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Introduction: The geroscience hypothesis proposes systemic biological aging is a root cause of cognitive decline.
Methods: We analyzed Framingham Heart Study Offspring Cohort data ( = 2296; 46% male; baseline age = 62, SD = 9, range = 25-101 y). We measured cognitive decline across two decades of neuropsychological-testing follow-up. We measured pace of aging using the DunedinPACE epigenetic clock. Analysis tested if participants with faster DunedinPACE values experienced more rapid cognitive decline compared with those with slower DunedinPACE values.
Results: Participants with faster DunedinPACE had poorer cognitive functioning at baseline and experienced more rapid cognitive decline over follow-up. Results were robust to confounders and consistent across population strata. Findings were similar for the PhenoAge and GrimAge epigenetic clocks.
Discussion: Faster pace of aging is a risk factor for preclinical cognitive decline. Metrics of biological aging may inform risk stratification in clinical trials and prognosis in patient care.
Highlights: Faster DunedinPACE is associated with preclinical cognitive aging.Higher baseline cognition was protective of DunedinPACE-associated cognitive decline.The DunedinPACE association with cognitive decline explained a fourth of dementia risk.
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http://dx.doi.org/10.1002/dad2.70038 | DOI Listing |
Clin Epigenetics
September 2025
Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany.
Background: Work-related stress is a well-established contributor to mental health decline, particularly in the context of burnout, a state of prolonged exhaustion. Epigenetic clocks, which estimate biological age based on DNA methylation (DNAm) patterns, have been proposed as potential biomarkers of chronic stress and its impact on biological aging and health. However, their role in mediating the relationship between work-related stress, physiological stress markers, and burnout remains unclear.
View Article and Find Full Text PDFAlzheimers Dement
September 2025
Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA.
Introduction: Mild cognitive impairment (MCI) represents a transitional stage between normal aging and dementia. We investigate associations among cardiovascular and metabolic disorders (hypertension, diabetes mellitus, and hyperlipidemia) and diagnosis (normal; amnestic [aMCI]; and non-amnestic [naMCI]).
Methods: Multinomial logistic regressions of participant data (N = 8737; age = 70.
J Mol Neurosci
September 2025
Department of Physiology, School of Medicine, Dokuz Eylul University, Izmir, Turkey.
The ketogenic diet (KD), a high-fat, low-carbohydrate regimen, has been shown to exert neuroprotective effects in various neurological models. This study explored how KD-alone or combined with antibiotic-induced gut microbiota depletion-affects cognition and neuroinflammation in aging. Thirty-two male rats (22 months old) were assigned to four groups (n = 8): control diet (CD), ketogenic diet (KD), antibiotics with control diet (AB), and antibiotics with KD (KDAB).
View Article and Find Full Text PDFGeroscience
September 2025
Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
Cognitive decline is common in multiple sclerosis (MS), although neural mechanisms are not fully understood. The objective was to investigate the impact of mild cognitive impairment (MCI) on the relationship between resting state functional connectivity (RSFC) and cognitive function in older adults with multiple sclerosis (OAMS) and age matched healthy controls. Participants underwent magnetic resonance imaging (MRI) scans and cognitive assessments.
View Article and Find Full Text PDFSci Rep
September 2025
Department of Endocrinology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China.
Visceral adiposity has been proposed to be closely linked to cognitive impairment. This cross-sectional study aimed to evaluate the predictive value of Chinese Visceral Adiposity Index (CVAI) for mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM) and to develop a quantitative risk assessment model. A total of 337 hospitalized patients with T2DM were included and randomly assigned to a training cohort (70%, n = 236) and a validation cohort (30%, n = 101).
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