Evidence map›Paper›PMID 38435999›Full record

ArticlePeerJ2024

Co-differential genes between DKD and aging: implications for a diagnostic model of DKD.

Hongxuan Du, Kaiying He, Jing Zhao, Qicai You, Xiaochun Zhou, Jianqin Wang

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
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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

6 authors at 3 institutions in 1 country.

Hongxuan Du *Lanzhou University, Lanzhou, Gansu, China.
Kaiying He *Lanzhou University, Lanzhou, Gansu, China.
Jing ZhaoDepartment of Pediatric Cardiology, nephrology, rheumatism and Immunology, Gansu Provincial Central Hospital, Lanzhou, Gansu, China.
Qicai YouLanzhou University, Lanzhou, Gansu, China.
Xiaochun ZhouDepartment of Nephrology, The Second Hospital of Lanzhou University, Lanzhou, Gansu, China.
Jianqin WangLanzhou University, Lanzhou, Gansu, China.
Lanzhou University Second Hospital · CNLanzhou University · CNGansu Provincial Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus (DM) that is closely related to aging. In this study, we found co-differential genes between DKD and aging and established a diagnostic model of DKD based on these genes. Methods: Differentially expressed genes (DEGs) in DKD were screened using GEO datasets. The intersection of the DEGs of DKD and aging-related genes revealed DKD and aging co-differential genes. Based on this, a genetic diagnostic model for DKD was constructed using LASSO regression. The characteristics of these genes were investigated using consensus clustering, WGCNA, functional enrichment, and immune cell infiltration. Finally, the expression of diagnostic model genes was analyzed using single-cell RNA sequencing (scRNA-seq) in DKD mice (model constructed by streptozotocin (STZ) injection and confirmed by tissue section staining). Results: First, there were 159 common differential genes between DKD and aging, 15 of which were significant. These co-differential genes were involved in stress, glucolipid metabolism, and immunological functions. Second, a genetic diagnostic model (including IGF1, CETP, PCK1, FOS, and HSPA1A) was developed based on these genes. Validation of these model genes in scRNA-seq data revealed statistically significant variations in FOS, HSPA1A, and PCK1 gene expression between the early DKD and control groups. Validation of these model genes in the kidneys of DKD mice revealed that Igf1, Fos, Pck1, and Hspa1a had lower expression in DKD mice, with Igf1 expression being statistically significant. Conclusion: Our findings suggest that DKD and aging co-differential genes are significant in DKD diagnosis, providing a theoretical basis for novel research directions on DKD.

Indexed as

AgingDiabetic NephropathiesAnimalsCluster AnalysisControl GroupsKidneyMiceAgingDiabetic kidney diseaseDiagnosisDKD mice modelsSingle-cell RNA-seq analysis

Identifiers

PMID38435999
PMCPMC10909364
OpenAlexW4392287337

What OpenQuestion holds

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LicenceCC BY
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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.