The stability and change of wellbeing across the lifespan: a longitudinal twin-sibling study.

BACKGROUND: Wellbeing is relatively stable over the life span. However, individuals differ in this stability and change. One explanation for these differences could be the influence of different genetic or environmental factors on wellbeing over time. METHODS: To investigate causes of stability and change of wellbeing across the lifespan, we used cohort-sequential data on wellbeing from twins and their siblings of the Netherlands Twin Register (NTR) (total N = 46.885, 56% females). We organized wellbeing data in multiple age groups, from childhood (age 5), to adolescence, up to old age (age 61+). Applying a longitudinal genetic simplex model, we investigated the phenotypic stability of wellbeing and continuity and change in genetic and environmental influences. RESULTS: Wellbeing peaked in childhood, decreased during adolescence, and stabilized during adulthood. In childhood and adolescence, around 40% of the individual differences was explained by genetic effects. The heritability decreased toward old adulthood (35-24%) and the contribution of unique environmental effects increased to 76%. Environmental innovation was found at every age, whereas genetic innovation was only observed during adolescence (10-18 years). In childhood and adulthood, the absence of genetic innovation indicates a stable underlying set of genes influencing wellbeing during these life phases. CONCLUSION: These findings provide insights into the stability and change of wellbeing and the genetic and environmental influences across the lifespan. Genetic effects were mostly stable, except in adolescence, whereas the environmental innovation at every age suggests that changing environmental factors are a source of changes in individual differences in wellbeing over time.

Disentangling potential causal effects of educational duration on well-being, and mental and physical health outcomes.

BACKGROUND: Extensive research has focused on the potential benefits of education on various mental and physical health outcomes. However, whether the associations reflect a causal effect is harder to establish. METHODS: To examine associations between educational duration and specific aspects of well-being, anxiety and mood disorders, and cardiovascular health in a sample of European Ancestry UK Biobank participants born in England and Wales, we apply four different causal inference methods (a natural policy experiment leveraging the minimum school-leaving age, a sibling-control design, Mendelian randomization [MR], and within-family MR), and assess if the methods converge on the same conclusion. RESULTS: A comparison of results across the four methods reveals that associations between educational duration and these outcomes appears predominantly to be the result of confounding or bias rather than a true causal effect of education on well-being and health outcomes. Although we do consistently find no associations between educational duration and happiness, family satisfaction, work satisfaction, meaning in life, anxiety, and bipolar disorder, we do not find consistent significant associations across all methods for the other phenotypes (health satisfaction, depression, financial satisfaction, friendship satisfaction, neuroticism, and cardiovascular outcomes). CONCLUSIONS: We discuss inconsistencies in results across methods considering their respective limitations and biases, and additionally discuss the generalizability of our findings in light of the sample and phenotype limitations. Overall, this study strengthens the idea that triangulation across different methods is necessary to enhance our understanding of the causal consequences of educational duration.

Distinguishing happiness and meaning in life from depressive symptoms: A GWAS-by-subtraction study in the UK Biobank.

Hedonic (happiness) and eudaimonic (meaning in life) well-being are negatively related to depressive symptoms. Genetic variants play a role in this association, reflected in substantial genetic correlations. We investigated the overlap and differences between well-being and depressive symptoms, using results of Genome-Wide Association studies (GWAS) in UK Biobank. Subtracting GWAS summary statistics of depressive symptoms from those of happiness and meaning in life, we obtained GWASs of respectively "pure" happiness (neffective = 216,497) and "pure" meaning (neffective = 102,300). For both, we identified one genome-wide significant SNP (rs1078141 and rs79520962, respectively). After subtraction, SNP heritability reduced from 6.3% to 3.3% for pure happiness and from 6.2% to 4.2% for pure meaning. The genetic correlation between the well-being measures reduced from 0.78 to 0.65. Pure happiness and pure meaning became genetically unrelated to traits strongly associated with depressive symptoms, including loneliness, and psychiatric disorders. For other traits, including ADHD, educational attainment, and smoking, the genetic correlations of well-being versus pure well-being changed substantially. GWAS-by-subtraction allowed us to investigate the genetic variance of well-being unrelated to depressive symptoms. Genetic correlations with different traits led to new insights about this unique part of well-being. Our results can be used as a starting point to test causal relationships with other variables, and design future well-being interventions.

Connecting the dots: using a network approach to study the wellbeing spectrum.

In positive psychology, defining the wellbeing construct has been a challenge. We used the psychometric network approach to study the structure of wellbeing. The sample consisted of Dutch adults registered with the Netherlands Twin Register. The variables were measured through standardized surveys. The network was estimated using the Mixed Graphical Models method and least absolute shrinkage and selection operator (LASSO) regularization to limit the number of spurious edges. We estimated a network in a trimming sample (N=1343, 63% females, M age = 53.18, SD age = 9.45) and in an estimation sample (N=726, 75% females, M age = 45.27, SD age = 11.12) to examine its performance and accuracy. Our final network consists of a positive cluster including satisfaction with life, subjective happiness, and flourishing items, and a negative cluster including depressive symptoms, loneliness, and neuroticism items. We identified the four most central nodes: one satisfaction with life item, one neuroticism item, and two depression items. This suggests that to get a general sense of the wellbeing construct, these items would serve as most informative.  The network approach clearly demonstrates the different, yet connected positive and negative clusters of wellbeing and therefore re-affirms the complex interconnectivity of wellbeing phenotypes. In addition, the network results reject the view of strictly delineated wellbeing domains. Having identified the most central nodes in the network, these can be used in futures studies with limited resources, as they are likely to be the most representative of the wellbeing spectrum. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-024-06363-0.

Bias in Gene-by-Environment Interaction Effects with Sum Scores; An Application to Well-being Phenotypes.

In the current study, we investigated the influence of using skewed sum scores on estimated gene-by-environment interaction effects (GxE) for life satisfaction and happiness with perceived social support. To this end, we analyzed item-level data from a large adult twin sample (Ns between 3610 and 11,305) of the Netherlands Twin Register. Item response theory (IRT) models were incorporated in unmeasured (univariate) GxE models, and measured GxE models (with social support as moderator). We found that skewness introduced spurious GxE effects, with the largest effect for the most skewed variable (social support). Finally, in the IRT model for life satisfaction, but not for happiness, heritability estimates decreased with higher social support, while this was not observed when analyzing sum scores. Together, our results indicate that IRT can be used to address psychometric issues related to the use of sum scores, especially in the context of GxE, for complex traits like well-being.

Daily affect intensity and variability of adolescents and their parents before and during a COVID-19 lockdown.

INTRODUCTION: The corona virus (COVID-19) pandemic may have a prolonged impact on people's lives, with multiple waves of infections and lockdowns, but how a lockdown may alter emotional functioning is still hardly understood. METHODS: In this 100-daily diaries study, we examined how to affect intensity and variability of adolescents (N = 159, Mage = 13.3, 61.6% female) and parents (N = 159, Mage = 45.3, 79.9% female) changed after the onset and during (>50 days) the second COVID-19 lockdown in the Netherlands, using preregistered piecewise growth models. RESULTS: We found only an unexpected increase in parents' positive affect intensity after the lockdown onset, but no immediate changes in negative affect intensity or variability. However, both adolescents and parents reported gradual increases in negative affect intensity and variability as the lockdown prolonged. Lockdown effects did not differ between adolescents and parents. However, within groups, individuals differed. The individual differences in the effects were partly explained by life satisfaction, depressive symptoms, and self-reported lockdown impact. CONCLUSIONS: Overall, these findings suggests that a lockdown triggers changes in daily affective well-being especially as the lockdown prolongs. Individual differences in the effects indicate heterogeneity in the impact of the lockdown on daily affect that was partly explained by baseline life satisfaction and depressive symptoms. However, more knowledge on the causes of this heterogeneity is needed to be able to increase resilience to lockdown effects in the population.

Gene-by-Crisis Interaction for Optimism and Meaning in Life: The Effects of the COVID-19 Pandemic.

The corona virus disease 2019 (COVID-19) pandemic and the restrictions to reduce the spread of the virus has had a large impact on daily life. We investigated the individual differences in the effect of the COVID-19 pandemic and first lockdown on optimism and meaning in life in a sample from the Netherlands Twin Register. Participants completed surveys before (N = 9964, Mean age: 48.2, SD = 14.4) and during the first months of the pandemic (i.e. April-May 2020, N = 17,464, Mean age: 44.6 SD = 14.8), with a subsample completing both surveys (N = 6461, Mean age T1: 48.8, SD = 14.5). We applied genetic covariance structure models to twin data to investigate changes in the genetic architecture of the outcome traits due to the pandemic and the interaction of genes with the environmental exposure. Although 56% and 35% of the sample was negatively affected by the pandemic in their optimism and meaning in life, many participants were stable (32% and 43%) or even showed increased optimism and meaning in life (11% and 22%). Subgroups, specifically women, higher educated people, and people with poorer health, experienced larger negative effects. During the first months of the pandemic, slightly lower heritability estimates for optimism and meaning in life (respectively 20% and 25%) were obtained compared to pre-pandemic (respectively 26% and 32%), although confidence intervals overlap. The lower than unity genetic correlations across time (.75 and .63) suggest gene-environment interactions, where the expression of genes that influence optimism and meaning in life differs before and during the pandemic. The COVID-19 pandemic is a strong exposure that leads to imbalanced effects on the well-being of individuals. Some people decrease in well-being, while others get more optimistic and consider their lives as more meaningful during the pandemic. These differences are partly explained by individual differences in genetic sensitivity to extreme environmental change. More knowledge on the person-specific response to specific environmental variables underlying these individual differences is urgently needed to prevent further inequality.

Genetic factors explain a significant part of associations between adolescent well-being and the social environment.

Socio-environmental factors play an important role in adolescent well-being, but potential genetic contributions to these associations are rarely assessed. To address this gap in the literature, associations between well-being and family conflict and functioning, number of friends, friendship importance and satisfaction, and leisure time variables were studied in N = ~ 4700 twin pairs from the Netherlands Twin Register, us ing generalized estimating equations and twin-difference scores. When twin-difference scores indicated a role for genetic factors, we used bivariate genetic models to quantify genetic and environmental contributions to these associations. We identify significant associations between well-being and family functioning, family conflict, different leisure time activities, number of friends, and satisfaction with friendships. Additionally, we find evidence for large (73-91%) genetic influence on the associations between well-being and family conflict and functioning, leisure time sport/scouting clubs, and satisfaction with friendships. Finally, findings support the hypothesis of a causal association between well-being and family conflict and functioning. These findings have important implications for research into the social correlates of well-being in adolescence, as not taking genetic factors into account leads to overestimations of the influence of identified correlates and consequently to recommendations of these correlates as intervention targets.

A Re-evaluation of Candidate Gene Studies for Well-Being in Light of Genome-Wide Evidence.

Ever since twin-family studies found that a substantial amount (± 40%) of the variation in well-being can be explained by genetic variation, several candidate genes have been proposed explaining this variation. However, these candidate gene and candidate gene-by-environment interaction studies have been surrounded by controversy regarding the validity and replication of their results. In the present study, we review the existing candidate gene literature for well-being. First, we perform a systematic literature search that results in the inclusion of 41 studies. After describing the results of the included studies, we evaluated the included candidate polymorphisms by (1) looking up the results for the studied candidate SNPs in a large well-being genome-wide association study, (2) performing association analyses in UK biobank (UKB) data for the candidate variable number tandem repeats (VNTR) and the APOE ε4 allele, and (3) studying possible candidate interactions with positive and negative environmental moderators using UKB data. We find no support for any of the candidate genes or candidate gene-environment interactions for well-being, with the exception of two SNPs that were chosen based on genome-wide evidence. While the generalizability of our findings is limited by our phenotype and environment definitions, we strongly advise well-being researchers to abandon the candidate gene approach in the field of well-being and move toward genome-wide approaches. Supplementary Information: The online version contains supplementary material available at 10.1007/s10902-022-00538-x.

Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort.

Effective personalized well-being interventions require the ability to predict who will thrive or not, and the understanding of underlying mechanisms. Here, using longitudinal data of a large population cohort (the Netherlands Twin Register, collected 1991-2022), we aim to build machine learning prediction models for adult well-being from the exposome and genome, and identify the most predictive factors (N between 702 and 5874). The specific exposome was captured by parent and self-reports of psychosocial factors from childhood to adulthood, the genome was described by polygenic scores, and the general exposome was captured by linkage of participants' postal codes to objective, registry-based exposures. Not the genome (R 2 = -0.007 [-0.026-0.010]), but the general exposome (R 2 = 0.047 [0.015-0.076]) and especially the specific exposome (R 2 = 0.702 [0.637-0.753]) were predictive of well-being in an independent test set. Adding the genome (P = 0.334) and general exposome (P = 0.695) independently or jointly (P = 0.029) beyond the specific exposome did not improve prediction. Risk/protective factors such as optimism, personality, social support and neighborhood housing characteristics were most predictive. Our findings highlight the importance of longitudinal monitoring and promises of different data modalities for well-being prediction.

Comparison of wellbeing structures based on survey responses and social media language: A network analysis.

Wellbeing is predominantly measured through surveys but is increasingly measured by analysing individuals' language on social media platforms using social media text mining (SMTM). To investigate whether the structure of wellbeing is similar across both data collection methods, we compared networks derived from survey items and social media language features collected from the same participants. The dataset was split into an independent exploration (n = 1169) and a final subset (n = 1000). After estimating exploration networks, redundant survey items and language topics were eliminated. Final networks were then estimated using exploratory graph analysis (EGA). The networks of survey items and those from language topics were similar, both consisting of five wellbeing dimensions. The dimensions in the survey- and SMTM-based assessment of wellbeing showed convergent structures congruent with theories of wellbeing. Specific dimensions found in each network reflected the unique aspects of each type of data (survey and social media language). Networks derived from both language features and survey items show similar structures. Survey and SMTM methods may provide complementary methods to understand differences in human wellbeing.

Self-rated health when population health is challenged by the COVID-19 pandemic; a longitudinal study.

RATIONALE: The coronavirus disease 2019 (COVID-19) pandemic and consequent lockdown measures have had a large impact on people's lives. Recent evidence suggests that self-rated health (SRH) scores remained relatively stable or increased during the pandemic. OBJECTIVE: For the current project, we examine potential changes in the variance decomposition of SRH before and during the COVID-19 pandemic in the Netherlands. METHODS: We analyse data from the Netherlands Twin Register to examine pre-pandemic SRH scores (N = 16,127), pandemic SRH scores (N = 17,451), and SRH difference scores (N = 7464). Additionally, we perform bivariate genetic analyses to estimate genetic and environmental variance components in pre-pandemic and pandemic SRH, and estimate the genetic correlation to assess potential gene-environment interaction. RESULTS: The majority of the sample (66.7%) reported the same SRH before and during the pandemic, while 10.8% reported a decrease, and 22.5% an increase. Individuals who reported good/excellent SRH before the pandemic were most likely to report unchanged SRH during the pandemic, and individuals with bad/mediocre/reasonable SRH more often reported increased SRH. The bivariate longitudinal genetic model reveals no significant change in variance decomposition of SRH from before to during the pandemic, with a heritability estimate of 45% (CI 36%-52%). We found that the genetic correlation could be constrained to 1, and a moderate unique environmental correlation (rE = 0.49, CI = 0.37 to 0.60). CONCLUSIONS: We theorize that the increases in SRH are explained by uninfected individuals evaluating their health more positively than under normal circumstances (partly through social comparison with infected individuals), rather than actual improvements. As the same genes are expressed under different environmental exposures, these results imply no evidence for gene-environment interaction. While different environmental factors might influence SRH at the two time-points, the influence of environmental factors does not become relatively more important during the pandemic.

Genetic and environmental influences on quality of life: The COVID-19 pandemic as a natural experiment.

By treating the coronavirus disease 2019 (COVID-19) pandemic as a natural experiment, we examine the influence of substantial environmental change (i.e., lockdown measures) on individual differences in quality of life (QoL) in the Netherlands. We compare QoL scores before the pandemic (N = 25,772) to QoL scores during the pandemic (N = 17,222) in a sample of twins and their family members. On a 10-point scale, we find a significant decrease in mean QoL from 7.73 (SD = 1.06) before the pandemic to 7.02 (SD = 1.36) during the pandemic (Cohen's d = 0.49). Additionally, variance decomposition shows an increase in unique environmental variance during the pandemic (0.30-1.08), and a decrease in the heritability estimate from 30.9% to 15.5%. We hypothesize that the increased environmental variance is the result of lockdown measures not impacting everybody equally. Whether these effects persist over longer periods and how they impact health inequalities remain topics for future investigation.

The Motivation and Opportunity for Socially Desirable Responding Does Not Alter the General Factor of Personality.

Socially desirable responding may affect the factor structure of personality questionnaires and may be one of the reasons for the common variance among personality traits. In this study, we test this hypothesis by investigating the influence of the motivational test-taking context (development vs. selection) and the opportunity to distort responses (forced-choice vs. Likert response format) on personality questionnaire scores. Data from real selection and assessment candidates (total N = 3,980) matched on gender, age, and educational level were used. Mean score differences were found between the selection and development groups, with smaller differences for the FC version. Yet, exploratory structural equation models showed that the overall factor structures as well as the general factor were highly similar across the four groups. Thus, although socially desirable responding may affect mean scores on personality traits, it does not appear to affect factor structures. This study further suggests that the common variance in personality questionnaires is consistent and appears to be little influenced by motivational pressures for response distortion.