Evidence map›Paper›PMID 30830377›Full record

Trial reportMetabolomics : Official journal of the Metabolomic Society2018

1,5-Anhydroglucitol predicts CKD progression in macroalbuminuric diabetic kidney disease: results from non-targeted metabolomics.

Gesiane Tavares, Gabriela Venturini, Kallyandra Padilha, Roberto Zatz, Alexandre C Pereira, Ravi I Thadhani, Eugene P Rhee, Silvia M O Titan

Open access · greenAbstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Metabolomics : Official journal of the Metabolomic Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
2.5field-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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.

  1. Pooled it
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  3. Urinary biomarkers of diabetic kidney disease.World journal of diabetes · 2026
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  5. Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease.Diabetic medicine : a journal of the British Diabetic Association · 2025
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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

8 authors at 2 institutions in 2 countries.

Gesiane TavaresNephrology Division, University of São Paulo Medical School, Av Dr Enéas de Carvalho Aguiar, 255, São Paulo, SP, 05403-000, Brazil. gesianeft@hotmail.com.
Gabriela VenturiniLaboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo Medical School, São Paulo, SP, Brazil.
Kallyandra PadilhaLaboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo Medical School, São Paulo, SP, Brazil.
Roberto ZatzNephrology Division, University of São Paulo Medical School, Av Dr Enéas de Carvalho Aguiar, 255, São Paulo, SP, 05403-000, Brazil.
Alexandre C PereiraLaboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo Medical School, São Paulo, SP, Brazil.
Ravi I ThadhaniDivision of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
Eugene P RheeDivision of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
Silvia M O TitanNephrology Division, University of São Paulo Medical School, Av Dr Enéas de Carvalho Aguiar, 255, São Paulo, SP, 05403-000, Brazil.
Universidade de São Paulo · BRMassachusetts General Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMetabolomics allows exploration of novel biomarkers and provides insights on metabolic pathways associated with disease. To date, metabolomics studies on CKD have been largely limited to Caucasian populations and have mostly examined surrogate end points.

objectiveIn this study, we evaluated the role of metabolites in predicting a primary outcome defined as dialysis need, doubling of serum creatinine or death in Brazilian macroalbuminuric DKD patients.

methodsNon-targeted metabolomics was performed on plasma from 56 DKD patients. Technical triplicates were done. Metabolites were identified using Agilent Fiehn GC/MS Metabolomics and NIST libraries (Agilent MassHunter Work-station Quantitative Analysis, version B.06.00). After data cleaning, 186 metabolites were left for analyses.

resultsDuring a median follow-up time of 2.5 years, the PO occurred in 17 patients (30.3%). In non-parametric testing, 13 metabolites were associated with the PO. In univariate Cox regression, only 1,5-anhydroglucitol (HR 0.10; 95% CI 0.01-0.63, p = .01), norvaline and L-aspartic acid were associated with the PO. After adjustment for baseline renal function, 1,5-anhydroglucitol (HR 0.10; 95% CI 0.02-0.63, p = .01), norvaline (HR 0.01; 95% CI 0.001-0.4, p = .01) and aspartic acid (HR 0.12; 95% CI 0.02-0.64, p = .01) remained significantly and inversely associated with the PO.

conclusionOur results show that lower levels of 1,5-anhydroglucitol, norvaline and L-aspartic acid are associated with progression of macroalbuminuric DKD. While norvaline and L-aspartic acid point to interesting metabolic pathways, 1,5-anhydroglucitol is of particular interest since it has been previously shown to be associated with incident CKD. This inverse biomarker of hyperglycemia should be further explored as a new tool in DKD.

Indexed as

MetabolomicsAlbuminuriaBiomarkersBrazilCreatinineDeoxyglucoseDiabetic NephropathiesDouble-Blind MethodGas Chromatography-Mass SpectrometryHumans1,5-anhydroglucitolBiomarkersCreatinineDeoxyglucose1,5-AnhydroglucitolDiabetic kidney diseaseMetabolomics

Identifiers

PMID30830377
OpenAlexW2792190290

What OpenQuestion holds

Textmetadata
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.