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Record W2991772886 · doi:10.1016/j.jhepr.2019.11.004

Methylation signatures in peripheral blood are associated with marked age acceleration and disease progression in patients with primary sclerosing cholangitis

2019· article· en· W2991772886 on OpenAlexaff
Michael Trauner, Yevgeniy Gindin, Zhaoshi Jiang, Chuhan Chung, G. Mani Subramanian, Robert P. Myers, Aliya Gulamhusein, Kris V. Kowdley, Cynthia Levy, Zachary Goodman, Michael P. Manns, Andrew J. Muir, Christopher L. Bowlus

Bibliographic record

VenueJHEP Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity of Toronto
FundersTarget Real-world evidenceIntercept PharmaceuticalsGilead SciencesNovartisMeso Scale DiagnosticsMerckDr. Falk PharmaGlaxoSmithKlineAllerganBristol-Myers Squibb
KeywordsPrimary sclerosing cholangitisMedicineInternal medicineGastroenterologyCohortHazard ratioCirrhosisLiver biopsyConfidence intervalBiopsyDisease

Abstract

fetched live from OpenAlex

Background & Aims A DNA methylation (DNAm) signature derived from 353 CpG sites (the Horvath clock) has been proposed as an epigenetic measure of chronological and biological age. This epigenetic signature is accelerated in diverse tissue types in various disorders, including non-alcoholic steatohepatitis, and is associated with mortality. Here, we assayed whole blood DNAm to explore age acceleration in patients with primary sclerosing cholangitis (PSC). Methods Using the MethylationEPIC BeadChip (850K) array, DNAm signatures in whole blood were analyzed in 36 patients with PSC enrolled in a 96-week trial of simtuzumab (Ishak F0-1, n = 13; F5-6, n = 23). Age acceleration was calculated as the difference between DNAm age and chronological age. Comparisons between patients with high and low age acceleration (≥ vs . < the median) were made and Cox regression evaluated the association between age acceleration and PSC-related clinical events ( e.g. decompensation, cholangitis, transplantation). Results Age acceleration was significantly higher in patients with PSC compared to a healthy reference cohort (median, 11.1 years, p <2.2 × 10 -16 ). In PSC, demographics, presence of inflammatory bowel disease, and ursodeoxycholic acid use were similar between patients with low and high age acceleration. However, patients with high age acceleration had increased serum alkaline phosphatase, gamma glutamyltransferase, alanine aminotransferase, enhanced liver fibrosis test scores, and greater hepatic collagen and α-smooth muscle actin expression on liver biopsy (all p <0.05). Moreover, patients with high age acceleration had an increased prevalence of cirrhosis (89% vs. 39%; p = 0.006) and greater likelihood of PSC-related events (hazard ratio 4.19; 95% CI 1.15–15.24). Conclusion This analysis of blood DNAm profiles suggests that compared with healthy controls, patients with PSC – particularly those with cirrhosis - exhibit significant acceleration of epigenetic age. Future studies are required to evaluate the prognostic implications and effect of therapies on global methylation patterns and age acceleration in PSC. Lay summary An epigenetic clock based on DNA methylation has been proposed as a marker of age. In liver diseases such as non-alcoholic steatohepatitis, age acceleration based on this epigenetic clock has been observed. Herein, we show that patients with primary sclerosing cholangitis have marked age acceleration, which is further accentuated by worsening fibrosis. This measure of age acceleration could be a useful marker for prognostication or risk stratification in primary sclerosing cholangitis.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.225
Teacher spread0.218 · 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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Citations18
Published2019
Admission routes1
Has abstractyes

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