Evidence map›Paper›PMID 28813537›Full record

ArticlePloS one2017

Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study.

Manuela J Rist, Alexander Roth, Lara Frommherz, Christoph H Weinert, Ralf Krüger, Benedikt Merz, Diana Bunzel, Carina Mack, Björn Egert, Achim Bub and 6 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
135citing papers in PubMed, 1 pooled it
8.5field-weighted citation impact, top 2% of its field
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

135 citing papers in PubMed, 1 synthesis or guideline pooled it, 207 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trimethylamine-Nutrients · 2020
    Trial
  4. Urinary metabolic age from high-resolution nuclear magnetic resonance spectroscopy reveals longitudinal aging patterns.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
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  16. ERJ open research · 2025
    Article
  17. Article
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75 more citing papers are in PubMed but not listed here.

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 at 2 institutions in 1 country.

Manuela J RistDepartment of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany.ORCID http://orcid.org/0000-0002-7067-2756
Alexander RothDepartment of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany.
Lara FrommherzDepartment of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany.
Christoph H WeinertDepartment of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany.
Ralf KrügerDepartment of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany.
Benedikt MerzDepartment of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany.
Diana BunzelDepartment of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany.
Carina MackDepartment of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany.
Björn EgertDepartment of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany.
Achim BubDepartment of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany.
Benjamin GörlingInstitute of Organic Chemistry and Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Pavleta TzvetkovaInstitute of Organic Chemistry and Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Burkhard LuyInstitute of Organic Chemistry and Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Ingrid HoffmannDepartment of Nutrition Behaviour, Max Rubner-Institut, Max Rubner-Institut, Karlsruhe, Germany.
Sabine E KullingDepartment of Quality and Safety of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany.
Bernhard WatzlDepartment of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany.
Max Rubner Institut · DEKarlsruhe Institute of Technology · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles. In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18-80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one- or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms. Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy. The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies.

Indexed as

AdolescentAdultAgedAged, 80 and overAge FactorsCholineChromatography, LiquidCross-Sectional StudiesFemaleGas Chromatography-Mass SpectrometryHeptosesHumansMagnetic Resonance SpectroscopyMaleMetabolomeMetabolomicsCholineHeptosessedoheptulose

Identifiers

PMID28813537
PMCPMC5558977
OpenAlexW2749646273

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.