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.

/pdf/the-impact-of-genes-and-environment-on-brain-ageing-in-males-1c71iez6.pdf

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

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker. 

BrainAGE, Brain Health, and Mental Disorders: a Systematic Review

Genetic and environmental influences on structural- and diffusion-based Alzheimer's disease neuroimaging signatures across midlife and early old age.

Whitsel and colleagues have conducted an elegant longitudinal study, incorporating structural magnetic resonance imaging (MRI) data [1]. Their aim was to test if a history of heavier smoking and alcohol consumption in early mid-life is associated with more advanced brain ageing later in life (a composite measure of cortical thickness, cortical surface area and subcortical volume). Their findings suggest that, in men, both heavier smoking and alcohol consumption at age 40 years predict advanced brain ageing 16 years later. The authors rightfully note that they cannot establish causality, because their study was observational and they were not able to correct for brain age at baseline. It is therefore interesting to compare these findings to results from research methods that can aid causal inferences. In particular, Mendelian randomization (MR) is an increasingly popular method that employs a set of genetic variants highly predictive of a proposed risk factor, e.g. smoking or alcohol use, as instrumental variables to test if there are causal effects on an outcome [2]. To conduct an MR study, effect estimates of the genetic variants on risk factor and on outcome are obtained from genome-wide association studies (GWAS). Now that large MRI GWAS are available it has become feasible to perform MR to investigate causal questions pertaining to brain measures. A number of relevant MR studies have been published very recently. The first was a study in which the authors conducted a GWAS of white matter hyperintensities—a radiological trait associated with brain ageing—and additionally performed MR [3]. Their findings provide evidence that smoking causally increases the occurrence of white matter hyperintensities, suggesting that smoking accelerates brain ageing (alcohol was not studied). Another MR study investigated effects of alcohol consumption and hazardous alcohol use on a composite measure of predicted brain age [4]. They found consistent evidence that higher alcohol consumption causally increases brain age, and limited evidence that alcohol use disorder has similar effects (with the cautionary note that the latter was largely driven by a single genetic variant). Finally, a third MR study investigated both smoking and alcohol use in relation to subcortical brain volume, and found evidence to suggest that there are causal decreasing effects (on amygdala and hippocampal volume for alcohol and on hippocampal volume only for smoking) [5]. Combined, these MR studies seem to bolster the findings of Whitsel and colleagues. However, much is still unclear and important (causal) questions remain. For instance, what is the role of polysubstance use? None of the MR studies investigated smoking and alcohol combined in a multivariable model, while we know that their use is correlated [6]. In addition, cannabis has not been taken into account in any of the studies described above, while its use is correlated strongly with smoking [7]. It would be valuable to conduct multivariable MR, which allows the inclusion of multiple risk factors, to test effects of different substances together. Another worthwhile direction is to link the tentative evidence for causal effects of smoking on brain structure to the hypothesis that smoking could be a causal risk factor for mental health disorders [8, 9]. If smoking causally affects brain structure, it seems plausible that this could lead to adverse changes in brain function and subsequently mental health problems [10]. However, studies that explicitly test (mediation) pathways from smoking to brain structure and then mental health are lacking. Unravelling the complex relationship of smoking and alcohol use with brain structure and function is challenging. However, given the potential benefits for prevention and treatment, it is worth increasing our efforts to do so. This may be most pressing for smoking, as it is not widely recognized that smoking could have a negative effect on the brain. In some cases it is even the opposite—health professionals are often hesitant to urge people with a mental health disorder to give up smoking, fearing that quitting might worsen their mental health problems [11]. In reality, this is probably the group that could benefit most from smoking cessation [12]. Going forward, it will be crucial to ‘triangulate’ findings from multiple approaches to gain a clearer understanding of how smoking and alcohol use affect the brain [13, 14]. If different methods, with different strengths and weaknesses, point in the same direction, this would provide robust evidence that can be acted upon—e.g. by guiding policy decisions or aiding the design of public health messages. Received: 4 January 2022 Accepted: 4 January 2022

Commentary on Whitsel et al.: Smoking, alcohol use and the brain‐ the challenge of answering causal questions

Abstract INTRODUCTION This study investigates the relationship between cognitive functioning and 59 modifiable and intrinsic factors at the cusp of midlife. METHODS We analyzed data from 1221 participants in the Colorado Adoption/Twin Study of Lifespan behavioral development and cognitive aging (CATSLife; M age = 33.20, %Female = 52.74). We assessed the impact of 59 factors on cognitive functioning using regularized regression and co‐twin control models, controlling for earlier‐life cognitive functioning and gray matter volume. RESULTS Eight robust factors were identified, including education attainment, cognitive complexity, purpose‐in‐life, and smoking status. Twins reporting higher levels of cognitive complexity and purpose‐in‐life showed better cognitive performance than their cotwin, while smoking was negatively associated. Using meta‐analytically derived effect size threshold, we additionally identified that twins experiencing more financial difficulty tend to perform less well compared with their cotwin. DISCUSSION The findings highlight the early midlife link between cognitive functioning and lifestyle/psychological factors, beyond prior cognitive performance, brain status, genetic and familial confounders. Our results further highlight the potential of established adulthood as a crucial window for dementia prevention interventions targeting lifestyle and psychosocial factors. Highlights Cog complexity(+), purpose‐in‐life(+) were associated with cognition in early midlife. Smoking(−) was also associated with cognition in early midlife. Results were consistent controlling for genetic and environmental confounds. Association between EA and cognition might be mostly genetic and familial confounded. 

Lifestyle and psychosocial associations with cognition at the cusp of midlife using twins and siblings

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.

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

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