| Background: Psoriasis is an immune-mediated chronic,systemic,and inflammatory disease,simultaneously affecting skin,nails,and joints.The specific mechanisms of the occurrence and development have not been fully elucidated.Psoriatic arthritis(Ps A)is a seronegative inflammatory musculoskeletal disease,occurring in 10%-40% of psoriasis patients.It overlaps with psoriasis in terms of pathogenesis,treatment methods,and other aspects,commonly appearing 10 years after the appearance of psoriasis lesions.The main manifestations of Ps A include varying degrees of swelling and tender in different joints,which can even progress to joint deformity,disability,and dysfunction in later stages.Previous studies have reported that the diagnosis delay in Ps A is significantly related to the reduction of quality of life(Qo L),impairment of physical function and therapeutic responsiveness.Therefore,the international consensus have suggested that it is of great necessary to seize the window of opportunity,to carry out early screening and diagnosis,start intervention as soon as possible,regularly monitor disease activity,and put timely adjustments to treatments.According to different information channels and research methods,data resulting from different perspectives can be collected for the same research project being psoriasis.When integrating the obtained mono-modal data into a multi-modal form,various types of data complement each other to comprehensively present the clinical characteristics of psoriasis and Ps A at different stages.Therefore,it could assist to promote the formation of a complete evaluation system,then achieving regular monitoring and reducing the possibility of serious adverse events such as joint deformities and disabilities.Based on affected joint sites,Ps A can be divided into peripheral psoriatic arthritis(p Ps A)and axial psoriatic arthritis(Ax Ps A).Due to the small number of Ax Ps A patients and unclear pathogenesis,the specific disease definition and clinical characteristics of Ax Ps A have not been fully studied.Early inflammatory pathological changes,such as bone marrow edema(BME),could be detected in sacroiliac joint(SIJ)and other axial parts ahead of the appearance of specific clinical symptoms.However,as BME could not be discovered in time by purely visual and palpation examinations or the patient reported outcomes,it often leads to missed diagnosis,misdiagnosis,and delayed diagnosis.The international consensus has proposed to use imaging methods for early screening and diagnosis in Ax Ps A,which helps to accurately reflect the microscopic lesions of bones and joints.Among many imaging examination methods,magnetic resonance imaging(MRI)stands out in detecting axial joint lesions by the relatively high sensitivity and specificity for BME,which is beneficial for further elucidation.In addition to early screening and diagnosis,timely monitoring of patients’ disease activity is also a key to control the progression.Lacking of Ps A specific biomarkers to determine disease condition have brought out difficulties in evaluating disease activity.Nowadays,the evaluation tools for disease activity status are mainly based on clinical features.In recent years,various specific evaluation tools for Ps A have been improved or developed,but the application has still been limited to clinical trials,not expanding to clinical practice yet.In addition,frequently used evaluation tools still have shortcomings such as high subjectivity,long time consumption,and complex calculations.Thus,based on objective laboratory examination results,screening and constructing new evaluation tools can improve the consistency of results under different treatment states,which is more conducive for the promotion in clinical practice.In terms of treatment intervention,with the continuous exploration of the pathogenesis,several treatment targets have gradually become clear,contributing to the treatment options expand from traditional drugs to immune interventions.Various biologics targeting different cytokines play a positive role.According to previous statistics,14%-46% of psoriasis patients can develop metabolic diseases,such as obesity and hyperlipidemia.The application of biologics is not only beneficial for clearing skin lesions,but also for metabolic disorders correction.However,the correlation between the therapeutic response of biologics and the metabolic status has not been fully elucidated.In recent years,the application of metabolomics has emerged in the field of metabolic status related to psoriasis.Metabolomics demonstrates the changes in endogenous metabolites through qualitative and quantitative analysis of metabolites in skin lesions,blood,urine and other sample.By utilizing metabolomics,analyzing the differences in metabolic status pre-and post-treatments of biologics can assist in identifying metabolic markers implying biologics responsiveness in the short-term,and provide new insights in metabolic characteristics of psoriasis.Chapter 1.Researches on Disease Characteristics of Psoriatic Arthritis and its Subtypes Based on Clinical and Imaging DataObjectives: To summarize the clinical characteristics,the degree of impact of Qo L,and imaging characteristics of Ax Ps A,and to explore potential imaging features suitable for early diagnosis,by collecting clinical and MRI examination results meanwhile compared to patients with psoriasis vulgaris(Ps V)and p Ps A.Methods: This chapter is divided into two parts,respectively collecting clinical data of Ps V and Ps A patients,and imaging data of Ps A patients.In the first part of the clinical feature research process,a questionnaire was designed and distributed to investigate the involvement of the axial joint in psoriasis patients.Psoriasis patients who completed the questionnaire were included in the study,based on the diagnostic criteria for psoriasis,the classification criteria for Ps A(CASPAR)Clinical and/or radiological evaluation of Ax Ps A(Ax Ps A-CR)for axial psoriasis arthritis,diagnosed by a multidisciplinary team and divided into Ps V group,p Ps A group,and Ax Ps A group;Improve the condition assessment,collect clinical data including demography characteristics,psoriasis related disease information,the generalized anxiety disorder questionaire-2(GAD-2),the patient health questionaire-2(PHQ-2),and the five level Euro Qol five dimensional questionaire(EQ-5D-5L).In the second part of the imaging feature research process,p Ps A and Ax Ps A patients were included separately to improve the MRI examination of the SIJ site.The imaging data was quantitatively scored using the Canadian Spinal Arthritis Research Consortium of Canada Sacroiliac Joint(SPARCC-SIJ)evaluation system to further explore the correlation between imaging scores and clinical characteristics.Results: 1.The clinical characteristics were as follows.(1)A total of 461 psoriasis patients were included,of which 284 were diagnosed and included in the Ps V group,132 in the p Ps A group,and 45 in the Ax Ps A group.(2)The comparison of clinical characteristics between the PsV group and the Ps A group(including the p Ps A group and Ax Ps A group)showed that the Ps V group was significantly lower in terms of age(39.0 ± 13.9years vs.45.0 ± 11.3 years,P<0.001),the proportion of patients with psoriasis nail changes(56.0% vs.80.2%,P<0.001),the proportion of patients feeling fatigue(72.9% vs.90.4%,P<0.001),and the degree of fatigue(3.7 ± 3.1 vs.4.5 ± 2.8,P<0.04).(3)The comparison of clinical characteristics between two types of Ps A subtypes,the p Ps A group and the Ax Ps A group,showed that the p Ps A group was significantly higher than the Ax Ps A group in terms of age(46.1 ± 11.1>42.0 ± 11.2,P=0.034)and the proportion of patients with skin lesions first(89.4%>71.1%,P=0.009).In terms of the proportion of patients with psoriasis nail changes(76.5%<91.1%,P=0.034),the proportion of patients with peripheral joint morning stiffness(43.9%<73.3%,P=0.001),the proportion of patients with axial joint morning stiffness(37.9%<100.0%,P<0.001),the proportion of patients with fatigue(87.1%< 100.0%,P<0.001),and the degree of fatigue(4.3 ± 2.8<5.9 ± 2.5,P<0.001),results in the p Ps A group were significantly lower than the Ax Ps A group.(4)The results of the EQ-5D-5L study showed that the Ax PsA group had a higher proportion of patients with problems or difficulties in five dimensions: mobility,self-care,daily life,pain/discomfort,and anxiety/depression,all of which were higher than the p Ps A group.The difference in the proportion of patients with pain/discomfort and anxiety/depression was the largest.2.The the imaging characteristics were as follows.(1)A total of 29 Ps A patients were included,including 17 confirmed and included in the p Ps A group and 12 in the Ax Ps A group.(2)The clinical evaluation and SPARCC-SIJ evaluation results showed that the overall evaluation results of physicians in the p Ps A group were significantly higher(28.5 ± 23.8>10.0 ± 10.5,P=0.034),while the SPARCC-SIJ evaluation results in the Ax Ps A group were significantly higher(5.1 ± 9.0<32.9 ± 37.7,P=0.026).(3)The SPARCC-SIJ score is positively correlated with the risk of disease progression to Ax Ps A.For every point increased in SPARCC-SIJ,the risk of disease progression to Ax Ps A increases 0.058 times(OR=1.058,95% CI: 1.001-1.119,P=0.048);The SPARCC-SIJ score range(0-192 points)corresponds to an increased risk range of0.058-11.136 times.Conclusions: 1.Joint involvement can significantly impair the Qo L of psoriasis patients,mainly focusing on physical and mental aspects.Progression to central axis joint involvement can further exacerbate the degree of impact.2.A higher SPARCC-SIJ score indicates a higher risk of progression to Ax Ps A(OR=1.058,95% CI: 1.001-1.119,P=0.048).Chapter 2.A Risk Predictive Model for Disease Activity Status for Psoriatic Arthritis Based on Laboratory Examination and Body DataObjectives: To build a novel Ps A disease activity status prediction model through machine learning mainly based on blood routine examination results and body data.Methods: 1.PsA patients meeting the CASPAR classification criteria were continually recruited,along with the clinical and blood biochemical data collected.All of the blood biochemical data were entered as the original data set,and randomly divide them into training sets and validation sets according to the ratio of 9:1.The former is used for the construction of the disease activity state prediction model,and the latter is used for the prediction efficiency test after the model construction.2.For the model construction,the disease activity index(DAPSA)score was used as the reference standard for the classification of disease activity status.The original data set in the training set and the validation set were respectively divided into two groups,low disease activity status(REM/LDA)and high disease activity status(MDA/HDA),representing achieving/failing to the treatment goal.3.The variables of the validation set after logarithmic transformation were output as parameters and into a new data set.Through fitting the least absolute contraction selection operator(LASSO)regression model,the parameters and corresponding coefficients of the final selected model were screened and the coefficients were calculated in the disease activity prediction model finally constructed.4.The risk scores of all patients in the validation set are calculated as the boundary value to distinguish the high/low disease activity status.By conducting follow-up analysis of the subject working curve(ROC)and the area under the curve(AUC),the test efficiency of the model was reflected.Results: 1.The final selected parameters included in the model include white blood cell(WBC)count,hemoglobin(HGB)concentration,red cell distribution width(RDW),C-reactive protein(CRP),and waist to hip ratio(WHR),totaling in 5.2.The constructed Ps A disease state risk prediction model is: Risk Score=0.128×Ln(WBC)+(-1.922)×Ln(HGB)+0.381×Ln(RDW)+0.824×L n(CRP)+0.612×Ln(WHR)+10.132.The risk score threshold was 0.708.During clinical application,parameters are substituted into the model for calculation.If the result is higher than 0.708,it represents a high disease activity state,while lower than 0.708 meaning the opposite.3.The AUC values calculated based on the training set and validation set are 0.852 and 0.817,respectively,which have preliminarily achieved ideal evaluation performance.Conclusions: The novel Ps A disease activity status prediction model based on blood routine parameters has initially reached an ideal state in identification efficiency.Chapter 3.Researches on the Effect Caused by Biologics in the Short-term to the Metabolic Status of Psoriasis Based on Metabolomics DataObjectives: To preliminarily explore the short-term impact of biologics on the metabolic status and the potential metabolic markers used to predict the short-term response of biologics to treatment,by combining metabolic and clinical characteristics.Methods: 1.Patients with moderate and severe plaque psoriasis initially treated with IL-17 inhibitors were included,and the information needed for the study,including general information,medical history,disease severity,etc.,as well as the fasting peripheral blood serum samples before(week 0)and after(week 4)treatment with IL-17 and TNF-α inhibitors were collected.2.The metabolic network and specific metabolite expression of the included patients were displayed by means of mass spectrometry with KEGG as the main reference.Various secondary analysis such as qualitative analysis,quantitative analysis,analytical screenings of metabolites and metabolic pathways were carried out to confirm the participation of metabolites and related metabolic pathways.Types and pathways of different and/or dominant metabolites corresponding to different research purposes were classified and summarized.3.According to different response targets,as whether the psoriasis area and severity index(PASI)has achieved a 50/75/90/100%improvement rate after treatment(PASI 50/75/90/100),patients using IL-17 or TNF-α inhibitors were respectively divided into response/non response groups.Qualitative,quantitative,and differential metabolomic data between the two groups were analyzed to preliminarily identify metabolic markers that may indicate short-term treatment responsiveness of biologics.Results: 1.The results of the correlation analysis between the clinical and metabolic characteristics showed that phosphatidylcholines(PCs),sphingomyelins(SMs),phosphatidylethanolamines(PEs),and prostaglandins were significantly related to the severity and quality of life of psoriasis.(1)PC(18:1e/18:2),PC(18:2E/18:1),PC(22:1/20:4),PE(16:0e/18:2),PE(16:0e/20:3),PE(16:1e/18:2),PE(18:2e/20:3),SM(d14:1/22:2),SM(d22:2/19:1),sucrose disaccharide,trans-10 heptane diacid,pentacosane acid,prostaglandin F1 and other substances were positively correlated to disease severity.(2)PC(11:0/12:0),PC(16:2e/18:2),PC(16:2e/2:0),PC(19:2/18:5),PC(2:0/16:1),PC(2:0/16:2),PC(20:4/20:4),PC(4:0/18:5),PC(9:0/9:0),SM(d14:0/16:2),SM(d18:1/20:2),prostaglandin H1,9,11-epoxymethyl prostaglandin H2 are negatively correlated to disease severity.2.The results of the IL-17 inhibitor group were as follows.(1)Compared with pre-treatment condition,the substances with significantly increased expression levels after treatment are mainly ceramides(Cers),including Cer-NS(d18:2/24:0)and Cer-NS(d18:1/24:1).SM(d14:1/22:2),arachidic acid,and branched fatty acid esters of hydroxy fatty acids(FAHFA)decreased significantly.The metabolic pathways significantly affected included pentose phosphate pathway,unsaturated fat acid biosynthesis,arachidonic acid metabolism,steroid hormone biosynthesis,and phenylalanine metabolism.(2)The proportion of patients who achieve PASI 50/75/90/100 in the short term is 78.9%,52.6%,31.6%,and 21.1%,respectively.(3)Compared with the non-responsive group,the substance with significantly increased expression level in the PASI 75 responsive group was mainly N-acetylvaline,while the substance with significantly decreased expression level was mainly PC(18:4e/18:5).The substances with significantly increased expression levels in the PASI 90 response group were mainly N-phenylacetyl-L-glutamine and 2-phenylacetamide,while the substances with significantly decreased expression levels were mainly PC(18:3e/22:2),palmitoyl L-L-carnitine,acylcarnitine(ACar)20:0,ACar 24:1,and ACar 26:0.The substances with significantly increased expression levels in the PASI 100 response group were mainly13,14-dihydro-15-ketoprostaglandin D2,N-acetyl-L-glutamine,and SM(d14:2/23:0),while the significantly decreased substances included ACar20:0,ACar 24:0,ACar 24:1,ACar 26:0,and ACar 26:1.3.The results of the TNF-α inhibitor group were as follows.(1)Compared with pre-treatment condition,the substances with significantly increased expression level after treatment were PC(18:1e/22:4),13,14-dihydro-15-one prostaglandin D2,Cer(18:0/24:1),Cer-NS(d18:1/24:1),Cer-NS(d18:2/24:0),and the substances with significantly decreased expression level were FAHFA,bilirubin,and arachidic acid.The metabolic pathways significantly affected after treatment include unsaturated fat acid biosynthesis,arachidonic acid metabolism,steroid hormone biosynthesis,and phenylalanine,tyrosine and tryptophan biosynthesis.(2)The proportion of patients who achieve PASI 50/75 in the short term is 41.2% and 11.8% respectively.(3)Compared with the non-responsive group,the substances with significantly increased expression levels in the PASI 50 responsive group were mainly PC(14:0e/18:1),PC(2:0/16:2),SM(d25:1/12:1),while the substances with significantly decreased expression levels were FAHFA and SM(d14:0/28:2).Conclusions: 1.The short-term responsiveness of IL-17 inhibitors is slightly better than TNF-α inhibitors.2.SM and PC with decreased expression levels suggests increased disease severity and impaired Qo L.3.ACar and Cer can be preliminarily used as metabolic markers to predict the short-term responsiveness of IL-17 inhibitors.The respective significant decreased and increased expression levels in ACar and Cer indicate higher responsiveness.4.PC and SM can be preliminarily used as metabolic markers to predict the short-term responsiveness of TNF-α inhibitors.The significant increased expression levels of these two types of substances indicate lower responsiveness. |
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