Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank

Anterior and posterior cerebral white matter show different patterns of microstructural alterations in young adult smokers

Neuroimaging studies revealed that smoking is associated with abnormal white matter (WM) microstructure. However, results are controversial, and the impact of smoking on the WM integrity in young smokers is still unclear. In this study, we used diffusion tensor imaging to investigate the smoking-related WM alterations in young adult smokers. One hundred and twenty-six subjects (60 current smokers and 66 nonsmokers) aged 18-29 years participated in the study. The tract-based spatial statistics with multiple diffusion indices was applied to explore diffusivity patterns associated with smoking. Correlation analysis was performed to evaluate relationships between fractional anisotropy (FA) and smoking-related variables in young adult smokers. Compared with nonsmokers, young adult smokers showed higher FA dominantly in the anterior cerebral WM regions, while lower FA mainly in the posterior cerebral WM areas. The dominant diffusivity pattern for regions with larger FA was characterized by lower radial and axial diffusion (Dr and Da), while in areas with smaller FA, higher Dr without significant difference in Da was the main diffusivity pattern. Moreover, diffusion indices in the genu and body of the corpus callosum were related with smoking-related variables. Our findings indicate that smoking may have differential effects on the WM integrity in the anterior and posterior parts of the brain, and may also accelerate brain aging in young adult smokers.

Adherence to Life's Essential 8 is associated with delayed white matter aging

Importance

The American Heart Association introduced Life's Essential 8 (LE8) as a checklist of healthy lifestyle factors to help older individuals maintain and improve cardiovascular health and live longer. How LE8 can foster healthy brain aging and interact with genetic risk factors to render the aging brain less vulnerable to dementia is not well understood.

Objective

To investigate the impact of LE8 on the white matter brain aging and the moderating effects of the APOE4 allele.

Design Setting and Participants

This cross-sectional study uses genetic, imaging, and other health-related data collected in the UK Biobank cohort. Participants included non-pregnant whites with LE8 variables, diffusion tensor imaging data, and genetic data on APOE4 available, and excluded the extreme white matter hyperintensities. The baseline assessment was performed from 2006 to 2010. The diffusion tensor imaging data were collected since 2014.

Exposures

LE8 variables, encompassing diet, physical activity, smoking, sleep, body mass index, lipids, hemoglobin, and blood pressure.

Main Outcomes and Measures

The white matter brain age was predicted from regional fractional anisotropy measures derived from diffusion tensor imaging data using the random forest regression method. The outcome white matter brain age gap was calculated by subtracting individuals' chronological age from their predicted brain age.

Results

The analysis included 9,430 women and 9,387 men (mean age 55.45 [SD: 7.46] years). Higher LE8 scores were associated with lower white matter brain age gap, indicating delayed brain aging. The findings are consistent for each of the individual LE8 variables. The effect was stronger among non- APOE4 carriers (124 days younger per 10-point increase, 95% CI, 102 to 146 days; P<0.001) than APOE4 carriers (84 days younger per 10-point increase, 95% CI, 47 to 120 days; P<0.001). Notably, early middle-aged women with APOE4 showed significant interactions between LE8 scores and brain aging (P interaction = 0.048), not observed in men.

Conclusions and Relevance

Adherence to LE8 is associated with delayed brain aging, moderated by genetic factors such as APOE4 . These findings highlight the potential of behavioral and lifestyle interventions in reducing dementia risk, emphasizing tailored prevention plans for those with different genetic predispositions to dementia and sex.

Altered functional connectivity within the primary visual networks and neurotransmitter activity in male smokers: A group ICA study

Smoking puts patients at high risk for cognitive and psychiatric disorders. The aim of this study was to explore the effects of nicotine use on primary visual network (PVN) and its association with neurotransmitters. A total of 59 tobacco use disorder (TUD) patients and 51 healthy controls (HC) participated in this study and underwent resting state functional magnetic resonance imaging scans. Functional connectivity (FC) within the network was explored using independent component analysis. In addition, the spatial correlations of PVN changes with neurotransmitters and their correlations with clinical characteristics of patients were evaluated using the JuSpace toolbox and SPSS. We found reduced FC within the PVN in patients with TUD compared with HC. In terms of relevant analysis, there is a spatial correlation between FC changes in the patient's PVN and a higher distribution of dopamine receptor and gamma-aminobutyric acid receptor. This study revealed changes in the FC and neurotransmitters of the PVN in patients with TUD, expanding the potential neural mechanisms underlying sensory perception and psychiatric disorders.

Nongenetic and Genetic Factors Associated with White Matter Brain Aging: Exposome-Wide and Genome-Wide Association Study

BACKGROUND/OBJECTIVES

Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g., environmental and lifestyle) and genetic risk factors are found to contribute to the varying rates at which the brain ages among individuals.

METHODS

In this paper, we conducted both an exposome-wide association study (XWAS) and a genome-wide association study (GWAS) on white matter brain aging in the UK Biobank, revealing the multifactorial nature of brain aging. We applied a machine learning algorithm and leveraged fractional anisotropy tract measurements from diffusion tensor imaging data to predict the white matter brain age gap (BAG) and treated it as the marker of brain aging. For XWAS, we included 107 variables encompassing five major categories of modifiable exposures that potentially impact brain aging and performed both univariate and multivariate analysis to select the final set of nongenetic risk factors.

RESULTS

We found current tobacco smoking, dietary habits including oily fish, beef, lamb, cereal, and coffee intake, length of mobile phone use, use of UV protection, and frequency of solarium/sunlamp use were associated with the BAG. In genetic analysis, we identified several SNPs on chromosome 3 mapped to genes IP6K1, GMNC, OSTN, and SLC25A20 significantly associated with the BAG, showing the high heritability and polygenic architecture of human brain aging.

CONCLUSIONS

The critical nongenetic and genetic risk factors identified in our study provide insights into the causal relationship between white matter brain aging and neurodegenerative diseases.

Alcohol, smoking, and brain structure: common or substance specific associations

Alcohol use and smoking are common substance-use behaviors with well-established negative health effects, including decreased brain health. We examined whether alcohol use and smoking were associated with the same neuroimaging-derived brain measures. We further explored whether the effects of alcohol use and smoking on the brain were additive or interactive. We leveraged a cohort of 36,309 participants with neuroimaging data from the UK Biobank. We used linear regression to determine the association between 354 neuroimaging-derived brain measures and alcohol use defined as drinks per week, pack years of smoking, and drinks per week × pack years smoking interaction. To assess whether the brain associations with alcohol are broadly similar or different from the associations with smoking, we calculated the correlation between z-scores of association for drinks per week and pack years smoking. Results indicated overall moderate positive correlation in the associations across measures representing brain structure, magnetic susceptibility, and white matter tract microstructure, indicating greater similarity than difference in the brain measures associated with alcohol use and smoking. The only evidence of an interaction between drinks per week and pack years smoking was seen in measures representing magnetic susceptibility in subcortical structures. The effects of alcohol use and smoking on brain health appeared to be additive rather than multiplicative for all other brain measures studied. 97% (224/230) of associations with alcohol and 100% (167/167) of the associations with smoking that surpassed a p value threshold are in a direction that can be interpreted to reflect reduced brain health. Our results underscore the similarity of the adverse associations between use of these substances and neuroimaging derived brain measures.

TIPS: a novel pathway-guided joint model for transcriptome-wide association studies

In the past two decades, genome-wide association studies (GWAS) have pinpointed numerous SNPs linked to human diseases and traits, yet many of these SNPs are in non-coding regions and hard to interpret. Transcriptome-wide association studies (TWAS) integrate GWAS and expression reference panels to identify the associations at gene level with tissue specificity, potentially improving the interpretability. However, the list of individual genes identified from univariate TWAS contains little unifying biological theme, leaving the underlying mechanisms largely elusive. In this paper, we propose a novel multivariate TWAS method that Incorporates Pathway or gene Set information, namely TIPS, to identify genes and pathways most associated with complex polygenic traits. We jointly modeled the imputation and association steps in TWAS, incorporated a sparse group lasso penalty in the model to induce selection at both gene and pathway levels and developed an expectation-maximization algorithm to estimate the parameters for the penalized likelihood. We applied our method to three different complex traits: systolic and diastolic blood pressure, as well as a brain aging biomarker white matter brain age gap in UK Biobank and identified critical biologically relevant pathways and genes associated with these traits. These pathways cannot be detected by traditional univariate TWAS + pathway enrichment analysis approach, showing the power of our model. We also conducted comprehensive simulations with varying heritability levels and genetic architectures and showed our method outperformed other established TWAS methods in feature selection, statistical power, and prediction. The R package that implements TIPS is available at https://github.com/nwang123/TIPS.

Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults

BACKGROUND AND OBJECTIVES

Cardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential 8 (LE8), with neuroimaging-based brain age and brain-predicted age difference (brain-PAD).

METHODS

This longitudinal community-based study was based on UK Biobank participants aged 40-69 years who were free from dementia and other neurologic diseases at baseline. LE8 score at baseline was assessed with 8 measures and tertiled as low, moderate, and high CVH. Structural and functional brain MRI scans were performed approximately 9 years after baseline, and 1,079 measures from 6 neuroimaging modalities were used to model brain age. A Least Absolute Shrinkage and Selection Operator regression model was trained in 4,355 healthy participants and then used to calculate brain age and brain-PAD in the whole population. Data were analyzed using linear regression models.

RESULTS

The study included 32,646 participants (mean age at baseline 54.74 years; 53.44% female; mean LE8 score: 71.90). In multivariable-adjusted linear regression, higher LE8 score was associated with younger brain age (β [95% CI] -0.037 [-0.043 to -0.031]) and more negative brain-PAD (β [95% CI] -0.043 [-0.048 to -0.038]) (brain looks younger for chronological age). Compared with high CVH, low/moderate CVH was associated with older brain age (β [95% CI] 1.030 [0.852-1.208]/0.475 [0.303-0.647]) and increased brain-PAD (β [95% CI] 1.193 [1.029-1.357]/0.528 [0.370-0.686]). The associations between low CVH and older brain age/brain-PAD remained similar and significant in both middle-aged (β [95% CI] 1.199 [0.992-1.405]/1.351 [1.159-1.542]) and older adults (β [95% CI] 0.764 [0.417-1.110]/0.948 [0.632-1.263]).

DISCUSSION

Low CVH is associated with older brain age and greater brain-PAD, even among middle-aged adults. Our findings suggest that optimizing CVH could support brain health. The main limitation of our study is that the study sample was healthier than the general population, thus caution is required when generalizing our findings to other populations.

Causal relationships involving brain imaging-derived phenotypes based on UKB imaging cohort: a review of Mendelian randomization studies

The UK Biobank (UKB) has the largest adult brain imaging dataset, which encompasses over 40,000 participants. A significant number of Mendelian randomization (MR) studies based on UKB neuroimaging data have been published to validate potential causal relationships identified in observational studies. Relevant articles published before December 2023 were identified following the PRISMA protocol. Included studies (n = 34) revealed that there were causal relationships between various lifestyles, diseases, biomarkers, and brain image-derived phenotypes (BIDPs). In terms of lifestyle habits and environmental factors, there were causal relationships between alcohol consumption, tea intake, coffee consumption, smoking, educational attainment, and certain BIDPs. Additionally, some BIDPs could serve as mediators between leisure/physical inactivity and major depressive disorder. Regarding diseases, BIDPs have been found to have causal relationships not only with Alzheimer's disease, stroke, psychiatric disorders, and migraine, but also with cardiovascular diseases, diabetes, poor oral health, osteoporosis, and ankle sprain. In addition, there were causal relationships between certain biological markers and BIDPs, such as blood pressure, LDL-C, IL-6, telomere length, and more.

Association between allostatic load and accelerated white matter brain aging: findings from the UK Biobank

White matter (WM) brain age, a neuroimaging-derived biomarker indicating WM microstructural changes, helps predict dementia and neurodegenerative disorder risks. The cumulative effect of chronic stress on WM brain aging remains unknown. In this study, we assessed cumulative stress using a multi-system composite allostatic load (AL) index based on inflammatory, anthropometric, respiratory, lipidemia, and glucose metabolism measures, and investigated its association with WM brain age gap (BAG), computed from diffusion tensor imaging data using a machine learning model, among 22 951 European ancestries aged 40 to 69 (51.40% women) from UK Biobank. Linear regression, Mendelian randomization, along with inverse probability weighting and doubly robust methods, were used to evaluate the impact of AL on WM BAG adjusting for age, sex, socioeconomic, and lifestyle behaviors. We found increasing one AL score unit significantly increased WM BAG by 0.29 years in association analysis and by 0.33 years in Mendelian analysis. The age- and sex-stratified analysis showed consistent results among participants 45-54 and 55-64 years old, with no significant sex difference. This study demonstrated that higher chronic stress was significantly associated with accelerated brain aging, highlighting the importance of stress management in reducing dementia and neurodegenerative disease risks.

Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank

BACKGROUND

Elevated blood pressure (BP) is a modifiable risk factor associated with cognitive impairment and cerebrovascular diseases. However, the causal effect of BP on white matter brain aging remains unclear.

METHODS

In this study, we focused on N  = 228 473 individuals of European ancestry who had genotype data and clinical BP measurements available (103 929 men and 124 544 women, mean age = 56.49, including 16 901 participants with neuroimaging data available) collected from UK Biobank (UKB). We first established a machine learning model to compute the outcome variable brain age gap (BAG) based on white matter microstructure integrity measured by fractional anisotropy derived from diffusion tensor imaging data. We then performed a two-sample Mendelian randomization analysis to estimate the causal effect of BP on white matter BAG in the whole population and subgroups stratified by sex and age brackets using two nonoverlapping data sets.

RESULTS

The hypertension group is on average 0.31 years (95% CI = 0.13-0.49; P  < 0.0001) older in white matter brain age than the nonhypertension group. Women are on average 0.81 years (95% CI = 0.68-0.95; P  < 0.0001) younger in white matter brain age than men. The Mendelian randomization analyses showed an overall significant positive causal effect of DBP on white matter BAG (0.37 years/10 mmHg, 95% CI 0.034-0.71, P  = 0.0311). In stratified analysis, the causal effect was found most prominent among women aged 50-59 and aged 60-69.

CONCLUSION

High BP can accelerate white matter brain aging among late middle-aged women, providing insights on planning effective control of BP for women in this age group.

Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults

Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.