Background: With increasing life expectancy in China, no large population-based studies have been done on the trend for musculoskeletal disorders in China. We have investigated the pattern and trend of five major musculoskeletal disorders in China from the Global Burden of Disease Study 2017 and its association with sociodemographic index (SDI). Methods: The main outcome measures were incidence, prevalence, and disability-adjusted life years (DALYs) for rheumatoid arthritis, osteoarthritis, low back pain, neck pain, and gout. Average annual percent change (AAPC) and annual percent change (APC) between 1990 and 2017 were analyzed with Joinpoint regression. Results: The age-standardized rate of incidence, prevalence, and DALYs for the five major musculoskeletal disorders increased with age. For SDI, the age-standardized rate of DALYs was zigzagged increasing for rheumatoid arthritis and curvilinear increasing for gout, curvilinear decreasing for low back pain, and reaching to the highest point for osteoarthritis and neck pain with an SDI value of 0.61. The AAPC in age-standardized rate of DALYs indicated an increasing trend for rheumatoid arthritis (0.20, 95% CI 0.07, 0.34), osteoarthritis (0.26, 95% CI 0.20, 0.31), neck pain (0.09, 95% CI 0.07, 0.12), and gout (0.25, 95% CI 0.23, 0.27), but a decreasing trend for low back pain (− 0.96, 95% CI − 0.98, − 0.93). The AAPC of risk factors indicated a decreasing trend in smoking (− 0.14, 95% CI − 0.24, − 0.04) for rheumatoid arthritis, smoking (− 0.22, 95% CI − 0.24, − 0.19) and occupational ergonomic factors (− 1.25, 95% CI − 1.29, − 1.21) for low back pain, and impaired kidney function (− 0.95, 95% CI − 1.00, − 0.90) for gout, but an increasing trend in high body-mass index for osteoarthritis (3.10, 95% CI 3.03, 3.17), low back pain (3.07, 95% CI 2.99, 3.14), and gout (3.12, 95% CI 3.04, 3.20). Comparing the burden of five musculoskeletal diseases in China with the 19 countries of G20, China ranked first to second in the number of DALYs, and 12th to 16th in age-standardized rate of DALYs. Conclusion: There are remarkably complex temporal patterns in disease burden and risk factors for five major musculoskeletal disorders across past three decades. Population-wide initiatives targeting high body-mass index may mitigate the burden of musculoskeletal disorders.

Background: China has a large population; however, medical resources are unevenly distributed and extremely limited, and more medical services are needed. With the development and ever-increasing popularity of mobile internet communication, China has created a mode of mobile health (mHealth) care to resolve this problem. Objective: The aim of this study was (1) to describe the problems associated with China’s medical care practice, (2) explore the need for and the feasibility of internet-based medical care in China, and (3) analyze the functionality of and services offered by internet-based health care platforms for the management of chronic diseases. Methods: Data search was performed by searching national websites, the popular search engine Baidu, the App Store, and websites of internet medical care institutions, using search terms like “mobile health,” “Internet health,” “mobile medical,” “Internet medical,” “digital medical,” “digital health,” and “online doctor.” A total of 6 mobile apps and websites with the biggest enrollment targeting doctors and end users with chronic diseases in China were selected. Results: We recognized the limitations of medical and health care providers and unequal distribution of medical resources in China. An mHealth care platform is a novel and efficient way for doctors and patients to follow up and manage chronic diseases. Services offered by these platforms include reservation and payment, medical consultation, medical education assessment, pharmaceutical and medical instruments sales, electronic medical records, and chronic disease management. China’s health policies are now strongly promoting the implementation of mHealth solutions, particularly in response to the increasing burden of chronic diseases and aging in the population. Conclusions: China's internet-based medical and health care mode can benefit the populace by providing people with high-quality medical resources. This can help other countries and regions with high population density and unevenly distributed medical resources manage their health care concerns.

MicroRNAs (miRNAs) play a key role in the regulation of almost all the physiological and pathological processes, including bone metabolism. Recent studies have suggested that miR-27 might play a key role in osteoblast differentiation and bone formation. Increasing evidence indicates that the canonical Wnt signaling pathway contributes to different stages of bone formation. In this study, we identify miR-27a can promote osteoblast differentiation by repressing a new target, secreted frizzled-related proteins 1 (sFRP1) expression at the transcriptional level. Here, 21 candidate targets of miR-27a involved in canonical Wnt/β-catenin signaling were predicted, and a significant decrease in sFRP1 luciferase activity was observed both in 293T and MG63 cells co-transfected with the matched luciferase reporter constructs and miR-27a mimic. Furthermore, the presence of exogenous miR-27a significantly decreased sFRP1 mRNA and protein expression in hFOB1.19 cells during both proliferation and osteogenic differentiation. The over-expression of miR-27a or knockdown sFRP1 significantly increased the percentage of apoptotic hFOBs, the percentage of cells in the G2-M phase of the cell cycle and the expression of key osteoblastic markers, including ALP, SPP1, RUNX2 and ALP activity. Over-expression of miR-27a or knockdown of endogenous sFRP1 led to an accumulation of β-catenin in hFOBs. In the present study, we demonstrate that miR-27a induced gene silencing effect is a vital mechanism contributing to bone metabolism in hFOB cells in vitro, which is partly affected by the post-transcriptional regulation of sFRP1, during osteoblast proliferation, apoptosis and differentiation.

Background: Dyslipidemia often concurs with hyperuricemia. Our study was to discover different lipid levels of gout and asymptomatic hyperuricemia and the predictors of sUA (serum uric acid) levels. Methods: A cross-sectional study was performed to collect demographic, clinical variables, comorbidities and laboratory testing in patients with gout and asymptomatic hyperuricemia. Group comparison was performed with Student’s t-test or Mann Whitney U test for continuous variables and chi-squared tests for categorical variables (Fisher’s exact test where appropriate) and to screen potential risk factors. Correlation of sUA levels with demographic and biochemical variables were performed by using correlation analysis. The variable with s p-value less than 0.20 during the group comparison or clinical relevance was introduced into the stepwise multiple regression model. Results: Six hundred fifty-three patients with gout and 63 patients with asymptomatic hyperuricemia (> 420 μmol/L in male and > 360 μmol/L in female) were enrolled, including 553 (84.7%) males. The mean age was 47.8 ± 16.0 years old. Elevated total cholesterol (TC) was observed in 173 (26.5%) cases with gout. Increased triglycerides (TG) and low-density lipoprotein (LDL-C) levels were observed in 242 (37.1%) cases and 270 (41.3%) cases with gout, individually. In contrast, elevated TC, TG and LDL-C levels were observed in 10 (15.9%) cases, 30 (47.6%) cases and 22 (34.9%) cases with hyperuricemia, individually. Significant differences were found in age, serum creatine, TC and erythrocyte sedimentation rate (ESR) between gout and asymptomatic hyperuricemia groups (p < 0.05). In patients with asymptomatic hyperuricemia, 12 (19.0%) patients had hypertension and 5 (7.9%) suffered from coronary heart diseases. Male (B = -112.7, p < 0.001), high-density lipoprotein (HDL-C) (B = -60.797, p = 0.013), body mass index (BMI) (B = 5.168, p = 0.024), age (B = -3.475, p = 0.006), age of hyperuricemia onset (B = 2.683, p = 0.032), and serum creatine (B = 0.534, p < 0.001) were predictors of sUA levels in gout patients (adjusted R² = 28.7%). Conclusions: Dyslipidemia is more commonly seen in patients with gout, compared to asymptomatic hyperuricemia. HDL-C is a protective predictor of sUA levels in gout.