Evidence map›Paper›PMID 36557310›Full record

ArticleMetabolites2022

Metabolomics and Lipidomics Signatures of Insulin Resistance and Abdominal Fat Depots in People Living with Obesity.

Yen Chin Koay, Adelle C F Coster, Daniel L Chen, Brad Milner, Amani Batarseh, John F O'Sullivan, Jerry R Greenfield, Dorit Samocha-Bonet

Abstract read
In one paragraph

Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Hepatic Steatosis and Fibrosis, Cardiorespiratory Fitness, and Metabolic Mediators in the Community.Liver international : official journal of the International Association for the Study of the Liver · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
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

8 authors.

Yen Chin KoaySchool of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0002-6704-5495
Adelle C F CosterSchool of Mathematics and Statistics, UNSW Sydney, Kensington, NSW 2052, Australia.
Daniel L ChenClinical Diabetes, Appetite and Metabolism Laboratory, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.
Brad MilnerDepartment of Medical Imaging, St Vincent's Hospital, Darlinghurst, NSW 2010, Australia.
Amani BatarsehSchool of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.
John F O'SullivanSchool of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia.
Jerry R GreenfieldClinical Diabetes, Appetite and Metabolism Laboratory, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.
Dorit Samocha-BonetClinical Diabetes, Appetite and Metabolism Laboratory, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.ORCID 0000-0001-9422-7852

Funding

Diabetes Australia Research ProgramHeart Research Institute FellowshipNSW Early-Mid Career Researcher FellowshipSt Vincent's Clinic Foundationthe Garvan Research Foundationthe University of Sydney Medical School Chapman Fellowship
6 · The paper itself

Abstract

The liver, skeletal muscle, and adipose tissue are major insulin target tissues and key players in glucose homeostasis. We and others have described diverse insulin resistance (IR) phenotypes in people at risk of developing type 2 diabetes. It is postulated that identifying the IR phenotype in a patient may guide the treatment or the prevention strategy for better health outcomes in populations at risk. Here, we performed plasma metabolomics and lipidomics in a cohort of men and women living with obesity not complicated by diabetes (mean [SD] BMI 36.0 [4.5] kg/m2, n = 62) to identify plasma signatures of metabolites and lipids that align with phenotypes of IR (muscle, liver, or adipose tissue) and abdominal fat depots. We used 2-step hyperinsulinemic-euglycemic clamp with deuterated glucose, oral glucose tolerance test, dual-energy X-ray absorptiometry and abdominal magnetic resonance imaging to assess muscle-, liver- and adipose tissue- IR, beta cell function, body composition, abdominal fat distribution and liver fat, respectively. Spearman’s rank correlation analyses that passed the Benjamini−Hochberg statistical correction revealed that cytidine, gamma-aminobutyric acid, anandamide, and citrate corresponded uniquely with muscle IR, tryptophan, cAMP and phosphocholine corresponded uniquely with liver IR and phenylpyruvate and hydroxy-isocaproic acid corresponded uniquely with adipose tissue IR (p < 7.2 × 10−4). Plasma cholesteryl sulfate (p = 0.00029) and guanidinoacetic acid (p = 0.0001) differentiated between visceral and subcutaneous adiposity, while homogentisate correlated uniquely with liver fat (p = 0.00035). Our findings may help identify diverse insulin resistance and adiposity phenotypes and enable targeted treatments in people living with obesity.

Indexed as

abdominal fat depositionliver fatobesity phenotypesplasma lipidomicsplasma metabolomicstissue insulin resistance

Identifiers

PMID36557310
PMCPMC9781703

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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.