Evidence map›Paper›PMID 33918080›Full record

ArticleMetabolites2021

Plasma Metabolome and Lipidome Associations with Type 2 Diabetes and Diabetic Nephropathy.

Yan Ming Tan, Yan Gao, Guoshou Teo, Hiromi W L Koh, E Shyong Tai, Chin Meng Khoo, Kwok Pui Choi, Lei Zhou, Hyungwon Choi

Abstract read
In one paragraph

Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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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

9 authors.

Yan Ming TanDepartment of Statistics and Applied Probability, Faculty of Science, National University of Singapore, Singapore 117546, Singapore.
Yan GaoSingapore Eye Research Institute, The Academia, 20 College Road, Singapore 169856, Singapore.
Guoshou TeoDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.
Hiromi W L KohDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.
E Shyong TaiDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.
Chin Meng KhooDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.ORCID 0000-0003-1601-2391
Kwok Pui ChoiDepartment of Statistics and Applied Probability, Faculty of Science, National University of Singapore, Singapore 117546, Singapore.
Lei ZhouSingapore Eye Research Institute, The Academia, 20 College Road, Singapore 169856, Singapore.
Hyungwon ChoiDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.ORCID 0000-0002-6687-3088

Funding

Ministry of Education - Singapore MOE2016-T2-1-001National Research Foundation Singapore IAF-ICP I1901E0040Singapore National Medical Research Council NMRC/CG/M009/2017Singapore National Medical Research Council NMRC/CG/SERI/2017Singapore National Medical Research Council NMRC/OF-LCG/DYNAMO/2017Venerable Yen Pei - National Kidney Foundation NKFRC/2015/07/06
6 · The paper itself

Abstract

We conducted untargeted metabolomics analysis of plasma samples from a cross-sectional case-control study with 30 healthy controls, 30 patients with diabetes mellitus and normal renal function (DM-N), and 30 early diabetic nephropathy (DKD) patients using liquid chromatography-mass spectrometry (LC-MS). We employed two different modes of MS acquisition on a high-resolution MS instrument for identification and semi-quantification, and analyzed data using an advanced multivariate method for prioritizing differentially abundant metabolites. We obtained semi-quantification data for 1088 unique compounds (~55% lipids), excluding compounds that may be either exogenous compounds or treated as medication. Supervised classification analysis over a confounding-free partial correlation network shows that prostaglandins, phospholipids, nucleotides, sugars, and glycans are elevated in the DM-N and DKD patients, whereas glutamine, phenylacetylglutamine, 3-indoxyl sulfate, acetylphenylalanine, xanthine, dimethyluric acid, and asymmetric dimethylarginine are increased in DKD compared to DM-N. The data recapitulate the well-established plasma metabolome changes associated with DM-N and suggest uremic solutes and oxidative stress markers as the compounds indicating early renal function decline in DM patients.

Indexed as

data independent acquisitiondiabetic nephropathyoxidative stressphospholipidsprostaglandinsuremic toxins

Identifiers

PMID33918080
PMCPMC8069978

What OpenQuestion holds

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Registered trials

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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.