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Liang J, Wan L, Yao Y, Cui X, He Y, Li S, Jiang M, Sun Y, Cao H, Lin J. An externally validated clinical-laboratory nomogram for myocardial involvement in adult idiopathic-inflammatory-myopathy patients. Clin Rheumatol 2024; 43:1959-1969. [PMID: 38587715 PMCID: PMC11111495 DOI: 10.1007/s10067-024-06948-x] [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: 05/12/2023] [Revised: 01/09/2024] [Accepted: 03/22/2024] [Indexed: 04/09/2024]
Abstract
OBJECTIVES This study aimed at identifying clinical and laboratory risk factors for myocardial involvement (MI) in idiopathic inflammatory myopathies (IIMs) patients as well as constructing a risk-predicted nomogram for prediction and early identification of MI. METHODS An IIMs cohort in southeastern China was constructed, including 504 adult IIMs patients who met the inclusion and exclusion criteria, and were hospitalized at four divisions of the First Affiliated Hospital, Zhejiang University School of Medicine from January 1st 2018 to April 30st 2022. After dividing patients into the training cohort and the validation cohort, risk factors for MI were identified through least absolute shrinkage and selection operator regression and multivariate logistic regression. A risk-predicted nomogram was established and validated internally and externally for discrimination, calibration and practicability. RESULTS In this cohort, 17.7% of patients developed MI and the survival was significantly inferior to that of IIMs patients without MI (P < 0.001). In the training cohort, age > 55 years old (P < 0.001), disease activity > 10 points (P < 0.001), interleukin-17A (IL-17A) > 7.5 pg/ml (P < 0.001), lactic dehydrogenase (LDH) > 425 U/L (P < 0.001), anti-mitochondrial antibodies (AMAs, P = 0.017), and anti-MDA5 antibody (P = 0.037) were significantly correlated with development of MI. A nomogram was established by including the above values to predict MI and was found efficient in discrimination, calibration, and practicability through internal and external validation. CONCLUSION This study developed and validated a nomogram model to predict the risk of MI in adult IIMs patients, which can benefit the prediction and early identification of MI as well as timely intervention in these patients.
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Affiliation(s)
- Junyu Liang
- Department of Rheumatology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Zhejiang Province, Hangzhou, 310003, China
| | - Liyan Wan
- Department of Rheumatology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Zhejiang Province, Hangzhou, 310003, China
| | - Yake Yao
- Department of Respiratory Diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Cui
- Department of Cardiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ye He
- Department of Rheumatology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Zhejiang Province, Hangzhou, 310003, China
| | - Shuangshuang Li
- Department of Rheumatology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Zhejiang Province, Hangzhou, 310003, China
| | - Mengdi Jiang
- Department of Rheumatology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Zhejiang Province, Hangzhou, 310003, China
| | - Yiduo Sun
- Department of Rheumatology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Zhejiang Province, Hangzhou, 310003, China
| | - Heng Cao
- Department of Rheumatology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Zhejiang Province, Hangzhou, 310003, China.
| | - Jin Lin
- Department of Rheumatology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Zhejiang Province, Hangzhou, 310003, China.
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Tang Q, Yuan Y, Li L, Xu Y, Ji W, Xiao S, Han Y, Miao W, Cai J, You P, Chen M, Ding S, Li Z, Qi Z, Hou W, Luo H. Comprehensive analysis reveals that LTBR is a immune-related biomarker for glioma. Comput Biol Med 2024; 174:108457. [PMID: 38599071 DOI: 10.1016/j.compbiomed.2024.108457] [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: 09/22/2023] [Revised: 04/02/2024] [Accepted: 04/07/2024] [Indexed: 04/12/2024]
Abstract
Glioma is a common malignant brain tumor with great heterogeneity and huge difference in clinical outcomes. Although lymphotoxin (LT) beta receptor (LTBR) has been linked to immune system and response development for decades, the expression and function in glioma have not been investigated. To confirm the expression profile of LTBR, integrated RNA-seq data from glioma and normal brain tissues were analyzed. Functional enrichment analysis, TMEscore analysis, immune infiltration, the correlation of LTBR with immune checkpoints and ferroptosis, and scRNAseq data analysis in gliomas were in turn performed, which pointed out that LTBR was pertinent to immune functions of macrophages in gliomas. In addition, after being trained and validated in the tissue samples of the integrated dataset, an LTBR DNA methylation-based prediction model succeeded to distinguish gliomas from non-gliomas, as well as the grades of glioma. Moreover, by virtue of the candidate LTBR CpG sites, a prognostic risk-score model was finally constructed to guide the chemotherapy, radiotherapy, and immunotherapy for glioma patients. Taken together, LTBR is closely correlated with immune functions in gliomas, and LTBR DNA methylation could serve as a biomarker for diagnosis and prognosis of gliomas.
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Affiliation(s)
- Qisheng Tang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, 200040, China
| | - Yifan Yuan
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, 200040, China
| | - Lingjuan Li
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, 200040, China
| | - Yue Xu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, Department of General Dentistry, College of Stomatology, Xi'an Jiaotong University, Xi'an, 710004, Shaanxi Province, China
| | - Wei Ji
- Department of Anesthesiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264000, Shandong Province, China
| | - Siyu Xiao
- Department of Rehabilitation, Gongan Hospital of Traditional Chinese Medicine Affiliated to Hubei University of Chinese Medicine, Jingzhou, 434300, Hubei Province, China
| | - Yi Han
- Naval Medical Center of PLA, Naval Medical University, Shanghai, 200052, China
| | - Wenrong Miao
- Naval Medical Center of PLA, Naval Medical University, Shanghai, 200052, China
| | - Jing Cai
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, 200040, China
| | - Pu You
- Shanghai QuietD Biotechnology Co., Ltd., Shanghai, 201210, China
| | - Ming Chen
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, 200040, China
| | - Saineng Ding
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, 200040, China
| | - Zhen Li
- Shanghai QuietD Biotechnology Co., Ltd., Shanghai, 201210, China.
| | - Zengxin Qi
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, 200040, China.
| | - Weiliang Hou
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, 200040, China.
| | - Hao Luo
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, 200040, China.
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Khoo T, Lilleker JB, Thong BYH, Leclair V, Lamb JA, Chinoy H. Epidemiology of the idiopathic inflammatory myopathies. Nat Rev Rheumatol 2023; 19:695-712. [PMID: 37803078 DOI: 10.1038/s41584-023-01033-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 10/08/2023]
Abstract
The idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of systemic autoimmune diseases that affect the skeletal muscles and can also involve the skin, joints, lungs and heart. The epidemiology of IIM is obscured by changing classification criteria and the inherent shortcomings of case identification using healthcare record diagnostic coding. The incidence of IIM is estimated to range from 0.2 to 2 per 100,000 person-years, with prevalence from 2 to 25 per 100,000 people. Although the effects of age and gender on incidence are known, there is only sparse understanding of ethnic differences, particularly in indigenous populations. The incidence of IIM has reportedly increased in the twenty-first century, but whether this is a genuine increase is not yet known. Understanding of the genetic risk factors for different IIM subtypes has advanced considerably. Infections, medications, malignancy and geography are also commonly identified risk factors. Potentially, the COVID-19 pandemic has altered IIM incidence, although evidence of this occurrence is limited to case reports and small case series. Consideration of the current understanding of the epidemiology of IIM can highlight important areas of interest for future research into these rare diseases.
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Affiliation(s)
- Thomas Khoo
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- School of Medicine, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Department of Rheumatology, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Science Centre, Salford, UK
| | - James B Lilleker
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Manchester Centre for Clinical Neuroscience, Manchester Academic Health Science Centre, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Bernard Yu-Hor Thong
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore, Singapore
| | - Valérie Leclair
- Department of Medicine, Division of Rheumatology, McGill University, Montreal, Canada
| | - Janine A Lamb
- Epidemiology and Public Health Group, School of Health Sciences, The University of Manchester, Manchester, UK
| | - Hector Chinoy
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
- Department of Rheumatology, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Science Centre, Salford, UK.
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Patasova K, Lundberg IE, Holmqvist M. Genetic Influences in Cancer-Associated Myositis. Arthritis Rheumatol 2023; 75:153-163. [PMID: 36053262 PMCID: PMC10107284 DOI: 10.1002/art.42345] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/28/2022] [Accepted: 08/31/2022] [Indexed: 02/02/2023]
Abstract
Idiopathic inflammatory myopathies (IIMs) comprise a heterogeneous group of rare immune-mediated disorders that primarily affect muscles but also lead to dysfunction in other organs. Five different clinical subphenotypes of IIM have been distinguished: dermatomyositis, polymyositis, inclusion body myositis, antisynthetase syndrome, and immune-mediated necrotizing myopathy. Excess mortality and morbidity associated with IIM are largely attributed to comorbidities, particularly cancer. The risk of malignancy is not equally distributed among IIM groups and is particularly high among patients with dermatomyositis. The cancer risk peaks around 3 years on either side of the IIM diagnosis and remains elevated even 10 years after the onset of the disease. Lung, colorectal, and ovarian neoplasms typically arise before the onset of IIM, whereas melanoma, cervical, oropharyngeal, and nonmelanoma skin cancers usually develop after IIM diagnosis. Given the close temporal proximity between IIM diagnosis and the emergence of malignancy, it has been proposed that IIM could be a consequence rather than a cause of cancer, a process known as a paramalignant phenomenon. Thus, a separate group of IIMs related to paramalignant phenomenon has been distinguished, known as cancer-associated myositis (CAM). Although the relationship between IIM and cancer is widely recognized, the pathophysiology of CAM remains elusive. Given that genetic factors play a role in the development of IIM, dissection of the molecular mechanisms shared between IIM and cancer presents an opportunity to examine the role of autoimmunity in cancer development and progression. In this review, the evidence supporting the contribution of genetics to CAM will be discussed.
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Affiliation(s)
- Karina Patasova
- Clinical Epidemiology Division, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | - Ingrid E Lundberg
- Rheumatology Division, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Marie Holmqvist
- Clinical Epidemiology Division, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
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Kronzer VL, Kimbrough BA, Crowson CS, Davis JM, Holmqvist M, Ernste FC. Incidence, Prevalence, and Mortality of Dermatomyositis: A Population-Based Cohort Study. Arthritis Care Res (Hoboken) 2023; 75:348-355. [PMID: 34549549 PMCID: PMC8934743 DOI: 10.1002/acr.24786] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/07/2021] [Accepted: 09/14/2021] [Indexed: 11/07/2022]
Abstract
OBJECTIVE We aimed to determine the population-based incidence, prevalence, and mortality of dermatomyositis (DM) using European Alliance of Associations for Rheumatology (EULAR)/American College of Rheumatology (ACR) criteria. METHODS This population-based cohort study included incident DM from January 1, 1995 to December 31, 2019. We manually reviewed all individuals with at least 1 code for DM or polymyositis to determine if they met EULAR/ACR criteria, subspecialty physician diagnosis, and/or Bohan and Peter criteria. We age- and sex-adjusted incidence and prevalence estimates to the US non-Hispanic White year 2000 population and estimated prevalence on January 1, 2015. Standardized mortality ratios (SMRs) with 95% confidence intervals (95% CIs) compared observed to expected mortality adjusting for age, sex, and year. RESULTS We identified 40 cases of verified DM, with 29 cases incident in Olmsted County from 1995 to 2019. The mean age was 57 years, 26 (90%) were female, and 12 (41%) had clinically amyopathic DM (CADM). The median follow-up time was 8.2 years. The overall adjusted incidence of DM was 1.1 (95% CI 0.7-1.5) per 100,000 person-years, and prevalence was 13 (95% CI 6-19) per 100,000. The SMR was significantly elevated among the myopathic DM cases (3.1 [95% CI 1.1-6.8]) but not CADM cases (1.1 [95% CI 0.2-3.3]). The positive predictive value of ≥2 DM codes was only 40 of 82 (49%). CONCLUSION This population-based study found that DM incidence and prevalence were higher than previously reported. Mortality was significantly elevated for myopathic DM but not for CADM.
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Affiliation(s)
| | | | - Cynthia S. Crowson
- Division of Rheumatology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - John M. Davis
- Division of Rheumatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Marie Holmqvist
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
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Epidemiologic Opportunities and Challenges in Studying Environmental Risk Factors for Rheumatic Diseases. Rheum Dis Clin North Am 2022; 48:763-779. [PMID: 36332994 DOI: 10.1016/j.rdc.2022.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Most rheumatic diseases have a stronger environmental than hereditary etiology. This article summarizes the key environmental risk factors for rheumatic diseases, the data sources that generated these findings, and the key pitfalls with existing research that every rheumatology clinician should know. Emerging research opportunities hold promise to revolutionize this field, and soon.
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Abstract
The idiopathic inflammatory myopathies (IIM) are rare, heterogeneous systemic autoimmune disorders, characterized by inflammation of skeletal muscle and multi-organ involvement. Studies to identify genetic risk factors and dysregulated gene expression in IIM aim to increase our understanding of disease pathogenesis. Genome-wide association studies have confirmed the HLA region as the most strongly associated region in IIM, with different associations between clinically-defined subgroups. Associated genes are involved in both the innate and adaptive immune response, while identification of variants reported in other autoimmune disorders suggests shared biological pathways. Targeted imputation analysis has identified key associated amino acid residues within HLA molecules that may influence antigen recognition. These amino acids increase risk for specific clinical phenotypes and autoantibody subgroups, and suggest that serology-defined subgroups may be more homogeneous. Recent data support the contribution of rare genetic variation to disease susceptibility in IIM, including mitochondrial DNA variation in sporadic inclusion body myositis and somatic mutations and loss of heterozygosity in cancer-associated myositis. Gene expression studies in skeletal muscle, blood and skin from individuals with IIM has confirmed the role of interferon signalling and other dysregulated pathways, and identified cell-type specific signatures. These dysregulated genes differentiate IIM subgroups and identify potential biomarkers. Here, we review recent genetic studies in IIM, and how these inform our understanding of disease pathogenesis and provide mechanistic insights into biological pathways.
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