IMPORTANCE: Digital health applications have been shown to be effective in the management of chronic diseases with simple treatment targets. The potential clinical value of digital health applications in rheumatoid arthritis (RA) has not been well studied.OBJECTIVE: To investigate whether assessing patient-reported outcomes using digital health applications could result in disease control for patients with RA.DESIGN, SETTING, AND PARTICIPANTS: This is a multicenter, open-label randomized clinical trial in 22 tertiary hospitals across China. Eligible participants were adult patients with RA. Participants were enrolled from November 1, 2018, to May 28, 2019, with a 12-month follow-up. The statisticians and rheumatologists who assessed disease activity were blinded. Investigators and participants were not blind to group assignment. Analysis was conducted from October 2020 to May 2022.INTERVENTIONS: Participants were randomly assigned at a 1:1 ratio (block size of 4) to a smart system of disease management group (SSDM) or a conventional care control group. Upon the completion of the 6-month parallel comparison, patients in the conventional care control group were instructed to use the SSDM application for an extension of 6 months.MAIN OUTCOMES AND MEASURES: The primary outcome was the rate of patients with disease activity score in 28 joints using the C-reactive protein (DAS28-CRP) of 3.2 or less at month 6.RESULTS: Of 3374 participants screened, 2204 were randomized, and 2197 patients with RA (mean [SD] age, 50.5 [12.4] years; 1812 [82.5%] female) were enrolled. The study included 1099 participants in the SSDM group and 1098 participants in the control group. At month 6, the rate of patients with DAS28-CRP of 3.2 or less was 71.0% (780 of 1099 patients) in the SSDM group vs 64.5% (708 of 1098 patients) in the control group (difference between groups, 6.6%; 95% CI, 2.7% to 10.4%; P = .001). At month 12, the rate of patients with DAS28-CRP of 3.2 or less in the control group increased to a level (77.7%) that was comparable with that (78,2%) in the SSDM group (difference between groups, −0.2%; 95% CI, −3.9% to 3.4%; P = .90).CONCLUSIONS AND RELEVANCE: In this randomized clinical trial of RA, the use of a digital health application with patient-reported outcomes was associated with an increase in disease control rate.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03715595

Αlpha-lipoic acid is a naturally occurring antioxidant in human body and has been widely used as an antioxidant clinically. Accumulating evidences suggested thatα-lipoic acid might have immunomodulatory effects on both adaptive and innate immune systems. This review focuses on the evidences and potential targets involved in the immunomodulatory effects ofα-lipoic acid. It highlights the fact thatα-lipoic acid may have beneficial effects in autoimmune diseases once the immunomodulatory effects can be confirmed by further investigation.

Background: ANCA-associated vasculitis (AAV) is a group of autoimmune diseases characterized by small vessel inflammation, diagnosed primarily through clinical features, histopathology, and ANCA testing. Novel biomarkers derived from routine blood counts, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI), may support disease assessment. This study evaluated their utility in distinguishing AAV patients, reflecting disease activity, and predicting prognosis.Methods: In this retrospective case-control study, 65 AAV patients and 65 age- and sex-matched healthy controls were enrolled. AAV diagnosis adhered to the 2012 Chapel Hill Consensus and the American College of Rheumatology 1990 criteria. NLR, PLR, MLR, SII, and SIRI were calculated from complete blood counts. Disease activity (Birmingham Vasculitis Activity Score, BVAS), extent (Disease Extent Index, DEI), damage (Vasculitis Damage Index, VDI), and prognosis (Five-Factor Score, FFS 2009) were assessed. Statistical analyses included Mann-Whitney U tests, Spearman correlations, and receiver operating characteristic (ROC) curves to evaluate discriminatory and predictive capacities.Results: AAV patients exhibited significantly higher NLR (6.94 ± 0.76 vs. 1.88 ± 0.08), PLR (242.44 ± 23.09 vs. 125.97 ± 4.34), MLR (0.44 ± 0.03 vs. 0.20 ± 0.01), SII (1813.71 ± 221.85 vs. 446.62 ± 22.40), and SIRI (3.19 ± 0.31 vs. 0.72 ± 0.06) compared to controls (all P < 0.001). ROC analysis showed strong discriminatory power, with SIRI (AUC = 0.902) and NLR (AUC = 0.885) performing best. NLR, PLR, SII, and SIRI correlated positively with BVAS (rs = 0.325-0.356, P < 0.01) and FFS 2009 (rs = 0.358-0.386, P < 0.05), and all markers correlated with DEI (rs = 0.396-0.488, P < 0.01), but not VDI. For predicting active disease (BVAS ≥ 15), SII had the highest AUC (0.726, P = 0.003).Conclusions: NLR, PLR, MLR, SII, and SIRI effectively distinguish AAV patients from controls and reflect disease activity, extent, and prognosis. While not standalone diagnostic tools, these markers offer valuable support to standard AAV assessment, particularly in challenging cases. Their accessibility suggests potential for enhancing clinical management, pending validation in larger cohorts.

The global incidence of hyperuricemia is increasing in parallel with rising obesity rates. This study investigated the relationship between weight change patterns and the risk of hyperuricemia. Utilizing data from the NHANES, we conducted a retrospective cohort analysis of 20,545 U.S. adults aged 40–75 years. We categorized weight changes into six patterns based on BMI measurements at baseline and 10 years prior. Hyperuricemia was defined as uric acid ≥ 7.0 mg/dL in males and ≥ 6.0 mg/dL in females. Multivariable logistic regression models were used to assess the associations between weight change patterns and hyperuricemia. Participants with weight gain or obesity had significantly higher odds of hyperuricemia (ORs = 2.32 [95% CI: 1.93–2.80, P < 0.001] to 4.72 [95% CI: 3.95–5.63, P < 0.001]). Those transitioning from obese to normal weight had reduced odds of hyperuricemia (OR = 0.41 [95% CI: 0.21–0.79, P = 0.008]). A nonlinear relationship between BMI percentage change and hyperuricemia was observed, with a change of ≥ 6% associated with a higher OR of 6.72 (95% CI 3.76–12.02, P < 0.001). The impact was more pronounced in females. This study provides evidence for a significant nonlinear relationship between weight change and hyperuricemia, emphasizing the need for gender-specific, tailored weight management strategies.

Background: Hyperuricemia is a metabolic disorder linked to gout, kidney disease, and cardiovascular complications. Understanding its prevalence and risk factors across demographic groups is crucial for effective prevention and management. Objectives: To evaluate the prevalence of hyperuricemia among U.S. adults by sex, age, and racial/ethnic groups, and identify common and sex-specific risk factors. Methods: Data from 34,144 U.S. adults aged ≥20 years, obtained from the National Health and Nutrition Examination Survey (NHANES) 2007–2018, were analyzed. Hyperuricemia was defined as serum uric acid levels >7.0 mg/dL in males and >6.0 mg/dL in females. Prevalence estimates were calculated, and multivariate logistic regression models identified risk factors, adjusting for confounders such as body mass index (BMI), alcohol consumption, hypertension, renal function, and other variables. A sensitivity analysis excluding participants with a history of gout was conducted to evaluate the robustness of identified associations. Results: The overall prevalence of hyperuricemia was significantly higher in males (21.1%) compared to females (17.1%, P < 0.001). Females surpassed males in both prevalence and absolute numbers after age 50–59; by age ≥ 80, the number of female cases was more than twice that of males. Non-Hispanic Black adults had the highest prevalence (23.9% in males, 23.4% in females). Key risk factors for both sexes included obesity (OR = 3.91 in males; OR = 4.76 in females), hypertension (OR = 1.65 in males; OR = 2.09 in females), and impaired renal function (eGFR < 30 mL/min/1.73 m²: OR = 3.72 in males; OR = 15.37 in females). Alcohol consumption was positively associated with hyperuricemia in males (OR = 1.25), but not significantly so in females. Diabetes showed opposite associations: protective in males (OR = 0.72) but a risk factor in females (OR = 1.22). Medication use exhibited expected directional effects: diuretic use was associated with significantly increased risk of hyperuricemia (OR = 2.67 in males; OR = 2.55 in females), while urate-lowering therapy was associated with reduced risk (OR = 0.57 in males; OR = 0.55 in females). Conclusions: Hyperuricemia remains highly prevalent in the U.S., with notable disparities by sex, age, and race/ethnicity. Older women bear a particularly high burden, partly due to obesity, renal dysfunction, and diuretic use. Incorporating medication use into analyses strengthens the evidence for sex-specific risk profiles. These findings highlight the importance of considering targeted screening and prevention strategies in specific high-risk groups, such as older women and patients receiving diuretics.