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BackgroundClarifying the relationship between depression symptoms and cardiometabolic and related health could clarify risk factors and treatment targets. The objective of this study was to assess whether depression symptoms in midlife are associated with the subsequent onset of cardiometabolic health problems.MethodsThe study sample comprised 787 male twin veterans with polygenic risk score data who participated in the Harvard Twin Study of Substance Abuse (‘baseline’) and the longitudinal Vietnam Era Twin Study of Aging (‘follow-up’). Depression symptoms were assessed at baseline [mean age 41.42 years (s.d. = 2.34)] using the Diagnostic Interview Schedule, Version III, Revised. The onset of eight cardiometabolic conditions (atrial fibrillation, diabetes, erectile dysfunction, hypercholesterolemia, hypertension, myocardial infarction, sleep apnea, and stroke) was assessed via self-reported doctor diagnosis at follow-up [mean age 67.59 years (s.d. = 2.41)].ResultsTotal depression symptoms were longitudinally associated with incident diabetes (OR 1.29, 95% CI 1.07–1.57), erectile dysfunction (OR 1.32, 95% CI 1.10–1.59), hypercholesterolemia (OR 1.26, 95% CI 1.04–1.53), and sleep apnea (OR 1.40, 95% CI 1.13–1.74) over 27 years after controlling for age, alcohol consumption, smoking, body mass index, C-reactive protein, and polygenic risk for specific health conditions. In sensitivity analyses that excluded somatic depression symptoms, only the association with sleep apnea remained significant (OR 1.32, 95% CI 1.09–1.60).ConclusionsA history of depression symptoms by early midlife is associated with an elevated risk for subsequent development of several self-reported health conditions. When isolated, non-somatic depression symptoms are associated with incident self-reported sleep apnea. Depression symptom history may be a predictor or marker of cardiometabolic risk over decades.

The volume of subcortical structures represents a reliable, quantitative, and objective phenotype that captures genetic effects, environmental effects such as trauma, and disease effects such as posttraumatic stress disorder (PTSD). Trauma and PTSD represent potent exposures that may interact with genetic markers to influence brain structure and function. Genetic variants, associated with subcortical volumes in two large normative discovery samples, were used to compute polygenic scores (PGS) for the volume of seven subcortical structures. These were applied to a target sample enriched for childhood trauma and PTSD. Subcortical volume PGS from the discovery sample were strongly associated in our trauma/PTSD enriched sample (n = 7580) with respective subcortical volumes of the hippocampus (p = 1.10 × 10−20), thalamus (p = 7.46 × 10−10), caudate (p = 1.97 × 10−18), putamen (p = 1.7 × 10−12), and nucleus accumbens (p = 1.99 × 10−7). We found a significant association between the hippocampal volume PGS and hippocampal volume in control subjects from our sample, but was absent in individuals with PTSD (GxE; (beta = −0.10, p = 0.027)). This significant GxE (PGS × PTSD) relationship persisted (p −19) in four out of five threshold peaks (0.024, 0.133, 0.487, 0.730, and 0.889) used to calculate hippocampal volume PGSs. We detected similar GxE (G × ChildTrauma) relationships in the amygdala for exposure to childhood trauma (rs4702973; p = 2.16 × 10−7) or PTSD (rs10861272; p = 1.78 × 10−6) in the CHST11 gene. The hippocampus and amygdala are pivotal brain structures in mediating PTSD symptomatology. Trauma exposure and PTSD modulate the effect of polygenic markers on hippocampal volume (GxE) and the amygdala volume PGS is associated with PTSD risk, which supports the role of amygdala volume as a risk factor for PTSD.

The locus coeruleus (LC) is one of the earliest sites of tau pathology, making it a key structure in early Alzheimer’s disease (AD) progression. As the primary source of norepinephrine for the brain, reduced LC integrity may have negative consequences for brain health, yet macrostructural brain measures (e.g. cortical thickness) may not be sensitive to early stages of neurodegeneration. We therefore examined whether LC integrity was associated with differences in cortical gray matter microstructure among 435 men (mean age = 67.5; range = 62–71.7). LC structural integrity was indexed by contrast-to-noise ratio (LCCNR) from a neuromelanin-sensitive MRI scan. Restriction spectrum imaging (RSI), an advanced multi-shell diffusion technique, was used to characterize cortical microstructure, modeling total diffusion in restricted, hindered, and free water compartments. Higher LCCNR (greater integrity) was associated with higher hindered and lower free water diffusion in multiple cortical regions. In contrast, no associations between LCCNR and cortical thickness survived correction. Results suggest lower LC integrity is associated with patterns of cortical microstructure that may reflect a reduction in cytoarchitectural barriers due to broader neurodegenerative processes. These findings highlight the potential utility for LC imaging and advanced diffusion measures of cortical microstructure in assessing brain health and early identification of neurodegenerative processes.

The relationship of verbal fluency to executive functions (EFs) remains somewhat unclear. Verbal fluency is sometimes considered an EF ability, but is not often included in the same models as other well-studied EFs (inhibition, shifting, and working memory updating). We examined the associations between verbal fluency and EFs at 2 ages with the unity/diversity model, which includes common and domain-specific EF factors. Participants were 813 adolescent twins from the Colorado Longitudinal Twin Sample (mean age 17 years) and 1,290 middle-aged twins from the Vietnam Era Twin Study of Aging (mean age 56 years) who completed multiple measures of EFs, verbal fluency, vocabulary, and nonverbal cognitive ability. Results revealed that, in both samples, a General Fluency factor (i.e., comprising both phonemic and semantic fluency measures) was associated with the Common EF factor, but also with variance unique to working memory updating, working memory span, and set-shifting. In adolescents, semantic fluency also had unique associations with shifting beyond its shared variance with phonemic fluency and Common EF. After accounting for EFs and other cognitive abilities, there were unique genetic and environmental influences on the General Fluency and Semantic-Specific latent factors. These results suggest that verbal fluency ability may best be viewed as an amalgamation of general EF variance (i.e., Common EF ability), variance shared with other EFs (e.g., Updating-Specific ability), and multiple sources of unique genetic/environmental variance (i.e., General Fluency and Semantic-Specific abilities). These associations between verbal fluency and EFs generalize to populations that differ in age by approximately 40 years. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Author(s): Elman, Jeremy A; Puckett, Olivia K; Beck, Asad; Fennema-Notestine, Christine; Cross, Latonya K; Dale, Anders M; Eglit, Graham ML; Eyler, Lisa T; Gillespie, Nathan A; Granholm, Eric L; Gustavson, Daniel E; Hagler, Donald J; Hatton, Sean N; Hauger, Richard; Jak, Amy J; Logue, Mark W; McEvoy, Linda K; McKenzie, Ruth E; Neale, Michael C; Panizzon, Matthew S; Reynolds, Chandra A; Sanderson-Cimino, Mark; Toomey, Rosemary; Tu, Xin M; Whitsel, Nathan; Williams, McKenna E; Xian, Hong; Lyons, Michael J; Franz, Carol E; Kremen, William S | Abstract: IntroductionThe locus coeruleus (LC) undergoes extensive neurodegeneration in early Alzheimer's disease (AD). The LC is implicated in regulating the sleep-wake cycle, modulating cognitive function, and AD progression.MethodsParticipants were 481 men (ages 62 to 71.7) from the Vietnam Era Twin Study of Aging. LC structural integrity was indexed by neuromelanin-sensitive magnetic resonance imaging (MRI) contrast-to-noise ratio (LCCNR ). We examined LCCNR , cognition, amnestic mild cognitive impairment (aMCI), and daytime dysfunction.ResultsHeritability of LCCNR was .48. Participants with aMCI showed greater daytime dysfunction. Lower LCCNR was associated with poorer episodic memory, general verbal fluency, semantic fluency, and processing speed, as well as increased odds of aMCI and greater daytime dysfunction.DiscussionReduced LC integrity is associated with widespread differences across cognitive domains, daytime sleep-related dysfunction, and risk for aMCI. These findings in late-middle-aged adults highlight the potential of MRI-based measures of LC integrity in early identification of AD risk.

Objective Processing Speed (PS) is an important domain in cognitive aging that is characterized by multiple related but nonidentical abilities. Few studies have directly investigated the relationships among PS measures, and none have used genetically informed methods. In this study, we examined the relationship between measures of PS at up to two time points during middle age. Method We examined data from 1,262 middle-aged men when the sample was at a mean age of 56 and a mean age 62 years. Participants completed 6 measures of PS from three different cognitive tests. We used a genetically informative, confirmatory factor analytic approach to evaluate the phenotypic and genetic relationships cross-sectionally at both single time points and across time. Results A higher-order common PS factor accounted for the covariance among three test-specific factors, and each test-specific factor accounted for the covariance between two observed measures. The general PS factor was explained primarily by genetic influences at both time points (a²age56 = .75, a²age62 = .64), and all test-specific factors were heritable (a² range: .45 to .65). Factor loadings from the test-specific factors to the general PS factor varied from λ = .46 to .82. The model was stable over time, although there were differing rates of phenotypic change among latent factors (.20 SD to .44 SD). Cross-time genetic correlations were near unity for all latent factors. Conclusion These results suggest that PS is highly heritable when considered at a latent variable level, and that there are different rates of change in tests of PS within this sample. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

BackgroundInternalizing and externalizing psychopathology factors explain much of the covariance among psychiatric conditions, especially at the level of genetic risk. However, few studies have examined internalizing and externalizing factors in middle-aged samples, especially their ability to predict later symptoms across midlife. The goals of the current study were (i) to quantify the genetic and environmental influences on internalizing and externalizing psychopathology in individuals in their early 40s, and (ii) examine the extent to which these genetic and environmental influences predict self-reported measures of internalizing and externalizing symptoms 15–20 years later.Method1484 male twins completed diagnostic interviews of psychopathology at mean age 41 and self-reported measures of anxiety, depression, substance use, and related variables at up to two time-points in late middle age (mean ages 56 and 62).ResultsStructural equation modeling of the diagnostic interviews confirmed that internalizing and externalizing factors accounted for most of the genetic variance in individual disorders, with substantial genetic (ra = 0.70) and environmental (re = 0.77) correlations between the factors. Internalizing psychopathology at age 41 was correlated with latent factors capturing anxiety, depression, and/or post-traumatic stress symptoms at ages 56 (r = 0.51) and 62 (r = 0.43). Externalizing psychopathology at age 41 was correlated r = 0.67 with a latent factor capturing aggression, tobacco use, and alcohol use at age 56. Stability of both factors was driven by genetic influences.ConclusionsThese findings demonstrate the considerable stability of internalizing and externalizing psychopathology symptoms across middle age, especially their genetic influences. Diagnostic interviews effectively predict self-reported symptoms and behaviors 15–20 years later.

Despite the relevance of semantic fluency measures to risk for dementia and psychiatric disorders, little is known about their genetic and environmental architecture in mid-to-late life. Participants represent 21,684 middle-aged and older adult twins (M = 60.84 years, SD = 11.21; Range 40-89) from six studies from three countries participating in the Interplay of Genes and Environment across Multiple Studies (IGEMS) consortium. All completed the same measure of semantic fluency (naming animals in 60 seconds). Results revealed small-to-moderate phenotypic associations with age and education, with education more strongly and positively associated with fluency performance in females than males. Heritability and environmental influences did not vary by age. Environmental variance was smaller with higher levels of education, but this effect was observed only in males. This is the largest study to examine the genetic and environmental architecture of semantic fluency, and the first to demonstrate that environmental influences vary based on levels of education.

Objective: Abnormal white matter (AWM) on magnetic resonance imaging is associated with cognitive performance in older adults. We explored cognitive associations with AWM during late-midlife. Method: Participants were community-dwelling men (n = 242; M = 61.90 years; range = 56-66). Linear-mixed effects regression models examined associations of total, periventricular, and deep AWM with cognitive performance, controlling for multiple comparisons. Models considering specific cognitive domains controlled for current general cognitive ability (GCA). We hypothesized that total AWM would be associated with worse processing speed, executive function, and current GCA; deep AWM would correlate with GCA and periventricular AWM would relate to specific cognitive abilities. We also assessed the potential influence of cognitive reserve by examining a moderation effect of early life (mean age of 20) cognition. Results: Greater total and deep AWM were associated with poorer current GCA. Periventricular AWM was associated with worse executive function, working memory, and episodic memory. When periventricular and deep AWM were modeled simultaneously, both retained their respective significant associations with cognitive performance. Cognitive reserve did not moderate associations. Conclusions: Our findings suggest that AWM contributes to poorer cognitive function in late-midlife. Examining only total AWM may obscure the potential differential impact of regional AWM. Separating total AWM into subtypes while controlling for current GCA revealed a dissociation in relationships with cognitive performance; deep AWM was associated with nonspecific cognitive ability whereas periventricular AWM was associated with specific frontal-related abilities and memory. Management of vascular or other risk factors that may increase the risk of AWM should begin during or before early late-midlife. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Growing research suggests that posttraumatic stress disorder (PTSD) may be a risk factor for poor cardiovascular health, and yet our understanding of who might be at greatest risk of adverse cardiovascular outcomes after trauma is limited. In this study, we conducted the first examination of the individual and synergistic contributions of PTSD symptoms and blood pressure genetics to continuous blood pressure levels. We harnessed the power of the Psychiatric Genomics Consortium-PTSD Physical Health Working Group and investigated these associations across 11 studies of 72,224 trauma-exposed individuals of European (n = 70,870) and African (n = 1,354) ancestry. Genetic contributions to blood pressure were modeled via polygenic scores (PGS) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) that were derived from a prior trans-ethnic blood pressure genome-wide association study (GWAS). Results of trans-ethnic meta-analyses revealed significant main effects of the PGS on blood pressure levels [SBP: β = 2.83, standard error (SE) = 0.06, p < 1E-20; DBP: β = 1.32, SE = 0.04, p < 1E-20]. Significant main effects of PTSD symptoms were also detected for SBP and DBP in trans-ethnic meta-analyses, though there was significant heterogeneity in these results. When including data from the largest contributing study – United Kingdom Biobank – PTSD symptoms were negatively associated with SBP levels (β = −1.46, SE = 0.44, p = 9.8E-4) and positively associated with DBP levels (β = 0.70, SE = 0.26, p = 8.1E-3). However, when excluding the United Kingdom Biobank cohort in trans-ethnic meta-analyses, there was a nominally significant positive association between PTSD symptoms and SBP levels (β = 2.81, SE = 1.13, p = 0.01); no significant association was observed for DBP (β = 0.43, SE = 0.78, p = 0.58). Blood pressure PGS did not significantly moderate the associations between PTSD symptoms and blood pressure levels in meta-analyses. Additional research is needed to better understand the extent to which PTSD is associated with high blood pressure and how genetic as well as contextual factors may play a role in influencing cardiovascular risk.

Memory performance results from plasticity, the ability to change with experience. We show that benefit from practice over a few trials, learning slope, is predictive of long-term recall and hippocampal volume across a broad age range and a long period of time, relates to memory training benefit, and is heritable. First, in a healthy lifespan sample (n = 1825, age 4–93 years), comprising 3483 occasions of combined magnetic resonance imaging (MRI) scans and memory tests over a period of up to 11 years, learning slope across 5 trials was uniquely related to performance on a delayed free recall test, as well as hippocampal volume, independent from first trial memory or total memory performance across the five learning trials. Second, learning slope was predictive of benefit from memory training across ten weeks in an experimental subsample of adults (n = 155). Finally, in an independent sample of male twins (n = 1240, age 51–50 years), learning slope showed significant heritability. Within-session learning slope may be a useful marker beyond performance per se, being heritable and having unique predictive value for long-term memory function, hippocampal volume and training benefit across the human lifespan.

We investigated the heritability of educational attainment and how it differed between birth cohorts and cultural-geographic regions. A classical twin design was applied to pooled data from 28 cohorts representing 16 countries and including 193,518 twins with information on educational attainment at 25 years of age or older. Genetic factors explained the major part of individual differences in educational attainment (heritability: a(2)=0.43; 0.41-0.44), but also environmental variation shared by co-twins was substantial (c(2)=0.31; 0.30-0.33). The proportions of educational variation explained by genetic and shared environmental factors did not differ between Europe, North America and Australia, and East Asia. When restricted to twins 30 years or older to confirm finalized education, the heritability was higher in the older cohorts born in 1900-1949 (a(2)=0.44; 0.41-0.46) than in the later cohorts born in 1950-1989 (a(2)=0.38; 0.36-0.40), with a corresponding lower influence of common environmental factors (c(2)=0.31; 0.29-0.33 and c(2)=0.34; 0.32-0.36, respectively). In conclusion, both genetic and environmental factors shared by co-twins have an important influence on individual differences in educational attainment. The effect of genetic factors on educational attainment has decreased from the cohorts born before to those born after the 1950s. This study was conducted within the CODATwins project. We thank Wendy Cozen, University of Southern California, for sharing data with us. Funding for collaborators: Netherlands Twin Register acknowledges the Netherlands Organization for Scientific Research (NWO) and MagW/ZonMW grants 904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004, 463-06-001, 451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192; VU University's Institute for Health and Care Research (EMGO+); the European Research Council (ERC - 230374), the Avera Institute, Sioux Falls, South Dakota (USA). Data collection and analyses in Finnish twin cohorts have been supported by ENGAGE - European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, Grant Agreement Number 201413, National Institute of Alcohol Abuse and Alcoholism (Grants AA-12502, AA-00145, and AA-09203 to R J Rose, the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant Numbers: 213506, 129680), and the Academy of Finland (Grants 100499, 205585, 118555, 141054, 265240, 263278 and 264146 to J Kaprio). Since its origin the East Flanders Prospective Survey has been partly supported by grants from the Fund of Scientific Research, Flanders and Twins, a non-profit Association for Scientific Research in Multiple Births (Belgium). Anthropometric measurements of the Hungarian twins were supported by Medexpert Ltd., Budapest, Hungary. The Murcia Twin Registry is supported by Fundacion Seneca, Regional Agency for Science and Technology, Murcia, Spain (08633/PHCS/08, 15302/PHCS/10 & 19479/PI/14) and Ministry of Science and Innovation, Spain (PSI2009-11560 & PSI2014-56680-R). K Silventoinen is supported by Osaka University's International Joint Research Promotion Program. We acknowledge The Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant no 2017-00641. This research was facilitated through access to Twins Research Australia, a national resource supported by a Centre of Research Excellence Grant (ID: 1079102), from the National Health and Medical Research Council. California Twin Program was supported by The California Tobacco-Related Disease Research Program (7RT-0134H, 8RT-0107H, 6RT-0354H) and the National Institutes of Health (1R01ESO15150-01). The Carolina African American Twin Study of Aging (CAATSA) was funded by a grant from the National Institute on Aging (grant 1RO1-AG13662-01A2) to K. E. Whitfield. Colorado Twin Registry is funded by NIDA funded center grant DA011015, & Longititudinal Twin Study HD10333; Author Huibregtse is supported by 5T32DA017637 and 5T32AG052371. Washington State Twin Registry (formerly the University of Washington Twin Registry) was supported in part by grant NIH RC2 HL103416 (D. Buchwald, PI). Vietnam Era Twin Study of Aging was supported by National Institute of Health grants NIA R01 AG018384, R01 AG018386, R01 AG022381, and R01 AG022982, and, in part, with resources of the VA San Diego Center of Excellence for Stress and Mental Health. The Cooperative Studies Program of the Office of Research & Development of the United States Department of Veterans Affairs has provided financial support for the development and maintenance of the Vietnam Era Twin (VET) Registry. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIA/NIH, or the VA. The NAS-NRC Twin Registry acknowledges financial support from the National Institutes of Health grant number R21 AG039572. Korean Twin-Family Register was supported by the Global Research Network Program of the National Research Foundation (NRF 2011-220-E00006). Osaka University Aged Twin Registry is supported by grants from JSPS KAKENHI JP (23593419, 24792601, 26671010, 24590695, 26293128, 16K15385, 16K15978, 16K15989, 16H03261).

Objectives: First, we test for differences in various cognitive abilities across trajectories of body mass index (BMI) over the later life course. Second, we examine whether genetic risk factors for unhealthy BMIs—assessed via polygenic risk scores (PRS)—predict cognitive abilities in late-life. Methods: The study used a longitudinal sample of Vietnam veteran males to explore the associations between BMI trajectories, measured across four time points, and later cognitive abilities. The sample of 977 individuals was drawn from the Vietnam Era Twin Study of Aging. Cognitive abilities evaluated included executive function, abstract reasoning, episodic memory, processing speed, verbal fluency, and visual spatial ability. Multilevel linear regression models were used to estimate the associations between BMI trajectories and cognitive abilities. Then, BMI PRS was added to the models to evaluate polygenic associations with cognitive abilities. Results: There were no significant differences in cognitive ability between any of the BMI trajectory groups. There was a significant inverse relationship between BMI-PRS and several cognitive ability measures. Discussion: While no associations emerged for BMI trajectories and cognitive abilities at the phenotypic levels, BMI PRS measures did correlate with key cognitive domains. Our results suggest possible polygenic linkages cutting across key components of the central and peripheral nervous system.

Loos, Ruth J F/0000-0002-8532-5087; Huibregtse, Brooke M./0000-0003-0977-7249; Kandler, Christian/0000-0002-9175-235X; Hjelmborg, Jacob/0000-0001-9630-9149; mangino, massimo/0000-0002-2167-7470; Siribaddana, Sisira/0000-0001-5821-2557; Plomin, Robert/0000-0002-0756-3629; Latvala, Antti/0000-0001-5695-117X; Kaprio, Jaakko/0000-0002-3716-2455; Willemsen, Gonneke/0000-0003-3755-0236; Tan, Qihua/0000-0003-3194-0030; Pahlen, Shandell/0000-0003-0753-4155; Pedersen, Nancy/0000-0001-8057-3543; Haworth, Claire/0000-0002-8608-289X; Nistico, Lorenza/0000-0003-1805-6240; Skytthe, Axel/0000-0002-8629-4913; van Beijsterveldt, Toos/0000-0002-6617-4201; Rebato, Esther/0000-0003-1221-8501; Li, Lintao/0000-0002-0604-9660; Bartels, Meike/0000-0002-9667-7555; Silventoinen, Karri/0000-0003-1759-3079; Cunha Oliveira, Vinicius/0000-0002-8658-3774; Sund, Reijo/0000-0002-6268-8117; Sodemann, Morten/0000-0001-8992-2500; Rasmussen, Finn/0000-0001-7915-7809; Harden, Kathryn/0000-0002-1557-6737; Medda, Emanuela/0000-0003-4837-4549; Kyvik, Kirsten Ohm/0000-0003-2981-0245; Colodro Conde, Lucia/0000-0002-9004-364X WOS: 000517442200060 PubMed: 31364586 The COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) project is a large international collaborative effort to analyze individual-level phenotype data from twins in multiple cohorts from different environments. The main objective is to study factors that modify genetic and environmental variation of height, body mass index (BMI, kg/m(2)) and size at birth, and additionally to address other research questions such as long-term consequences of birth size. The project started in 2013 and is open to all twin projects in the world having height and weight measures on twins with information on zygosity. Thus far, 54 twin projects from 24 countries have provided individual-level data. The CODATwins database includes 489,981 twin individuals (228,635 complete twin pairs). Since many twin cohorts have collected longitudinal data, there is a total of 1,049,785 height and weight observations. For many cohorts, we also have information on birth weight and length, own smoking behavior and own or parental education. We found that the heritability estimates of height and BMI systematically changed from infancy to old age. Remarkably, only minor differences in the heritability estimates were found across cultural-geographic regions, measurement time and birth cohort for height and BMI. In addition to genetic epidemiological studies, we looked at associations of height and BMI with education, birth weight and smoking status. Within-family analyses examined differences within same-sex and opposite-sex dizygotic twins in birth size and later development. The CODATwins project demonstrates the feasibility and value of international collaboration to address gene-by-exposure interactions that require large sample sizes and address the effects of different exposures across time, geographical regions and socioeconomic status. Academy of FinlandAcademy of Finland [266592, 100499, 205585, 118555, 141054, 265240, 263278, 264146]; Osaka University's International Joint Research Promotion Program; Centre of Research Excellence Grant from the National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [1079102]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01 HD068435, R01 MH062375, 1R01ESO15150-01, R21 AG039572]; National Medical Research Council Research Fellowship; California Tobacco-Related Disease Research ProgramUniversity of California System [7RT-0134H, 8RT-0107H, 6RT-0354H]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [1RO1-AG13662-01A2]; Swedish Research Council through the Swedish Initiative for Research on Microdata in the Social And Medical Sciences (SIMSAM) [340-2013-5867]; Swedish Heart-Lung FoundationSwedish Heart-Lung Foundation; Swedish Asthma and Allergy Association's Research Foundation; Special Fund for Health Scientific Research in the Public Welfare, China [201502006]; NIDAUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Drug Abuse (NIDA) [DA011015, HD10333]; National Program for Research Infrastructure 2007 from the Danish Agency for Science, Technology and Innovation; Research Council for Health and Disease; Velux Foundation; US National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P01 AG08761]; Fund of Scientific Research, FlandersFWO; ENGAGE - European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007 [201413]; National Institute of Alcohol Abuse and AlcoholismUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Alcohol Abuse & Alcoholism (NIAAA) [AA-12502, AA-00145, AA-09203]; Academy of Finland Center of Excellence in Complex Disease GeneticsAcademy of Finland [213506, 129680]; Cancer Research UKCancer Research UK [C1418/A7974]; W T Grant Foundation; University of London Central Research fund; Medical Research CouncilMedical Research Council UK (MRC) [G81/343, G120/635]; Economic and Social Research CouncilEconomic & Social Research Council (ESRC) [RES-000-22-2206]; Institute of Social Psychiatry [06/07-11]; Leverhulme Research FellowshipLeverhulme Trust [RF/2/RFG/2008/0145]; Goldsmiths, University of London; UK Medical Research CouncilMedical Research Council UK (MRC) [MR/M021475/1, G0901245]; National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01AG053217, RC2 HL103416]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [81125007]; Global Research Network Program of the National Research Foundation [NRF 2011-220-E00006]; European Research Council (ERC)European Research Council (ERC) [240994]; Michigan State University; National Institute of Mental Health (NIMH)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01-MH081813, R01-MH0820-54, R01-MH092377-02, R21-MH070542-01, R03-MH63851-01, 1R01-MH118848-01]; Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD) [R01-HD066040]; MSU Foundation [11-SPG-2518]; Fundacion Seneca, Regional Agency for Science and Technology, Murcia, Spain [08633/PHCS/08, 15302/PHCS/10, 19479/PI/14]; Ministry of Science and Innovation, SpainSpanish Government [PSI2009-11560, PSI2014-56680-R]; MagW/ZonMW [904-61-090, 985-10-002, 912-10-020, 904-61-193, 480-04-004, 463-06-001, 451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192]; European Research CouncilEuropean Research Council (ERC) [ERC-230374]; JSPS KAKENHI JP [23593419, 24792601, 26671010, 24590695, 26293128, 16K15385, 16K15978, 16K15989, 16H03261]; Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [437015, 607358]; Bonnie Babes Foundation [BBF20704]; Financial Markets Foundation for Children [032-2007]; Victorian Government; Portuguese agency for research (The Foundation for Science and Technology [FCT]) [POCI/DES/56834/2004]; Wellcome TrustWellcome Trust; Medical Research CouncilMedical Research Council UK (MRC); European UnionEuropean Union (EU); National Institute for Health Research (NIHR)National Institute for Health Research (NIHR); King's College London; Fonds Quebecois de la Recherche sur la Societe et la CultureFQRNT; Fonds de la Recherche en Sante du QuebecFonds de la Recherche en Sante du Quebec; Social Science and Humanities Research Council of Canada; National Health Research Development Program; Canadian Institutes for Health ResearchCanadian Institutes of Health Research (CIHR); Sainte-Justine Hospital's Research Center; Canada Research Chair ProgramCanada Research Chairs; National Research Foundation of KoreaNational Research Foundation of Korea [NRF-371-2011-1 B00047]; Swedish Research CouncilSwedish Research Council [2017-00641]; UK Biotechnology and Biological Sciences Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC) [31/D19086]; MRCMedical Research Council UK (MRC) [MR/M021475/1]; Krkkale University [2009/43]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [114C117]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH58354]; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [NIA R01 AG018384, R01 AG018386, R01 AG022381, R01 AG022982]; VA San Diego Center of Excellence for Stress and Mental Health; Cooperative Studies Program of the Office of Research & Development of the United States Department of Veterans Affairs; Japan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science [15H05105]; Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust; Stockholm County Council (ALF-projects)Stockholm County Council; Twins, a nonprofit Association for Scientific Research in Multiple Births (Belgium); [5T32DA017637]; [5T32AG052371] This study was conducted within the CODATwins project (Academy of Finland #266592). K Silventoinen is supported by Osaka University's International Joint Research Promotion Program. This research was facilitated through access to Twins Research Australia, a national resource supported by a Centre of Research Excellence Grant (ID: 1079102), from the National Health and Medical Research Council. The Boston University Twin Project is funded by grants (#R01 HD068435 #R01 MH062375) from the National Institutes of Health to K. Saudino. Paulo Ferreira is funded by a National Medical Research Council Research Fellowship. California Twin Program was supported by The California Tobacco-Related Disease Research Program (7RT-0134H, 8RT-0107H, 6RT-0354H) and the National Institutes of Health (1R01ESO15150-01).; The Carolina African American Twin Study of Aging (CAATSA) was funded by a grant from the National Institute on Aging (grant 1RO1-AG13662-01A2) to K. E. Whitfield. The CATSS-Study is supported by the Swedish Research Council through the Swedish Initiative for Research on Microdata in the Social And Medical Sciences (SIMSAM) framework grant no 340-2013-5867, grants provided by the Stockholm County Council (ALF-projects), the Swedish Heart-Lung Foundation and the Swedish Asthma and Allergy Association's Research Foundation.; Chinese National Twin Registry is funded by Special Fund for Health Scientific Research in the Public Welfare (Project No: 201502006), China. Colorado Twin Registry is funded by NIDA-funded center grant DA011015, & Longitudinal Twin Study HD10333; Author Huibregtse is supported by 5T32DA017637 and 5T32AG052371. Danish Twin Registry is supported by the National Program for Research Infrastructure 2007 from the Danish Agency for Science, Technology and Innovation, The Research Council for Health and Disease, the Velux Foundation and the US National Institute of Health (P01 AG08761). Since its origin, the East Flanders Prospective Survey has been partly supported by grants from the Fund of Scientific Research, Flanders and Twins, a nonprofit Association for Scientific Research in Multiple Births (Belgium). Data collection and analyses in Finnish twin cohorts have been supported by ENGAGE - European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement number 201413, National Institute of Alcohol Abuse and Alcoholism (grants AA-12502, AA-00145 and AA-09203 to R J Rose, the Academy of Finland Center of Excellence in Complex Disease Genetics (grant numbers: 213506, 129680), and the Academy of Finland (grants 100499, 205585, 118555, 141054, 265240, 263278 and 264146 to J Kaprio). Gemini was supported by a grant from Cancer Research UK (C1418/A7974). Waves 1-3 of Genesis 12-19 were funded by the W T Grant Foundation, the University of London Central Research fund and a Medical Research Council Training Fellowship (G81/343) and Career Development Award (G120/635) to Thalia C. Eley. Wave 4 was supported by grants from the Economic and Social Research Council (RES-000-22-2206) and the Institute of Social Psychiatry (06/07-11) to Alice M. Gregory, who was also supported at that time by a Leverhulme Research Fellowship (RF/2/RFG/2008/0145). Wave 5 was supported by funding to Alice M. Gregory from Goldsmiths, University of London. T. C. Eley is partly funded by a program grant from the UK Medical Research Council (MR/M021475/1).; This study presents independent research [partly] funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The Minnesota Twin Registry (MTR) acknowledges support from NIH grant R01AG053217. Guangzhou Twin Eye Study is supported by National Natural Science Foundation of China (grant #81125007). Anthropometric measurements of the Hungarian twins were supported by Medexpert Ltd., Budapest, Hungary. Korean Twin-Family Register was supported by the Global Research Network Program of the National Research Foundation (NRF 2011-220-E00006). Longitudinal Israeli Study of Twins was funded by the Starting Grant no. 240994 from the European Research Council (ERC) to Ariel Knafo. The Michigan State University Twin Registry has been supported by Michigan State University, as well as grants R01-MH081813, R01-MH0820-54, R01-MH092377-02, R21-MH070542-01, R03-MH63851-01 and 1R01-MH118848-01 from the National Institute of Mental Health (NIMH), R01-HD066040 from the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD) and 11-SPG-2518 from the MSU Foundation. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIMH, the NICHD or the National Institutes of Health. The Murcia Twin Registry is supported by Fundacion Seneca, Regional Agency for Science and Technology, Murcia, Spain (08633/PHCS/08, 15302/PHCS/10 & 19479/PI/14) and Ministry of Science and Innovation, Spain (PSI2009-11560 & PSI2014-56680-R). The NAS-NRC Twin Registry acknowledges financial support from the National Institutes of Health grant number R21 AG039572. Netherlands Twin Register acknowledges the Netherlands Organization for Scientific Research (NWO) and MagW/ZonMW grants 904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004, 463-06-001, 451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192; VU University's Institute for Health and Care Research (EMGO+); the European Research Council (ERC-230374), the Avera Institute, Sioux Falls, South Dakota (USA). Osaka University Aged Twin Registry is supported by grants from JSPS KAKENHI JP (23593419, 24792601, 26671010, 24590695, 26293128, 16K15385, 16K15978, 16K15989, 16H03261). PETS was supported by grants from the Australian National Health and Medical Research Council (grant numbers 437015 and 607358 to JC, and RS), the Bonnie Babes Foundation (grant number BBF20704 to JMC), the Financial Markets Foundation for Children (grant no. 032-2007 to JMC), and by the Victorian Government's Operational Infrastructure Support Program.; Madeira data comes from the following project: Genetic and environmental influences on physical activity, fitness and health: the Madeira family study Project reference: POCI/DES/56834/2004 Founded by the Portuguese agency for research (The Foundation for Science and Technology [FCT]). TwinsUK receives funding from the Wellcome Trust, Medical Research Council and European Union.; TwinsUK and M. Mangino are supported by the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. The Quebec Newborn Twin Study acknowledges financial support from the Fonds Quebecois de la Recherche sur la Societe et la Culture, the Fonds de la Recherche en Sante du Quebec, the Social Science and Humanities Research Council of Canada, the National Health Research Development Program, the Canadian Institutes for Health Research, Sainte-Justine Hospital's Research Center, and the Canada Research Chair Program (Michel Boivin). South Korea Twin Registry is supported by National Research Foundation of Korea (NRF-371-2011-1 B00047). We acknowledge The Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant no. 2017-00641.; The Twins Early Development Study (TEDS) is supported by a program grant (G0901245) from the UK Medical Research Council and the work on obesity in TEDS is supported in part by a grant from the UK Biotechnology and Biological Sciences Research Council (31/D19086). Currently TEDS is supported by MRC grant 'MR/M021475/1'. The Texas Twin Project is currently funded by grant R01HD083613 from the National Institutes of Health. S. Y. oncel and F. Aliev are supported by Krkkale University Research Grant: KKU, 2009/43 and TUBITAK grant 114C117. The University of Southern California Twin Study is funded by a grant from the National Institute of Mental Health (R01 MH58354). Washington State Twin Registry (formerly the University of Washington Twin Registry) was supported in part by grant NIH RC2 HL103416 (D. Buchwald, PI). Vietnam Era Twin Study of Aging was supported by National Institute of Health grants NIA R01 AG018384, R01 AG018386, R01 AG022381 and R01 AG022982, and, in part, with resources of the VA San Diego Center of Excellence for Stress and Mental Health. The Cooperative Studies Program of the Office of Research & Development of the United States Department of Veterans Affairs has provided financial support for the development and maintenance of the Vietnam Era Twin (VET) Registry. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIA/NIH, or the VA. The West Japan Twins and Higher Order Multiple Births Registry was supported by Grant-in-Aid for Scientific Research (B) (grant number 15H05105) from the Japan Society for the Promotion of Science.

Mounting evidence suggests that measures of phonemic fluency and semantic fluency are differentially associated with other cognitive and health phenotypes, but few studies have examined their shared and unique variance, especially using genetically-informative designs. In this study, 1464 middle-aged twins completed six fluency subtests at up to two time-points (mean age 56 and 62 years). Confirmatory factor analyses supported a two-factor solution: a General Fluency latent factor explained variation in all six subtests and a Semantic-Specific factor accounted for additional variance in semantic subtests. Both factors were explained primarily by genetic influences at both waves (a2 = 0.57–0.76). There was considerable stability of individual differences over 6 years (r = .90 for General Fluency, r = .81 for Semantic-Specific), especially for genetic influences (rg = .94 and 1.0, respectively). These results suggest that semantic fluency can be viewed as a combination of general and semantic-specific variance, but phonemic fluency is captured entirely by the general factor.

We examined whether the longitudinal association between lifestyle behaviors and brain age is moderated by early general cognitive ability (GCA). The sample comprises 356 participants from the Vietnam Era Twin Study of Aging (VETSA). At mean age 40 (SD 2.7; range 34-44) a positive lifestyle index was created comprising three self-reported behaviors: not smoking, zero to moderate alcohol consumption, and high social engagement. GCA at mean age 20 was assessed with the Armed Forces Qualification Test. At mean age 68 (SD 2.6; range 61-72), participants underwent structural magnetic resonance imaging which was used to create predicted brain age difference (PBAD) scores. Multivariate models included GCA, lifestyle and their interaction as IVs, adjusted for age, ethnicity, APOE genotype, height, and family membership. Age 20 GCA and age 40 lifestyle significantly predicted age 68 PBAD [F=5.83; p=.02 and F=15.14; p

Objectives: Sleep quality affects memory and executive function in older adults, but little is known about its effects in midlife. If it affects cognition in midlife, it may be a modifiable factor for later-life functioning. Methods: We examined the association between sleep quality and cognition in 1220 middle-aged male twins (age 51–60 years) from the Vietnam Era Twin Study of Aging. We interviewed participants with the Pittsburgh Sleep Quality Index and tested them for episodic memory as well as executive functions of inhibitory and interference control, updating in working memory, and set shifting. Interference control was assessed during episodic memory, inhibitory control during working memory, and non-memory conditions and set shifting during working memory and non-memory conditions. Results: After adjusting for covariates and correcting for multiple comparisons, sleep quality was positively associated with updating in working memory, set shifting in the context of working memory, and better visual-spatial (but not verbal) episodic memory, and at trend level, with interference control in the context of episodic memory. Conclusions: Sleep quality was associated with visual-spatial recall and possible resistance to proactive/retroactive interference. It was also associated with updating in working memory and with set shifting, but only when working memory demands were relatively high. Thus, effects of sleep quality on midlife cognition appear to be at the intersection of executive function and memory processes. Subtle deficits in these age-susceptible cognitive functions may indicate increased risk for decline in cognitive abilities later in life that might be reduced by improved midlife sleep quality. (JINS, 2018, 24, 67–76)

Recent work on procrastination has begun to unravel the genetic and environmental correlates of this problematic behavior. However, little is known about how strongly procrastination is associated with internalizing and externalizing psychopathology, and the extent to which shared genetic/environmental factors or relevant personality constructs (e.g., fear of failure, impulsivity, and neuroticism) can inform the structure of these associations. The current study examined data from 764 young adult twins who completed questionnaires assessing procrastination and personality and structured interviews regarding psychopathology symptoms. Results indicated that procrastination was positively correlated with both internalizing and externalizing latent variables and that these correlations were driven by shared genetic influences. Moreover, the association between procrastination and internalizing was accounted for by fear of failure and neuroticism, whereas the association between procrastination and externalizing was primarily explained by impulsivity. The role of procrastination in psychopathology is discussed using a framework that highlights common and broadband-specific variance.

Structural brain changes that occur during development and ageing are related to mental health and general cognitive functioning. Individuals differ in the extent to which their brain volumes change over time, but whether these differences can be attributed to differences in their genotypes has not been widely studied. Here we estimate heritability (h2) of changes in global and subcortical brain volumes in five longitudinal twin cohorts from across the world and in different stages of the lifespan (N = 861). Heritability estimates of brain changes were significant and ranged from 16% (caudate) to 42% (cerebellar gray matter) for all global and most subcortical volumes (with the exception of thalamus and pallidum). Heritability estimates of change rates were generally higher in adults than in children suggesting an increasing influence of genetic factors explaining individual differences in brain structural changes with age. In children, environmental influences in part explained individual differences in developmental changes in brain structure. Multivariate genetic modeling showed that genetic influences of change rates and baseline volume significantly overlapped for many structures. The genetic influences explaining individual differences in the change rate for cerebellum, cerebellar gray matter and lateral ventricles were independent of the genetic influences explaining differences in their baseline volumes. These results imply the existence of genetic variants that are specific for brain plasticity, rather than brain volume itself. Identifying these genes may increase our understanding of brain development and ageing and possibly have implications for diseases that are characterized by deviant developmental trajectories of brain structure.

Magnetic resonance imaging (MRI) has become an important tool in the early detection of age-related and neuropathological brain changes. Recent studies suggest that changes in mean diffusivity (MD) of cortical gray matter derived from diffusion MRI scans may be useful in detecting early effects of Alzheimer’s disease (AD), and that these changes may be detected earlier than alterations associated with standard structural MRI measures such as cortical thickness. Thus, due to its potential clinical relevance, we examined the genetic and environmental influences on cortical MD in middle-aged men to provide support for the biological relevance of this measure and to guide future gene association studies. It is not clear whether individual differences in cortical MD reflect neuroanatomical variability similarly detected by other MRI measures, or whether unique features are captured. For instance, variability in cortical MD may reflect morphological variability more commonly measured by cortical thickness. Differences among individuals in cortical MD may also arise from breakdowns in myelinated fibers running through the cortical mantle. Thus, we investigated whether genetic influences on variation in cortical MD are the same or different from those influencing cortical thickness and MD of white matter (WM) subjacent to the cortical ribbon. Univariate twin analyses indicated that cortical MD is heritable in the majority of brain regions; the average of regional heritability estimates ranged from 0.38 in the cingulate cortex to 0.66 in the occipital cortex, consistent with the heritability of other MRI measures of the brain. Trivariate analyses found that, while there was some shared genetic variance between cortical MD and each of the other two measures, this overlap was not complete (i.e., the correlation was statistically different from 1). A significant amount of distinct genetic variance influences inter-individual variability in cortical MD; therefore, this measure could be useful for further investigation in studies of neurodegenerative diseases and gene association studies.

Associations between smoking behavior and lower cognitive functioning have been observed but there is a paucity of evidence examining the etiological impact of smoking on cognition. The current study explored the moderation of genetic and environmental contributions to cognition across mid and late-adulthood by smoking behaviors in 8 twin studies from the international IGEMS consortium (N=11,764; Mage=63.1 years). Mixed effects regression models between smoking behavior and cognition found the strongest negative effects for smoking on Symbol Digit (Bpackyears=-1.42, p

ObjectiveTest the hypothesis that individual differences in episodic memory and verbal fluency in cognitively normal middle-aged adults will predict progression to amnestic MCI after 6 years.MethodThe analysis sample included 842 male twins who were cognitively normal at baseline (M=56 years), completed measures of episodic memory and verbal fluency at baseline and again 6 years later (M=62 years).ResultsPoor episodic memory predicted progression to both amnestic MCI (OR=4.42, 95% CI [2.44, 10.60]) and non-amnestic MCI (OR=1.92, 95% CI [1.32, 3.44]). Poor semantic verbal fluency also independently predicted progression to amnestic MCI (OR=1.86, 95% CI [1.12, 3.52]). In the full sample, a semantic-specific fluency latent variable at wave 1 (which controls for letter fluency) predicted change in episodic memory at wave 2 (β=.13), but not vice-versa (β=.04). Associations between episodic memory and verbal fluency factors were primarily explained by genetic, rather than environmental, correlations.ConclusionsAmong individuals who were cognitively normal at wave 1, episodic memory moderately-to-strongly predicted progression to MCI at average age 62, emphasizing the fact that there is still meaningful variability even among cognitively normal individuals. Episodic memory, which is typically a primary focus for AD risk, declined earlier and more quickly than fluency. However, semantic fluency at average age 56 predicted 6-year change in memory as well as progression to amnestic MCI even after accounting for baseline memory performance. These findings emphasize the utility of memory and fluency measures in early identification of AD risk.

Multiple executive functions (EFs) are associated with trait anxiety and depression symptoms, but it is unclear how genetic and/or environmental factors account for these associations and whether they are explained by general variance underlying multiple EFs (i.e., common EF). In this study, 1,207 male twins completed seven EF tasks and measures of trait anxiety and depression symptoms at an average age of 62 years. The common EF factor was associated with both anxiety ( r = −.24) and depression ( r = −.32) symptoms. Anxiety and depression had a near-complete genetic overlap ( rg = .96). Genetic influences shared with depression accounted for 83% of the phenotypic correlation between anxiety and common EF. For depression, the phenotypic correlation with common EF was explained by genetic influences shared with anxiety (69%) but also nonshared environmental influences distinct from anxiety (20%). These results suggest that genetic influences on common EF play a role in the observed genetic overlap between anxiety and depression.

In this study, we characterized the genetic/environmental commonality and heterogeneity of impulsivity facets and tested the hypothesis that goal-management is central to their common variance. 764 young-adult twins completed the UPPS-P Impulsive Behavior Scale and measures of goal management, personality, and psychopathology. We found common genetic influences across all impulsivity facets except sensation seeking. These impulsivity genetic influences explained 40.0% of variance in goal-management ability. Other results supported three hypotheses concerning heterogeneity: that sensation seeking is independent of other facets, that urgency is more related to psychopathology than lack of premeditation, and that lack of perseverance is more similar to urgency than lack of premeditation. Thus, impulsivity facets show considerable heterogeneity in addition to common variation related to goal-management abilities.

How and when education improves cognitive capacity is an issue of profound societal importance. Education and later-life education-related factors, such as occupational complexity and engagement in cognitive-intellectual activities, are frequently considered indices of cognitive reserve, but whether their effects are truly causal remains unclear. In this study, after accounting for general cognitive ability (GCA) at an average age of 20 y, additional education, occupational complexity, or engagement in cognitive-intellectual activities accounted for little variance in late midlife cognitive functioning in men age 56–66 ( n = 1009). Age 20 GCA accounted for 40% of variance in the same measure in late midlife and approximately 10% of variance in each of seven cognitive domains. The other factors each accounted for n = 367). In our view, the most parsimonious explanation of our results, a meta-analysis of the impact of education, and epidemiologic studies of the Flynn effect is that intellectual capacity gains due to education plateau in late adolescence/early adulthood. Longitudinal studies with multiple cognitive assessments before completion of education would be needed to confirm this speculation. If cognitive gains reach an asymptote by early adulthood, then strengthening cognitive reserve and reducing later-life cognitive decline and dementia risk may really begin with improving educational quality and access in childhood and adolescence.

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.

We examined whether attained socioeconomic status (SES) moderated genetic and environmental sources of individual differences in cognitive performance using pooled data from 9 adult twin studies. Prior work concerning SES moderation of cognitive performance has focused on rearing SES. The current adult sample of 12,196 individuals (aged 27-98 years) allowed for the examination of common sources of individual differences between attained SES and cognitive performance (signaling potential gene-environment correlation mechanisms, rGE), as well as sources of individual differences unique to cognitive performance (signaling potential gene-environment interaction mechanisms, G × E). Attained SES moderated sources of individual differences in 4 cognitive domains, assessed via performance on 5 cognitive tests ranging 2,149 to 8,722 participants. Attained SES moderated common sources of influences for 3 domains and influences unique to cognition in all 4 domains. The net effect was that genetic influences on the common pathway tended to be relatively more important at the upper end of attained SES indicating possible active rGE, whereas, genetic influences for the unique pathway were proportionally stable or less important at the upper end of attained SES. As a noted exception, at the upper end of attained SES, genetic influences unique to perceptual speed were amplified and genetic influences on the common pathway were dampened. Accounting for rearing SES did not alter attained SES moderation effects on cognitive performance, suggesting mechanisms germane to adulthood. Our findings suggest the importance of gene-environment mechanisms through which attained SES moderates sources of individual differences in cognitive performance. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Genetics and neuropsychology have historically been two rather distant and unrelated fields. With the very rapid advances that have been taking place in genetics, research and treatment of disorders of cognition in the 21st century are likely to be increasingly informed by individual differences in genetics and epigenetics. Although neuropsychologists are not expected to become geneticists, it is our view that increased training in genetics should become more central to training in neuropsychology. This relationship should not be unidirectional. Here we note ways in which an understanding of genetics and epigenetics can inform neuropsychology. On the other hand, given the complexity of cognitive phenotypes, neuropsychology can also play a valuable role in informing and refining genetic studies. Greater integration of the two should advance both fields.

White matter disease in the brain increases with age and cardiovascular disease, emerging in midlife, and these associations may be influenced by both genetic and environmental factors. We examined the frequency, distribution, and heritability of abnormal white matter and its association with hypertension in 395 middle-aged male twins (61.9 ± 2.6 years) from the Vietnam Era Twin Study of Aging, 67% of whom were hypertensive. A multi-channel segmentation approach estimated abnormal regions within the white matter. Using multivariable regression models, we characterized the frequency distribution of abnormal white matter in midlife and investigated associations with hypertension and Apolipoprotein E-ε4 status and the impact of duration and control of hypertension. Then, using the classical twin design, we estimated abnormal white matter heritability and the extent of shared genetic overlap with blood pressure. Abnormal white matter was predominantly located in periventricular and deep parietal and frontal regions; associated with age (t = 1.9, p = 0.05) and hypertension (t = 2.9, p = 0.004), but not Apolipoprotein ε4 status; and was greater in those with uncontrolled hypertension relative to controlled (t = 3.0, p = 0.003) and normotensive (t = 4.0, p = 0.0001) groups, suggesting that abnormal white matter may reflect currently active cerebrovascular effects. Abnormal white matter was highly heritable (a2 = 0.81) and shared some genetic influences with systolic blood pressure (rA = 0.26), although there was evidence for distinct genetic contributions and unique environmental influences. Future longitudinal research will shed light on factors impacting white matter disease presentation, progression, and potential recovery., Highlights • White matter abnormalities were highly heritable and associated with hypertension. • Only some genetic influences were shared with systolic blood pressure. • There is evidence for unique environmental influences unrelated to hypertension. • Poorly controlled hypertension was associated with more abnormal white matter. • Abnormal white matter may reflect currently active cerebrovascular effects.

Anxiety symptoms are common in later life and are associated with diverse adverse health outcomes. Little is known about how genetic and environmental influences on anxiety symptoms might vary across older adulthood. The purpose of this study was to explore change and stability of contributions to anxiety symptoms across older adulthood. We examined data from the Swedish Adoption/Twin Study of Aging (SATSA). Between the years 1984 and 2010, 2,021 participants (including 753 complete twin pairs) completed up to seven assessments containing two measures of anxiety symptoms. Longitudinal genetic simplex models were fit to examine the stability and change in genetic and environmental influences. Amplification of genetic factors at ages 75–80 suggests tentative new genetic contributions to anxiety symptoms. These findings suggest that the heritability of anxiety symptoms may increase later in life. Physiological factors associated with aging are discussed as potential factors explaining this increase.

Introduction We asked whether anxiety is associated with prospective risk of dementia, and the extent to which genetic influences mediate this association. Methods Nondemented twins (n = 1082) from the Swedish Adoption Twin Study of Aging completed an assessment of anxiety symptoms in 1984 and were followed for 28 years. Results Baseline anxiety score, independent of depressive symptoms, was significantly associated with incident dementia over follow-up (hazard ratio [HR] = 1.04; 95% confidence interval [CI] = 1.01–1.06). There was 48% increased risk of becoming demented for those who had experienced high anxiety at any time compared with those who had not. In co-twin analyses, the association between anxiety symptoms and dementia was greater for dizygotic (HR = 1.11; 95% CI = 1.02–1.20) compared with monozygotic twins (HR = 1.06; 95% CI = 0.95–1.20), indicating genetic mediation. Discussion Anxiety symptoms were associated with increased risk of dementia. Genetic factors common to dementia and anxiety partially mediated this association.

Despite emerging interest in gene-environment interaction (GxE) effects, there is a dearth of studies evaluating its potential relevance apart from specific hypothesized environments and biometrical variance trends. Using a monozygotic within-pair approach, we evaluated evidence of G×E for body mass index (BMI), depressive symptoms, and cognition (verbal, spatial, attention, working memory, perceptual speed) in twin studies from four countries. We also evaluated whether APOE is a 'variability gene' across these measures and whether it partly represents the 'G' in G×E effects. In all three domains, G×E effects were pervasive across country and gender, with small-to-moderate effects. Age-cohort trends were generally stable for BMI and depressive symptoms; however, they were variable-with both increasing and decreasing age-cohort trends-for different cognitive measures. Results also suggested that APOE may represent a 'variability gene' for depressive symptoms and spatial reasoning, but not for BMI or other cognitive measures. Hence, additional genes are salient beyond APOE. Despite emerging interest in gene-environment interaction (GxE) effects, there is a dearth of studies evaluating its potential relevance apart from specific hypothesized environments and biometrical variance trends. Using a monozygotic within-pair approach, we evaluated evidence of G×E for body mass index (BMI), depressive symptoms, and cognition (verbal, spatial, attention, working memory, perceptual speed) in twin studies from four countries. We also evaluated whether APOE is a 'variability gene' across these measures and whether it partly represents the 'G' in G×E effects. In all three domains, G×E effects were pervasive across country and gender, with small-to-moderate effects. Age-cohort trends were generally stable for BMI and depressive symptoms; however, they were variable-with both increasing and decreasing age-cohort trends-for different cognitive measures. Results also suggested that APOE may represent a 'variability gene' for depressive symptoms and spatial reasoning, but not for BMI or other cognitive measures. Hence, additional genes are salient beyond APOE.

Negative fateful life events (FLEs) such as interpersonal conflict, death in the family, financial hardship, and serious medical emergencies can act as allostatic stressors that accelerate biological aging. However, the relationship between FLEs and neuroanatomical aging is not well understood. We examined 359 men (mean age 62years) participating in the Vietnam Era twin study of aging (VETSA) to determine whether negative midlife FLEs are associated with advanced brain aging after controlling for physical, psychological, and lifestyle factors. At two different time points, participants were assessed for negative FLEs, health and well-being, general cognitive ability, socioeconomic status, depression, and ethnicity. Participants underwent a magnetic resonance imaging examination, and T1-weighted images were processed with FreeSurfer. Subsequent neuroanatomical measurements were entered into the Brain-Age Regression Analysis and Computation Utility software (BARACUS) to predict brain age. Having more midlife FLEs, particularly relating to interpersonal relationships, was associated with advanced predicted brain aging (i.e., higher predicted brain age relative to chronological age). This association remained after controlling for the significant covariates of alcohol consumption, cardiovascular risk, adult socioeconomic status, and ethnicity.

Age moderation of genetic and environmental contributions to Digits Forward, Digits Backward, Block Design, Symbol Digit, Vocabulary, and Synonyms was investigated in a sample of 14,534 twins aged 26 to 98 years. The Interplay of Genes and Environment across Multiple Studies (IGEMS) consortium contributed the sample, which represents nine studies from three countries (USA, Denmark, and Sweden). Average test performance was lower in successively older age groups for all tests. Significant age moderation of additive genetic, shared environmental, and non-shared environmental variance components was observed, but the pattern varied by test. The genetic contribution to phenotypic variance across age was smaller for both Digit Span tests, greater for Synonyms, and stable for Block Design and Symbol Digit. The non-shared environmental contribution was greater with age for the Digit Span tests and Block Design, while the shared environmental component was small for all tests, often more so with age. Vocabulary showed similar age-moderation patterns as Synonyms, but these effects were nonsignificant. Findings are discussed in the context of theories of cognitive aging.

Research on executive functions (EFs) has revealed that individual differences in general EF abilities are highly correlated across the first few decades of life, especially at the level of genetic influences. Our work has also provided evidence for substantial heritability of this Common EF factor in midlife, but it remains unclear whether individual differences in Common EFs continue to show strong stability in middle age. We examined data from 1,464 middle-aged twins from the Vietnam Era Twin Study of Aging, most of whom completed 7 neuropsychological measures of EFs at 2 points in middle age (Mages = 56 and 62). Confirmatory factor analysis indicated that individual differences in Common EF, a latent factor explaining variation in seven neuropsychological EF tasks, were highly correlated across this 6-year period (r = .97), and that the same genetic and environmental influences were operating across this interval (genetic and shared environmental correlations = 1.0, nonshared environment correlation = .95). Similar phenotypic and genetic stability was observed for a Working Memory (WM)-Specific latent factor, which explained additional variance in working memory span tasks not captured by Common EF (r = .98, genetic correlation = 1.0, nonshared environmental correlation = .88). There was a large mean-level performance decline in Common EF (d = -.60) but not WM-Specific (d = -.03). These results suggest that there is substantial decline in Common EF abilities across middle age but that individual differences are almost perfectly stable. (PsycINFO Database Record

Transcriptome-wide screens of peripheral blood during the onset and development of posttraumatic stress disorder (PTSD) indicate widespread immune dysregulation. However, little is known as to whether biological sex and the type of traumatic event influence shared or distinct biological pathways in PTSD. We performed a combined analysis of five independent PTSD blood transcriptome studies covering seven types of trauma in 229 PTSD and 311 comparison individuals to synthesize the extant data. Analyses by trauma type revealed a clear pattern of PTSD gene expression signatures distinguishing interpersonal (IP)-related traumas from combat-related traumas. Co-expression network analyses integrated all data and identified distinct gene expression perturbations across sex and modes of trauma in PTSD, including one wound-healing module down-regulated in men exposed to combat traumas, one IL12-mediated signaling module up-regulated in men exposed to IP-related traumas, and two modules associated with lipid metabolism and MAPK-activity up-regulated in women exposed to IP-related traumas. Remarkably, a high degree of sharing of transcriptional dysregulation across sex and modes of trauma in PTSD was also observed converging on common signaling cascades, including cytokine, innate immune and type I interferon pathways. Collectively, these findings provide a broad view of immune dysregulation in PTSD and demonstrate inflammatory pathways of molecular convergence and specificity, which may inform mechanisms and diagnostic biomarkers for the disorder.

Background and Objectives Childhood socioeconomic status (cSES) is found to predict later-life cognitive abilities, yet the mechanisms underlying these associations remain unclear. The objective of this longitudinal study was to examine the direct and indirect paths through which cSES influences late midlife cognitive outcomes. Research Design and Methods Participants were 1,009 male twins in the Vietnam Era Twin Study of Aging (VETSA). At mean ages 20 and 62, participants completed a standardized test for general cognitive ability (GCA). The age 62 cognitive assessment also included in-person tests of processing speed, episodic memory, abstract reasoning, working memory, verbal fluency, visual-spatial ability, and executive functions. At mean age 56, participants were interviewed regarding their own and their parents’ education and occupation, and completed questionnaires about cognitive leisure activities and sociodemographic information. Multiple mediation analyses were conducted to examine the direct path effects and indirect path effects of cSES through age 20 GCA, adult SES, and cognitive leisure activities on seven cognitive outcomes at age 62, adjusting for age, ethnicity, and non-independence of observations. Results Total (direct plus indirect) effects were significant for all measures with the exception of executive functions. Men from lower cSES backgrounds had poorer cognitive functioning in late midlife. The direct effect of cSES was partially mediated for abstract reasoning, and was fully mediated for the remaining six cognitive outcomes. Total indirect effects accounted for at least half of the total effects in each model, with paths through age 20 GCA explaining most of the total indirect effects. Discussion and Implications cSES predicted cognitive functioning in late middle age Using multiple mediation models, we show that lower cSES predicts poorer cognition in late midlife primarily through young adult cognitive ability and to a lesser extent through SES in adulthood and engagement in cognitively stimulating activities.

Pupillary responses during cognitive tasks are linked to functioning of the locus coeruleus (LC). The LC is an early site of abnormal tau deposition, which may contribute to key aspects of Alzheimer’s disease (AD) pathophysiology. We previously found attenuation of pupillary responses to increases in cognitive load in individuals with mild cognitive impairment (MCI), suggesting pupillary responses may provide a biomarker of early risk for AD associated with LC dysfunction. The LC modulates cortical activity through two modes of operation: tonic and phasic. Early LC damage has been predicted to result in a state of persistent high tonic LC activity that may disrupt task-related phasic activity. To further examine whether pupillary responses are associated with early LC dysfunction, we measured pupil dilation during a digit span task as a measure of phasic activity, and low frequency BOLD variance (LFBV) during resting-state fMRI in key nodes of the ventral attention network as a measure of cortical reactivity related to LC tonic activity in 358 middle-aged men. Individuals with greater LFBV in ventral attention network nodes, i.e., higher tonic brain activity at rest, showed a smaller increase in pupil dilation from low to moderate cognitive loads. Thus, higher tonic LFBV activity at rest was related to reduced task-appropriate phasic dilation increases. The results support predictions from prominent models of LC functioning in which early LC dysfunction leads to persistent high tonic rates of activity during rest and lower signal-to-noise of phasic responses during task performance. Taken together with previous findings of early AD pathophysiology in LC and reduced phasic dilation responses to increased cognitive load in individuals with MCI, the present results suggest that pupillary responses may index early LC dysfunction and should receive further study as a potential biomarker of risk for AD.

Objective In an effort to address earliest detection of mild cognitive impairment (MCI), we examined hippocampal volumes and atrophy in middle-aged men to explore neuroanatomical support for different neuropsychological definitions of MCI. Methods 460 men aged 51–60 years underwent neuropsychological testing and MRI. MCI was defined according to five criteria sets. MRI-derived hippocampal volume and hippocampal occupancy (HOC) were obtained via FreeSurfer. Statistical analyses were performed using linear mixed models. Results Differences in HOC between normal cognitive functioning, amnestic, and non-amnestic MCI were observed using MCI criteria that required one impaired (>1.5 SD) cognitive measure in a given cognitive domain or a cognitive composite score method with a cut-point 2 SD below the mean. Differences in standard hippocampal volume were only found between normal and amnestic presentations and only when using the composite score method. Conclusion Results provide empirical support for detection of pre-MCI in younger cohorts. Convergence of neuropsychological and neuroanatomical data, particularly HOC (as opposed to standard cross-sectional volume), supports early identification of MCI as defined by some neuropsychological criteria.

We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets., National Cancer Institute (U.S.) (1K25-CA181632-01), Athinoula A. Martinos Center for Biomedical Imaging (P41-RR014075), Athinoula A. Martinos Center for Biomedical Imaging (P41-EB015896), National Alliance for Medical Image Computing (U.S.) (U54-EB005149), Neuroimaging Analysis Center (U.S.) (P41-EB015902), National Center for Research Resources (U.S.) (U24 RR021382), National Institute of Biomedical Imaging and Bioengineering (U.S.) (5P41EB015896-15), National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758), National Institute on Aging (AG022381), National Institute on Aging (5R01AG008122-22), National Institute on Aging (AG018344), National Institute on Aging (AG018386), National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01), National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01), National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01), National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963), National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534), National Institutes of Health (U.S.) ((5U01-MH093765)

Objective Research on executive functions (EFs) has revealed evidence for general abilities that underlie performance across multiple EF tasks and domains. This Common EF factor is highly stable in adolescence through young adulthood, correlates with other important cognitive abilities, and is explained largely by genetic influences. However, little is known about Common EF beyond young adulthood. This study examines 3 hypotheses regarding the latent structure, genetic/environmental etiology, and cognitive correlates of Common EF in middle age. Method We examined data from 1,284 middle-aged twins (51-60 years) in the Vietnam Era Twin Study of Aging who completed 7 neuropsychological measures of EFs, as well as measures of general cognitive ability and processing speed. Results Our confirmatory factor analysis indicated that Common EF explained variation across all 7 EF tasks. Inhibition and shifting were subsumed entirely under the Common EF factor, and there was an additional working memory span-specific factor. Common EF was heritable in midlife (a2 = .46), with additional evidence for both shared environmental influences (c2 = .41) and nonshared environmental influences (e2 = .13). Higher Common EF was moderately associated with higher general cognitive ability, measured both in early adulthood and midlife, and faster processing speed in midlife. These correlations were primarily driven by shared genetic influences. Conclusions These results support the hypothesis that Common EF captures similar EF abilities in midlife as in adolescence and young adulthood. However, environmental influences may explain a larger portion of variance in this construct as individuals age. (PsycINFO Database Record

Cognitive decline and anxiety symptoms commonly co-occur in later life, but the temporal order of changes on these two attributes is unclear. Specifically, it is unknown if greater anxiety leads to subsequent declines in cognitive performance or if worse cognitive performance leads to increased anxiety. In this study, we sought to elucidate the temporal dynamics between anxiety symptoms and cognitive performance across old age-that is, the extent to which level and change in one variable influence subsequent changes in a second variable. We examined data from 721 nondemented participants from the Swedish Adoption/Twin Study of Aging. Participants completed as many as eight assessments of cognitive performance and anxiety over a 26-year period. Bivariate dual-change score models were fit to examine the dynamic association between anxiety and cognitive performance. Bidirectional associations between anxiety and cognitive performance were found among measures of processing speed, attention, and memory but not visuospatial abilities. Higher anxiety was associated with greater declines in processing speed over the duration of 6 years and worsening attention over a span of 3 years. The reverse direction was also significant in that slower processing speed, worse attention, and poorer nonverbal and working memory performance were associated with larger increases in anxiety 3 years later. These findings highlight that in cognitively intact older adults, the association between anxiety and worse cognitive performance is bidirectional and complex. (PsycINFO Database Record

In this longitudinal study we examined the stability of general cognitive ability (GCA), as well as heterogeneity and genetic and environmental influences underlying individual differences in change. We investigated GCA from young adulthood through late midlife in 1,288 Vietnam Era Twin Study of Aging participants at ages ∼20, ∼56, and ∼62 years. The correlations among the 3 occasions ranged from .73 to .85, reflecting substantial stability. The heritability was significant on each of the 3 occasions and ranged from .59 to .66. The influence of the shared environment was not significant at any of the ages. The genetic correlations across the 3 occasions ranged from .95 to .99 and did not differ significantly from 1.0. The nonshared environmental correlations ranged from .21 to .47. Latent growth curve analysis was applied to characterize trajectories over the 42-year period. Slope was significantly different from 0 and indicated that there was modest change over time. There was a significant genetic influence on initial level of GCA (h2 = .67), but not change (h2 = .23). Genetic factors primarily contribute to stability, while change reflects the influence of nonshared environmental influences. There was a significant negative correlation between initial level of GCA and change (r = -.31). Latent class growth analysis identified 4 trajectories. In general, the 4 groups followed parallel trajectories and were differentiated mainly by differences in AFQT performance level at the time of military induction. (PsycINFO Database Record