Evidence map›Paper›PMID 31540069›Full record

ArticleMetabolites2019

Targeted Clinical Metabolite Profiling Platform for the Stratification of Diabetic Patients.

Linda Ahonen, Sirkku Jäntti, Tommi Suvitaival, Simone Theilade, Claudia Risz, Risto Kostiainen, Peter Rossing, Matej Orešič, Tuulia Hyötyläinen

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

18 citing papers in PubMed, 28 citations in OpenAlex.

  1. Trial
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Liraglutide Lowers Palmitoleate Levels in Type 2 Diabetes. AFrontiers in clinical diabetes and healthcare · 2022
    Article
  11. Article
  12. Article
  13. DecipheringMetabolites · 2021
    Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. 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

9 authors at 5 institutions in 3 countries.

Linda AhonenSteno Diabetes Center Copenhagen, 2820 Gentofte, Denmark. la@biosyntia.com.
Sirkku JänttiDrug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, 00014 Helsinki, Finland. sirkku.e.jantti@gmail.com.
Tommi SuvitaivalSteno Diabetes Center Copenhagen, 2820 Gentofte, Denmark. tommi.raimo.leo.suvitaival@regionh.dk.
Simone TheiladeSteno Diabetes Center Copenhagen, 2820 Gentofte, Denmark. stheilade@hotmail.com.
Claudia RiszSteno Diabetes Center Copenhagen, 2820 Gentofte, Denmark. claudia.risz@chello.at.
Risto KostiainenDrug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, 00014 Helsinki, Finland. risto.kostiainen@helsinki.fi.
Peter RossingSteno Diabetes Center Copenhagen, 2820 Gentofte, Denmark. peter.rossing@regionh.dk.
Matej OrešičTurku Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520 Turku, Finland. matej.oresic@oru.se.
Tuulia HyötyläinenDepartment of Chemistry, Örebro University, 702 81 Örebro, Sweden. tuulia.hyotylainen@oru.se.
Steno Diabetes Centers · DKUniversity of Helsinki · FIÅbo Akademi University · FIÖrebro University · SEUniversity of Copenhagen · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several small molecule biomarkers have been reported in the literature for prediction and diagnosis of (pre)diabetes, its co-morbidities, and complications. Here, we report the development and validation of a novel, quantitative method for the determination of a selected panel of 34 metabolite biomarkers from human plasma. We selected a panel of metabolites indicative of various clinically-relevant pathogenic stages of diabetes. We combined these candidate biomarkers into a single ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method and optimized it, prioritizing simplicity of sample preparation and time needed for analysis, enabling high-throughput analysis in clinical laboratory settings. We validated the method in terms of limits of detection (LOD) and quantitation (LOQ), linearity (

Indexed as

clinical diagnosticsdiabetesmass spectrometrymetabolomics

Identifiers

PMID31540069
PMCPMC6780060
OpenAlexW2973124386

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.