Evidence map›Paper›PMID 41089320›Full record

ArticleFrontiers in psychiatry2025

Exploring the relationship and shared mechanisms of major depressive disorder and diabetic kidney disease: a comprehensive clinical and genetic analysis.

Huan-Xi Liu, Ting-Ting Wang, You-di Cheng, Chun-Hui Qu, Shun-Kang Feng, Xiang-Wen Wang, Xiao-Hui Wu, Wen-Xi Sun, Ping Sun

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Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Huan-Xi LiuQingdao University, Qingdao Medical College, Qingdao, China.
Ting-Ting WangQingdao Mental Health Center, Qingdao, Shandong, China.
You-di ChengQingdao Mental Health Center, Qingdao, Shandong, China.
Chun-Hui QuQingdao Mental Health Center, Qingdao, Shandong, China.
Shun-Kang FengQingdao Mental Health Center, Qingdao, Shandong, China.
Xiang-Wen WangQingdao Mental Health Center, Qingdao, Shandong, China.
Xiao-Hui WuClinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wen-Xi Sun *Suzhou Guangji Hospital, Suzhou, Jiangsu, China.
Ping Sun *Qingdao Mental Health Center, Qingdao, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Major depressive disorder (MDD) is a common comorbidity in diabetes mellitus (DM), while diabetic kidney disease (DKD) represents a severe complication of DM. However, the clinical and genetic associations between MDD and DKD remain unclear. This study aimed to investigate their shared biomarkers, molecular pathways, and immune features. Methods: We analyzed data from the National Health and Nutrition Examination Survey (NHANES, 2005-2018) to assess the association between MDD and DKD. Genetic correlation was evaluated using linkage disequilibrium score regression (LDSC), and causality was tested with Mendelian randomization (MR). Gene expression datasets were integrated to identify crosstalk genes, followed by protein-protein interaction (PPI) analysis to detect hub genes. Diagnostic performance was validated using least absolute shrinkage and selection operator (LASSO) regression and receiver operating characteristic (ROC) curves. Immune infiltration was assessed, and potential therapeutic compounds were predicted through connectivity map (cMAP) analysis and molecular docking. Results: Clinical analysis revealed a significant association between MDD and DKD (OR = 1.45, 95% CI: 1.28-1.64). LDSC indicated a significant genetic correlation (r = 0.2153, P = 0.008), although MR analysis did not support a causal relationship. A total of 83 crosstalk genes were identified, primarily enriched in inflammation and immune regulation pathways. PPI analysis highlighted eight hub genes, with CD163 and KLRB1 emerging as promising shared diagnostic biomarkers. Validation using LASSO and ROC confirmed their diagnostic potential. Immune infiltration analysis revealed shared immune cell alterations. Furthermore, cMAP analysis and molecular docking suggested rucaparib and levocetirizine as candidate therapeutic agents. Discussion: Our findings demonstrate a genetic and immunological link between MDD and DKD. CD163 and KLRB1 may serve as potential biomarkers and therapeutic targets, offering new insights into the shared mechanisms and treatment strategies for comorbid MDD and DKD.

Indexed as

diabetic kidney diseasegenetic correlationmajor depressive disorderNHANEStranscriptomic analysis

Identifiers

PMID41089320
PMCPMC12515961

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