Evidence map›Paper›PMID 38773208›Full record

ArticleScientific reports2024

Predicting exacerbation of renal function by DNA methylation clock and DNA damage of urinary shedding cells: a pilot study.

Akihito Hishikawa, Erina Sugita Nishimura, Norifumi Yoshimoto, Ran Nakamichi, Eriko Yoshida Hama, Wataru Ito, Tomomi Maruki, Kengo Nagashima, Ryoko Shimizu-Hirota, Hiromasa Takaishi and 2 more

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease.Diabetic medicine : a journal of the British Diabetic Association · 2025
    Review
  5. Epigenetics, Microbiome and Personalized Medicine: Focus on Kidney Disease.International journal of molecular sciences · 2024
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Akihito HishikawaDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan. a-hishikawa@keio.jp.
Erina Sugita NishimuraDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
Norifumi YoshimotoDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
Ran NakamichiDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
Eriko Yoshida HamaDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
Wataru ItoDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
Tomomi MarukiDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
Kengo NagashimaBiostatistics Unit, Clinical and Translational Research Center, Keio University School of Medicine, Tokyo, Japan.
Ryoko Shimizu-HirotaCenter for Preventive Medicine, Keio University School of Medicine, Tokyo, Japan.
Hiromasa TakaishiCenter for Preventive Medicine, Keio University School of Medicine, Tokyo, Japan.
Hiroshi ItohCenter for Preventive Medicine, Keio University School of Medicine, Tokyo, Japan.
Kaori HayashiDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan. kaorihayashi@keio.jp.

Funding

Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan Grants for Scientific Research (22H03091)
6 · The paper itself

Abstract

Recent reports have shown the feasibility of measuring biological age from DNA methylation levels in blood cells from specific regions identified by machine learning, collectively known as the epigenetic clock or DNA methylation clock. While extensive research has explored the association of the DNA methylation clock with cardiovascular diseases, cancer, and Alzheimer's disease, its relationship with kidney diseases remains largely unexplored. In particular, it is unclear whether the DNA methylation clock could serve as a predictor of worsening kidney function. In this pilot study involving 20 subjects, we investigated the association between the DNA methylation clock and subsequent deterioration of renal function. Additionally, we noninvasively evaluated DNA damage in urinary shedding cells using a previously reported method to examine the correlation with the DNA methylation clock and worsening kidney function. Our findings revealed that patients with an accelerated DNA methylation clock exhibited increased DNA damage in urinary shedding cells, along with a higher rate of eGFR decline. Moreover, in cases of advanced CKD (G4-5), the DNA damage in urinary shedding cells was significantly increased, highlighting the interplay between elevated DNA damage and eGFR decline. This study suggests the potential role of the DNA methylation clock and urinary DNA damage as predictive markers for the progression of chronic kidney disease.

Indexed as

DNA DamageDNA MethylationGlomerular Filtration RateRenal Insufficiency, ChronicAgedBiomarkersDisease ProgressionEpigenesis, GeneticFemaleHumansKidneyMaleMiddle AgedPilot ProjectsBiomarkers

Identifiers

PMID38773208
PMCPMC11109093

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.