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Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults.
Maintaining high levels of daily activity and physical capability have been proposed as important constituents to promote healthy brain and cognitive aging. Studies investigating the associations between brain health and physical activity in late life have, however, mainly been based on self-reported data or measures designed for clinical populations. In the current study, we examined cross-sectional associations between physical activity, recorded by an ankle-positioned accelerometer for seven days, physical capability (grip strength, postural control, and walking speed), and neuroimaging based surrogate markers of brain health in 122 healthy older adults aged 65-88 years. We used a multimodal brain imaging approach offering complementary structural MRI based indicators of brain health: global white matter fractional anisotropy (FA) and mean diffusivity (MD) based on diffusion tensor imaging, and subcortical and global brain age based on brain morphology inferred from T1-weighted MRI data. In addition, based on the results from the main analysis, follow-up regression analysis was performed to test for association between the volume of key subcortical regions of interest (hippocampus, caudate, thalamus and cerebellum) and daily steps, and a follow-up voxelwise analysis to test for associations between walking speed and FA across the white matter Tract-Based Spatial Statistics (TBSS) skeleton. The analyses revealed a significant association between global FA and walking speed, indicating higher white matter integrity in people with higher pace. Voxelwise analysis supported widespread significant associations. We also found a significant interaction between sex and subcortical brain age on number of daily steps, indicating younger-appearing brains in more physically active women, with no significant associations among men. These results provide insight into the intricate associations between different measures of brain and physical health in old age, and corroborate established public health advice promoting physical activity.
Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: U.K. Biobank 18,608 example.
Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population-based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large-scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract-based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data (n = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction.
Development and validation of a dementia risk score in the UK Biobank and Whitehall II cohorts.
BACKGROUND: Current dementia risk scores have had limited success in consistently identifying at-risk individuals across different ages and geographical locations. OBJECTIVE: We aimed to develop and validate a novel dementia risk score for a midlife UK population, using two cohorts: the UK Biobank, and UK Whitehall II study. METHODS: We divided the UK Biobank cohort into a training (n=176 611, 80%) and test sample (n=44 151, 20%) and used the Whitehall II cohort (n=2934) for external validation. We used the Cox LASSO regression to select the strongest predictors of incident dementia from 28 candidate predictors and then developed the risk score using competing risk regression. FINDINGS: Our risk score, termed the UK Biobank Dementia Risk Score (UKBDRS), consisted of age, education, parental history of dementia, material deprivation, a history of diabetes, stroke, depression, hypertension, high cholesterol, household occupancy, and sex. The score had a strong discrimination accuracy in the UK Biobank test sample (area under the curve (AUC) 0.8, 95% CI 0.78 to 0.82) and in the Whitehall cohort (AUC 0.77, 95% CI 0.72 to 0.81). The UKBDRS also significantly outperformed three other widely used dementia risk scores originally developed in cohorts in Australia (the Australian National University Alzheimer's Disease Risk Index), Finland (the Cardiovascular Risk Factors, Ageing, and Dementia score), and the UK (Dementia Risk Score). CLINICAL IMPLICATIONS: Our risk score represents an easy-to-use tool to identify individuals at risk for dementia in the UK. Further research is required to determine the validity of this score in other populations.
Investigating the synergistic effects of hormone replacement therapy, apolipoprotein E and age on brain health in the UK Biobank.
Global prevalence of Alzheimer's Disease has a strong sex bias, with women representing approximately two-thirds of the patients. Yet, the role of sex-specific risk factors during midlife, including hormone replacement therapy (HRT) and their interaction with other major risk factors for Alzheimer's Disease, such as apolipoprotein E (APOE)-e4 genotype and age, on brain health remains unclear. We investigated the relationship between HRT (i.e., use, age of initiation and duration of use) and brain health (i.e., cognition and regional brain volumes). We then consider the multiplicative effects of HRT and APOE status (i.e., e2/e2, e2/e3, e3/e3, e3/e4 and e4/e4) via a two-way interaction and subsequently age of participants via a three-way interaction. Women from the UK Biobank with no self-reported neurological conditions were included (N = 207,595 women, mean age = 56.25 years, standard deviation = 8.01 years). Generalised linear regression models were computed to quantify the cross-sectional association between HRT and brain health, while controlling for APOE status, age, time since attending centre for completing brain health measure, surgical menopause status, smoking history, body mass index, education, physical activity, alcohol use, ethnicity, socioeconomic status, vascular/heart problems and diabetes diagnosed by doctor. Analyses of structural brain regions further controlled for scanner site. All brain volumes were normalised for head size. Two-way interactions between HRT and APOE status were modelled, in addition to three-way interactions including age. Results showed that women with the e4/e4 genotype who have used HRT had 1.82% lower hippocampal, 2.4% lower parahippocampal and 1.24% lower thalamus volumes than those with the e3/e3 genotype who had never used HRT. However, this interaction was not detected for measures of cognition. No clinically meaningful three-way interaction between APOE, HRT and age was detected when interpreted relative to the scales of the cognitive measures used and normative models of ageing for brain volumes in this sample. Differences in hippocampal volume between women with the e4/e4 genotype who have used HRT and those with the e3/e3 genotype who had never used HRT are equivalent to approximately 1-2 years of hippocampal atrophy observed in typical health ageing trajectories in midlife (i.e., 0.98%-1.41% per year). Effect sizes were consistent within APOE e4/e4 group post hoc sensitivity analyses, suggesting observed effects were not solely driven by APOE status and may, in part, be attributed to HRT use. Although, the design of this study means we cannot exclude the possibility that women who have used HRT may have a predisposition for poorer brain health.
Mediation Analyses Link Cardiometabolic Factors and Liver Fat With White Matter Hyperintensities and Cognitive Performance: A UK Biobank Study
Background: Liver fat is associated with cardiometabolic disease, cerebrovascular disease, and dementia. Cerebrovascular disease, most often cerebral small vessel disease, identified by magnetic resonance imaging as white matter hyperintensities (WMHs) often contributes to dementia. However, liver fat's role in the relationship between cardiometabolic risk, WMHs, and cognitive performance is unclear. Methods: In the UK Biobank cohort (N = 32,461, 52.6% female; mean age 64.2 ± 7.7 years; n = 23,354 in the cognitive performance subsample), we used linear regression to investigate associations between cardiometabolic factors measured at baseline and liver fat, WMHs, and cognitive performance measured at follow-up, which was 9.3 ± 2.0 years later on average. We used structural equation modeling to investigate whether liver fat mediated associations between cardiometabolic factors and WMHs and whether WMHs mediated associations between liver fat and cognitive performance. Results: Nearly all cardiometabolic factors were significantly associated with liver fat (|r| range = 0.03–0.41, p = 3.4 × 10−8 to 0) and WMHs (|r| = 0.04–0.15, p = 5.8 × 10−13 to 7.0 × 10−159) in regression models. Liver fat was associated with WMHs (r = 0.11, p = 4.3 × 10−82) and cognitive performance (r = −0.03, p = 1.6 × 10−7). Liver fat mediated the associations between cardiometabolic factors and WMHs (|βmediation| = 0.003–0.027, pmediation = 1.9 × 10−8 to 0), and WMHs mediated the associations between liver fat and cognitive performance (βmediation = −0.01, pmediation = 0). Conclusions: Our findings indicate that liver fat mediates associations between cardiometabolic factors and WMHs and that WMHs mediate the association between liver fat and cognitive performance. This suggests that liver fat may be important for understanding the effects of cardiometabolic factors on cerebrovascular disease and cognitive function. Experimental studies are warranted to determine relevant targets for preventing vascular-driven cognitive impairment.
A Bayesian analysis of diagnostic timelines across Alzheimer's disease, frontotemporal dementia, and other neurodegenerative conditions
INTRODUCTION: Timely diagnosis is crucial for managing neurodegenerative conditions. This study investigated whether time from symptom onset to diagnosis differs by clinical syndrome and sex. METHODS: This retrospective, cross-sectional study included 591 participants with Alzheimer's disease (AD), frontotemporal dementia (FTD) subtypes (behavioral variant FTD [bvFTD], semantic dementia [SD], and progressive non-fluent aphasia), logopenic progressive aphasia (LPA), and syndromes associated with movement disorders (corticobasal syndrome, FTD with motor neuron disease [FTD-MND], and progressive supranuclear palsy). Bayesian regression models were used to compute diagnostic timelines. RESULTS: Compared to AD (3.35 years; 95% credible interval [CrI]: 3.03–3.72), SD and bvFTD had additional delays of 9.7 (95% CrI: 1.96–20.64) and 14.82 months (95% CrI: 6.94–25.42), respectively, while FTD-MND was shorter by 11.62 months (95% CrI: −15.7 to −4.68). Men with bvFTD had 23.64 month longer delays than women (95% CrI: 10.35–44.33). DISCUSSION: Diagnostic delays may reflect syndrome-specific clinical features, diagnostic complexity, and sociocultural factors. Findings highlight the need for improved diagnostic pathways and pre-clinical biomarkers to facilitate earlier identification. Highlights: Bayesian analyses revealed that diagnostic delays differ by syndrome and sex. Alzheimer's disease (AD) was diagnosed on average 3.35 years after symptom onset. Diagnoses were delayed in semantic and behavioral variant frontotemporal dementia (bvFTD) compared to AD. Men with bvFTD had longer delays than women. Findings support need for improved diagnostic pathways and pre-clinical biomarkers.
Menopausal hormone therapy and the female brain: leveraging neuroimaging and prescription registry data from the UK Biobank cohort.
BACKGROUND AND OBJECTIVES: Menopausal hormone therapy (MHT) is generally thought to be neuroprotective, yet results have been inconsistent. Here, we present a comprehensive study of MHT use and brain characteristics in middle- to older aged females from the UK Biobank, assessing detailed MHT data, APOE ε4 genotype, and tissue-specific gray (GM) and white matter (WM) brain age gap (BAG), as well as hippocampal and white matter hyperintensity (WMH) volumes. METHODS: A total of 19,846 females with magnetic resonance imaging data were included (current-users = 1,153, 60.1 ± 6.8 years; past-users = 6,681, 67.5 ± 6.2 years; never-users = 12,012, mean age 61.6 ± 7.1 years). For a sub-sample (n = 538), MHT prescription data was extracted from primary care records. Brain measures were derived from T1-, T2- and diffusion-weighted images. We fitted regression models to test for associations between the brain measures and MHT variables including user status, age at initiation, dosage and duration, formulation, route of administration, and type (i.e., bioidentical vs synthetic), as well as active ingredient (e.g., estradiol hemihydrate). We further tested for differences in brain measures among MHT users with and without a history of hysterectomy ± bilateral oophorectomy and examined associations by APOE ε4 status. RESULTS: We found significantly higher GM and WM BAG (i.e., older brain age relative to chronological age) as well as smaller left and right hippocampus volumes in current MHT users, not past users, compared to never-users. Effects were modest, with the largest effect size indicating a group difference of 0.77 years (~9 months) for GM BAG. Among MHT users, we found no significant associations between age at MHT initiation and brain measures. Longer duration of use and older age at last use post menopause was associated with higher GM and WM BAG, larger WMH volume, and smaller left and right hippocampal volumes. MHT users with a history of hysterectomy ± bilateral oophorectomy showed lower GM BAG relative to MHT users without such history. Although we found smaller hippocampus volumes in carriers of two APOE ε4 alleles compared to non-carriers, we found no interactions with MHT variables. In the sub-sample with prescription data, we found no significant associations between detailed MHT variables and brain measures after adjusting for multiple comparisons. DISCUSSION: Our results indicate that population-level associations between MHT use, and female brain health might vary depending on duration of use and past surgical history. Future research is crucial to establish causality, dissect interactions between menopause-related neurological changes and MHT use, and determine individual-level implications to advance precision medicine in female health care.
Dimensions of Early-Life Adversity Are Differentially Associated With Patterns of Delayed and Accelerated Brain Maturation.
BACKGROUND: Different types of early-life adversity (ELA) have been associated with children's brain structure and function. However, understanding the disparate influence of distinct adversity exposures on the developing brain remains a major challenge. METHODS: This study investigates the neural correlates of 10 robust dimensions of ELA identified through exploratory factor analysis in a large community sample of youth from the Adolescent Brain Cognitive Development Study. Brain age models were trained, validated, and tested separately on T1-weighted (n = 9524), diffusion tensor (n = 8834), and resting-state functional (n = 8233) magnetic resonance imaging data from two time points (mean age = 10.7 years, SD = 1.2, age range = 8.9-13.8 years). RESULTS: Bayesian multilevel modeling supported distinct associations between different types of ELA exposures and younger- and older-looking brains. Dimensions generally related to emotional neglect, such as lack of primary and secondary caregiver support and lack of caregiver supervision, were associated with lower brain age gaps, i.e., younger-looking brains. In contrast, dimensions generally related to caregiver psychopathology, trauma exposure, family aggression, substance use and separation from biological parent, and socioeconomic disadvantage and neighborhood safety were associated with higher brain age gaps, i.e., older-looking brains. CONCLUSIONS: The findings suggest that dimensions of ELA are differentially associated with distinct neurodevelopmental patterns, indicative of dimension-specific delayed and accelerated brain maturation.
Sex-Dependent Effects of Cardiometabolic Health and APOE4 on Brain Age: A Longitudinal Cohort Study.
BACKGROUND AND OBJECTIVES: The aging population is growing faster than all other demographic strata. With older age comes a greater risk of health conditions such as obesity and high blood pressure (BP). These cardiometabolic risk factors (CMRs) exhibit prominent sex differences in midlife and aging, yet their influence on brain health in females vs males is largely unexplored. In this study, we investigated sex differences in relationships between BP, body mass index (BMI), and brain age over time and tested for interactions with APOE ε4 genotype (APOE4), a known genetic risk factor of Alzheimer disease. METHODS: The sample included participants from 2 United Kingdom-based longitudinal birth cohorts, the Lothian Birth Cohort (1936) and Insight 46 (1946). Participants with MRI data from at least 1 time point were included to evaluate sex differences in associations between CMRs and brain age. The open-access software package brainageR 2.1 was used to estimate brain age for each participant. Linear mixed-effects models were used to assess the relationships between brain age, BMI, BP, and APOE4 status (i.e., carrier vs noncarrier) in males and females over time. RESULTS: The combined sample comprised 1,120 participants (48% female) with a mean age (SD) of 73 (0.72) years in the Lothian Birth Cohort and 71 (0.68) years in Insight 46 at the time point 1 assessment. Approximately 30% of participants were APOE4 carriers. Higher systolic and diastolic BP was significantly associated with older brain age in females only (β = 0.43-0.56, p < 0.05). Among males, higher BMI was associated with older brain age across time points and APOE4 groups (β = 0.72-0.77, p < 0.05). In females, higher BMI was linked to older brain age among APOE4 noncarriers (β = 0.68-0.99, p < 0.05), whereas higher BMI was linked to younger brain age among carriers, particularly at the last time point (β = -1.75, p < 0.05). DISCUSSION: This study indicates sex-dependent and time-dependent relationships between CMRs, APOE4 status, and brain age. Our findings highlight the necessity of sex-stratified analyses to elucidate the role of CMRs in individual aging trajectories, providing a basis for developing personalized preventive interventions.
Linking menopause-related factors, history of depression, APOE ε4, and proxies of biological aging in the UK biobank cohort.
In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39-82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.
Parental status and markers of brain and cellular age: A 3D convolutional network and classification study.
Recent research shows prominent effects of pregnancy and the parenthood transition on structural brain characteristics in humans. Here, we present a comprehensive study of how parental status and number of children born/fathered links to markers of brain and cellular ageing in 36,323 UK Biobank participants (age range 44.57-82.06 years; 52% female). To assess global effects of parenting on the brain, we trained a 3D convolutional neural network on T1-weighted magnetic resonance images, and estimated brain age in a held-out test set. To investigate regional specificity, we extracted cortical and subcortical volumes using FreeSurfer, and ran hierarchical clustering to group regional volumes based on covariance. Leukocyte telomere length (LTL) derived from DNA was used as a marker of cellular ageing. We employed linear regression models to assess relationships between number of children, brain age, regional brain volumes, and LTL, and included interaction terms to probe sex differences in associations. Lastly, we used the brain measures and LTL as features in binary classification models, to determine if markers of brain and cellular ageing could predict parental status. The results showed associations between a greater number of children born/fathered and younger brain age in both females and males, with stronger effects observed in females. Volume-based analyses showed maternal effects in striatal and limbic regions, which were not evident in fathers. We found no evidence for associations between number of children and LTL. Classification of parental status showed an Area under the ROC Curve (AUC) of 0.57 for the brain age model, while the models using regional brain volumes and LTL as predictors showed AUCs of 0.52. Our findings align with previous population-based studies of middle- and older-aged parents, revealing subtle but significant associations between parental experience and neuroimaging-based surrogate markers of brain health. The findings further corroborate results from longitudinal cohort studies following parents across pregnancy and postpartum, potentially indicating that the parenthood transition is associated with long-term influences on brain health.
An evolutionary timeline of the oxytocin signaling pathway.
Oxytocin is a neuropeptide associated with both psychological and somatic processes like parturition and social bonding. Although oxytocin homologs have been identified in many species, the evolutionary timeline of the entire oxytocin signaling gene pathway has yet to be described. Using protein sequence similarity searches, microsynteny, and phylostratigraphy, we assigned the genes supporting the oxytocin pathway to different phylostrata based on when we found they likely arose in evolution. We show that the majority (64%) of genes in the pathway are 'modern'. Most of the modern genes evolved around the emergence of vertebrates or jawed vertebrates (540 - 530 million years ago, 'mya'), including OXTR, OXT and CD38. Of those, 45% were under positive selection at some point during vertebrate evolution. We also found that 18% of the genes in the oxytocin pathway are 'ancient', meaning their emergence dates back to cellular organisms and opisthokonta (3500-1100 mya). The remaining genes (18%) that evolved after ancient and before modern genes were classified as 'medium-aged'. Functional analyses revealed that, in humans, medium-aged oxytocin pathway genes are highly expressed in contractile organs, while modern genes in the oxytocin pathway are primarily expressed in the brain and muscle tissue.
Recommendations for a Better Understanding of Sex and Gender in the Neuroscience of Mental Health.
There are prominent sex/gender differences in the prevalence, expression, and life span course of mental health and neurodiverse conditions. However, the underlying sex- and gender-related mechanisms and their interactions are still not fully understood. This lack of knowledge has harmful consequences for those with mental health problems. Therefore, we set up a cocreation session in a 1-week workshop with a multidisciplinary team of 25 researchers, clinicians, and policy makers to identify the main barriers in sex and gender research in the neuroscience of mental health. Based on this work, here we provide recommendations for methodologies, translational research, and stakeholder involvement. These include guidelines for recording, reporting, analysis beyond binary groups, and open science. Improved understanding of sex- and gender-related mechanisms in neuroscience may benefit public health because this is an important step toward precision medicine and may function as an archetype for studying diversity.
Brain asymmetries from mid- to late life and hemispheric brain age.
The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.