Daylength and the rates of changes in daylength have been associated with seasonal fluctuations in psychiatric symptoms and in cognition and mood in healthy adults. However, variations in human brain glucose metabolism in concordance with seasonal changes remain under explored.

In this cross-sectional study, we examined seasonal effects on brain glucose metabolism, which we measured using 18F-fluorodeoxyglucose-PET in 97 healthy participants. To maximize the sensitivity of regional effects, we computed relative metabolic measures by normalizing the regional measures to white matter metabolism. Additionally, we explored the role of rest–activity rhythms/sleep–wake activity measured with actigraphy in the seasonal variations of regional brain metabolic activity.

We found that seasonal variations of cerebral glucose metabolism differed across brain regions. Glucose metabolism in prefrontal regions increased with longer daylength and with greater day-to-day increases in daylength. The cuneus and olfactory bulb had the maximum and minimum metabolic values around the summer and winter solstice respectively (positively associated with daylength), whereas the temporal lobe, brainstem, and postcentral cortex showed maximum and minimum metabolic values around the spring and autumn equinoxes, respectively (positively associated with faster daylength gain). Longer daylength was associated with greater amplitude and robustness of diurnal activity rhythms suggesting circadian involvement.

The current findings advance our knowledge of seasonal patterns in a key indicator of brain function relevant for mood and cognition. These data could inform treatment interventions for psychiatric symptoms that peak at specific times of the year.

The DSM Level 1 Cross-Cutting Symptom Measure (DSM-XC) allows for assessing multiple psychopathological domains. However, its capability to screen for mental disorders in a population-based sample and the impact of adverbial framings (intensity and frequency) on its performance are unknown.

The study was based on cross-sectional data from the 1993 Pelotas birth cohort in Brazil. Participants with completed DSM-XC and structured diagnostic interviews (n = 3578, aged 22, 53.6% females) were included. Sensitivity, specificity, positive (LR+), and negative (LR−) likelihood ratios for each of the 13 DSM-XC domains were estimated for detecting five internalizing disorders (bipolar, generalized anxiety, major depressive, post-traumatic stress, and social anxiety disorders) and three externalizing disorders (antisocial personality, attention-deficit/hyperactivity, and alcohol use disorders). Sensitivities and specificities >0.75, LR+ > 2 and LR− < 0.5 were considered meaningful. Values were calculated for the DSM-XC's original scoring and for adverbial framings.

Several DSM-XC domains demonstrated meaningful screening properties. The anxiety domain exhibited acceptable sensitivity and LR− values for all internalizing disorders. The suicidal ideation, psychosis, memory, repetitive thoughts and behaviors, and dissociation domains displayed acceptable specificity for all disorders. Domains also yielded small but meaningful LR+ values for internalizing disorders. However, LR+ and LR− values were not generally meaningful for externalizing disorders. Frequency-framed questions improved screening properties.

The DSM-XC domains showed transdiagnostic screening properties, providing small but meaningful changes in the likelihood of internalizing disorders in the community, which can be improved by asking frequency of symptoms compared to intensity. The DSM-XC is currently lacking meaningful domains for externalizing disorders.

Although suicide bereavement is associated with suicide and self-harm, evidence regarding mechanisms is lacking. We investigated whether depression and substance use (alcohol and/or other drugs) explain the association between partner suicide bereavement and suicide.

Linkage of nationwide, longitudinal data from Denmark for the period 1980–2016 facilitated a comparison of 22 668 individuals exposed to bereavement by a partner's suicide with 913 402 individuals bereaved by a partner's death due to other causes. Using causal mediation models, we estimated the degree to which depression and substance use (considered separately) mediated the association between suicide bereavement and suicide.

Suicide-bereaved partners were found to have a higher risk of suicide (HRadj = 1.59, 95% CI 1.36–1.86) and of depression (ORadj 1.16, 95% CI 1.09–1.25) when compared to other-bereaved partners, but a lower risk of substance use (ORadj 0.83; 95% CI 0.78–0.88). An increased risk of suicide was found among any bereaved individuals with a depression diagnosis recorded post-bereavement (ORadj 3.92, 95% CI 3.55–4.34). Mediation analysis revealed that depression mediated 2% (1.68%; 95% CI 0.23%–3.14%; p = 0.024) of the association between suicide bereavement and suicide in partners when using bereaved controls.

Depression is a partial mediator of the association between suicide bereavement and suicide. Efforts to prevent and optimize the treatment of depression in suicide-bereaved people could reduce their suicide risk. Our findings might be conservative because we did not include cases of depression diagnosed in primary care. Further work is needed to understand this and other mediators.

Motor activity fluctuations in healthy adults exhibit fractal patterns characterized by consistent temporal correlations across wide-ranging time scales. However, these patterns are disrupted by aging and psychiatric conditions. This study aims to investigate how fractal patterns vary across the sleep–wake cycle, differ based on individuals' recency of depression diagnosis, and change before and after a depressive episode.

Using actigraphy from two cohorts (n = 378), we examined fractal motor activity patterns both between individuals without depression and with varying recencies of depression and within individuals before and after depressive symptom recurrence. To evaluate fractal patterns, we quantified temporal correlations in motor activity fluctuations across different time scales using a scaling exponent, α. Linear mixed models were utilized to assess the influence of the sleep–wake cycle, (recency of) depression, and their interaction on α.

Fractal activity patterns in all individuals varied across the sleep–wake cycle, showing stronger temporal correlations during wakefulness (larger α = 1.035 ± 0.003) and more random activity fluctuations during sleep (smaller α = 0.784 ± 0.004, p < 0.001). This sleep–wake difference was reduced in recently depressed individuals (1–6 months), leading to larger α during sleep (0.836 ± 0.017), compared to currently depressed (0.781 ± 0.018, p = 0.006), remitted (0.776 ± 0.014, p < 0.001), and never-depressed individuals (0.773 ± 0.016, p < 0.001). Moreover, remitted individuals who experienced depressive symptom recurrence during antidepressant tapering exhibited a larger α during sleep after the symptom onset as compared to before (after: α = 0.703 ± 0.022; before: α = 0.680 ± 0.022; p < 0.001).

These findings suggest a link between fractal motor activity patterns during sleep and depressive symptom recurrence in remitted individuals and those with recent depression.

Major theories link threat learning processes to anxiety symptoms, which typically emerge during adolescence. While this developmental stage is marked by substantial maturation of the neural circuity involved in threat learning, research directly examining adolescence-specific patterns of neural responding during threat learning is scarce. This study compared adolescents and adults in acquisition and extinction of conditioned threat responses assessed at the cognitive, psychophysiological, and neural levels, focusing on the late positive potential (LPP), an event-related potential (ERP) component indexing emotional valence.

Sixty-five adults and 63 adolescents completed threat acquisition and extinction, 24 h apart, using the bell conditioning paradigm. Self-reported fear, skin conductance responses (SCR), and ERPs were measured.

Developmental differences emerged in neural and psychophysiological responses during threat acquisition, with adolescents displaying heightened LPP responses to threat and safety cues as well as heightened threat-specific SCR compared to adults. During extinction, SCR suggested comparable reduction in conditioned threat responses across groups, while LPP revealed incomplete extinction only among adolescents. Finally, age moderated the link between anxiety severity and LPP-assessed extinction, whereby greater anxiety severity was associated with reduced extinction among younger participants.

In line with developmental theories, adolescence is characterized by a specific age-related difficulty adapting to diminishing emotional significance of prior threats, contributing to heightened vulnerability to anxiety symptoms. Further, LPP appears to be sensitive to developmental differences in threat learning and may thus potentially serve as a useful biomarker in research on adolescents, threat learning, and anxiety.

Multimorbidity, known as the co-occurrence of at least two chronic conditions, has become of increasing concern in the current context of ageing populations, though it affects all ages. Early life risk factors of multimorbidity include adverse childhood experiences (ACEs), particularly associated with psychological conditions and weight problems. Few studies have considered related mechanisms and focus on old age participants. We are interested in estimating, from young adulthood, the risk of overweight-depression comorbidity related to ACEs while adjusting for early life confounders and intermediate variables.

We used data from the 1958 National Child Development Study, a prospective birth cohort study (N = 18 558). A four-category outcome (no condition, overweight only, depression only and, overweight-depression comorbidity) was constructed at 23, 33, and 42 years. Multinomial logistic regression models adjusting for intermediate variables co-occurring with this outcome were created. ACEs and sex interaction on comorbidity risk was tested.

In our study sample (N = 7762), we found that ACEs were associated with overweight-depression comorbidity risk throughout adulthood (RRR [95% CI] at 23y = 3.80 [2.10–6.88]) though less overtime. Comorbidity risk was larger than risk of separate conditions. Intermediate variables explained part of the association. After full-adjustment, an association remained (RRR [95% CI] at 23y = 2.00 [1.08–3.72]). Comorbidity risk related to ACEs differed by sex at 42.

Our study provides evidence on the link and potential mechanisms between ACEs and the co-occurrence of mental and physical diseases throughout the life-course. We suggest addressing ACEs in intervention strategies and public policies to go beyond single disease prevention.

Schools are central places for adolescent social lives, which is a major factor greatly affecting adolescent mental health; school climate (i.e. quality of the school social environments) can be a proximal social determinant for adolescent mental health. Supportive school environments may serve as a protective factor during crises like COVID-19, which disrupt social lives and worsen adolescent mental health. This is the first study examining whether the pandemic effects differed based on the levels of school climate on depressive symptoms (DS) and psychotic experiences (PEs) among adolescents.

School climate (score range: 0–28), DS (0–26), and PEs (0–5) were self-reported in a population-based cohort (Tokyo Teen Cohort; N = 3171) at four timepoints (10y, 12y, 14y, and 16y) before and during COVID-19. COVID-19 occurred midway through the 16y survey, allowing us to examine its impact and interaction effect with school climate while accounting for within-person changes over time using mixed-effects models.

Significant interaction effects were found on DS (unstandardized coefficient [B] = −0.166, 95% confidence interval [CI] −0.225 to −0.107) and PEs (B = −0.020, 95% CI −0.028 to −0.012). The pandemic effects were not significant for adolescents with high school climate scores (around the 80th percentile or higher), although the pandemic significantly worsened these outcomes among the overall sample.

The negative mental health effects of the pandemic were significantly mitigated among adolescents experiencing a supportive school climate. A positive school climate can protect adolescent mental health during challenging social conditions, such as pandemics.

Although behavioral mechanisms in the association among depression, anxiety, and cancer are plausible, few studies have empirically studied mediation by health behaviors. We aimed to examine the mediating role of several health behaviors in the associations among depression, anxiety, and the incidence of various cancer types (overall, breast, prostate, lung, colorectal, smoking-related, and alcohol-related cancers).

Two-stage individual participant data meta-analyses were performed based on 18 cohorts within the Psychosocial Factors and Cancer Incidence consortium that had a measure of depression or anxiety (N = 319 613, cancer incidence = 25 803). Health behaviors included smoking, physical inactivity, alcohol use, body mass index (BMI), sedentary behavior, and sleep duration and quality. In stage one, path-specific regression estimates were obtained in each cohort. In stage two, cohort-specific estimates were pooled using random-effects multivariate meta-analysis, and natural indirect effects (i.e. mediating effects) were calculated as hazard ratios (HRs).

Smoking (HRs range 1.04–1.10) and physical inactivity (HRs range 1.01–1.02) significantly mediated the associations among depression, anxiety, and lung cancer. Smoking was also a mediator for smoking-related cancers (HRs range 1.03–1.06). There was mediation by health behaviors, especially smoking, physical inactivity, alcohol use, and a higher BMI, in the associations among depression, anxiety, and overall cancer or other types of cancer, but effects were small (HRs generally below 1.01).

Smoking constitutes a mediating pathway linking depression and anxiety to lung cancer and smoking-related cancers. Our findings underline the importance of smoking cessation interventions for persons with depression or anxiety.

One potential cause of comorbidity is the direct causal effect of one disorder – A – on risk for subsequent onset of disorder B. Could genetic risk scores be utilized to test for such an effect? If disorder A causally impacts on risk for disorder B, then genetic risk for disorder A should be lower in cases of disorder A with v. without a prior onset of B.

In all individuals (n = 905 736) born in Sweden from 1980 to 1990, from six psychiatric and drug use disorders (major depression, anxiety disorders, alcohol use disorder, drug use disorder, bipolar disorder, and schizophrenia), we formed 14 pairs of disorders A and B. In these pairs, we compared, using Cox proportional hazards models, the predictive effect of the familial-genetic risk score (FGRS) for disorder B in those who had v. had not had a prior onset of disorder A.

In all pairs, the impact of the FGRS for disorder B was significantly stronger in cases without v. with a prior history of disorder A. These effects were similar across sex, stable across levels of FGRS and not likely due to clinician bias. In many of our disorder pairs, previous clinical studies suggest a mechanism for a causal effect of disorder A on B.

Our findings provide indirect evidence that the occurrence of one psychiatric or substance use disorder often has a causal effect on risk for subsequent disorders. This mechanism may substantially contribute to the widespread comorbidity among psychiatric conditions.

Past studies indicate daily increases in estrogen across the menstrual cycle protect against binge-eating (BE) phenotypes (e.g. emotional eating), whereas increases in progesterone enhance risk. Two previous studies from our laboratory suggest these associations could be due to differential genomic effects of estrogen and progesterone. However, these prior studies were unable to directly model effects of daily changes in hormones on etiologic risk, instead relying on menstrual cycle phase or mean hormone levels. The current study used newly modified twin models to examine, for the first time, the effects of daily changes in estradiol and progesterone on genetic/environmental influences on emotional eating in our archival twin sample assessed across 45 consecutive days.

Participants included 468 female twins from the Michigan State University Twin Registry. Daily emotional eating was assessed with the Dutch Eating Behavior Questionnaire, and daily saliva samples were assayed for ovarian hormone levels. Modified genotype × environment interaction models examined daily changes in genetic/environmental effects across hormone levels.

Findings revealed differential effects of daily changes in hormones on etiologic risk, with increasing genetic influences across progesterone levels, and increasing shared environmental influences at the highest estradiol levels. Results were consistent across primary analyses examining all study days and sensitivity analyses within menstrual cycle phases.

Findings are significant in being the first to identify changes in etiologic risk for BE symptoms across daily hormone levels and highlighting novel mechanisms (e.g. hormone threshold effects, regulation of conserved genes) that may contribute to the etiology of BE.

Persistent cognitive deficits and functional impairments are associated with bipolar disorder (BD), even during the euthymic phase. The dysfunction of default mode network (DMN) is critical for self-referential and emotional mental processes and is implicated in BD. The current study aims to explore the balance of excitatory and inhibitory neurotransmitters, i.e. glutamate and γ-aminobutyric acid (GABA), in hubs of the DMN during the euthymic patients with BD (euBD).

Thirty-four euBD and 55 healthy controls (HC) were recruited to the study. Using proton magnetic resonance spectroscopy (1H-MRS), glutamate (with PRESS sequence) and GABA levels (with MEGAPRESS sequence) were measured in the medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC) and the posterior cingulate gyrus (PCC). Measured concentrations of excitatory glutamate/glutamine (Glx) and inhibitory GABA were used to calculate the excitatory/inhibitory (E/I) ratio. Executive and attentional functions were respectively assessed using the Wisconsin card-sorting test and continuous performance test.

euBD performed worse on attentional function than controls (p = 0.001). Compared to controls, euBD had higher E/I ratios in the PCC (p = 0.023), mainly driven by a higher Glx level in the PCC of euBD (p = 0.002). Only in the BD group, a marginally significant negative association between the mPFC E/I ratio (Glx/GABA) and executive function was observed (p = 0.068).

Disturbed E/I balance, particularly elevated Glx/GABA ratio in PCC is observed in euBD. The E/I balance in hubs of DMN may serve as potential biomarkers for euBD, which may also contribute to their poorer executive function.

Previous studies have suggested that the habenula (Hb) may be involved in the mechanism of obsessive-compulsive disorder (OCD). However, the specific role of Hb in OCD remains unclear. This study aimed to explore the structural and functional abnormalities of Hb in OCD and their relationship with the clinical symptoms.

Eighty patients with OCD and 85 healthy controls (HCs) were recruited as the primary dataset. The grey matter volume, resting-state functional connectivity (FC), and effective connectivity (EC) of the Hb were calculated and compared between OCD group and HCs. An independent replication dataset was used to verify the stability and robustness of the results.

Patients with OCD exhibited smaller Hb volume and increased FC of right Hb-left hippocampus than HCs. Dynamic causal model revealed an increased EC from left hippocampus to right Hb and a less inhibitory causal influence from the right Hb to left hippocampus in the OCD group compared to HCs. Similar results were found in the replication dataset.

This study suggested that abnormal structure of Hb and hippocampus-Hb connectivity may contribute to the pathological basis of OCD.

Psychotic-like experiences (PLEs) are subclinical phenomena that often precede the onset of psychosis and occur in various mental disorders. Social determinants of psychosis and PLEs are important and have been operationalized within the social defeat (SD) hypothesis. The SD hypothesis posits that low social status and exposure to repeated humiliation can lead to imbalanced dopamine neuron activity, and thus increased risk of psychosis. We aimed to assess the role of dynamic interactions between SD components in shaping the occurrence of PLEs using a network analysis.

A total of 2241 non-clinical, young adults were assessed at baseline and invited for reassessment after a 6-month follow-up. Self-reports recording the occurrence of PLEs, aberrant salience (AS), depressive, and anxiety symptoms as well as SD characteristics (socioeconomic status, minority status, humiliation, perceived constraints, and domain control) were administered. Two networks were analyzed (the first one covering all baseline measures and the second one with the baseline SD components and follow-up measures of AS and psychopathology).

The SD components were not directly connected to the measures of PLEs in both networks. However, in both networks, SD components were connected to PLEs through a mediating effect of AS. Among SD components, humiliation had the highest bridge centrality across three predefined communities of variables (SD; depressive and anxiety symptoms; AS, and PLEs).

The findings indicate that SD might make individuals vulnerable to develop PLEs through the mediating effects of AS. Among SD components, humiliation might play the most important role in the development of PLEs.

Machine learning could predict binge behavior and help develop treatments for bulimia nervosa (BN) and alcohol use disorder (AUD). Therefore, this study evaluates person-specific and pooled prediction models for binge eating (BE), alcohol use, and binge drinking (BD) in daily life, and identifies the most important predictors.

A total of 120 patients (BN: 50; AUD: 51; BN/AUD: 19) participated in an experience sampling study, where over a period of 12 months they reported on their eating and drinking behaviors as well as on several other emotional, behavioral, and contextual factors in daily life. The study had a burst-measurement design, where assessments occurred eight times a day on Thursdays, Fridays, and Saturdays in seven bursts of three weeks. Afterwards, person-specific and pooled models were fit with elastic net regularized regression and evaluated with cross-validation. From these models, the variables with the 10% highest estimates were identified.

The person-specific models had a median AUC of 0.61, 0.80, and 0.85 for BE, alcohol use, and BD respectively, while the pooled models had a median AUC of 0.70, 0.90, and 0.93. The most important predictors across the behaviors were craving and time of day. However, predictors concerning social context and affect differed among BE, alcohol use, and BD.

Pooled models outperformed person-specific models and the models for alcohol use and BD outperformed those for BE. Future studies should explore how the performance of these models can be improved and how they can be used to deliver interventions in daily life.

Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine.

We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort.

Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable.

Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

The pain analgesia hypothesis suggests that reduced pain sensitivity (PS) is a specific risk factor for the engagement in non-suicidal self-injury (NSSI). Consistent with this, several studies found reduced PS in adults as well as adolescents with NSSI. Cross-sectional studies in adults with borderline personality disorder (BPD) suggest that PS may (partially) normalize after remission or reduction of BPD symptoms. The objective of the present study was to investigate the development of PS over 1 year in a sample of adolescents with NSSI and to investigate whether PS at baseline predicts longitudinal change in NSSI.

N = 66 adolescents who underwent specialized treatment for NSSI disorder participated in baseline and 1-year follow-up assessments, including heat pain stimulation for the measurement of pain threshold and tolerance. Associations between PS and NSSI as well as BPD and depressive symptoms were examined using negative binomial, logistic, and linear regression analyses.

We found that a decrease in pain threshold over time was associated with reduced NSSI (incident rate ratio = 2.04, p = 0.047) and that higher pain tolerance at baseline predicted lower probability for NSSI (odds ratio = 0.42, p = 0.016) 1 year later. However, the latter effect did not survive Holm correction (p = 0.059). No associations between PS and BPD or depressive symptoms were observed.

Our findings suggest that pain threshold might normalize with a decrease in NSSI frequency and could thus serve as a state marker for NSSI.

Previous evidence suggests that early life complications (ELCs) interact with polygenic risk for schizophrenia (SCZ) in increasing risk for the disease. However, no studies have investigated this interaction on neurobiological phenotypes. Among those, anomalous emotion-related brain activity has been reported in SCZ, even if evidence of its link with SCZ-related genetic risk is not solid. Indeed, it is possible this relationship is influenced by non-genetic risk factors. Thus, this study investigated the interaction between SCZ-related polygenic risk and ELCs on emotion-related brain activity.

169 healthy participants (HP) in a discovery and 113 HP in a replication sample underwent functional magnetic resonance imaging (fMRI) during emotion processing, were categorized for history of ELCs and genome-wide genotyped. Polygenic risk scores (PRSs) were computed using SCZ-associated variants considering the most recent genome-wide association study. Furthermore, 75 patients with SCZ also underwent fMRI during emotion processing to verify consistency of their brain activity patterns with those associated with risk factors for SCZ in HP.

Results in the discovery and replication samples indicated no effect of PRSs, but an interaction between PRS and ELCs in left ventrolateral prefrontal cortex (VLPFC), where the greater the activity, the greater PRS only in presence of ELCs. Moreover, SCZ had greater VLPFC response than HP.

These results suggest that emotion-related VLPFC response lies in the path from genetic and non-genetic risk factors to the clinical presentation of SCZ, and may implicate an updated concept of intermediate phenotype considering early non-genetic factors of risk for SCZ.

Late-life depression (LLD) is characterized by repeated recurrent depressive episodes even with maintenance treatment. It is unclear what clinical and cognitive phenotypic characteristics present during remission predict future recurrence.

Participants (135 with remitted LLD and 69 comparison subjects across three institutions) completed baseline phenotyping, including psychiatric, medical, and social history, psychiatric symptom and personality trait assessment, and neuropsychological testing. Participants were clinically assessed every two months for two years while receiving standard antidepressant treatment. Analyses examined group differences in phenotypic measure using general linear models. Concurrent associations between phenotypic measures and diagnostic groups were examined using LASSO logistic regression.

Sixty (44%) LLD participants experienced a relapse over the two-year period. Numerous phenotypic measures across all domains differed between remitted LLD and comparison participants. Only residual depressive symptom severity, rumination, medical comorbidity, and executive dysfunction significantly predicted LLD classification. Fewer measures differed between relapsing and sustained remission LLD subgroups, with the relapsing group exhibiting greater antidepressant treatment intensity, greater fatigue, rumination, and disability, higher systolic blood pressure, greater life stress and lower instrumental social support. Relapsing group classification was informed by antidepressant treatment intensity, lower instrumental social support, and greater life stress.

A wide range of phenotypic factors differed between remitted LLD and comparison groups. Fewer measures differed between relapsing and sustained remission LLD subgroups, with less social support and greater stress informing vulnerability to subsequent relapse. This research suggests potential targets for relapse prevention and emphasizes the need for clinically translatable relapse biomarkers to inform care.