Estimating Likelihood of Dementia in the Absence of Diagnostic Data: A Latent Dementia Index in 10 Genetically Informed Studies

BACKGROUND

Epidemiological research on dementia is hampered by differences across studies in how dementia is classified, especially where clinical diagnoses of dementia may not be available.

OBJECTIVE

We apply structural equation modeling to estimate dementia likelihood across heterogeneous samples within a multi-study consortium and use the twin design of the sample to validate the results.

METHODS

Using 10 twin studies, we implement a latent variable approach that aligns different tests available in each study to assess cognitive, memory, and functional ability. The model separates general cognitive ability from components indicative of dementia. We examine the validity of this continuous latent dementia index (LDI). We then identify cut-off points along the LDI distributions in each study and align them across studies to distinguish individuals with and without probable dementia. Finally, we validate the LDI by determining its heritability and estimating genetic and environmental correlations between the LDI and clinically diagnosed dementia where available.

RESULTS

Results indicate that coordinated estimation of LDI across 10 studies has validity against clinically diagnosed dementia. The LDI can be fit to heterogeneous sets of memory, other cognitive, and functional ability variables to extract a score reflective of likelihood of dementia that can be interpreted similarly across studies despite diverse study designs and sampling characteristics. Finally, the same genetic sources of variance strongly contribute to both the LDI and clinical diagnosis.

CONCLUSION

This latent dementia indicator approach may serve as a model for other research consortia confronted with similar data integration challenges.

Associations between depression and cardiometabolic health: A 27-year longitudinal study

BACKGROUND

Clarifying 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.

METHODS

The 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)].

RESULTS

Total 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).

CONCLUSIONS

A 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.

Associations between parenting characteristics and adolescent substance use: A genetically informed, longitudinal adoption study

The present study examined putative environmental predictors of adolescent substance use, using a prospective adoption design to distinguish between environmental mediation (i.e., parenting influencing adolescent substance use), passive gene-environment correlation (i.e., parental genetic predisposition influencing the association between parenting characteristics and adolescent substance use), and evocative gene-environment correlation (i.e., children's genetic predisposition influencing parenting). Longitudinal data from the Colorado Adoption Project (395 adoptees, 491 nonadoptees, 485 adoptive parents, and 490 biological parents) were examined. Children (48% girls) were assessed at ages 1 to 17 years. Over 90% of the sample were non-Hispanic White. Associations between parenting and adolescent substance use were compared between adoptive and nonadoptive families. Positive, negative, and inconsistent parenting measures in early childhood through adolescence were not consistently associated with adolescent substance use, with only 6% of correlations being statistically significant (r = -0.152 to .207). However, parent-child relationship quality assessed from childhood to adolescence and orientation to parents assessed during adolescence were significantly, negatively associated with adolescent substance use, with 71% of correlations being statistically significant (r = -0.88 to -0.11). There was little evidence of sex differences in the associations. Environmental mediation, rather than passive or evocative gene-environment correlation, explained most associations.

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

Does sleep duration moderate genetic and environmental contributions to cognitive performance?

While prior research has demonstrated a relationship between sleep and cognitive performance, how sleep relates to underlying genetic and environmental etiologies contributing to cognitive functioning, regardless of the level of cognitive function, is unclear. The present study assessed whether the importance of genetic and environmental contributions to cognition vary depending on an individual's aging-related sleep characteristics. The large sample consisted of twins from six studies within the Interplay of Genes and Environment across Multiple Studies (IGEMS) consortium spanning mid- to late-life (Average age [Mage] = 57.6, range = 27-91 years, N = 7052, Female = 43.70%, 1525 complete monozygotic [MZ] pairs, 2001 complete dizygotic [DZ] pairs). Quantitative genetic twin models considered sleep duration as a primary moderator of genetic and environmental contributions to cognitive performance in four cognitive abilities (Semantic Fluency, Spatial-Visual Reasoning, Processing Speed, and Episodic Memory), while accounting for age moderation. Results suggested genetic and both shared and nonshared environmental contributions for Semantic Fluency and genetic and shared environmental contributions for Episodic Memory vary by sleep duration, while no significant moderation was observed for Spatial-Visual Reasoning or Processing Speed. Results for Semantic Fluency and Episodic Memory illustrated patterns of higher genetic influences on cognitive function at shorter sleep durations (i.e. 4 hours) and higher shared environmental contributions to cognitive function at longer sleep durations (i.e. 10 hours). Overall, these findings may align with associations of upregulation of neuroinflammatory processes and ineffective beta-amyloid clearance in short sleep contexts and common reporting of mental fatigue in long sleep contexts, both associated with poorer cognitive functioning.

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

The Impact of Genes and Environment on Brain Ageing in Males Aged 51 to 72 Years

Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimer's Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 51-72 years. Biometrical genetic analyses "twin models" revealed significant and highly correlated estimates of additive genetic heritability ranging from 59 to 75%. Multivariate longitudinal modeling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences.

Independent and joint effects of body mass index and metabolic health in mid- and late-life on all-cause mortality: a cohort study from the Swedish Twin Registry with a mean follow-up of 13 Years

BACKGROUND

There is robust evidence that in midlife, higher body mass index (BMI) and metabolic syndrome (MetS), which often co-exist, are associated with increased mortality risk. However, late-life findings are inconclusive, and few studies have examined how metabolic health status (MHS) affects the BMI-mortality association in different age categories. We, therefore, aimed to investigate how mid- and late-life BMI and MHS interact to affect the risk of mortality.

METHODS

This cohort study included 12,467 participants from the Swedish Twin Registry, with height, weight, and MHS measures from 1958-2008 and mortality data linked through 2020. We applied Cox proportional hazard regression with age as a timescale to examine how BMI categories (normal weight, overweight, obesity) and MHS (identification of MetS determined by presence/absence of hypertension, hyperglycemia, low HDL, hypertriglyceridemia), independently and in interaction, are associated with the risk of all-cause mortality. Models were adjusted for sex, education, smoking, and cardiovascular disease.

RESULTS

The midlife group included 6,252 participants with a mean age of 59.6 years (range = 44.9-65.0) and 44.1% women. The late-life group included 6,215 participants with mean age 73.1 years (65.1-95.3) and 46.6% women. In independent effect models, metabolically unhealthy status in midlife increased mortality risks by 31% [hazard ratio 1.31; 95% confidence interval 1.12-1.53] and in late-life, by 18% (1.18;1.10-1.26) relative to metabolically healthy individuals. Midlife obesity increased the mortality risks by 30% (1.30;1.06-1.60) and late-life obesity by 15% (1.15; 1.04-1.27) relative to normal weight. In joint models, the BMI estimates were attenuated while those of MHS were less affected. Models including BMI-MHS categories revealed that, compared to metabolically healthy normal weight, the metabolically unhealthy obesity group had increased mortality risks by 53% (1.53;1.19-1.96) in midlife, and across all BMI categories in late-life (normal weight 1.12; 1.01-1.25, overweight 1.10;1.01-1.21, obesity 1.31;1.15-1.49). Mortality risk was decreased by 9% (0.91; 0.83-0.99) among those with metabolically healthy overweight in late-life.

CONCLUSIONS

MHS strongly influenced the BMI-mortality association, such that individuals who were metabolically healthy with overweight or obesity in mid- or late-life did not carry excess risks of mortality. Being metabolically unhealthy had a higher risk of mortality independent of their BMI.

Long-term associations of cigarette smoking in early mid-life with predicted brain aging from mid- to late life

BACKGROUND AND AIMS

Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later.

DESIGN

Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated.

SETTING

Population-based United States sample.

PARTICIPANTS/CASES

Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves.

MEASUREMENTS

Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19).

FINDINGS

In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD.

CONCLUSIONS

Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men.

Genetic associations between executive functions and intelligence: A combined twin and adoption study

Much debate has concerned the separability of executive function abilities and intelligence, with some evidence that the 2 constructs are genetically indistinguishable in children and adolescents but phenotypically and genetically distinct in older adolescents and adults. The current study leveraged data from twin and adoption studies to examine executive function's genetic structure in adulthood (M = 33.15 years, SD = 4.96) and its overlap with intelligence. 1,238 individuals (170 MZ twin pairs, 154 DZ twin pairs, 95 biological sibling pairs, 80 adoptive sibling pairs, and 240 unpaired individuals) completed 6 executive function tasks as well as the Weschler Adult Intelligence Scale-III as part of the Colorado Adoption/Twin study of Life span behavioral development and cognitive aging (CATSLife). Results replicated the unity/diversity model of executive function that distinguishes general executive function abilities (Common EF) from abilities specific to working memory updating (Updating-specific) and mental set shifting (Shifting-specific). In the final model, broad-sense heritability was high for Common EF (h² = .72), Updating-specific (h² = 1.0), and Shifting-specific (h² = .60) factors, as well as for full-scale intelligence (h² = .74). Intelligence was phenotypically and genetically correlated with Common EF (r = .49, broad-sense r_g = .44) and Updating-specific (r = .60, r_g = .69) abilities. This study represents the first executive function study to apply the adoption design. Leveraging the combined twin and adoptive design allowed us to estimate both additive and nonadditive genetic effects underlying these associations. These findings highlight the commonality and separability of executive function and intelligence. Common EF abilities are distinct from intelligence in adulthood, with intelligence also strongly associated with Updating-specific abilities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Measuring heritable contributions to Alzheimer's disease: polygenic risk score analysis with twins

The heritability of Alzheimer's disease estimated from twin studies is greater than the heritability derived from genome-based studies, for reasons that remain unclear. We apply both approaches to the same twin sample, considering both Alzheimer's disease polygenic risk scores and heritability from twin models, to provide insight into the role of measured genetic variants and to quantify uncaptured genetic risk. A population-based heritability and polygenic association study of Alzheimer's disease was conducted between 1986 and 2016 and is the first study to incorporate polygenic risk scores into biometrical twin models of Alzheimer's disease. The sample included 1586 twins drawn from the Swedish Twin Registry which were nested within 1137 twin pairs (449 complete pairs and 688 incomplete pairs) with clinically based diagnoses and registry follow-up (M age = 85.28, SD = 7.02; 44% male; 431 cases and 1155 controls). We report contributions of polygenic risk scores at P < 1 × 10-5, considering a full polygenic risk score (PRS), PRS without the APOE region (PRS.no.APOE) and PRS.no.APOE plus directly measured APOE alleles. Biometric twin models estimated the contribution of environmental influences and measured (PRS) and unmeasured genes to Alzheimer's disease risk. The full PRS and PRS.no.APOE contributed 10.1 and 2.4% to Alzheimer's disease risk, respectively. When APOE ɛ4 alleles were added to the model with the PRS.no.APOE, the total contribution was 11.4% to Alzheimer's disease risk, where APOE ɛ4 explained 9.3% and PRS.no.APOE dropped from 2.4 to 2.1%. The total genetic contribution to Alzheimer's disease risk, measured and unmeasured, was 71% while environmental influences unique to each twin accounted for 29% of the risk. The APOE region accounts for much of the measurable genetic contribution to Alzheimer's disease, with a smaller contribution from other measured polygenic influences. Importantly, substantial background genetic influences remain to be understood.

Paradoxical cognitive trajectories in men from earlier to later adulthood

Because longitudinal studies of aging typically lack cognitive data from earlier ages, it is unclear how general cognitive ability (GCA) changes throughout the life course. In 1173 Vietnam Era Twin Study of Aging (VETSA) participants, we assessed young adult GCA at average age 20 and current GCA at 3 VETSA assessments beginning at average age 56. The same GCA index was used throughout. Higher young adult GCA and better GCA maintenance were associated with stronger specific cognitive abilities from age 51 to 73. Given equivalent GCA at age 56, individuals who had higher age 20 GCA outperformed those whose GCA remained stable in terms of memory, executive function, and working memory abilities from age 51 to 73. Thus, paradoxically, despite poorer maintenance of GCA, high young adult GCA still conferred benefits. Advanced predicted brain age and the combination of elevated vascular burden and APOE-ε4 status were associated with poorer maintenance of GCA. These findings highlight the importance of distinguishing between peak and current GCA for greater understanding of cognitive aging.

Adiposity and the risk of dementia: mediating effects from inflammation and lipid levels

While midlife adiposity is a risk factor for dementia, adiposity in late-life appears to be associated with lower risk. What drives the associations is poorly understood, especially the inverse association in late-life. Using results from genome-wide association studies, we identified inflammation and lipid metabolism as biological pathways involved in both adiposity and dementia. To test if these factors mediate the effect of midlife and/or late-life adiposity on dementia, we then used cohort data from the Swedish Twin Registry, with measures of adiposity and potential mediators taken in midlife (age 40-64, n = 5999) or late-life (age 65-90, n = 7257). Associations between body-mass index (BMI), waist-hip ratio (WHR), C-reactive protein (CRP), lipid levels, and dementia were tested in survival and mediation analyses. Age was used as the underlying time scale, and sex and education included as covariates in all models. Fasting status was included as a covariate in models of lipids. One standard deviation (SD) higher WHR in midlife was associated with 25% (95% CI 2-52%) higher dementia risk, with slight attenuation when adjusting for BMI. No evidence of mediation through CRP or lipid levels was present. After age 65, one SD higher BMI, but not WHR, was associated with 8% (95% CI 1-14%) lower dementia risk. The association was partly mediated by higher CRP, and suppressed when high-density lipoprotein levels were low. In conclusion, the negative effects of midlife adiposity on dementia risk were driven directly by factors associated with body fat distribution, with no evidence of mediation through inflammation or lipid levels. There was an inverse association between late-life adiposity and dementia risk, especially where the body's inflammatory response and lipid homeostasis is intact.

Heritability × SES Interaction for IQ: Is it Present in US Adoption Studies?

An interaction between socioeconomic status (SES) and the heritability of IQ, such that the heritability of IQ increases with higher SES, has been reported in some US twin studies, although not in others, and has generally been absent in studies outside the US (England, Europe, Australia). Is such an interaction present in US adoption studies? Data from two such studies, the Texas and the Colorado Adoption Projects, were examined, involving 238-469 adopted children given IQ tests at various ages. A mini multi-level analysis was made of the prediction of the IQs by the SES of the rearing home (a composite of parental education and occupation), by the birth mother's intelligence, and by the interaction of the two. Neither study showed any substantial heritability × SES interaction: the effect size estimates in units comparable to twin moderation models were negative (- 0.042 and - 0.004), and the meta-analytic estimate for the combined analysis was - 0.27 (SE = 0.042) with a 95% confidence interval of - 0.109 to 0.054. Thus, while we cannot rule out positive moderation based on our two studies, the joint agreement across these studies, and with the non-US twin studies, warrants attention in further research. SES may not fully capture proximal familial-environmental aspects that moderate child IQ.

The effects of cannabis use on physical health: A co-twin control study

BACKGROUND

Research on the influence of cannabis use on anthropometrics, cardiovascular and pulmonary function, and other indicators of physical health has reported mixed results. We examined whether cannabis frequency is associated with physical health outcomes phenotypically and after controlling for shared genetic and environmental factors via a longitudinal co-twin control design.

METHODS

We tested the phenotypic associations of adolescent, young adult, and adult cannabis frequency with adult physical health. Next, we ran multilevel models to test if significant phenotypic associations remained at the between-family and within-twin pair levels. Participants include 677 individual twins (308 twin pairs) aged 25-35.

RESULTS

At the phenotypic level, adolescent cannabis use was associated with less adult exercise engagement (b = - 0.846 min, p = .000). Adult cannabis use was associated with a lower resting heart rate (HR; b = - 0.170 bpm, p = .001) and more frequent appetite loss (b = 0.018, p = .000). Only between-family effects were significant for adolescent cannabis use and exercise engagement (b = - 1.147 min, p = .000) and adult cannabis use and appetite loss frequency (b = 0.041, p = .002). The total within-twin (b = - 0.184, p = .014), MZ only (b = - 0.304, p = .003), and between-family effects (b = - 0.164, p = .025) were significant between adult cannabis use and a lower resting HR, which persisted after controlling for familial confounds and other substance use.

CONCLUSIONS

The associations between cannabis use with exercise engagement and frequency of appetite loss are explained by familial confounding while the association between cannabis use and resting HR was not. These results do not support a causal association between cannabis use once a week and poorer physical health effects among adults aged 25-35.

Personality predictors of cognitive dispersion: A coordinated analysis of data from seven international studies of older adults

OBJECTIVES

Dispersion in cognitive test performance within a single testing session is proposed as an early marker of poor brain health. Existing research, however, has not investigated factors that may explain individual differences in cognitive dispersion. We investigate the extent to which the Big Five personality traits are associated with cognitive dispersion in older adulthood.

METHOD

To promote transparency and reliability, we applied preregistration and conceptual replication via coordinated analysis. Drawing data from seven longitudinal studies of aging (N_total = 33,581; M_age range = 56.4-71.2), cognitive dispersion scores were derived from cognitive test results. Independent linear regression models were fit in each study to examine personality traits as predictors of dispersion scores, adjusting for mean cognitive performance and sociodemographics (age, sex, education). Results from individual studies were synthesized using random effects meta-analyses.

RESULTS

Synthesized results revealed that openness was positively associated with cognitive dispersion, 0.028, 95% CI [0.003, 0.054]. There was minimal evidence for associations between cognitive dispersion and the other personality traits in independent analyses or meta-analyses. Mean cognitive scores were negatively associated with cognitive dispersion across the majority of studies, while sociodemographic variables were not consistently associated with cognitive dispersion.

CONCLUSION

Higher levels of openness were associated with greater cognitive dispersion across seven independent samples, indicating that individuals higher in openness had more dispersion across cognitive tests. Further research is needed to investigate mechanisms that may help to explain the link between openness and cognitive dispersion, as well as to identify additional individual factors, beyond personality traits, that may be associated with cognitive dispersion. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Musical instrument engagement in adolescence predicts verbal ability 4 years later: A twin and adoption study

Individual differences in music traits are heritable and correlated with the development of cognitive and communication skills, but little is known about whether diverse modes of music engagement (e.g., playing instruments vs. singing) reflect similar underlying genetic/environmental influences. Moreover, the biological etiology underlying the relationship between musicality and childhood language development is poorly understood. Here we explored genetic and environmental associations between music engagement and verbal ability in the Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife). Adolescents (N = 1,684) completed measures of music engagement and intelligence at approximately age 12 and/or multiple tests of verbal ability at age 16. Structural equation models revealed that instrument engagement was highly heritable (a² = .78), with moderate heritability of singing (a² = .43) and dance engagement (a² = .66). Adolescent self-reported instrument engagement (but not singing or dance engagement) was genetically correlated with age 12 verbal intelligence and still was associated with age 16 verbal ability, even when controlling for age 12 full-scale intelligence, providing evidence for a longitudinal relationship between music engagement and language beyond shared general cognitive processes. Together, these novel findings suggest that shared genetic influences in part accounts for phenotypic associations between music engagement and language, but there may also be some (weak) direct benefits of music engagement on later language abilities. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Lifestyle and the aging brain: interactive effects of modifiable lifestyle behaviors and cognitive ability in men from midlife to old age

We examined the influence of lifestyle on brain aging after nearly 30 years, and tested the hypothesis that young adult general cognitive ability (GCA) would moderate these effects. In the community-dwelling Vietnam Era Twin Study of Aging (VETSA), 431 largely non-Hispanic white men completed a test of GCA at mean age 20. We created a modifiable lifestyle behavior composite from data collected at mean age 40. During VETSA, MRI-based measures at mean age 68 included predicted brain age difference (PBAD), Alzheimer's disease (AD) brain signature, and abnormal white matter scores. There were significant main effects of young adult GCA and lifestyle on PBAD and the AD signature (ps ≤ 0.012), and a GCA-by-lifestyle interaction on both (ps ≤ 0.006). Regardless of GCA level, having more favorable lifestyle behaviors predicted less advanced brain age and less AD-like brain aging. Unfavorable lifestyles predicted advanced brain aging in those with lower age 20 GCA, but did not affect brain aging in those with higher age 20 GCA. Targeting early lifestyle modification may promote dementia risk reduction, especially among lower reserve individuals.

A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease

Late-onset Alzheimer's disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer's disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer's disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer's disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer's disease to identify further genetic variants that contribute to Alzheimer's pathology.

12-year prediction of mild cognitive impairment aided by Alzheimer's brain signatures at mean age 56

Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer's disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Towards that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: (i) a validated MRI-derived Alzheimer's disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and (ii) a novel grey matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246-367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51-60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer's disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61-71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply ageing-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P = 0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step towards improving very early identification of Alzheimer's disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.