scholarly journals Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth-cohort

2019 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Daniel W. Belsky ◽  
Annchen R. Knodt ◽  
David Ireland ◽  
Tracy R. Melzer ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Birth Cohort ◽  
Brain Aging ◽  
Chronological Age ◽  
Biological Aging ◽  
Imaging Data ◽  
Cohort Differences ◽  
Age Related ◽  
Brain Age ◽  
The Brain

AbstractAn individual’s brain-age is the difference between chronological age and age predicted from machine-learning models of brain-imaging data. Brain-age has been proposed as a biomarker of age-related deterioration of the brain. Having an older brain-age has been linked to Alzheimer’s, dementia and mortality. However, these findings are largely based on cross-sectional associations which can confuse age differences with cohort differences. To illuminate the validity of brain-age a biomarker of accelerated brain aging, a study is needed of a large cohort all born the same year who nevertheless vary on brain-age. In a population-representative 1972-73 birth cohort we measured brain-age at age 45, as well as the pace of biological aging and cognitive decline in longitudinal data from childhood to midlife (N=869). In this cohort, all chronological age 45 years, brain-age was measured reliably (ICC=.81) and ranged from 24 to 72 years. Those with older midlife brain-ages tended to have poorer cognitive function in both adulthood and childhood, as well as impaired brain health at age 3. Furthermore, those with older brain-ages had an accelerated pace of biological aging, older facial appearance and early signs of cognitive decline from childhood to midlife. These findings help to validate brain-age as a potential surrogate biomarker for midlife intervention studies that seek to measure treatment response to dementia-prevention efforts in midlife. However, the findings also caution against the assumption that brain-age scores represent only age-related deterioration of the brain as they may also index central nervous system variation present since childhood.

scholarly journals Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort

2019 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Daniel W. Belsky ◽  
Annchen R. Knodt ◽  
David Ireland ◽  
Tracy R. Melzer ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Birth Cohort ◽  
Brain Aging ◽  
Chronological Age ◽  
Biological Aging ◽  
Imaging Data ◽  
Cross Sectional ◽  
Cohort Differences ◽  
Age Related ◽  
The Brain

AbstractAn individual’s brainAGE is the difference between chronological age and age predicted from machine-learning models of brain-imaging data. BrainAGE has been proposed as a biomarker of age-related deterioration of the brain. Having an older brainAGE has been linked to Alzheimer’s, dementia, and mortality. However, these findings are largely based on cross-sectional associations which can confuse age differences with cohort differences. To illuminate the validity of brainAGE as a biomarker of accelerated brain aging, a study is needed of a large cohort all born in the same year who nevertheless vary on brainAGE. In the Dunedin Study, a population-representative 1972–73 birth cohort, we measured brainAGE at age 45 years, as well as the pace of biological aging and cognitive decline in longitudinal data from childhood to midlife (N = 869). In this cohort, all chronological age 45 years, brainAGE was measured reliably (ICC = 0.81) and ranged from 24 to 72 years. Those with older midlife brainAGEs tended to have poorer cognitive function in both adulthood and childhood, as well as impaired brain health at age 3. Furthermore, those with older brainAGEs had an accelerated pace of biological aging, older facial appearance, and early signs of cognitive decline from childhood to midlife. These findings help to validate brainAGE as a potential surrogate biomarker for midlife intervention studies that seek to measure dementia-prevention efforts in midlife. However, the findings also caution against the assumption that brainAGE scores represent only age-related deterioration of the brain as they may also index central nervous system variation present since childhood.

Aging

2019 ◽  
pp. 105-112
Author(s):  
Risto Näätänen ◽  
Teija Kujala ◽  
Gregory Light
Keyword(s):  
Hearing Loss ◽  
Speech Perception ◽  
Cognitive Decline ◽  
Temporal Processing ◽  
Brain Aging ◽  
Brain Plasticity ◽  
Part Of Speech ◽  
Memory Traces ◽  
Age Related ◽  
The Brain

This chapter shows that MMN and its magnetoencephalographic (MEG) equivalent MMNm are sensitive indices of aging-related perceptual and cognitive decline. Importantly, the age-related neural changes are associated with a decrease of general brain plasticity, i.e. that of the ability of the brain to form and maintain sensory-memory traces, a necessary basis for veridical perception and appropriate cognitive brain function. MMN/MMNm to change in stimulus duration is particularly affected by aging, suggesting the increased vulnerability of temporal processing to brain aging and accounting, for instance, for a large part of speech-perception difficulties of the aged beyond the age-related peripheral hearing loss.

scholarly journals Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

2021 ◽  
Author(s):  
Sivaniya Subramaniapillai ◽  
Sana Suri ◽  
Claudia Barth ◽  
Ivan Maximov ◽  
Irene Voldsbekk ◽  
...  
Keyword(s):  
White Matter ◽  
Body Fat ◽  
Cardiometabolic Risk ◽  
Brain Aging ◽  
Chronological Age ◽  
Uk Biobank ◽  
Imaging Data ◽  
Increased Risk ◽  
Males And Females ◽  
Brain Age

Cardiometabolic risk factors (CMRs) are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer’s Disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ε4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied i) between males and females, ii) according to age at menopause in females, and iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males com- pared to females. Higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81yrs), where higher BF% was linked to lower BAG (younger brain age relative to chronological age). Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. APOE4 status was not significantly associated with BAG, and no significant interactions with CMRs were found. In conclusion, the findings point to sex- and age-specific associations between body fat composition and brain age. Incorporating sex as a factor of interest in studies addressing cardiometabolic risk may promote sex-specific precision medicine, consequently improving health care for both males and females.

scholarly journals No association between age-related hearing loss and accelerated brain aging derived from structural neuroimaging data

2020 ◽  
Author(s):  
Stephanie Rosemann ◽  
Christiane Thiel
Keyword(s):  
Hearing Loss ◽  
Brain Aging ◽  
Normal Hearing ◽  
Chronological Age ◽  
Listening Effort ◽  
Structural Neuroimaging ◽  
Age Related ◽  
Neuroimaging Data ◽  
Age Related Hearing Loss ◽  
Brain Age

Aging affects the brain’s underlying biophysical structure as well as its’ cellular and molecular functioning. Brain aging varies largely across individuals and is accelerated in a variety of disease states. Age-related hearing loss affects a large part of the older population and has been shown to impact cognition, brain structure and function. The main aim of this study was to investigate whether age-related hearing loss accelerates brain aging and is related to a decrease in cognitive function and increase in daily listening effort. We used structural neuroimaging data from a large sample of elderly subjects (n=163) with mild to moderate uncompensated age-related hearing loss or normal hearing. An established machine learning approach was applied to predict brain age from grey and white matter maps. Predicted brain age and chronological age significantly correlated across all participants. However, the difference between the predicted brain age and chronological age was neither significantly different between hard of hearing and normal-hearing participants, nor was this age difference significantly associated with general cognitive status or daily life listening effort. We conclude that uncompensated mild to moderate age-related hearing loss has negligible effects on brain age derived from structural neuroimaging data.

scholarly journals Predicting Brain Age Using Structural Neuroimaging and Deep Learning

2018 ◽  
Author(s):  
Yogatheesan Varatharajah ◽  
Sujeeth Baradwaj ◽  
Atilla Kiraly ◽  
Diego Ardila ◽  
Ravishankar Iyer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Healthy Subjects ◽  
Domain Knowledge ◽  
Brain Aging ◽  
Biological Aging ◽  
Elderly Subjects ◽  
Cognitively Impaired ◽  
Mean Squared Errors ◽  
Age Related ◽  
Brain Age

Early detection of age-related diseases will greatly benefit from a model of the underlying biological aging process. In this paper, we develop a brain-age predictor by using structural magnetic resonance imaging (SMRI) and deep learning and evaluate the predicted brain age as a marker of brain-aging in Alzheimer's disease. Our approach does not require any domain knowledge in that it trains end-to-end on the SMRI image itself, and has been validated on real SMRI data collected from elderly subjects. We developed two different models by using convolutional neural network (CNN) based regression and bucket classification to predict brain ages from SMRI images. Our models achieved root mean squared errors (RMSE) of 5.54 and 6.44 years in predicting brain ages of healthy subjects. Further analysis showed that there is a substantial difference between the predicted brain ages of cognitively impaired and healthy subjects with similar chronological ages.

scholarly journals Aging-associated deficit in CCR7 is linked to worsened glymphatic function, cognition, neuroinflammation, and β-amyloid pathology

2021 ◽  
Vol 7 (21) ◽  
pp. eabe4601
Author(s):  
Sandro Da Mesquita ◽  
Jasmin Herz ◽  
Morgan Wall ◽  
Taitea Dykstra ◽  
Kalil Alves de Lima ◽  
...  
Keyword(s):  
T Cells ◽  
Cognitive Decline ◽  
Brain Aging ◽  
Therapeutic Potential ◽  
T Cell Response ◽  
Age Related ◽  
Lymphatic Vasculature ◽  
Amyloid Aβ ◽  
Β Amyloid ◽  
Old Mice

Aging leads to a progressive deterioration of meningeal lymphatics and peripheral immunity, which may accelerate cognitive decline. We hypothesized that an age-related reduction in C-C chemokine receptor type 7 (CCR7)–dependent egress of immune cells through the lymphatic vasculature mediates some aspects of brain aging and potentially exacerbates cognitive decline and Alzheimer’s disease–like brain β-amyloid (Aβ) pathology. We report a reduction in CCR7 expression by meningeal T cells in old mice that is linked to increased effector and regulatory T cells. Hematopoietic CCR7 deficiency mimicked the aging-associated changes in meningeal T cells and led to reduced glymphatic influx and cognitive impairment. Deletion of CCR7 in 5xFAD transgenic mice resulted in deleterious neurovascular and microglial activation, along with increased Aβ deposition in the brain. Treating old mice with anti-CD25 antibodies alleviated the exacerbated meningeal regulatory T cell response and improved cognitive function, highlighting the therapeutic potential of modulating meningeal immunity to fine-tune brain function in aging and in neurodegenerative diseases.

scholarly journals Impairments in Brain Bioenergetics in Aging and Tau Pathology: A Chicken and Egg Situation?

Cells ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2531
Author(s):  
Amandine Grimm
Keyword(s):  
Brain Metabolism ◽  
Brain Aging ◽  
Cognitive Impairments ◽  
Tau Phosphorylation ◽  
The Body ◽  
Tau Pathology ◽  
Age Related ◽  
Energy Consuming ◽  
Effects Of Aging ◽  
The Brain

The brain is the most energy-consuming organ of the body and impairments in brain energy metabolism will affect neuronal functionality and viability. Brain aging is marked by defects in energetic metabolism. Abnormal tau protein is a hallmark of tauopathies, including Alzheimer’s disease (AD). Pathological tau was shown to induce bioenergetic impairments by affecting mitochondrial function. Although it is now clear that mutations in the tau-coding gene lead to tau pathology, the causes of abnormal tau phosphorylation and aggregation in non-familial tauopathies, such as sporadic AD, remain elusive. Strikingly, both tau pathology and brain hypometabolism correlate with cognitive impairments in AD. The aim of this review is to discuss the link between age-related decrease in brain metabolism and tau pathology. In particular, the following points will be discussed: (i) the common bioenergetic features observed during brain aging and tauopathies; (ii) how age-related bioenergetic defects affect tau pathology; (iii) the influence of lifestyle factors known to modulate brain bioenergetics on tau pathology. The findings compiled here suggest that age-related bioenergetic defects may trigger abnormal tau phosphorylation/aggregation and cognitive impairments after passing a pathological threshold. Understanding the effects of aging on brain metabolism may therefore help to identify disease-modifying strategies against tau-induced neurodegeneration.

scholarly journals COMPARABILITY OF BIOLOGICAL AGING MEASURES IN THE NATIONAL HEALTH AND NUTRITION EXAMINATION STUDY, 1999-2002

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S479-S479
Author(s):  
Waylon J Hastings ◽  
Daniel Belsky ◽  
Idan Shalev
Keyword(s):  
Risk Factors ◽  
Telomere Length ◽  
Cellular Level ◽  
Biological Age ◽  
Effect Sizes ◽  
Chronological Age ◽  
Biological Aging ◽  
Patient Level ◽  
Age Related ◽  
Level Information

Abstract Biological processes of aging are thought to be modifiable causes of many chronic diseases. Measures of biological aging could provide sensitive endpoints for studies of risk factors hypothesized to shorten healthy lifespan and/or interventions that extend it. However, uncertainty remains about how to measure biological aging and if proposed measures assess the same thing. We tested four proposed measures of biological aging with available data from NHANES 1999-2002: Klemera-Doubal method (KDM) Biological Age, homeostatic dysregulation, Levine Method (LM) Biological Age, and leukocyte telomere length. All measures of biological aging were correlated with chronological age. KDM Biological Age, homeostatic dysregulation, and LM Biological Age were all significantly associated with each other, but were each not associated with telomere length. NHANES participants with older biological ages performed worse on tests of physical, cognitive, perceptual, and subjective functions known to decline with advancing chronological age and thought to mediate age-related disability. Further, NHANES participants with higher levels of exposure to life-course risk factors were measured as having older biological ages. In both sets of analyses, effect-sizes tended to be larger for KDM Biological Age, homeostatic dysregulation, and LM Biological Age as compared to telomere length. Composite measures combining cellular- and patient-level information tended to have the largest effect-sizes. The cellular-level aging biomarker telomere length may measure different aspects of the aging process relative to the patient-level physiological measures. Studies aiming to test if risk factors accelerate aging or if interventions may slow aging should not treat proposed measures of biological aging as interchangeable.

scholarly journals Exploring Epigenetic Age in Response to Intensive Relaxing Training: A Pilot Study to Slow Down Biological Age

2019 ◽  
Vol 16 (17) ◽  
pp. 3074 ◽  
Author(s):  
Pavanello ◽  
Campisi ◽  
Tona ◽  
Lin ◽  
Iliceto
Keyword(s):  
Myocardial Infarction ◽  
Age Estimation ◽  
Healthy Subjects ◽  
Molecular Mechanisms ◽  
Biological Age ◽  
Chronological Age ◽  
Biological Aging ◽  
Epigenetic Clock ◽  
Age Related ◽  
Epigenetic Age

DNA methylation (DNAm) is an emerging estimator of biological aging, i.e., the often-defined “epigenetic clock”, with a unique accuracy for chronological age estimation (DNAmAge). In this pilot longitudinal study, we examine the hypothesis that intensive relaxing training of 60 days in patients after myocardial infarction and in healthy subjects may influence leucocyte DNAmAge by turning back the epigenetic clock. Moreover, we compare DNAmAge with another mechanism of biological age, leucocyte telomere length (LTL) and telomerase. DNAmAge is reduced after training in healthy subjects (p = 0.053), but not in patients. LTL is preserved after intervention in healthy subjects, while it continues to decrease in patients (p = 0.051). The conventional negative correlation between LTL and chronological age becomes positive after training in both patients (p < 0.01) and healthy subjects (p < 0.05). In our subjects, DNAmAge is not associated with LTL. Our findings would suggest that intensive relaxing practices influence different aging molecular mechanisms, i.e., DNAmAge and LTL, with a rejuvenating effect. Our study reveals that DNAmAge may represent an accurate tool to measure the effectiveness of lifestyle-based interventions in the prevention of age-related diseases.

scholarly journals Nutrients, Microglia Aging, and Brain Aging

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Zhou Wu ◽  
Janchun Yu ◽  
Aiqin Zhu ◽  
Hiroshi Nakanishi
Keyword(s):  
Oxidative Stress ◽  
Cognitive Decline ◽  
Systemic Inflammation ◽  
Brain Aging ◽  
Oxygen Species ◽  
Cellular Activation ◽  
Proinflammatory Mediators ◽  
The World ◽  
Age Dependent ◽  
The Brain

As the life expectancy continues to increase, the cognitive decline associated with Alzheimer’s disease (AD) becomes a big major issue in the world. After cellular activation upon systemic inflammation, microglia, the resident immune cells in the brain, start to release proinflammatory mediators to trigger neuroinflammation. We have found that chronic systemic inflammatory challenges induce differential age-dependent microglial responses, which are in line with the impairment of learning and memory, even in middle-aged animals. We thus raise the concept of “microglia aging.” This concept is based on the fact that microglia are the key contributor to the acceleration of cognitive decline, which is the major sign of brain aging. On the other hand, inflammation induces oxidative stress and DNA damage, which leads to the overproduction of reactive oxygen species by the numerous types of cells, including macrophages and microglia. Oxidative stress-damaged cells successively produce larger amounts of inflammatory mediators to promote microglia aging. Nutrients are necessary for maintaining general health, including the health of brain. The intake of antioxidant nutrients reduces both systemic inflammation and neuroinflammation and thus reduces cognitive decline during aging. We herein review our microglia aging concept and discuss systemic inflammation and microglia aging. We propose that a nutritional approach to controlling microglia aging will open a new window for healthy brain aging.

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