Murray B. Stein’s research while affiliated with University of California and other places

This study explores the genetic and epidemiologic correlates of long-term photoplethysmography-derived pulse rate variability (PRV) measurements with anxiety disorders. Individuals with whole-genome sequencing, Fitbit, and electronic health record data (N = 920; 61,333 data points) were selected from the All of Us Research Program. Anxiety polygenic risk scores (PRS) were derived with PRS-CS after meta-analyzing anxiety genome-wide association studies from three major cohorts- UK Biobank, FinnGen, and the Million Veterans Program (NTotal =364,550). PRV was estimated as the standard deviation of average five-minute pulse wave intervals over full 24-hour pulse rate measurements (SDANN). Antidepressant exposure was defined as an active antidepressant prescription at the time of the PRV measurement in the EHR. Anxiety PRS and antidepressant use were tested for association with daily SDANN. The potential causal effect of anxiety on PRV was assessed with one-sample Mendelian randomization (MR). Anxiety PRS was independently associated with reduced SDANN (beta = −0.08; p = 0.003). Of the eight antidepressant medications and four classes tested, venlafaxine (beta = −0.12, p = 0.002) and bupropion (beta = −0.071, p = 0.01), tricyclic antidepressants (beta = −0.177, p = 0.0008), selective serotonin reuptake inhibitors (beta = −0.069; p = 0.0008) and serotonin and norepinephrine reuptake inhibitors (beta = −0.16; p = 2×10⁻⁶) were associated with decreased SDANN. One-sample MR indicated an inverse effect of anxiety on SDANN (beta = −2.22, p = 0.03). Anxiety and antidepressants are independently associated with decreased PRV, and anxiety appears to exert a causal effect on reduced PRV. Those observational findings provide insights into the impact of anxiety on PRV.

Myasthenia gravis (MG) is a rare autoantibody-mediated disease affecting the neuromuscular junction. We performed a genome-wide association study of 5708 MG cases and 432,028 controls of European ancestry and a replication study in 3989 cases and 226,643 controls provided by 23andMe Inc. We identified 12 independent genome-wide significant hits (P < 5e⁻⁸) across 11 loci. Subgroup analyses revealed two of these were associated with early-onset (at age <50) and four with late-onset MG (at age ≥ 50). Imputation of human leukocyte antigen alleles revealed inverse effect sizes for late- and early-onset, suggesting a potential modulatory influence on the time of disease manifestation. We assessed the performance of polygenic risk scores for MG, which significantly predicted disease status in an independent target cohort, explaining 4.21% of the phenotypic variation (P = 5.12e⁻⁹). With this work, we aim to enhance our understanding of the genetic architecture of MG.

Background Contemporary surgical practices for traumatic brain injury (TBI) remain unclear. We describe the clinical profile of an 18-centre US TBI cohort with cranial surgery. Methods The prospective, observational Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study (2014–2018; ClinicalTrials.gov #NCT02119182) enrolled subjects who presented to trauma centre and received head computed tomography within 24-h (h) post-TBI. We performed a secondary data analysis in subjects aged ≥17-years with hospitalisation. Clinical characteristics, surgery type/timing, hospital and six-month outcomes were reported. Findings Of 2032 subjects (age: mean = 41.4-years, range = 17–89-years; male = 71% female = 29%), 260 underwent cranial surgery, comprising 65% decompressive craniectomy, 23% craniotomy, 12% other surgery. Subjects with surgery (vs. without surgery) presented with worse neurological injury (median Glasgow Coma Scale = 6 vs. 15; midline shift ≥5 mm: 48% vs. 2%; cisternal effacement: 61% vs. 4%; p < 0.0001). Median time-to-craniectomy/craniotomy was 1.8 h (interquartile range = 1.1–5.0 h), and 67% underwent intracranial pressure monitoring. Seventy-three percent of subjects with decompressive craniectomy and 58% of subjects with craniotomy had ≥3 intracranial lesion types. Decompressive craniectomy (vs. craniotomy) was associated with intracranial injury severity (median Rotterdam Score = 4 vs. 3, p < 0.0001), intensive care length of stay (median = 13 vs. 4-days, p = 0.0002), and six-month unfavourable outcome (62% vs. 30%; p = 0.0001). Earlier time-to-craniectomy was associated with intracranial injury severity. Interpretation In a large representative cohort of patients hospitalised with TBI, surgical decision-making and time-to-surgery aligned with intracranial injury severity. Multifocal TBIs predominated in patients with cranial surgery. These findings summarise current TBI surgical practice across US trauma centres and provide the foundation for analyses in targeted subpopulations. Funding National Institute of Neurological Disorders and Stroke; US 10.13039/100000005Department of Defense; Neurosurgery Research and Education Foundation.

Objectives To develop a composite score for differential resilience to effects of combat‐related stressors (CRS) on persistent DSM‐IV post‐traumatic stress disorder (PTSD) among US Army combat arms soldiers using survey data collected before deployment. Methods A sample of n = 2542 US Army combat arms soldiers completed a survey shortly before deployment to Afghanistan and then again two to three and 8–9 months after redeployment. Retrospective self‐reports were obtained about CRS. Precision treatment methods were used to determine whether differential resilience to persistent PTSD in the follow‐up surveys could be developed from pre‐deployment survey data in a 60% training sample and validated in a 40% test sample. Results 40.8% of respondents experienced high CRS and 5.4% developed persistent PTSD. Significant test sample heterogeneity was found in resilience (t = 2.1, p = 0.032), with average treatment effect (ATE) of high CRS in the 20% least resilient soldiers of 17.1% (SE = 5.5%) compared to ATE = 3.8% (SE = 1.2%) in the remaining 80%. The most important predictors involved recent and lifetime pre‐deployment distress disorders. Conclusions A reliable pre‐deployment resilience score can be constructed to predict variation in the effects of high CRS on persistent PTSD among combat arms soldiers. Such a score could be used to target preventive interventions to reduce PTSD or other resilience‐related outcomes.

Background Around the world, people living in objectively difficult circumstances who experience symptoms of generalized anxiety disorder (GAD) do not qualify for a diagnosis because their worry is not ‘excessive’ relative to the context. We carried out the first large-scale, cross-national study to explore the implications of removing this excessiveness requirement. Methods Data come from the World Health Organization World Mental Health Survey Initiative. A total of 133 614 adults from 12 surveys in Low- or Middle-Income Countries (LMICs) and 16 surveys in High-Income Countries (HICs) were assessed with the Composite International Diagnostic Interview. Non-excessive worriers meeting all other DSM-5 criteria for GAD were compared to respondents meeting all criteria for GAD, and to respondents without GAD, on clinically-relevant correlates. Results Removing the excessiveness requirement increases the global lifetime prevalence of GAD from 2.6% to 4.0%, with larger increases in LMICs than HICs. Non-excessive and excessive GAD cases worry about many of the same things, although non-excessive cases worry more about health/welfare of loved ones, and less about personal or non-specific concerns, than excessive cases. Non-excessive cases closely resemble excessive cases in socio-demographic characteristics, family history of GAD, and risk of temporally secondary comorbidity and suicidality. Although non-excessive cases are less severe on average, they report impairment comparable to excessive cases and often seek treatment for GAD symptoms. Conclusions Individuals with non-excessive worry who meet all other DSM-5 criteria for GAD are clinically significant cases. Eliminating the excessiveness requirement would lead to a more defensible GAD diagnosis.

Background While previous studies have reported high rates of documented suicide attempts (SAs) in the U.S. Army, the extent to which soldiers make SAs that are not identified in the healthcare system is unknown. Understanding undetected suicidal behavior is important in broadening prevention and intervention efforts. Methods Representative survey of U.S. Regular Army enlisted soldiers ( n = 24 475). Reported SAs during service were compared with SAs documented in administrative medical records. Logistic regression analyses examined sociodemographic characteristics differentiating soldiers with an undetected SA v. documented SA. Among those with an undetected SA, chi-square tests examined characteristics associated with receiving a mental health diagnosis (MH-Dx) prior to SA. Discrete-time survival analysis estimated risk of undetected SA by time in service. Results Prevalence of undetected SA (unweighted n = 259) was 1.3%. Annual incidence was 255.6 per 100 000 soldiers, suggesting one in three SAs are undetected. In multivariable analysis, rank ⩾E5 (OR = 3.1[95%CI 1.6–5.7]) was associated with increased odds of undetected v. documented SA. Females were more likely to have a MH-Dx prior to their undetected SA (Rao-Scott χ ² 1 = 6.1, p = .01). Over one-fifth of undetected SAs resulted in at least moderate injury. Risk of undetected SA was greater during the first four years of service. Conclusions Findings suggest that substantially more soldiers make SAs than indicated by estimates based on documented attempts. A sizable minority of undetected SAs result in significant injury. Soldiers reporting an undetected SA tend to be higher ranking than those with documented SAs. Undetected SAs require additional approaches to identifying individuals at risk.

Importance The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions. Objective To develop a model based on administrative data for regular US Army soldiers that can predict suicides 1 to 120 months after leaving active service. Design, Setting, and Participants In this prognostic study, a consolidated administrative database was created for all regular US Army soldiers who left service from 2010 through 2019. Machine learning models were trained to predict suicides over the next 1 to 120 months in a random 70% training sample. Validation was implemented in the remaining 30%. Data were analyzed from March 2023 through March 2024. Main outcome and measures The outcome was suicide in the National Death Index. Predictors came from administrative records available before leaving service on sociodemographics, Army career characteristics, psychopathologic risk factors, indicators of physical health, social networks and supports, and stressors. Results Of the 800 579 soldiers in the cohort (84.9% male; median [IQR] age at discharge, 26 [23-33] years), 2084 suicides had occurred as of December 31, 2019 (51.6 per 100 000 person-years). A lasso model assuming consistent slopes over time discriminated as well over all but the shortest risk horizons as more complex stacked generalization ensemble machine learning models. Test sample area under the receiver operating characteristic curve ranged from 0.87 (SE = 0.06) for suicides in the first month after leaving service to 0.72 (SE = 0.003) for suicides over 120 months. The 10% of soldiers with highest predicted risk accounted for between 30.7% (SE = 1.8) and 46.6% (SE = 6.6) of all suicides across horizons. Calibration was for the most part better for the lasso model than the super learner model (both estimated over 120-month horizons.) Net benefit of a model-informed prevention strategy was positive compared with intervene-with-all or intervene-with-none strategies over a range of plausible intervention thresholds. Sociodemographics, Army career characteristics, and psychopathologic risk factors were the most important classes of predictors. Conclusions and relevance These results demonstrated that a model based on administrative variables available at the time of leaving active Army service can predict suicides with meaningful accuracy over the subsequent decade. However, final determination of cost-effectiveness would require information beyond the scope of this report about intervention content, costs, and effects over relevant horizons in relation to the monetary value placed on preventing suicides.