Etiology of Nephrotic Syndrome: Insights from Univariate and Multivariate Mendelian Randomization Study
Renal failure（2025）
Abstract
Nephrotic syndrome (NS) is a common cause of chronic glomerular disease. However, the precise way in which one or more risk exposure traits of renal injury lead to NS remains unclear. In this study, we systematically examined the causal relationships between NS and various exposure traits, including traits related to chronic hepatitis B/C infection, COVID-19 (hospitalized), general allergy status, herbal tea intake, immunoglobulin E, childhood obesity, and the human leukocyte antigen (HLA)-II histocompatibility DM α/DP β1/DQ α2 chain, via multivariate Mendelian randomization (MVMR). A previously reported exposure trait, ulcerative colitis, was also included to analyze the independent effect of each significant exposure on the risk of developing NS. In the univariable MR analysis, immunoglobulin E (OR = 5.62, 95% CI = 2.91-10.84, p = 2.67 × 10-7) and the HLA-II histocompatibility DQ α2 chain (OR = 0.70, 95% CI = 0.63-0.80, p = 2.83 × 10-7) were shown to have effect estimates consistent with a greater risk of developing NS. The reverse MR analysis showed no evidence of causal effect from NS to histocompatibility DQ α2 chain (p = 0.76). In MVMR, only the HLA-II histocompatibility DQ α2 chain retained a robust effect (OR = 0.71, 95% CI = 0.61-0.82; p = 9.39 × 10-6), and the estimate for immunoglobulin E was weakened (OR = 1.04, 95% CI = 0.60-2.13; p = 0.92). With two independent ulcerative colitis resources used for validation, ulcerative colitis was not significantly associated with NS. This study provides genetic evidence that the HLA-II histocompatibility DQ α2 chain has a predominant causal effect on the risk of developing NS. HLA-II histocompatibility-mediated immune abnormalities may lead to subtypes of NS and its pathological changes.
MoreTranslated text
Key words
Multivariate Mendelian randomization,nephrotic syndrome,steroid-sensitive nephrotic syndrome,human leukocyte antigen
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper