Evidence map›Paper›PMID 24976265›Full record

ReviewCurrent opinion in biotechnology2014

Systems analysis and the prediction and prevention of Type 2 diabetes mellitus.

Richard N Bergman, Darko Stefanovski, Stella P Kim

Abstract readReview
In one paragraph

Review in Current opinion in biotechnology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

8 citing papers in PubMed.

  1. Article
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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

3 authors.

Richard N BergmanCedars-Sinai Diabetes and Obesity Research Institute, Los Angeles, CA 90048, USA. Electronic address: Richard.Bergman@cshs.org.
Darko StefanovskiCedars-Sinai Diabetes and Obesity Research Institute, Los Angeles, CA 90048, USA.
Stella P KimCedars-Sinai Diabetes and Obesity Research Institute, Los Angeles, CA 90048, USA.

Funding

QUANTITATION OF FACTORS REGULATING GLUCOSE TOLERANCER01DK029867 · NIDDK · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MARILYN ADER · 1986 to 2026
$11.0M
Quantitative Studies of Metabolic Organ DynamicsR37DK027619 · NIDDK · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BERGMAN, RICHARD NATHAN · 2007 to 2016
$6.2M
NIDDK NIH HHS R01 DK029867NIDDK NIH HHS R37 DK027619
6 · The paper itself

Abstract

Prevalence of Type 2 diabetes has increased at an alarming rate, highlighting the need to correctly predict the development of this disease in order to allow intervention and thus, slow progression of the disease and resulting metabolic derangement. There have been many recent 'advances' geared toward the detection of pre-diabetes, including genome wide association studies and metabolomics. Although these approaches generate a large amount of data with a single blood sample, studies have indicated limited success using genetic and metabolomics information alone for identification of disease risk. Clinical assessment of the disposition index (DI), based on the hyperbolic law of glucose tolerance, is a powerful predictor of Type 2 diabetes, but is not easily assessed in the clinical setting. Thus, it is evident that combining genetic or metabolomic approaches for a more simple assessment of DI may provide a useful tool to identify those at highest risk for Type 2 diabetes, allowing for intervention and prevention.

Indexed as

Systems BiologyDiabetes Mellitus, Type 2Genome-Wide Association StudyHumansInsulinMetabolomicsPredictive Value of TestsInsulin

Identifiers

PMID24976265
PMCPMC5931209

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.