scholarly journals Age 20 Cognitive Ability Moderates the Long-Term Influence of Lifestyle Behaviors on Brain Aging in Late Midlife

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 116-117
Author(s):  
Carol Franz ◽  
Teresa Warren ◽  
William Kremen
Keyword(s):  
Cognitive Ability ◽  
Social Engagement ◽  
Cognitive Reserve ◽  
Brain Aging ◽  
Apoe Genotype ◽  
Age Difference ◽  
Lifestyle Behaviors ◽  
Brain Health ◽  
Positive Behaviors ◽  
Brain Age

Abstract We examined whether the longitudinal association between lifestyle behaviors and brain age is moderated by early general cognitive ability (GCA). The sample comprises 356 participants from the Vietnam Era Twin Study of Aging (VETSA). At mean age 40 (SD 2.7; range 34-44) a positive lifestyle index was created comprising three self-reported behaviors: not smoking, zero to moderate alcohol consumption, and high social engagement. GCA at mean age 20 was assessed with the Armed Forces Qualification Test. At mean age 68 (SD 2.6; range 61-72), participants underwent structural magnetic resonance imaging which was used to create predicted brain age difference (PBAD) scores. Multivariate models included GCA, lifestyle and their interaction as IVs, adjusted for age, ethnicity, APOE genotype, height, and family membership. Age 20 GCA and age 40 lifestyle significantly predicted age 68 PBAD [F=5.83; p=.02 and F=15.14; p<.001, respectively]. Both positive behaviors and higher age 20 GCA were associated with less brain aging. The GCA-lifestyle interaction was also significant. Those with both lower age 20 GCA and fewer positive behaviors had older brains relative to chronological age [F=5.00; p=. 03]. When GCA was high, however, participants had younger brains, regardless of lifestyle behaviors, suggesting a protective effect of early high GCA or cognitive reserve on later brain health. However, for those with lower cognitive reserve, positive lifestyle behaviors appeared to be protective against brain aging nearly three decades later. Results highlight the important role of cognitive reserve and lifestyle factors for later life brain health.

scholarly journals Associations between Early Midlife Lifestyle Behaviors, Young Adult Cognitive Reserve and Advanced Predicted Brain Age in Late Midlife

2020 ◽  
Author(s):  
Carol E. Franz ◽  
Sean N. Hatton ◽  
Jeremy A. Elman ◽  
Teresa Warren ◽  
Nathan A. Gillespie ◽  
...  
Keyword(s):  
Young Adult ◽  
Alcohol Consumption ◽  
Risk Reduction ◽  
Social Engagement ◽  
Cognitive Reserve ◽  
Brain Aging ◽  
Lifestyle Behaviors ◽  
Dementia Risk ◽  
Brain Age ◽  
Late Midlife

ABSTRACTImportanceBoth cognitive reserve and modifiable lifestyle behaviors are associated with dementia risk. The effect of early lifestyle behaviors and cognitive reserve on late midlife brain aging could inform early identification and risk reduction of future dementia.ObjectiveDetermine associations of young adult cognitive reserve, early midlife lifestyle behaviors, and the reserve-by-lifestyle interaction on late midlife brain age. Examine the relationship between mild cognitive impairment (MCI) and brain age.DesignParticipants were from the nationally representative Vietnam Era Twin Study of Aging (VETSA). Cognitive reserve was assessed at median age 20 years (IQR=1.38) with the Armed Forces Qualification Test (AFQT). Lifestyle behaviors (smoking, alcohol consumption, and social engagement) were assessed at median age 41 (IQR=5.00). Structural brain imaging conducted at median age 69 (IQR=4.74) was used to construct predicted brain age difference scores (PBAD=chronological age minus predicted brain age) and MCI was ascertained.SettingIn-person cognitive testing (ages 20 and 69); mailed survey (age 41); structural MRI, MCI diagnosis at University of California, San Diego (age 69).Participants431 community-dwelling men.ExposuresAFQT; self-reported health and lifestyle behaviors.Main outcomes and measuresPBAD scores; MCI.ResultsIn fully adjusted mixed linear models, age 20 cognitive reserve and the age 41 lifestyle composite were associated with age 69 PBAD [t (104)=2.62, p=0.01, 95%CI 0.874, 6.285; t (104)=3.37, p=0.001, 95%CI 0.583, 2.249 respectively] as was the reserve-by-lifestyle interaction [t (104) = −2.29, p=0.02, 95%CI −2.330, −0.167]. Unfavorable lifestyle predicted more advanced brain age, but only for those with lower young adult cognitive reserve. The MCI group had more advanced brain age (F (2,130) = 3.13; p=0.05).Conclusions and relevanceFavorable lifestyle behaviors promoted resistance to accelerated brain aging 3 decades later for those with lower young adult cognitive reserve. High reserve appeared to be protective regardless of lifestyle. The association with MCI suggests that advanced PBAD scores reflect poorer brain integrity, although it is unclear if PBAD is related to Alzheimer’s dementia specifically. Lower cognitive reserve increases risk for dementia, but early lifestyle modification may promote healthier brain aging and dementia risk reduction, particularly in those with lower reserve.Study TypeCohort StudyKey PointsQuestionDo modifiable lifestyle behaviors in early midlife predict later accelerated brain aging and is that association moderated by cognitive reserve?FindingsA lifestyle composite of smoking, alcohol consumption and social engagement at age 41 was associated with estimated brain age in late midlife. There was a significant moderation effect whereby more unfavorable lifestyle behaviors predicted more advanced brain aging, but only in those with low-to-moderate cognitive reserve.MeaningFavorable lifestyle behaviors appear to be protective for brain integrity especially among those with lower cognitive reserve. Early midlife efforts at prevention could be prioritized among those with lower cognitive reserve.

scholarly journals Young adult cognitive ability moderates effects of lifestyle behaviors on brain aging in late midlife: A longitudinal study of cognitive reserve

2020 ◽  
Vol 16 (S10) ◽  
Author(s):  
Carol E. Franz ◽  
Sean N. Hatton ◽  
Michael J. Lyons ◽  
Olivia K. Puckett ◽  
Nathan Whitsell ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Young Adult ◽  
Cognitive Ability ◽  
Cognitive Reserve ◽  
Brain Aging ◽  
Lifestyle Behaviors ◽  
Late Midlife

scholarly journals Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

2019 ◽  
Author(s):  
Stephen M. Smith ◽  
Lloyd T. Elliott ◽  
Fidel Alfaro-Almagro ◽  
Paul McCarthy ◽  
Thomas E. Nichols ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Brain Aging ◽  
Functional Change ◽  
Genetic Associations ◽  
Imaging Data ◽  
Brain Health ◽  
Health And Disease ◽  
Brain Imaging Data ◽  
Structural Brain Change ◽  
Brain Age

AbstractBrain imaging can be used to study how individuals’ brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single “brain age” is estimated per subject, whereas here we we identified 62 modes of subject variability, from 21,407 subjects’ multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

scholarly journals Longitudinal trajectories of brain age in young individuals at familial risk of mood disorder from the Scottish Bipolar Family Study

2020 ◽  
Vol 4 ◽  
pp. 206
Author(s):  
Laura de Nooij ◽  
Mathew A. Harris ◽  
Emma L. Hawkins ◽  
Toni-Kim Clarke ◽  
Xueyi Shen ◽  
...  
Keyword(s):  
Mood Disorder ◽  
Brain Structure ◽  
Family Study ◽  
Brain Aging ◽  
Familial Risk ◽  
Age Difference ◽  
Age Related ◽  
Prospective Longitudinal ◽  
Brain Age

Background: Within young individuals, mood disorder onset may be related to changes in trajectory of brain structure development. To date, however, longitudinal prospective studies remain scarce and show partly contradictory findings, with a lack of emphasis on changes at the level of global brain patterns. Cross-sectional adult studies have applied such methods and show that mood disorders are associated with accelerated brain aging. Currently, it remains unclear whether young individuals show differential brain structure aging trajectories associated with onset of mood disorder and/or presence of familial risk. Methods: Participants included young individuals (15-30 years, 53%F) from the prospective longitudinal Scottish Bipolar Family Study with and without close family history of mood disorder. All were well at time of recruitment. Implementing a structural MRI-based brain age prediction model, we globally assessed individual trajectories of age-related structural change using the difference between predicted brain age and chronological age (brain-predicted age difference (brain-PAD)) at baseline and at 2-year follow-up. Based on follow-up clinical assessment, individuals were categorised into three groups: (i) controls who remained well (C-well, n = 93), (ii) high familial risk who remained well (HR-well, n = 74) and (iii) high familial risk who developed a mood disorder (HR-MD, n = 35). Results: At baseline, brain-PAD was comparable between groups. Results showed statistically significant negative trajectories of brain-PAD between baseline and follow-up for HR-MD versus C-well (β = -0.60, pcorrected < 0.001) and HR-well (β = -0.36, pcorrected = 0.02), with a potential intermediate trajectory for HR-well (β = -0.24 years, pcorrected = 0.06).   Conclusions: These preliminary findings suggest that within young individuals, onset of mood disorder and familial risk may be associated with a deceleration in brain structure aging trajectories. Extended longitudinal research will need to corroborate findings of emerging maturational lags in relation to mood disorder risk and onset.

scholarly journals Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

2020 ◽  
Vol 30 (11) ◽  
pp. 5844-5862
Author(s):  
Chen-Yuan Kuo ◽  
Pei-Lin Lee ◽  
Sheng-Che Hung ◽  
Li-Kuo Liu ◽  
Wei-Ju Lee ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Aging ◽  
Neurological Diseases ◽  
Brain Network ◽  
Machine Learning Algorithms ◽  
Model Complexity ◽  
Brain Health ◽  
Structural Covariance ◽  
Neuropsychiatric Diseases ◽  
Brain Age

Abstract The aging process is accompanied by changes in the brain’s cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework’s ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer’s disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

scholarly journals HEAVY ALCOHOL CONSUMPTION IN MIDLIFE IS ASSOCIATED WITH ACCELERATED BRAIN AGING SIX YEARS LATER

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S911-S911
Author(s):  
Riki E Slayday ◽  
Carol E Franz ◽  
Sean N Hatton ◽  
Linda K McEvoy ◽  
Michael J Lyons ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Brain Aging ◽  
Age Difference ◽  
Protective Effects ◽  
Heavy Alcohol ◽  
Heavy Drinkers ◽  
Heavy Alcohol Consumption ◽  
Excessive Alcohol ◽  
Brain Age ◽  
Late Midlife

Abstract Excessive alcohol consumption is associated with cognitive decline, exacerbated brain atrophy, and dementia in older adults, but associations with midlife brain health are less well understood. We hypothesized that heavy drinkers would have older-looking brains in late midlife. We examined alcohol consumption at mean age 56 (range 51-59) in 364 men from the Vietnam Era Twin Study of Aging (VETSA) and their predicted brain age at mean age 62 (range 56-67). We created five midlife alcohol consumption groups based on drinks consumed over the past two weeks: never, former, light (1-14), moderate (15-28), and heavy (&gt;28). Participants underwent structural magnetic resonance imaging at mean age 62. Predicted brain age was measured using the Brain-Age Regression Analysis and Computation Utility software (BARACUS). Models adjusted for age, scanner, race/ethnicity, SES, smoking, health, depressive symptoms, alcohol dependence, general cognitive ability at age 20, and non-independence of twins within pairs. Heavy drinkers had a significantly older predicted than chronological brain age (M= 5.93, SE= 0.88) compared to each of the other four groups (p’s &lt; 0.05). There were no significant differences among the never (M= 2.88, SE= 0.98), former (M= 2.76, SE= 0.74), light (M= 3.00, SE= 0.94), or moderate (M= 5.93, SE= 0.88) consumption groups. Heavy alcohol consumption at age 56 was associated with an approximately 3-year greater predicted brain age difference at age 62. There was no evidence of protective effects of light/moderate drinking over non-drinking. The neurotoxic effects of excessive alcohol may exacerbate brain aging in late midlife.

scholarly journals Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Stephen M Smith ◽  
Lloyd T Elliott ◽  
Fidel Alfaro-Almagro ◽  
Paul McCarthy ◽  
Thomas E Nichols ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Brain Aging ◽  
Functional Change ◽  
Genetic Associations ◽  
Imaging Data ◽  
Brain Health ◽  
Health And Disease ◽  
Brain Imaging Data ◽  
Structural Brain Change ◽  
Brain Age

Brain imaging can be used to study how individuals’ brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single ‘brain age’ is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects’ multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

scholarly journals Brain age as a surrogate marker for information processing speed in multiple sclerosis

2021 ◽  
Author(s):  
S Denissen ◽  
DA Engemann ◽  
A De Cock ◽  
L Costers ◽  
J Baijot ◽  
...  
Keyword(s):  
Information Processing ◽  
Processing Speed ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Surrogate Marker ◽  
Chronological Age ◽  
Age Difference ◽  
Information Processing Speed ◽  
Brain Health ◽  
Brain Age

BackgroundData from neuro-imaging techniques allow us to estimate a brain’s age. Brain age is easily interpretable as “how old the brain looks”, and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility.ObjectivesTo investigate the relationship between brain age and information processing speed in MS.MethodsA ridge-regression model was trained to predict age from brain MRI volumetric features and sex in a healthy control dataset (HC_train, n=1690). This model was used to predict brain age in two test sets: HC_test (n=50) and MS_test (n=201). Brain-Predicted Age Difference (BPAD) was calculated as BPAD=brain age minus chronological age. Information processing speed was assessed with the Symbol Digit Modalities Test (SDMT).ResultsBrain age was significantly related to SDMT scores in the MS_test dataset (r=-0.44, p<.001), and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r=-0.21, p=0.003) and a significant weight (−0.21, p=0.011) in a multivariate regression equation with age.ConclusionsBrain age is a candidate biomarker for information processing speed in MS and an easy to grasp metric for brain health.

scholarly journals Increased brain-predicted aging in treated HIV disease

Neurology ◽  
2017 ◽  
Vol 88 (14) ◽  
pp. 1349-1357 ◽  
Author(s):  
James H. Cole ◽  
Jonathan Underwood ◽  
Matthan W.A. Caan ◽  
Davide De Francesco ◽  
Rosan A. van Zoest ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Cognitive Performance ◽  
Brain Aging ◽  
Chronological Age ◽  
Hiv Positive ◽  
Age Difference ◽  
Hiv Disease ◽  
Hiv Positive Adults ◽  
Brain Age ◽  
Hiv Negative

Objective:To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters.Methods:A large sample of virologically suppressed HIV-positive adults (n = 162, age 45–82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18–90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age − chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out.Results:HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (−0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures.Conclusion:Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging.

scholarly journals Differential Associations of White Matter Brain Age With Language-Related Mechanisms in Word-Finding Ability Across the Adult Lifespan

2021 ◽  
Vol 13 ◽  
Author(s):  
Pin-Yu Chen ◽  
Chang-Le Chen ◽  
Hui-Ming Tseng ◽  
Yung-Chin Hsu ◽  
Chi-Wen Christina Huang ◽  
...  
Keyword(s):  
White Matter ◽  
Cognitive Aging ◽  
Brain Aging ◽  
Related Word ◽  
Word Retrieval ◽  
Age Difference ◽  
Chronological Aging ◽  
Domain Specific ◽  
Word Finding ◽  
Brain Age

Research on cognitive aging has established that word-finding ability declines progressively in late adulthood, whereas semantic mechanism in the language system is relatively stable. The aim of the present study was to investigate the associations of word-finding ability and language-related components with brain aging status, which was quantified by using the brain age paradigm. A total of 616 healthy participants aged 18–88 years from the Cambridge Centre for Ageing and Neuroscience databank were recruited. The picture-naming task was used to test the participants’ language-related word retrieval ability through word-finding and word-generation processes. The naming response time (RT) and accuracy were measured under a baseline condition and two priming conditions, namely phonological and semantic priming. To estimate brain age, we established a brain age prediction model based on white matter (WM) features and estimated the modality-specific predicted age difference (PAD). Mass partial correlation analyses were performed to test the associations of WM-PAD with the cognitive performance measures under the baseline and two priming conditions. We observed that the domain-specific language WM-PAD and domain-general WM-PAD were significantly correlated with general word-finding ability. The phonological mechanism, not the semantic mechanism, in word-finding ability was significantly correlated with the domain-specific WM-PAD. In contrast, all behavioral measures of the conditions in the picture priming task were significantly associated with chronological age. The results suggest that chronological aging and WM aging have differential effects on language-related word retrieval functions, and support that cognitive alterations in word-finding functions involve not only the domain-specific processing within the frontotemporal language network but also the domain-general processing of executive functions in the fronto-parieto-occipital (or multi-demand) network. The findings further indicate that the phonological aspect of word retrieval ability declines as cerebral WM ages, whereas the semantic aspect is relatively resilient or unrelated to WM aging.

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