Eigenlipids for exploring lipid biology.

Nicholas Sing, Adam Alexander T Smith, Aleksandar Dakic, Changyu Yi, Agus Salim, Corey Giles, Peter J Meikle

Abstract readReview

In one paragraph

Review in Journal of lipid research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Nicholas SingMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Department of Mathematical and Physical Sciences La Trobe University, Melbourne, VIC, Australia; Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia. Electronic address: nicholas.sing@baker.edu.au.

Adam Alexander T SmithFriedrich Miescher Institute for Biomedical Research, Basel, Switzerland.

Aleksandar DakicMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia.

Changyu YiMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia.

Agus SalimMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Department of Mathematical and Physical Sciences La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia; Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia; School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia.

Corey GilesMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.

Peter J MeikleMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia. Electronic address: peter.meikle@baker.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lipidomics involves the analysis of hundreds or even thousands of lipids within biological systems. Many of these lipids are highly correlated due to similarities in structure or relationships within metabolic pathways. Eigenlipids, as a concept ported over from transcriptomics, can be used to capture these associations and to explore relationships between their constituent lipids and phenotypes of interest. However, gene expression and lipid concentration data differ in how they are structured, requiring additional considerations for eigenlipid analysis. In this review, we discuss how eigenlipids are generated, how they are used to explore lipid metabolism and biology, possible usage pitfalls and finally potential future methodological developments, such as alternative dimensionality reduction methods and generating eigenlipids for predefined lipid sets instead of unsupervised clusters.

Indexed as

Lipid MetabolismLipidomicsLipidsAnimalsHumansLipidsBioinformaticseigengeneeigenlipideigenmetabolitelipid•lipid modelinglipid modellinglipidomicslipidsmetabolomics

Identifiers

PMID42155612

DOI10.1016/j.jlr.2026.101060

PMCPMC13279155

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

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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.