scholarly journals Identification of Markers Predicting Clinical Course in Patients with IgG4-Related Ophthalmic Disease by Unbiased Clustering Analysis

2020 ◽  
Vol 9 (12) ◽  
pp. 4084
Author(s):  
Kinya Tsubota ◽  
Yoshihiko Usui ◽  
Rey Nemoto ◽  
Hiroshi Goto
Keyword(s):  
Cluster Analysis ◽  
Clinical Features ◽  
Test Data ◽  
Extraocular Muscle ◽  
Blood Test ◽  
Roc Curves ◽  
High Serum ◽  
Serum Ige ◽  
Serum Igg4 ◽  
Ophthalmic Disease

Purpose: To describe the clinical features of patients with immunoglobulin G4 (IgG4)-related ophthalmic disease (IgG4-ROD) grouped by unbiased cluster analysis using peripheral blood test data and to find novel biomarkers for predicting clinical features. Methods: One hundred and seven patients diagnosed with IgG4-ROD were divided into four groups by unsupervised hierarchical cluster analysis using peripheral blood test data. The clinical features of the four groups were compared and novel markers for prediction of clinical course were explored. Results: Unbiased cluster analysis divided patients into four groups. Group B had a significantly higher frequency of extraocular muscle enlargement (p < 0.001). The frequency of patients with decreased best corrected visual acuity (BCVA) was significantly higher in group D (p = 0.002). Receiver operating characteristic (ROC) curves for the prediction of extraocular muscle enlargement and worsened BCVA using a panel consisting of important blood test data identified by machine learning yielded areas under the curve of 0.78 and 0.86, respectively. Clinical features were compared between patients divided into two groups by the cutoff serum IgE or IgG4 level obtained from ROC curves. Patients with serum IgE above 425 IU/mL had a higher frequency of extraocular muscle enlargement (25% versus 6%, p = 0.004). Patients with serum IgG4 above 712 mg/dL had a higher frequency of decreased BCVA (37% versus 5%, p ≤ 0.001). Conclusion: Unsupervised hierarchical clustering analysis using routine blood test data differentiates four distinct clinical phenotypes of IgG4-ROD, which suggest differences in pathophysiologic mechanisms. High serum IgG4 is a potential predictor of worsened BCVA, and high serum IgE is a potential predictor of extraocular muscle enlargement in IgG4-ROD patients.

scholarly journals Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Jieqiong Li ◽  
Yu Peng ◽  
Yuelun Zhang ◽  
Panpan Zhang ◽  
Zheng Liu ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Prediction Model ◽  
Disease Severity ◽  
Principal Component ◽  
High Serum ◽  
Composite Score ◽  
Dominant Group ◽  
Related Disease ◽  
Igg4 Related Disease ◽  
Serum Igg4

Abstract Background To explore the clinical patterns of patients with IgG4-related disease (IgG4-RD) based on laboratory tests and the number of organs involved. Methods Twenty-two baseline variables were obtained from 154 patients with IgG4-RD. Based on principal component analysis (PCA), patients with IgG4-RD were classified into different subgroups using cluster analysis. Additionally, IgG4-RD composite score (IgG4-RD CS) as a comprehensive score was calculated for each patient by principal component evaluation. Multiple linear regression was used to establish the “IgG4-RD CS” prediction model for the comprehensive assessment of IgG4-RD. To evaluate the value of the IgG4-RD CS in the assessment of disease severity, patients in different IgG4-RD CS groups and in different IgG4-RD responder index (RI) groups were compared. Results PCA indicated that the 22 baseline variables of IgG4-RD patients mainly consisted of inflammation, high serum IgG4, multi-organ involvement, and allergy-related phenotypes. Cluster analysis classified patients into three groups: cluster 1, inflammation and immunoglobulin-dominant group; cluster 2, internal organs-dominant group; and cluster 3, inflammation and immunoglobulin-low with superficial organs-dominant group. Moreover, there were significant differences in serum and clinical characteristics among subgroups based on the CS and RI scores. IgG4-RD CS had a similar ability to assess disease severity as RI. The “IgG4-RD CS” prediction model was established using four independent variables including lymphocyte count, eosinophil count, IgG levels, and the total number of involved organs. Conclusion Our study indicated that newly diagnosed IgG4-RD patients could be divided into three subgroups. We also showed that the IgG4-RD CS had the potential to be complementary to the RI score, which can help assess disease severity.

scholarly journals Serum IgE in the Clinical Features and Disease Outcome of IgG4-related Disease: A Large Retrospective Cohort Study

2020 ◽  
Author(s):  
Jiaxin Zhou ◽  
Yu Peng ◽  
Linyi Peng ◽  
Di Wu ◽  
Jing Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Features ◽  
Eosinophil Count ◽  
Disease Outcome ◽  
Remission Induction ◽  
Baseline Serum ◽  
Serum Ige ◽  
Related Disease ◽  
Serum Igg4 ◽  
Group Serum

Abstract Objective: The aim of this study was to investigate the role of serum IgE level in the clinical features and outcome of IgG-4 related disease (IgG4-RD). Methods: We retrospectively enrolled 459 newly diagnosed IgG4-RD patients with serum IgE examined at baseline from 2012 to 2019, and compared the clinical features between high IgE group (Serum IgE level>60KU/L) and normal IgE group (Serum IgE level≤60KU/L) patients. Then 312 patients who have been followed up for ≥ 1 year were further selected to evaluate the correlation of serum IgE level and disease outcome. Results: At baseline, Serum IgE level was positively correlated with serum IgG4 level (r=0.1779, P=0.0001), eosinophil count (r=0.3004, P<0.0001), serum IgG level (r=0.2189, P<0.0001) in IgG4-RD patients. Compared with normal IgE group patients, high IgE group patients had more patients with allergy disease (P=0.004), more organ involvements (P=0.003), and higher IgG4-RD RI scores (P=0.002). During follow-up, normal IgE group patients had higher remission induction rate than high IgE group patients (88.4% vs. 73.6%, P=0.035), while high IgE group patients had more relapse rate compared with normal IgE group patients (29.0% vs. 16.2%, P=0.039), and relapsed patients had higher serum IgE level at baseline (P=0.046). Cox regression analysis showed that elevation of the eosinophil count was the risk factors of relapse for both normal and high IgE groups patients (HR 8.504 (1.071-42.511), P=0.009; HR 2.078 (1.277-3.380), P=0.003), and the involvement of lacrimal gland (HR 1.756 (1.108-2.782), P=0.017), submandibular gland (HR 1.654 (1.037-2.639), P=0.035), kidney (HR 3.413 (1.076-10.831), P=0.037) were also the risk factors of relapse for high IgE group patients.Conclusion: IgG4-RD patients with high serum IgE level at baseline were more likely to have higher disease activity, and baseline high IgE level were associated with disease relapse.

scholarly journals Clinical Features of Patients with Basedow's Disease and High Serum IgG4 Levels

2017 ◽  
Vol 56 (9) ◽  
pp. 1009-1013 ◽  
Author(s):  
Keiichi Torimoto ◽  
Yosuke Okada ◽  
Akira Kurozumi ◽  
Manabu Narisawa ◽  
Tadashi Arao ◽  
...  
Keyword(s):  
Clinical Features ◽  
High Serum ◽  
Serum Igg4 ◽  
Basedow’S Disease

scholarly journals Clinical features and relapse risks of IgG4-related ophthalmic disease: a single-center experience in China

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Zhen Zhao ◽  
Dapeng Mou ◽  
Ziqiao Wang ◽  
Qiaozhu Zeng ◽  
Zhenfan Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Survival Time ◽  
Clinical Features ◽  
Protective Factor ◽  
Laboratory Data ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Cox Regression Analysis ◽  
Serum Igg4 ◽  
Ophthalmic Disease

Abstract Background IgG4-related ophthalmic disease (IgG4-ROD) is one of the phenotypes of IgG4-related disease (IgG4-RD), and its lesions are mainly located in the ocular. Currently, there are few studies on IgG4-ROD and no study has compared the phenotypic differences between IgG4-ROD and non IgG4-ROD (nIgG4-ROD). Thus, it is difficult to establish the optimal treatment strategy for IgG4-ROD. The aim of this study was to identify the disparities between the two groups and to clarify the risk factors for IgG4-ROD relapse. Methods 434 IgG4-RD patients met comprehensive diagnostic criteria and diagnosed at Peking University People’s Hospital between January 2009 and January 2020 were recruited in this study. Patients were divided into IgG4-ROD and nIgG4-ROD group according to the ophthalmic involvement. Demographic, clinical, and laboratory data of two groups were collected and compared. Cox regression analysis was used to identify the independent risk factors for IgG4-ROD relapse. Results 255 IgG4-ROD patients were identified in this study. IgG4-ROD group had almost equal sex ratio, younger age of disease onset and diagnosis comparing with nIgG4-ROD patients. As compared to nIgG4-ROD group, higher percentage of IgG4-ROD patients met the 2019 American College of Rheumatology/European League Against Rheumatism classification criteria (AECC) for IgG4-RD; moreover, IgG4-ROD patients had higher AECC scores and IgG4-RD responder index (RI). Allergic diseases and multiorgan involvement were more common in IgG4-ROD group. IgG4-ROD was frequently associated with salivary gland, paranasal sinus, lung, and lymph node involvement, while retroperitoneal fibrosis and biliary system lesions were more common in nIgG4-ROD. IgG4-ROD patients had higher serum IgG4 levels, IgG4/IgG ratio, IgE levels, and lower CRP levels. The initial glucocorticoid plus immunosuppressant was a protective factor for IgG4-ROD relapse. IgG4-ROD patients treated with initial glucocorticoid plus immunosuppressant had longer relapse-free survival time than patients treated with initial glucocorticoid monotherapy. Conclusions IgG4-ROD patients had distinctive clinical features compared with nIgG4-ROD patients. The initial glucocorticoid plus immunosuppressant was a protective factor for IgG4-ROD relapse, which could prolong the relapse-free survival time of IgG4-ROD patients. These findings may have important implications for understanding and management of IgG4-ROD.

scholarly journals Immunoglobulin G4-related masses surrounding coronary arteries: a case report

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Takuya Nakamura ◽  
Yutaka Goryo ◽  
Takuya Isojima ◽  
Hiroyuki Kawata
Keyword(s):  
Coronary Artery ◽  
Coronary Angiography ◽  
Cardiovascular System ◽  
Coronary Arteries ◽  
Elevated Serum ◽  
High Serum ◽  
Immunoglobulin G4 ◽  
Coronary Arteritis ◽  
Serum Igg4 ◽  
Mass Formation

Abstract Background Immunoglobulin G4 (IgG4)-related disease (IgG4-RD) is an immune-mediated fibroinflammatory condition with high serum IgG4 levels affecting various organs, such as the pancreas, lacrimal and salivary glands, thyroid, kidney, and lung. Typical cardiovascular manifestations of IgG4-RD include periaortitis, coronary arteritis, and pericarditis. However, reports of IgG4-RD associated with coronary arteritis are rare. Here, we report a case of IgG4-related masses surrounding the coronary arteries. Case summary A 59-year-old man was referred to our hospital because of mediastinal masses detected by computed tomography (CT). Coronary CT angiography revealed masses surrounding the right coronary artery and the left anterior descending coronary artery. An elevated serum level of IgG4 and histological findings led to the diagnosis of IgG4-related coronary arteritis with mass formation. Coronary angiography showed numerous feeding arteries to the masses, which were demonstrated as multiple microchannels in the intravascular ultrasound (IVUS) images. Discussion IgG4-RD involving the cardiovascular system has been reported. However, coronary artery disease associated with IgG4-RD is very rare, and the mechanism of mass formation in IgG4-related coronary arteritis is unclear. In our case, within the cardiovascular system, IgG4-RD was limited to the coronary arteries, suggesting that the affected coronary arteries may provide the necessary blood supply to the mass, thus, aiding its growth. These findings were supported by the images from coronary angiography and IVUS.

Prediction of Recurrence in Patients with Stage III Colon Cancer Using Conventional Clinicopathological Factors and Peripheral Blood Test Data: A New Analysis with Artificial Intelligence

Oncology ◽  
2021 ◽  
Vol 99 (5) ◽  
pp. 318-326
Author(s):  
Yutaro Kamei ◽  
Tetsuro Takayama ◽  
Toshiyuki Suzuki ◽  
Kenichi Furihata ◽  
Megumi Otsuki ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colon Cancer ◽  
Serum Albumin ◽  
Test Data ◽  
Predictive Factors ◽  
Blood Test ◽  
Stage Iii ◽  
Clinicopathological Factors ◽  
Stage Iii Colon Cancer ◽  
Staging Systems

Background: Survival rate may be predicted by tumor-node-metastasis staging systems in colon cancer. In clinical practice, about 20 to 30 clinicopathological factors and blood test data have been used. Various predictive factors for recurrence have been advocated; however, the interactions are complex and remain to be established. We used artificial intelligence (AI) to examine predictive factors related to recurrence. Methods: The study group comprised 217 patients who underwent curative surgery for stage III colon cancer. Using a self-organizing map (SOM), an AI-based method, patients with only 23 clinicopathological factors, patients with 23 clinicopathological factors and 34 of preoperative blood test data (pre-data), and those with 23 clinicopathological factors and 31 of postoperative blood test data (post-data) were classified into several clusters with various rates of recurrence. Results: When only clinicopathological factors were used, the percentage of T4b disease, the percentage of N2 disease, and the number of metastatic lymph nodes were significantly higher in a cluster with a higher rate of recurrence. When clinicopathological factors and pre-data were used, three described pathological factors and the serum C-reactive protein (CRP) levels were significantly higher and the serum total protein (TP) levels, serum albumin levels, and the percentage of lymphocytes were significantly lower in a cluster with a higher rate of recurrence. When clinicopathological factors and post-data were used, three described pathological factors, serum CRP levels, and serum carcinoembryonic antigen levels were significantly higher and serum TP levels, serum albumin levels, and the percentage of lymphocytes were significantly lower in a cluster with a higher rate of recurrence. Conclusions: This AI-based analysis extracted several risk factors for recurrence from more than 50 pathological and blood test factors before and after surgery separately. This analysis may predict the risk of recurrence of a new patient by confirming which clusters this patient belongs to.

CancerEMC: frontline non-invasive cancer screening from circulating protein biomarkers and mutations in cell-free DNA

2021 ◽  
Author(s):  
Saifur Rahaman ◽  
Xiangtao Li ◽  
Jun Yu ◽  
Ka-Chun Wong
Keyword(s):  
Test Data ◽  
Cancer Detection ◽  
Blood Test ◽  
Class Imbalance ◽  
Cancer Type ◽  
Protein Biomarkers ◽  
Class Imbalance Problem ◽  
Multiple Cancer ◽  
Cell Free Dna ◽  
Free Dna

Abstract Motivation The early detection of cancer through accessible blood tests can foster early patient interventions. Although there are developments in cancer detection from cell-free DNA (cfDNA), its accuracy remains speculative. Given its central importance with broad impacts, we aspire to address the challenge. Methods A bagging Ensemble Meta Classifier (CancerEMC) is proposed for early cancer detection based on circulating protein biomarkers and mutations in cfDNA from the blood. CancerEMC is generally designed for both binary cancer detection and multi-class cancer type localization. It can address the class imbalance problem in multi-analyte blood test data based on robust oversampling and adaptive synthesis techniques. Results Based on the clinical blood test data, we observe that the proposed CancerEMC has outperformed other algorithms and state-of-the-arts studies (including CancerSEEK published in Science, 2018) for cancer detection. The results reveal that our proposed method (i.e., CancerEMC) can achieve the best performance result for both binary cancer classification with 99.1748% accuracy (AUC = 0.999) and localized multiple cancer detection with 74.1214% accuracy (AUC = 0.938). For addressing the data imbalance issue with oversampling techniques, the accuracy can be increased to 91.4966% (AUC = 0.992), where the state-of-the-art method can only be estimated at 69.64% (AUC = 0.921). Similar results can also be observed on independent and isolated testing data. Availability https://github.com/saifurcubd/Cancer-Detection

scholarly journals High Serum IgG4 Concentrations in Patients with Hashimoto’s Thyroiditis

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Anna Popławska-Kita ◽  
Maria Kościuszko-Zdrodowska ◽  
Katarzyna Siewko ◽  
Beata Telejko ◽  
Justyna Hryniewicka ◽  
...  
Keyword(s):  
Fas Ligand ◽  
Hashimoto Thyroiditis ◽  
High Serum ◽  
Composition Analysis ◽  
Rapid Progression ◽  
Igg4 Related Disease ◽  
Serum Igg4 ◽  
Receptor Antibodies ◽  
Higher Doses ◽  
Igg4 Level

Purpose. Since recent reports suggest that Hashimoto thyroiditis (HT) may be associated with IgG4-related disease, we aimed to find out whether the measurement of serum IgG4 allows for the identification of distinct types of HT, with different clinical, sonographic, and serologic characteristics.Methods. The group studied consisted of 53 patients with HT and 28 healthy individuals who underwent thyroid ultrasonography and body composition analysis. Serum concentrations of IgG4, TSH, anti-peroxidase antibodies (TPOAb), anti-TSH receptor antibodies, TNF-α, TGF-β1, Fas Ligand, TRAIL, and chemokines (CXCL9, CXCL11, and CXCL10) were measured by ELISA or radioimmunoassay.Results. The group with IgG4 level >135 IU/ml accounted for 32.5% of the patients. The signs of fibrosis were present in 27.0% of the high-IgG4 patients and in 9.1% of the normal-IgG4 group. The patients with elevated IgG4 required higher doses of L-thyroxine and had significantly lower level of TPOAb (P=0.02) than the non-IgG4-HT individuals and higher TNF-αlevel in comparison with the controls (P=0.01).Conclusions. Our results suggest that the measurement of serum IgG4 allows for an identification of patients with more rapid progression of HT, requiring higher doses of L-thyroxine. Low TPOAb level and the absence of coexisting autoimmune diseases may suggest distinct pathomechanism of this type of thyroiditis.

Predominant expression of 950delCAG of IL-18R α chain cDNA is associated with reduced IFN-γ production and high serum IgE levels in atopic Japanese children

2002 ◽  
Vol 109 (4) ◽  
pp. 669-675 ◽  
Author(s):  
Mizuho Watanabe ◽  
Hideo Kaneko ◽  
Hiroaki Shikano ◽  
Minako Aoki ◽  
Heima Sakaguchi ◽  
...  
Keyword(s):  
High Serum ◽  
Japanese Children ◽  
Serum Ige ◽  
Ifn Γ ◽  
Α Chain ◽  
Predominant Expression

scholarly journals An Augmented Model with Inferred Blood Features for the Self-diagnosis of Metabolic Syndrome

2020 ◽  
Vol 59 (01) ◽  
pp. 018-030
Author(s):  
Tianshu Zhou ◽  
Ying Zhang ◽  
Chengkai Wu ◽  
Chao Shen ◽  
Jingsong Li ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Test Data ◽  
Classification Accuracy ◽  
Blood Test ◽  
Health Management ◽  
Fasting Blood Glucose ◽  
Density Lipoprotein ◽  
Developed Countries ◽  
The Third ◽  
Health Checks

Abstract Background and Objectives The penetration rate of physical examinations in China is substantially lower than that in developed countries. Therefore, an auxiliary approach that does not depend on hospital health checks for the diagnosis of metabolic syndrome (MetS) is needed. Methods In this study, we proposed an augmented method with inferred blood features that uses self-care inputs available at home for the auxiliary diagnosis of MetS. The dataset used for modeling contained data on 91,420 individuals who had at least 2 consecutive years of health checks. We trained three separate models using a regularized gradient-boosted decision tree. The first model used only home-based features; additional blood test data (including triglyceride [TG] data, fasting blood glucose data, and high-density lipoprotein cholesterol [HDL-C] data) were included in the second model. However, in the augmented approach, the blood test data were manipulated using multivariate imputation by chained equations prior to inclusion in the third model. The performance of the three models for MetS auxiliary diagnosis was then quantitatively compared. Results The results showed that the third model exhibited the highest classification accuracy for MetS in comparison with the other two models (area under the curve [AUC]: 3rd vs. 2nd vs. 1st = 0.971 vs. 0.950 vs. 0.905, p < 0.001). We further revealed that with full sets of the three measurements from earlier blood test data, the classification accuracy of MetS can be further improved (AUC: without vs. with = 0.971 vs. 0.993). However, the magnitude of improvement was not statistically significant at the 1% level of significance (p = 0.014). Conclusion Our findings demonstrate the feasibility of the third model for MetS homecare applications and lend novel insights into innovative research on the health management of MetS. Further validation and implementation of our proposed model might improve quality of life and ultimately benefit the general population.

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