Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults

Cheng

et al. 2022

Human Brain Mapping

Disrupted brain structures and several life environmental factors have been shown to influence depression and anxiety, but their interactions with anxiety and depression remain elusive. Genome‐wide association study datasets of 15 brain structure longitudinal changes ( N = 15,640) were obtained from the published study. Genotype and phenotype‐related data of depression, anxiety, and life environmental factors (including smoking, alcohol drinking, coffee intake, maternal smoking, physical activity, vitamin D, insomnia, sleep duration, and family satisfaction) were collected from UK Biobank. We calculated the polygenic risk scores (PRS) of 15 brain structure changes and then conducted linear regression analyses to explore the interactions of brain structure changes and life environmental factors on depression and anxiety using 15 brain structure change‐related PRS, life environmental factors and interactions of them as instrumental variables, and depression score or anxiety score as outcomes. Sex stratification in all analyses was performed to reveal sex‐specific differences in the interactions. We found 14 shared interactions related to both depression and anxiety in total sample, such as alcohol drinking × cerebellum white matter 3 (WM; beta = −.003, p = .018 for depression; beta = −003, p = .008 for anxiety) and maternal smoking × nucleus accumbens 2 (beta = .088, p = .002 for depression; beta = .070, p = .008 for anxiety). We also observed sex‐specific differences in the interactions, for instance, alcohol drinking × cerebellum WM 3 was negatively associated with depression and anxiety in males (beta = −.004, p = .020 for depression; beta = −.005, p = .002 for anxiety). Our study results reveal the important interactions between brain structure changes and several life environmental factors on depression and anxiety, which may help to explore the pathogenesis of depression and anxiety.

Section: Discussionmentioning

confidence: 99%

Assessing the interaction effects of brain structure longitudinal changes and life environmental factors on depression and anxiety

Cheng

et al. 2022

Human Brain Mapping

“…Brain aging is associated with a natural ‘wear and tear’, but is highly influenced by environmental factors throughout life. Although linked to genetics (Kaufmann et al, 2019; Cole et al, 2017) and birth weight (Vidal-Pineiro et al, 2021), brain structure and related brain age is associated with a range of proxies for general and cardiometabolic health (Montine et al, 2019; Beck et al, 2022a; Beck et al, 2022b) and life-style choices such as unhealthy diet (Onaolapo et al, 2019), lack of physical exercise (Stillmann et al, 2020; Sanders et al, 2021) and heavy alcohol use (Sullivan & Pfefferbaum, 2019). In line with our current findings, a younger appearing brain is not only associated with lower risk of cognitive deficits, but also increases resilience to consequences after injury, such as a stroke (Montine et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…For model training we used an independent sample of healthy controls (n = 934, age range = 18-88 years, mean = 55.8, SD = 17.4) from the Cambridge centre for Aging and Neuroscience (CamCAN) (Shafto et al, 2014; Taylor et al, 2017) and StrokeMRI (Richard et al, 2018; Sanders et al, 2021). In line with previous work (Kaufmann et al, 2019; Richard et al, 2020; Høgestøl et al, 2019; Beck et al, 2022a; Anatürk et al, 2020), the age-prediction model was trained using XGBoost regression (Chen & Guestrin, 2016) in Python 3 with scikit-learn (Pedregosa et al, 2011) including nested cross-validation for hyperparameter tuning and model evaluation (5 inner and 10 outer folds).…”

Section: Methodsmentioning

confidence: 99%

Longitudinal brain age prediction and cognitive function after stroke

Aamodt

Alnæs

Lange

et al. 2022

Preprint

BackgroundAdvanced age and poor brain health have been associated with higher risk for more severe clinical and cognitive outcomes following stroke, but more accurate models for clinical prediction are needed. Machine learning based on brain scans can be used to estimate brain age of individual patients, and the corresponding difference from chronological age, the brain age gap (BAG) has been investigated in a range of clinical conditions. Yet, the predictive value for post stroke NCD has not been established. To this end, using longitudinal data we aimed to investigate the association between BAG and post-stroke neurocognitive disorder (NCD) over time.Methods269 stroke survivors (55.4% women, mean (SD) age = 71 (11) years) were included from the Norwegian Cognitive Impairment After Stroke (Nor-COAST) study. MRI and clinical data were collected shortly after the acute stroke (neuropsychological data first after 3 months) and at 18- and 36 months follow-up. The brain age model was trained in an independent sample using established protocols based on machine learning and brain structural features from Freesurfer and applied to the current dataset. We used linear mixed effects (LME) models and survival analysis to assess the associations between cognitive status and longitudinal brain age.ResultsLMEs revealed a main effect of BAG on post stroke NCD across time, confirming our main hypothesis that a younger appearing brain may protect against post stroke NCD. For patients with no NCD at baseline, survival analysis suggested that higher baseline BAG was associated with higher risk of post stroke NCD at 18 and 36 months.ConclusionA younger appearing brain is associated with lower risk of post stroke NCD up to 36 months after a stroke, even among those showing no evidence of impairments 3 months after hospital admission. Brain age prediction based on brain scans provides a reliable assessment of brain structure and is sensitive to post stroke NCD with predictive value for cognitive decline among patients with no impairment at the initial assessment.

“…The initial sample consisted of 341 participants from the StrokeMRI project, which aims to investigate lifestyle predictors of brain and cognitive health, including ageing and stroke. Enrolment criteria and recruitment procedures have previously been described (Richard et al, 2018; Sanders et al, 2021). In brief, participants were healthy volunteers aged 18-94 years at enrolment, without history of neurological or psychiatric diseases, or current drug and/or alcohol abuse, and no MRI contraindications (e.g., pacemaker, ferrous implants, pregnancy, claustrophobia).…”

Section: Methodsmentioning

confidence: 99%

“…Participants used an ankle-worn step activity measure (The Modus StepWatchTM3 Activity Monitor) for in median seven consecutive days, ranging from three to nine days. The protocol has been described elsewhere (Sanders et al, 2021). Briefly, the step activity measure recorded the stride count for every one-minute period for the whole test duration, and the recorded numbers were doubled to capture strides from both legs.…”

Section: Methodsmentioning

confidence: 99%

Associations between everyday activities and arterial spin labelling-derived cerebral blood flow: A longitudinal study in community-dwelling elderly volunteers

Sanders

Richard

Kolskår

et al. 2022

Preprint

Self Cite

Background: Cerebral blood flow (CBF) is critical for brain metabolism and overall function. Age-related changes in CBF have been associated with cognitive deficits and increased risk of neurocognitive disorders and vascular events such as stroke. Exercise is considered among the protective factors for age-related brain and cognitive impairment, but how different lifestyle characteristics, such as low or moderate-to-vigorous physical activity or frequency of everyday activities, relate to cross-sectional and longitudinal measures of CBF has not been established. Objective: With the main aim of identifying potential targets for interventions, our objective was to examine associations between cortical and subcortical CBF and frequency of diverse everyday activities in healthy community-dwelling adults aged 65-89 years, and assess to which degree activity level at baseline is associated with longitudinal changes in CBF across a one-to-two years interval. Method: One hundred nineteen (N = 119) adults underwent brain magnetic resonance imaging (MRI), neurocognitive, physical, and activity assessment at baseline. CBF was obtained using pseudo-continuous arterial spin labelling (ASL) MRI. Frequency of everyday activities were measured using Frenchay Activities index, while minutes of low and moderate-to-vigorous intensity physical activity were measured using a StepWatch Activity Monitor. Eighty-six participants completed a follow-up ASL MRI, on average 506 (SD = 113) days after the baseline scan. Results: Bayesian multilevel modelling revealed positive associations between baseline cortical and subcortical CBF and various everyday activities. Higher baseline accumbens, putamen and pallidum CBF was associated with more time spent on low intensity (> 0 steps/minute to < 100 steps/minute) physical activity, higher accumbens and caudate CBF with more moderate to vigorous intensity (≥ 100 steps/minute) physical activity, higher cerebral cortical CBF with more participation in social activity, higher cortical and thalamic CBF with more reading, and higher baseline pallidum CBF with more actively pursuing hobbies. We did not find evidence for an association between baseline activity level and longitudinal changes in CBF. Conclusion: The identified associations between everyday activity measures and CBF provide new knowledge on malleable lifestyle factors that may indicate or contribute to healthy brain aging. In addition to the relevance for prioritizing targets for public health guidelines, our findings contribute to disclose parts of the intricate connection between brain metabolism and everyday activities in aging.