120017
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.6 Breast Cancer (Dove Med Press)
- 4.3 Clin Epidemiol
- 2.6 Cancer Manag Res
- 3.2 Infect Drug Resist
- 4.1 Clin Interv Aging
- 6.1 Drug Des Dev Ther
- 4.1 Int J Chronic Obstr
- 8.7 Int J Nanomed
- 2.5 Int J Women's Health
- 3.2 Neuropsych Dis Treat
- 2.4 OncoTargets Ther
- 2.6 Patient Prefer Adher
- 2.6 Ther Clin Risk Manag
- 3.1 J Pain Res
- 3.5 Diabet Metab Synd Ob
- 4.5 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 2.4 Pharmgenomics Pers Med
- 2.6 Risk Manag Healthc Policy
- 4.6 J Inflamm Res
- 2.3 Int J Gen Med
- 3.9 J Hepatocell Carcinoma
- 3.3 J Asthma Allergy
- 2.5 Clin Cosmet Investig Dermatol
- 3.0 J Multidiscip Healthc
已发表论文
阐明自闭症谱系障碍的生物学联系：从血液风险因素识别到经验证的早期风险分层列线图
Authors Lin L, Wang X, Wang P, Wang Q, Sun J, Li H, Cong F
Received 17 September 2025
Accepted for publication 6 January 2026
Published 17 January 2026 Volume 2026:22 568119
DOI https://doi.org/10.2147/NDT.S568119
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Taro Kishi
Lin Lin,1– 3,* Xixi Wang,2,* Peng Wang,1 Qi Wang,1 Jin Sun,1 Huanjie Li,2 Fengyu Cong1,2,4– 6
1Women and Children’s Hospital of Dalian University of Technology, Dalian, Liaoning Province, People’s Republic of China; 2School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning Province, People’s Republic of China; 3Department of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, People’s Republic of China; 4Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland; 5Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education, Dalian, People’s Republic of China; 6School of Software Engineering, Dalian University, Dalian, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Huanjie Li, Email hj_li@dlut.edu.cn Jin Sun, Email 916054417@qq.com
Objective: Blood-based biomarkers are valuable for investigating Autism Spectrum Disorder (ASD). This study aimed to identify key blood-based risk factors for ASD and to develop and validate a clinical model for predicting disease risk.
Methods: We retrospectively analyzed data from 879 children, including patients with ASD and healthy controls. After using propensity score matching to balance for age and sex, we employed multivariate logistic regression to identify independent risk factors from routine blood parameters, vitamins, and trace elements. We then characterized dose-response relationships and a nomogram for ASD risk prediction was constructed and internally validated.
Results: Four independent risk factors were identified: calcium, serum iron, vitamin D, and platelet distribution width (PDW). Lower levels of serum iron and vitamin D, along with higher PDW levels, were significantly associated with an increased risk of ASD. Calcium exhibited a non-linear relationship, with risk peaking at a concentration of 1.13 mmol/L. The nomogram based on these four markers demonstrated strong predictive performance, with sensitivities of 84.6% and 78.6% and specificities of 73.4% and 79.0% for the training and validation sets, respectively.
Conclusion: These findings highlight the feasibility of calcium, iron, vitamin D, and PDW as independent blood-based risk factors for ASD predicting and elucidated their specific patterns of association with ASD prevalence. The nomogram ASD risk prediction model, constructed utilizing these key markers, demonstrated commendable discrimination, calibration, and clinical utility. This model holds considerable promise as an efficacious tool for early ASD screening and risk assessment, with significant potential for clinical translation.
Keywords: autism spectrum disorder, ASD, propensity score matching, PSM, clinical risk factors, dose-response relationship, restricted cubic spline, RCS, nomogram prediction model
1Women and Children’s Hospital of Dalian University of Technology, Dalian, Liaoning Province, People’s Republic of China; 2School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning Province, People’s Republic of China; 3Department of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, People’s Republic of China; 4Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland; 5Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education, Dalian, People’s Republic of China; 6School of Software Engineering, Dalian University, Dalian, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Huanjie Li, Email hj_li@dlut.edu.cn Jin Sun, Email 916054417@qq.com
Objective: Blood-based biomarkers are valuable for investigating Autism Spectrum Disorder (ASD). This study aimed to identify key blood-based risk factors for ASD and to develop and validate a clinical model for predicting disease risk.
Methods: We retrospectively analyzed data from 879 children, including patients with ASD and healthy controls. After using propensity score matching to balance for age and sex, we employed multivariate logistic regression to identify independent risk factors from routine blood parameters, vitamins, and trace elements. We then characterized dose-response relationships and a nomogram for ASD risk prediction was constructed and internally validated.
Results: Four independent risk factors were identified: calcium, serum iron, vitamin D, and platelet distribution width (PDW). Lower levels of serum iron and vitamin D, along with higher PDW levels, were significantly associated with an increased risk of ASD. Calcium exhibited a non-linear relationship, with risk peaking at a concentration of 1.13 mmol/L. The nomogram based on these four markers demonstrated strong predictive performance, with sensitivities of 84.6% and 78.6% and specificities of 73.4% and 79.0% for the training and validation sets, respectively.
Conclusion: These findings highlight the feasibility of calcium, iron, vitamin D, and PDW as independent blood-based risk factors for ASD predicting and elucidated their specific patterns of association with ASD prevalence. The nomogram ASD risk prediction model, constructed utilizing these key markers, demonstrated commendable discrimination, calibration, and clinical utility. This model holds considerable promise as an efficacious tool for early ASD screening and risk assessment, with significant potential for clinical translation.
Keywords: autism spectrum disorder, ASD, propensity score matching, PSM, clinical risk factors, dose-response relationship, restricted cubic spline, RCS, nomogram prediction model