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We examined whether personality traits are differently associated with coronary heart disease and stroke mortality. Participants were pooled from three prospective cohort studies (Health and Retirement Study, Wisconsin Longitudinal Study graduate and sibling samples; n = 24,543 men and women, mean age 61.4 years, mortality follow-up between 3 and 15 years). There were 423 coronary heart disease deaths and 88 stroke deaths during 212,542 person-years at risk. Higher extraversion was associated with an increased risk of stroke (hazard ratio per each standard deviation increase in personality trait HR = 1.41, 95 % CI 1.10–1.80) but not with coronary heart disease mortality (HR = 0.93, 0.83–1.05). High neuroticism, in turn, was more strongly related to the risk of coronary heart disease (HR = 1.16, 1.04–1.29) than stroke deaths (HR = 0.95, 0.78–1.17). High conscientiousness was associated with lower mortality risk from both coronary heart disease (HR = 0.74, 0.67–0.81) and stroke (HR = 0.78, 0.63–0.97). Cardiovascular risk associated with personality traits appears to vary between main cardiac and cerebral disease endpoints.

Human personality is 30–60% heritable according to twin and adoption studies. Hundreds of genetic variants are expected to influence its complex development, but few have been identified. We used a machine learning method for genome-wide association studies (GWAS) to uncover complex genotypic–phenotypic networks and environmental interactions. The Temperament and Character Inventory (TCI) measured the self-regulatory components of personality critical for health (i.e., the character traits of self-directedness, cooperativeness, and self-transcendence). In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified five clusters of people with distinct profiles of character traits regardless of genotype. Third, we found 42 SNP sets that identified 727 gene loci and were significantly associated with one or more of the character profiles. Each character profile was related to different SNP sets with distinct molecular processes and neuronal functions. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of 95% of the 42 SNP sets in healthy Korean and German samples, as well as their associations with character. The identified SNPs explained nearly all the heritability expected for character in each sample (50 to 58%). We conclude that self-regulatory personality traits are strongly influenced by organized interactions among more than 700 genes despite variable cultures and environments. These gene sets modulate specific molecular processes in brain for intentional goal-setting, self-reflection, empathy, and episodic learning and memory.

Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic–phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37–53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.

Personality traits are basicdimensions of behavioral variation, and twin, family, and adoption studies show that around 30% of the between-individual variation is due to genetic variation. There is rapidly growing interest in understanding the evolutionary basis of this genetic variation. Several evolutionary mechanisms could explain how genetic variation is maintained in traits, and each of these makes predictions in terms of the relative contribution of rare and common genetic variants to personality variation, the magnitude of nonadditive genetic influences, and whether personality is affected by inbreeding. Using genome-wide single nucleotide polymorphism (SNP) data from > 8000 individuals, we estimated that little variation in the Cloninger personality dimensions (7.2% on average) is due to the combined effect of common, additive genetic variants across the genome, suggesting that most heritable variation in personality is due to rare variant effects and/or a combination of dominance and epistasis. Furthermore, higher levels of inbreeding were associated with less socially desirable personality trait levels in three of the four personality dimensions. These findings are consistent with genetic variation in personality traits having been maintained by mutation—selection balance.

BackgroundA study was undertaken to examine the association between multiple indicators of socioeconomic position (SEP) at the age of 30 and the subsequent risk of the most common mental disorders.MethodsAll persons born in Finland between 1966 and 1986 who were alive and living in Finland at the end of the year when they turned 30 were included. Educational attainment, employment status and personal total income were used as the alternative measures of SEP. Cox proportional hazards models were used to examine the association of SEP at the age of 30 with later risk of mental disorders. Additional analyses were conducted using a sibling design to account for otherwise unobserved shared family characteristics. Competing risks models were used to estimate absolute risks.ResultsThe study population included 1 268 768 persons, 26% of whom were later diagnosed with a mental disorder. Lower SEP at age 30 was consistently associated with a higher risk of being later diagnosed with a mental disorder, even after accounting for shared family characteristics and prior history of a mental disorder. Diagnosis-specific analyses showed that the associations were considerably stronger when substance misuse or schizophrenia spectrum disorders were used as an outcome. Absolute risk analyses showed that, by the age of 52 years, 58% of persons who had low educational attainment at the age of 30 were later diagnosed with a mental disorder.ConclusionsPoor SEP at the age of 30 is associated with an increased risk of being later diagnosed with a mental disorder.

School grades in adolescence have been linked to later psychiatric outcomes, but large-scale nationwide studies across the spectrum of mental disorders are scarce. In the present study, we examined the risk of a wide array of mental disorders in adulthood, as well as the risk of comorbidity, associated with school achievement in adolescence. We used population-based cohort data comprising all individuals born in Finland over the period 1980–2000 ( N  = 1,070,880) who were followed from age 15 or 16 until a diagnosis of mental disorder, emigration, death, or December 2017, whichever came first. Final grade average from comprehensive school was the exposure, and the first diagnosed mental disorder in a secondary healthcare setting was the outcome. The risks were assessed with Cox proportional hazards models, stratified Cox proportional hazard models within strata of full-siblings, and multinomial regression models. The cumulative incidence of mental disorders was estimated using competing risks regression. Better school achievement was associated with a smaller risk of all subsequent mental disorders and comorbidity, except for eating disorders, where better school achievement was associated with a higher risk. The largest associations were observed between school achievement and substance use disorders. Overall, individuals with school achievement more than two standard deviations below average had an absolute risk of 39.6% of a later mental disorder diagnosis. By contrast, for individuals with school achievement more than two standard deviations above average, the absolute risk of a later mental disorder diagnosis was 15.7%. The results show that the largest mental health burden accumulates among those with the poorest school achievement in adolescence.

Phylogenetic, developmental, and brain-imaging studies suggest that human personality is the integrated expression of three major systems of learning and memory that regulate (1) associative conditioning, (2) intentionality, and (3) self-awareness. We have uncovered largely disjoint sets of genes regulating these dissociable learning processes in different clusters of people with (1) unregulated temperament profiles (i.e., associatively conditioned habits and emotional reactivity), (2) organized character profiles (i.e., intentional self-control of emotional conflicts and goals), and (3) creative character profiles (i.e., self-aware appraisal of values and theories), respectively. However, little is known about how these temperament and character components of personality are jointly organized and develop in an integrated manner. In three large independent genome-wide association studies from Finland, Germany, and Korea, we used a data-driven machine learning method to uncover joint phenotypic networks of temperament and character and also the genetic networks with which they are associated. We found three clusters of similar numbers of people with distinct combinations of temperament and character profiles. Their associated genetic and environmental networks were largely disjoint, and differentially related to distinct forms of learning and memory. Of the 972 genes that mapped to the three phenotypic networks, 72% were unique to a single network. The findings in the Finnish discovery sample were blindly and independently replicated in samples of Germans and Koreans. We conclude that temperament and character are integrated within three disjoint networks that regulate healthy longevity and dissociable systems of learning and memory by nearly disjoint sets of genetic and environmental influences.

Background Personality is suggested to be a major risk factor for depression but large‐scale individual participant meta‐analyses on this topic are lacking. Method Data from 10 prospective community cohort studies with 117,899 participants (mean age 49.0 years; 54.7% women) were pooled for individual participant meta‐analysis to determine the association between personality traits of the five‐factor model and risk of depressive symptoms. Results In cross‐sectional analysis, low extraversion (pooled standardized regression coefficient (B) = –.08; 95% confidence interval = –0.11, –0.04), high neuroticism (B = .39; 0.32, 0.45), and low conscientiousness (B = –.09; –0.10, –0.06) were associated with depressive symptoms. Similar associations were observed in longitudinal analyses adjusted for baseline depressive symptoms (n = 56,735; mean follow‐up of 5.0 years): low extraversion (B = –.03; –0.05, –0.01), high neuroticism (B = .12; 0.10, 0.13), and low conscientiousness (B = –.04; –0.06, –0.02) were associated with an increased risk of depressive symptoms at follow‐up. In turn, depressive symptoms were associated with personality change in extraversion (B = –.07; 95% CI = –0.12, –0.02), neuroticism (B = .23; 0.09, 0.36), agreeableness (B = –.09; –0.15, –0.04), conscientiousness (B = –.14; –0.21, –0.07), and openness to experience (B = –.04; –0.08, 0.00). Conclusions Personality traits are prospectively associated with the development of depressive symptoms. Depressive symptoms, in turn, are associated with changes in personality that may be temporary or persistent.

ObjectiveTo examine whether social isolation and loneliness (1) predict acute myocardial infarction (AMI) and stroke among those with no history of AMI or stroke, (2) are related to mortality risk among those with a history of AMI or stroke, and (3) the extent to which these associations are explained by known risk factors or pre-existing chronic conditions.MethodsParticipants were 479 054 individuals from the UK Biobank. The exposures were self-reported social isolation and loneliness. AMI, stroke and mortality were the outcomes.ResultsOver 7.1 years, 5731 had first AMI, and 3471 had first stroke. In model adjusted for demographics, social isolation was associated with higher risk of AMI (HR 1.43, 95% CI 1.3 to –1.55) and stroke (HR 1.39, 95% CI 1.25 to 1.54). When adjusted for all the other risk factors, the HR for AMI was attenuated by 84% to 1.07 (95% CI 0.99 to 1.16) and the HR for stroke was attenuated by 83% to 1.06 (95% CI 0.96 to 1.19). Loneliness was associated with higher risk of AMI before (HR 1.49, 95% CI 1.36 to 1.64) but attenuated considerably with adjustments (HR 1.06, 95% CI 0.96 to 1.17). This was also the case for stroke (HR 1.36, 95% CI 1.20 to 1.55 before and HR 1.04, 95% CI 0.91 to 1.19 after adjustments). Social isolation, but not loneliness, was associated with increased mortality in participants with a history of AMI (HR 1.25, 95% CI 1.03 to 1.51) or stroke (HR 1.32, 95% CI 1.08 to 1.61) in the fully adjusted model.ConclusionsIsolated and lonely persons are at increased risk of AMI and stroke, and, among those with a history of AMI or stroke, increased risk of death. Most of this risk was explained by conventional risk factors.

Research on the importance of the family environment on children’s health behaviors is ubiquitous, yet critical gaps in the literature exist. Many studies have focused on one family characteristic and have relied on variable-centered approaches as opposed to person-centered approaches (e.g., latent profile analysis). The purpose of the current study was to use latent profile analysis to identify family typologies characterized by parental acceptance, parental monitoring, and family conflict, and to examine whether such typologies are associated with the number of movement behavior recommendations (i.e., physical activity, screen time, and sleep) met by children. Data for this cross-sectional observational study were part of the baseline data from the Adolescent Brain Cognitive Development (ABCD) study. Data were collected across 21 study sites in the United States. Participants included 10,712 children (female = 5143, males = 5578) aged 9 and 10 years (M = 9.91, SD = 0.62). Results showed that children were meaningfully classified into one of five family typologies. Children from families with high acceptance, medium monitoring, and medium conflict (P2; OR = 0.54; 95% CI, 0.39–0.76); high acceptance, medium monitoring, and high conflict (P3; OR = 0.28; 95% CI, 0.20, 0.40); low acceptance, low monitoring, and medium conflict (P4; OR = 0.24; 95% CI, 0.16, 0.36); and medium acceptance, low monitoring, and high conflict (P5; OR = 0.19; 95% CI, 0.12–0.29) were less likely to meet all three movement behavior recommendations compared to children from families with high acceptance, high monitoring, and low conflict (P1). These findings highlight the importance of the family environment for promoting healthy movement behaviors among children.