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Background The effectiveness of fish oil in preventing cardiovascular events is still debating. Some studies indicate a correlation between the use of fish oil supplements and reduced mortality or decreased incidence of stroke. However, other studies show no significant association between fish oil intake and stroke prevention, indicating an ongoing debate. This study aimed at exploring which subjects may benefit more from fish oil supplementation. Methods This study utilized the data obtained through face-to-face interview from the Taiwan Longitudinal Study in Aging (TLSA). A total of 3,652 participants were included from the 2003 baseline data, after excluding patients with pre-existing ischemic heart disease or stroke. Participants were divided into two groups based on whether taking fish oil supplement or not. Participants were followed until 2015, estimating and comparing the all-cause mortality and cumulative incidence rate of stroke between both groups. Results The results of the 12-year longitudinal study showed that the cumulative incidence rate of stroke in the fish oil supplementation group was 5.7%, compared to 7.7% in the non-supplemented group ( P  < 0.05). Additionally, the crude hazard ratio for stroke was significantly lower in the fish oil supplementation group (HR = 0.686;95% CI 0.476–0.987). However, after adjusting potential confounders, the adjusted risk of stroke was lower only for the diabetic patients supplemented with fish oil (aHR = 0.123; 95% CI 0.016–0.930) compared to non-diabetic patients (aHR = 0.917; 95% CI 0.616–1.364). Conclusion This study suggests that there is an association between fish oil supplementation and a lower cumulative incidence rate of subsequent stroke among diabetic patients.

Background Maternal overweight and gestational metabolic disorders are established risk factors for adverse pregnancy outcomes and may influence the risk of overweight or obesity in offspring. However, the extent to which these factors independently or jointly affect early childhood adiposity remains unclear, particularly in Asian populations. This study examined the independent and combined effects of maternal pre-pregnancy body mass index (BMI), gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), and excessive gestational weight gain (EGWG) on offspring adiposity, with an emphasis on population-level and policy implications. Methods Data were derived from the Taiwan Birth Cohort Study (TBCS), a nationally representative longitudinal dataset of 20,764 mother–child pairs followed from birth to 66 months. Generalized estimating equation (GEE) models were used to analyze BMI trajectories across seven time points, and logistic regression estimated adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for childhood overweight and obesity at 66 months. Analyses were conducted in SAS 9.4; a two-sided p value < 0.001 was considered statistically significant. Results Children born to mothers with overweight or obesity showed persistently higher BMI trajectories and an earlier adiposity rebound than those of mothers with normal weight. At 66 months, adjusted ORs for overweight or obesity (OWOB) were 1.39 (95% CI 1.22–1.58) and 1.49 (95% CI 1.27–1.74) for maternal overweight and obesity, respectively; corresponding ORs for obesity were 1.58 (95% CI 1.30–1.91) and 1.82 (95% CI 1.46–2.28). The effects of GDM and HDP were attenuated after adjusting for maternal BMI, suggesting mediation through maternal adiposity. In continuous analyses, maternal overweight and obesity were associated with increases of 0.62 (SE 0.10) and 1.26 (SE 0.13) in offspring BMI z-scores, respectively (both p  < 0.001). Conclusions Maternal pre-pregnancy adiposity emerged as the strongest and most consistent predictor of early-childhood adiposity in this cohort. Integrating preconception weight management and structured gestational weight gain monitoring into maternal health services may help prevent early-onset overweight and break the intergenerational cycle of obesity.

Background The prevalence of diabetes mellitus (DM) during pregnancy and childhood obesity is increasing worldwide. Factors affecting the growth of children with overweight whose mothers had DM are complicated and inconclusive. Few longitudinal studies have focused on the growth of infants with macrosomia born to mothers with DM and the factors influencing their overweight. This study explored risk factors for childhood overweight/obesity (OWOB) among children of mothers with DM. Perinatal, maternal socio-demographic, infant care, and maternal body weight characteristics as well as child growth until age 3 years were analyzed using a longitudinal design. Methods In total, 24,200 pairs of mothers and their children from the Taiwan Birth Cohort Study were included. Combined Taiwan Children Growth Curve report classifications were analyzed for infant growth at birth and at 6, 12, 18, 24, and 36 months old (m/o). A multiple logistic regression analysis with different model settings was used to assess factors affecting the growth of high birth weight children of mothers with diabetic mellitus (HODM). Results Children in the HODM group had a higher average body weight than did those in the non-DM group at different age stages. Relative to the non-DM group, weight gain in the HODM group was slower before 18 m/o but faster from 18 to 36 m/o, particularly after 24 m/o. Maternal DM was a major risk factor for childhood OWOB (odds ratio [OR]: 3.25–3.95). After adjustment for related confounders, the OR was 2.19–3.17. Maternal overweight or obesity and higher gestational weight gain were greater risk factors for childhood OWOB at 3 years old after adjusted maternal DM and other selected confounders (OR: 1.45 and 1.23, respectively). Breastfeeding until 6 m/o was a protective factor against childhood OWOB (OR: 0.95). The HODM and non-DM groups did not differ significantly in perinatal, maternal socio-demographic, or infant care characteristics. Conclusions Maternal DM is a major factor of childhood OWOB. Maternal body weight before and after pregnancy affects childhood OWOB, and this effect increases with the child’s age.

BackgroundHyperinflammation with dysregulated production of galectins and cytokines may develop in COVID-19 or adult-onset Still’s disease (AOSD). Given the similar clinical features in both diseases, it is necessary to identify biomarkers that can differentiate COVID-19 from AOSD. However, the related data remain scarce currently.MethodsIn this cross-sectional study, plasma levels of galectin-3, galectin-9, and soluble TIM-3 (sTIM-3) were determined by ELISA in 55 COVID-19 patients (31 non-severe and 24 severe), 23 active AOSD patients, and 31 healthy controls (HC). The seropositivity for SARS-CoV-2 was examined using an immunochromatographic assay, and cytokine profiles were determined with the MULTIPLEX platform.ResultsSignificantly higher levels of galectin-3, galectin-9, IL-1β, IL-1Ra, IL-10, IFN-α2, IL-6, IL-18, and TNF-α were observed in severe COVID-19 and active AOSD patients compared with HC (all p<0.001). AOSD, but not COVID-19, showed significantly higher IFN-γ and IL-17A compared with HC (both p<0.01). Moreover, active AOSD patients had 68-fold higher IL-18 levels and 5-fold higher ferritin levels than severe COVID-19 patients (both p<0.001). IL-18 levels at the cut-off value 190.5pg/mL had the highest discriminative power for active AOSD and severe COVID-19, with AUC 0.948, sensitivity 91.3%, specificity 95.8%, and accuracy of 91.5% (p<0.005). Multivariate regression analysis revealed IL-18 as a significant predictor of active AOSD (p<0.05).ConclusionActive AOSD patients share features of hyperinflammation and cytokine storm with severe COVID-19 patients but possess a distinct cytokine profile, including elevated IL-18, IL-6, IFN-γ, and IL-17A. IL-18 is a potential discriminator between AOSD and COVID-19 and may significantly predict active AOSD.

Rheumatoid arthritis (RA) is associated with a high risk of osteoporosis and fracture. Interleukin (IL)-6 inhibitors may suppress osteoclast activation. Anticitrullinated protein antibody (ACPA) titers are inversely associated with bone mineral density (BMD). However, the differential effect of ACPA on bone turnover marker (BTM) and BMD changes after IL-6 inhibition remains unclear. This prospective study recruited patients with active RA with inadequate response to methotrexate or biologics. BMD was measured before and after 2-year tocilizumab (TCZ) treatment. Serum osteocalcin, N-terminal propeptide of type I collagen (P1NP), and C-terminal cross-linking telopeptide of type I collagen (CTX) levels were assessed at the baseline and after treatment. We enrolled 76 patients with RA (89.5% women, age: 57.2 ± 13.3 years) receiving TCZ. The 28-joint disease activity score was negatively correlated with BMD and T-scores of the lumbar spine and bilateral femoral neck. ACPA-positive patients had lower lumbar spine and femoral neck T-scores. After 2-year TCZ treatment, CTX levels significantly decreased (0.32 ± 0.21 vs. 0.26 ± 0.17, p = 0.038). Femoral neck BMD increased significantly (0.71 ± 0.22 vs. 0.69 ± 0.55, p = 0.008). Decreased CTX levels and improved BMD were observed only in ACPA-positive patients. After treatment, femoral neck BMD significantly increased only in patients receiving a glucocorticoid dose of ≥5 mg/day. Two-year TCZ treatment reduced bone resorption and increased femoral BMD in ACPA-positive patients. The net effects of glucocorticoids and IL-6 inhibition on BMD imply that strict inflammation control might affect bone metabolism.

The objective of this study is to determine the risk of tuberculosis (TB) disease in biologics users among rheumatoid arthritis (RA) patients in Taiwan from 2000 to 2015. This retrospective cohort study enrolled adult RA patients initiated on first biologics at Taichung Veterans General Hospital. TB risks were determined as hazard ratio (HR) with 95% confidence interval (CI) using cox regression. A total of 951 patients were recruited; etanercept (n = 443), adalimumab (n = 332), abatacept (n = 74), golimumab (n = 60), tocilizumab (n = 31) and tofacitinib (n = 11). Twenty-four TB cases were identified; 13 in etanercept and 11 in adalimumab group with the TB incidence rate of 889.3/ 100,000 and 1055.6/ 100,000 patient-years respectively. There was no significant difference in TB risk between adalimumab and etanercept users with an incidence rate ratio of 1.27 (p = 0.556 by Poisson model). Significant 2-year TB risk factors included elderly patient >65 year-old (HR: 2.72, 95% CI: 1.06-6.99, p = 0.037), history of TB (HR: 6.24, 95% CI: 1.77-22.00, p = 0.004) and daily glucocorticoid use ≥5mg (HR:5.01, 95% CI: 1.46-17.21, p = 0.010). Sulfasalazine treatment appeared to be protective (HR: 0.32, 95% CI: 0.11-0.97, p = 0.043). Risk management plan (RMP) for TB before initiation of biologics commenced in 2012. The 2-year TB risks after RMP was compared with that before 2012 (HR:0.67, 95% CI: 0.30-1.49, p = 0.323). Elderly RA patients with a history of previous TB infection and concomitant moderate dose glucocorticoid were at higher risk of TB disease. Concurrent sulfasalazine treatment appeared to be a protective factor against TB disease.

Background: Detecting vertebral structural damage in patients with ankylosing spondylitis (AS) is crucial for understanding disease progression and in research settings. Objectives: This study aimed to use deep learning to score the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) using lateral X-ray images of the cervical and lumbar spine in patients with AS in Asian populations. Design: A deep learning model was developed to automate the scoring of mSASSS based on X-ray images. Methods: We enrolled patients with AS at a tertiary medical center in Taiwan from August 1, 2001 to December 30, 2020. A localization module was used to locate the vertebral bodies in the images of the cervical and lumbar spine. Images were then extracted from these localized points and fed into a classification module to determine whether common lesions of AS were present. The scores of each localized point were calculated based on the presence of these lesions and summed to obtain the total mSASSS score. The performance of the model was evaluated on both validation set and testing set. Results: This study reviewed X-ray image data from 554 patients diagnosed with AS, which were then annotated by 3 medical experts for structural changes. The accuracy for judging various structural changes in the validation set ranged from 0.886 to 0.985, whereas the accuracy for scoring the single vertebral corner in the test set was 0.865. Conclusion: This study demonstrated a well-trained deep learning model of mSASSS scoring for detecting the vertebral structural damage in patients with AS at an accuracy rate of 86.5%. This artificial intelligence model would provide real-time mSASSS assessment for physicians to help better assist in radiographic status evaluation with minimal human errors. Furthermore, it can assist in a research setting by offering a consistent and objective method of scoring, which could enhance the reproducibility and reliability of clinical studies.

Objective To investigate the association between the risk of rheumatoid arthritis (RA) and a history of periodontitis. Methods This nationwide, population-based, case–control study used administrative data to identify 13 779 newly diagnosed patients with RA (age ≥16 years) as the study group and 137 790 non-patients with RA matched for age, sex, and initial diagnosis date (index date) as controls. Using conditional logistic regression analysis after adjustment for potential confounders, including geographical region and a history of diabetes and Sjögren's syndrome, ORs with 95% CI were calculated to quantify the association between RA and periodontitis. To evaluate the effects of periodontitis severity and the lag time since the last periodontitis visit on RA development, ORs were calculated for subgroups of patients with periodontitis according to the number of visits, cumulative cost, periodontal surgery and time interval between the last periodontitis-related visit and the index date. Results An association was found between a history of periodontitis and newly diagnosed RA (OR=1.16; 95% CI 1.13 to 1.21). The strength of this association remained statistically significant after adjustment for potential confounders (OR=1.16; 95% CI 1.12 to 1.20), and after variation of periodontitis definitions. The association was dose- and time-dependent and was strongest when the interval between the last periodontitis-related visit and the index date was <3 months (OR=1.64; 95% CI 1.49 to 1.79). Conclusions This study demonstrates an association between periodontitis and incident RA. This association is weak and limited to lack of individual smoking status.

Rheumatoid arthritis (RA), a chronic inflammatory disease, carries a significant burden of atherosclerotic cardiovascular diseases (ASCVD). With their heterogeneous composition, high-density lipoprotein (HDL) particles have varied athero-protective properties, and some may even increase ASCVD risk. In this prospective and cross-sectional study, we aimed to examine the relationship between HDL sizes/metabolites and inflammation in RA. Using 1H-NMR-based lipid/metabolomics, differential HDL-related metabolites were identified between RA patients and healthy control (HC) subjects and between RA patients with and without anti-citrullinated peptide antibodies (ACPA). The correlation between the discriminative HDL-related metabolites and C-reactive protein (CRP) was evaluated in RA patients. RA patients demonstrated higher particle number, lipids, cholesterol, cholesterol ester, free cholesterol, and phospholipids in large/very large-sized HDLs. ACPA-positive patients had higher L-HDL-C and L-HDL-CE but lower small-/medium-sized HDL-TG levels than ACPA-negative patients. An inverse correlation was found between CRP levels and small-sized HDLs. Janus kinase inhibitor treatment was associated with increased serum small-sized HDL-related metabolites and decreased CRP levels. We are the first to reveal the significant associations between RA inflammation and HDL sizes/metabolites. A potential link between ACPA positivity and changes in serum levels of HDL-related metabolites was also observed in RA patients.

In this study we aimed to identify the predictors of drug survival for biologic and targeted synthetic DMARDs (bDMARDs and tsDMARDs) among patients with rheumatoid arthritis (RA) in a real-world setting. Data from RA patients receiving bDMARDs and tsDMARDs between 2007 and 2019 were extracted from the Taiwan Rheumatology Association Clinical Electronic Registry (TRACER). Patients were categorized into tumor necrosis factor-alpha (TNF-α) inhibitors, non-TNF-α inhibitors, and tofacitinib groups. The primary outcome was 3-year drug retention and the causes of bDMARDs and tsDMARDs discontinuation were recorded. Baseline demographic data before the initiation of bDMARDs and tsDMARDs treatment were analyzed to identify the predictors of 3-year drug survival. A total of 1,270 RA patients were recruited (TNF-α inhibitors: 584; non-TNF-α inhibitors: 535; tofacitinib: 151). The independent protective factors for 3-year drug survival were positive rheumatoid factor (RF) (HR: 0.48, 95% CI: 0.27–0.85, p = 0.013) and biologics-naïve RA (HR: 0.61, 95% CI: 0.39–0.94, p = 0.024). In contrast, positive anti-citrullinated protein antibody (ACPA) (HR: 2.24, 95% CI: 1.32–3.79, p = 0.003) and pre-existing latent tuberculosis (HR: 2.90, 95% CI: 2.06–4.09, p<0.001) were associated with drug discontinuation. RA patients treated with TNF-α inhibitors exhibited better drug retention, especially in the biologics-naïve subgroup ( p = 0.037). TNF-α inhibitors were associated with lower cumulative incidence of discontinuation due to inefficacy and adverse events (both p <0.001). Baseline RF and ACPA positivity in abatacept-treated patients were associated with a better 3-year drug survival. However, negative ACPA levels predicted superior drug survival of TNF-α inhibitors and tofacitinib. In conclusion, bio-naïve status predicted better drug survival in TNF-α inhibitors-treated RA patients. RF and ACPA positivity predicted better abatacept drug survival. In contrast, ACPA negativity was associated with superior TNF-α inhibitors and tofacitinib survival.