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Record W4412960897 · doi:10.1101/2025.07.16.25331683

Integrative effects of Telomere Length, Epigenetic Age, and Mitochondrial DNA abundance in Alzheimer’s Disease

2025· preprint· en· W4412960897 on OpenAlexfundno aff
Shea J. Andrews, Brendan A. Mitchell, Tong Tong, Luke W. Bonham, Alan E. Renton, Xiaoling Zhang, Marina Sirota, Duygu Tosun, Kristine Yaffe

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierPfizerNovartis Pharmaceuticals CorporationRadiological Society of North AmericaBiogenEli Lilly and CompanyBristol-Myers SquibbBioClinicaEisaiU.S. Department of DefenseMeso Scale DiagnosticsNational Institute on AgingAlzheimer's Association
KeywordsdNaMDementiaCognitionCognitive declineAlzheimer's Disease Neuroimaging InitiativeEpigeneticsTelomereOncologyNeuroimagingAlzheimer's diseasePsychologyDNA methylationDiseaseInternal medicineMedicineBiologyNeuroscienceGeneticsGene expression

Abstract

fetched live from OpenAlex

ABSTRACT Background and Objectives Biological age, reflecting the cumulative molecular and cellular damage such as telomere attrition, epigenetic alterations and mitochondrial dysfunction, may better capture ageLrelated decline and Alzheimer’s disease (AD) risk than chronological age. Most studies have focused on one measure of biological age and not investigated joint or interactive contributions to AD pathogenesis. Methods We estimated bloodLderived telomere length (TL) via qPCR, epigenetic age (DNAm age) using the CausAge clock, and mitochondrial DNA copy number (mtDNAcn) from whole genome sequencing in 640 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; Age: 74.91±7.56, Female: 44.8%, Cognitively Unimpaired: 34.3%, Mild Cognitive Impairment: 52%, AD: 12.9%). Linear mixedLeffects models examined the associations and interactions of these markers with cognitive decline for memory, executive function, language ability, visuospatial ability, and global cognition, while linear regression tested associations with cross-sectional AD biomarkers (CSF Aβ 42 , totalLtau, pTau 181 , and meta-ROI for cortical thickness and gray matter volume). Models adjusted for baseline age, sex, clinical dementia rating scale, APOE , blood cell composition, and outcomeLspecific covariates (education and intracranial volume). Results Individually, TL and DNAm age, were not associated with cognition, CSF biomarkers, or neuroimaging outcomes, while higher mtDNAcn was associated with lower CSF tau and pTau 181 . Interaction models revealed that mtDNAcn modified the effects of both TL and DNAm age: at higher mtDNAcn, shorter TL predicted poorer global cognition (β = 0.033 ± 0.014, p = 0.020) and older DNAm age predicted poorer language performance (β = –0.059 ± 0.028, p = 0.038). A significant three-way interaction showed that the combination of higher mtDNAcn, longer TL, and older DNAm age was associated with lower grey-matter volume. Discussion These findings suggest that increased mtDNAcn may act as a compensatory response to accelerated epigenetic aging and telomere attrition. Our results underscore the importance of evaluating the interplay among multiple biological aging markers when investigating AD pathogenesis. Glossary mtDNAcn: mitochondrial DNA copy number; TL: telomere length; AD: Alzheimer’s disease; CDR-SB: Clinical dementia rating sum of boxes; CSF: cerebrospinal fluid

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.292
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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