Evidence mapPaperPMID 38944038Full record

ArticleCell reports. Medicine2024

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening.

Tarek Eissa, Cristina Leonardo, Kosmas V Kepesidis, Frank Fleischmann, Birgit Linkohr, Daniel Meyer, Viola Zoka, Marinus Huber, Liudmila Voronina, Lothar Richter and 2 more

Abstract read
In one paragraph

Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
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  12. The Perils of Molecular Interpretations from Vibrational Spectra of Complex Samples.Angewandte Chemie (International ed. in English) · 2024
    Review
  13. 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

12 authors.

Tarek EissaDepartment of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany; School of Computation, Information and Technology, Technical University of Munich (TUM), Garching, Germany. Electronic address: tarek.eissa@mpq.mpg.de.
Cristina LeonardoDepartment of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany.
Kosmas V KepesidisDepartment of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany; Center for Molecular Fingerprinting (CMF), Budapest, Hungary.
Frank FleischmannDepartment of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany.
Birgit LinkohrInstitute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.
Daniel MeyerLaboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany; Center for Molecular Fingerprinting (CMF), Budapest, Hungary.
Viola ZokaDepartment of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Center for Molecular Fingerprinting (CMF), Budapest, Hungary.
Marinus HuberDepartment of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany.
Liudmila VoroninaDepartment of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany.
Lothar RichterSchool of Computation, Information and Technology, Technical University of Munich (TUM), Garching, Germany.
Annette PetersInstitute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany; School of Public Health, Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer, Ludwig Maximilian University of Munich (LMU), Munich, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Munich, Munich, Germany.
Mihaela ŽigmanDepartment of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany. Electronic address: mihaela.zigman@mpq.mpg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.

Indexed as

Diabetes Mellitus, Type 2Machine LearningPhenotypeAdultAgedDyslipidemiasFemaleHumansHypertensionMaleMetabolic SyndromeMiddle AgedPrediabetic StateSpectroscopy, Fourier Transform Infrareddisease detectioninfrared spectroscopyin vitro diagnosticsmachine learningmetabolic syndromemolecular fingerprintingmultilabelmultimorbidity

Identifiers

PMID38944038
PMCPMC11293328

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.