Evidence map›Paper›PMID 38977808›Full record

ArticleScientific reports2024

Identifying critical age and gender-based metabolomic shifts in a Japanese population of the Tohoku Medical Megabank cohort.

Miyuki Sakurai, Ikuko N Motoike, Eiji Hishinuma, Yuichi Aoki, Shu Tadaka, Mana Kogure, Masatsugu Orui, Mami Ishikuro, Taku Obara, Naoki Nakaya and 6 more

Abstract 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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

16 authors.

Miyuki SakuraiTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Ikuko N MotoikeTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Eiji HishinumaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Yuichi AokiTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Shu TadakaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Mana KogureTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Masatsugu OruiTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Mami IshikuroTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Taku ObaraTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Naoki NakayaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Kazuki KumadaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Atsushi HozawaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Shinichi KuriyamaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Masayuki YamamotoTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Seizo KoshibaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Kengo KinoshitaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan. kengo@tohoku.ac.jp.

Funding

B-Cure from Japan Agency for Medical Research and Development JP21tm0424601Japan Agency for Medical Research and Development JP21tm0124005Japan Agency for Medical Research and Development JP22ama121019Japan Society for the Promotion of Science 16H05241Japan Society for the Promotion of Science 20H05939Ministry of Education, Culture, Sports, Science and Technology.The Project for Promoting Public Utilization of Advanced Research Infrastructure JPMXS0450100321
6 · The paper itself

Abstract

Understanding the physiological changes associated with aging and the associated disease risks is essential to establish biomarkers as indicators of biological aging. This study used the NMR-measured plasma metabolome to calculate age-specific metabolite indices. In doing so, the scope of the study was deliberately simplified to capture general trends and insights into age-related changes in metabolic patterns. In addition, changes in metabolite concentrations with age were examined in detail, with the period from 55-59 to 60-64 years being a period of significant metabolic change, particularly in men, and from 45-49 to 50-54 years in females. These results illustrate the different variations in metabolite concentrations by sex and provide new insights into the relationship between age and metabolic diseases.

Indexed as

AgingMetabolomeMetabolomicsAdultAgedAge FactorsBiomarkersCohort StudiesEast Asian PeopleFemaleHumansJapanMagnetic Resonance SpectroscopyMaleMiddle AgedSex FactorsBiomarkers

Identifiers

PMID38977808
PMCPMC11231361

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Registered trials

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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.