Association and Predictive Value of Immunoglobulin and Complement Levels for Incident Coronary Heart Disease: a Nested Case-Control Study in Chinese Adults.
European journal of preventive cardiology(2025)
Abstract
AIMS:To investigate the associations of serum immunoglobulin (Ig) and complement levels with incident coronary heart disease (CHD), and to explore the potential mediating role of C-reactive protein (CRP). METHODS AND RESULTS:We measured serum levels of IgA, IgE, IgG, IgM, complement 3 (C3), complement 4 (C4), and CRP in a nested case-control study within the Dongfeng-Tongji cohort, consisting of 1605 CHD cases and 1605 age- and sex-matched controls. We quantified the associations of serum Ig and complement levels with incident CHD using conditional logistic regression and restricted cubic spline models. Mediation analysis was conducted to explore the role of CRP in these associations. The additional predictive ability of an immune indicator score beyond traditional risk factors was also evaluated. Higher IgA and C3 levels were associated with an increased risk of CHD in a linear manner [odds ratio (OR) (95% confidence interval, CI): 1.35 (1.11-1.62), P = 0.002; OR (95% CI): 2.01 (1.17-3.44), P = 0.01, respectively]. Conversely, higher IgG exhibited a significant linear decrease in CHD risk [OR (95% CI): 0.55 (0.36-0.83), P = 0.005]. C-reactive protein mediated 5.70% and 12.51% in the associations of IgA and C3 with incident CHD, respectively. Adding an immune indicator score to the traditional risk model improved CHD prediction more effectively than adding CRP [area under receiver operating characteristic curve (AUC): 0.85% vs. 0.21%; net reclassification improvement (NRI): 15.33% vs. 7.85%; integrated discrimination improvement (IDI): 0.80% vs. 0.17%]. CONCLUSION:Our study identified IgA and C3 as independent risk factors and IgG as a protective factor for CHD. These immune markers may improve CHD risk prediction beyond traditional and CRP models, highlighting their potential for better risk assessment.
MoreTranslated text
求助PDF
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
Summary is being generated by the instructions you defined