Utility of multi-biomarker panel on discriminating disease activity in patients with psoriatic arthritis.

OBJECTIVE: To investigate the correlation of serum protein biomarkers and disease activity in patients with PsA. METHODS: 176 patients fulfilled the CASPAR (ClASsification criteria for Psoriatic ARthritis) were recruited in this cross-sectional study. The level of 48 protein biomarkers, cartilage and bone turn-over markers were assessed. The patients were randomly divided into a derivation-cohort and a validation-cohort at a ratio of 7:3. Patients were further categorized based on their disease activity states using cDAPSA (remission/low disease activity and moderate/high disease activity). Least absolute shrinkage and selection operator (LASSO) was used to select biomarkers which were associated with moderate/high disease activity in the derivation cohort. Receiver operating characteristic (ROC) curve, GiViTI calibration belt were used to assess the performance of the model in both cohorts. RESULTS: The cohort [age: 55.5 (44.0-62.75) years, male: 80 (45.5 %)] had moderate disease activity [DAPSA: 15.9 (8.3-26.9); PASI: 3.2 (0.5-6.8)]. 101 PsA patients (57.4 %) had clinical DAPSA moderate/high disease activity. Biomarker levels associated with moderate/high disease activity included SAA (Serum amyloid A), IL-8 (Interleukin 8), IP10 (Interferon gamma-induced protein 10)/CXCL10, M-CSF (Macrophage colony-stimulating factor), SCGF-ß (Stem cell growth factor), SDF-1α (Stromal cell-derived factor 1α)/CXCL12. The model's equation including the 6 biomarker levels was applied to the validation-cohort. The area under the ROC curve (AUC) for discriminating moderate/high disease activity was 0.802 and 0.835 for the derivation-and-validation-cohorts, respectively. The multi-biomarkers panel model had higher-AUC when compared with that of C-reactive protein (CRP) (AUC = 0.727, p = 0.022). The P-values of calibration charts in the two sets were 0.902 and 0.123. CONCLUSIONS: The multi-biomarkers panel demonstrated the ability to discriminate patients with moderate/high disease activity from those with low disease activity/remission.

DCES-PA: Deformation-controllable elastic shape model for 3D bone proliferation analysis using hand HR-pQCT images.

Bone proliferation is an important pathological feature of inflammatory rheumatic diseases. Although recent advance in high-resolution peripheral quantitative computed tomography (HR-pQCT) enables physicians to study microarchitectures, physicians' annotation of proliferation suffers from slice inconsistency and subjective variations. Also, there are only few effective automatic or semi-automatic tools for proliferation detection. In this study, by integrating pathological knowledge of proliferation formation with the advancement of statistical shape analysis theory, we present an unsupervised method, named Deformation-Controllable Elastic Shape model, for 3D bone Proliferation Analysis (DCES-PA). Unlike previous shape analysis methods that directly regularize the smoothness of the displacement field, DCES-PA regularizes the first and second-order derivative of the displacement field and decomposes these vector fields according to different deformations. For the first-order elastic metric, DCES-PA orthogonally decomposes the first-order derivative of the displacement field by shearing, scaling and bending deformation, and then penalize deformations triggering proliferation formation. For the second-order elastic metric, DCES-PA encodes both intrinsic and extrinsic surface curvatures into the second-order derivative of the displacement field to control the generation of high-curvature regions. By integrating the elastic shape metric with the varifold distances, DCES-PA achieves correspondence-free shape analysis. Extensive experiments on both simulated and real clinical datasets demonstrate that DCES-PA not only shows an improved accuracy than other state-of-the-art shape-based methods applied to proliferation analysis but also produces highly sensitive proliferation annotations to assist physicians in proliferation analysis.

Role of inflammatory burden and treatment on joint space width in psoriatic arthritis-a high-resolution peripheral quantitative computed tomography study.

BACKGROUND: To investigate the relationship between disease-related parameters and joint space width (JSW) on high-resolution peripheral quantitative computed tomography (HR-pQCT) in psoriatic arthritis (PsA) patients. METHODS: PsA patients who underwent HR-pQCT examination of the second to fourth metacarpophalangeal joint (MCPJ 2-4) were recruited in this cross-sectional study. The joint space metrics included joint space volume (JSV), mean, minimum, and maximum JSW, JSW asymmetry, and distribution. Correlation analysis and multivariable linear regression models were used to determine the association between disease-related variables and JSW. RESULTS: Sixty-seven patients [37 (55.2%) males; median (IQR) age: 57.0 (53.0, 63.0); median disease duration: 21 (16, 28) years] were included in this analysis. Multivariable linear regression analysis demonstrated that males had larger JSV (MCPJ 2-4), mean (MCPJ 4), and maximum JSW (MCPJ 3). Longer disease duration (MCPJ 2-3) and higher ESR values (MCPJ 3) were negatively associated with mean and maximum JSW, while higher damage joint count was negatively associated with mean and minimum JSW (MCPJ 2). Use of conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs) was negatively associated with minimum JSW (MCPJ 3) while use of biologic DMARDs (bDMARDs) was positively associated with minimum JSW (MCPJ 2). CONCLUSION: Higher inflammatory burden as reflected by longer disease duration, higher ESR levels, and damage joint count was negatively associated with mean, maximum, and minimum JSW, while suppression of inflammation using bDMARDs seems to limit the decline in JSW.

The temporal trend of disease burden attributable to metabolic risk factors in China, 1990-2019: An analysis of the Global Burden of Disease study.

Background and aims: The disease burden attributable to metabolic risk factors is rapidly increasing in China, especially in older people. The objective of this study was to (i) estimate the pattern and trend of six metabolic risk factors and attributable causes in China from 1990 to 2019, (ii) ascertain its association with societal development, and (iii) compare the disease burden among the Group of 20 (G20) countries. Methods: The main outcome measures were disability-adjusted life-years (DALYs) and mortality (deaths) attributable to high fasting plasma glucose (HFPG), high systolic blood pressure (HSBP), high low-density lipoprotein (HLDL) cholesterol, high body-mass index (HBMI), kidney dysfunction (KDF), and low bone mineral density (LBMD). The average annual percent change (AAPC) between 1990 and 2019 was analyzed using Joinpoint regression. Results: For all six metabolic risk factors, the rate of DALYs and death increased with age, accelerating for individuals older than 60 and 70 for DALYs and death, respectively. The AAPC value in rate of DALYs and death were higher in male patients than in female patients across 20 age groups. A double-peak pattern was observed for AAPC in the rate of DALYs and death, peaking at age 20-49 and at age 70-95 plus. The age-standardized rate of DALYs increased for HBMI and LBMD, decreased for HFPG, HSBP, KDF, and remained stable for HLDL from 1990 to 2019. In terms of age-standardized rate of DALYs, there was an increasing trend of neoplasms and neurological disorders attributable to HFPG; diabetes and kidney diseases, neurological disorders, sense organ diseases, musculoskeletal disorders, neoplasms, cardiovascular diseases, digestive diseases to HBMI; unintentional injuries to LBMD; and musculoskeletal disorders to KDF. Among 19 countries of Group 20, in 2019, the age-standardized rate of DALYs and death were ranked fourth to sixth for HFPG, HSBP, and HLDL, but ranked 10th to 15th for LBMD, KDF, and HBMI, despite the number of DALYs and death ranked first to second for six metabolic risk factors. Conclusions: Population aging continuously accelerates the metabolic risk factor driven disease burden in China. Comprehensive and tight control of metabolic risk factors before 20 and 70 may help to mitigate the increasing disease burden and achieve healthy aging, respectively.