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The association of diet and metabolic system on the ageing brain: a systematic review and meta-analysis
Review question Aim: To systematically review and analyse the literature examining diet and metabolic system influences on the ageing brain. Questions: What variables are used to measure diet and metabolic health? Which brain areas and networks are associated with diet and metabolism? Are the associations of diet and metabolism with brain structure and function persistent across the lifespan? What confounding variables need to be considered when researching diet?
Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women.
The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.
Associations Between Longitudinal Trajectories of Cognitive and Social Activities and Brain Health in Old Age.
IMPORTANCE: Prior neuroimaging studies have found that late-life participation in cognitive (eg, reading) and social (eg, visiting friends and family) leisure activities are associated with magnetic resonance imaging (MRI) markers of the aging brain, but little is known about the neural and cognitive correlates of changes in leisure activities during the life span. OBJECTIVES: To examine trajectories of cognitive and social activities from midlife to late life and evaluate whether these trajectories are associated with brain structure, functional connectivity, and cognition. DESIGN, SETTING, AND PARTICIPANTS: This prospective cohort included participants enrolled in the Whitehall II study and its MRI substudy based in the UK. Participants provided information on their leisure activities at 5 times during calendar years 1997 to 1999, 2002 to 2004, 2006, 2007 to 2009, and 2011 to 2013 and underwent MRI and cognitive battery testing from January 1, 2012, to December 31, 2016. Data analysis was performed from October 7, 2017, to July 15, 2019. MAIN OUTCOME AND MEASURES: Growth curve models and latent class growth analysis were used to identify longitudinal trajectories of cognitive and social activities. Multiple linear regression was used to evaluate associations between activity trajectories and gray matter, white matter microstructure, functional connectivity, and cognition. RESULTS: A total of 574 individuals (468 [81.5%] men; mean [SD] age, 69.9 [4.9] years; median Montreal Cognitive Assessment score, 28 [interquartile range, 26-28]) were included in the present analysis. During a mean (SD) of 15 (4.2) years, cognitive and social activity levels increased during midlife before reaching a plateau in late life. Both baseline (global cognition: unstandardized β [SE], 0.955 [0.285], uncorrected P = .001; executive function: β [SE], 1.831 [0.499], uncorrected P
Associations of depression and regional brain structure across the adult lifespan: Pooled analyses of six population-based and two clinical cohort studies in the European Lifebrain consortium.
OBJECTIVE: Major depressive disorder has been associated with lower prefrontal thickness and hippocampal volume, but it is unknown whether this association also holds for depressive symptoms in the general population. We investigated associations of depressive symptoms and depression status with brain structures across population-based and patient-control cohorts, and explored whether these associations are similar over the lifespan and across sexes. METHODS: We included 3,447 participants aged 18-89 years from six population-based and two clinical patient-control cohorts of the European Lifebrain consortium. Cross-sectional meta-analyses using individual person data were performed for associations of depressive symptoms and depression status with FreeSurfer-derived thickness of bilateral rostral anterior cingulate cortex (rACC) and medial orbitofrontal cortex (mOFC), and hippocampal and total grey matter volume (GMV), separately for population-based and clinical cohorts. RESULTS: Across patient-control cohorts, depressive symptoms and presence of mild-to-severe depression were associated with lower mOFC thickness (rsymptoms = -0.15/ rstatus = -0.22), rACC thickness (rsymptoms = -0.20/ rstatus = -0.25), hippocampal volume (rsymptoms = -0.13/ rstatus = 0.13) and total GMV (rsymptoms = -0.21/ rstatus = -0.25). Effect sizes were slightly larger for presence of moderate-to-severe depression. Associations were similar across age groups and sex. Across population-based cohorts, no associations between depression and brain structures were observed. CONCLUSIONS: Fitting with previous meta-analyses, depressive symptoms and depression status were associated with lower mOFC, rACC thickness, and hippocampal and total grey matter volume in clinical patient-control cohorts, although effect sizes were small. The absence of consistent associations in population-based cohorts with mostly mild depressive symptoms, suggests that significantly lower thickness and volume of the studied brain structures are only detectable in clinical populations with more severe depressive symptoms.
Brain aging differs with cognitive ability regardless of education.
Higher general cognitive ability (GCA) is associated with lower risk of neurodegenerative disorders, but neural mechanisms are unknown. GCA could be associated with more cortical tissue, from young age, i.e. brain reserve, or less cortical atrophy in adulthood, i.e. brain maintenance. Controlling for education, we investigated the relative association of GCA with reserve and maintenance of cortical volume, -area and -thickness through the adult lifespan, using multiple longitudinal cognitively healthy brain imaging cohorts (n = 3327, 7002 MRI scans, baseline age 20-88 years, followed-up for up to 11 years). There were widespread positive relationships between GCA and cortical characteristics (level-level associations). In select regions, higher baseline GCA was associated with less atrophy over time (level-change associations). Relationships remained when controlling for polygenic scores for both GCA and education. Our findings suggest that higher GCA is associated with cortical volumes by both brain reserve and -maintenance mechanisms through the adult lifespan.
Associations between arterial stiffening and brain structure, perfusion, and cognition in the Whitehall II Imaging Sub-study: A retrospective cohort study.
BACKGROUND: Aortic stiffness is closely linked with cardiovascular diseases (CVDs), but recent studies suggest that it is also a risk factor for cognitive decline and dementia. However, the brain changes underlying this risk are unclear. We examined whether aortic stiffening during a 4-year follow-up in mid-to-late life was associated with brain structure and cognition in the Whitehall II Imaging Sub-study. METHODS AND FINDINGS: The Whitehall II Imaging cohort is a randomly selected subset of the ongoing Whitehall II Study, for which participants have received clinical follow-ups for 30 years, across 12 phases. Aortic pulse wave velocity (PWV) was measured in 2007-2009 (Phase 9) and at a 4-year follow-up in 2012-2013 (Phase 11). Between 2012 and 2016 (Imaging Phase), participants received a multimodal 3T brain magnetic resonance imaging (MRI) scan and cognitive tests. Participants were selected if they had no clinical diagnosis of dementia and no gross brain structural abnormalities. Voxel-based analyses were used to assess grey matter (GM) volume, white matter (WM) microstructure (fractional anisotropy (FA) and diffusivity), white matter lesions (WMLs), and cerebral blood flow (CBF). Cognitive outcomes were performance on verbal memory, semantic fluency, working memory, and executive function tests. Of 542 participants, 444 (81.9%) were men. The mean (SD) age was 63.9 (5.2) years at the baseline Phase 9 examination, 68.0 (5.2) at Phase 11, and 69.8 (5.2) at the Imaging Phase. Voxel-based analysis revealed that faster rates of aortic stiffening in mid-to-late life were associated with poor WM microstructure, viz. lower FA, higher mean, and radial diffusivity (RD) in 23.9%, 11.8%, and 22.2% of WM tracts, respectively, including the corpus callosum, corona radiata, superior longitudinal fasciculus, and corticospinal tracts. Similar voxel-wise associations were also observed with follow-up aortic stiffness. Moreover, lower mean global FA was associated with faster rates of aortic stiffening (B = -5.65, 95% CI -9.75, -1.54, Bonferroni-corrected p < 0.0125) and higher follow-up aortic stiffness (B = -1.12, 95% CI -1.95, -0.29, Bonferroni-corrected p < 0.0125). In a subset of 112 participants who received arterial spin labelling scans, faster aortic stiffening was also related to lower cerebral perfusion in 18.4% of GM, with associations surviving Bonferroni corrections in the frontal (B = -10.85, 95% CI -17.91, -3.79, p < 0.0125) and parietal lobes (B = -12.75, 95% CI -21.58, -3.91, p < 0.0125). No associations with GM volume or WMLs were observed. Further, higher baseline aortic stiffness was associated with poor semantic fluency (B = -0.47, 95% CI -0.76 to -0.18, Bonferroni-corrected p < 0.007) and verbal learning outcomes (B = -0.36, 95% CI -0.60 to -0.12, Bonferroni-corrected p < 0.007). As with all observational studies, it was not possible to infer causal associations. The generalisability of the findings may be limited by the gender imbalance, high educational attainment, survival bias, and lack of ethnic and socioeconomic diversity in this cohort. CONCLUSIONS: Our findings indicate that faster rates of aortic stiffening in mid-to-late life were associated with poor brain WM microstructural integrity and reduced cerebral perfusion, likely due to increased transmission of pulsatile energy to the delicate cerebral microvasculature. Strategies to prevent arterial stiffening prior to this point may be required to offer cognitive benefit in older age. TRIAL REGISTRATION: ClinicalTrials.gov NCT03335696.
Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates.
It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.
Mind the gap: Performance metric evaluation in brain-age prediction.
Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R2 ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R2 values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy-also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.
Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging.
The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging-related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no "gold standard" for measuring these constructs. Using machine-learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34-82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.
Associations Between Depression and Brain Structure Across the Lifespan: Preliminary Results From the European Lifebrain Consortium
Background Depression diagnosis is associated with decreased prefrontal thickness and hippocampal volume. Less is known about these associations in the general population and across the lifespan. We investigate associations between (subclinical) depression and brain structure, and moderating effects of age and sex. Methods We included 1870 participants between 18 and 85 years old from 3 sites (UK, Norway, Netherlands) of the European Lifebrain consortium. Meta-analyses were performed, calculating per-site and pooled effect sizes for associations between mild/moderate depression and FreeSurfer-derived rACC, mOFC thickness or hippocampal volume. In ongoing analyses, we include additional cohorts (Spain, Germany, Sweden, UK, Denmark; final N∼3500) and explore the effects of age and sex. Results 374 participants met criteria for mild-to-moderate depression, with 181 meeting criteria for moderate depression. Mild-to-moderate depression (vs. no depression) was not significantly associated with rACC (Cohen’s d=-0.090) or mOFC (d=-0.114) thickness, or hippocampal volume (d=0.018). Similarly, moderate depression was not significantly associated with rACC (d=-0.164) or mOFC thickness (d=-0.088), or hippocampal volume (d=-0.012). Conclusions Mild or moderate depression was not associated with medial prefrontal thickness, or hippocampal volume, although effect-sizes for medial prefrontal areas were similar to previous observations in the ENIGMA consortium. However, associations with hippocampal volume were not in line with earlier ENIGMA findings, potentially due to few participants with moderate depression. Of relevance, earlier studies often had a more limited age range, and associations might change across the lifespan. Therefore, in ongoing analyses we investigate potential moderating effects of age which will be presented at the conference.
Association of cerebral small vessel disease burden with brain structure and cognitive and vascular risk trajectories in mid-to-late life
We characterize the associations of total cerebral small vessel disease (SVD) burden with brain structure, trajectories of vascular risk factors, and cognitive functions in mid-to-late life. Participants were 623 community-dwelling adults from the Whitehall II Imaging Sub-study with multi-modal MRI (mean age 69.96 SD=5.18, 79% men). We used linear mixed-effects models to investigate associations of SVD burden with up to 25-year retrospective trajectories of vascular risk and cognitive performance. General linear modelling was used to investigate concurrent associations with grey matter (GM) density and white matter (WM) microstructure, and whether these associations were modified by cognitive status (Montreal Cognitive Assessment, MoCA). Severe SVD burden in older age was associated with higher mean arterial pressure throughout midlife (β=3.36, 95% CI [0.42- 6.30]), and faster 25-year cognitive decline in letter fluency (β=-0.07, 95% CI [-0.13–-0.01]), and verbal reasoning (β=-0.05, 95% CI [-0.11–-0.001]). Moreover, SVD burden was related to lower GM volumes in 9.7% of total GM, and widespread WM microstructural decline (FWE-corrected p<0.05). The latter association was most pronounced in individuals with cognitive impairments on MoCA (F3,608=2.14, p=0.007). These findings highlight the importance of managing midlife vascular health to preserve brain structure and cognitive function in old age.
The effects of APOE on the functional architecture of the resting brain.
There is a well-established association between APOE genotype and the risk of developing Alzheimer's disease (AD). Relative to individuals with the common ε3/ε3 genotype, carriers of the ε4 allele are at increased risk of developing AD, while carriers of the ε2 allele appear to be protected against the disease. However, we recently reported that in a sample of cognitively healthy adults, both ε4 and ε2 carriers showed nearly identical changes in the pattern of fMRI activity during memory and non-memory tasks, relative to ε3 homozygotes. These findings suggest that the effects of APOE on brain function are not tightly linked to the effects of this gene on AD risk. Here we test the hypothesis that APOE has an intrinsic effect on the brain's functional networks. Resting-state fMRI was used to compare the pattern of functional connectivity of a variety of resting-state networks between 77 cognitively healthy participants aged 32 to 55 with different APOE genotypes (23 ε2/ε3, 20 ε3/ε3, 26 ε3/ε4, and 8 ε4/ε4). Differences between genotype groups were found in two hippocampal networks, the auditory network, the left frontal-parietal network, and the lateral visual network. While there was considerable variety in the brain regions affected and the direction of change across networks, the main finding was that changes in functional connectivity were similar in ε4 and ε2 carriers, relative to ε3 homozygotes. APOE appears to have an intrinsic effect on the differentiation of functional networks in the brain. This effect is apparent in cognitively healthy adults and does not manifest in a manner reflective of the link between APOE and AD risk. Rather, the effects of APOE on brain function may relate to the role of this gene in neurodevelopment.
Magnetic resonance imaging in late-life depression: multimodal examination of network disruption.
CONTEXT: Disruption of frontal-subcortical and limbic networks is hypothesized to have a key role in late-life depression (LLD) and can be examined using magnetic resonance imaging (MRI) techniques. Gray matter can be examined using T1-weighted MRI, white matter using T2-weighted MRI and diffusion tensor imaging, and functional connectivity in resting-state networks using functional MRI. Although independent MRI studies have supported gray and white matter abnormalities in frontosubcortical and limbic networks and increased functional connectivity in the default-mode network in depression, no study has concurrently examined gray matter, white matter, and functional connectivity. OBJECTIVE: To examine whether results of different MRI techniques are complementary, multimodal MRI was used to compare gray matter, white matter, and resting-state networks between LLD and control groups. DESIGN: Cross-sectional, case-control, multimodal MRI analysis. SETTING: University research department. PARTICIPANTS: Thirty-six recovered participants with LLD (mean age, 71.8 years) and 25 control participants (mean age, 71.8 years). MAIN OUTCOME MEASURES: Gray matter was examined across the whole brain using voxel-based morphometry. Subcortical gray matter structures were also automatically segmented, and volumetric and shape analyses were performed. For white matter analysis, fractional anisotropy, axial diffusivity, and radial diffusivity values were examined using tract-based spatial statistics. For resting-state network analysis, correlation coefficients were compared using independent components analysis followed by dual regression. RESULTS: White matter integrity was widely reduced in LLD, without significant group differences in gray matter volumes or functional connectivity. CONCLUSIONS: The present work strongly supports the hypothesis that white matter abnormalities in frontal-subcortical and limbic networks play a key role in LLD even in the absence of changes in resting functional connectivity and gray matter. Factors that could contribute to the lack of significant differences in gray matter and functional connectivity measures, including current symptom severity, medication status, and age of participants with LLD, are discussed.
Neuroticism as a predictor of mood change
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Transcranial magnetic stimulation in the management of mood disorders.
BACKGROUND: Many trials of transcranial magnetic stimulation (TMS) have used small samples and, therefore, lack power. Here we present an up-to-date meta-analysis of TMS in the treatment of depression. METHODS: We searched Medline and Embase from 1996 until 2008 for randomized sham-controlled trials, with patients and investigators blinded to treatment, and outcome measured using a version of the Hamilton Depression Rating Scale (or similar). We identified 1,789 studies. Thirty-one were suitable for inclusion, with a cumulative sample of 815 active and 716 sham TMS courses. RESULTS: We found a moderately sized effect in favour of TMS [Random Effects Model Hedges' g = 0.64, 95% confidence interval (95% CI) = 0.50-0.79]. The corresponding Pooled Peto Odds Ratio for treatment response (≤50% reduction in depression scores) was 4.1 (95% CI = 2.9-5.9). There was significant variability between study effect sizes. Meta-regressions with relevant study variables did not reveal any predictors of treatment efficacy. Nine studies included follow-up data with an average follow-up time of 4.3 weeks; there was no mean change in depression severity between the end of treatment and follow-up (Hedges' g = -0.02, 95% CI = -0.22 to +0.18) and no heterogeneity in outcome. DISCUSSION: TMS appears to be an effective treatment; however, at 4 weeks' follow-up after TMS, there had been no further change in depression severity. Problems with finding a suitably blind and ineffective placebo condition may have confounded the published effect sizes. If the TMS effect is specific, only further large double-blind randomized controlled designs with systematic exploration of treatment and patient parameters will help to define optimum treatment indications and regimen.
Association of trajectories of depressive symptoms with vascular risk, cognitive function and adverse brain outcomes: The Whitehall II MRI sub-study.
BACKGROUND: Trajectories of depressive symptoms over the lifespan vary between people, but it is unclear whether these differences exhibit distinct characteristics in brain structure and function. METHODS: In order to compare indices of white matter microstructure and cognitive characteristics of groups with different trajectories of depressive symptoms, we examined 774 participants of the Whitehall II Imaging Sub-study, who had completed the depressive subscale of the General Health Questionnaire up to nine times over 25 years. Twenty-seven years after the first examination, participants underwent magnetic resonance imaging to characterize white matter hyperintensities (WMH) and microstructure and completed neuropsychological tests to assess cognition. Twenty-nine years after the first examination, participants completed a further cognitive screening test. OUTCOMES: Using K-means cluster modelling, we identified five trajectory groups of depressive symptoms: consistently low scorers ("low"; n = 505, 62·5%), a subgroup with an early peak in depression scores ("early"; n = 123, 15·9%), intermediate scorers ("middle"; n = 89, 11·5%), a late symptom subgroup with an increase in symptoms towards the end of the follow-up period ("late"; n = 29, 3·7%), and consistently high scorers ("high"; n = 28, 3·6%). The late, but not the consistently high scorers, showed higher mean diffusivity, larger volumes of WMH and impaired executive function. In addition, the late subgroup had higher Framingham Stroke Risk scores throughout the follow-up period, indicating a higher load of vascular risk factors. INTERPRETATION: Our findings suggest that tracking depressive symptoms in the community over time may be a useful tool to identify phenotypes that show different etiologies and cognitive and brain outcomes.