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Age-related macular degeneration in individuals with clonal hematopoiesis.

2025· article· en· W4410809597 on OpenAlexaff
Keishla Marie Arce-Ruiz, Lachelle D. Weeks, Caitlyn Vlasschaert, Shai Shimony, Yating Wang, Md Mesbah Uddin, Pradeep Natarajan, Alexander G. Bick, Benjamin L. Ebert, Demetrios G. Vavvas

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsQueen's University
FundersConquer Cancer Foundation
KeywordsMacular degenerationMedicineGerontologyOncologyOphthalmology

Abstract

fetched live from OpenAlex

6517 Background: Clonal hematopoiesis (CH), an age-related condition involving somatic mutations in blood stem cells, increases the risk of myelodysplastic syndrome (MDS), blood cancers and cardiovascular disease through inflammatory pathways. Age-related macular degeneration (AMD), the leading cause of blindness in the developed world, is also characterized by chronic inflammation. An increased prevalence of AMD has been observed in older adults with MDS, but the association between CH and AMD remains unexplored. Understanding this relationship could reveal shared inflammatory mechanisms in age-related diseases and guide prevention strategies. Methods: This retrospective cohort study used exome sequencing and electronic medical records (EMRs) from 467,200 adults ≥40 years of age in the UK Biobank (UKB), recruited between 2006–2010 and followed until 2020. Participants with prevalent blood cancer, AMD, or with missing AMD diagnosis dates were excluded. CH was defined as pathogenic somatic mutations with a variant allele fraction (VAF) ≥0.02. Incident AMD was identified using ICD-10 codes (H35.3). Kaplan-Meier estimates and log-rank tests assessed cumulative incidence, while Cox regression models calculated hazard ratios (HRs), adjusted for age, sex, smoking and hypertension. A separate cohort of 4,079 patients from Dana-Farber Cancer Institute (DFCI) validated findings and enabled granular clinical data abstraction from EMRs. Results: CH was detected in 29,550 (6.8%) individuals of the UKB. The 12-year cumulative incidence (C.I.) of AMD was higher in individuals with CH (n=671, C.I. 2.45%) compared to those without (n=6,728, 1.61%; p<2x10-16). In unadjusted Cox models, individuals with CH had a 51% higher risk of AMD compared to those without (HR =1.51 (95% CI: 1.39–1.63; p < 2×10⁻¹⁶), remaining significant after adjusting for covariates (p=0.023). CH genotypes most associated with AMD risk included ASXL1 (HR: 1.32; p = 0.0146) and splicing factors (HR: 1.54; p = 0.0345). Individuals with CH and AMD had a 33% higher risk of progressing to blindness compared to those without CH, though this was not statistically significant (p = 0.242). In the DFCI cohort (n= 4,079), CH was present in 1,028 (25.2%) individuals. 86 (8.37%) individuals with CH had AMD diagnoses compared to those without CH (n= 86, 3.21%; p = 2.53×10⁻¹⁰), with exudative AMD, a more severe subtype, being more prevalent in CH patients (n=11; p = 1.1×10⁻⁵). Conclusions: There is a significant association between CH and AMD, suggesting that AMD prevalent in individuals with MDS is related to presence of CH in the pre-MDS state. Real world data support these findings, highlighting a trend towards severe AMD subtypes in individuals with CH. The identification of specific genes linked to AMD incidence suggests that certain CH genotypes may confer a higher risk for AMD, highlighting the role of AMD screening in individuals with myeloid malignancy precursor conditions.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.076
GPT teacher head0.467
Teacher spread0.390 · 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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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