BACKGROUND:Affect regulation models suggest high negative and low positive affect may drive binge eating and purging in bulimia nervosa (BN). While ecological momentary assessment (EMA) studies often support these theories, inconsistent outcomes in affect-targeted interventions suggest causal relations vary across individuals. This study applied causal discovery analysis (CDA) to EMA data to characterize such heterogeneity in person-specific causal models for BN. METHODS:EMA data from 118 adult women with BN, collected over 14 days, assessed momentary negative affect, positive affect, binge eating, and self-induced vomiting. Using the Greedy Fast Causal Inference algorithm, we derived individual causal models and estimated effect sizes via structural equation modeling. Heterogeneity was evaluated by the proportion of participants with affect as a causal factor for BN behaviors. RESULTS:Causal patterns were highly heterogeneous. Elevated negative affect was causal for binge eating in 16.9% of participants, vomiting in 18.6%, and either behavior in 27.1%. Low positive affect was causal for binge eating in 8.5%, vomiting in 11.0%, and either behavior in 15.3%. Behavior-behavior causality was also common: vomiting caused binge eating in 26.3% of participants, and binge eating caused vomiting in 22.0%. CONCLUSIONS:CDA revealed marked heterogeneity in causal factors underlying BN behaviors, with some models showing affect-driven behaviors and others indicating behavior-driven patterns. Ultimately, this work indicates that the link between momentary affect and BN behaviors is highly individualized, underscoring the need for precision-targeted interventions rather than one-size-fits-all treatments.

BACKGROUND:Antithrombotic drug-drug interactions (DDIs) impact patient safety. This study used causal inference framework to examine the association between interacting drugs, direct oral anticoagulants (DOACs), antiplatelets, warfarin and incident gastrointestinal (GI) bleeding risk. RESEARCH DESIGN AND METHODS:Data from Merative MarketScan commercial and Medicare claims (2013-2021), enriched with Micromedex data were used. Adults (≥18 years) with antithrombotics claims were included. Incident GI bleeding was primary outcome. Adaptive post-double selection using least absolute shrinkage and selection operator (LASSO) was implemented for variable selection. Following propensity score matching, final estimates were obtained from weighted logistic models. RESULTS:The final matched cohorts included 368,934 patients for DOACs vs Antiplatelets; 261,184 for DOACs vs warfarin and 192,955 for antiplatelets vs warfarin. Baseline characteristics were well balanced (standardized mean difference (SMD) <0.05). Compared to antiplatelets, DOACs were associated with a significant reduction in incident GI bleeding risk (odds ratio (OR): 0.94; 95% CI: 0.91-0.98; PFDR = 0.006). None of the interacting drugs reached statistical significance, bleeding risk was driven by age (>70 years) and comorbidities. CONCLUSIONS:Within a causal inference framework, DOACs exposure was associated with less risk of GI bleeding compared to antiplatelets. However, class-level grouping may mask individual drug-specific metabolic risks. IRB:The protocol (STUDY00016941) was reviewed and received exemption determination by the University of Minnesota Institutional Review Board.

Early alcohol sipping with permission, here defined as tasting alcohol without necessarily taking a full drink by late childhood with parental permission, is an early marker of future problematic alcohol use. This study aimed to generate an initial data-driven causal model of the potential risk factors underlying early alcohol sipping and probe gender differences in this model. We used deidentified data from the baseline visit of Adolescent Brain and Cognitive Development (ABCD) Study (N = 9,253; 47.2

Abstract Background Prenatal substance exposure (PSE) occurs when an individual is exposed to substances in utero. PSEs may have lasting effects on mental health. We tested whether PSEs show threshold, cumulative, or individual substance associations with childhood psychiatric diagnoses. Methods Clinical variables (demographics, ICD-9/10 diagnoses, PSE history) were extracted from electronic health records from the University of Minnesota Adoption Medicine Clinic. PSEs were identified from caregiver and child-protective-services narratives and/or toxicology (cord tissue/blood, meconium). For each ICD-9/10 diagnostic category, we fit logistic regression models comparing (1) exposure thresholds (0, 1, 2, 3, ≥4 exposures), (2) a cumulative exposure count, and (3) individual substances to estimate marginal odds ratios (ORs) with 95% Confidence Intervals (CIs). Results Psychiatric diagnoses increased with the number of PSEs. Relative to no exposure, odds of an Anxiety Disorder rose from OR 1.47 (95% CI 1.16–1.87) with one exposure to OR 2.03 (1.64–2.52) with ≥4 exposures. Higher cumulative exposure scores were associated with Anxiety Disorders (OR 1.28, 1.18–1.38), Behavioral and Emotional Disorders (OR 1.42, 1.31–1.54), Substance Use Disorders (OR 1.52, 1.29–1.79), and Mood Disorders (OR 1.16, 1.04–1.30). Alcohol, tobacco, and marijuana exposures were associated with increased odds of at least one psychiatric diagnosis, and each substance showed at least one significant diagnostic cluster when modeled independently. Conclusion Increasing numbers of PSEs were associated with higher odds of psychiatric diagnoses, with patterns varying by substance and outcome. These findings motivate research on exposure timing and combinations to support earlier identification and intervention for at-risk children.

OBJECTIVE:Theory and evidence indicate that some of the key dysregulations in the brain's stress and mood systems induced by substance use and resulting in addiction are also evident in those with anxiety and depression ("internalizing") disorder (INTD). Based on this, we hypothesized that those with INTD develop alcohol-related symptoms with greater neurobiological efficiency (i.e., with less alcohol use) than others. In support of this hypothesis, we reported earlier that, after controlling for level of alcohol use, drinkers with a current INTD experience a greater number of alcohol-related symptoms than those without INTD. However, the cross-sectional design employed in that study circumscribed the conclusions that could be drawn concerning our hypothesis. The present study extends the earlier work in a prospective nationally representative dataset. METHOD:Using the NESARC waves 1 and 2 datasets (the same individuals assessed approximately 3 years apart), we evaluated the association of INTD status at both waves to alcohol symptom count at wave 1, while controlling typical daily drinking level and other relevant demographic and clinical variables. RESULTS:Those with INTD at wave 2 but not at or before wave 1 experienced more alcohol-related symptoms relative to their drinking level at wave 1 than did those who did not have INTD at either wave. The magnitude of this effect was greater when INTD was also present at wave 1. CONCLUSION:These findings are consistent with a shared vulnerability for the development of INTD and alcohol-related problems, which may, in part, account for their common co-occurrence.

Alzheimer's disease (AD) is increasing in prevalence, and early detection is essential for timely care. Clinical services face growing demand, leading to delays in diagnostic appointments and increasing the risk of disease progression before evaluation. This work examines artificial intelligence (AI) methods for assessing cognitive status from linguistic features. The proposed architecture uses small language models (SLMs) to analyze speech patterns, and its compact design allows deployment on mobile devices. Recent reasoning-focused models, including Deepseek-R1 and Llama, were evaluated for dementia classification. Multiple fine-tuning strategies were compared, and the best model achieved 91% accuracy and an F1 score. The findings show that AI systems built on SLMs can achieve performance comparable to large language models, indicating their potential as efficient tools that may support health care providers through accessible pre-clinical screening for AD. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The project has been supported by the Alzheimer's Association Research Grant. There is no commercial interest to be disclosed. ### 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: The data is publicly available from Dementia Bank database, this study utlized the Pitt Corpus which is a subset of the database. The study initally obtained the required persmissions from the authors of the database and with the help of IRB at Univeristy of Missouri, the analysis was performed. The links to the data can be found in this link: https://talkbank.org/dementia/access/English/Pitt.html 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, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors of the DementiaBank database. All the data can be found online at: https://talkbank.org/dementia/access/English/Pitt.html

To investigate the relevance of small RNAs to human longevity, we pursued three goals: (a) to validate epigenetic (small RNA) factors underlying survival of older adults, (b) to develop and validate prediction models of survival for potential clinical application, and (c) to identify plausible druggable targets prolonging longevity. We evaluated 828 small non-coding RNAs-687 microRNAs (miRNAs) and 141 piwi-interacting RNAs (piRNAs)-in baseline plasma from 1271 community-dwelling older adults (≥ 71 years) in the Duke-EPESE study. Our predictive model incorporating smRNAs, clinical variables (demographics, lifestyle, mood, physical function, standard clinical laboratory tests, NMR-derived lipids and metabolites, and medical conditions) and age achieved strong performance, with cross-validated AUCs of 0.92 for 2-year survival in Discovery and 0.87 in external Validation. Nine piRNAs, all reduced in longer-lived individuals, were identified as potential therapeutic targets. Under the assumption of causal sufficiency, these data provide causal evidence linking circulating small RNAs with survival outcomes in humans. While such inference does not replace experimental validation, it complements mechanistic studies by identifying candidate molecular drivers most relevant to human longevity. Supporting biological plausibility, reduced piRNA biogenesis has been shown to double lifespan in C elegans. Together, our findings identify circulating piRNAs and miRNAs as promising biomarkers and potential therapeutic targets to advance human longevity.

Background:Adverse childhood experiences (ACEs) are traumatic or adverse events in early life that can have lasting effects on behavioral, emotional, and psychological functioning. Prior research suggests ACEs relate to later psychiatric outcomes through threshold, cumulative, and individual-specific risk patterns. Few studies, however, have operationalized all three models to test ACE-specific associations with diagnosed psychiatric disorders in individuals who are adopted or with foster care histories. Methods:We conducted a cross-sectional retrospective study using electronic health record data from foster care and adopted patients aged 0-21 years old seen at the University of Minnesota Adoption Medicine Clinic (UMN-AMC) between 2014-2024. Extracted measures included ACE history, demographics, and psychiatric diagnoses. We used latent class analysis and logistic regression to identify clusters of adversity and estimate associations with psychiatric diagnosis domains, adjusting for Sex and Age at Initial Visit. Results:ACEs showed a threshold pattern across psychiatric domains, with higher ACE counts associated with greater odds of psychiatric diagnoses. Individual risk modeling indicated that exposure to abuse or violence was associated with higher odds of psychiatric diagnoses. Across cumulative and individual risk approaches, Anxiety Disorders, Mood Disorders, and Behavioral or Emotional Disorders showed the greatest sensitivity to adversity. Conclusion:Current ACE models may not fully capture neurodevelopmental impacts reflected in diagnosed psychiatric disorders among adolescents, particularly in high-risk groups such as foster and adopted individuals. In a large clinic sample our findings support a nuanced association between ACEs and later psychiatric diagnoses and highlight the need for ACE-focused assessment, prevention, and treatment strategies tailored to foster care and adopted populations.

Suicidality is common among people at clinical high risk (CHR) for psychosis. Delineating causal pathways to suicidality and identifying its determinants would inform tailored intervention efforts for these individuals. To this end, we analyzed data on CHR samples from the second and third North American Prodrome Longitudinal Studies (NAPLS-2, n = 355; NAPLS-3, n = 266). Data on correlates of suicidality-including depression and attenuated psychosis symptoms, sleep, and childhood trauma-from two initial study timepoints were submitted to the greedy relaxations of the sparsest permutation algorithm. Intervention calculus was used to estimate the (lower bound) total empirically plausible causal effects of each variable on suicidality. Across both samples, greedy relaxations of the sparsest permutation suggested that symptoms of depression-particularly hopelessness, self-deprecation, and depressed mood-were likely direct causes of suicidality among people at CHR for psychosis. Across samples and measurement time points, intervention calculus indicated that depressed mood exerted the greatest influence over suicidality of all measured variables. This study provides data-driven, testable hypotheses about the causal pathways leading to suicidality among people at CHR for psychosis and suggests promising targets for interventions on suicidality tailored to these individuals. Future experimental research should test these hypotheses by, for example, comparing the suicide risk reduction afforded by interventions aimed at each aforementioned target. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

INTRODUCTION:Suicide is the second leading cause of death in adolescents in the United States. There is an urgent need to advance understanding of risk mechanisms in adolescents to guide early interventions. While prior research has implicated cognition, neural connectivity, and psychopathology in relation to adolescent suicidal ideation (SI) and nonsuicidal self-injury (NSSI), there is a relative lack of clarity regarding the causal structure of these factors, particularly in early adolescence. METHODS:Causal discovery analysis was applied to neuroimaging, neurocognition, and clinical assessment data from the baseline visit of the Adolescent Brain Cognitive Development Study when the participants were 9-10 years old (N = 8937; 49.6% female) to produce models of causal relationships. RESULTS:In the discovered model, causal pathways from resting state functional connectivity to externalizing and internalizing psychopathology were observed. Greater externalizing psychopathology increased SI and NSSI. Cognitive performance indirectly increased SI and NSSI via its negative relationship with externalizing psychopathology. Finally, more SI increased NSSI. CONCLUSIONS:In this developmental window prior to when the risk of suicide accelerates, it is critical to begin to advance our understanding of the processes that may undergird suicide risk (neural, cognitive performance), features of psychopathology and the potential progression of SI and NSSI (both risk factors for suicide). Future research should incorporate other factors related to SI and NSSI to produce a more comprehensive understanding of the mechanisms of risk. This line of research has the potential for a more comprehensive understanding of risk and provides avenues for prevention.

Sensitivity analysis informs causal inference by assessing the sensitivity of conclusions to departures from assumptions. The consistency assumption states that there are no hidden versions of treatment and that the outcome arising naturally equals the outcome arising from intervention. When reasoning about the possibility of consistency violations, it can be helpful to distinguish between covariates and versions of treatment. In the context of surgery, for example, genomic variables are covariates and the skill of a particular surgeon is a version of treatment. There may be hidden versions of treatment, and this paper addresses that concern with a new kind of sensitivity analysis. Whereas many methods for sensitivity analysis are focused on confounding by unmeasured covariates, the methodology of this paper is focused on confounding by hidden versions of treatment. In this paper, new mathematical notation is introduced to support the novel method, and example applications are described.

Despite the accelerating presence of exploratory causal analysis in modern science and medicine, the available non-experimental methods for validating causal models are not well characterized. One of the most popular methods is to evaluate the stability of model features after resampling the data, similar to resampling methods for estimating confidence intervals in statistics. Many aspects of this approach have received little to no attention, however, such as whether the choice of resampling method should depend on the sample size, algorithms being used, or algorithm tuning parameters. We present theoretical results proving that certain resampling methods closely emulate the assignment of specific values to algorithm tuning parameters. We also report the results of extensive simulation experiments, which verify the theoretical result and provide substantial data to aid researchers in further characterizing resampling in the context of causal discovery analysis. Together, the theoretical work and simulation results provide specific guidance on how resampling methods and tuning parameters should be selected in practice.

Prevalence in autism spectrum disorder (ASD) diagnosis has long been strongly male-biased. Yet, consensus has not been reached on mechanisms and clinical features that underlie sex-based discrepancies. Whereas females may be under-diagnosed because of inconsistencies in diagnostic/ascertainment procedures (sex-biased criteria, social camouflaging), diagnosed males may have exhibited more overt behaviors (e.g., hyperactivity, aggression) that prompted clinical evaluation. Applying a novel network-theory-based approach, we extracted data-driven, clinically-relevant insights from a large, well-characterized sample (Simons Simplex Collection) of 2175 autistic males (Ages = 8.9±3.5 years) and 334 autistic females (Ages = 9.2±3.7 years). Exploratory factor analysis (EFA) and expert clinical review reduced data dimensionality to 15 factors of interest. To offset inherent confounds of an imbalanced sample, we identified a subset of males (N=331) matched to females on key variables (Age, IQ) and applied data-driven CDA using Greedy Fast Causal Inference (GFCI) for three groups (All Females, All Males, and Matched Males). Structural equation modeling (SEM) extracted measures of model fit and effect sizes for causal relationships between sex, age, and, IQ on EFA-selected factors capturing phenotypic representations of autism across sensory, social, and restricted and repetitive behavior domains. Our methodology unveiled sex-specific directional relationships to inform developmental outcomes and targeted interventions.

BACKGROUND:Individuals with internalizing (anxiety and depressive) disorder (INTD) suffer from an alcohol-related "harm paradox"; that is, they experience more alcohol-related symptoms in aggregate than do others who drink at the same level. Here, we extend this earlier finding by examining the association of INTD with a wide range of individual alcohol-related symptoms. METHODS:The study sample included respondents in the NESARC Wave 3 who reported having consumed alcohol in the past year (N = 24,485). We used logistic regression analysis to identify the association between INTD and risk for 37 individual alcohol symptoms. We used the BOSS causal discovery algorithm to identify the best-fitting causal model for the full dataset and for 100 resampled datasets, each composed of a randomly selected 50% of the full dataset. Causal edges that appeared in at least 80% of the resampled datasets were deemed "highly stable." RESULTS:After controlling for the level of daily alcohol volume and demographic variables, INTD significantly increased the relative odds of having all 37 alcohol-related symptoms measured (ORs ranged from 1.5 to 4.6). Interactions between INTD and the level of alcohol use were largely nonsignificant. Highly stable direct (unmediated) causal edges emanated primarily from INTD to the symptoms of alcohol withdrawal and dependence. CONCLUSIONS:Those with INTD are at greater risk for a wide range of alcohol symptoms than others who drink at the same level, even at relatively low levels of alcohol use. We consider that INTD could exert a direct causal influence specifically on withdrawal and dependence symptoms due to overlapping experiential and/or neurobiological aspects of these alcohol use symptoms and INTD. We conclude that the harm paradox likely contributes to the elevated risk of developing alcohol use disorder comorbidity among those with INTD.