Evidence map›Paper›PMID 38067119›Full record

ReviewCells2023

The Role of Autophagy in Type 2 Diabetic Kidney Disease Management.

Che-Hao Tseng, Kavya M Shah, I-Jen Chiu, Li-Li Hsiao

Abstract readReview
In one paragraph

Review in Cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Lipid homeostasis in diabetic kidney disease.International journal of biological sciences · 2024
    Pooled it
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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

4 authors.

Che-Hao TsengRenal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Kavya M ShahRenal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.ORCID 0000-0002-8722-4095
I-Jen ChiuRenal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Li-Li HsiaoRenal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.

Funding

Sundry fund, L.L. H. N/A
6 · The paper itself

Abstract

Diabetic kidney disease (DKD), or diabetic nephropathy (DN), is one of the most prevalent complications of type 2 diabetes mellitus (T2DM) and causes severe burden on the general welfare of T2DM patients around the world. While several new agents have shown promise in treating this condition and potentially halting the progression of the disease, more work is needed to understand the complex regulatory network involved in the disorder. Recent studies have provided new insights into the connection between autophagy, a physiological metabolic process known to maintain cellular homeostasis, and the pathophysiological pathways of DKD. Typically, autophagic activity plays a role in DKD progression mainly by promoting an inflammatory response to tissue damage, while both overactivated and downregulated autophagy worsen disease outcomes in different stages of DKD. This correlation demonstrates the potential of autophagy as a novel therapeutic target for the disease, and also highlights new possibilities for utilizing already available DN-related medications. In this review, we summarize findings on the relationship between autophagy and DKD, and the impact of these results on clinical management strategies.

Indexed as

Diabetes Mellitus, Type 2Diabetic NephropathiesAutophagyHumansautophagydiabetic kidney diseasediabetic nephropathy

Identifiers

PMID38067119
PMCPMC10705810

What OpenQuestion holds

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LicenceCC BY
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.