scholarly journals Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging

2020 ◽  
Author(s):  
Melis Anatürk ◽  
Tobias Kaufmann ◽  
James H. Cole ◽  
Sana Suri ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
Cognitive Reserve ◽  
Older Age ◽  
Study Cohort ◽  
Cognitive Age ◽  
Novel Method ◽  
Brain Changes ◽  
Brain Maintenance ◽  
Normative Aging ◽  
No Gold Standard ◽  
Brain Age

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, we estimated brain and cognitive maintenance based on deviations from normative aging patterns in the Whitehall II MRI sub-study cohort, 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 cognitive maintenance independent of the degree of brain maintenance. No strong evidence was found for associations between lifestyle trajectories and brain or cognitive maintenance. In conclusion, we present a novel method to characterize 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.

scholarly journals A history of previous childbirths is linked to women's white matter brain age in midlife and older age

2021 ◽  
Author(s):  
Irene Voldsbekk ◽  
Claudia Barth ◽  
Ivan I. Maximov ◽  
Tobias Kaufmann ◽  
Dani Beck ◽  
...  
Keyword(s):  
White Matter ◽  
Older Age ◽  
History Of ◽  
Brain Age

Predictors of Short-Term Mortality after Heart Valve Surgery: A Single-Center Study; Retrospective analysis

2020 ◽  
Author(s):  
Khalid ibrahim ◽  
Khalid Kheirallah ◽  
Fadia Mayyas ◽  
Nizar Alwaqfi ◽  
Murtaha Alawami ◽  
...  
Keyword(s):  
Valve Replacement ◽  
Beta Blockers ◽  
Artery Bypass ◽  
Median Sternotomy ◽  
Valve Surgery ◽  
Older Age ◽  
Study Cohort ◽  
Short Term ◽  
Biological Valve ◽  
Short Term Mortality

Abstract Objective To investigate predictors of short-term mortality after valve surgery at our center. Methods The study cohort included 346 patients who underwent different types of valve surgery, excluding redo and Bentall operations. All operations were performed through a median sternotomy using cardiopulmonary bypass. Results Mean patient age was 51.6 ± 16.1 years, and 51% were male. Approximately 21% had diabetes, and 44.6% were hypertensive. Aortic valve replacement (AVR) was performed in 125 patients (37%), mitral valve replacement (MVR) in 95 (28%), combined AVR and MVR in 42 (13%), AVR plus coronary artery bypass grafting (CABG) in 19 (6%), and MVR plus CABG in 32 (10%). Operative mortality was 5.8% (n = 20). In the bivariate-level analysis, older age, operation type, hypertension, emergency surgery, use of a biological valve in the aortic or mitral position, pump time greater than 120 minutes, and aortic clamp time greater than 60 minutes were significant predictors of 30-day mortality. Use of angiotensin-converting enzyme inhibitors, digoxin, beta-blockers, statins, and loop diuretics was associated with mortality. Older age, emergency/salvage surgery, use of beta-blockers for less than 1 month preoperatively, and use of a biological valve in the aortic position were significant and independent predictors of 30-day mortality. Conclusion Older age, emergency valve surgery, use of a biological valve, and use of beta-blockers for less than 1 month before surgery were all found to be independent predictors of mortality in patients undergoing valve surgery.

Creating Value for Customers 50+ Taking into Account Their Cognitive Age

2017 ◽  
Vol 2017 (1) ◽  
pp. 147-168
Author(s):  
Marina Sheresheva ◽  
Natalia Kalmykova ◽  
Liliya Valitova ◽  
Svetlana Berezka
Keyword(s):  
Consumer Behavior ◽  
Economic Activity ◽  
Older Age ◽  
Physiological Characteristics ◽  
Research Project ◽  
Age Cohorts ◽  
Needs Satisfaction ◽  
Cognitive Age ◽  
Behavior Models

The paper discusses the results of the consumer behavior survey conducted in the research project “Social and economic activity of the mature age people (50+): needs, satisfaction, consumer behavior models, interaction with business and authorities”. The findings confirm that creation of products and services for the older age cohorts must take into account both physiological characteristics of ageing, and psychological (cognitive) perception of age by target consumers.

scholarly journals Neuroimaging in aging: brain maintenance

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1215 ◽  
Author(s):  
Lars Nyberg
Keyword(s):  
Multimodal Imaging ◽  
Longitudinal Design ◽  
Older Age ◽  
Aging Brain ◽  
Brain Pathology ◽  
New Techniques ◽  
Relative Lack ◽  
Brain Maintenance ◽  
Critical Task ◽  
Different Levels

Neuroimaging studies of the aging brain provide support that the strongest predictor of preserved memory and cognition in older age is brain maintenance, or relative lack of brain pathology. Evidence for brain maintenance comes from different levels of examination, but up to now relatively few studies have used a longitudinal design. Examining factors that promote brain maintenance in aging is a critical task for the future and may be combined with the use of new techniques for multimodal imaging.

scholarly journals Cognitive Reserve and Brain Maintenance: Orthogonal Concepts in Theory and Practice

2016 ◽  
Author(s):  
C. Habeck ◽  
Q. Razlighi ◽  
Y. Gazes ◽  
D. Barulli ◽  
J. Steffener ◽  
...  
Keyword(s):  
Cognitive Reserve ◽  
Theory And Practice ◽  
Brain Maintenance

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 The Influence of Covid-19 Vaccine on Daily Cases, Hospitalization, and Death Rate in Tennessee: A Case Study in the United States

2021 ◽  
Author(s):  
Ali Roghani
Keyword(s):  
Death Rate ◽  
Age Groups ◽  
Vaccination Strategy ◽  
The Elderly ◽  
The United States ◽  
Hospitalization Rate ◽  
Older Age ◽  
Study Cohort ◽  
Lower Death Rate

The COVID-19 outbreak highlights the vulnerability to novel infections, and vaccination remains a foreseeable method to return to normal life. However, infrastructure is inadequate for the whole population to be vaccinated immediately. Therefore, policies have adopted a strategy to vaccinate the elderly and vulnerable population while delaying others. This study uses the Tennessee official statistic from the onset of COVID vaccination (17th of December 2021) to understand how age-specific vaccination strategies reduce daily cases, hospitalization, and death rate. The result shows that vaccination strategy can significantly influence the numbers of patients with COVID-19 in all age groups and lower hospitalization and death rates just in older age groups. The Elderly had a 95% lower death rate from December to March; however, and no change in the death rate in other age groups. The Hospitalization rate was reduced by 80% in this study cohort for people aged 80 or older, while people who were between 50 to 70 had almost the same hospitalization rate. The study indicates that vaccination targeting older age groups is the optimal way to avoid higher transmissions and reduce hospitalization and death rate for older groups.

scholarly journals Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression

2019 ◽  
Author(s):  
Marco Palma ◽  
Shahin Tavakoli ◽  
Julia Brettschneider ◽  
Thomas E. Nichols ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Quantile Regression ◽  
Prediction Interval ◽  
Learning Approaches ◽  
Quantile Regression Model ◽  
Brain Changes ◽  
Anatomical Mri ◽  
Brain Age ◽  
Quantifying Uncertainty

AbstractPrediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysis. We propose a penalised functional quantile regression model of age on brain structure with cognitively normal (CN) subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and use it to predict brain age in Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) subjects. Unlike the machine learning approaches available in the literature of brain age prediction, which provide only point predictions, the outcome of our model is a prediction interval for each subject.

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