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Introduction Neuroticism has societal, mental and physical health relevance, with an etiology involving genetic predisposition, psychological influence, and their interaction. Objectives To understand whether the association between polygenic risk score for neuroticism (PRS-N) and neuroticism is moderated by affective well-being. Methods Data were derived from TwinssCan, a general population twin cohort (age range=15-35 years, 478 monozygotic twins). Self-report questionnaires were used to measure well-being and neuroticism. PRS-N was trained from the Genetics of Personality Consortium (GPC) and United Kingdom Biobank (UKB). Multilevel mixed-effects models were used to test baseline and changes in well-being and neuroticism. Results Baseline wellbeing and neuroticism were associated (β=-1.35, p

Introduction Prior evidence suggests that men and women might be differentially susceptible to distinct types of childhood adversity (CA), but research on gender-specific associations between CA subtypes and psychiatric symptoms is limited. Objectives To test the gender-specific associations of CA subtypes and psychiatric symptoms in the general population. Methods Data from 791 twins and siblings from the TwinssCan project were used. Psychopathology and CA exposure were assessed using the Symptom Checklist-90 Revised (SCL-90) and the Childhood Trauma Questionnaire (CTQ), respectively. The associations between the total CTQ scores and SCL-90 scores (i.e. total SCL-90, psychoticism, paranoid ideation, anxiety, depression, somatization, obsessive-compulsive, interpersonal sensitivity, hostility, and phobic anxiety) were tested in men and women separately. The associations between the five CA subtypes (i.e. physical abuse, emotional abuse, sexual abuse, physical neglect, and emotional neglect) and total SCL-90 were tested in a mutually adjusted model. As exploratory analyses, the associations between all CA subtypes and the nine SCL-90 subdomain scores were similarly tested. The regression coefficients between men and women were compared using Chow’s test. All models were adjusted for age and family structure. Results Total CTQ was significantly associated with total SCL-90 in men (B = 0.013, SE = 0.003, P < .001) and women (B = 0.011, SE = 0.002, P < .001). The associations with the nine symptom domains were also significant in both genders (P < .001). No significant gender differences in the regression coefficients of total CTQ were detected. The analyses of CA subtypes showed a significant association between emotional abuse and total SCL-90 in women (B = 0.173, SE = 0.030, P < .001) and men (B = 0.080, SE = 0.035, P = .023), but the association was significantly stronger in women (ꭓ2(1) = 4.10, P = .043). The association of sexual abuse and total SCL-90 was only significant in women (B = 0.217, SE = 0.053, P < .001). The associations of emotional neglect (B = 0.061, SE = 0.027, P = .026) and physical neglect (B = 0.167, SE = 0.043, P < .001) with total SCL-90 were only significant in men. The explorative analyses of SCL-90 subdomains revealed significant associations of emotional abuse with all nine symptom domains and of sexual abuse with seven symptom domains in women. Significant associations of physical neglect with six symptom domains and of emotional neglect with depression were also detected in men. No other significant associations between CT subtypes and total SCL-90 or symptom domain scores were observed in men and women. Conclusions CA exposure was associated with diverse psychopathology similarly in both genders. However, women are more sensitive to abuse, but men are more sensitive to neglect. Gender-specific influences of CA subtypes on psychopathology should be considered in future studies. Disclosure of InterestNone Declared

Introduction Group-level studies showed cross-sectional and prospective between-person associations between circadian rest-activity rhythms (RAR), physical activity (PA), sleep, and depressive symptoms. However, whether these associations replicate at the within-person level remains unclear. Therefore, it is clinically relevant to investigate these associations within persons and study whether changes in depressive symptoms are related to changes in circadian rhythm and sleep variables. Objectives To identify changes in circadian rhythm elements in proximity to a transition in depressive symptoms, whether changes are less frequent in individuals without compared to those with transitions, and whether there are individual differences in the direction of change of circadian rhythm variables. Methods Data of remitted individuals tapering antidepressants were used: 12 with and 14 without a transition in depressive symptoms. RAR, PA, and sleep variables were calculated as predictors from four months of actigraphy data. Transitions in depressive symptoms were based on weekly SCL-90 scores and evaluation interviews. Kernel Change Point analyses were used to detect change points (CPs) and CP timing in circadian rhythm variables for each individual separately. Results In 67% of individuals with depressive symptoms transitions, CPs were identified in proximity to symptom transitions. CPs were detected less frequently in the no-transition group with 7 CPs in 14 individuals, compared to transition groups with 10 CPs in 12 individuals. For several RAR and sleep variables, consistent changes were detected in expected directions. Conclusions Circadian rhythm variables provide potentially clinically relevant information although their patterns around transitions are highly person-specific. Future research is needed to disentangle which variables are predictive for which patients. Disclosure No significant relationships.

Introduction In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remains unidentified. Introducing additional screening tools may facilitate the diagnostic process. Objectives This study aims to examine whether Experience Sampling Method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from non-depressed individuals. In addition, the added value of actigraphy-based measures was examined. Methods We used data from two samples to develop and validate prediction models. The development dataset included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and non-depressed individuals (n=82). The validation dataset included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and non-depressed individuals (n=27). Backward stepwise logistic regression analyses were applied to build the prediction models. The performance of the models was assessed with the goodness of fit indices, calibration curves, and discriminative ability (AUC, the area under the receiver operating characteristic curve). Results In the development dataset, the discriminative ability was good for the actigraphy model (AUC=0.790) and excellent for the ESM (AUC=0.991) and combined-domains model (AUC=0.993). In the validation dataset, the discriminative ability was reasonable for the actigraphy model (AUC=0.648) and excellent for the ESM (AUC=0.891) and combined-domains model (AUC=0.892). Conclusions ESM is a good diagnostic predictor and is easy to calculate, and, therefore, holds promise for implementation in clinical practice. Actigraphy shows no added value to ESM as a diagnostic predictor, but might still be useful when active monitoring with ESM is not feasible. Disclosure No significant relationships.

Introduction For patients with bipolar disorder, early recognition of impending mood episodes is crucial to enable timely intervention. Longitudinal digital mood monitoring using ecological momentary assessment (EMA) enable prospective study of early warning signals (EWS) in momentary affective estates prior to symptom transitions. Objectives The present study examined in a unique longitudinal EMA data set whether EWS prospectively signal transitions to manic or depressive episodes. Methods Twenty bipolar type I/II patients completed EMA questionnaires five times a day for four months (average 491 observations per person), as well as weekly symptom questionnaires concerning depressive (Quick Inventory for Depressive Symptomatology) and manic (Altman Self-Rating Mania Scale) symptoms. Weekly data was used to determine transitions (i.e., abrupt increase in symptoms). Prior to these transitions, EWS (autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective EMA states. Kendall’s tau was calculated to detect significant rises in the EWS indicator prior to the transition. Results Eleven patients reported one or two transitions to a mood episode. All transitions were preceded by at least one EWS. Average sensitivity for detecting EWS was slightly higher for manic episodes (36%) than for depressive episodes (25%). For manic episodes, EWS in thoughts racing, being full of ideas, and feeling agitated showed the highest sensitivity and specificity, whereas for depression, only feeling tired showed high sensitivity and specify. Conclusions EWS show promise in anticipating transitions to mood episodes in bipolar disorder. Further investigation is warranted. Disclosure No significant relationships.

Introduction Stress is a risk factor for developing psychopathology. Emerging evidence suggests that daily experiences of stress may also predict symptoms during the day. It is unclear to what extent the influence of stress on psychopathology during the day is the same across individuals (including across diagnostic boundaries), and which effects are individual-specific Objectives This study aims to reveal how stress and symptoms are interrelated in a cross-diagnostic context by modeling individual level temporal networks, and examining subgroups with similar dynamics. Methods Hundred twenty two young adults (43.4% women) with a wide range of psychopathology in terms of severity and type of problems completed a six-month daily diary study. We used a temporal network approach (i.e., group iterative multiple model estimation) to model how stress and ten specific symptoms (e.g., feeling down, paranoia, restlessness) were related across time at the individual-specific, subgroup, and group level. Results After controlling for the lagged influence of stress on itself, stress level predicted the level of restlessness, worrying, nervousness, and feeling down during the same day for >70% of individuals. We observed three larger subgroups with each over 20 individuals, whose temporal networks showed different dynamic patterns involving specific symptoms. Effects of stress on other specific symptoms differed across individuals, and these were not subgroup-specific. Conclusions This study showed important overlap between individuals in terms of impact of stress on psychopathology in daily life. Subtle differences between individuals were also observed. Possibly, such differences are relevant for examining individual-specific vulnerability for future psychopathology. This requires further investigation. Disclosure No significant relationships.

Introduction The reward system regulates the processes that motivate people to pursue evolutionary beneficial stimuli. Effective functioning of the reward system can protect against the development of anhedonia. In the daily life, the reward system can be expressed as the dynamic interplay of positive affect (liking), reward anticipation (wanting), and active behavior (engaging). Applying network analysis to daily life experience data allows us to identify such reward dynamics and use them to predict future depressive symptoms. Objectives We investigated whether at baseline (i) higher network positive affect in-strength, reflecting how strongly positive affect is influenced by other components and hence the level of anhedonia, and (ii) higher network connectivity, reflecting overall functioning of the reward system, are associated with fewer depressive symptoms on follow-up. Methods We used data from 43 participants with mild depressive symptoms from the SMARTSCAN study. The dynamic interplay between momentary positive affect, reward anticipation, and active behavior was assessed with individual vector-autoregressive models and the network analysis. Network positive affect in-strength and connectivity indices were used to predict a six-month depressive symptoms trajectory. Results Reward systems networks vary greatly between individuals. On the group level, higher positive affect in-strength (Beta=-3.66, p=0.05) and network connectivity (Beta=-4.06, p=0.03) at baseline were associated with fewer symptoms at follow-up. Conclusions Higher influences of reward anticipation and active behavior on positive affect and stronger connections between reward cycle components are associated with fewer future symptoms, suggesting the importance of daily life reward cycle dynamics in depression. Disclosure No significant relationships.

Introduction Transitions in mental health, such as the onset or sudden progression of psychopathology, are difficult to foresee. If mental health behaves like other complex systems, drops in mental health may be anticipated by early warning signals (EWS), which manifest in the dynamics of time series data. Objectives This study aimed to establish the sensitivity and specificity of EWS as personalized risk markers for sudden drops mental health. Methods Individuals (N=122, mean age 23.6 ±0.7 years, 57% males) at increased risk for psychopathology completed daily questionnaires on mental states for six consecutive months. Transitions in mental health were identified by change point analyses. EWS, operationalized as rising trends in the autoregressive coefficient of 36 negative mental states, were identified using generalized additive models. Results EWS were found for 59% of individuals with a drop in mental health, and for 47% without such a drop (sensitivity: 0-.12; specificity: .88-1). There were considerable individual differences in the prevalence, strength, and timing of EWS. Conclusions EWS might be informative of impeding transitions, yet they are also highly conservative. Present findings may inspire future research into the prerequisites for detecting EWS in the context of mental health, for instance with respect to the stability of pre- and post-transition phases, the magnitude of transitions, and the timescale at which EWS manifest. An improved understanding of the dynamics that govern psychopathology could ultimately allow us to determine whether a specific individual at a specific moment in time is at risk for a sudden onset or progression of mental health problems.

Introduction Greater affective inertia during the day (higher carry-over effects of prior affect to the current moment) is associated with depression and its development. However, the role of overnight affective inertia (from evening to morning) in depression, and the role of sleep therein, has been scarcely studied. Objectives We examined i) the difference in overnight inertia for positive (PA) and negative affect (NA) between individuals with past depression, current depression, and no depression; ii) how sleep duration and quality influence overnight affective inertia in these groups, and iii) whether overnight affective inertia predicts depression development. Methods We used data of 579 women from the East-Flanders Prospective Twin Survey. First, individuals with past (n=82), current (n=26), and no depression (n=471) at baseline were examined, and then individuals who did (n=58) and did not (n=319) develop depression at 12-months follow-up. Affect was assessed 10 times a day for 5 days. Sleep was assessed with sleep diaries. Affective inertia was operationalized as the influence of affectt-1 on affectt. Linear mixed-effect models were used to test the hypotheses. Results Overnight affective inertia was not associated with depression, neither was it differently associated with sleep characteristics in the depression groups. However, sleep characteristics were more negatively associated with morning NA in both depression groups compared to the non-depressed group. Overnight affective inertia did not predict the development of depression at follow-up. Conclusions Depression and sleep characteristics might be more related to mean affect levels rather than to more complex emotion dynamics measures. Replication of these findings with longer time-series is needed.