Evidence mapPaperPMID 37728808Full record

ArticleInternational urology and nephrology2024

Metabolic implications of amino acid metabolites in chronic kidney disease progression: a metabolomics analysis using OPLS-DA and MBRole2.0 database.

Jianhao Kang, Xinghua Guo, Hongquan Peng, Ying Deng, Jiahui Lai, Leile Tang, Chiwa Aoieong, Tou Tou, Tsungyang Tsai, Xun Liu

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Article in International urology and nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.5field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

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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

14 citing papers in PubMed, 16 citations in OpenAlex.

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  7. ATF4 in proximal tubules modulates kidney function and modifies the metabolome.Journal of molecular medicine (Berlin, Germany) · 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

10 authors at 2 institutions in 1 country.

Jianhao Kang *Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Xinghua Guo *Department of Rheumatology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
Hongquan PengDepartment of Nephrology, Kiang Wu Hospital, Macau, Macao SAR, China. hpeng93170@gmail.com.
Ying DengDivision of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Jiahui LaiDivision of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Leile TangDepartment of Cardiovasology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
Chiwa AoieongDepartment of Nephrology, Kiang Wu Hospital, Macau, Macao SAR, China.
Tou TouDepartment of Nephrology, Kiang Wu Hospital, Macau, Macao SAR, China.
Tsungyang TsaiDepartment of Nephrology, Kiang Wu Hospital, Macau, Macao SAR, China.
Xun LiuDivision of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China. naturestyle@163.com.ORCID http://orcid.org/0000-0002-2360-1429
Sun Yat-sen University · CNKiang Wu Hospital · CN

Funding

The Guangzhou Science and Technology Planning Project Grant No. 202002020047The National Natural Science Foundation of China 81070612The National Natural Science Foundation of China 81370866The National Natural Science Foundation of China Grant No. 81873631The Science and Technology Development Fund, Macau SAR File no. 0032/2018/A1
6 · The paper itself

Abstract

backgroundAs chronic kidney disease (CKD) progresses, metabolites undergo diverse transformations. Nevertheless, the impact of these metabolic changes on the etiology, progression, and prognosis of CKD remains uncertain. Our objective is to conduct a metabolomics analysis to scrutinize metabolites and identify significant metabolic pathways implicated in CKD progression, thereby pinpointing potential therapeutic targets for CKD management.

methodsWe recruited 145 patients with CKD and determined their mGFR by measuring the plasma iohexol clearance, whereupon we partitioned them into four groups based on their mGFR values. Non-targeted metabolomics analysis was conducted using UPLC-MS/MS assays. Differential metabolites were identified via one-way ANOVA, PCA, PLS-DA, and OPLS-DA analyses employing the MetaboAnalyst 5.0 platform. Ultimately, we performed differential metabolite pathway enrichment analysis, using both the MetaboAnalyst 5.0 platform and the MBRole2.0 database.

resultsAccording to the findings of the MBRole2.0 and MetaboAnalyst 5.0 enrichment analysis, six amino acid metabolism pathways were discovered to have significant roles in the progression of CKD, with the glycine, serine, and threonine metabolism pathway being the most prominent. The latter enriched 14 differential metabolites, of which six decreased while two increased concomitantly with renal function deterioration.

conclusionsThe metabolic analysis unveiled that glycine, serine, and threonine metabolism plays a pivotal role in the progression of CKD. Specifically, glycine was found to increase while serine decreased with the deterioration of CKD.

Indexed as

Amino AcidsRenal Insufficiency, ChronicBiomarkersChromatography, LiquidGlycineHumansMetabolomicsSerineTandem Mass SpectrometryThreonineAmino AcidsBiomarkersGlycineSerineThreonineAmino acids metabolitesChronic kidney diseaseGlycineMetabolomicsOPLS-DASerineThreonine metabolism

Identifiers

PMID37728808
OpenAlexW4386878576

What Socratic 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 Socratic graph.