Evidence map›Paper›PMID 39430286›Full record

ArticleKidney diseases (Basel, Switzerland)2024

Integrative Analysis by Mendelian Randomization and Large-Scale Single-Cell Transcriptomics Reveals Causal Links between B Cell Subtypes and Diabetic Kidney Disease.

Yuan Ma, Jing Ji, Xintong Liu, Xizi Zheng, Lingyi Xu, Qingqing Zhou, Zehua Li, Li Yang

Abstract read
In one paragraph

Article in Kidney diseases (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. CD4Frontiers in immunology · 2026
    Review
  4. Review
  5. Article
  6. Article
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.

Yuan MaKey Laboratory of Renal Disease-Ministry of Health of China, Key Laboratory of CKD Prevention and Treatment (Peking University)-Ministry of Education of China, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
Jing JiKey Laboratory of Renal Disease-Ministry of Health of China, Key Laboratory of CKD Prevention and Treatment (Peking University)-Ministry of Education of China, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
Xintong LiuKey Laboratory of Renal Disease-Ministry of Health of China, Key Laboratory of CKD Prevention and Treatment (Peking University)-Ministry of Education of China, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
Xizi ZhengKey Laboratory of Renal Disease-Ministry of Health of China, Key Laboratory of CKD Prevention and Treatment (Peking University)-Ministry of Education of China, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
Lingyi XuKey Laboratory of Renal Disease-Ministry of Health of China, Key Laboratory of CKD Prevention and Treatment (Peking University)-Ministry of Education of China, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
Qingqing ZhouKey Laboratory of Renal Disease-Ministry of Health of China, Key Laboratory of CKD Prevention and Treatment (Peking University)-Ministry of Education of China, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
Zehua LiKey Laboratory of Renal Disease-Ministry of Health of China, Key Laboratory of CKD Prevention and Treatment (Peking University)-Ministry of Education of China, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
Li YangKey Laboratory of Renal Disease-Ministry of Health of China, Key Laboratory of CKD Prevention and Treatment (Peking University)-Ministry of Education of China, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The increasing incidence of diabetic kidney disease (DKD) and the challenges in its management highlight the necessity for a deeper understanding of its pathogenesis. While recent studies have underscored the substantial impact of circulating immunity on the development of diabetic microvascular complications such as retinopathy and neuropathy, research on circulating immunity in DKD remains limited. Methods: This study utilized Mendelian randomization analysis to explore the potential independent causal relationships between circulating immune cells and DKD pathogenesis. Additionally, a combination of single-cell disease relevance score (scDRS) and immune cell infiltration analysis was employed to map the circulating immunity landscape in DKD patients. Results: Ten immune traits, including 5 of B cells, 2 of T cells, 2 of granulocytes, and one of monocytes, were defined to be associated with the pathogenesis of DKD. Notably, Conclusion: Our study revealed the causal relations between circulating immunity and DKD susceptibility, particularly highlighted the potential roles of B cell subtypes in DKD development. Further studies addressing the related mechanisms would broaden the current understanding of DKD pathogenesis.

Indexed as

B cellCirculating immune cellsDiabetic kidney diseaseImmune cell infiltration analysisMendelian randomizationSingle-cell disease relevance score

Identifiers

PMID39430286
PMCPMC11488840

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.