Evidence map›Paper›PMID 35113332›Full record

ReviewCurrent diabetes reports2022

Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms.

Daniel Gonzalez Izundegui, Matthew Nayor

Open access · greenAbstract readReview
In one paragraph

Review in Current diabetes reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

17 citing papers in PubMed, 29 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Daniel Gonzalez IzundeguiDepartment of Medicine, Boston University School of Medicine, Boston, MA, USA.
Matthew NayorSections of Cardiology and Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA. mnayor@bu.edu.ORCID 0000-0002-6993-9396
Boston University · US

Funding

Metabolic Responses to an Oral Mixed Meal Tolerance Test: Intra-individual changes, correlates, and prognostic significanceR01HL156975 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI NAYOR, MATTHEW G. · 2021 to 2025
$3.3M
Metabolic Signatures of Impaired Cardiorespiratory Fitness: Correlates, Prognostic Significance and Modulation with Exercise TrainingK23HL138260 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI NAYOR, MATTHEW G. · 2017 to 2021
$998k
NHLBI NIH HHS K23 HL138260NHLBI NIH HHS R01 HL156975
6 · The paper itself

Abstract

purpose of reviewMetabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding. RECENT

findingsFindings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses. Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.

Indexed as

Diabetes ComplicationsDiabetes Mellitus, Type 2Metabolic DiseasesAmino Acids, Branched-ChainBiomarkersHumansMetabolomicsAmino Acids, Branched-ChainBiomarkersMetabolomicsRisk predictionType 1 diabetesType 2 diabetes

Identifiers

PMID35113332
PMCPMC8934149
OpenAlexW4214533935

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.