. We developed a light, accurate, and interpretable natural language processing (NLP) algorithm to extract psychiatric phenotypes from Spanish clinical notes. Methods:We selected a set of 136 core psychiatric phenotypes and annotated 4,000 clinical note sections (e.g., Chief Complaint, Plan; called "documents") and 240 complete visit notes (called "entries") from two psychiatric hospitals in Colombia: Hospital Mental de Antioquia (HOMO) and Clínica San Juan de Dios Manizales (CSJDM). For phenotypes meeting frequency and inter-annotator reliability thresholds, we developed three NLP algorithms (HOMO, CSJDM, and COMBINED) for phenotype extraction and context labeling (e.g., negation, family history, uncertainty). We evaluated performance at the document and entry levels, as well as across hospitals. Results:Document-level performance at both hospitals was high (average F1 scores of 0.84 and 0.85). Moreover, on phenotypes meeting our document-level performance threshold of F1 ≥0.7, entry-level performance was high as well (average F1 of 0.75 and 0.78), as was the cross-hospital transportability of the algorithms (F1 of 0.75 HOMO-to-CSJDM and 0.77 CSJDM-to-HOMO). The COMBINED algorithm improved overall recall, without significantly decreasing precision (F1 of 0.78 and 0.77 on HOMO and CSJDM, respectively). The application of our algorithm for 50 high-performing phenotypes to the notes of 9,737 SMI patients highlighted the transdiagnostic nature of many core SMI phenotypes; 44/50 phenotypes were recorded in over 10% of patients across diagnoses. Multiple correspondence analysis further revealed variation in symptom space across diagnoses; while major depressive disorder and schizophrenia form distinct clusters, patients with bipolar disorder span the entire phenotypic spectrum. Conclusion:Our tool enables the systematic investigation of psychiatric symptoms from psychiatric notes, facilitating large-scale investigations in Spanish-speaking populations.

Introduction: Clinical notes in electronic health records offer valuable insight into the symptom profiles and trajectories of patients with severe mental illness (SMI). However, systematically extracting symptoms at scale remains a challenge, especially in languages other than English. We developed a light, accurate, and interpretable natural language processing (NLP) algorithm to extract psychiatric phenotypes from Spanish clinical notes. Methods: We selected a set of 136 core psychiatric phenotypes and annotated 4,000 clinical note sections (e.g., Chief Complaint, Plan; called “documents”) and 240 complete visit notes (called “entries”) from two psychiatric hospitals in Colombia: Hospital Mental de Antioquia (HOMO) and Clínica San Juan de Dios Manizales (CSJDM). For phenotypes meeting frequency and inter-annotator reliability thresholds, we developed three NLP algorithms (HOMO, CSJDM, and COMBINED) for phenotype extraction and context labeling (e.g., negation, family history, uncertainty). We evaluated performance at the document and entry levels, as well as across hospitals. Results: Document-level performance at both hospitals was high (average F1 scores of 0.84 and 0.85). Moreover, on phenotypes meeting our document-level performance threshold of F1 ≥0.7, entry-level performance was high as well (average F1 of 0.75 and 0.78), as was the cross-hospital transportability of the algorithms (F1 of 0.75 HOMO-to-CSJDM and 0.77 CSJDM-to-HOMO). The COMBINED algorithm improved overall recall, without significantly decreasing precision (F1 of 0.78 and 0.77 on HOMO and CSJDM, respectively). The application of our algorithm for 50 high-performing phenotypes to the notes of 9,737 SMI patients highlighted the transdiagnostic nature of many core SMI phenotypes; 44/50 phenotypes were recorded in over 10% of patients across diagnoses. Multiple correspondence analysis further revealed variation in symptom space across diagnoses; while major depressive disorder and schizophrenia form distinct clusters, patients with bipolar disorder span the entire phenotypic spectrum. Conclusion: Our tool enables the systematic investigation of psychiatric symptoms from psychiatric notes, facilitating large-scale investigations in Spanish-speaking populations.

Circulating cell-free mitochondrial DNA (ccf-mtDNA) is a biomarker of cellular injury or cellular stress and is a potential novel biomarker of psychological stress and of various brain, somatic, and psychiatric disorders. No studies have yet analyzed ccf-mtDNA levels in post-traumatic stress disorder (PTSD), despite evidence of mitochondrial dysfunction in this condition. In the current study, we compared plasma ccf-mtDNA levels in combat trauma-exposed male veterans with PTSD ( n = 111) with those who did not develop PTSD ( n = 121) and also investigated the relationship between ccf mt-DNA levels and glucocorticoid sensitivity. In unadjusted analyses, ccf-mtDNA levels did not differ significantly between the PTSD and non-PTSD groups ( t = 1.312, p = 0.191, Cohen’s d = 0.172). In a sensitivity analysis excluding participants with diabetes and those using antidepressant medication and controlling for age, the PTSD group had lower ccf-mtDNA levels than did the non-PTSD group (F(1, 179) = 5.971, p = 0.016, partial η 2 = 0.033). Across the entire sample, ccf-mtDNA levels were negatively correlated with post-dexamethasone adrenocorticotropic hormone (ACTH) decline ( r = −0.171, p = 0.020) and cortisol decline ( r = −0.149, p = 0.034) (viz., greater ACTH and cortisol suppression was associated with lower ccf-mtDNA levels) both with and without controlling for age, antidepressant status and diabetes status. Ccf-mtDNA levels were also significantly positively associated with IC 50-DEX (the concentration of dexamethasone at which 50% of lysozyme activity is inhibited), a measure of lymphocyte glucocorticoid sensitivity, after controlling for age, antidepressant status, and diabetes status ( β = 0.142, p = 0.038), suggesting that increased lymphocyte glucocorticoid sensitivity is associated with lower ccf-mtDNA levels. Although no overall group differences were found in unadjusted analyses, excluding subjects with diabetes and those taking antidepressants, which may affect ccf-mtDNA levels, as well as controlling for age, revealed decreased ccf-mtDNA levels in PTSD. In both adjusted and unadjusted analyses, low ccf-mtDNA levels were associated with relatively increased glucocorticoid sensitivity, often reported in PTSD, suggesting a link between mitochondrial and glucocorticoid-related abnormalities in PTSD.

BackgroundBrain-derived neurotrophic factor (BDNF) has been implicated in the therapeutic action of antidepressants and possibly in the pathophysiology of Major Depressive Disorder (MDD). Clinical studies of peripheral blood levels of BDNF in MDD have provided conflicting results, and there are also conflicting reports regarding the predictive value of peripheral BDNF levels for antidepressant treatment response. The present study investigated the association between serum BDNF levels, the BDNF Val66Met polymorphism (rs6265), clinical characteristics and SSRI treatment response.MethodsThis open-label clinical trial included 99 physically healthy, unmedicated MDD participants and 70 healthy controls. Following a baseline assessment, 53 of the MDD participants completed an eight-week, open-label course of SSRI antidepressant treatment. Serum BDNF levels and Hamilton Rating Scale for Depression (HDRS) ratings were examined at baseline and after eight weeks of treatment. Antidepressant response was defined as a decrease in HDRS ratings of > 50% from baseline to the end-of-treatment. Finally, serum BDNF levels and SSRI treatment response were compared between MDD participants who were heterozygous or homozygous for the Met allele (“Met-carriers”) and individuals homozygous for the Val allele.ResultsSerum BDNF levels at baseline were significantly higher in the unmedicated MDD participants compared to healthy controls (15.90ng/ml vs 13.75ng/ml, t (167) = -2.041, p = 0.043). In a post-hoc analysis, this difference was seen in the female but not male participants (16.85ng/ml vs 14.06ng/ml, t (91) = -2.067, p = 0.042; 14.86ng/ml vs 13.31ng/ml, t (74) = -0.923, p = 0.359). Baseline serum BDNF levels were not associated with treatment responder status or with absolute change in depression ratings over the course of 8-week SSRI treatment (p = 0.599). In both Responders and Non-responders, no significant changes in serum BDNF levels were found over the 8-week period of SSRI-treatment (16.32ng/ml vs 16.23ng/ml, t (18) = 0.060, p = 0.953; 16.04ng/ml vs 15.61ng/ml, t (29) = 0.438, p = 0.665, respectively). Further, no differences were found in serum BDNF levels prior to treatment between MDD Met-carriers and MDD Val/Val homozygotes (15.32ng/ml vs 16.36ng/ml, t (85) = 0.747, p = 0.457), and no differences were found in post-treatment serum BDNF (F1,42= 0.031, p = 0.862). However, MDD Val/Val homozygotes showed significantly greater antidepressant responses at week 8 than did MDD Met-carriers (F1,46 = 4.366, p = 0.043).ConclusionOur results do not support sufficient reliability of using peripheral BDNF to characterize depression or to predict antidepressant response in clinical use. The role of sex in moderating BDNF differences in depression, and the role of BDNF gene polymorphisms in predicting antidepressant response, remain to be further investigated. We conclude that, while central nervous system BDNF is likely involved in antidepressant efficacy and in aspects of MDD pathophysiology, its reflection in serum BDNF levels is of limited diagnostic or prognostic utility.

Background:The Populations Underrepresented in Mental illness Association Studies (PUMAS) project is attempting to remediate the historical underrepresentation of African and Latin American populations in psychiatric genetics through large-scale genetic association studies of individuals diagnosed with a serious mental illness [SMI, including schizophrenia (SCZ), schizoaffective disorder (SZA) bipolar disorder (BP), and severe major depressive disorder (MDD)] and matched controls. Given growing evidence indicating substantial symptomatic and genetic overlap between these diagnoses, we sought to enable transdiagnostic genetic analyses of PUMAS data by conducting phenotype alignment and harmonization for 89,320 participants (48,165 cases and 41,155 controls) from four cohorts, each of which used different ascertainment and assessment methods: PAISA n=9,105; PUMAS-LATAM n=14,638; NGAP n=42,953 and GPC n=22,624. As we describe here, these efforts have yielded harmonized datasets enabling us to analyze PUMAS genetic variation data at three levels: SMI overall, diagnoses, and individual symptoms. Methods:In aligning item-level phenotypes obtained from 14 different clinical instruments, we incorporated content, branching nature, and time frame for each phenotype; standardized diagnoses; and selected 19 core SMI item-level phenotypes for analyses. The harmonization was evaluated in PUMAS cases using multiple correspondence analysis (MCA), co-occurrence analyses, and item-level endorsement. Outcomes:We mapped >6,895 item-level phenotypes in the aggregated PUMAS data, in which SCZ (44.97%) and severe BP (BP-I, 31.53%) were the most common diagnoses. Twelve of the 19 core item-level phenotypes occurred at frequencies of > 10% across all diagnoses, indicating their potential utility for transdiagnostic genetic analyses. MCA of the 14 phenotypes that were present for all cohorts revealed consistency across cohorts, and placed MDD and SCZ into separate clusters, while other diagnoses showed no significant phenotypic clustering. Interpretation:Our alignment strategy effectively aggregated extensive phenotypic data obtained using diverse assessment tools. The MCA yielded dimensional scores which we will use for genetic analyses along with the item level phenotypes. After successful harmonization, residual phenotypic heterogeneity between cohorts reflects differences in branching structure of diagnostic instruments, recruitment strategies, and symptom interpretation (due to cultural variation).

Objective Electronic health record (EHR) databases enable scalable investigations of serious mental illness (SMI), including bipolar disorder (BD), severe or recurrent major depressive disorder (MDD), schizophrenia (SCZ), and other chronic psychoses. The authors analyzed structured and unstructured EHR data from a large mental health facility to characterize SMI clinical features and trajectories. Methods Diagnostic codes, information from clinical notes, and healthcare use data, were extracted from the EHR database of Clínica San Juan de Dios in Manizales, Colombia for the years 2005-2022, including 22,447 individuals (ages 4-90, 60% female) treated for SMI. The reliability of diagnostic codes was assessed in relation to diagnoses obtained from manual chart review (n=105). A Natural Language Processing (NLP) pipeline was developed to extract features from clinical notes. Diagnostic stability was quantified in patients with ≥ 3 visits (n=12,962). Finally, mixed-effect logistic regression models were used to identify factors associated with diagnostic stability. Results Assigned EHR diagnoses showed very good agreement with those obtained from manual chart review (Cohen’s kappa 0.78). The NLP algorithm (which demonstrated excellent balance between precision and recall with average F1=0.88) identified high frequencies of suicidality and psychosis, transdiagnostically. Most SMI patients (64%) displayed multiple EHR diagnoses, including switches between primary diagnoses (19%), comorbidities (30%), and combinations of both (15%). Predictors of changes in EHR diagnoses include Delusions in clinical notes (OR=1.50, p=2e-18) and a history of previous diagnostic changes (OR=4.02, p=3e-250). Conclusions Longitudinal EHR databases enable scalable investigation of transdiagnostic clinical features and delineation of granular SMI trajectories through the integration of information from clinical notes and diagnostic codes. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Research reported here was supported by R01MH123157 (to LMOL, CLJ, and NBF), R01MH113078 (to CEB, CLJ, and NBF), R00MH116115 (to LMOL), T32MH073526 (to JFDLH) and the Fulbright Commission in Colombia through a Fulbright-Colciencias grant (to JFDLH). The content is solely the responsibility of the authors and does not necessarily represent the official views of Fulbright or the National Institutes of Health. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All procedures involving human subjects/patients were approved by the Institutional Review Boards at Clinica San Juan de Dios Manizales and University of California, Los Angeles I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes NA

Adverse childhood experiences have been consistently linked with physical and mental health disorders in adulthood that may be mediated, in part, via the effects of such exposures on biological aging. Using recently developed “epigenetic clocks”, which provide an estimate of biological age, several studies have demonstrated a link between the cumulative exposure to childhood adversities and accelerated epigenetic aging. However, not all childhood adversities are equivalent and less is known about how distinct dimensions of childhood adversity relate to epigenetic aging metrics. Using two measures of childhood adversity exposure, we assess how the dimensions of Maltreatment and Household Dysfunction relate to epigenetic aging using two “second-generation” clocks, GrimAge and PhenoAge, in a cohort of unmedicated somatically healthy adults with moderate to severe major depression ( n = 82). Our results demonstrate that the dimension of Maltreatment is associated with epigenetic age acceleration (EAA) using the PhenoAge but not the GrimAge clock. This association was observed using both the Childhood Trauma questionnaire (CTQ; β = 0.272, p = 0.013 ) and the Adverse Childhood Experiences (ACEs) questionnaire (β = 0.307, p = 0.005 ) and remained significant when adjusting for exposure to the dimension of Household Dysfunction (β = 0.322, p = 0.009 ). In contrast, the dimension of Household Dysfunction is associated with epigenetic age deceleration (β = −0.194, p = 0.083 ) which achieved significance after adjusting for exposure to the dimension of Maltreatment (β = −0.304, p = 0.022 ). This study is the first to investigate these effects among individuals with Major Depressive Disorder and suggests that these dimensions of adversity may be associated with disease via distinct biological mechanisms.

Investigations using Electronic Health Records (EHR) databases could enable accurate delineation of psychiatric disease trajectories at an unprecedented scale. Using EHR from a single institution (Clinica San Juan de Dios in Manizales, Colombia), we characterize diagnostic trajectories of >22,000 (ages 4-90, 60% female) individuals treated for severe mental illness (SMI), including schizophrenia (SCZ), bipolar disorder (BD), and severe or recurrent major depressive disorder (MDD). We extracted diagnostic codes, clinical notes, and healthcare use data collected since 2005. Using a subsample of 105 SMI patients, we assessed diagnostic reliability, comparing EHR to clinical chart review. EHR diagnostic codes showed very good agreement with chart review diagnoses (Cohens kappa 0.78). Using 3,600 annotated sentences from 2,788 patients, we developed a pipeline for extracting clinical features from the electronic text, which showed high agreement with gold-standard annotations (average F1 0.88). Factors associated with diagnostic instability, defined as changes in diagnosis between successive visits, were identified using mixed-effect logistic regression models. Of SMI patients with >3 visits (n=12,962), 64% had multiple EHR diagnoses; diagnostic switches (19%), comorbidities (30%), and both (15%). While some diagnostic switches are common, such as the switch from MDD to BD (observed in 22% of BD patients), trajectories are highly heterogeneous, with rare trajectories (occurring in <1% of patients) making up the majority (58% of all patients). Predictors of diagnostic instability include time since initial visit (OR 0.56 by visit number, p-value 2e-66), previous diagnostic change (OR= 4.02, p-value 3e-250) and NLP-derived descriptions of delusions (OR 1.50, p-values 2e-18). Our results underline the importance of considering longitudinal rather than cross-sectional diagnoses in psychiatric research and show how high-quality EHR data can contribute to global efforts to understand disease trajectories.