Evidence map›Paper›PMID 38535308›Full record

ArticleMetabolites2024

Diagnostic and Prognostic Performance of Metabolic Signatures in Pancreatic Ductal Adenocarcinoma: The Clinical Application of Quantitative NextGen Mass Spectrometry.

Paulo D'Amora, Ismael D C G Silva, Steven S Evans, Adam J Nagourney, Katharine A Kirby, Brett Herrmann, Daniela Cavalheiro, Federico R Francisco, Paula J Bernard, Robert A Nagourney

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 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
0.6field-weighted citation impact, top 31% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  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

10 authors at 2 institutions in 2 countries.

Paulo D'AmoraMetabolomycs, Inc., 750 E. 29th Street, Long Beach, CA 90806, USA.
Ismael D C G SilvaMetabolomycs, Inc., 750 E. 29th Street, Long Beach, CA 90806, USA.
Steven S EvansMetabolomycs, Inc., 750 E. 29th Street, Long Beach, CA 90806, USA.
Adam J NagourneyNagourney Cancer Institute, 750 E. 29th Street, Long Beach, CA 90806, USA.
Katharine A KirbyCenter for Statistical Consulting, Department of Statistics, University of California Irvine, (UC Irvine), 843 Health Science Rd., Irvine, CA 92697, USA.
Brett HerrmannNagourney Cancer Institute, 750 E. 29th Street, Long Beach, CA 90806, USA.
Daniela CavalheiroNagourney Cancer Institute, 750 E. 29th Street, Long Beach, CA 90806, USA.
Federico R FranciscoNagourney Cancer Institute, 750 E. 29th Street, Long Beach, CA 90806, USA.
Paula J BernardMetabolomycs, Inc., 750 E. 29th Street, Long Beach, CA 90806, USA.
Robert A NagourneyMetabolomycs, Inc., 750 E. 29th Street, Long Beach, CA 90806, USA.
Universidade Federal de São Paulo · BRUniversity of California, Irvine · US

Funding

University of California Health Participation in the National COVID Cohort Collaborative (N3C)UL1TR001414 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI COOPER, DAN M, VILAIN, ERIC J. · 2015 to 2023
$35.1M
NCATS NIH HHS UL1 TR001414
6 · The paper itself

Abstract

With 64,050 new diagnoses and 50,550 deaths in the US in 2023, pancreatic ductal adenocarcinoma (PDAC) is among the most lethal of all human malignancies. Early detection and improved prognostication remain critical unmet needs. We applied next-generation metabolomics, using quantitative tandem mass spectrometry on plasma, to develop biochemical signatures that identify PDAC. We first compared plasma from 10 PDAC patients to 169 samples from healthy controls. Using metabolomic algorithms and machine learning, we identified ratios that incorporate amino acids, biogenic amines, lysophosphatidylcholines, phosphatidylcholines and acylcarnitines that distinguished PDAC from normal controls. A confirmatory analysis then applied the algorithms to 30 PDACs compared with 60 age- and sex-matched controls. Metabolic signatures were then analyzed to compare survival, measured in months, from date of diagnosis to date of death that identified metabolite ratios that stratified PDACs into distinct survival groups. The results suggest that metabolic signatures could provide PDAC diagnoses earlier than tumor markers or radiographic measures and offer insights into disease severity that could allow more judicious use of therapy by stratifying patients into metabolic-risk subgroups.

Indexed as

biomarkerearly detectionmetabolic profilingNextGen metabolomicspancreatic cancerprognosticationsurvival analysis

Identifiers

PMID38535308
PMCPMC10972340
OpenAlexW4392293638

What OpenQuestion 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 OpenQuestion graph.