Alcohol, Clinical and Experimental Research

BackgroundSubstance use initiation in adolescence is influenced by both genetic and environmental factors; however, large-scale genetic studies often treat initiation as a binary outcome and underuse longitudinal timing information. MethodsWe conducted time-to-event (survival) genome-wide association analyses (GWAS) of initiation for four outcomes--alcohol, nicotine, cannabis, and any substance use--using longitudinal follow-up data from the Adolescent Brain Cognitive Development (ABCD) Study. We performed ancestry-stratified GWAS within European (EUR), African (AFR), and Hispanic (HISP) groups, applying consistent quality control and covariate adjustment. Summary statistics were harmonized across ancestries and meta-analyzed using inverse-variance weighted fixed-effects and DerSimonian-Laird random-effects models. We evaluated genomic inflation and heterogeneity (Cochrans Q and I2), identified independent lead variants at genome-wide and suggestive significance thresholds, and assessed cross-trait overlap of associated loci. ResultsIn the multi-ancestry meta-analysis, we observed suggestive association signals across traits (minimum p-values: alcohol [~] 1 x 10-7, any [~] 1 x 10-7, cannabis [~] 5 x 10-8, nicotine [~] 1 x 10-8). Nicotine initiation showed one genome-wide significant variant in both fixed- and random-effects meta-analyses (p < 5 x 10-8). Across traits, suggestive loci demonstrated limited overlap, with the strongest concordance between alcohol and any substance use, consistent with shared liability. Heterogeneity statistics indicated that some loci exhibited cross-ancestry variation in effect estimates. ConclusionsSurvival GWAS leveraging initiation timing can identify genetic signals that may be missed by binary designs and enables principled multi-ancestry synthesis. Our results highlight both shared and trait-specific genetic contributions to early substance initiation and provide a foundation for downstream functional annotation and integrative modeling with environmental risk factors. These findings demonstrate the value of incorporating developmental timing into genetic discovery and provide a framework for integrating longitudinal risk modeling with genomic analyses.

Importance: Suicide is a leading cause of death in the United States with risk strongly influenced by Interpersonal trauma, contributing to treatment resistance and clinical complexity. Objective: To assess clinical and genetic factors in individuals who died from suicide, with and without interpersonal trauma exposure. Design: Individuals who died from suicide with and without trauma were compared in a retrospective case-case design. Prevalence of 19 broad clinical categories was assessed between groups. Results directed selection of 42 clinical subcategories, and 40 polygenic scores (PGS) for further assessment. Multivariable logistic regression models, adjusted for critical covariates and multiple tests, were formulated. Models were also stratified by age group (<26yo and >=26yo), sex, and age/sex. Setting: A population-based evaluation of comorbidity and polygenic scoring in two suicide death subgroups. Participants: A total of 8 738 Utah Suicide Mortality Research Study individuals (23.9% female, average age = 42.6 yo) who died from suicide were evaluated, divided into trauma (N = 1 091) and non-trauma exposed (N = 7 647) individuals. A subset of unrelated European genotyped individuals was also assessed in PGS analyses (Trauma N = 491; Non-trauma N = 3 233). Exposures: Trauma is here defined as interpersonal trauma exposure, including abuse, assault, and neglect from International Classification of Disease coding. Main Outcomes and Measures: Prevalence of comorbid clinical sub/categories and PGS enrichment in trauma versus non-trauma exposed suicide deaths. Results: Overall, trauma-exposed individuals died from suicide earlier (mean age of 38.1 yo versus 43.3 yo; P <0.0001) and were disproportionately female (38% versus 21%, OR = 3.3, CI = 2.9-3.8). Prevalence of asphyxiation and overdose methods, prior suicidality, psychiatric diagnoses, and substance use (OR range = 1.3-3.7) were elevated in trauma exposed individuals who died from suicide. Genetic PGS were also elevated in trauma-exposed individuals who died from suicide for depression, bipolar disorder, cannabis use, PTSD, insomnia, and schizophrenia (OR range = 1.1-1.4) with ADHD and opioid use showing uniquely elevated PGS in trauma exposed males (OR range = 1.2-1.4). Conclusions and Relevance: Results demonstrated multiple convergent lines of age- and sex-specific evidence differentiating trauma-exposed from non-trauma exposed suicide death. Such findings suggest unique biological backgrounds and may refine identification and treatment of this high-risk group.

Background: The Adolescent Brain Cognitive Development (ABCD) Study provides rich longitudinal data on environmental, genetic, and behavioral factors related to substance use initiation. Classical marginal structural models (MSMs) require selecting covariates for propensity models, which is challenging when there are many correlated predictors. Methods: We analyzed longitudinal panel data from 11,868 ABCD participants with repeated observations over time. Interval-level binary outcomes were defined for initiation of alcohol, nicotine, cannabis, and any substance, including only participants at risk before initiation. All predictors were constructed as lagged variables to preserve temporal ordering. We used a two-stage machine learning-based causal framework. First, we performed graph discovery using a Granger-inspired lagged predictive modeling approach with elastic-net logistic regression to identify relationships between past predictors and future outcomes. Stable candidate edges were selected using subject-level bootstrap stability selection. Second, we estimated adjusted effects for stable predictors using double machine learning (DML) with partialling-out and cross-fitting. For each predictor, the lagged variable was treated as the exposure and adjusted for high-dimensional lagged covariates. Cross-fitting with group-based splitting accounted for within-subject dependence. Nuisance functions were estimated using random forests, and cluster-robust standard errors were used for inference. Results: We identified stable predictors across multiple domains, including sleep patterns, family environment, peer relationships, behavioral traits, and genetic risk. Many predictors were shared across substance outcomes, while some were outcome-specific. Effect sizes were modest, typically ranging from -0.01 to 0.02 per standard deviation increase in the predictor. Both risk-increasing and protective associations were observed. Risk factors included sleep disturbance and behavioral risk indicators, while protective factors included parental monitoring and structured environments. Conclusions: This study presents a practical framework for analyzing high-dimensional longitudinal data and identifying time-varying predictors of substance use initiation. The approach combines machine learning for variable selection with causal inference for effect estimation. The results highlight both shared and outcome-specific risk factors and identify modifiable targets, such as family environment and sleep, that may inform prevention strategies.

Background: Early initiation of substance use is linked to later adverse outcomes, and risk factors come from multiple domains and are shared across substances. In our previous work, traditional time-to-event Cox models identified individual risk factors, but these models are not designed to jointly model multiple outcomes or capture complex non-linear relationships. Multi-task learning (MTL) can leverage shared structure across related outcomes to improve prediction and distinguish common versus substance-specific predictors. However, most MTL studies rely on baseline features and focus on single outcomes, which limits their ability to capture shared risk and temporal changes. Substance use initiation is a time-dependent process that unfolds during development and reflects changing exposures over time. Baseline-only models cannot capture these changes or represent risk dynamics. Discrete-time modeling provides a practical approach by estimating interval-level initiation risk and combining it into cumulative risk at the subject level. By integrating multi-task learning with dynamic modeling, it is possible to share information across outcomes while capturing how risk evolves over time, which may improve prediction performance. Methods: Using the Adolescent Brain Cognitive Development (ABCD) Study (release 5.1), we developed two complementary multi-task learning (MTL) frameworks to predict initiation of alcohol, nicotine, cannabis, and any substance use. A baseline MTL model predicted fixed- horizon (48-month) initiation using one record per participant, while a dynamic discrete-time MTL model incorporated longitudinal interval data to model time-varying risk. Both models used multi-domain environmental exposures, core covariates, and polygenic risk scores (PRS). Performance was evaluated on a held-out test set using AUROC, PR-AUC, and calibration metrics, and compared with single-task logistic regression (LR). Feature importance was assessed using permutation importance and compared with Cox proportional hazards models. Results: MTL showed comparable or improved performance relative to LR, with larger gains for low-prevalence outcomes (cannabis and nicotine). Incorporating longitudinal information led to consistent improvements across all outcomes. Dynamic models increased AUROC by +0.044 to +0.062 for MTL and +0.050 to +0.084 for LR, indicating that temporal information was the primary driver of performance gains. Feature importance analyses showed modest overlap across methods, with higher agreement between dynamic MTL and Cox models than static MTL. A small set of features, including externalizing behavior, parental monitoring, and developmental factors, were consistently identified across all approaches. Conclusions: Dynamic multi-task learning improves the prediction of substance use initiation by leveraging longitudinal structure and shared information across outcomes. While MTL provides additional gains, incorporating time-varying information is the dominant factor for improving performance. Combining baseline and dynamic frameworks offers a comprehensive strategy for identifying robust risk factors and modeling adolescent substance use initiation.

Introduction: Eating behaviors are consistently associated with weight-related traits, yet the biological factors contributing to individual differences in these behaviors remain poorly characterized. Plasma proteomics offers an opportunity to investigate the biological processes underlying eating behaviors. Methods: Participants were 730 young adult twins from the FinnTwin12 cohort. Eating behaviors were measured through self-report questionnaires, including the Three-Factor Eating Questionnaire-R18 and four additional items on eating styles. Associations between plasma proteins and eating behaviors were examined using generalized estimating equation models adjusted for age and sex, with additional analyses adjusting for body mass index (BMI). Within-pair analyses were conducted in both monozygotic (MZ) and dizygotic twin pairs to assess whether associations were influenced by genetic or environmental factors. Results: We identified 51 significant protein-eating behavior associations involving 35 unique proteins (FDR <0.05). We observed 19 associations for the item "overeating when feeling down" and 12 for the TFEQ factor of emotional eating. The identified proteins were predominantly enriched in immune system pathways, including the complement cascade and adaptive immune signaling. After further adjustment for BMI, 12 associations persisted, most of which were associated with eating-style items, suggesting that BMI had a substantial influence on protein-eating behavior associations. Within-pair analyses of MZ pairs indicated that several associations persist after accounting for genetic effects. Conclusion: Our study identifies plasma proteins associated with eating behaviors, largely involving immune-related pathways. While some associations attenuated in twin analyses, several persisted, suggesting environmental influences. These results highlight potential biomarker candidates and indicate that modifiable environmental factors may contribute to the proteomic profiles associated with eating behaviors, with possible implications for weight-related traits.

BackgroundLoneliness is a psychosocial stressor associated with elevated risk of severe mental illness (SMI), including major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BD). Loneliness is theorized to become biologically embedded via inflammation-related mechanisms, yet its causal relationship with SMI and the role of inflammatory signaling remain unclear. AimsTo investigate whether loneliness causally influences SMI risk and whether inflammatory cytokines mediate this relationship. MethodWe applied univariable Mendelian randomization (MR) to estimate the causal effect of loneliness on SMI and multivariable MR (MVMR) to assess mediation by inflammatory signaling. We integrated genome-wide association study (GWAS) summary statistics for loneliness and SMI with genetic instruments for inflammatory cytokines. MVMR models estimated the direct effect of loneliness after accounting for inflammatory signaling using eQTL and pQTLs for interleukin-1 receptor antagonist (IL-1RA), interleukin-6 (IL-6), IL-6 receptor (IL-6R), tumor necrosis factor alpha (TNF-), and TNF receptors (TNF-R1/2). Bidirectional MR examined potential reverse causal pathways between inflammation, SMI, and loneliness. ResultsMR provided evidence consistent with a causal effect of loneliness on SCZ and MDD. Results were also consistent with inflammatory cytokine pathways for IL-1RA, IL-6R, and TNF-R1, partially mediating the loneliness-SCZ and loneliness-MDD causal effect. No significant effects were identified for BD in UVMR or MVMR models. Bidirectional MR suggested evidence of reverse causation between SCZ and loneliness. ConclusionsThe findings support a causal risk-increasing effect of loneliness on SCZ and MDD, partially mediated by systemic inflammatory signaling, implicating pathways as a plausible mechanistic link between psychosocial stress and mental illness risk and highlighting potential opportunities for prevention and targeted intervention through inflammation and social pathways.

Genetic counselling outcome measures are increasingly adapted for diverse clinical contexts. While the Genetic Counselling Outcome Scale (GCOS-24) is available in Swedish, no autism-specific version has been developed. Therefore, we adapted the Swedish GCOS-24 using the English version of the modified GCOS-24 (mGCSOS-24) to create a Swedish autism-specific mGCOS-24. Thereafter, we evaluated both the Swedish autism mGCOS-24 and the Swedish general GCOS-24 using Rasch analysis to assess their psychometric properties. Both instruments exhibited structural challenges, including multidimensionality, disordered thresholds, local item dependence, and invariance issues. For the Swedish autism mGCOS-24, we were able to identify subscales with acceptable measurement properties. However, applying the same structure to the Swedish general GCOS-24 did not resolve its broader limitations. This study introduces the first Swedish autism-specific mGCOS-24 and represents the first Rasch-based evaluation of any GCOS-24 or mGCOS-24 in Swedish. Our findings highlight important opportunities for measure refinement but also indicate that new or more substantially adapted tools may be needed to capture outcomes of genetic counselling in autistic populations.

BackgroundNegative symptoms are core features of schizophrenia that relate strongly to functional impairment, yet interventions targeting these symptoms remain largely ineffective. Emerging theoretical work highlights how environmental factors may shape and maintain negative symptoms. Although racial disparities in schizophrenia diagnosis among Black Americans are well documented and linked to racial stress and psychosis, the impact of racial stress on negative symptoms has not been examined. This study provides an initial test of a novel theory proposing that racial stress - here measured by racial discrimination - influences negative symptom severity through exacerbation of negative cognitions about the self, particularly defeatist performance beliefs (DPB). Study DesignParticipants diagnosed with schizophrenia-spectrum disorder (SSD) (N = 208; 80 Black, 128 White) completed the Positive and Negative Syndrome Scale (PANSS), the Defeatist Beliefs Scale, and self-report measures of subjective racial and ethnic discrimination (Racial and Ethnic Minority Scale and General Ethnic Discrimination Scale). Relationships among variables were tested using linear regression and mediation analysis. Study ResultsBlack participants exhibited significantly greater total and experiential negative symptoms than White participants with no group difference in DPB. Racial discrimination explained 46% of the relationship between race and negative symptoms. Among Black participants, higher DPB were associated with greater negative symptom severity. Discrimination was positively related to both DPB and negative symptoms. DPB partially mediated the relationship between discrimination and negative symptoms. ConclusionsFindings suggest that racial stress contributes to negative symptom severity via defeatist beliefs among Black individuals, highlighting potential targets for culturally informed interventions.

Cannabis use is an increasingly common therapeutic for a variety of chronic diseases. In addition, people with sleep problems may self-medicate using cannabis products. However, genetic architecture of cannabis use and its shared genetic predispositions with sleep traits has not been systematically examined. We performed a meta-analysis of cannabis use within the All of Us and UK Biobank cohorts, consisting of 152,807 cases and 220,272 controls. Our meta-analysis identified 39 independent loci, including the previously reported CADM2 locus associated with cannabis use and replicating previous work. Additionally our associations include neuronal and sleep-regulating genes such as HTR1A, RAI1, SLC39A8, and NCAM1. Moreover, tissue-specific analyses revealed that the genetic architecture of cannabis use is heavily enriched within the central nervous system and specific brain cell types. In addition, we observed significant positive genetic correlations with clinical insomnia, insomnia-related medication usage, and objectively measured nighttime physical activity, alongside negative correlations with morningness chronotype and daytime activity. Fine-mapping and colocalization analyses identified shared genetic signals between cannabis use and clinical insomnia including a near-perfect colocalization at SLC39A8 and CADM2. Together, these results highlight the shared genetic risk between cannabis use and sleep disorders. Additionally, our findings indicate the importance of investigating the genetic effects of cannabis use as its use becomes more widespread, both recreationally and medicinally.

Severe mental disorders (SMDs), including bipolar disorder, schizophrenia, and major depressive disorder, are highly complex conditions associated with a substantial clinical burden and an increased suicide risk. Here, we present the Madrid Manic Cohort (MadManic), a large-scale initiative from Spain designed to integrate genomic, multi-omics, clinical, and digital phenotyping data to investigate the biological basis and clinical heterogeneity of SMDs. The cohort is still expanding and currently includes over 4,400 participants (~2,300 psychiatric patients and ~2,100 controls) and >11,000 biospecimens. Genotyping, transcriptomic and epigenetic data are available for different subsets of the cohort. By establishing the MadManic cohort we aim to integrate molecular data with detailed clinical and longitudinal digital information, allowing a more precise characterization of patient subgroups based on biological and phenotypic profiles. The MadManic cohort is well positioned to contribute to major international efforts in psychiatric genetics by enhancing the representation of Southern European populations, and advancing the identification of genetic risk, clinical predictors, and pharmacogenomic markers of treatment response. This cohort represents a valuable resource for advancing precision psychiatry, with the potential to improve risk prediction and guide personalized interventions in SMDs.

ImportanceBlood neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are entering clinical use in neurology as markers of neuroaxonal and astrocytic injury, but their utility in psychiatry is unclear. ObjectiveTo determine whether psychiatric diagnoses are associated with altered plasma NfL and GFAP levels. Design, Setting, and ParticipantsThis population-based study examined plasma NfL and GFAP among 47,495 participants from the UK Biobank (54.0% female; 93.5% White; mean [SD] age 56.8 [8.2] years) who provided blood samples and sociodemographic and clinical data between 2006 and 2010. Normative modeling was applied to assess associations between 7 lifetime psychiatric diagnostic categories and deviations from expected NfL and GFAP levels, while accounting for neurological diagnoses, cardiometabolic burden, and substance use. Data were analyzed between July 2025 and March 2026. Main Outcomes and MeasuresDeviations in plasma NfL and GFAP levels from normative predictions. ResultsRelative to the reference population, plasma NfL levels were higher among individuals with bipolar disorder (d=0.20; 95% CI, 0.03-0.37; p=0.03), recurrent depressive disorder (d=0.23; 95% CI, 0.07-0.38; p=0.009), and depressive episodes (d=0.06; 95% CI, 0.02-0.10; p=0.01), lower among individuals with anxiety disorders (d=-0.07; 95% CI, -0.12 to -0.02; p=0.008), but did not differ in schizophrenia spectrum, stress-related, or other psychiatric disorders. Plasma GFAP levels were not elevated in any psychiatric disorders. Variability in NfL levels was greater among individuals with schizophrenia spectrum disorders (variance ratio [VR]=1.30; p=0.005), depressive episodes (VR=1.06; p=0.006), and anxiety disorders (VR=1.08; p=0.005). Variability in GFAP levels was increased only in anxiety disorders (VR=1.08; p=0.01). Plasma NfL levels exceeding percentile-based normative thresholds were more common among individuals with schizophrenia spectrum disorders, bipolar disorder, recurrent depressive disorder, and depressive episodes. Neurological diagnoses, cardiometabolic burden, and substance use were associated with plasma NfL and GFAP levels. Conclusions and RelevanceThis study provides population-level evidence of plasma NfL elevation in bipolar and depressive disorders and increased variability in schizophrenia spectrum, bipolar and depressive disorders, supporting its potential as a biomarker in psychiatry and informing its ongoing neurological applications. Plasma GFAP levels, in contrast, were largely unaltered across psychiatric disorders. Key PointsO_ST_ABSQuestionC_ST_ABSAre plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) levels altered in psychiatric disorders? FindingsIn this cohort study including 47,495 individuals, normative modeling revealed that plasma NfL levels were elevated in bipolar and depressive disorders, whereas plasma GFAP levels were not elevated in any psychiatric disorder. Plasma NfL levels also showed higher variability in schizophrenia spectrum, bipolar, and depressive disorders. MeaningPlasma NfL shows distinct alterations in schizophrenia spectrum and affective disorders, supporting its further investigation as a biomarker in clinical psychiatry and highlighting the need to consider psychiatric comorbidity in neurological applications.

Background Higher levels of sedentary behaviour, such as leisure screen time (LST), and lower levels of physical activity are associated with diseases across multiple body systems which contribute to a large global health burden. Whether these associations are causal is unclear. The primary aim of this study is to investigate the causal effects of higher LST (given greater power) and, secondarily, lower moderate-to-vigorous intensity physical activity (MVPA), on a wide range of diseases in a hypothesis-free approach. Methods A two-sample Mendelian randomisation phenome-wide association study was conducted for the main analyses. Genetic single nucleotide polymorphisms (SNPs) were first selected as exposure genetic instruments for LST (hours of television watched per day; 117 SNPs) and MVPA (higher vs. lower; 18 SNPs) based on the genome-wide significant threshold (p < 5*10-8) from the largest relevant genome-wide association study (GWAS). For disease outcomes, we used summary results from FinnGen GWAS, including 1,719 diseases defined by hospital discharge International Classification of Diseases (ICD) codes in 453,733 European participants. For the main analyses, we used the inverse-variance weighting method with a Bonferroni corrected p-value of p [&le;] 3.47*10-4. Sensitivity analyses included Steiger filtering, MR-Egger and weighted median analyses, and data from UK Biobank were used to explore replication. Findings Genetically predicted higher LST was associated with increased risk of 87 (5.1% of the 1,719) diseases. Most of these diseases were in musculoskeletal and connective tissue (n=37), genitourinary (n=12) and respiratory (n=8) systems. Genetic liability to lower MVPA was associated with six diseases: three in musculoskeletal and connective tissue and genitourinary systems (with greater risk of these diseases also identified with higher LST), and three in respiratory and genitourinary systems. Sensitivity analyses largely supported the main analyses. Results replicated in UK Biobank, where data available. Conclusions Higher levels of sedentary behaviour, and lower levels of physical activity, causally increase the risk of diseases across multiple body systems, making them promising targets for reducing multimorbidity.

Objectives: Longer lifespans lead to longer time on retirement, despite the efforts to raise the retirement age. Therefore, it is important to study how the retirement years can be spent without diseases. This study examined socioeconomic and sociodemographic differences in healthy years spent on retirement. Methods: We followed a cohort of retired Finnish municipal employees (N=4231, average follow-up 15.4 years) on national administrative registers for major chronic diseases: cancer, coronary heart disease, cerebrovascular disease, diabetes, asthma or chronic obstructive pulmonary disease, dementia, mental disorders, and alcohol-related disorders. Median healthy years on retirement and age at first occurrence of illness (ICD-10 and ATC-based) in each combination of sex, occupational class, and age of retirement were predicted using Royston-Parmar models. Prevalence rates for each diagnostic group were calculated. Results: Most healthy years on retirement were spent by women having worked in semi-professional jobs who retired at age 60-62 (median predicted healthy years 11.6, 95% CI 10.4-12.7). The least healthy years on retirement were spent by men having worked in routine non-manual jobs who retired after age 62 (median predicted healthy years 6.5, 95% CI 4.4-9.5). Diabetes was slightly more common among lower occupational class women, and dementia among manual working women having retired at age 60-62. Discussion: Healthy years on retirement are not enjoyed equally by women and men and those who retire early or later. Policies aiming to increase the retirement age should consider the effects of these gaps on retirees and the equitability of those effects.

The global ambition to end the human immunodeficiency virus (HIV) epidemic requires understanding which system-level policy levers, enacted under the framework of Universal Health Coverage (UHC), are most effective in achieving both transmission reduction and diagnostic coverage. This study addresses an important evidence gap by quantifying the within-country association between measurable UHC policy indicators and the estimated rate of new HIV infections across nine Southeast Asian countries between 2013 and 2022. Employing a Fixed-Effects panel data methodology, the analysis controls for time-invariant national heterogeneity, ensuring reliable estimates of policy impact. We found that marginal changes in total current health expenditure (CHE) as a percentage of gross domestic product (GDP) were not statistically significantly associated with changes in HIV incidence. However, increases in the UHC Infectious Disease Service Coverage Index were statistically significantly associated with concurrent reductions in HIV incidence (p < 0.001), suggesting the efficacy of targeted service implementation as the principal driver of curbing new HIV infections. In addition, the UHC Reproductive, Maternal, Newborn, and Child Health Service Coverage Index exhibited a statistically significant positive association with changes in HIV incidence (p < 0.01), which is interpreted as a vital surveillance artefact resulting from expanded detection and reporting of previously undiagnosed HIV cases. Furthermore, out-of-pocket (OOP) health expenditure as a percentage of CHE showed a counter-intuitive negative association with changes in HIV incidence (p < 0.01), suggesting this metric primarily shows ongoing indirect cost burdens on the established patient cohort, or, alternatively, presents a diagnostic access barrier that results in lower case finding. These findings suggest that policymakers should prioritise investment in targeted infectious disease service efficacy over aggregate fiscal commitment and utilise integrated sexual health platforms for strengthened HIV surveillance and case identification.

Human papillomavirus (HPV) testing is the primary method for cervical cancer screening, and a negative HPV test is associated with a very low subsequent risk of invasive cancer. Nevertheless, a small number of cervical cancers are diagnosed following an HPV-negative testing result, posing challenges within HPV-based screening pathways. Using nationwide Swedish registry data of HPV testing, we identified women diagnosed with invasive cervical cancer between 2019 and 2024 and reconstructed HPV testing histories from the National Cervical Screening Registry (NKCx). The most recent HPV test prior to diagnosis was defined as the index test, and longitudinal HPV testing trajectories were classified among women with an HPV-negative index test. Of 3,000 women diagnosed with invasive cancer, 243 (8.1%) had an HPV-negative index test. These women were older at diagnosis and more frequently diagnosed at advanced stages compared with women with an HPV-positive index test. Most HPV-negative index tests (66.3%) were performed in the peri-diagnostic period (+/- 30 days). Among women with an HPV-negative index test, 52.7% (128/243) had no prior HPV testing recorded, while the remainder had consistently HPV-negative histories (33.3%, 83/243) or evidence of prior HPV positivity before the index negative test (14%, 32/243). Possible recurrent HPV positivity following an intervening negative test was rare (0.4%, 1/243). HPV-negative screening results preceding invasive cancer reflect heterogeneous screening histories and cannot be explained solely by test failure. Findings highlighting the importance of reaching women earlier in screening programs and show that fluctuating HPV detectability is rare.

PurposeAntibiotic use (ABU) is a major driver of antimicrobial resistance (AMR), but ABU patterns are poorly understood in low-income countries where the burden of AMR is great and ABU is insufficiently regulated. Here, we report ABU from ten sites ranging from rural villages to small cities in Madagascar, a country with high AMR levels, and present results from modeling to identify factors that may be associated with ABU in this setting. MethodsWe conducted surveys of 290 individuals from ten sites in the SAVA Region of northeast Madagascar to gather data on sociodemographic characteristics, agricultural and animal husbandry practices, recent antibiotic use, the antibiotics that participants recalled using in their lifetimes, and the sources of their antibiotics. Using these data, we conducted statistical analyses with a mixed-effects logistic model to determine which characteristics were associated with recent antibiotic use. ResultsNearly all respondents (N=283, 97.6%) reported ABU in their lifetimes, with amoxicillin being the most widely reported antibiotic (N=255, 90.1% of those reporting ABU). All recalled antibiotics were classified as frontline drugs except for ciprofloxacin. Most respondents who reported antibiotic use also reported obtaining antibiotics without prescriptions from local stores (N=273, 96.5%), while only 52.3% (N=148) reported obtaining antibiotics through a prescriptive route, such as from a health clinic or private doctor. Of the 127 individuals (44.9%) who reported recent ABU, men were found to be significantly less likely to have recently taken antibiotics than women. ConclusionsOur findings provide new insights into ABU in agricultural settings in low-income countries, which have historically been understudied in AMR and pharmacoepidemiologic research. Knowledge of ABU patterns supports understanding of AMR dynamics and AMR control efforts in these contexts, such as interventions on inappropriate antibiotic dispensing. Key pointsO_LIAntibiotic use (ABU) in Madagascar is largely unstudied despite its role in antimicrobial resistance (AMR), which Madagascar faces a high burden of. C_LIO_LIABU was widespread among livestock owners in northeast Madagascar, with the majority of study participants reporting ABU in their lifetimes and most people reporting ABU also having taken antibiotics in the previous three months. C_LIO_LIMost respondents reported obtaining their antibiotics from non-pharmaceutical stores, indicating high levels of unregulated ABU, though more than half also reported sourcing their antibiotics through prescriptive means (like doctors and health clinics). C_LIO_LIMen were less likely than women to have taken antibiotics in the previous three months. C_LIO_LIThese findings support the development of interventions to mitigate the burden of AMR in Madagascar and similar contexts while underscoring the need for more comprehensive research on the drivers and patterns of ABU. C_LI Plain language summaryIn this study, we provide basic information on antibiotic use (ABU) patterns in Madagascar, a country that experiences high levels of resistance but has been particularly understudied in AMR and pharmacological research. We surveyed 290 farmers with livestock from ten sites across northeast Madagascar about their ABU and found that nearly all study participants (N=283, 97.6%) have used antibiotics in their lifetimes, while a little under half of those who reported ABU also reported using antibiotics in the previous three months (N=127, 44.9%). The most used antibiotic was amoxicillin (N=255, 90.1%). Most people obtained their antibiotics from sources that do not require prescriptions, like general stores, indicating that most ABU is unregulated. Through modeling, we also found that men were less likely than women to have taken antibiotics in the previous three months (OR=0.50, CI 0.30-0.82). These findings help us better understand the dynamics of ABU in low-income countries, which have historically been understudied in AMR and pharmacological research. They also support efforts to mitigate the burden of AMR by revealing ABU dynamics that may contribute to the emergence and spread of AMR, as well as identifying targets for intervention to curb inappropriate ABU.

BackgroundEarly-life adversity (ELA) is a risk factor for enduring pain in youth and is associated with alterations in brain morphology and function. However, it remains unclear whether ELA-related neurobiological changes contribute to the development of enduring pain in early adolescence. MethodsUsing data from the Adolescent Brain Cognitive Development (ABCD) Study, we examined multimodal magnetic resonance imaging (MRI) markers in children assessed at baseline (ages 9-11 years) and at 2-year follow-up (ages 11-13 years). ELA exposure was defined at baseline to maximise temporal separation between early adversity and later enduring pain. Participants with enduring pain at follow-up (n = 322) were compared to matched pain-free controls (n = 644). Structural MRI, diffusion MRI (fractional anisotropy, mean diffusivity), and resting-state functional connectivity data were analysed. Linear models tested main effects of enduring pain, ELA, and their interaction on brain metrics, controlling for relevant covariates. ResultsELA exposure was associated with smaller caudate and nucleus accumbens volumes, and reduced surface area of the left rostral middle frontal gyrus. No significant effects of enduring pain or ELA-by-enduring pain interaction were observed across grey matter, white matter, or functional connectivity measures. ConclusionsELA was associated with alterations in fronto-striatal regions in late childhood, but these changes were not linked to enduring pain in early adolescence. These findings suggest that ELA-related neurobiological alterations may represent early markers of vulnerability rather than concurrent correlates of enduring pain. Longitudinal follow-up is needed to determine whether these alterations contribute to later chronic pain risk.

BackgroundThis study aimed to assess caregiver knowledge of Infant and Young Child Feeding (IYCF), child health, severe acute malnutrition (SAM) screening, and Community-Based Management of Acute Malnutrition (CMAM), its determinants, and associations with IYCF/ WaSH (water, sanitation, and hygiene) practices among caregivers of children 6-59 months with SAM in Ethiopian agrarian and pastoralist settings. MethodData were from the baseline survey of the R-SWITCH Ethiopia cluster-randomized controlled trial (cRCT), which screened [~]28,000 children aged 6-59 months and identified 686 SAM cases. Caregiver knowledge was evaluated using a validated 32-item questionnaire (Cronbachs for internal reliability) and analyzed via linear mixed-effects and Poisson regression models in Stata 17. ResultsCaregiver knowledge was positively associated with improved IYCF/WaSH practices among children aged 6-23 months with SAM, including higher minimum dietary diversity (MDD: IRR=1.50), minimum acceptable diet (MAD: IRR=1.63), and reduced zero vegetable/fruit intake (IRR=0.77), as well as MDD in children aged 24-59 months, improved water access (IRR=1.19), water treatment (IRR=2.02), and handwashing stations (IRR=1.41). Literate ({beta} = 4.1; 95% CI:1.5-6.6, p= 0.016), pregnant({beta} = 4.4; 95% CI:0.9-7.8, 0.018), having child weighing at a health post/ health center ({beta} = 8.9;95% CI:3.5-14.2,p [&le;] 0.001), and higher household wealth index ({beta} = 11.8;95% CI:3.6-20.1,p= 0.005) were associated with higher knowledge, while possible depression ({beta} = -0.3;95% CI: -0.5 to 0.0, p= 0.015) was associated with lower knowledge. ConclusionCaregiver knowledge determines better IYCF/WaSH practices among children aged 6-59 months with SAM. Literacy, pregnancy, having child weighing at a health post or health center, and greater household wealth were associated with caregivers knowledge, whereas possible depression was associated with lower knowledge. Integrating context-specific caregiver education and mental health support into CMAM, GMP(Growth monitoring and promotion), and primary care services could enhance feeding/WaSH practices in Ethiopia.

BackgroundInsecticides remain the cornerstone of mosquito vector control for malaria, dengue, and other mosquito-borne diseases, yet global patterns of deployment and their socioeconomic and environmental drivers are poorly characterized. Understanding where and why insecticides are used is essential for better targeting control efforts and ensuring they are effective, equitable, and efficient. MethodsWe analyzed annual country-level insecticide-use data from 122 countries (1990-2019), reported as standard spray coverage for insecticide-treated nets (ITNs), residual spraying (RS), spatial spraying (SS), and larviciding (LA). Generalized linear mixed models and hurdle models quantified associations between deployment and disease incidence, human development index (HDI), human population density, temperature, and precipitation. Models were evaluated using repeated cross-validation and applied to generate downscaled predictions of insecticide use at subnational administrative region level 2 (ADM2) globally. FindingsInsecticide deployment increased with malaria and dengue incidence, but this response was substantially stronger in higher-HDI countries, indicating that deployment depends on socioeconomic capacity as well as disease burden that leads to weaker scaling in lower-resource settings. Intervention types exhibited distinct patterns; ITN use tracked malaria burden, whereas infrastructure-intensive approaches (e.g., RS and SS) were concentrated in higher-HDI settings and increased with Aedes-borne disease incidence. Downscaled ADM2-level maps uncovered substantial within-country heterogeneity that is obscured at the national scale, highlighting regions where predicted deployment remains low relative to disease risk across sub-Saharan Africa, South Asia, and parts of Latin America. InterpretationGlobal insecticide deployment reflects not only epidemiological need but also economic and logistical capacity, creating mismatches between risk and control. High-resolution mapping can support more equitable allocation of interventions, guide insecticide resistance stewardship, and improve strategic planning as climate and urbanization reshape mosquito-borne disease risk.

Background Comprehensive sexuality education (CSE) is essential to the health and well-being of young people. In the Democratic Republic of Congo (DRC), where more than 65% of the population is under the age of 25, access to interpersonal CSE remains limited owing to sociocultural and structural barriers. This exposes young people to persistent socio-sanitary vulnerabilities. In this context, mobile health apps (MHAs) constitute a promising solution, supported by the growing use of smartphones among young Congolese. However, this group's intention to use MHAs for CSE has been the subject of little research to date. Objective The aim of this study was to identify predictors of intention to use MHAs among young Congolese, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). Methods A predictive correlational study was conducted in eight public secondary schools in Bukavu (DRC) with a stratified random sample of 859 students. Predictors of intention to use--performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and perceived risk (PR)--and moderators--age, gender, and past MHA experience--were measured from data collected through a self-administered UTAUT questionnaire. Descriptive and multivariate analyses were run on SPSS version 28. Results Mean age of participants was 16.3 years (SD = 1.5). Boys made up 55.1% of the sample. Overall, 51.0% of the sample owned a smartphone, of which 62.3% reported having easy access to mobile data and 16.2% were already using MHAs to learn about sexual health. Intention to use MHAs was positively influenced by PE ({beta} = 0.523, p < 0.001), EE ({beta} = 0.115, p < 0.001), and SI ({beta} = 0.113, p < 0.001). FC (p = 0.260) and PR (p = 0.631), however, had no significant influence. Age moderated all of the relationships tested (F (1, 849-854) = 9.97-20.82; p [&le;] 0.002), with more marked effects observed among younger participants 14-15 years old. The final model explained 44% of the variance, indicating good predictive power. Conclusion Intention to use digital CSE was explained primarily by PE, EE, and SI and moderated by age. To strengthen this intention, stakeholders will need to promote e-interventions that are pertinent, easy to use, socially valorized, and tailored to young people's needs and to the local context.