Evidence map›Paper›PMID 37558891›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2023

Methods for estimating insulin resistance from untargeted metabolomics data.

Fang-Chi Hsu, Nicholette D Palmer, Shyh-Huei Chen, Maggie C Y Ng, Mark O Goodarzi, Jerome I Rotter, Lynne E Wagenknecht, Michael P Bancks, Richard N Bergman, Donald W Bowden

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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 4 institutions in 1 country.

Fang-Chi HsuDepartment of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Nicholette D PalmerDepartment of Biochemistry, Wake Forest University School of Medicine, 1 Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
Shyh-Huei ChenDepartment of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Maggie C Y NgVanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Mark O GoodarziDivision of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jerome I RotterThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
Lynne E WagenknechtDivision of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Michael P BancksDepartment of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA.
Richard N BergmanDiabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Donald W BowdenDepartment of Biochemistry, Wake Forest University School of Medicine, 1 Medical Center Boulevard, Winston-Salem, NC, 27157, USA. dbowden@wakehealth.edu.ORCID http://orcid.org/0000-0003-4861-8005
Wake Forest University · USCedars-Sinai Medical Center · USUCLA Medical Center · USVanderbilt University Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

contextInsulin resistance is associated with multiple complex diseases; however, precise measures of insulin resistance are invasive, expensive, and time-consuming.

objectiveDevelop estimation models for measures of insulin resistance, including insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR) from metabolomics data.

designInsulin Resistance Atherosclerosis Family Study (IRASFS).

settingCommunity based.

participantsMexican Americans (MA) and African Americans (AA). MAIN OUTCOME: Estimation models for measures of insulin resistance, i.e. SI and HOMA-IR.

resultsLeast Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net regression were used to build insulin resistance estimation models from 1274 metabolites combined with clinical data, e.g. age, sex, body mass index (BMI). Metabolite data were transformed using three approaches, i.e. inverse normal transformation, standardization, and Box Cox transformation. The analysis was performed in one MA recruitment site (San Luis Valley, Colorado (SLV); N = 450) and tested in another MA recruitment site (San Antonio, Texas (SA); N = 473). In addition, the two MA recruitment sites were combined and estimation models tested in the AA recruitment sample (Los Angeles, California; N = 495). Estimated and empiric SI were correlated in the SA (r

conclusionsWe have developed a method for estimating insulin resistance with metabolomics data that has the potential for application to a wide range of biomedical studies and conditions.

Indexed as

AtherosclerosisInsulin ResistanceHumansMetabolomicsElastic netInsulin resistanceLASSOMachine learningMetabolomics

Identifiers

PMID37558891
PMCPMC10412652
OpenAlexW4385690790

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.