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Chen M, Qi Y, Zhang S, Du Y, Cheng H, Gao S. Molecular insights into programmed cell death in esophageal squamous cell carcinoma. PeerJ 2024; 12:e17690. [PMID: 39006030 PMCID: PMC11246021 DOI: 10.7717/peerj.17690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 06/14/2024] [Indexed: 07/16/2024] Open
Abstract
Background Esophageal squamous cell carcinoma (ESCC) is a deadly type of esophageal cancer. Programmed cell death (PCD) is an important pathway of cellular self-extermination and is closely involved in cancer progression. A detailed study of its mechanism may contribute to ESCC treatment. Methods We obtained expression profiling data of ESCC patients from public databases and genes related to 12 types of PCD from previous studies. Hub genes in ESCC were screened from PCD-related genes applying differential expression analysis, machine learning analysis, linear support vector machine (SVM), random forest and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. In addition, based on the HTFtarget and TargetScan databases, transcription factors (TFs) and miRNAs interacting with the hub genes were selected. The relationship between hub genes and immune cells were analyzed using the CIBERSORT algorithm. Finally, to verify the potential impact of the screened hub genes on ESCC occurrence and development, a series of in vitro cell experiments were conducted. Results We screened 149 PCD-related DEGs, of which five DEGs (INHBA, LRRK2, HSP90AA1, HSPB8, and EIF2AK2) were identified as the hub genes of ESCC. The area under the curve (AUC) of receiver operating characteristic (ROC) curve of the integrated model developed using the hub genes reached 0.997, showing a noticeably high diagnostic accuracy. The number of TFs and miRNAs regulating hub genes was 105 and 22, respectively. INHBA, HSP90AA1 and EIF2AK2 were overexpressed in cancer tissues and cells of ESCC. Notably, INHBA knockdown suppressed ECSS cell migration and invasion and altered the expression of important apoptotic and survival proteins. Conclusion This study identified significant molecules with promising accuracy for the diagnosis of ESCC, which may provide a new perspective and experimental basis for ESCC research.
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Affiliation(s)
- Min Chen
- School of Information Engineering, Henan University of Science and Technology, Luoyang, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College of Henan University of Science and Technology, Luoyang, China
| | - Yijun Qi
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College of Henan University of Science and Technology, Luoyang, China
| | - Shenghua Zhang
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College of Henan University of Science and Technology, Luoyang, China
| | - Yubo Du
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College of Henan University of Science and Technology, Luoyang, China
| | - Haodong Cheng
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College of Henan University of Science and Technology, Luoyang, China
| | - Shegan Gao
- School of Information Engineering, Henan University of Science and Technology, Luoyang, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College of Henan University of Science and Technology, Luoyang, China
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Huang J, Zhu Q, Wang B, Wang H, Xie Z, Zhu X, Zhao T, Yang Z. Antiphospholipid antibodies and the risk of adverse pregnancy outcomes in patients with systemic lupus erythematosus: a systematic review and meta-analysis. Expert Rev Clin Immunol 2024; 20:793-801. [PMID: 38445835 DOI: 10.1080/1744666x.2024.2324005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 02/12/2024] [Indexed: 03/07/2024]
Abstract
OBJECTIVE This article aims to evaluate the magnitude of adverse pregnancy outcomes (APOs) risks associated with different antiphospholipid antibody (aPL) profiles in women with systemic lupus erythematosus (SLE). METHODS Multiple databases were investigated to identify articles that explored the relationship between aPLs and APOs in SLE patients. A random effects model was used for calculating pooled odds ratios (OR). Stata version 15.0 was utilized to conduct the meta-analysis. RESULTS There were 5234 patients involved in 30 studies. Overall aPL was linked to an increased incidence of any kind of APOs, fetal loss, and preterm birth. Any kind of APOs and preterm delivery were more common in patients with lupus anticoagulant (LA) positive. Anticardiolipin antibody (aCL) was associated with an increased risk of any kind of APOs and fetal loss. The association between aCL-IgM and fetal loss was also significant. Patients with anti-beta2-glycoprotein1 antibody (antiβ2GP1) positivity had an increased risk of fetal loss. CONCLUSIONS Both LA and aCL were risk factors of APOs in patients with SLE. Not only ACL, particularly aCL-IgM, but antiβ2GP1 were associated with an increased risk of fetal loss, while LA appeared to indicate the risk of preterm birth.PROSPERO (CRD42023388122).
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Affiliation(s)
- Jinge Huang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Qingmiao Zhu
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Baizhou Wang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Hanzheng Wang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhijun Xie
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xingyu Zhu
- Department of Nephrology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Ting Zhao
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zi Yang
- The Third School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
- Teaching Faculty, New Zealand College of Chinese Medicine, Greenlane, New Zealand
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Zhou X, Cai F, Li S, Li G, Zhang C, Xie J, Yang Y. Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges. Int Immunopharmacol 2024; 134:112238. [PMID: 38735259 DOI: 10.1016/j.intimp.2024.112238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/01/2024] [Accepted: 05/08/2024] [Indexed: 05/14/2024]
Abstract
Autoimmune rheumatic diseases are chronic conditions affecting multiple systems and often occurring in young women of childbearing age. The diseases and the physiological characteristics of pregnancy significantly impact maternal-fetal health and pregnancy outcomes. Currently, the integration of big data with healthcare has led to the increasing popularity of using machine learning (ML) to mine clinical data for studying pregnancy complications. In this review, we introduce the basics of ML and the recent advances and trends of ML in different prediction applications for common pregnancy complications by autoimmune rheumatic diseases. Finally, the challenges and future for enhancing the accuracy, reliability, and clinical applicability of ML in prediction have been discussed. This review will provide insights into the utilization of ML in identifying and assisting clinical decision-making for pregnancy complications, while also establishing a foundation for exploring comprehensive management strategies for pregnancy and enhancing maternal and child health.
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Affiliation(s)
- Xiaoshi Zhou
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Feifei Cai
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiran Li
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Guolin Li
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Changji Zhang
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jingxian Xie
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; College of Pharmacy, Southwest Medical University, Luzhou, China
| | - Yong Yang
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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Shahwar DE, Rehmani D, Raza A. A Comparison of Obstetric Outcomes in Antiphospholipid Syndrome Among Pregnant Women With Systemic Lupus Erythematosus. Cureus 2024; 16:e62126. [PMID: 38993403 PMCID: PMC11238018 DOI: 10.7759/cureus.62126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/11/2024] [Indexed: 07/13/2024] Open
Abstract
Objective The aim of this study was to evaluate the maternal and perinatal outcomes in systemic lupus erythematosus (SLE) women with antiphospholipid syndrome (APS). Methods This retrospective case-control study was conducted among pregnant women with SLE with and without APS. Group A included SLE patients with APS, whereas group B included pregnant SLE women without APS. Data were expressed as mean ± standard deviation (SD). Frequency and percentage were computed for categorical data. The chi-square test was used to analyze the difference between categorical data. Results Out of 125 cases of SLE, APS was found in 72 (57.6%) women. Almost 95.8% of patients were on treatment (aspirin and enoxaparin) in group A. Preterm delivery (31.89±7.36 versus 34.46±4.97; p=0.021) and termination of pregnancy (18.1% [13/72] versus 5.7% [3/53]; p=0.04) were statistically significant in group A. Among these terminations, second-trimester intrauterine death is found to be more in group A (SLE with APS) (16.7% [12/72]) as compared to group B (SLE without APS) (5.7% [3/53]) with a p-value of 0.05. Perinatal outcomes including NICU admissions (39% [23/59] versus 24% [12/50]; p=0.071) and neonatal death (12.3% [7/57]; p=0.015) were also found to be statistically significant between the two groups. Conclusion APS with SLE is associated with adverse pregnancy outcomes such as preterm birth, termination of pregnancy due to second-trimester fetal loss, more NICU admission, and neonatal deaths when compared to the control group. Hence, pregnancies with APS with SLE require vigilant monitoring and frequent follow-ups to ensure a positive pregnancy outcome.
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Affiliation(s)
- Dur E Shahwar
- Obstetrics and Gynecology, Aga Khan University, Karachi, PAK
| | - Duriya Rehmani
- Obstetrics and Gynecology, Aga Khan University, Karachi, PAK
| | - Amir Raza
- Obstetrics and Gynecology, Aga Khan University, Karachi, PAK
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Mitranovici MI, Chiorean DM, Moraru R, Moraru L, Caravia L, Tiron AT, Craina M, Cotoi OS. Understanding the Pathophysiology of Preeclampsia: Exploring the Role of Antiphospholipid Antibodies and Future Directions. J Clin Med 2024; 13:2668. [PMID: 38731197 PMCID: PMC11084819 DOI: 10.3390/jcm13092668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Preeclampsia (PE) is a hypertensive disorder in pregnancy associated with significant fetal and maternal complications. Antiphospholipid syndrome (APS) is an acquired form of thrombophilia characterized by recurrent venous or arterial thrombosis and obstetric complications that significantly increases morbidity and mortality rates. While preeclampsia may not be the most prevalent obstetric complication in APS, it significantly impacts the long-term health of both mother and child. The treatment of preeclampsia in antiphospholipid syndrome is different from the treatment of preeclampsia as an independent disease. Despite current treatments involving anticoagulants, antiplatelet agents, and antihypertensive drugs, obstetric complications may persist, underscoring the need for cohesive management and effective treatments. The objective of our review is to briefly present knowledge about the physiopathology of preeclampsia and the role of antiphospholipid antibodies in this process. Based on the existing literature, our review aims to identify future directions in molecular pathology toward the discovery of biomarkers and targeted treatments. The application of multidisciplinary approaches and prognostic models, including new biomarkers, could be beneficial in the prediction of PE.
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Affiliation(s)
- Melinda-Ildiko Mitranovici
- Department of Obstetrics and Gynecology, Emergency County Hospital Hunedoara, 14 Victoriei Street, 331057 Hunedoara, Romania
| | - Diana Maria Chiorean
- Department of Pathology, County Clinical Hospital of Targu Mures, 540072 Targu Mures, Romania;
- Department of Pathophysiology, “George Emil Palade” University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu Street, 540142 Targu Mures, Romania
| | - Raluca Moraru
- Faculty of Medicine, “George Emil Palade” University of Medicine, Pharmacy, Sciences and Technology, 540142 Targu Mures, Romania;
| | - Liviu Moraru
- Department of Anatomy, “George Emil Palade” University of Medicine, Pharmacy, Sciences and Technology, 540142 Targu Mures, Romania;
| | - Laura Caravia
- Division of Cellular and Molecular Biology and Histology, Department of Morphological Sciences, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Andreea Taisia Tiron
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Marius Craina
- Department of Obstetrics and Gynecology, University of Medicine and Pharmacy “Victor Babes”, 300001 Timisoara, Romania;
| | - Ovidiu Simion Cotoi
- Department of Pathology, County Clinical Hospital of Targu Mures, 540072 Targu Mures, Romania;
- Department of Pathophysiology, “George Emil Palade” University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu Street, 540142 Targu Mures, Romania
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Zhan K, Buhler KA, Chen IY, Fritzler MJ, Choi MY. Systemic lupus in the era of machine learning medicine. Lupus Sci Med 2024; 11:e001140. [PMID: 38443092 PMCID: PMC11146397 DOI: 10.1136/lupus-2023-001140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 01/26/2024] [Indexed: 03/07/2024]
Abstract
Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.
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Affiliation(s)
- Kevin Zhan
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Katherine A Buhler
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Irene Y Chen
- Computational Precision Health, University of California Berkeley and University of California San Francisco, Berkeley, California, USA
- Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California, USA
| | - Marvin J Fritzler
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - May Y Choi
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Calgary, Alberta, Canada
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Yu QY, Lin Y, Zhou YR, Yang XJ, Hemelaar J. Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms. Front Big Data 2024; 7:1291196. [PMID: 38495848 PMCID: PMC10941650 DOI: 10.3389/fdata.2024.1291196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 02/12/2024] [Indexed: 03/19/2024] Open
Abstract
We aimed to develop, train, and validate machine learning models for predicting preterm birth (<37 weeks' gestation) in singleton pregnancies at different gestational intervals. Models were developed based on complete data from 22,603 singleton pregnancies from a prospective population-based cohort study that was conducted in 51 midwifery clinics and hospitals in Wenzhou City of China between 2014 and 2016. We applied Catboost, Random Forest, Stacked Model, Deep Neural Networks (DNN), and Support Vector Machine (SVM) algorithms, as well as logistic regression, to conduct feature selection and predictive modeling. Feature selection was implemented based on permutation-based feature importance lists derived from the machine learning models including all features, using a balanced training data set. To develop prediction models, the top 10%, 25%, and 50% most important predictive features were selected. Prediction models were developed with the training data set with 5-fold cross-validation for internal validation. Model performance was assessed using area under the receiver operating curve (AUC) values. The CatBoost-based prediction model after 26 weeks' gestation performed best with an AUC value of 0.70 (0.67, 0.73), accuracy of 0.81, sensitivity of 0.47, and specificity of 0.83. Number of antenatal care visits before 24 weeks' gestation, aspartate aminotransferase level at registration, symphysis fundal height, maternal weight, abdominal circumference, and blood pressure emerged as strong predictors after 26 completed weeks. The application of machine learning on pregnancy surveillance data is a promising approach to predict preterm birth and we identified several modifiable antenatal predictors.
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Affiliation(s)
- Qiu-Yan Yu
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Department of Preventive Medicine, School of Public Health, Wenzhou Medical University, Wenzhou, China
| | - Ying Lin
- Wenzhou Women and Children Health Guidance Center, Wenzhou, China
| | - Yu-Run Zhou
- Wenzhou Women and Children Health Guidance Center, Wenzhou, China
| | - Xin-Jun Yang
- Department of Preventive Medicine, School of Public Health, Wenzhou Medical University, Wenzhou, China
| | - Joris Hemelaar
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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Hwang YM, Wei Q, Piekos SN, Vemuri B, Molani S, Mease P, Hood L, Hadlock J. Maternal-fetal outcomes in patients with immune-mediated inflammatory diseases, with consideration of comorbidities: a retrospective cohort study in a large U.S. healthcare system. EClinicalMedicine 2024; 68:102435. [PMID: 38586478 PMCID: PMC10994966 DOI: 10.1016/j.eclinm.2024.102435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/25/2023] [Accepted: 01/10/2024] [Indexed: 04/09/2024] Open
Abstract
Background Immune-mediated inflammatory diseases (IMIDs) are likely to complicate maternal health. However, literature on patients with IMIDs undergoing pregnancy is scarce and often overlooks the presence of comorbidities. We aimed to evaluate the impact of IMIDs on adverse pregnancy outcomes after assessing and addressing any discrepancies in the distribution of covariates associated with adverse pregnancy outcomes between patients with and without IMIDs. Methods We conducted a retrospective cohort study using data from an integrated U.S. community healthcare system that provides care across Alaska, California, Montana, Oregon, New Mexico, Texas, and Washington. We used a database containing all structured data from electronic health record (EHRs) and analyzed the cohort of pregnant people who had live births from January 1, 2013, through December 31, 2022. We investigated 12 selected IMIDs: psoriasis, inflammatory bowel disease, rheumatoid arthritis, spondyloarthritis, multiple sclerosis, systemic lupus erythematosus, psoriatic arthritis, antiphospholipid syndrome, Sjögren's syndrome, vasculitides, sarcoidosis, and systemic sclerosis. We characterized patients with IMIDs prior to pregnancy (IMIDs group) based on pregnancy/maternal characteristics, comorbidities, and pre-pregnancy/prenatal immunomodulatory medications (IMMs) prescription patterns. We 1:1 propensity score matched the IMIDs cohort with people who had no IMID diagnoses prior to pregnancy (non-IMIDs cohort). Outcome measures were preterm birth (PTB), low birth weight (LBW), small for gestational age (SGA), and caesarean section. Findings Our analytic cohort had 365,075 people, of which 5784 were in the IMIDs group and 359,291 were in the non-IMIDs group. The prevalence rate of pregnancy of at least 20 weeks duration in people with a previous IMID diagnosis has doubled in the past ten years. 17% of the IMIDs group had at least one prenatal IMM prescription. Depending on the type of IMM, 48%-70% of the patients taking IMMs before pregnancy continued them throughout pregnancy. Overall, patients with one or more of these 12 IMIDs had increased risk of PTB (Relative risk (RR) = 1.1 [1.0, 1.3]; p = 0.08), LBW (RR = 1.2 [1.0, 1.4]; p = 0.02), SGA (RR = 1.1 [1.0, 1.2]; p = 0.03), and caesarean section (RR = 1.1 [1.1, 1.2], p < 0.0001) compared to a matched cohort of people without IMIDs. When adjusted for comorbidities, patients with rheumatoid arthritis (PTB RR = 1.2, p = 0.5; LBW RR = 1.1, p = 0.6) and/or inflammatory bowel disease (PTB RR = 1.2, p = 0.3; LBW RR = 1.0, p = 0.8) did not have significantly increased risk for PTB and LBW. Interpretation For patients who have been pregnant for 20 weeks or greater, the association between IMIDs and adverse pregnancy outcomes depends on both the nature of the IMID and the presence of comorbidities. Because this study was limited to pregnancies resulting in live births, results must be interpreted together with other studies on early pregnancy loss and stillbirth in patient with IMIDs. Funding National Institutes of Health.
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Affiliation(s)
- Yeon Mi Hwang
- Institute for Systems Biology, Seattle, WA, USA
- University of Washington, Seattle, WA, USA
| | - Qi Wei
- Institute for Systems Biology, Seattle, WA, USA
| | | | - Bhargav Vemuri
- Institute for Systems Biology, Seattle, WA, USA
- University of Washington, Seattle, WA, USA
| | | | - Philip Mease
- University of Washington, Seattle, WA, USA
- Providence Health and Services and Affiliates, WA, USA
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, USA
| | - Jennifer Hadlock
- Institute for Systems Biology, Seattle, WA, USA
- University of Washington, Seattle, WA, USA
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Demicheva E, Dordiuk V, Polanco Espino F, Ushenin K, Aboushanab S, Shevyrin V, Buhler A, Mukhlynina E, Solovyova O, Danilova I, Kovaleva E. Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review. Metabolites 2024; 14:54. [PMID: 38248857 PMCID: PMC10820779 DOI: 10.3390/metabo14010054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/05/2024] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.
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Affiliation(s)
- Ekaterina Demicheva
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
- Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg 620049, Russia
| | - Vladislav Dordiuk
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
| | - Fernando Polanco Espino
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
| | - Konstantin Ushenin
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
- Autonomous Non-Profit Organization Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
| | - Saied Aboushanab
- Institute of Chemical Engineering, Ural Federal University, Ekaterinburg 620002, Russia; (S.A.); (V.S.); (E.K.)
| | - Vadim Shevyrin
- Institute of Chemical Engineering, Ural Federal University, Ekaterinburg 620002, Russia; (S.A.); (V.S.); (E.K.)
| | - Aleksey Buhler
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
| | - Elena Mukhlynina
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
- Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg 620049, Russia
| | - Olga Solovyova
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
- Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg 620049, Russia
| | - Irina Danilova
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
- Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg 620049, Russia
| | - Elena Kovaleva
- Institute of Chemical Engineering, Ural Federal University, Ekaterinburg 620002, Russia; (S.A.); (V.S.); (E.K.)
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Chung CW, Chou SC, Hsiao TH, Zhang GJ, Chung YF, Chen YM. Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records. BioData Min 2024; 17:1. [PMID: 38183082 PMCID: PMC10770905 DOI: 10.1186/s13040-023-00352-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 12/19/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND Although the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) has required at least a positive anti-nuclear antibody (ANA) titer (≥ 1:80), it remains challenging for clinicians to identify patients with SLE. This study aimed to develop a machine learning (ML) approach to assist in the detection of SLE patients using genomic data and electronic health records. METHODS Participants with a positive ANA (≥ 1:80) were enrolled from the Taiwan Precision Medicine Initiative cohort. The Taiwan Biobank version 2 array was used to detect single nucleotide polymorphism (SNP) data. Six ML models, Logistic Regression, Random Forest (RF), Support Vector Machine, Light Gradient Boosting Machine, Gradient Tree Boosting, and Extreme Gradient Boosting (XGB), were used to identify SLE patients. The importance of the clinical and genetic features was determined by Shapley Additive Explanation (SHAP) values. A logistic regression model was applied to identify genetic variations associated with SLE in the subset of patients with an ANA equal to or exceeding 1:640. RESULTS A total of 946 SLE and 1,892 non-SLE controls were included in this analysis. Among the six ML models, RF and XGB demonstrated superior performance in the differentiation of SLE from non-SLE. The leading features in the SHAP diagram were anti-double strand DNA antibodies, ANA titers, AC4 ANA pattern, polygenic risk scores, complement levels, and SNPs. Additionally, in the subgroup with a high ANA titer (≥ 1:640), six SNPs positively associated with SLE and five SNPs negatively correlated with SLE were discovered. CONCLUSIONS ML approaches offer the potential to assist in diagnosing SLE and uncovering novel SNPs in a group of patients with autoimmunity.
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Affiliation(s)
- Chih-Wei Chung
- Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Seng-Cho Chou
- Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Public Health, Fu Jen Catholic University, New Taipei City, Taiwan
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan
| | - Grace Joyce Zhang
- Department of Cellular and Physiological Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Yu-Fang Chung
- Department of Electrical Engineering, Tunghai University, Taichung, Taiwan
| | - Yi-Ming Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, 1650, Section 4, Taiwan Boulevard, Xitun Dist., Taichung City, 407, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Rong Hsing Research Center for Translational Medicine & Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung, Taiwan.
- Precision Medicine Research Center, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
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Grovu R, Huo Y, Nguyen A, Mourad O, Pan Z, El-Gharib K, Wei C, Mustafa A, Quan T, Slobodnick A. Machine learning: Predicting hospital length of stay in patients admitted for lupus flares. Lupus 2023; 32:1418-1429. [PMID: 37831499 DOI: 10.1177/09612033231206830] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
BACKGROUND Although rare, severe systemic lupus erythematosus (SLE) flares requiring hospitalization account for most of the direct costs of SLE care. New machine learning (ML) methods may optimize lupus care by predicting which patients will have a prolonged hospital length of stay (LOS). Our study uses a machine learning approach to predict the LOS in patients admitted for lupus flares and assesses which features prolong LOS. METHODS Our study sampled 5831 patients admitted for lupus flares from the National Inpatient Sample Database 2016-2018 and collected 90 demographics and comorbidity features. Four machine learning (ML) models were built (XGBoost, Linear Support Vector Machines, K Nearest Neighbors, and Logistic Regression) to predict LOS, and their performance was evaluated using multiple metrics, including accuracy, receiver operator area under the curve (ROC-AUC), precision-recall area under the curve (PR- AUC), and F1-score. Using the highest-performing model (XGBoost), we assessed the feature importance of our input features using Shapley value explanations (SHAP) to rank their impact on LOS. RESULTS Our XGB model performed the best with a ROC-AUC of 0.87, PR-AUC of 0.61, an F1 score of 0.56, and an accuracy of 95%. The features with the most significant impact on the model were "the need for a central line," "acute dialysis," and "acute renal failure." Other top features include those related to renal and infectious comorbidities. CONCLUSION Our results were consistent with the established literature and showed promise in ML over traditional methods of predictive analyses, even with rare rheumatic events such as lupus flare hospitalizations.
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Affiliation(s)
- Radu Grovu
- Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA
| | - Yanran Huo
- Department of Engineering, University of Massachusetts, Dartmouth, MA, USA
| | - Andrew Nguyen
- Medicine Department, Harvard Medical School, Boston, MA, USA
| | - Omar Mourad
- Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA
| | - Zihang Pan
- Medicine Department, Duke-NUS Medical School, Singapore
| | - Khalil El-Gharib
- Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA
| | - Chapman Wei
- Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA
| | - Ahmad Mustafa
- Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA
| | - Theodore Quan
- Medicine Department, George Washington University School of Medicine, Washington, DC, USA
| | - Anastasia Slobodnick
- Rheumatology Department, Staten Island University Hospital, Staten Island, NY, USA
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12
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Hwang YM, Wei Q, Piekos SN, Vemuri B, Molani S, Mease P, Hood L, Hadlock JJ. Maternal-fetal outcomes in patients with immune mediated inflammatory diseases, with consideration of comorbidities: a retrospective cohort study in a large U.S. healthcare system. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.07.23293726. [PMID: 37609126 PMCID: PMC10441487 DOI: 10.1101/2023.08.07.23293726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Background Immune-mediated inflammatory diseases (IMIDs) are likely to complicate maternal health. However, literature data on patients with IMIDs undergoing pregnancy is scarce and often overlooks the impact of comorbidities. Methods We investigated 12 selected IMIDs: psoriasis, inflammatory bowel disease, rheumatoid arthritis, spondyloarthritis, multiple sclerosis, systemic lupus erythematosus, psoriatic arthritis, antiphospholipid syndrome, Sjögren's syndrome, vasculitis, sarcoidosis, systemic sclerosis. We characterized patients with IMIDs prior to pregnancy (IMIDs group) based on pregnancy/maternal characteristics, comorbidities, and pre-pregnancy/prenatal immunomodulatory medications (IMMs) prescription patterns. We 1:1 propensity score matched the IMIDs cohort with people who had no IMID diagnoses prior to pregnancy (non-IMIDs cohort). Outcome measures were preterm birth (PTB), low birth weight (LBW), small for gestational age (SGA), and cesarean section. Findings The prevalence rate of pregnancy occurring with people with a previous IMID diagnosis has doubled in the past ten years. We identified 5,784 patients with IMIDs. 17% of the IMIDs group had at least one prenatal IMM prescription. Depending on the type of IMM, from 48% to 70% of the patients taking IMMs before pregnancy continued them throughout pregnancy. Patients with IMIDs had similar but slightly increased risks of PTB (Relative risk (RR)=1·1[1·0, 1·3]), LBW (RR=1·2 [1·0,1·4]), SGA (RR=1·1 [1·0,1·2]), and cesarean section (RR=1·1 [1·1,1·2]) compared to a matched cohort of people without IMIDs. Out of the 12 selected IMIDs, three for PTB, one for LBW, two for SGA, and six for cesarean section had results supporting increased risk. Interpretation The association between IMIDs and the increased risk of adverse pregnancy outcomes depend on both the nature of the IMID and the presence of comorbidities.
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Affiliation(s)
- Yeon Mi Hwang
- Institute for Systems Biology, Seattle, WA, USA
- University of Washington, Seattle, WA, USA
| | - Qi Wei
- Institute for Systems Biology, Seattle, WA, USA
| | | | - Bhargav Vemuri
- Institute for Systems Biology, Seattle, WA, USA
- University of Washington, Seattle, WA, USA
| | | | | | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, USA
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Kharb S, Joshi A. Editorial: eDiagnostics and monitoring for precision endocrinology. Front Endocrinol (Lausanne) 2023; 14:1229475. [PMID: 37469987 PMCID: PMC10353017 DOI: 10.3389/fendo.2023.1229475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 06/21/2023] [Indexed: 07/21/2023] Open
Affiliation(s)
- Simmi Kharb
- Department of Biochemistry, Postgraduate Institute of Medical Sciences, Rohtak, Haryana, India
| | - Anagha Joshi
- Computational Biology Unit (CBU), Department of Clinical Science, University of Bergen, Bergen, Norway
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Application of Machine Learning Models in Systemic Lupus Erythematosus. Int J Mol Sci 2023; 24:ijms24054514. [PMID: 36901945 PMCID: PMC10003088 DOI: 10.3390/ijms24054514] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/14/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease and is extremely heterogeneous in terms of immunological features and clinical manifestations. This complexity could result in a delay in the diagnosis and treatment introduction, with impacts on long-term outcomes. In this view, the application of innovative tools, such as machine learning models (MLMs), could be useful. Thus, the purpose of the present review is to provide the reader with information about the possible application of artificial intelligence in SLE patients from a medical perspective. To summarize, several studies have applied MLMs in large cohorts in different disease-related fields. In particular, the majority of studies focused on diagnosis and pathogenesis, disease-related manifestations, in particular Lupus Nephritis, outcomes and treatment. Nonetheless, some studies focused on peculiar features, such as pregnancy and quality of life. The review of published data demonstrated the proposal of several models with good performance, suggesting the possible application of MLMs in the SLE scenario.
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