Serum Metabolomic And Lipidomic Profiling Identifies Diagnostic Biomarkers For Seropositive And Seronegative Rheumatoid Arthritis Patients

2021 ◽  
Author(s):  
Hemi Luan ◽  
Wanjian Gu ◽  
Hua Li ◽  
Zi Wang ◽  
Lu Lu ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Disease Activity ◽  
Acid Metabolism ◽  
Serum Markers ◽  
Classification Model ◽  
Diagnostic Biomarkers ◽  
Validation Set ◽  
Lipids Metabolism ◽  
Normal Controls

Abstract Background: Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods: We carried out metabolomic and lipidomic profiling of metabolites and lipids in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results: Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions: A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.

scholarly journals Serum metabolomic and lipidomic profiling identifies diagnostic biomarkers for seropositive and seronegative rheumatoid arthritis patients

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Hemi Luan ◽  
Wanjian Gu ◽  
Hua Li ◽  
Zi Wang ◽  
Lu Lu ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Disease Activity ◽  
Acid Metabolism ◽  
Serum Markers ◽  
Classification Model ◽  
Diagnostic Biomarkers ◽  
Validation Set ◽  
Lipids Metabolism ◽  
Normal Controls

Abstract Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.

scholarly journals Isotypes of autoantibodies against novel differential 4-hydroxy-2-nonenal-modified peptide adducts in serum is associated with rheumatoid arthritis in Taiwanese women

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kai-Leun Tsai ◽  
Che-Chang Chang ◽  
Yu-Sheng Chang ◽  
Yi-Ying Lu ◽  
I-Jung Tsai ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Disease Activity ◽  
Characteristic Curve ◽  
Factor H ◽  
Protein Adducts ◽  
Complement Factor ◽  
Increased Risk ◽  
Tandem Mass Spectrometric ◽  
Peptide Adducts

Abstract Background Rheumatoid arthritis (RA) is an autoimmune disorder with systemic inflammation and may be induced by oxidative stress that affects an inflamed joint. Our objectives were to examine isotypes of autoantibodies against 4-hydroxy-2-nonenal (HNE) modifications in RA and associate them with increased levels of autoantibodies in RA patients. Methods Serum samples from 155 female patients [60 with RA, 35 with osteoarthritis (OA), and 60 healthy controls (HCs)] were obtained. Four novel differential HNE-modified peptide adducts, complement factor H (CFAH)1211–1230, haptoglobin (HPT)78–108, immunoglobulin (Ig) kappa chain C region (IGKC)2–19, and prothrombin (THRB)328–345, were re-analyzed using tandem mass spectrometric (MS/MS) spectra (ProteomeXchange: PXD004546) from RA patients vs. HCs. Further, we determined serum protein levels of CFAH, HPT, IGKC and THRB, HNE-protein adducts, and autoantibodies against unmodified and HNE-modified peptides. Significant correlations and odds ratios (ORs) were calculated. Results Levels of HPT in RA patients were greatly higher than the levels in HCs. Levels of HNE-protein adducts and autoantibodies in RA patients were significantly greater than those of HCs. IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgM anti-IGKC2−19 HNE may be considered as diagnostic biomarkers for RA. Importantly, elevated levels of IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgG anti-THRB328−345 were positively correlated with the disease activity score in 28 joints for C-reactive protein (DAS28-CRP). Further, the ORs of RA development through IgM anti-HPT78−108 HNE (OR 5.235, p < 0.001), IgM anti-IGKC2−19 (OR 12.655, p < 0.001), and IgG anti-THRB328−345 (OR 5.761, p < 0.001) showed an increased risk. Lastly, we incorporated three machine learning models to differentiate RA from HC and OA, and performed feature selection to determine discriminative features. Experimental results showed that our proposed method achieved an area under the receiver operating characteristic curve of 0.92, which demonstrated that our selected autoantibodies combined with machine learning can efficiently detect RA. Conclusions This study discovered that some IgG- and IgM-NAAs and anti-HNE M-NAAs may be correlated with inflammation and disease activity in RA. Moreover, our findings suggested that IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgG anti-THRB328−345 may play heavy roles in RA development.

scholarly journals OP0018 PREDICTING RESPONSES TO ANTI-TNF TREATMENTS IN RHEUMATOID ARTHRITIS PATIENTS FROM GENETIC AND CLINICAL DATA USING A MACHINE LEARNING APPROACH

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 10-11
Author(s):  
S. Ju ◽  
M. Hudson ◽  
I. Colmegna ◽  
S. Bernatsky ◽  
Y. LI
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Random Forest ◽  
Tumor Necrosis Factor ◽  
Disease Activity ◽  
Tumor Necrosis ◽  
Genomic Data ◽  
Genome Wide ◽  
Non Linear ◽  
Necrosis Factor

Background:Tumor necrosis factor (TNF) inhibitors are key therapies in rheumatoid arthritis (RA). However, a third of patients fail to respond to these agents, and there are no reliable predictors for response. Predictive models, potentially based on clinical and genomic data, are vital to personalizing therapy. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) RA Responder Challenge invited research teams to create models for patient response to anti-TNF therapy. The winning model relied heavily on limited genetic input and was unable to correctly predict responses in a large number of individuals.Objectives:We compared non-linear and linear analytic methods to predict response and non-response to anti-TNF treatment for RA patients in the DREAM database, using moth clinical variables and a large number of potential genome-wide predictors.Methods:DREAM data on anti-TNF treated RA patients were accessed through Synapse (synapse.sagebase.org). Analogously to the DREAM challenge, we were provided with the clinical and genomic data of 2706 patients with at least moderate disease activity according to their composite disease activity scores for 28 joints (DAS28). In contrast to the previous analysis that focused on single nucleotide polymorphisms (SNPs) based on existing knowledge of RA, we used the full genome-wide dataset of 2.5 million SNPs. We first reduced this to 284 SNPs by considering the marginal p-value of 0.001 for each SNP based on whether or not it predicted response. Then, we removed SNPs with borderline significant p-values if they were in linkage disequilibrium with the most significant SNPs. Instead of predicting a binary outcome of responder or non-responder, we trained both linear (e.g. least absolute shrinkage and selection operator, or LASSO) and non-linear models (e.g. Random Forest) to predict a continuous outcome, the change in DAS28 from baseline to 3-12 months after initiation of anti-TNF therapy. We split the patients into training (N=2031) and testing (N=675) subsets and used the predicted response scores to evaluate the true binary response labels for the test patients.Results:The best performing method was Random Forest (RF), a non-linear model that uses decision trees to progressively separate subjects into groups based on the most predictive features. Support Vector Regression (SVR) also out-performed linear methods. Compared to only clinical covariates such as age and sex, adding SNPs improved the prediction from an area under the receiver operating curve (AUROC) of 0.63 to 0.67, i.e., 0.04 improvement. This AUROC of 0.67 was 0.046 greater than the DREAM challenge winner.Conclusion:Non-linear methods such as RF and SVR gave larger predictive improvements compared to linear methods. This may imply some interaction between SNPs and clinical covariatesas potential predictors of response to anti-TNF therapy in RA. We are further investigating these 284 SNPs and their interactions in this regard.References:[1]Guan, Y., Zhang, H., Quang, D., Wang, Z., Parker, S., Pappas, D. A., Kremer, J. M., & Zhu, F. (2019). Machine Learning to Predict Anti-Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers. Arthritis & rheumatology (Hoboken, N.J.), 71(12), 1987–1996. https://doi.org/10.1002/art.41056[2]Sieberts, S. K., Zhu, F., García-García, J., Stahl, E., Pratap, A., Pandey, G., Pappas, D., Aguilar, D., Anton, B., Bonet, J., Eksi, R., Fornés, O., Guney, E., Li, H., Marín, M. A., Panwar, B., Planas-Iglesias, J., Poglayen, D., Cui, J., Falcao, A. O., … Mangravite, L. M. (2016). Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature communications, 7, 12460. https://doi.org/10.1038/ncomms12460Figure.Disclosure of Interests:None declared

scholarly journals OP0300 USE OF THERMOGRAPHY OF HANDS AND MACHINE LEARNING TO QUANTIFY JOINT INFLAMMATION AND ESTIMATE DAS28, CDAI, SDAI IN PATIENTS WITH RHEUMATOID ARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 184.1-184
Author(s):  
I. Morales-Ivorra ◽  
C. Gómez Vaquero ◽  
C. Moragues Pastor ◽  
J. M. Nolla ◽  
J. Narváez ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Linear Regression ◽  
Disease Activity ◽  
Joint Inflammation ◽  
Training Dataset ◽  
Test Dataset ◽  
Thermal Images ◽  
Joint Assessment

Background:Disease activity scores such as DAS28, CDAI and SDAI are used in the follow-up of patients with rheumatoid arthritis (RA). These scores include variables obtained on physical examination such as the tender joint count (TJC) and the swollen joint count (SJC). In telematic consultations, it is not possible to determine these variables by physical joint assessment. Therefore, it is necessary to develop new tools that allow detecting joint inflammation in places close to the patient. Thermography is a safe and fast technique that measures heat through infrared imaging. Inflammation of the joints causes an increase in temperature and can therefore be detect by thermography. Machine learning methods are highly accurate in analyzing medical images automatically.Objectives:To develop an algorithm that, based on thermographic images of hands and machine learning, learn to quantify joint inflammation in patients with RA and estimate the DAS28, CDAI, SDAI by including the patient global health (PGH).Methods:Multicenter observational study conducted in the rheumatology and radiology service of two hospitals. Patients with RA, psoriatic arthritis (PA), undifferentiated arthritis (UA) and arthritis of hands secondary to other diseases (SA) that attended the follow-up visits were recruited. Companions of patients and healthcare professionals were also recruited as healthy subjects (HS). In all cases, a thermographic image of the hands was taken using a Flir One Pro or a Thermal Expert TE-Q1 camera connected to a smartphone. Ultrasound (US) of both hands was performed in patients with RA, PA, UA and SA. The degree of synovial hypertrophy (SH) and power doppler (PD) was assessed for each joint (score from 0 to 3). Machine learning was used to quantify joint inflammation (SH+PD) from the thermal images using US as ground truth. RA patients whose thermal image was taken with the Thermal Expert TE-Q1 camera were used to evaluate the performance (test dataset). The other participants were used as training dataset. The TJC, SJC, PGH, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were also assessed in the test dataset. A linear regression was used to estimate the DAS28, CDAI and SDAI with the resultant joint inflammation quantification from the thermal images and the PGH. Performance was evaluated by means of Pearson’s correlation coefficient. The study was approved by the Clinical Ethics and Research Committee of both centers.Results:The total number of recruited subjects was 521 (422 for the training and 99 for the testing dataset). In the training dataset, the thermography of 296 patients was taken with the Flir One Pro (163 RA, 17 PA, 22 UA, 12 SA and 82 HS) and 126 with the Thermal Expert TE-Q1 camera (6 RA without clinical data, 20 PA, 7 UA, 23 SA and 70 HS).We found higher correlations between joint inflammation variables (US and SJC) and thermography (0.48, p<0.01 for US and 0.48, p<0.01 for SJC) than between joint inflammation variables (US and SJC) and the PGH (0.29, p<0.01 for US and 0.35, p<0.01 for SJC). Thermography did not show statistically significant correlation with the PGH (0.14, p=0.164). The linear regression of thermography and the PGH showed strong correlation with the DAS28 (0.73, p<0.01), CDAI (0.84, p<0.01) and SDAI (0.82, p<0.01).Conclusion:Thermography of hands and machine learning can effectively quantify joint inflammation and can be used in combination with the PGH to estimate disease activity scores. These results open an opportunity to develop tools that facilitate telematic consultations in patients with RA.References:[1]Brenner M, Braun C, Oster M, Gulko PS. Thermal signature analysis as a novel method for evaluating inflammatory arthritis activity. Ann Rheum Dis. 2006;65(3):306-11[2]Lynch CJ, Liston C. New machine-learning technologies for computer-aided diagnosis. Nat Med. 2018;24(9):1304-1305[3]Tan YK, Hong C, Li H, Allen JC Jr, Thumboo J. Thermography in rheumatoid arthritis: a comparison with ultrasonography and clinical joint assessment. Clin Radiol. 2020;75(12):963Disclosure of Interests:None declared.

scholarly journals Validation of a machine learning approach to estimate Clinical Disease Activity Index Scores for rheumatoid arthritis

RMD Open ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. e001781
Author(s):  
Alison K. Spencer ◽  
Jigar Bandaria ◽  
Michelle B. Leavy ◽  
Benjamin Gliklich ◽  
Zhaohui Su ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Disease Activity ◽  
Activity Index ◽  
Disease Activity Index ◽  
Learning Model ◽  
Real World Data ◽  
Clinical Notes ◽  
Machine Learning Model ◽  
R Value

ObjectiveDisease activity measures, such as the Clinical Disease Activity Index (CDAI), are important tools for informing treatment decisions and monitoring patient outcomes in rheumatoid arthritis (RA). Yet, documentation of CDAI scores in electronic medical records and other real-world data sources is inconsistent, making it challenging to use these data for research. The purpose of this study was to validate a machine learning model to estimate CDAI scores for patients with RA using clinical notes.MethodsA machine learning model was developed to estimate CDAI score values using clinical notes from a specific rheumatology visit. Data from the OM1 RA Registry were used to create a training cohort of 56 177 encounters and a separate validation cohort of 18 726 encounters, 11 985 of which passed a model-derived confidence filter; all included encounters had both a clinician-recorded CDAI score and a clinical note. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), positive predictive value (PPV) and negative predictive value (NPV), calculated using a binarised version of the outcome. The Spearman’s R and Pearson’s R values were also calculated.ResultsThe model had a PPV of 0.80, NPV of 0.84 and AUC of 0.88 when evaluating performance using the binarised version of the outcome. The model had a Spearman’s R value of 0.72 and a Pearson’s R value of 0.69 when evaluating performance using the continuous CDAI numeric scores.ConclusionA machine learning model estimates CDAI scores from clinical notes with good performance. Application of the model to real-world data sets may allow estimated CDAI scores to be used for research purposes.

scholarly journals Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

2020 ◽  
Author(s):  
Dmitry Rychkov ◽  
Jessica Neely ◽  
Tomiko Oskotsky ◽  
Steven Yu ◽  
Noah Perlmutter ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Machine Learning ◽  
Feature Selection ◽  
Disease Activity ◽  
Gene Expression Data ◽  
Clinical Utility ◽  
Differentially Expressed ◽  
Healthy Controls ◽  
Expression Data

AbstractBackground/PurposeThere is an urgent need to identify effective biomarkers for early diagnosis of rheumatoid arthritis (RA) and to accurately monitor disease activity. Here we define an RA meta-profile using publicly available cross-tissue gene expression data and apply machine learning to identify putative biomarkers, which we further validate on independent datasets.MethodsWe carried out a comprehensive search for publicly available microarray gene expression data in the NCBI Gene Expression Omnibus database for whole blood and synovial tissues from RA patients and healthy controls. The raw data from 13 synovium datasets with 284 samples and 14 blood datasets with 1,885 samples were downloaded and processed. The datasets for each tissue were merged, batch corrected and split into training and test sets. We then developed and applied a robust feature selection pipeline to identify genes dysregulated in both tissues and highly associated with RA. From the training data, we identified a set of overlapping differentially expressed genes following the condition of co-directionality. The classification performance of each gene in the resulting set was evaluated on the testing sets using the area under a receiver operating characteristic curve. Five independent datasets were used to validate and threshold the feature selected (FS) genes. Finally, we defined the RA Score, composed of the geometric mean of the selected RA Score Panel genes, and demonstrated its clinical utility.ResultsThis feature selection pipeline resulted in a set of 25 upregulated and 28 downregulated genes. To assess the robustness of these FS genes, we trained a Random Forest machine learning model with this set of 53 genes and then with the set of 33 overlapping genes differentially expressed in both tissues and tested on the validation cohorts. The model with FS genes outperformed the model with common DE genes with AUC 0.89 ± 0.04 vs 0.87 ± 0.04. The FS genes were further validated on the 5 independent datasets resulting in 10 upregulated genes, TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, and ATP6V0E1, which are involved in innate immune system pathways, including neutrophil degranulation and apoptosis. There were also three downregulated genes, HSP90AB1, NCL, and CIRBP, that are involved in metabolic processes and T-cell receptor regulation of apoptosis.To investigate the clinical utility of the 13 validated genes, the RA Score was developed and found to be highly correlated with the disease activity score based on the 28 examined joints (DAS28) (r = 0.33 ± 0.03, p = 7e-9) and able to distinguish osteoarthritis (OA) from RA samples (OR 0.57, 95% CI [0.34, 0.80], p = 8e-10). Moreover, the RA Score was not significantly different for rheumatoid factor (RF) positive and RF-negative RA sub-phenotypes (p = 0.9) and also distinguished polyarticular juvenile idiopathic arthritis (polyJIA) from healthy individuals in 10 independent pediatric cohorts (OR 1.15, 95% CI [1.01, 1.3], p = 2e-4) suggesting the generalizability of this score in clinical applications. The RA Score was also able to monitor the treatment effect among RA patients (t-test of treated vs untreated, p = 2e-4). Finally, we performed immunoblotting analysis of 6 proteins in unstimulated PBMC lysates from an independent cohort of 8 newly diagnosed RA patients and 7 healthy controls, where two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90, were validated and the S100A8 protein showed near significant up-regulation.ConclusionThe RA Score, consisting of 13 putative biomarkers identified through a robust feature selection procedure on public data and validated using multiple independent data sets, could be useful in the diagnosis and treatment monitoring of RA.

scholarly journals SAT0567 USE OF THERMOGRAPHY OF HANDS AND MACHINE LEARNING TO DIFFERENTIATE PATIENTS WITH ARTHRITIS FROM HEALTHY SUBJECTS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1241.1-1242
Author(s):  
I. Morales-Ivorra ◽  
D. Grados Canovas ◽  
C. Gómez Vaquero ◽  
J. M. Nolla ◽  
J. Narváez ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Early Diagnosis ◽  
Healthy Subjects ◽  
Power Doppler ◽  
Thermal Image ◽  
Learning Technologies ◽  
Classification Model ◽  
P Value ◽  
Research Committee

Background:The early diagnosis of rheumatic diseases improves their prognosis. However, patients take several months to reach the rheumatologist from the beginning of the first symptoms. Thermography is a safe and fast technique that captures the heat of an object through infrared photography. The inflammation of the joints causes an increase in temperature and, therefore, can be measured by thermography. Machine learning methods have shown that they are capable of analyzing medical images with an accuracy similar or superior to that of a healthcare professional.Objectives:Develop an algorithm that, based on thermographic images of hands and machine learning, differentiates healthy subjects from patients with rheumatoid arthritis (RA), psoriatic arthritis (PA), undifferentiated arthritis (UA) and arthritis of hands secondary to other diseases (SA).Methods:Multicenter observational study conducted in the rheumatology and radiology service of two hospitals. Patients with RA, PA, UA and SA who attended the followup visit and healthy subjects (companions and healthcare proffesionals) were recruited. In all cases, a thermal image of the hands was taken using a Flir One Pro or Thermal Expert TE-Q1 camera connected to the mobile and an ultrasound of both hands. The degree of synovial hypertrophy (SH) and power doppler (PD) was assessed for each joint (score from 0 to 3). Inflammation was defined as the presence of SH> 1 or PD> 0. Machine learning was used to classify patients with RA, PA, UA and SA with inflammation evidenced by ultrasound and healthy subjects from thermographic images. The evaluation of the classifier was performed by leave-one-out cross-validation and the area under the ROC curve (AUCROC) in those subjects whose thermal image was performed with the Thermal Expert TE-Q1 camera. The study was approved by the Clinical Ethics and Research Committee of the centers.Results:500 subjects were recruited from March 2018 to January 2020, of these 73 were excluded due to poor quality in the thermal image (moved or absence of temperature contrast between hand and background). Of the 427 subjects analyzed, 129 corresponded to healthy subjects, 138 to patients without evidence of inflammation and 160 to patients with inflammation evidenced by ultrasound (116 RA and 44 PA, UA or SA). Of these, 42% were taken using the Thermal Expert TE-Q1 camera. An AUCROC of 0.73 (p-value <0.01) was obtained for the healthy classifier vs RA and 0.72 (p-value <0.01) for the healthy classifier vs PA, UA and SA.Conclusion:A classification model has been developed capable of differentiating patients with RA, PA, UA and SA with evidence of inflammation from healthy subjects. These results open an opportunity to develop tools that facilitate early diagnosis.References:[1]Barhamain AS, Magliah RF, Shaheen MH, Munassar SF, Falemban AM, Alshareef MM, Almoallim HM. The journey of rheumatoid arthritis patients: a review of reported lag times from the onset of symptoms. Open Access Rheumatol. 2017 Jul 28;9:139-150. doi: 10.2147/OARRR.S138830. eCollection 2017. Review.[2]Lynch CJ, Liston C. New machine-learning technologies for computer-aided diagnosis. Nat Med. 2018 Sep;24(9):1304-1305. doi: 10.1038/s41591-018-0178-4.[3]Brenner M, Braun C, Oster M, Gulko PS. Thermal signature analysis as a novel method for evaluating inflammatory arthritis activity. Ann Rheum Dis. 2006 Mar;65(3):306-11.Disclosure of Interests:None declared

scholarly journals Central role of the glutamate metabolism in long-term antiretroviral treated HIV-infected individuals with metabolic syndrome: a cross-sectional cohort study

2021 ◽  
Author(s):  
Marco Gelpi ◽  
Flora Mikaeloff ◽  
Andreas Dehlbæk Knudsen ◽  
Rui Benfeitas ◽  
Shuba Krishnan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Amino Acid ◽  
Hiv Infection ◽  
Amino Acid Metabolism ◽  
Research Council ◽  
Acid Metabolism ◽  
Classification Model ◽  
Glutamate Metabolism

AbstractBackgroundMetabolic syndrome (MetS) is one of the major factors for cardiometabolic comorbidities in people living with HIV (PLWH). The long-term consequences of HIV-infection and combination antiretroviral therapy (cART) in metabolic reprogramming are unknown. In this study, we aim to investigate metabolic alterations in long-term well-treated PLWH with MetS to identify the potential mechanism behind the MetS phenotype using advanced statistical and machine learning algorithms.MethodsWe included 200 PLWH ≥40 years old from the Copenhagen Comorbidity in HIV-infection (COCOMO) study. PLWH were grouped into PLWH with MetS (n=100) and without MetS (n=100). The clinical data were collected from the COCOMO database and untargeted plasma metabolomics was performed using ultra-high-performance liquid chromatography/mass spectrometry (UHPLC/MS/MS). Both clinical characteristics and plasma samples were collected at study baseline. We applied several conventional approaches, machine learning algorithm and linear classification model to identify the biologically relevant metabolites associated with MetS in PLWH.FindingsA total of 877 characterized biochemicals were identified. Of these, 9% (76/877) biochemicals differed significantly between PLWH with and without MetS (false discovery rate <0.05). The majority belonged to the amino acid metabolism (n=33, 43%). A consensus identification by combining supervised and unsupervised methods indicates 11 biomarkers of MetS phenotype in PLWH, of which seven (63%) have higher abundance in PLWH with MetS compared to the PLWH without MetS. A weighted co-expression network by Leiden partitioning analysis identified seven communities of positively intercorrelated metabolites, of which a single community contained six of the potential biomarkers mainly related to glutamate metabolism (glutamate, 4-hydroxyglutamate, α-ketoglutamate and γ-glutamylglutamate).InterpretationAltered amino acid metabolism is a central characteristic of PLWH with MetS and a potential central role for glutamate metabolism in establishing this phenotype is suggested.FundingRigshospitalet Research Council, Danish National Research Foundation (DNRF126) NovoNordisk Foundation, the Swedish Research Council (2017-01330 and 2018-06156)

scholarly journals Serum Markers in Rheumatoid Arthritis: A Longitudinal Study of Patients Undergoing Infliximab Treatment

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Oddgeir Selaas ◽  
Hilde H. Nordal ◽  
Anne-Kristine Halse ◽  
Johan G. Brun ◽  
Roland Jonsson ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Activity Index ◽  
Joint Destruction ◽  
Disease Activity Index ◽  
Serum Levels ◽  
Serum Markers ◽  
Matrix Metalloproteases ◽  
High Disease Activity ◽  
Infliximab Treatment

Objective.The aim of this study was to investigate the clinical effect and serum markers in a cohort of rheumatoid arthritis patients with moderate to high disease activity, participating in an open clinical phase IV study conducted in Norway between 2001 and 2003 receiving infliximab treatment.Method.A total of 39 patients were studied, with a mean age of 54 years and 12-year disease duration. The analyses were performed using serum from patients at four assessment time points: baseline and 3, 6, and 12 months after starting treatment with infliximab. A wide variety of clinical data was collected and disease activity of 28 joints and Simple Disease Activity Index were calculated. The joint erosion was determined by X-ray imaging and the Sharp/van der Heijde score was determined. Serum analysis included multiplex immunoassays for 12 cytokines, 5 matrix metalloproteases, and 2 VEGFs.Results.The majority of the RA patients in this study had initially moderate to high disease activity and the infliximab treatment reduced the disease activity significantly and also reduced any further joint destruction and improved disease status. Most of the serum levels of cytokines and metalloproteases remained unchanged during the course of the study, and we were unable to detect changes in TNF-αin serum. Serum levels of IL-6 and VEGF-A decreased significantly after initiation of infliximab treatment.Conclusion.The serum levels of IL-6 and VEGF-A may be promising disease markers as they vary with disease progression. The clinical significance of these findings is yet to be determined and has to be confirmed in future clinical trials before being applied in the clinics.

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