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.