Evidence map›Paper›PMID 34564462›Full record

ArticleMetabolites2021

Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study.

Habtamu B Beyene, Gavriel Olshansky, Corey Giles, Kevin Huynh, Michelle Cinel, Natalie A Mellett, Adam Alexander T Smith, Jonathan E Shaw, Dianna J Magliano, Peter J Meikle

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 19% 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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

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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 1 institution in 1 country.

Habtamu B BeyeneMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.
Gavriel OlshanskyMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.
Corey GilesMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.ORCID 0000-0002-6050-1259
Kevin HuynhMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.ORCID 0000-0001-6170-2207
Michelle CinelMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.
Natalie A MellettMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.
Adam Alexander T SmithMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.ORCID 0000-0003-4593-3042
Jonathan E ShawSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
Dianna J MaglianoSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
Peter J MeikleMetabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.ORCID 0000-0002-2593-4665
Baker Heart and Diabetes Institute · AU

Funding

National Health and Medical Research Council APP1101320
6 · The paper itself

Abstract

Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we investigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25-95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (β-coefficient = 0.125 cm increment per SD increment in baseline lipid level,

Indexed as

change in BMIchange in WCmetabolic scoresplasma lipidomics

Identifiers

PMID34564462
PMCPMC8471381
OpenAlexW3201650504

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.