Evidence map›Paper›PMID 37805498›Full record

ArticleNature communications2023

Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts.

Habtamu B Beyene, Corey Giles, Kevin Huynh, Tingting Wang, Michelle Cinel, Natalie A Mellett, Gavriel Olshansky, Thomas G Meikle, Gerald F Watts, Joseph Hung and 8 more

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
–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

46 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
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  20. Precision Medicine for Obesity Treatment.Journal of the Endocrine Society · 2025
    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

18 authors.

Habtamu B Beyene *Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.ORCID http://orcid.org/0000-0001-7075-6629
Corey Giles *Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.ORCID http://orcid.org/0000-0002-6050-1259
Kevin HuynhBaker Heart and Diabetes Institute, Melbourne, VIC, Australia.ORCID http://orcid.org/0000-0001-6170-2207
Tingting WangBaker Heart and Diabetes Institute, Melbourne, VIC, Australia.
Michelle CinelBaker Heart and Diabetes Institute, Melbourne, VIC, Australia.
Natalie A MellettBaker Heart and Diabetes Institute, Melbourne, VIC, Australia.
Gavriel OlshanskyBaker Heart and Diabetes Institute, Melbourne, VIC, Australia.ORCID http://orcid.org/0000-0002-5122-5547
Thomas G MeikleBaker Heart and Diabetes Institute, Melbourne, VIC, Australia.
Gerald F WattsSchool of Medicine, University of Western Australia, Perth, WA, Australia.ORCID http://orcid.org/0000-0003-2276-1524
Joseph HungSchool of Medicine, University of Western Australia, Perth, WA, Australia.ORCID http://orcid.org/0000-0002-4468-6097
Jennie HuiPathWest Laboratory Medicine of Western Australia, Nedlands, WA, Australia.
Gemma CadbySchool of Population and Global Health, University of Western Australia, Crawley, WA, Australia.ORCID http://orcid.org/0000-0001-7317-6531
John BeilbySchool of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia.
John BlangeroSouth Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA.ORCID http://orcid.org/0000-0001-6250-5723
Eric K MosesSchool of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia.ORCID http://orcid.org/0000-0001-7781-4465
Jonathan E ShawBaker Heart and Diabetes Institute, Melbourne, VIC, Australia.ORCID http://orcid.org/0000-0002-6187-2203
Dianna J MaglianoBaker Heart and Diabetes Institute, Melbourne, VIC, Australia. Dianna.Magliano@baker.edu.au.ORCID http://orcid.org/0000-0002-9507-6096
Peter J MeikleBaker Heart and Diabetes Institute, Melbourne, VIC, Australia. peter.meikle@baker.edu.au.ORCID http://orcid.org/0000-0002-2593-4665

Funding

Identification of the Exposome in Fatty Liver Disease in Mexican American Families Using Genetic CorrectionR01MD012564 · NIMHD · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI BLANGERO, JOHN, CLARKE, GEOFFREY DAVID · 2018 to 2022
$3.7M
NIMHD NIH HHS R01 MD012564
6 · The paper itself

Abstract

Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2Metabolic SyndromeBody Mass IndexHumansObesityRisk Factors

Identifiers

PMID37805498
PMCPMC10560260

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.