Evidence map›Paper›PMID 32925908›Full record

ArticlePLoS genetics2020

The causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance.

Zong Miao, Marcus Alvarez, Arthur Ko, Yash Bhagat, Elior Rahmani, Brandon Jew, Sini Heinonen, Linda Liliana Muñoz-Hernandez, Miguel Herrera-Hernandez, Carlos Aguilar-Salinas and 6 more

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
5.4field-weighted citation impact, top 3% 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

45 citing papers in PubMed, 70 citations in OpenAlex.

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  16. Higher neighborhood disadvantage is associated with weaker interactions among cardiometabolic drivers.International journal of cardiology. Cardiovascular risk and prevention · 2024
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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

16 authors at 5 institutions in 3 countries.

Zong MiaoDepartment of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.ORCID 0000-0002-6183-4565
Marcus AlvarezDepartment of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.ORCID 0000-0002-6369-5028
Arthur KoDepartment of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.ORCID 0000-0002-1523-7225
Yash BhagatDepartment of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.ORCID 0000-0002-3437-1817
Elior RahmaniComputer Science Department in the School of Engineering, UCLA, Los Angeles, California, United States of America.ORCID 0000-0002-9017-2070
Brandon JewBioinformatics Interdepartmental Program, UCLA, Los Angeles, California, United States of America.ORCID 0000-0002-1848-1880
Sini HeinonenObesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.ORCID 0000-0002-6342-0577
Linda Liliana Muñoz-HernandezUnidad de Investigación en Enfermedades Metabólicas, Dirección de Nutrición, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
Miguel Herrera-HernandezDepartamento de Cirugía, Instituto Nacional de Ciencias Médicas y Nutrición, Mexico City, Mexico.
Carlos Aguilar-SalinasUnidad de Investigación en Enfermedades Metabólicas, Dirección de Nutrición, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
Teresa Tusie-LunaUnidad de Biología Molecular y Medicina Genómica Instituto de Investigaciones Biomédicas UNAM / Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubiran, Mexico City, Mexico.
Karen L MohlkeDepartment of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America.ORCID 0000-0001-6721-153X
Markku LaaksoInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.ORCID 0000-0002-3394-7749
Kirsi H PietiläinenObesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.ORCID 0000-0002-8522-1288
Eran HalperinDepartment of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.ORCID 0000-0002-2373-3691
Päivi PajukantaDepartment of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.
University of California, Los Angeles · USInstituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán · MXUniversity of Helsinki · FIUniversity of Eastern Finland · FIUniversity of North Carolina at Chapel Hill · US

Funding

Systems genomics of metabolic syndrome traitsP01HL028481 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CANTOR, RITA M. · 1985 to 2019
$50.2M
Genetic epidemiology of rare and regulatory variants for metabolic traitsR01DK093757 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN L. MOHLKE · 2011 to 2026
$8.2M
Genomics of dyslipidemia in MexicansR01HL095056 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PAJUKANTA, PAIVI · 2009 to 2018
$5.9M
Methods for Genomic Analysis in Heterogeneous TissuesR01HG010505 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HALPERIN, ERAN · 2019 to 2022
$2.6M
Functional genetic variants for type 2 diabetesU01DK105561 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI MOHLKE, KAREN L. · 2015 to 2019
$2.3M
Identification of lipid biomarkers via integrative genomic approaches in Hispanic populationsF31HL127921 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI KO, ARTHUR · 2016 to 2018
$82k
Howard Hughes Medical InstituteNHGRI NIH HHS R01 HG010505NHLBI NIH HHS F31 HL127921NHLBI NIH HHS P01 HL028481NHLBI NIH HHS R01 HL095056NIDDK NIH HHS R01 DK093757NIDDK NIH HHS U01 DK105561
6 · The paper itself

Abstract

Reverse causality has made it difficult to establish the causal directions between obesity and prediabetes and obesity and insulin resistance. To disentangle whether obesity causally drives prediabetes and insulin resistance already in non-diabetic individuals, we utilized the UK Biobank and METSIM cohort to perform a Mendelian randomization (MR) analyses in the non-diabetic individuals. Our results suggest that both prediabetes and systemic insulin resistance are caused by obesity (p = 1.2×10-3 and p = 3.1×10-24). As obesity reflects the amount of body fat, we next studied how adipose tissue affects insulin resistance. We performed both bulk RNA-sequencing and single nucleus RNA sequencing on frozen human subcutaneous adipose biopsies to assess adipose cell-type heterogeneity and mitochondrial (MT) gene expression in insulin resistance. We discovered that the adipose MT gene expression and body fat percent are both independently associated with insulin resistance (p≤0.05 for each) when adjusting for the decomposed adipose cell-type proportions. Next, we showed that these 3 factors, adipose MT gene expression, body fat percent, and adipose cell types, explain a substantial amount (44.39%) of variance in insulin resistance and can be used to predict it (p≤2.64×10-5 in 3 independent human cohorts). In summary, we demonstrated that obesity is a strong determinant of both prediabetes and insulin resistance, and discovered that individuals' adipose cell-type composition, adipose MT gene expression, and body fat percent predict their insulin resistance, emphasizing the critical role of adipose tissue in systemic insulin resistance.

Indexed as

AdipocytesAdipose TissueAdiposityAdultBody Mass IndexCohort StudiesDiabetes Mellitus, Type 2FemaleHumansInsulin ResistanceMaleMiddle AgedObesityPrediabetic StateSubcutaneous Fat

Identifiers

PMID32925908
PMCPMC7515203
OpenAlexW3085724896

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.