Pseudotime Analysis Reveals Exponential Trends in DNA Methylation Aging with Mortality Associated Timescales.

Kalsuda Lapborisuth, Colin Farrell, Matteo Pellegrini

Abstract read

In one paragraph

Article in Cells, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Age-associated DNA methylation patterns in mouse blood and tail: feasibility of tissue-specific epigenetic clock development.GeroScience · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kalsuda LapborisuthDepartment of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.

Colin FarrellDepartment of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.

Matteo PellegriniDepartment of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0001-9355-9564

Funding

Validation and optimization of epigenetic clocksU01AG060908 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI RITZ, BEATE R. · 2018 to 2022

$3.4M

NIA NIH HHS U01 AG060908

6 · The paper itself

Abstract

The epigenetic trajectory of DNA methylation profiles has a nonlinear relationship with time, reflecting rapid changes in DNA methylation early in life that progressively slow with age. In this study, we use pseudotime analysis to determine the functional form of these trajectories. Unlike epigenetic clocks that constrain the functional form of methylation changes with time, pseudotime analysis orders samples along a path, based on similarities in a latent dimension, to provide an unbiased trajectory. We show that pseudotime analysis can be applied to DNA methylation in human blood and brain tissue and find that it is highly correlated with the epigenetic states described by the Epigenetic Pacemaker. Moreover, we show that the pseudotime trajectory can be modeled with respect to time, using a sum of two exponentials, with coefficients that are close to the timescales of human age-associated mortality. Thus, for the first time, we can identify age-associated molecular changes that appear to track the exponential dynamics of mortality risk.

Indexed as

DNA MethylationEpigenesis, GeneticAgingBrainEpigenomicsHumansDNA methylationepigenetic agingpseudotime analysistrajectory inference

Identifiers

PMID35269389

DOI10.3390/cells11050767

PMCPMC8909670

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