Evidence map›Paper›PMID 29626220›Full record

Observational studyDiabetologia2018

Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose.

Jordi Merino, Aaron Leong, Ching-Ti Liu, Bianca Porneala, Geoffrey A Walford, Marcin von Grotthuss, Thomas J Wang, Jason Flannick, Josée Dupuis, Daniel Levy and 3 more

Open access · bronzeAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Diabetologia, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed, 5 pooled it
6.7field-weighted citation impact, top 2% 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

85 citing papers in PubMed, 5 syntheses or guidelines pooled it, 147 citations in OpenAlex.

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25 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 7 institutions in 1 country.

Jordi MerinoDiabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Aaron LeongPrograms in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Ching-Ti LiuDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Bianca PornealaDivision of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St, Boston, MA, 02114, USA.
Geoffrey A WalfordDiabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Marcin von GrotthussPrograms in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Thomas J WangDivision of Cardiovascular Medicine, Vanderbilt University, Nashville, TN, USA.
Jason FlannickDiabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Josée DupuisDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Daniel LevyThe Framingham Heart Study, National Heart, Lung and Blood Institute, National Institutes of Health, Framingham, MA, USA.
Robert E GersztenDivision of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Jose C FlorezDiabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
James B MeigsPrograms in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. JMEIGS@mgh.harvard.edu.
Broad Institute · USMassachusetts General Hospital · USBoston University · USFramingham Heart Study · USHarvard University · USNational Heart Lung and Blood Institute · USVanderbilt University · US

Funding

ROLE OF DIETARY CONSTITUENTS ON GENE EXPRESSION IN INTESTINAL EPITHELIUMP30DK040561 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI ALEXANDER A SOUKAS · 1994 to 2026
$31.6M
Common Genetic Variation and Quantitative Diabetes TraitsR01DK078616 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI MEIGS, JAMES B · 2008 to 2014
$5.0M
TOPMed Omics of Type 2 Diabetes and Quantitative TraitsUM1DK078616 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI MANNING, ALISA KNODLE · 2021 to 2025
$3.8M
Rare Sequence Variation and Diabetes Quantitative TraitsU01DK078616 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI MEIGS, JAMES B · 2015 to 2019
$3.6M
Epidemiology of Precursors to Type 2 DiabetesK24DK080140 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI MEIGS, JAMES B · 2007 to 2016
$1.9M
Trace Elements and CVD Risks Factors Among Young AdultsR01HL081572 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAHE, KA · 2006 to 2008
$1.2M
Mentoring Investigators on the Clinical Translation of Genetic Discoveries in Type 2 DiabetesK24DK110550 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI FLOREZ, JOSE CARLOS · 2016 to 2020
$812k
THE FRAMINGHAM HEART STUDY-N01HC25195-268025195-268025195N01HC025195 · HC · TRUSTEES OF BOSTON UNIVERSITY · PI WOLF, PHILIP A · 2002 to 2006
–
H2020 Marie Skłodowska-Curie Actions 2015-703787NHLBI NIH HHS N01 HC025195NHLBI NIH HHS N01-HC-25195NHLBI NIH HHS R01 HL081572NIDDK NIH HHS K24 DK080140NIDDK NIH HHS K24 DK110550NIDDK NIH HHS K24-DK110550NIDDK NIH HHS P30 DK040561NIDDK NIH HHS R01 DK078616NIDDK NIH HHS U01 DK078616NIDDK NIH HHS UM1 DK078616
6 · The paper itself

Abstract

aims/hypothesisIdentifying the metabolite profile of individuals with normal fasting glucose (NFG [<5.55 mmol/l]) who progressed to type 2 diabetes may give novel insights into early type 2 diabetes disease interception and detection.

methodsWe conducted a population-based prospective study among 1150 Framingham Heart Study Offspring cohort participants, age 40-65 years, with NFG. Plasma metabolites were profiled by LC-MS/MS. Penalised regression models were used to select measured metabolites for type 2 diabetes incidence classification (training dataset) and to internally validate the discriminatory capability of selected metabolites beyond conventional type 2 diabetes risk factors (testing dataset).

resultsOver a follow-up period of 20 years, 95 individuals with NFG developed type 2 diabetes. Nineteen metabolites were selected repeatedly in the training dataset for type 2 diabetes incidence classification and were found to improve type 2 diabetes risk prediction beyond conventional type 2 diabetes risk factors (AUC was 0.81 for risk factors vs 0.90 for risk factors + metabolites, p = 1.1 × 10 CONCLUSIONS/

interpretationIn individuals with NFG, information from a discrete set of 19 metabolites improved prediction of type 2 diabetes beyond conventional risk factors. In addition, the nitrogen metabolism pathway and its components emerged as a potential effector of earliest stages of type 2 diabetes pathophysiology.

Indexed as

MetabolomicsAdultAgedArea Under CurveBlood GlucoseComputational BiologyDiabetes Mellitus, Type 2FemaleGlycated HemoglobinGlycineHumansIncidenceMaleMendelian Randomization AnalysisMiddle AgedPhenylalanineBlood GlucoseGlycated HemoglobinGlycinePhenylalanineTaurineMetabolomicsNormoglycaemiaType 2 diabetes pathophysiologyType 2 diabetes prediction

Identifiers

PMID29626220
PMCPMC5940516
OpenAlexW2796144553

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.