scholarly journals 1195Brain-predicted age difference is associated with cognitive processing and delayed recall in later life

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Jo Wrigglesworth ◽  
Nurathifah Yaacob ◽  
Phillip Ward ◽  
Robyn Woods ◽  
John McNeil ◽  
...  
Keyword(s):  
Community Dwelling ◽  
Chronological Age ◽  
Accelerated Ageing ◽  
Age Difference ◽  
Psychomotor Speed ◽  
Delayed Recall ◽  
Memory Decline ◽  
Brain Ageing ◽  
Community Dwelling Older Adults ◽  
Brain Age

Abstract Background Brain age is a novel neuroimaging-based marker of ageing that uses machine learning to predict a person’s biological brain age. A higher brain age relative to chronological age (i.e., brain-predicted age difference [brain-PAD]) is considered a sign of accelerated ageing. We examined whether brain-PAD is associated with cognition and the change in cognitive function over time. Methods This study involved 531 cognitively healthy community-dwelling older adults (70+ years). Using a previously trained algorithm, brain age was estimated using T1-weighted structural magnetic resonance images acquired at baseline. Psychomotor speed, delayed recall, verbal fluency and global cognition were assessed at baseline, years 1 and 3. Results At baseline, a significant negative association was observed between brain-PAD and psychomotor speed (r=-0.14, p = 0.001), delayed recall (r=-0.09, p = 0.04), and the three-year change in delayed recall (r=-0.15, p = 0.02), which persisted after adjusting for covariates. Conclusions These findings indicate that accelerated brain ageing in cognitively unimpaired older people is associated with worse psychomotor speed, and delayed recall. This study also provides new evidence that accelerated brain ageing is a risk factor for progressive memory decline. Future research would benefit from further prospective analyses of associations between brain-PAD and cognitive function in community dwelling older adults. Key messages Brain age is a neuroimaging-based marker of biological ageing. A higher estimate of brain age relative to chronological age (i.e., accelerated ageing) is associated with worse psychomotor speed and memory, and memory decline.

scholarly journals Bidirectional Relationship Between Subjective Age and Frailty: Findings From the NHATS

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 271-271
Author(s):  
Yuxiao Li ◽  
Minhui Liu ◽  
Christina Miyawaki ◽  
Xiaocao Sun ◽  
Tianxue Hou ◽  
...  
Keyword(s):  
Older Adults ◽  
Clinical Syndrome ◽  
Community Dwelling ◽  
Chronological Age ◽  
Subjective Age ◽  
Bidirectional Relationship ◽  
Study Participants ◽  
Community Dwelling Older Adults ◽  
The Relationship ◽  
Hispanic White

Abstract Frailty is a clinical syndrome that becomes increasingly common as people age. Subjective age refers to how young or old individuals experience themselves to be. It is associated with many risk factors of frailty, such as increased depression, worse cognitive function, and poorer psychological wellbeing. In this study, we examined the relationship between subjective age and frailty using the 2011-2015 waves of the National Health and Aging Trends Study. Participants were community-dwelling older adults without frailty in the initial wave (N=1,165). Subjective age was measured by asking participants, “What age do you feel most of the time?” Based on the Fried five phenotypic criteria: exhaustion, unintentional weight loss, low physical activity, slow gait, and weak grip strength, frailty was categorized into robust=0, pre-frail=1 or 2; frail=3 or more criteria met. Participants were, on average, 74.1±6.5 years old, female (52%), and non-Hispanic White (81%). Eighty-five percent of the participants felt younger, and 3% felt older than their chronological age, but 41% of them were pre-frail/frail. Generalized estimating equations revealed that an “older” subjective age predicted a higher likelihood of pre-frailty and frailty (OR, 95%CI= 1.01, 1.01-1.02). In contrast, frailty predicted an “older” subjective age (OR, 95%CI= 2.97, 1.65-5.35) adjusting for demographics and health conditions. These findings suggest a bidirectional relationship between subjective age and frailty. Older people who feel younger than their chronological age are at reduced risk of becoming pre-frail/frail. Intervention programs to delay frailty progression should include strategies that may help older adults perceive a younger subjective age.

Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication

2021 ◽  
pp. 1-8
Author(s):  
Yi-Bin Xi ◽  
Xu-Sha Wu ◽  
Long-Biao Cui ◽  
Li-Jun Bai ◽  
Shuo-Qiu Gan ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
First Episode ◽  
Age Difference ◽  
Healthy Controls ◽  
Data Set ◽  
Brain Ageing ◽  
First Episode Schizophrenia ◽  
Brain Age ◽  
The Brain ◽  
Age Prediction

Background Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear. Aims To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age. Method The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed. Results The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014). Conclusions The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

scholarly journals Factors associated with brain ageing - a systematic review

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jo Wrigglesworth ◽  
Phillip Ward ◽  
Ian H. Harding ◽  
Dinuli Nilaweera ◽  
Zimu Wu ◽  
...  
Keyword(s):  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Community Dwelling ◽  
Systematic Search ◽  
Current Evidence ◽  
Chronological Age ◽  
Future Research ◽  
Age Related ◽  
Brain Ageing

Abstract Background Brain age is a biomarker that predicts chronological age using neuroimaging features. Deviations of this predicted age from chronological age is considered a sign of age-related brain changes, or commonly referred to as brain ageing. The aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors and diseases in adult populations, with brain ageing. Methods This systematic review was undertaken in accordance with the PRISMA guidelines. A systematic search of Embase and Medline was conducted to identify relevant articles using search terms relating to the prediction of age from neuroimaging data or brain ageing. The tables of two recent review papers on brain ageing were also examined to identify additional articles. Studies were limited to adult humans (aged 18 years and above), from clinical or general populations. Exposures and study design of all types were also considered eligible. Results A systematic search identified 52 studies, which examined brain ageing in clinical and community dwelling adults (mean age between 21 to 78 years, ~ 37% were female). Most research came from studies of individuals diagnosed with schizophrenia or Alzheimer’s disease, or healthy populations that were assessed cognitively. From these studies, psychiatric and neurologic diseases were most commonly associated with accelerated brain ageing, though not all studies drew the same conclusions. Evidence for all other exposures is nascent, and relatively inconsistent. Heterogenous methodologies, or methods of outcome ascertainment, were partly accountable. Conclusion This systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing. Overall there is good evidence to suggest schizophrenia and Alzheimer’s disease are associated with accelerated brain ageing. Evidence for all other exposures was mixed or limited. This was mostly due to a lack of independent replication, and inconsistency across studies that were primarily cross sectional in nature. Future research efforts should focus on replicating current findings, using prospective datasets. Trial registration A copy of the review protocol can be accessed through PROSPERO, registration number CRD42020142817.

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 ◽  
Familial Risk ◽  
Age Difference ◽  
Age Related ◽  
Brain Ageing ◽  
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 ageing. 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 Prevalence of anxiety disorders in community dwelling older adults in Hong Kong

2016 ◽  
Vol 29 (2) ◽  
pp. 259-267 ◽  
Author(s):  
Ada Wai Tung Fung ◽  
Wai-Chi Chan ◽  
Corine Sau-Man Wong ◽  
Eric Yu-Hai Chen ◽  
Roger Man-Kin Ng ◽  
...  
Keyword(s):  
Older Adults ◽  
Hong Kong ◽  
Anxiety Disorders ◽  
Common Mental Disorders ◽  
Female Gender ◽  
Community Dwelling ◽  
Delayed Recall ◽  
Physical Burden ◽  
Community Dwelling Older Adults ◽  
Physical Functioning Score

ABSTRACTBackground:Anxiety disorders are prevalent yet under-recognized in late life. We examined the prevalence of anxiety disorders in a representative sample of community dwelling older adults in Hong Kong.Method:Data on 1,158 non-demented respondents aged 60–75 years were extracted from the Hong Kong Mental Morbidity survey (HKMMS). Anxiety was assessed with the revised Clinical Interview Schedule (CIS-R).Result:One hundred and thirty-seven respondents (11.9%, 95% CI = 10–13.7%) had common mental disorders with a CIS-R score of 12 or above. 8% (95% CI = 6.5–9.6%) had anxiety, 2.2% (95% CI = 1.3–3%) had an anxiety disorder comorbid with depressive disorder, and 1.7% (95% CI = 1–2.5%) had depression. Anxious individuals were more likely to be females (χ2 = 25.3, p < 0.001), had higher chronic physical burden (t = −9.3, p < 0.001), lower SF-12 physical functioning score (t = 9.2, p < 0.001), and poorer delayed recall (t = 2.3, p = 0.022). The risk of anxiety was higher for females (OR 2.8, 95% C.I. 1.7–4.6, p < 0.001) and those with physical illnesses (OR 1.4, 95% C.I. 1.3–1.6, p < 0.001). The risk of anxiety disorders increased in those with disorders of cardiovascular (OR 1.9, 95% C.I. 1.2–2.9, p = 0.003), musculoskeletal (OR 2.0, 95% C.I. 1.5–2.7, p < 0.001), and genitourinary system (OR 2.0, 95% C.I. 1.3–3.2, p = 0.002).Conclusions:The prevalence of anxiety disorders in Hong Kong older population was 8%. Female gender and those with poor physical health were at a greater risk of developing anxiety disorders. Our findings also suggested potential risk for early sign of memory impairment in cognitively healthy individuals with anxiety disorders.

The APOE ∍4 allele Is Associated with Decline on Delayed Recall Performance in Community-Dwelling Older Adults

1998 ◽  
Vol 46 (12) ◽  
pp. 1493-1498 ◽  
Author(s):  
Ruth O'Hara ◽  
Jerome A. Yesavage ◽  
Helena C. Kraemer ◽  
Maritess Mauricio ◽  
Leah F. Friedman ◽  
...  
Keyword(s):  
Older Adults ◽  
Recall Performance ◽  
Community Dwelling ◽  
Delayed Recall ◽  
Community Dwelling Older Adults

Episodic Memory Decline Predicts Cortical Amyloid Status in Community-Dwelling Older Adults

2011 ◽  
Vol 27 (3) ◽  
pp. 627-637 ◽  
Author(s):  
David G. Darby ◽  
Amy Brodtmann ◽  
Robert H. Pietrzak ◽  
Julia Fredrickson ◽  
Michael Woodward ◽  
...  
Keyword(s):  
Older Adults ◽  
Episodic Memory ◽  
Community Dwelling ◽  
Memory Decline ◽  
Community Dwelling Older Adults

scholarly journals Brain-predicted age difference score is related to specific cognitive functions: A multi-site replication analysis

2019 ◽  
Author(s):  
R. Boyle ◽  
L. Jollans ◽  
L.M. Rueda-Delgado ◽  
R. Rizzo ◽  
G.G. Yener ◽  
...  
Keyword(s):  
Visual Attention ◽  
Cognitive Flexibility ◽  
Cognitive Functions ◽  
Cognitive Status ◽  
Chronological Age ◽  
Accelerated Ageing ◽  
Age Difference ◽  
Difference Scores ◽  
Mri Scans ◽  
The Relationship

AbstractBrain-predicted age difference scores are calculated by subtracting chronological age from ‘brain’ age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1,359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n=175), the Cognitive Reserve/Reference Ability Neural Network study (n=380), and The Irish Longitudinal Study on Ageing (n=487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.

scholarly journals Biological Aging, Mortality, and Alzheimer’s Disease Related Biomarkers from Midlife to Old Age

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 666-666
Author(s):  
Carol Franz ◽  
Erik Buchholz ◽  
Amy Yongmei Qin ◽  
Xin Tu ◽  
William Kremen
Keyword(s):  
Learning Algorithm ◽  
Frailty Index ◽  
Community Dwelling ◽  
Physiological Age ◽  
Biological Aging ◽  
Age Difference ◽  
Machine Learning Algorithm ◽  
Neurofilament Light ◽  
Subcortical Regions ◽  
Brain Age

Abstract People age at different rates and in different biological systems that may differentially contribute to accelerated decline. Better understanding of biological aging may contribute to identification of better targets for intervention. In 1005 VETSA participants we created 3 indicators of biological age: physiological age (PA), frailty, and brain age. PA included hemoglobin, glucose, lipids, height, weight, waist, systolic and diastolic blood pressure, and age. PA was calculated using the Klemera and Doubal (2006) method. The frailty index summed 37 health deficits (Jiang et al. 2017). A machine learning algorithm was used to estimate brain age across cortical and subcortical regions (Liem et al, 2017); predicted brain age subtracted from chronological age comprised the predicted brain age difference score (PBAD). Frailty and PBAD were calculated at waves 1, 2 and 3 when participants were average age 56, 62, and 68, respectively. PA markers were only available at waves 2 and 3. Outcome measures included mortality by wave 3 and scores on AD-related plasma biomarkers—Neurofilament light (NFL), Tau, and AB40 and AB42 at wave 3. Frailty at wave 1 and 2 predicted mortality. Frailty at wave 1 was significantly associated with wave 3 NFL, AB42 and AB40. Wave 2 & 3 frailty was associated with all biomarkers. Neither PA nor PBAD predicted biomarkers or mortality. The results are striking given the relatively young age of the sample. Even as early as one’s 50s, frailty in a community-dwelling sample predicted accelerated decline and mortality when the outcome age was only 66-73.

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