Slower speed of blood pressure recovery after standing is associated with accelerated brain ageing: Evidence from The Irish Longitudinal Study on Ageing (TILDA) DOI Creative Commons

Morgana Afonso Shirsath,

John D. O’Connor, Rory Boyle

и другие.

Cerebral Circulation - Cognition and Behavior, Год журнала: 2024, Номер 6, С. 100212 - 100212

Опубликована: Янв. 1, 2024

Impaired recovery of blood pressure (BP) in response to standing up is a prevalent condition older individuals. We evaluated the relationship between early hemodynamic responses and brain health adults over 50.

Язык: Английский

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex DOI Creative Commons
Irene Cumplido‐Mayoral,

Marina García‐Prat,

Grégory Operto

и другие.

eLife, Год журнала: 2023, Номер 12

Опубликована: Апрель 17, 2023

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer’s disease (AD), but its validation against markers neurodegeneration AD is lacking. Here, imaging-derived measures the UK Biobank dataset (N=22,661) were used predict brain-age 2,314 cognitively unimpaired (CU) individuals at higher risk mild cognitive impaired (MCI) patients four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, OASIS. delta was associated abnormal amyloid-β, more advanced stages (AT) pathology APOE -ε4 status. positively plasma neurofilament light, neurodegeneration, sex differences effects this found. These results validate non-invasive non-demented levels biomarkers axonal injury.

Язык: Английский

Процитировано

37

A framework of biomarkers for brain aging: a consensus statement by the Aging Biomarker Consortium DOI Creative Commons
Yujuan Jia, Jun Wang,

Jun-Rong Ren

и другие.

Life Medicine, Год журнала: 2023, Номер 2(3)

Опубликована: Май 6, 2023

China and the world are facing severe population aging an increasing burden of age-related diseases. Aging brain causes major diseases, such as neurodegenerative diseases stroke. Identifying biomarkers for effective assessment establishing a system could facilitate development intervention strategies prevention treatment aging-related Thus, experts from Biomarker Consortium (ABC) have combined latest research results practical experience to recommend form expert consensus, aiming provide basis assessing degree conducting brain-aging-related with ultimate goal improving health elderly individuals in both world.

Язык: Английский

Процитировано

32

Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN) DOI Creative Commons
Nicole S. McKay, Brian A. Gordon, Russ C. Hornbeck

и другие.

Nature Neuroscience, Год журнала: 2023, Номер 26(8), С. 1449 - 1460

Опубликована: Июль 10, 2023

Abstract The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons case–control studies. age onset highly predictable within families, allowing researchers to estimate individual’s point the trajectory. These characteristics allow candidate AD biomarker measurements reliably mapped during preclinical phase. Although represents small proportion cases, understanding neuroimaging-based changes that occur period may provide insight into early stages ‘sporadic’ also. Additionally, this study provides rich data research healthy aging through inclusion controls. Here we introduce neuroimaging dataset collected and describe how resource used by range researchers.

Язык: Английский

Процитировано

31

BrainAGE, brain health, and mental disorders: A systematic review DOI
Johanna Seitz‐Holland, Shalaila S. Haas, Nora Penzel

и другие.

Neuroscience & Biobehavioral Reviews, Год журнала: 2024, Номер 159, С. 105581 - 105581

Опубликована: Фев. 13, 2024

Язык: Английский

Процитировано

11

Association of biological age with health outcomes and its modifiable factors DOI Creative Commons

Wei‐Shi Liu,

Jia You,

Yi‐Jun Ge

и другие.

Aging Cell, Год журнала: 2023, Номер 22(12)

Опубликована: Сен. 18, 2023

Abstract Identifying the clinical implications and modifiable unmodifiable factors of aging requires measurement biological age (BA) gap. Leveraging biomedical traits involved with physical measures, biochemical assays, genomic data, cognitive functions from healthy participants in UK Biobank, we establish an integrative BA model consisting multi‐dimensional indicators. Accelerated (age gap >3.2 years) at baseline is associated incident circulatory diseases, related chronic disorders, all‐cause, cause‐specific mortality. We identify 35 for ( p < 4.81 × 10 −4 ), where pulmonary functions, body mass, hand grip strength, basal metabolic rate, estimated glomerular filtration C‐reactive protein show most significant associations. Genetic analyses replicate possible associations between health‐related outcomes further CST3 as essential gene aging, which highly expressed brain immune traits. Our study profiles landscape provides insights into preventive strategies therapeutic targets aging.

Язык: Английский

Процитировано

22

Brain age prediction across the human lifespan using multimodal MRI data DOI
Sihai Guan,

Runzhou Jiang,

Chun Meng

и другие.

GeroScience, Год журнала: 2023, Номер 46(1), С. 1 - 20

Опубликована: Сен. 21, 2023

Язык: Английский

Процитировано

16

Exercise mitigates age-related metabolic diseases by improving mitochondrial dysfunction DOI
Dandan Jia, Zhenjun Tian, Ru Wang

и другие.

Ageing Research Reviews, Год журнала: 2023, Номер 91, С. 102087 - 102087

Опубликована: Окт. 11, 2023

Язык: Английский

Процитировано

15

Multi-center brain age prediction via dual-modality fusion convolutional network DOI

Xuebin Chang,

Xiaoyan Jia, Simon B. Eickhoff

и другие.

Medical Image Analysis, Год журнала: 2025, Номер 101, С. 103455 - 103455

Опубликована: Янв. 10, 2025

Язык: Английский

Процитировано

0

Brain Age Modeling and Cognitive Outcomes in Young Adults With and Without Sickle Cell Anemia DOI Creative Commons
Andria L. Ford, Slim Fellah, Yan Wang

и другие.

JAMA Network Open, Год журнала: 2025, Номер 8(1), С. e2453669 - e2453669

Опубликована: Янв. 17, 2025

Importance Both sickle cell anemia (SCA) and socioeconomic status have been associated with altered brain structure cognitive disability, yet precise mechanisms underlying these associations are unclear. Objective To determine whether brains of individuals without SCA appear older than chronological age if modeling using gap (BAG) can estimate outcomes mediate the association disease outcomes. Design, Setting, Participants In this cross-sectional study 230 adults SCA, underwent magnetic resonance imaging (MRI) assessment. Brain was estimated DeepBrainNet, a model trained to from 14 468 structural MRIs healthy across lifespan. BAG defined as minus age. Linear regression examined clinical factors ability performance compared neuroimaging metrics health ischemic injury, such normalized whole volume, white matter mean diffusivity (MD), infarct volume. MD were tested further mediators performance. Data analyzed October 15, 2023, July 1, 2024. Exposures economic deprivation measured area index (ADI). Main Outcome Measures Executive function, crystallized processing speed, full-scale intelligence quotient (FSIQ) derived National Institutes Health (NIH) Toolbox Wechsler Abbreviated Scale Intelligence, Second Edition. Results Among included adults, 123 had (median [IQR] age, 26.4 [21.8-34.3] years; 77 female [63%]) 107 did not (control cohort; median 30.1 [26.3-34.8] [72%]). larger (IQR) in control cohort (14.2 [8.0-19.2] vs 7.3 [3.2-11.1] difference, 6.13 95% CI, 4.29-8.05 P &amp;lt; .001). Individuals demonstrated relative reference population (mean 7.52 6.32-8.72 Higher (β [SE] per 1% ADI increase, 0.079 [0.028]; 0.023 0.135; = .006), while intracranial vasculopathy [SE], 6.562 [1.883]; 2.828 10.296; .001) hemoglobin S percentage 0.089 [0.032]; 0.026 0.151; .006) participants SCA. Across health, largest effect size for (eg, executive function: r −0.430; .001), −0.365; cohort. population, mediated β 1-unit decrease ADI, −0.031 [0.014]; −0.061 −0.006), FSIQ: −3.79 [1.42]; −6.87 −1.40) −4.55 [1.82]; −8.14 −0.94) Conclusions Relevance Adults greater suggestive insufficient development, premature aging, or both. estimates may inform between chronic populations, will require confirmation longitudinal studies.

Язык: Английский

Процитировано

0

Cross‐Sectional Comparison of Structural MRI Markers of Impairment in a Diverse Cohort of Older Adults DOI Creative Commons
Julie K. Wisch, Kalen J. Petersen, Peter R Millar

и другие.

Human Brain Mapping, Год журнала: 2025, Номер 46(2)

Опубликована: Янв. 27, 2025

Neurodegeneration is presumed to be the pathological process measure most proximal clinical symptom onset in Alzheimer Disease (AD). Structural MRI routinely collected research and trial settings. Several quantitative MRI-based measures of atrophy have been proposed, but their low correspondence with each other has previously documented. The purpose this study was identify which commonly used structural (hippocampal volume, cortical thickness AD signature regions, or brain age gap [BAG]) had best Clinical Dementia Rating (CDR) an ethno-racially diverse sample. 2870 individuals recruited by Healthy Aging Brain Study-Health Disparities completed both CDR evaluation. Of these, 1887 were matched on ethno-racial identity (Mexican American [MA], non-Hispanic Black [NHB], White [NHW]) (27% > 0). We estimated using two pipelines (DeepBrainNet, BrainAgeR) then calculated BAG as difference between chronological age. also quantified hippocampal volumes HippoDeep thicknesses (both AD-specific average whole brain) FreeSurfer. ordinal regression evaluate associations neuroimaging test whether these differed groups. Higher (pDeepBrainNet = 0.0002; pBrainAgeR 0.00117) lower volume (p 0.0015) < 0.0001) associated worse status (higher CDR). strongest relationship (AICDeepBrainNet 2623, AICwhole cortex 2588, AICBrainAgeR 2533, AICHippocampus 2293, AICSignature Cortical Thickness 1903). groups for estimates not thickness. interpret lack interaction evidence that effectively captures sources disease-related may differ across racial ethnic association CDR. These results suggest a more sensitive generalizable marker neurodegeneration than cohorts.

Язык: Английский

Процитировано

0