Self- and informant-reported personality traits and vaccination against COVID-19.

As COVID-19 vaccines' accessibility has grown, so has the role of personal choice in vaccination, and not everybody is willing to vaccinate. Exploring personality traits' associations with vaccination could highlight some person-level drivers of, and barriers to, vaccination. We used self- and informant-ratings of the Five-Factor Model domains and their subtraits (a) measured approximately at the time of vaccination with the 100 Nuances of Personality (100NP) item pool (N = 56,575) and (b) measured on average ten years before the pandemic with the NEO Personality Inventory-3 (NEO-PI-3; N = 3,168). We tested individual domains' and either items' (in the 100NP sample) or facets' (in the NEO-PI-3 sample) associations with vaccination, as well as their collective ability to predict vaccination using elastic net models trained and tested in independent sample partitions. Although the NEO-PI-3 domains and facets did not predict vaccination ten years later, the domains correlated with vaccination in the 100NP sample, with vaccinated people scoring slightly higher on neuroticism and agreeableness and lower on openness, controlling for age, sex, and education. Collectively, the five domains predicted vaccination with an accuracy of r = .08. Associations were stronger at the item level. Vaccinated people were, on average, more science-minded, politically liberal, respectful of rules and authority, and anxious but less spiritual, religious, and self-assured. The 100NP items collectively predicted vaccination with r = .31 accuracy. We conclude that unvaccinated people may be a psychologically heterogeneous group and highlight some potential areas for action in vaccination campaigns.

A bottom-up approach dramatically increases the predictability of body mass from personality traits.

Personality traits consistently relate to and allow predicting body mass index (BMI), but these associations may not be adequately captured with existing inventories' domains or facets. Here, we aimed to test the limits of how accurately BMI can be predicted from and described with personality traits. We used three large datasets (combined N ≈ 100,000) with nearly 700 personality assessment items to (a) empirically identify clusters of personality traits linked to BMI and (b) identify relatively small sets of items that predict BMI as accurately as possible. Factor analysis revealed 14 trait clusters showing well-established personality trait-BMI associations (disorganization, anger) and lesser-known or novel ones (altruism, obedience). Most of items' predictive accuracy (up to r = .24 here but plausibly much higher) was captured by relatively few items. Brief scales that predict BMI have potential clinical applications-for instance, screening for risk of excessive weight gain or related complications.

Triangulating causality between childhood obesity and neurobehavior: Behavioral genetic and longitudinal evidence.

Childhood obesity is a serious health concern that is not yet fully understood. Previous research has linked obesity with neurobehavioral factors such as behavior, cognition, and brain morphology. The causal directions of these relationships remain mostly untested. We filled this gap by using the Adolescent Brain Cognitive Development study cohort comprising 11,875 children aged 9-10. First, correlations between the age- and sex-specific 95th BMI percentile (%BMIp95) and neurobehavioral measures were cross-sectionally analyzed. Effects were then aggregated by neurobehavioral domain for causal analyses. Behavioral genetic Direction of Causation modeling was used to test the direction of each relationship. Findings were validated by longitudinal cross-lagged panel modeling. %BMIp95 correlated with impulsivity, motivation, psychopathology, eating behavior, and cognitive tests (executive functioning, language, memory, perception, working memory). Greater %BMIp95 was also associated with reduced cortical thickness in frontal and temporal brain areas but with increased thickness in parietal and occipital areas. Similar although weaker patterns emerged for cortical surface area and volume. Behavioral genetic modeling suggested causal effects of %BMIp95 on eating behavior (ß = 0.26), cognition (ß = 0.05), cortical thickness (ß = 0.15), and cortical surface area (ß = 0.07). Personality/psychopathology (ß = 0.09) and eating behavior (ß = 0.16) appeared to influence %BMIp95. Longitudinal evidence broadly supported these findings. Results regarding cortical volume were inconsistent. Results supported causal effects of obesity on brain functioning and morphology. The present study highlights the importance of physical health for brain development and may inform interventions aimed at preventing or reducing pediatric obesity. RESEARCH HIGHLIGHTS: A continuous measure related to obesity, %BMIp95, has correlations with various measures of brain functioning and structure Behavioral genetic and longitudinal modeling suggest causal links from personality, psychopathology, and eating behavior to %BMIp95 Results also indicate directional links from %BMIp95 to eating behavior, cognition, cortical thickness, and cortical surface area Obesity may play a role for healthy brain development during childhood.

Body mass predicts personality development across 18 years in middle to older adulthood.

OBJECTIVE: Various personality traits have longitudinal relations with body mass index (BMI), a measure of body weight and a risk factor for numerous health concerns. We tested these associations' compatibility with causality in either direction. METHOD: Using three waves of the Wisconsin Longitudinal Study (N = 12,235, Mage  = 53.33 at baseline), we tested how accurately the Five-Factor Model personality domains and their items could collectively predict BMI and change in it with elastic net models. With multilevel models, we tested (a) bidirectional and (b) within-person associations between BMI and personality traits. RESULTS: The five domains were able to predict concurrent (r = 0.08), but not future BMI. Twenty-nine personality items predicted concurrent and future BMI at r = 0.21 and r = 0.16 to 0.25, respectively. Neither the domains nor items could collectively predict change in BMI. Similarly, no individual trait predicted change in BMI, but BMI predicted changes in Conscientiousness, Agreeableness, and several items (|b*| = 0.03 to 0.08). BMI had within-person correlations with these same traits; time-invariant third factors like genetics or childhood environments therefore could not (fully) account for their relations. CONCLUSIONS: Body weight may contribute to adults' personality development, but the reverse appears less likely.

Beyond BMI: Personality traits' associations with adiposity and metabolic rate.

Various personality traits are known to correlate with body mass index (BMI). However, this index of adiposity conflates fat mass with lean body mass and may therefore lead to biased estimates of correlations. Yet, rarely have studies looked beyond BMI to understand how adiposity and other physiological characteristics relate to these psychological traits. Using previously validated formulas, we calculated an improved measure of adiposity (relative fat mass, RFM), as well as basal metabolic rate (BMR); explored their associations with various personality traits; and assessed how personality traits' associations with RFM differ from their associations with BMI. In a subsample of the Estonian Biobank (N = 3535), we compared how the five domains and 30 facets of NEO Personality Inventory-3 correlated with RFM, BMI, and BMR. Various traits, notably Openness to Experience and its facets, were associated with RFM above and beyond BMI; these traits may relate to lower adiposity through eating habits. Assertiveness, a facet of Extraversion, correlated more strongly with BMI than with RFM and also correlated with BMR. These correlations mirror associations of metabolic rate with conceptually similar traits in non-human animals and are consistent with Assertiveness being based on biological processes. Finally, BMI-personality trait correlations appeared to conflate personality traits' associations with fat mass and lean mass; the use of BMI as an indicator of adiposity can lead to both attenuated and inflated estimates of personality trait-adiposity associations.

Two genetic analyses to elucidate causality between body mass index and personality.

BACKGROUND/OBJECTIVES: Many personality traits correlate with BMI, but the existence and direction of causal links between them are unclear. If personality influences BMI, knowing this causal direction could inform weight management strategies. Knowing that BMI instead influences personality would contribute to a better understanding of the mechanisms of personality development and the possible psychological effects of weight change. We tested the existence and direction of causal links between BMI and personality. SUBJECTS/METHODS: We employed two genetically informed methods. In Mendelian randomization, allele scores were calculated to summarize genetic propensity for the personality traits neuroticism, worry, and depressive affect and used to predict BMI in an independent sample (N = 3 541). Similarly, an allele score for BMI was used to predict eating-specific and domain-general phenotypic personality scores (PPSs; aggregate scores of personality traits weighted by BMI). In a direction of causation (DoC) analysis, twin data from five countries (N = 5424) were used to assess the fit of four alternative models: PPSs influencing BMI, BMI influencing PPSs, reciprocal causation, and no causation. RESULTS: In Mendelian randomization, the allele score for BMI predicted domain-general (ß = 0.05; 95% CI: 0.02, 0.08; P = 0.003) and eating-specific PPS (ß = 0.06; 95% CI: 0.03, 0.09; P < 0.001). The allele score for worry also predicted BMI (ß = -0.05; 95% CI: -0.08, -0.02; P < 0.001), while those for neuroticism and depressive affect did not (P ≥ 0.459). In DoC, BMI similarly predicted domain-general (ß = 0.21; 95% CI:, 0.18, 0.24; P < 0.001) and eating-specific personality traits (ß = 0.19; 95% CI:, 0.16, 0.22; P < 0.001), suggesting causality from BMI to personality traits. In exploratory analyses, links between BMI and domain-general personality traits appeared reciprocal for higher-weight individuals (BMI > ~25). CONCLUSIONS: Although both genetic analyses suggested an influence of BMI on personality traits, it is not yet known if weight management interventions could influence personality. Personality traits may influence BMI in turn, but effects in this direction appeared weaker.

Assessing the Overlap Between Three Measures of Food Reward.

Food reward is an important concept for research in eating behaviors. Many food reward tasks have been developed and are in active use. However, little is known how much these tasks overlap. Here, we sought to compare three promising food reward tasks: (1) the Leeds Food Preference Questionnaire (LFPQ; a procedure combining explicit ratings of wanting and liking and an implicit wanting task based on forced choice), (2) a hand grip force task, and (3) an emotional attentional blink (EAB) task. Specifically, we assessed whether the tasks are sensitive to changes in hunger, correlate with each other, and correlate with trait binge eating and snack food calorie intake. Thirty-nine women aged 25.51 ± 5.99 years, with a BMI of 22.51 ± 3.58 kg/m2 completed the three tasks twice: after a 6-h fast and following a breakfast meal. In the fasted condition, participants were also given ad libitum access to snack foods to assess calorie intake. Prior to the two laboratory sessions, participants completed a trait binge eating questionnaire. Results revealed that the LFPQ's explicit wanting and explicit liking subscales, as well as grip force reflected higher food reward scores in the fasted condition. The three metrics also correlated positively with each other. Explicit wanting and liking correlated with snack food intake, while grip force did not. None of the tasks were related to trait binge eating. Reaction times in the forced choice procedure did not reflect changes in hunger, but the task was nevertheless able to differentiate between foods varying in taste and fat content. The EAB was not sensitive to the hunger manipulation; neither did the task correlate with binge eating or energy intake. Collectively, our findings suggest that the explicit wanting and liking scales and the grip force task measure the same construct, whereas EAB results may be obscured by a variety of potential confounding factors. Future research could include additional food reward tasks in comparisons, measure covariates that may moderate the variables' associations, and compare hunger-dependent changes in food reward in different subgroups.