Research Letter: Characterizing spin in psychiatric clinical research literature using large language models.

Importance: Spin is a common form of biased reporting that misrepresents study results in publications as more positive than an objective assessment would indicate, but its prevalence in psychiatric journals is unknown. Objective: To apply a large language model to characterize the extent to which original reports of pharmacologic and non-pharmacologic interventions in psychiatric journals reflect spin. Design: We identified abstracts from studies published between 2013 and 2023 in 3 high-impact psychiatric journals describing randomized trials or meta-analyses of interventions. Main Outcome and Measure: Presence or absence of spin estimated by a large language model (GPT4-turbo, turbo-2024-04-09), validated using gold standard abstracts with and without spin. Results: Among a total of 663 abstracts, 296 (44.6%) exhibited possible or probable spin - 230/529 (43.5%) randomized trials, 66/134 (49.3%) meta-analyses; 148/310 (47.7%) for medication, 107/238 (45.0%) for psychotherapy, and 41/115 (35.7%) for other interventions. In a multivariable logistic regression model, reports of randomized trials, and non-pharmacologic/non-psychotherapy interventions, were less likely to exhibit spin, as were more recent publications. Conclusions and Relevance: A substantial subset of psychiatric intervention abstracts in high-impact journals may contain results presented in a potentially misleading way, with the potential to impact clinical practice. The success in automating spin detection via large language models may facilitate identification and revision to minimize spin in future publications.

Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians.

Importance: While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care. Objective: To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics. Design, Setting, and Participants: This was a longitudinal cohort study using data derived from electronic health records at 2 large academic medical centers and 6 community hospitals, and their affiliated outpatient networks, in eastern Massachusetts. Participants were deidentified clinicians who billed at least 10 International Classification of Diseases, Ninth Revision (ICD-9) or Tenth Revision (ICD-10) diagnoses of major depressive disorder per year between 2008 and 2022. Data analysis occurred between September 2023 and January 2024. Main Outcomes and Measures: Heterogeneity of prescribing, defined as the number of distinct antidepressants accounting for 75% of prescriptions by a given clinician; proportion of patients who did not return for follow-up after an index prescription; and proportion of patients receiving stable, ongoing antidepressant treatment. Results: Among 11 934 clinicians treating major depressive disorder, unsupervised learning identified 10 distinct clusters on the basis of ICD codes, corresponding to outpatient psychiatry as well as oncology, obstetrics, and primary care. Between these clusters, substantial variability was identified in the proportion of selective serotonin reuptake inhibitors, selective norepinephrine reuptake inhibitors, and tricyclic antidepressants prescribed, as well as in the number of distinct antidepressants prescribed. Variability was also detected between clinician clusters in loss to follow-up and achievement of stable treatment, with the former ranging from 27% to 69% and the latter from 22% to 42%. Clinician clusters were significantly associated with treatment outcomes. Conclusions and Relevance: Groups of clinicians treating individuals diagnosed with major depressive disorder exhibit marked differences in prescribing patterns as well as longitudinal patient outcomes defined by electronic health records. Incorporating these group identifiers yielded similar prediction to more complex models incorporating individual codes, suggesting the importance of considering treatment context in efforts at risk stratification.

Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity.

Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in utero microglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs. Comparison with human datasets demonstrated conservation of placental resident macrophage signatures between mice and humans. Single-cell RNA-seq identified common alterations in fetal microglial and Hofbauer cell gene expression induced by maternal obesity, as well as sex differences in these alterations. We propose that Hofbauer cells, which are easily accessible at birth, provide insights into fetal brain microglial programs and may facilitate the early identification of offspring vulnerable to neurodevelopmental disorders.

Maternal SARS-CoV-2 impacts fetal placental macrophage programs and placenta-derived microglial models of neurodevelopment.

BACKGROUND: The SARS-CoV-2 virus activates maternal and placental immune responses. Such activation in the setting of other infections during pregnancy is known to impact fetal brain development. The effects of maternal immune activation on neurodevelopment are mediated at least in part by fetal brain microglia. However, microglia are inaccessible for direct analysis, and there are no validated non-invasive surrogate models to evaluate in utero microglial priming and function. We have previously demonstrated shared transcriptional programs between microglia and Hofbauer cells (HBCs, or fetal placental macrophages) in mouse models. METHODS AND RESULTS: We assessed the impact of maternal SARS-CoV-2 on HBCs isolated from 24 term placentas (N = 10 SARS-CoV-2 positive cases, 14 negative controls). Using single-cell RNA-sequencing, we demonstrated that HBC subpopulations exhibit distinct cellular programs, with specific subpopulations differentially impacted by SARS-CoV-2. Assessment of differentially expressed genes implied impaired phagocytosis, a key function of both HBCs and microglia, in some subclusters. Leveraging previously validated models of microglial synaptic pruning, we showed that HBCs isolated from placentas of SARS-CoV-2 positive pregnancies can be transdifferentiated into microglia-like cells (HBC-iMGs), with impaired synaptic pruning behavior compared to HBC models from negative controls. CONCLUSION: These findings suggest that HBCs isolated at birth can be used to create personalized cellular models of offspring microglial programming.

Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.

Objective: Postpartum depression (PPD) represents a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. Thus, we aimed to develop and estimate the performance of a generalizable risk stratification model for PPD in patients without a history of depression using information collected as part of routine clinical care. Methods: We performed a retrospective cohort study of all individuals who delivered between 2017 and 2022 in one of two large academic medical centers and six community hospitals. An elastic net model was constructed and externally validated to predict PPD using sociodemographic factors, medical history, and prenatal depression screening information, all of which was known before discharge from the delivery hospitalization. Results: The cohort included 29,168 individuals; 2,703 (9.3%) met at least one criterion for postpartum depression in the 6 months following delivery. In the external validation data, the model had good discrimination and remained well-calibrated: area under the receiver operating characteristic curve 0.721 (95% CI: 0.707-0.734), Brier calibration score 0.088 (95% CI: 0.084 - 0.092). At a specificity of 90%, the positive predictive value was 28.0% (95% CI: 26.0-30.1%), and the negative predictive value was 92.2% (95% CI: 91.8-92.7%). Conclusions: These findings demonstrate that a simple machine-learning model can be used to stratify the risk for PPD before delivery hospitalization discharge. This tool could help identify patients within a practice at the highest risk and facilitate individualized postpartum care planning regarding the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms.

Dimensional measures of psychopathology in children and adolescents using large language models.

BACKGROUND: To enable greater use of NIMH Research Domain Criteria (RDoC) in real-world settings, we applied large language models to estimate dimensional psychopathology from narrative clinical notes. METHODS: We conducted a cohort study using health records from individuals age 18 years or younger evaluated in the psychiatric emergency department of a large academic medical center between November 2008 and March 2015. Outcomes were hospital admission and length of emergency department stay. RDoC domains were estimated using a HIPAA-compliant large language model (gpt-4-1106-preview), and compared to a previously-validated token-based approach. RESULTS: The cohort included 3,059 individuals (median age 16 (25%-75% 13-18); 1580 (52%) female, 1479 (48%) male; 105 (3.4%) identified as Asian, 329 (11%) as Black, 288 (9.4%) Hispanic, 474 (15%) as another race, and 1863 (61%) as white), of whom 1695 (55%) were admitted. Correlation between LLM-extracted RDoC scores and the token-based scores ranged from small to medium by Kendall's Tau (0.14-0.22). In logistic regression models adjusted for sociodemographic and clinical features, admission likelihood was associated with greater scores on all domains, with the exception of sensorimotor, which was inversely associated (p<.001 for all adjusted associations). Tests for bias suggested modest but statistically significant differences in positive valence scores by race (p<.05 for Asian, Hispanic, and Black individuals). CONCLUSION: A large language model extracted estimates of 6 RDoC domains in an explainable manner, which were associated with clinical outcomes. This approach can contribute to a new generation of prediction models or biological investigations based on dimensional psychopathology.

Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits.

OBJECTIVE: Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD. METHODS: Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks. RESULTS: Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications. CONCLUSIONS: This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.

Identifying data-driven subtypes of major depressive disorder with electronic health records.

BACKGROUND: Efforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration. METHODS: We utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups. RESULTS: Among 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %). LIMITATIONS: Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions. CONCLUSION: These data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

Psychiatric Symptoms, Treatment Uptake, and Barriers to Mental Health Care Among US Adults With Post-COVID-19 Condition.

Importance: Psychiatric symptoms are reportedly common among adults with post-COVID-19 condition (PCC). However, nationally representative data regarding symptom prevalence, treatment uptake, and barriers to care are needed to inform the development of care models. Objectives: To evaluate the prevalence of psychiatric symptoms in US adults with PCC compared with those without PCC and assess treatment uptake and cost-related barriers to treatment. Design, Setting, and Participants: Data from the 2022 National Health Interview Survey (NHIS), a nationally representative US cross-sectional survey, were analyzed between October 2023 and February 2024. Exposure: Current PCC, defined as new symptoms following SARS-CoV-2 infection lasting more than 3 months and ongoing at the time of interview. Main Outcomes and Measures: Depression symptoms were evaluated by the Patient Health Questionnaire-8 and anxiety symptoms were assessed using the General Anxiety Disorder-7 instrument. Participants were classified as having received treatment if they received mental health counseling or therapy or medications for mental health. Sleep difficulties, cognitive difficulties, disabling fatigue, and cost-related barriers were assessed from additional NHIS questions. Results: Of the 25 122 participants representing approximately 231 million US adults (median [IQR] age, 46 [32-61] years; 49.8% male and 50.2% female participants), a weighted prevalence (wPr) of 3.4% (95% CI, 3.1%-3.6%) had current PCC. Compared with other US adults, participants with current PCC were more likely to have depression symptoms (wPr, 16.8% vs 7.1%; adjusted odds ratio [AOR], 1.96; 95% CI, 1.51-2.55), anxiety symptoms (wPr, 16.7% vs 6.3%; AOR, 2.21; 95% CI, 1.53-3.19), sleep difficulties (wPr, 41.5% vs 22.7%; AOR 1.95; 95% CI, 1.65-2.29), cognitive difficulties (wPr, 35.0% vs 19.5%; AOR, 2.04; 95% CI, 1.66-2.50), and disabling fatigue (wPr, 4.0% vs 1.6%; AOR, 1.85; 95% CI, 1.20-2.86). Among participants who had depression or anxiety symptoms, those with PCC had a similar likelihood of not having received treatment (wPr, 28.2% vs 34.9%; AOR, 1.02; 95% CI, 0.66-1.57). However, participants with current PCC were more likely to report a cost-related barrier to accessing mental health counseling or therapy (wPr, 37.2% vs 23.3%; AOR, 2.05; 95% CI, 1.40-2.98). Conclusions and Relevance: The findings of this study suggest that people with PCC have a higher prevalence of psychiatric symptoms than other adults but are more likely to experience cost-related barriers to accessing therapy. Care pathways for PCC should consider prioritizing mental health screening and affordable treatment.

Clinical decision support for bipolar depression using large language models.

Management of depressive episodes in bipolar disorder remains challenging for clinicians despite the availability of treatment guidelines. In other contexts, large language models have yielded promising results for supporting clinical decisionmaking. We developed 50 sets of clinical vignettes reflecting bipolar depression and presented them to experts in bipolar disorder, who were asked to identify 5 optimal next-step pharmacotherapies and 5 poor or contraindicated choices. The same vignettes were then presented to a large language model (GPT4-turbo; gpt-4-1106-preview), with or without augmentation by prompting with recent bipolar treatment guidelines, and asked to identify the optimal next-step pharmacotherapy. Overlap between model output and gold standard was estimated. The augmented model prioritized the expert-designated optimal choice for 508/1000 vignettes (50.8%, 95% CI 47.7-53.9%; Cohen's kappa = 0.31, 95% CI 0.28-0.35). For 120 vignettes (12.0%), at least one model choice was among the poor or contraindicated treatments. Results were not meaningfully different when gender or race of the vignette was permuted to examine risk for bias. By comparison, an un-augmented model identified the optimal treatment for 234 (23.0%, 95% CI 20.8-26.0%; McNemar's p < 0.001 versus augmented model) of the vignettes. A sample of community clinicians scoring the same vignettes identified the optimal choice for 23.1% (95% CI 15.7-30.5%) of vignettes, on average; McNemar's p < 0.001 versus augmented model. Large language models prompted with evidence-based guidelines represent a promising, scalable strategy for clinical decision support. In addition to prospective studies of efficacy, strategies to avoid clinician overreliance on such models, and address the possibility of bias, will be needed.

Offspring cardiometabolic outcomes and postnatal growth trajectories after exposure to maternal SARS-CoV-2 infection.

OBJECTIVE: The objective of this study is to determine whether in utero exposure to SARS-CoV-2 is associated with increased risk for a cardiometabolic diagnosis by 18 months of age. METHODS: This retrospective electronic health record (EHR)-based cohort study included the live-born offspring of all individuals who delivered during the COVID-19 pandemic (April 1, 2020-December 31, 2021) at eight hospitals in Massachusetts. Offspring exposure was defined as a positive maternal SARS-CoV-2 polymerase chain reaction test during pregnancy. The primary outcome was presence of an ICD-10 code for a cardiometabolic disorder in offspring EHR by 18 months. Weight-, length-, and BMI-for-age z scores were calculated and compared at 6-month intervals from birth to 18 months. RESULTS: A total of 29,510 offspring (1599 exposed and 27,911 unexposed) were included. By 18 months, 6.7% of exposed and 4.4% of unexposed offspring had received a cardiometabolic diagnosis (crude odds ratio [OR] 1.47 [95% CI: 1.10 to 1.94], p = 0.007; adjusted OR 1.38 [1.06 to 1.77], p = 0.01). Exposed offspring had a significantly greater mean BMI-for-age z score versus unexposed offspring at 6 months (z score difference 0.19 [95% CI: 0.10 to 0.29], p < 0.001; adjusted difference 0.04 [-0.06 to 0.13], p = 0.4). CONCLUSIONS: Exposure to maternal SARS-CoV-2 infection was associated with an increased risk of receiving a cardiometabolic diagnosis by 18 months preceded by greater BMI-for-age at 6 months.

Performance of a Maternal Risk Stratification System for Predicting Low Apgar Scores.

OBJECTIVE: Maternal risk stratification systems are increasingly employed in predicting and preventing obstetric complications. These systems focus primarily on maternal morbidity, and few tools exist to stratify neonatal risk. We sought to determine if a maternal risk stratification score was associated with neonatal morbidity. STUDY DESIGN: Retrospective cohort study of patients with liveborn infants born at ≥24 weeks at four hospitals in one health system between January 1, 2020, and December 31, 2020. The Expanded Obstetric Comorbidity Score (EOCS) is used as the maternal risk score. The primary neonatal outcome was 5-minute Apgar <7. Logistic regression models determined associations between EOCS and neonatal morbidity. Secondary analyses were performed, including stratifying outcomes by gestational age and limiting analysis to "low-risk" term singletons. Model discrimination assessed using the area under the receiver operating characteristic curves (AUC) and calibration via calibration plots. RESULTS: A total of 14,497 maternal-neonatal pairs were included; 236 (1.6%) had 5-minute Apgar <7; EOCS was higher in 5-minute Apgar <7 group (median 41 vs. 11, p < 0.001). AUC for EOCS in predicting Apgar <7 was 0.72 (95% Confidence Interval (CI) 0.68, 0.75), demonstrating relatively good discrimination. Calibration plot revealed that those in the highest EOCS decile had higher risk of neonatal morbidity (7.6 vs. 1.7%, p < 0.001). When stratified by gestational age, discrimination weakened with advancing gestational age: AUC 0.70 for <28 weeks, 0.63 for 28 to 31 weeks, 0.64 for 32 to 36 weeks, and 0.61 for ≥37 weeks. When limited to term low-risk singletons, EOCS had lower discrimination for predicting neonatal morbidity and was not well calibrated. CONCLUSION: A maternal morbidity risk stratification system does not perform well in most patients giving birth, at low risk for neonatal complications. The findings suggest that the association between EOCS and 5-minute Apgar <7 likely reflects a relationship with prematurity. This study cautions against intentional or unintentional extrapolation of maternal morbidity risk for neonatal risk, especially for term deliveries. KEY POINTS: · EOCS had moderate discrimination for Apgar <7.. · Predictive performance declined when limited to low-risk term singletons.. · Relationship between EOCS and Apgar <7 was likely driven by prematurity..

SARS-CoV-2 infection elucidates unique features of pregnancy-specific immunity.

Pregnancy is a risk factor for increased severity of SARS-CoV-2 and other respiratory infections. The mechanisms underlying this risk have not been well-established, partly due to a limited understanding of how pregnancy shapes immune responses. To gain insight into the role of pregnancy in modulating immune responses at steady state and upon perturbation, we collected peripheral blood mononuclear cells (PBMC), plasma, and stool from 226 women, including 152 pregnant individuals (n = 96 with SARS-CoV-2 infection and n = 56 healthy controls) and 74 non-pregnant women (n = 55 with SARS-CoV-2 and n = 19 healthy controls). We found that SARS-CoV-2 infection was associated with altered T cell responses in pregnant compared to non-pregnant women. Differences included a lower percentage of memory T cells, a distinct clonal expansion of CD4-expressing CD8 + T cells, and the enhanced expression of T cell exhaustion markers, such as programmed cell death-1 (PD-1) and T cell immunoglobulin and mucin domain-3 (Tim-3), in pregnant women. We identified additional evidence of immune dysfunction in severely and critically ill pregnant women, including a lack of expected elevation in regulatory T cell (Treg) levels, diminished interferon responses, and profound suppression of monocyte function. Consistent with earlier data, we found maternal obesity was also associated with altered immune responses to SARS-CoV-2 infection, including enhanced production of inflammatory cytokines by T cells. Certain gut bacterial species were altered in pregnancy and upon SARS-CoV-2 infection in pregnant individuals compared to non-pregnant women. Shifts in cytokine and chemokine levels were also identified in the sera of pregnant individuals, most notably a robust increase of interleukin-27 (IL-27), a cytokine known to drive T cell exhaustion, in the pregnant uninfected control group compared to all non-pregnant groups. IL-27 levels were also significantly higher in uninfected pregnant controls compared to pregnant SARS-CoV-2-infected individuals. Using two different preclinical mouse models of inflammation-induced fetal demise and respiratory influenza viral infection, we found that enhanced IL-27 protects developing fetuses from maternal inflammation but renders adult female mice vulnerable to viral infection. These combined findings from human and murine studies reveal nuanced pregnancy-associated immune responses, suggesting mechanisms underlying the increased susceptibility of pregnant individuals to viral respiratory infections.

Cognitive Symptoms of Post-COVID-19 Condition and Daily Functioning.

Importance: The frequent occurrence of cognitive symptoms in post-COVID-19 condition has been described, but the nature of these symptoms and their demographic and functional factors are not well characterized in generalizable populations. Objective: To investigate the prevalence of self-reported cognitive symptoms in post-COVID-19 condition, in comparison with individuals with prior acute SARS-CoV-2 infection who did not develop post-COVID-19 condition, and their association with other individual features, including depressive symptoms and functional status. Design, Setting, and Participants: Two waves of a 50-state nonprobability population-based internet survey conducted between December 22, 2022, and May 5, 2023. Participants included survey respondents aged 18 years and older. Exposure: Post-COVID-19 condition, defined as self-report of symptoms attributed to COVID-19 beyond 2 months after the initial month of illness. Main Outcomes and Measures: Seven items from the Neuro-QoL cognition battery assessing the frequency of cognitive symptoms in the past week and patient Health Questionnaire-9. Results: The 14 767 individuals reporting test-confirmed COVID-19 illness at least 2 months before the survey had a mean (SD) age of 44.6 (16.3) years; 568 (3.8%) were Asian, 1484 (10.0%) were Black, 1408 (9.5%) were Hispanic, and 10 811 (73.2%) were White. A total of 10 037 respondents (68.0%) were women and 4730 (32.0%) were men. Of the 1683 individuals reporting post-COVID-19 condition, 955 (56.7%) reported at least 1 cognitive symptom experienced daily, compared with 3552 of 13 084 (27.1%) of those who did not report post-COVID-19 condition. More daily cognitive symptoms were associated with a greater likelihood of reporting at least moderate interference with functioning (unadjusted odds ratio [OR], 1.31 [95% CI, 1.25-1.36]; adjusted [AOR], 1.30 [95% CI, 1.25-1.36]), lesser likelihood of full-time employment (unadjusted OR, 0.95 [95% CI, 0.91-0.99]; AOR, 0.92 [95% CI, 0.88-0.96]) and greater severity of depressive symptoms (unadjusted coefficient, 1.40 [95% CI, 1.29-1.51]; adjusted coefficient 1.27 [95% CI, 1.17-1.38). After including depressive symptoms in regression models, associations were also found between cognitive symptoms and at least moderate interference with everyday functioning (AOR, 1.27 [95% CI, 1.21-1.33]) and between cognitive symptoms and lower odds of full-time employment (AOR, 0.92 [95% CI, 0.88-0.97]). Conclusions and Relevance: The findings of this survey study of US adults suggest that cognitive symptoms are common among individuals with post-COVID-19 condition and associated with greater self-reported functional impairment, lesser likelihood of full-time employment, and greater depressive symptom severity. Screening for and addressing cognitive symptoms is an important component of the public health response to post-COVID-19 condition.

Loss of Function in the Neurodevelopmental Disease and Schizophrenia-Associated Gene CYFIP1 in Human Microglia-like Cells Supports a Functional Role in Synaptic Engulfment.

BACKGROUND: The CYFIP1 gene, located in the neurodevelopmental risk locus 15q11.2, is highly expressed in microglia, but its role in human microglial function as it relates to neurodevelopment is not well understood. METHODS: We generated multiple CRISPR (clustered regularly interspaced short palindromic repeat) knockouts of CYFIP1 in patient-derived models of microglia to characterize function and phenotype. Using microglia-like cells reprogrammed from peripheral blood mononuclear cells, we quantified phagocytosis of synaptosomes (isolated and purified synaptic vesicles) from human induced pluripotent stem cell (iPSC)-derived neuronal cultures as an in vitro model of synaptic pruning. We repeated these analyses in human iPSC-derived microglia-like cells derived from 3 isogenic wild-type/knockout line pairs derived from 2 donors and further characterized microglial development and function through morphology and motility. RESULTS: CYFIP1 knockout using orthogonal CRISPR constructs in multiple patient-derived cell lines was associated with a statistically significant decrease in synaptic vesicle phagocytosis in microglia-like cell models derived from both peripheral blood mononuclear cells and iPSCs. Morphology was also shifted toward a more ramified profile, and motility was significantly reduced. However, iPSC-CYFIP1 knockout lines retained the ability to differentiate to functional microglia. CONCLUSIONS: The changes in microglial phenotype and function due to the loss of function of CYFIP1 observed in this study implicate a potential impact on processes such as synaptic pruning that may contribute to CYFIP1-related neurodevelopmental disorders. Investigating risk genes in a range of central nervous system cell types, not solely neurons, may be required to fully understand the way in which common and rare variants intersect to yield neuropsychiatric disorders.

Impaired neural stress resistance and loss of REST in bipolar disorder.

Neurodevelopmental changes and impaired stress resistance have been implicated in the pathogenesis of bipolar disorder (BD), but the underlying regulatory mechanisms are unresolved. Here we describe a human cerebral organoid model of BD that exhibits altered neural development, elevated neural network activity, and a major shift in the transcriptome. These phenotypic changes were reproduced in cerebral organoids generated from iPS cell lines derived in different laboratories. The BD cerebral organoid transcriptome showed highly significant enrichment for gene targets of the transcriptional repressor REST. This was associated with reduced nuclear REST and REST binding to target gene recognition sites. Reducing the oxygen concentration in organoid cultures to a physiological range ameliorated the developmental phenotype and restored REST expression. These effects were mimicked by treatment with lithium. Reduced nuclear REST and derepression of REST targets genes were also observed in the prefrontal cortex of BD patients. Thus, an impaired cellular stress response in BD cerebral organoids leads to altered neural development and transcriptional dysregulation associated with downregulation of REST. These findings provide a new model and conceptual framework for exploring the molecular basis of BD.

Community Mobility and Depressive Symptoms During the COVID-19 Pandemic in the United States.

Importance: Marked elevation in levels of depressive symptoms compared with historical norms have been described during the COVID-19 pandemic, and understanding the extent to which these are associated with diminished in-person social interaction could inform public health planning for future pandemics or other disasters. Objective: To describe the association between living in a US county with diminished mobility during the COVID-19 pandemic and self-reported depressive symptoms, while accounting for potential local and state-level confounding factors. Design, Setting, and Participants: This survey study used 18 waves of a nonprobability internet survey conducted in the United States between May 2020 and April 2022. Participants included respondents who were 18 years and older and lived in 1 of the 50 US states or Washington DC. Main Outcome and Measure: Depressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9); county-level community mobility estimates from mobile apps; COVID-19 policies at the US state level from the Oxford stringency index. Results: The 192 271 survey respondents had a mean (SD) of age 43.1 (16.5) years, and 768 (0.4%) were American Indian or Alaska Native individuals, 11 448 (6.0%) were Asian individuals, 20 277 (10.5%) were Black individuals, 15 036 (7.8%) were Hispanic individuals, 1975 (1.0%) were Pacific Islander individuals, 138 702 (72.1%) were White individuals, and 4065 (2.1%) were individuals of another race. Additionally, 126 381 respondents (65.7%) identified as female and 65 890 (34.3%) as male. Mean (SD) depression severity by PHQ-9 was 7.2 (6.8). In a mixed-effects linear regression model, the mean county-level proportion of individuals not leaving home was associated with a greater level of depression symptoms (ß, 2.58; 95% CI, 1.57-3.58) after adjustment for individual sociodemographic features. Results were similar after the inclusion in regression models of local COVID-19 activity, weather, and county-level economic features, and persisted after widespread availability of COVID-19 vaccination. They were attenuated by the inclusion of state-level pandemic restrictions. Two restrictions, mandatory mask-wearing in public (ß, 0.23; 95% CI, 0.15-0.30) and policies cancelling public events (ß, 0.37; 95% CI, 0.22-0.51), demonstrated modest independent associations with depressive symptom severity. Conclusions and Relevance: In this study, depressive symptoms were greater in locales and times with diminished community mobility. Strategies to understand the potential public health consequences of pandemic responses are needed.

Reattribution to Mind-Brain Processes and Recovery From Chronic Back Pain: A Secondary Analysis of a Randomized Clinical Trial.

Importance: In primary chronic back pain (CBP), the belief that pain indicates tissue damage is both inaccurate and unhelpful. Reattributing pain to mind or brain processes may support recovery. Objectives: To test whether the reattribution of pain to mind or brain processes was associated with pain relief in pain reprocessing therapy (PRT) and to validate natural language-based tools for measuring patients' symptom attributions. Design, Setting, and Participants: This secondary analysis of clinical trial data analyzed natural language data from patients with primary CBP randomized to PRT, placebo injection control, or usual care control groups and treated in a US university research setting. Eligible participants were adults aged 21 to 70 years with CBP recruited from the community. Enrollment extended from 2017 to 2018, with the current analyses conducted from 2020 to 2022. Interventions: PRT included cognitive, behavioral, and somatic techniques to support reattributing pain to nondangerous, reversible mind or brain causes. Subcutaneous placebo injection and usual care were hypothesized not to affect pain attributions. Main Outcomes and Measures: At pretreatment and posttreatment, participants listed their top 3 perceived causes of pain in their own words (eg, football injury, bad posture, stress); pain intensity was measured as last-week average pain (0 to 10 rating, with 0 indicating no pain and 10 indicating greatest pain). The number of attributions categorized by masked coders as reflecting mind or brain processes were summed to yield mind-brain attribution scores (range, 0-3). An automated scoring algorithm was developed and benchmarked against human coder-derived scores. A data-driven natural language processing (NLP) algorithm identified the dimensional structure of pain attributions. Results: We enrolled 151 adults (81 female [54%], 134 White [89%], mean [SD] age, 41.1 [15.6] years) reporting moderate severity CBP (mean [SD] intensity, 4.10 [1.26]; mean [SD] duration, 10.0 [8.9] years). At pretreatment, 41 attributions (10%) were categorized as mind- or brain-related across intervention conditions. PRT led to significant increases in mind- or brain-related attributions, with 71 posttreatment attributions (51%) in the PRT condition categorized as mind- or brain-related, as compared with 22 (8%) in control conditions (mind-brain attribution scores: PRT vs placebo, g = 1.95 [95% CI, 1.45-2.47]; PRT vs usual care, g = 2.06 [95% CI, 1.57-2.60]). Consistent with hypothesized PRT mechanisms, increases in mind-brain attribution score were associated with reductions in pain intensity at posttreatment (standardized ß = -0.25; t127 = -2.06; P = .04) and mediated the effects of PRT vs control on 1-year follow-up pain intensity (ß = -0.35 [95% CI, -0.07 to -0.63]; P = .05). The automated word-counting algorithm and human coder-derived scores achieved moderate and substantial agreement at pretreatment and posttreatment (Cohen κ = 0.42 and 0.68, respectively). The data-driven NLP algorithm identified a principal dimension of mind and brain vs biomechanical attributions, converging with hypothesis-driven analyses. Conclusions and Relevance: In this secondary analysis of a randomized trial, PRT increased attribution of primary CBP to mind- or brain-related causes. Increased mind-brain attribution was associated with reductions in pain intensity.