Sex-specific disparities in disease activity scores among patients with axial spondyloarthritis and their implications for evaluating the response to tumor necrosis factor alpha inhibitor therapy.

BACKGROUND: We aimed to investigate whether there are sex differences in disease activity measures among patients with axial spondyloarthritis (axSpA) and to determine any potential impact on the assessment of treatment responses to tumor necrosis factor alpha inhibitors (TNFi). METHODS: Using the Korean College of Rheumatology Biologics and Targeted Therapy (KOBIO) registry data, we compared sex differences in changes in the Bath Ankylosing Spondylitis Disease Activity Score (BASDAI) and Ankylosing Spondylitis Disease Activity Score (ASDAS) levels at baseline and one year after TNFi initiation in patients with axSpA. RESULTS: This study included 1,753 patients with axSpA who started or changed TNFi, of whom 1,343 (76.6%) were male. At baseline, the mean BASDAI and ASDAS scores of all patients were 5.98 and 3.6, respectively. The BASDAI changes between baseline and the one-year follow-up were independently associated with sex (𝛽 = 0.343, p = 0.011), whereas ASDAS was not (𝛽 = 0.079, p = 0.235). When judging the effect of TNFi at one-year of treatment, male patients were more likely to be assessed as effective by the BASDAI-based criterion (ΔBASDAI ≥ 50% or ≥ 2; OR 1.700, 95% CI 1.200-2.406), while the ASDAS-based criterion (ΔASDAS ≥ 1.1) showed no significant difference between sexes (OR 0.993, 95% CI 0.678-1.455), after adjusting for other baseline characteristics. CONCLUSIONS: The changes in disease activity before and after TNFi use were significantly different between sexes when measured by BASDAI, but not ASDAS. TNFi treatment effects may be interpreted differently between sexes depending on the disease activity measure used.

Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis.

Background: Magnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI. Methods: This study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation. Results: A total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705-0.745), 0.936 (95% CI, 0.924-0.947), and 0.830 (95%CI, 0.792-0.868), respectively, at the image level and 0.947 (95% CI, 0.912-0.982), 0.691 (95% CI, 0.603-0.779), and 0.816 (95% CI, 0.776-0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493-0.780), 0.944 (95% CI, 0.933-0.955), and 0.731 (95% CI, 0.681-0.780), respectively, at the image level and 0.806 (95% CI, 0.729-0.883), 0.617 (95% CI, 0.523-0.711), and 0.711 (95% CI, 0.660-0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation. Conclusion: An AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting.

Women with Metabolic Syndrome and Unhealthy Lifestyle Factors Are at a Higher Risk for Hyperuricemia.

Hyperuricemia (HUA) has become a significant medical concern due to its complications and links to metabolic syndrome (MetS) and cardiovascular disease (CVD), which result in increased mortality. The pathogenic processes associated with unhealthy behaviors, MetS, and HUA can be cooperative and potentially synergistic in the activation of risk factors. Recent research has shown sex-based differences in the relationship between HUA and its associated risk factors. This study aimed to investigate these differences, particularly in the context of MetS and CVD risk factors and unhealthy lifestyles. We also aimed to evaluate the joint effects of these factors based on sex. We conducted a cross-sectional study using nationally representative survey data from the Korean National Health and Nutritional Examination Survey 2016-2018. We performed multivariable logistic regression analysis, calculating adjusted odds ratios (ORs) with their 95% confidence intervals (CIs). We also conducted subgroup analyses based on sex and the presence of MetS with or without unhealthy lifestyle factors (tobacco use, alcohol intake). We found sex-based differences in the relationships between HUA and MetS, CVD risk factors, and lifestyle behaviors. Our major finding was a significant association between MetS and HUA in both men and women, regardless of alcohol consumption and smoking status, and this association was stronger in women. We also observed a synergistic effect of MetS and lifestyle factors on the risk of HUA, particularly in women, in whom the risk of HUA increased up to four times compared to the reference group. A sex-based clinical strategy for HUA is necessary to reduce related complications and their socio-economic burden.

Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review.

OBJECTIVES: Summarise the evidence of the performance of the machine learning algorithm in discriminating sacroiliitis features on MRI and compare it with the accuracy of human physicians. METHODS: MEDLINE, EMBASE, CIHNAL, Web of Science, IEEE, American College of Rheumatology and European Alliance of Associations for Rheumatology abstract archives were searched for studies published between 2008 and 4 June 2023. Two authors independently screened and extracted the variables, and the results are presented using tables and forest plots. RESULTS: Ten studies were selected from 2381. Over half of the studies used deep learning models, using Assessment of Spondyloarthritis International Society sacroiliitis criteria as the ground truth, and manually extracted the regions of interest. All studies reported the area under the curve as a performance index, ranging from 0.76 to 0.99. Sensitivity and specificity were the second-most commonly reported indices, with sensitivity ranging from 0.56 to 1.00 and specificity ranging from 0.67 to 1.00; these results are comparable to a radiologist's sensitivity of 0.67-1.00 and specificity of 0.78-1.00 in the same cohort. More than half of the studies showed a high risk of bias in the analysis domain of quality appraisal owing to the small sample size or overfitting issues. CONCLUSION: The performance of machine learning algorithms in discriminating sacroiliitis features on MRI varied owing to the high heterogeneity between studies and the small sample sizes, overfitting, and under-reporting issues of individual studies. Further well-designed and transparent studies are required.

Altered Risk of Incident Gout According to Changes in Metabolic Syndrome Status: A Nationwide, Population-Based Cohort Study of 1.29 Million Young Men.

OBJECTIVE: Few data are available on whether changes in metabolic syndrome affect incident gout. This study was undertaken to assess associations between metabolic syndrome status and incident gout, as well as changes in the clinical characteristics of metabolic syndrome and incident gout, in a cohort of young men. METHODS: This nationwide, population-based cohort study included 20-39-year-old men who participated in serial health check-ups. The outcome, incident gout, was defined according to the claims database diagnostic code for gout. Associations among changes in metabolic syndrome status and incident gout were analyzed using Cox proportional hazards models. RESULTS: Among 1,293,166 individuals, 18,473 were diagnosed as having gout (incidence rate 3.36 per 1,000 person-years). Subjects who had chronic metabolic syndrome (defined as metabolic syndrome at all 3 health check-ups) had a nearly 4-fold higher risk of incident gout compared to subjects who did not have metabolic syndrome at any of the 3 health check-ups (adjusted hazard ratio [HRadj ] 3.82 [95% confidence interval (95% CI) 3.67-3.98]). Development of metabolic syndrome more than doubled the risk of incident gout (HRadj 2.31 [95% CI 2.20-2.43]). Conversely, recovery from metabolic syndrome reduced the risk of incident gout by nearly half (HRadj 0.52 [95% CI 0.49-0.56]). Among metabolic syndrome components, changes in elevated triglycerides (development of elevated triglycerides, HRadj 1.74 [95% CI 1.66-1.81]; recovery from elevated triglycerides, HRadj 0.56 [95% CI 0.54-0.59]) and abdominal obesity (development of abdominal obesity, HRadj 1.94 [95% CI 1.85-2.03]; recovery from abdominal obesity, HRadj 0.69 [95% CI 0.64-0.74]) showed the greatest association with altered risk of incident gout. Associations between changes in the status and clinical characteristics of metabolic syndrome and incident gout were more pronounced in subjects ages 20-29 years compared to those ages 30-39 years, and in subjects who were underweight or who had a normal weight. CONCLUSION: Changes in the status and clinical characteristics of metabolic syndrome were associated with altered risk of incident gout. These results suggest that metabolic syndrome is a modifiable risk factor for gout.

Increased risk of malignancy in patients with Takayasu's arteritis: a population-based cohort study in Korea.

This study aimed to evaluate the relative risk of malignancy in patients with Takayasu's arteritis compared to that in the general population. This retrospective nationwide cohort study used data from the Korean Health Insurance Review and Assessment Service database. All newly diagnosed patients with Takayasu's arteritis were identified between January 2009 and December 2019. They were observed until the diagnosis of malignancy, death, or end of the observational period, December 2020. The standardized incidence ratios (SIRs) of the overall and site-specific malignancies were estimated and compared with the incidence of cancer in the general population retrieved from the National Cancer Registry. We identified 1449 newly diagnosed patients with Takayasu's arteritis during the observational period (9196 person-years). A total of 74, 66, and 8 patients had overall, solid, and hematologic malignancies, respectively. The risks of overall [SIR, 1.62; 95% confidence interval (CI) 1.27-2.03], solid (SIR, 1.51; 95% CI 1.17-1.92), and hematologic (SIR, 4.05; 95% CI 1.75-7.98) malignancies were increased compared to those in the general population. In solid malignancies, breast (SIR, 2.07; 95% CI 1.16-3.42) and ovarian (SIR, 4.45; 95% CI 1.21-11.39) cancers had an increased risk. In hematologic malignancies, the risk of myelodysplasia increased (SIR, 18.02; 95% CI 3.72-52.66). Immunosuppressive agent use was not associated with malignancy. There was no specific period when cancer more frequently occurred. An increased risk of malignancy was observed in patients with Takayasu's arteritis compared to that in the general population in this large-scale nationwide population study of Korean health insurance data.

Increased risk of incident gout in young men with metabolic syndrome: A nationwide population-based cohort study of 3.5 million men.

Background: To date, few studies have focused on risk factors for gout in young people, and large-scale studies on the relationship between metabolic syndrome (MetS) and gout are lacking. We aimed to investigate the association between gout and MetS in a large nationwide population-based cohort of young men who participated in national health examination. Materials and methods: Cohort included men aged 20-39 years who participated in a health check-up in 2009-2012. A total of 3,569,104 subjects was included in the study, excluding those who had a previous diagnosis of gout or had renal impairment. The outcome was the occurrence of gout, which was defined using the diagnosis code of gout in the claims database. Cox proportional hazard model was used to evaluate the association between MetS and incident gout. Results: Mean follow-up duration was 7.35 ± 1.24 years and the incidence rate of gout was 3.36 per 1,000 person-years. The risk of gout in subjects with MetS was 2.4-fold higher than subjects without MetS. Among the components of MetS, hypertriglyceridemia and abdominal obesity showed the greatest association with gout. As the number of MetS components increased, the risk of gout increased. The association between gout and MetS was more pronounced in relatively young subjects and in low- or normal-weight subjects. Conclusion: Metabolic syndrome is an important risk factor for the gout in young men. In particular, the association between MetS and gout was greater in young and non-obese men. Management of MetS in young men will be important for future gout prevention.

Associations between Cardiovascular Outcomes and Rheumatoid Arthritis: A Nationwide Population-Based Cohort Study.

Despite a growing burden posed by cardiovascular disease (CVD) in rheumatoid arthritis (RA) patients, large-scale studies on the association between the characteristics of RA patients and CVD risks and studies adjusted for various confounding factors are lacking. In this large-scale nationwide cohort study, we aimed to investigate the association between CVD risk and RA and factors that may increase CVD risk using a dataset provided by the Korean National Health Insurance Service (NHIS). We enrolled 136,469 patients with RA who participated in national health examinations within two years of RA diagnosis between 2010 and 2017 and non-RA controls matched by age and sex (n = 682,345). The outcome was the occurrence of myocardial infarction (MI) or stroke. MI was defined as one hospitalization or two outpatient visits with ICD-10-CM codes I21 or I22. Stroke was defined as one hospitalization with ICD-10-CM codes I63 or I64 and a claim for brain imaging (CT or MRI). The Cox proportional hazard model and Kaplan-Meier curve were used for analysis. The mean follow-up duration was 4.7 years, and the incidence rate of CVD was higher in the RA group than the control group (MI: 3.20 vs. 2.08; stroke: 2.84 vs. 2.33 per 1000 person-years). The risk of MI and stroke was about 50% and 20% higher, respectively, in RA patients. The association between RA and CVD was prominent in females after adjusting for confounding variables. The association between RA and risk of MI was significant in individuals without DM. Therefore, appropriate screening for CVD is important in all RA patients including females and younger patients.

Cardiovascular risk associated with allopurinol or benzbromarone treatment in patients with gout.

Background: In previous studies, cardiovascular (CV) risk was increased in patients with gout. The effects of uric acid-lowering therapy on CV risk in gout patients have been investigated in numerous studies; however, allopurinol and benzbromarone have rarely been compared. Objectives: To compare CV risk based on allopurinol and benzbromarone treatment in Korean gout patients. Design: A nationwide population-based retrospective cohort study. Methods: We used South Korea database of the Health Insurance Review and Assessment (HIRA) service to identify gout patients ⩾18 years of age who newly started allopurinol or benzbromarone between 2009 and 2015. The primary outcome of the study was the occurrence of a composite CV endpoint, which included coronary revascularization, hospitalization due to myocardial infarction, ischemic stroke, and transient ischemic attack. Cox proportional hazard regression analysis and Kaplan-Meier curves were used for analysis. Results: The study included 257,097 allopurinol initiators and 7868 benzbromarone initiators. Compared with allopurinol initiators, the adjusted hazard ratio (aHR) of the composite CV endpoint of benzbromarone initiators was 1.01 [95% confidence interval (CI): 0.83-1.21], which was not significantly different. The results did not change even when 1:3 propensity score matching was performed for baseline characteristics. In subgroup analysis of high-risk patients with CV disease, significant difference was not observed between allopurinol and benzbromarone initiators. Conclusion: In this study, significant difference was not found in CV risk between allopurinol and benzbromarone initiators. In the high-CV-risk group, the incidence of CV events did not differ between allopurinol and benzbromarone initiators.

Clinical characteristics and radiographic outcomes of vascular Behçet's disease involving the aorta or its major branches.

OBJECTIVE: To describe the clinical characteristics and radiographic outcomes of vascular Behçet's disease (BD) involving the aorta or its major branches. METHODS: This retrospective cohort study was performed in patients with vascular BD involving the aorta or its major branches. All included patients underwent computed tomography angiography (CTA) at least two times with a 2- to 5-year interval. Radiographic progression was defined as newly developed and/or aggravated (> 20%) characteristic features on CTA. RESULTS: The cohort included 22 patients with BD with a median interval of 3.65 years between the initial and follow-up CTA. Five patients (22.7%) showed radiographic progression. Patients with radiographic progression had a longer disease duration at baseline than those without (6.67 vs. 0.26 years, p = 0.028). Of all patients, 21 (95.5%) had vascular aneurysms/pseudoaneurysms and 11 (50.0%) had thrombosis. The most frequently involved artery with aneurysmal change was the abdominal aorta (8/21, 38.1%), followed by the iliac arteries (5/21, 23.8%). In the case of thrombosis, the most frequently involved arteries were the femoral (4/11, 36.4%) and iliac (4/11, 36.4%) arteries. The characteristics and locations of vascular involvement did not significantly differ according to the radiographic outcome. CONCLUSIONS: A considerable proportion of patients with BD with arterial involvement showed radiographic progression within 2-5 years. Patients with radiographic progression had a longer disease duration at baseline. The most common form of arterial involvement of BD was aneurysmal change, followed by thrombus formation. KEY POINTS: • This study evaluated for the first time the radiographic outcomes of 22 patients with Behçet's disease involving the aorta or its major branches. • A considerable proportion of patients (5/22, 22.7%) showed radiographic progression. • Patients with radiographic progression had a longer disease duration at baseline than their counterparts; however, no other clinical factors were significantly different. • The most frequent form of vascular involvement was pseudoaneurysm followed by thrombosis.

Association between female reproductive factors and gout: a nationwide population-based cohort study of 1 million postmenopausal women.

BACKGROUND: Previous studies have shown that the incidence and risk factors of gout differs according to sex. However, little research has been done on the association between reproductive factors and gout. We conducted an analysis of a large nationwide population-based cohort of postmenopausal women to determine whether there is an association between reproductive factors and the incidence of gout. METHODS: A total of 1,076,378 postmenopausal women aged 40-69 years who participated in national health screenings in 2009 were included in the study. The outcome was the occurrence of incident gout, which was defined using the ICD-10 code of gout (M10) in the claim database. Cox proportional hazard models were used for the analyses and stratified analyses according to body mass index (BMI) and the presence/absence of chronic kidney disease (CKD) were performed. RESULTS: The mean follow-up duration was 8.1 years, and incident cases of gout were 64,052 (incidence rate 7.31 per 1000 person-years). Later menarche, earlier menopause, and a shorter reproductive span were associated with a high risk of gout. No association between parity and gout incidence was observed. Use of oral contraceptives (OC) and hormone replacement therapy (HRT) were associated with an increased risk of gout. The association between reproductive factors and gout was not statistical significant in the high BMI group. The effects of OC and HRT usage on gout were not significant in the CKD group. CONCLUSION: Shorter exposure to endogenous estrogen was associated with a high risk of gout. Conversely, exposure to exogenous estrogen such as OC and HRT was associated with an increased risk of gout.

A cluster analysis of patients with axial spondyloarthritis using tumour necrosis factor alpha inhibitors based on clinical characteristics.

BACKGROUND: This study aimed to classify the distinct group of patients with axial spondyloarthritis (SpA) on tumour necrosis factor alpha inhibitors (TNFi) according to the baseline characteristics using a clustering algorithm. METHODS: The clinical characteristics and demographic data of patients with axial SpA included in the Korean College of Rheumatology Biologics and Targeted Therapy registry were investigated. The patterns of disease manifestations were examined using divisive hierarchical cluster analysis. After clustering, we compared the clinical characteristics of patients and the drug survival of TNFi between the classified groups. RESULTS: A total of 1042 patients were analysed. The cluster analysis classified patients into two groups: axial group predominantly showing isolated axial manifestations (n = 828) and extra-axial group more frequently showing extra-axial symptoms (n = 214). Almost all extra-axial symptoms (peripheral arthritis, enthesitis, uveitis, and psoriasis) were more frequently observed in the extra-axial group than in the axial group. Moreover, patients in the extra-axial group had shorter disease duration, later disease onset, and higher disease activity than those in the axial group. The disease activity was comparable between the two groups after 1 year of treatment with TNFi. Interestingly, the extra-axial group had a lower drug survival with TNFi than the axial group (p = 0.001). CONCLUSIONS: Cluster analysis of patients with axial SpA using TNFi classified two distinct clinical phenotypes. These clusters had different TNFi drug survival, clinical characteristics, and disease activity.

Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis.

BACKGROUND: Few studies on rheumatoid arthritis (RA) have generated machine learning models to predict biologic disease-modifying antirheumatic drugs (bDMARDs) responses; however, these studies included insufficient analysis on important features. Moreover, machine learning is yet to be used to predict bDMARD responses in ankylosing spondylitis (AS). Thus, in this study, machine learning was used to predict such responses in RA and AS patients. METHODS: Data were retrieved from the Korean College of Rheumatology Biologics therapy (KOBIO) registry. The number of RA and AS patients in the training dataset were 625 and 611, respectively. We prepared independent test datasets that did not participate in any process of generating machine learning models. Baseline clinical characteristics were used as input features. Responders were defined as those who met the ACR 20% improvement response criteria (ACR20) and ASAS 20% improvement response criteria (ASAS20) in RA and AS, respectively, at the first follow-up. Multiple machine learning methods, including random forest (RF-method), were used to generate models to predict bDMARD responses, and we compared them with the logistic regression model. RESULTS: The RF-method model had superior prediction performance to logistic regression model (accuracy: 0.726 [95% confidence interval (CI): 0.725-0.730] vs. 0.689 [0.606-0.717], area under curve (AUC) of the receiver operating characteristic curve (ROC) 0.638 [0.576-0.658] vs. 0.565 [0.493-0.605], F1 score 0.841 [0.837-0.843] vs. 0.803 [0.732-0.828], AUC of the precision-recall curve 0.808 [0.763-0.829] vs. 0.754 [0.714-0.789]) with independent test datasets in patients with RA. However, machine learning and logistic regression exhibited similar prediction performance in AS patients. Furthermore, the patient self-reporting scales, which are patient global assessment of disease activity (PtGA) in RA and Bath Ankylosing Spondylitis Functional Index (BASFI) in AS, were revealed as the most important features in both diseases. CONCLUSIONS: RF-method exhibited superior prediction performance for responses of bDMARDs to a conventional statistical method, i.e., logistic regression, in RA patients. In contrast, despite the comparable size of the dataset, machine learning did not outperform in AS patients. The most important features of both diseases, according to feature importance analysis were patient self-reporting scales.