Meike Bartels

Background: Genetic liability of depressive disorder can be captured by psychopathology in relatives (family history). Various methods summarize family history in a single score, differing in included information as well as the underlying model. We systematically compared the performance of family history indicators, including promising new indicators based on the liability threshold model, in predicting depressive disorder. Methods: We calculated selected family history indicators for depression (dichotomous, proportion, novel genetically informed method – Pearson–Aitkens Family Genetic Risk Score [PAFGRS]) in 1339 participants of the Netherlands Study of Depression and Anxiety ( N _case = 1086). Polygenic scores (PGS) were computed from the most recent GWAS for major depression. We assessed correlations between genetic liability indicators, as well as their prediction of lifetime depressive disorder diagnosis. Results: Correlations of family history indicators with each other were high ( r = 0.71–0.99) and much lower with the PGS ( r = 0.15). There was a suggested increase in predictive accuracy for more elaborately computed scores, ranging from proportion to PAFGRS. The best-performing family history indicator and the PGS were independently associated with depressive disorder. Conclusions: Our analysis shows that more elaborate family history indicators, including family size, prevalence, heritability, and genetic theory, would be preferable over simpler methods. Family history and PGS were complementary in prediction, showing the added value of including both in future studies.

Stress can affect not only average levels of mental health, but also how positive and negative aspects of mental health relate to one another. The COVID-19 pandemic provides a unique context to examine those population-level differences. We investigated youth mental health before, during, and after the pandemic by examining mean levels, association between wellbeing and depressive symptoms, and latent mental health profiles.Using large population-based samples from the Netherlands of youth aged 12–25 years, we compared mental health before (N = 14,146, 58% female), during (N = 14,308, 55% female), and after the pandemic (N = 37,797, 62% female) in a repeated cross-sectional design.Wellbeing and depressive symptoms were stronger correlated during (r = −.70) and after the pandemic (r = −.69) compared to pre-pandemic (r = −.49, ps < .001). Latent profile analyses further showed differences in the population distribution of mental health. Pre-pandemic, two profiles were identified, whereas during and after the pandemic three-profile solutions provided the best fit, including a large group with intermediate levels of wellbeing and depressive symptoms (38-40%). Across periods, most youth were classified in a high mental health profile (51-86%), whereas a smaller proportion were classified in a low mental health profile (8-14%). Loneliness, self-rated health, optimism, and stress exposure consistently differed between profiles. Youth mental health differed across periods in average levels, the strength of the association, and distribution of wellbeing and depressive symptoms. These findings show the value of moving beyond average effects when studying population responses to large-scale stress.

Background: Differences in wellbeing are shaped by genetic predispositions and environmental influences, yet recommendations for improving wellbeing are often framed as universally beneficial. We examined whether wellbeing is higher when individuals’ observed phenotypes correspond more closely to their genetic predispositions. Methods: We used data from 18,748 participants of the Netherlands Twin Register. Phenotype-genotype congruency was examined across social, health, and lifestyle-related phenotypes using a triangulation approach with three complementary methods. First, cross-sectional generalized estimating equation models tested whether difference scores between phenotypes and corresponding polygenic scores were associated with wellbeing. Second, response surface analyses (RSA) modelling linear, non-linear, and interaction effects of phenotype and genotype. And third, longitudinal multi-level models using repeated measures of phenotype-genotype difference scores and wellbeing over time.Results: Greater phenotype–genotype congruency was associated with higher wellbeing for BMI, neuroticism, sleep duration, and social interaction in cross-sectional analyses,. Longitudinal analyses showed similar associations for BMI, neuroticism, self-rated health, sleep duration, social interaction, and TV time. Effect sizes were small but generally consistent across models. In contrast, RSA did not support phenotype–genotype congruency effects. BMI, neuroticism, and social interaction showed partial congruency patterns, but neither satisfied the congruency criteria (Humberg et al., 2019). Conclusions: Across phenotypes, higher congruency between phenotypes and genetic predispositions was associated with higher wellbeing in difference score models, but were not confirmed by RSA. The findings suggest that phenotype–genotype congruency may be relevant for wellbeing in some domains, and that replication is needed to better understand the complexity of wellbeing.

Objective. The COVID-19 pandemic was a major global stressor for children and adolescents, raising concerns about longer-term consequences for mental health and wellbeing. Many studies reported increased mental health problems during and shortly after the pandemic, but less is known about longer-term post-pandemic patterns across domains and informants.Method. We used repeated cross-sectional data from Dutch children and adolescents aged 8-18 years in the Netherlands Twin Register (NTR, N = 39,178, 50% girls) and a representative general-population sample (N = 2,782, 48% girls). Parent- and child-reported mental health problems and wellbeing were assessed using pre-pandemic, pandemic, and post-pandemic data through 2025. Post-pandemic patterns were examined by comparing 2024/2025 outcomes with 2023 and pre-pandemic levels using pre-registered multilevel models.Results. Parent-reported internalizing and externalizing problems in 2024/2025 did not differ from 2023 levels (B = -0.03 to 0.03), but remained higher than pre-pandemic levels (B = 0.28 to 0.31). Child-reported depressive symptoms were lower in 2025 than in 2023 (B =-2.31), anxiety did not differ (B =-0.75), and anger was higher (B =1.13). Compared with pre-pandemic levels, child-reported depression, anxiety, and anger were all higher in 2025 (p <.001). Wellbeing patterns were mixed across informants and samples (B =-1.05 to 0.10).Conclusions. Up to 2025, youth mental health and wellbeing did not show a general return to pre-pandemic levels. Patterns differed across domains and informants, highlighting the importance of continued multi-informant monitoring.

The top-down model of life satisfaction proposes that general dispositions drive individual differences in both general life satisfaction and satisfaction with specific life domains (e.g., job, health). In contrast, the bottom-up model posits that satisfaction across multiple domains aggregates into overall life satisfaction. Using data from twins and siblings of the Netherlands Twin Register (N = 6,160; 71% female; Mage = 36.8, SDage = 15.2), these competing theories were tested. General life satisfaction was assessed with the Satisfaction With Life Scale, alongside domain ratings of satisfaction with friendships, family, job, finances, and health. Top-down and bottom-up effects were tested using four complementary approaches: 1) phenotypic association patterns, 2) multivariate twin-sibling models on associations between the domains, 3) multivariate twin-sibling models on associations between domain satisfaction and general life satisfaction, and 4) intra-pair difference score analyses. Across analyses, findings supported the presence of both top-down and bottom-up processes. Substantial genetic and more modest environmental overlap was found between general and domain specific life satisfaction, suggesting potential causal links, which were further supported by the intra-pair difference score analyses. Importantly, domains differed in the extent to which they reflected top-down versus bottom-up influences. Together, results suggest overall life satisfaction is shaped by a combination of general dispositional and domain-specific processes, with genetic factors playing a substantial role and with more modest environmental influences. These findings underscore the importance of adopting genetically informed designs in future (longitudinal) research aiming to disentangle top-down and bottom-up processes in life satisfaction.

Early temperament, such as socio-emotional development and activity level, varies widely, yet its underlying biological associations are not understood. We identified genetic variation associated with infant and toddler temperament using genome-wide association meta-analyses. We studied parent-rated emotionality, activity, shyness and sociability ( n = 43,963–72,663) in the second and third postnatal years and a cross-age average. Cross-age single nucleotide polymorphism heritabilities for emotionality, activity, shyness and sociability were 6.79% (95% confidence interval (CI), (4.71%, 8.87%)), 9.55% (95% CI, (7.04%, 12.06%)), 15.26% (95% CI, (12.24%, 18.28%)) and 3.42% (95% CI, (1.30%, 5.54%)), respectively. Ten genome-wide significant loci were discovered. Two loci colocalized with expression quantitative trait loci in the adult cortex: RHEBL1 (posterior probability, 0.93; associated with activity) and MR1 (posterior probability, 0.99; with emotionality). Genetic correlations were observed between early temperament and later outcomes, such as emotionality and adult neuroticism, activity and attention deficit/hyperactivity disorder (ADHD), sociability and autism, and shyness and adult extraversion. Multi-ancestry ( n = 56,083–78,894) and European-ancestry analyses gave similar results. Infant and toddler temperament is associated with genetic variation and shows genetic continuity with later outcomes.

Adolescence and young adulthood are sensitive developmental periods in which stress exposure increases. Youth differ in their capacity to maintain or regain mental health after stressful experiences, reflecting individual differences in resilience. Resilience resources can support mental health by promoting resilient functioning and/or by buffering the association between stress exposure and mental health. The COVID-19 pandemic provides a unique context to investigate these processes across changing stress exposures.We examined youth resilience before, during, and after the COVID-19 pandemic in large Dutch population-based samples, including youth aged 12-25 years before (N = 12,937, 58% female), during (N = 8,934, 58% female), and after the pandemic (N = 37,797, 62% female). We investigated whether loneliness, optimism, and self-rated health were associated with resilient functioning and, to test stress-buffering effects, whether they moderated associations between stress exposure and mental health. Resilient functioning was operationalised as better-than-expected mental health given stress exposure, based on wellbeing, depressive symptoms, and a combination. Higher optimism, lower loneliness, and better self-rated health were consistently associated with higher resilient functioning across pandemic phases and operationalisations (p<.001). Moderation analyses showed inconsistent evidence that these characteristics buffered associations between stress exposure and mental health. Psychosocial characteristics may help explain which youth function better in the context of stress, but do not consistently indicate who is less affected by stress. The results support a view of resilience as a complex and context-dependent process, rather than the result of single protective factors with similar effects across time and contexts.

Introduction: Educational attainment (EA) is negatively associated with body mass index (BMI), but less is known about the association between EA and adult BMI change. We analysed the role of genetic and environmental factors in the associations between EA and BMI trajectory components over adulthood. Data and methods: Pooled data from 59,490 twins aged 31–99 years (49% women) across 11 cohorts with EA and repeated measures of BMI were used. BMI trajectory components (baseline BMI and BMI change per decade) were estimated using linear mixed-effects (LME) and delta slope methods. EA was derived by regressing years of education on birth year and cohort. Associations between EA and BMI trajectories were evaluated with LME models in both cohort-specific and pooled data. Genetic and environmental contributions were evaluated using structural equation modelling. Results: EA was more strongly negatively associated with baseline BMI and BMI change (mean of 1.31 and 1.32 kg/m ² per decade in men and women, respectively) in women ( β = –0.14 kg/m², 95% CI: –0.15 to –0.12; β = –0.02 kg/m²/decade, 95% CI: –0.03 to –0.01, respectively) than in men ( β = –0.07, 95% CI: –0.08 to –0.06; β = –0.01, 95% CI: –0.02 to –0.001, respectively). The associations between baseline BMI and EA were explained by genetic factors in men ( r _A = –0.10) and by both genetic ( r _A = –0.17) and unique environmental factors ( r _E = –0.07) in women. For BMI change, the associations with EA were explained by genetic factors ( r _A = –0.04 in men; –0.06 in women). Conclusion: Individuals with higher EA tend to have lower baseline BMI and slower BMI increases across adulthood. These associations are primarily genetically mediated.

Puberty through adulthood involves major psychological and social change. Prospective evidence on adolescent wellbeing and later outcomes remains limited. Here we show that higher adolescent wellbeing (age 14–16) is associated with more favourable psychological and health-related outcomes in early adulthood (ages 20–25 and 25–35). Using data of 14,518 twins and siblings of the Netherlands Twin Register, we examine associations between adolescent wellbeing and adult wellbeing, flourishing, personality traits, self-rated health, and sleep quality using both between-family and within-family designs. Between-family analyses indicate positive associations with early adulthood wellbeing (β = 0.32;0.38), flourishing (β = 0.27;0.34), conscientiousness (β = 0.18;0.22), self-rated health (β = 0.09;0.18), and sleep quality (β = 0.59;0.87), and inverse associations with early adulthood neuroticism (β = −0.34;−0.11)(pFDR20-25 = 0.12), flourishing (β _20-25 = 0.17), self-rated health (β _20-25 = 0.07), sleep quality (β = 0.46;0.71), and neuroticism (β _20-25 = −0.12) (pFDR<0.05) were also observed. The findings characterize long-term associations between adolescent wellbeing and early adulthood outcomes, with attenuation observed after accounting for shared familial factors.

Enduring Mental Health (EMH) has been defined as maintaining good psychological functioning without developing mental health problems. Estimates of the proportion of individuals who have EMH vary widely. We investigated EMH across the lifespan, defined not only as absence of mental health problems, but also as consistent presence of high wellbeing. To explore how defining EMH affects prevalence, we included both stricter and broader lenient thresholds for mental health problems and low wellbeing.Data came from 16,030 participants (35.2% male; average birth year 1970) from the longitudinal Netherlands Twin Register, who completed ≥4 assessments of mental health and/or wellbeing over 8–20 years. For each EMH definition, preregistered analyses included computation of prevalence, genetic and environmental influences using twin modeling, and phenotypic and genetic correlates including education, personality, social factors, and genetic predispositions for mental health outcomes.As expected, the choice of threshold had a substantial impact on EMH prevalence estimates, 71% for absence of problems and 93% for high wellbeing when applying stricter thresholds for mental health compared to lenient thresholds, 42% and 82%, respectively. Heritability estimates (46-61%) and patterns of associations for EMH were similar across the different mental health thresholds. Protective factors included optimism, social support, and good self-rated health (OR= 1.50-2.86), while neuroticism, loneliness, and perceived stress were risk factors (OR=0.31-0.84).We show the importance of defining EMH as both the absence of problems and the presence of wellbeing. While prevalence varied depending on threshold and definition used, the consistent genetic and psychosocial correlates for different definitions of EMH suggest stable underlying mechanisms that can inform prevention to help individuals maintain mental health.