Evidence map›Paper›PMID 34298074›Full record

ArticleAnnals of epidemiology2021

Biomarker-based visceral adiposity score and incident type 2 diabetes in the multiethnic cohort.

Gertraud Maskarinec, Phyllis Ash Raquinio, Veronica W Setiawan, Thomas Ernst, Adrian A Franke, Steven D Buchthal, John A Shepherd, Lynne R Wilkens, Unhee Lim, Loïc Le Marchand

Open access · greenAbstract read
In one paragraph

Article in Annals of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 20% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
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 5 institutions in 1 country.

Gertraud MaskarinecUniversity of Hawaii Cancer Center, Honolulu, HI. Electronic address: gertraud@cc.hawaii.edu.
Phyllis Ash RaquinioUniversity of Hawaii Cancer Center, Honolulu, HI. Electronic address: praquini@hawaii.edu.
Veronica W SetiawanKeck School of Medicine, University of Southern California, Los Angeles, CA. Electronic address: vsetiawa@usc.edu.
Thomas ErnstUniversity of Maryland School of Medicine, Baltimore, MD. Electronic address: thomas.ernst@umm.edu.
Adrian A FrankeUniversity of Hawaii Cancer Center, Honolulu, HI. Electronic address: adrian@cc.hawaii.edu.
Steven D BuchthalUniversity of Hawaii Cancer Center, Honolulu, HI. Electronic address: buchthal@hawaii.edu.
John A ShepherdUniversity of Hawaii Cancer Center, Honolulu, HI. Electronic address: johnshep@hawaii.edu.
Lynne R WilkensUniversity of Hawaii Cancer Center, Honolulu, HI. Electronic address: lynne@cc.hawaii.edu.
Unhee LimUniversity of Hawaii Cancer Center, Honolulu, HI. Electronic address: ulim@cc.hawaii.eddu.
Loïc Le MarchandUniversity of Hawaii Cancer Center, Honolulu, HI. Electronic address: loic@cc.hawaii.edu.
University of Hawaiʻi at Mānoa · USUniversity of Hawaii System · USUniversity of Hawaii Cancer CenterUniversity of Maryland, Baltimore · USUniversity of Southern California · US

Funding

Southern California Clinical and Translational Science InstituteUL1TR001855 · NCATS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Thomas A Buchanan, Michele D. Kipke · 2016 to 2026
$81.3M
University of Hawaii Cancer Center CCSGP30CA071789 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI Xin Chen · 1996 to 2026
$56.2M
Understanding Population Differences in Cancer: The MEC StudyU01CA164973 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI HAIMAN, CHRISTOPHER ALAN, LE MARCHAND, LOIC · 2015 to 2025
$37.4M
Southern California Clinical and Translational Science InstituteUL1TR000130 · NCATS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BUCHANAN, THOMAS A · 2012 to 2015
$36.0M
Recruitment and Data CollectionP01CA168530 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI LE MARCHAND, LOIC · 2012 to 2016
$19.9M
NCATS NIH HHS UL1 TR000130NCATS NIH HHS UL1 TR001855NCI NIH HHS P01 CA168530NCI NIH HHS P30 CA071789NCI NIH HHS U01 CA164973
6 · The paper itself

Abstract

purposeVisceral adipose tissue (VAT) may be more important than subcutaneous fat in type 2 diabetes (T2D) etiology. We examined a VAT score developed in reference to MRI measurement of VAT in the Multiethnic Cohort (MEC) as a risk factor for incident T2D.

methodsTwo nested case-control studies of cancer allowed calculation of the VAT score based on anthropometric measures and 8 biomarkers among 2,556 participants without T2D. Incident cases were identified from Medicare linkages and self-reports after blood draws in 2001-2006. Cox regression with age as time metric was applied to estimate the association of the VAT score with T2D.

resultsDuring 10.1 ± 2.4 years, 355 incident T2D cases were identified. VAT scores were higher in T2D cases than among those without disease (5.06±0.43 vs. 4.95±0.41; P<0.0001) and significantly associated with T2D (HR = 2.70; 95%CI 1.60, 4.58 per unit) with similar values in men (HR = 2.99; 95%CI 1.03, 8.73) and women (HR = 2.61; 95%CI 1.39, 4.91). A significant association was observed in all five ethnic groups but only statistically significant among Japanese Americans (HR = 6.24; 95%CI 2.34, 16.68).

conclusionThese findings support that VAT as estimated by a biomarker-based score predicts T2D incidence beyond BMI in particular among older adults of Japanese ancestry.

Indexed as

Diabetes Mellitus, Type 2AdiposityAgedBiomarkersBody Mass IndexFemaleHumansMaleMedicareUnited StatesBiomarkersAsianBody fat distributionEthnicityIncidenceProspective studyType 2 diabetesVisceral adipose tissue

Identifiers

PMID34298074
PMCPMC8500936
OpenAlexW3184300872

What OpenQuestion holds

Textmetadata
LicenceTDM
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.