Diagnosis Of Gout Research Articles

Understanding Gout – Pathogenesis, Diagnosis and Management

Gout is one of the most common types of inflammatory arthritis caused by accumulation of uric acid crystals within joints. It typically starts in the big toe with symptoms such as intense joint pain especially at night, swelling and redness. This sudden episode is also called a “flare” or an “attack”. Males are three times more likely to be affected with gout than females. Other key risk factors include obesity, hypertension, high alcohol and seafood intake. Diagnosis of gout is mostly clinical. Joint fluid analysis which shows urate crystals and ultrasound are used to confirm the diagnosis. An acute attack of gout can be relieved by anti-inflammatory agents like NSAIDs, Colchicine and Corticosteroids which is followed by long term management – uric acid lowering agents, lifestyle, weight and dietary management.

Enhancing Gout Diagnosis with Deep Learning in Dual-energy Computed Tomography: A Retrospective Analysis of Crystal and Artifact Differentiation.

To evaluate whether the application of deep learning (DL) could achieve high diagnostic accuracy in differentiating between green colour coding, indicative of tophi, and clumpy artifacts observed in dual-energy computed tomography (DECT) scans. A comprehensive analysis of 18 704 regions of interest (ROIs) extracted from green foci in DECT scans obtained from 47 patients with gout and 27 gout-free controls was performed. The ROIs were categorized into three size groups: small, medium, and large. Convolutional neural network (CNN) analysis on a per-lesion basis and support vector machine (SVM) analysis on a per-patient basis were performed. The area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value of the models were compared. For small ROIs, the sensitivity and specificity of the CNN model were 81.5% and 96.1%, respectively; for medium ROIs, 82.7% and 96.1%, respectively; for large ROIs, 91.8% and 86.9%, respectively. Additionally, the DL algorithm exhibited accuracies of 88.5%, 88.6%, and 91.0% for small, medium, and large ROIs, respectively. In the per-patient analysis, the SVM approach demonstrated a sensitivity of 87.2%, a specificity of 100%, and an accuracy of 91.8% in distinguishing between patients with gout and gout-free controls. Our study demonstrates the effectiveness of the DL algorithm in differentiating between green colour coding indicative of crystal deposition and clumpy artifacts in DECT scans. With high sensitivity, specificity, and accuracy, the utilization of DL in DECT for diagnosing gout enables precise lesion classification, facilitating early-stage diagnosis and promoting timely intervention approaches.

Performance of novel multiparametric second-generation dual-layer spectral detector CT in gouty arthritis.

This study aimed to compare the performance of different dual-energy computed tomography (DECT) technologies in detecting monosodium urate (MSU) crystals and evaluate the potential clinical value of novel second-generation dual-layer spectral detector CT (dlDECT) in gouty arthritis. Using data collected from a tertiary hospital, we examined the diagnostic accuracy of different DECT technologies for the diagnosis of MSU. We used two standards: (1) demonstration of MSU crystals in synovial fluid (gold) and (2) 2015 ACR/EULAR gout classification criteria (silver). Furthermore, six novel spectral parameters derived from dlDECT were quantitatively calculated and analyzed for MSU diagnostic efficiency. Of the 243 patients with 387 joints, 68 (27.98%) had synovial fluid analysis. Compared with the gold standard, MSU diagnostic accuracy statistics for dlDECT, dual-source DECT (dsDECT) and rapid kilovolt peak switching DECT (rsDECT) were as follows: area under the curve (AUC): 0.85, 0.80 and 0.75, respectively. Findings were replicated compared with the silver standard. Multiparametric analysis in dlDECT demonstrated the highest MSU detection rate (92.86%), significantly higher than rsDECT (42.08%) and dsDECT (85.80%). Among novel parameters in dlDECT, Calcium-suppressed index 25 (CaSupp-I 25) exhibited the best performance in distinguishing materials (MSU, muscle, and bone), with an AUC of 0.992. The differentiation was also aided by histograms, scatter plots, and attenuation curves. The novel dlDECT is likely not inferior to other DECT technologies in MSU detection, especially its spectral parameter CaSupp-I 25. Multiparameter analysis showed the potential value for detecting MSU crystals in gouty arthritis, providing valuable clinical insights for gout diagnosis. Question The performance of different DECT technologies in detecting monosodium urate (MSU), and the value of dual-layer spectral detector CT (dlDECT) in gouty arthritis remains unclear. Findings The dlDECT was likely not inferior to other DECT technologies in MSU detection, and its multiparametric analysis provided valuable information for MSU diagnosis. Clinical relevance Novel dlDECT may improve the accurate detection of MSU crystals in gouty arthritis compared to other DECT technologies, providing valuable clinical insights and potentially improving patient outcomes for more precise gout diagnosis.

Material decomposition approaches for monosodium urate (MSU) quantification in gouty arthritis: a (bio)phantom study

BackgroundDual-energy computed tomography (DECT) is a noninvasive diagnostic tool for gouty arthritis. This study aimed to compare two postprocessing techniques for monosodium urate (MSU) detection: conventional two-material decomposition and material map-based decomposition.MethodsA raster phantom and an ex vivo biophantom, embedded with four different MSU concentrations, were scanned in two high-end CT scanners. Scanner 1 used the conventional postprocessing method while scanner 2 employed the material map approach. Volumetric analysis was performed to determine MSU detection, and image quality parameters, such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), were computed.ResultsThe material map-based method demonstrated superior MSU detection. Specifically, scanner 2 yielded total MSU volumes of 5.29 ± 0.28 mL and 4.52 ± 0.29 mL (mean ± standard deviation) in the raster and biophantom, respectively, versus 2.35 ± 0.23 mL and 1.15 ± 0.17 mL for scanner 1. Radiation dose correlated positively with detection for the conventional scanner, while there was no such correlation for the material map-based decomposition method in the biophantom. Despite its higher detection rate, material map-based decomposition was inferior in terms of SNR, CNR, and artifacts.ConclusionWhile material map-based decomposition resulted in superior MSU detection, it is limited by challenges such as increased artifacts. Our findings highlight the potential of this method for gout diagnosis while underscoring the need for further research to enhance its clinical reliability.Relevance statementAdvanced postprocessing such as material-map-based two-material decomposition might improve the sensitivity for gouty arthritis in clinical practice, thus, allowing for lower radiation doses or better sensitivity for gouty tophi.Key PointsDual-energy CT showed limited sensitivity for tophi with low MSU concentrations.Materiel-map-based decomposition increased sensitivity compared to conventional two-material decomposition.The advantages of material-map-based decomposition outweigh lower image quality and increased artifact load.Graphical

Mechanism of neutrophil extracellular traps in the pathogenesis of gout.

Gout is a self-limited inflammatory disease caused by the deposition of monosodium urate (MSU) crystals in joints and surrounding tissues due to abnormal purine metabolism. Neutrophil extracellular traps (NETs) are formed by neutrophils in response to pathogen attack. During gout, NETs induced by MSU crystals exacerbate inflammation, and aggregated NETs (aggNETs) promote the resolution of gout-associated inflammation by encapsulating MSU crystals, degrading cytokines and chemokines, and blocking the recruitment and activation of neutrophils. With disease progression, NETs participate in the formation of tophi. Therefore, aggNETs are a possible mechanism of spontaneous gout regression. Studying the specific mechanism by which NETs affect inflammatory bursts and spontaneous regression in gout patients is important. This review summarises the role of NETs in different stages of gout and the specific pathogenesis of NETs in gout to provide new ideas for the diagnosis and treatment of gout.

Gout diagnoses uncertain when made by general practitioners without serum uric acid testing: an observational study.

General practitioners (GPs) diagnose the majority of all gout patients. They make their diagnosis clinically. Serum uric acid (SUA) level >0.35mmol/l is largely determinative for the clinical diagnosis of gout. We aimed to assess to what extent GPs test SUA when making a first gout diagnosis, and to consider consequences regarding diagnostic certainty of not testing it. We calculated proportions of patients from 87 Dutch general practices (1-1-2013 to 1-1-2022) with a first gout diagnosis and a recorded SUA test, evaluated if SUA testing was punctually timed with respect to the diagnosis date, whether SUA levels were >0.35mmol/L, and whether diagnoses corresponded with diagnoses according to the 'Acute Gout Diagnosis Rule' (AGD-rule). In 43.0% of 7782 patients (mean age 64.4 years; 68.8% male) no SUA test result was recorded, with substantial variation among practices: median 41.9% (IQR 20.2%). 'Gout very likely' according to the AGD-rule was seen in all males and in 83.1% of females when a punctually timed SUA >0.35mmol/L was present. When such SUA level was lacking, 'Questionable or indeterminate gout diagnosis' was seen in all males and in 67.1% of females, and 'Arthritis diagnosis other than gout' in 32.9% of females. GPs diagnosed gout without testing SUA in ~40% of cases. This implies avoidable diagnostic uncertainty with impact for the clinical care of many patients as well as for studies that include primary care patients with gout. Key Points • Serum uric acid (SUA) level > 0.35mmol is determinative when diagnosing gout: however, it is unknown how often SUA testing is applied as such. • More than 40% of patients with a first gout diagnosis according to general practitioners (GPs) had no SUA test result registered in their medical record. • Gout diagnosing by GPs without SUA testing appeared to lead to avoidable diagnostic uncertainty, as diagnoses are often questionable or sometimes even wrong. • Researchers investigating gout need to take into account diagnostic uncertainty if they include gout patients who are diagnosed in primary care in their studies without SUA information.

Short-Term Risk of Cardiovascular Events in People Newly Diagnosed With Gout.

To investigate the temporal association between the first diagnosis of gout and cardiovascular events in the short term. We performed a self-controlled case series analysis and a cohort study using data from linked primary care, hospitalization, and mortality records from the United Kingdom's Clinical Practice Research Database-GOLD. We included individuals with a new diagnosis of gout either in the primary care or secondary care between January 1, 1997 and December 31, 2020. The first consultation at which gout was diagnosed was the exposure of interest. The main outcome consisted of cardiovascular events (ie, a composite of fatal and nonfatal myocardial infarction, ischemic or hemorrhagic stroke, and transient ischemic attack). The 4,398 patients (66.9% male, mean age 74.6 years) had a cardiovascular event within at least two years of their first recorded diagnosis of gout. The incidence of cardiovascular events was significantly higher in the 30 days after the first diagnosis of gout compared to baseline (adjusted incidence rate ratio 1.55, 95% confidence interval [CI] 1.33-1.83). Among 76,440 patients (72.9% male, mean age 63.2 years) included in the cohort study, the incidence of cardiovascular events in the 30 days after the first gout diagnosis (31.2 events per 1,000 person-years, 95% CI 27.1-35.9) was significantly higher than in days 31 to 730 after gout diagnosis (21.6 events per 1,000 person-years, 95% CI 20.8-22.4) with a rate difference of -9.6 events per 1,000 person-years (95% CI -14.0 to -5.1). Individuals had a short-term increased risk of cardiovascular events in the 30 days following the first consultation at which gout was diagnosed.

Prevalence and factors associated to calcium pyrophosphate arthritis in patients with gout

Aim: To ascertain the prevalence of calcium pyrophosphate arthritis (CPPA) at diagnosis and during follow-up of patients with gout. Methods: Inception cohort of patients with gout prospectively recruited and followed-up from 1994–2023. Gout-case was defined as crystal-proved tophus or arthritis, or the presence of tophus plus double contour with ultrasonography. CPPA was defined as the presence of intra-leukocyte calcium pyrophosphate (CPP) crystals in synovial fluid (SF) and neat chondrocalcinosis in plain radiographs. Age, gender, time from onset of symptoms, number of flares, joint distribution, previous and prescribed treatments, colchicine prophylaxis, comorbidities, alcohol intake, use of diuretics, renal function, and previous vascular disease were available for analysis. Results: A total of 1,544 patients with gout, with an average of 4-year follow-up, were available for analysis. CPPA was observed in 127/1,544 cases (8.2%). In 37/1,544 patients (2.4%) CPP and monosodium urate (MSU) crystals were observed in the same SF sample at gout diagnosis, and 90/1,544 (5.8%) showed CPP crystals apart from the diagnosis of gout. CPPA-gout cases had more flares per year, but no more frequent polyarticular distribution at baseline compared to non-CPPA-gout. CPPA-gout cases were older at baseline and showed lower renal function. Women, patients using diuretics, patients with hypertension, and those with previous vascular events showed CPPA more frequently. Multivariate analysis showed that only age and use of diuretics were independently associated with CPPA, as other variables apparently associated were dependent on aging. Interestingly, an analysis of the prevalence in the three decades available showed an increased CPPA diagnosis through time, probably associated with increased awareness of the association. Conclusions: (1) CPPA is not infrequent in patients with gout; (2) it is associated with aging and diuretic use; (3) awareness of this association may increase the rate of diagnosis.

An objective diagnosis of gout and calcium pyrophosphate deposition disease with machine learning of Raman spectra acquired in a point-of-care setting.

Raman spectroscopy is proposed as a next-generation method for the identification of monosodium urate (MSU) and calcium pyrophosphate (CPP) crystals in synovial fluid. As the interpretation of Raman spectra requires specific expertise, the method is not directly applicable for clinicians. We developed an approach to demonstrate that the identification process can be automated with the use of machine learning techniques. The developed system is tested in a point-of-care-setting at our outpatient rheumatology department. We collected synovial fluid samples from 446 patients with various rheumatic diseases from three centra. We analyzed all samples with our Raman spectroscope and used 246 samples for training and 200 samples for validation. Trained observers classified every Raman spectrum as MSU, CPP or else. We designed two one-against-all classifiers, one for MSU and one for CPP. These classifiers consisted of a principal component analysis model followed by a support vector machine. The accuracy for classification of CPP using the 2023 ACR/EULAR CPPD classification criteria was 96.0% (95% CI 92.3-98.3), while the accuracy for classification of MSU with using the 2015 ACR/EULAR gout classification criteria was 92.5% (95% CI 87.9-95.7). Overall, the accuracy for classification of pathological crystals was 88.0% (95% CI 82.7-92.2). The model was able to discriminate between pathologic crystals, artifacts, and other particles such as microplastics. We here demonstrate that potentially complex Raman spectra from clinical patient samples can be successfully classified by a machine learning approach, resulting in an objective diagnosis independent of the opinion of the medical examiner.

Diagnosis of Gout as a Correlative Risk for Acute Myocardial Infarction in the Absence of Traditional Cardiovascular Risk Factors.

We aimed to study the impact of gout as a correlative risk factor in the incidence of acute myocardial infarction (AMI) among patients without known MI risk factors. Our study population was obtained from the National Inpatient Sample (NIS) 2011-2018 using the International Classification of Diseases, Ninth and Tenth Revisions. This study included patients without cardiovascular disease (CVD), and various outcomes were compared among patients with and without gout. Cohorts were weighted using an algorithm provided by the NIS, which allows for national estimates. Our primary endpoint was the odds of developing an MI, and secondary endpoints were adverse hospital events and length of stay. In total, 117,261,842 patients without CVD risk factors were included in this study, 187,619 (0.16%) of whom had a diagnosis of gout. Patients without CVD risk factors who had gout were older and more likely to be male compared with patients without gout. Among patients without CVD risk factors, the odds of having an AMI were significantly higher in those with gout compared with those without, even after adjusting for chronic nonsteroidal anti-inflammatory drug and oral steroid use. Moreover, patients without CVD risk factors and with gout were more likely to develop acute renal failure, acute thromboembolic event, shock, acute gastrointestinal bleed, and arrhythmia compared with those without gout. Furthermore, patients without CVD risk factors who were admitted with gout had higher mortality compared with those without gout. In our study, we found that patients without risk factors for AMI who had gout were more likely to develop AMI compared with those without gout. Furthermore, the same patients were more likely to develop other adverse outcomes. Even with proper management, these individuals should be monitored closely for coronary events.

Ultrasound Features in Gout: An Overview.

The accurate diagnosis of gout frequently constitutes a challenge in clinical practice, as it bears a close resemblance to other rheumatologic conditions. An undelayed diagnosis and an early therapeutic intervention using uric acid lowering therapy (ULT) is of the utmost importance for preventing bone destruction, the main point of managing gout patients. Advanced and less invasive imaging techniques are employed to diagnose the pathology and ultrasonography (US) stands out as a non-invasive, widely accessible and easily reproducible method with high patient acceptability, enabling the evaluation of the full clinical spectrum in gout. The 2023 EULAR recommendations for imaging in diagnosis and management of crystal-induced arthropathies in clinical practice state that US is a fundamental imagistic modality. The guidelines underline its effectiveness in detecting crystal deposition, particularly for identifying tophi and the double contour sign (DCS). Its utility also arises in the early stages, consequent to synovitis detection. US measures of monosodium urate (MSU) deposits are valuable indicators, sensitive to change consequent to even short-term administration of ULT treatment, and can be feasibly used both in current daily practice and clinical trials. This paper aimed to provide an overview of the main US features observed in gout patients with reference to standardized imaging guidelines, as well as the clinical applicability both for diagnosis accuracy and treatment follow-up. Our research focused on summarizing the current knowledge on the topic, highlighting key data that emphasize gout as one of the few rheumatological conditions where US is recognized as a fundamental diagnostic and monitoring tool, as reflected in the most recent classification criteria.

Analysis of experience in the clinical use of dual-energy computed tomography in patients with undifferentiated arthritis

Dual-energy computed tomography (DECT) is a radiation diagnostic method that is used in rheumatology to verify microcrystalline arthritis, but in Russia this method is little known and the experience of its use is rather scarce.Objective of the research. To analyze the experience of using DECT in patients with undifferentiated arthritis and lesions of the axial skeleton in clinical practice.Material and Methods. The retrospective study included 20 patients (14 men and 6 women) observed at the V. A. Nasonova Research Institute of Rheumatology, who underwent DECT for diagnostic purposes. 13 patients with undifferentiated arthritis underwent DECT of peripheral joints to diagnose gout; 7 patients with an established diagnosis of gout (based on the 2015 ACR/EULAR classification criteria for gout) – to clarify the genesis of lesions to the axial joints and spine. The study was performed on a Siemens SOMATOM Definition Flash computed tomograph.Results. Using DECT in patients with undifferentiated arthritis, deposits of monosodium urate crystals (MSUc) were detected in 7 out of 13 (54 %), which made it possible to verify the diagnosis of gout. In the second sample, 6 out of 7 patients (85 %) showed signs of MSUc deposition on DECT, which explained the genesis of the existing complaints. Among these 6 patients, in 1 person, DECT revealed the presence of MSUc and calcium pyrophosphate crystals in the shoulder joint, which made it possible to make two diagnoses at once – gout and calcium pyrophosphate deposition disease.Conclusion. DECT in patients with undifferentiated arthritis makes it possible to verify the diagnosis of gout in more than half of the cases (54 %). In 85 % of patients with gout and pain in the back and axial joints of unknown origin, urate deposits are detected according to DECT. This method may be a useful tool for identifying microcrystalline arthritis of the axial joints, but further research is needed to implement the method in routine practice.

Chronic pyogenic spondylitis in patients with spinal gout: a case series and literature review

Background. Spinal gout is a rare and poorly described group of noninfectious inflammatory spinal lesions characterized by the deposition of monosodium urate crystals in the epidural space, paravertebral soft tissues, and by facet joints damage. There is no systematized data on the course of infectious spondylitis and spinal gout in the literature. The aim of the study — to present long-term outcomes of surgical treatment of chronic pyogenic spondylitis in patients with spinal gout and systematize the literature data on the issue. Methods. The clinical part of the study included 6 consecutively operated patients in the period from 2018 to 2021. The diagnosis of spinal gout and chronic pyogenic spondylitis was verified according to the results of histologic and bacteriologic studies of surgical material. Catamnesis accounted for at least 12 months. We studied the peculiarities of clinical and laboratory, radiological and histologic manifestations of the disease. Literature data systematization was carried out in the databases PubMed, Google Scholar, eLIBRARY from 2013 to 2023. Inclusion criteria: level of evidence II B-IV, catamnesis more or equal to 6 months, information on diagnostic and treatment measures. Results. The average age of the patients was 55±9 years. The duration of the diagnostic pause was 8±5 years. Multisegmented lesions of the lumbar (n = 6) spine prevailed. According to the classification of Pola E. et al. (2017) the following types of spondylitis were identified: B.3 (n = 1), C.1 (n = 1), C.3 (n = 2) and C.4 (n = 2). The clinical manifestation was dominated by chronic back pain with an intensity of 6,1±1,4 VAS scores. Neurologic disorders of the lower extremities and pelvic (ASIA scale) corresponded to types B (n = 1) and C (n = 4). The type of surgery: three-column reconstruction through combined approach in 2 cases and isolated anterior column reconstruction in 2 cases, 360° fusion in 1 case, and debridement of the epidural space and anterior column through posterior approach in 1 case. The follow-up results show the absence of recurrence of spondylitis in 4 cases, in 2 patients there were postoperative complications that required revision, after which no recurrence of spondylitis was noted. We selected 17 publications for literature review. Conclusion. The combination of chronic pyogenic spondylitis and spinal gout is a rare and poorly described pathology in the literature. Long therapeutic pause, chronic character of vertebrogenic pain syndrome in combination with increased uric acid level and formation of tophi of vertebral localization are distinctive features of the disease. Neurologic disorders with the combination of spondylitis and gout result from stenosis of the spinal canal due to two factors — epidural abscesses and tophi. Achieving local control of the combined pathology is possible with a combination of surgical intervention and long-term administration of hypouricemic drugs.

A machine learning-based prediction model for gout in hyperuricemics: a nationwide cohort study.

To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout. A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients' demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model's performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients. Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78-0.784) and precision-recall AUC of 0.208 (95% CI 0.195-0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706-0.723) and a negative predictive value (NPV) of 95%. The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout.

Histopathological diagnosis of visceral gout in desi fowl

Histopathological diagnosis of visceral gout in desi fowl

Urate-lowering therapy in patients with hyperuricemia and heart failure: A retrospective cohort study using the UK Clinical Practice Research Datalink.

Elevated serum uric acid (sUA) is associated with heart failure (HF). Urate-lowering therapy (ULT) in HF is associated with lower risk of HF hospitalization (hHF) and mortality. Data on patients with HF and gout or hyperuricemia in the Clinical Practice Research Datalink database linked to the Hospital Episode Statistics and the Office for National Statistics in the United Kingdom were analyzed. Risks of hHF and all-cause mortality or cardiovascular-related mortality by ULT exposure (ULT initiated within ≤6 months of gout or hyperuricemia diagnosis) were analyzed in a propensity score-matched cohort using adjusted Cox proportional hazards regression models. Of 2174 propensity score-matched pairs, patients were predominantly male, aged >70 years, with mean ± standard deviation sUA 9.3 ± 1.8 (ULT-exposed) and 9.4 ± 1.9 mg/dL (ULT-unexposed). At 5 years, ULT-exposed patients had a 43% lower risk of hHF or all-cause mortality (adjusted hazard ratio [HR]: 0.57; 95% confidence interval [CI]: 0.51-0.65) and a 19% lower risk of hHF or cardiovascular-related mortality (adjusted HR: 0.81; 95% CI: 0.71-0.92) versus no ULT exposure. ULT was associated with reduced risk of adverse clinical outcomes in patients with HF and gout or hyperuricemia over 5 years.

Health-related quality of life in gout patients in Madinah Region, Saudi Arabia

ABSTRACT Background: Gout can significantly impact health-related quality of life (HRQoL) due to excruciating pain, chronic arthropathy, and associated comorbidities. The objective of our study was to evaluate HRQoL among patients diagnosed with gout in the Madinah region of Saudi Arabia. Materials and Methods: The research took place in the Madinah Region, where eligible adult participants aged 18 years or older, having a confirmed history, or present diagnosis of gout were enrolled using medical records from 2016 to 2022. To gather data, patients were invited to participate in telephone-based interviews and complete the Short Form-36 (SF-36) questionnaire. Results: The majority of participants were male (81.5%), and 31–39 years form the largest group (37.0%). Most participants were married (86.4%), and the majority possess university and postgraduate education (70.4%). The respondents’ self-reported assessments were as follows: physical function (69.9), limitation due to physical health (74.1), emotional problem (75.8), energy or fatigue (61.6), emotional well-being (68.9), social functioning (76.6), pain (78.9), and general health (63.8). Conclusions: HRQoL among patients with gout was not significantly affected.

Epidemiology and diagnosis of gout in sub-saharan Africa: a scoping review

BackgroundThe episodic nature of gout and diagnostic uncertainty in the absence of microcrystal evidence make it particularly difficult to estimate the frequency of gout. Our aim was to review the literature on the epidemiological and diagnostic aspects of gout in sub-Saharan Africa.MethodsThis literature review was conducted using the MEDLINE database (via PUBMED), Google Scholar, and conference abstracts. The selection process was based on reading the titles first, then the abstracts, and then the full texts once the articles had been selected. Studies were included in this review if they presented original findings on the epidemiological and/or diagnostic aspects of gout in sub-Saharan Africa. Two groups of two investigators independently reviewed the studies. The results were analysed descriptively.ResultsThe literature search identified 131 articles and 22 conference abstracts. Nineteen articles were included in our review. Twelve studies were retrospective, five were cross-sectional, one was prospective, and one was both retrospective and cross-sectional. The duration of the studies ranged from 1 to 15 years, and the sample size ranged from 15 to 511 patients, for a total of 2557 patients. Gout was quite common, with a maximum frequency of 11.87%. Fourteen articles diagnosed gout via criteria, including 9 studies totaling 1174 patients via the 1977 ACR criteria. Gout tophi were reported in 15 articles involving 464 patients. Of these studies, seven looked for monosodium urate crystals in 317 (43.85%) of 723 patients. Among the 317 patients, monosodium urate crystals were detected in 263 (82.97%) patients. Eleven studies reported mean uricemia values ranging from 452.09 µmol/L to 642.44 µmol/L, with a mean of 510.63 µmol/L.ConclusionsThis review revealed that all the studies conducted in sub-Saharan Africa were intrahospital studies, and the majority were retrospective. Consequently, there is a clear need for population-based studies.

Skeletal muscle mass and quality in gout patients versus non-gout controls: A computed tomography imaging study

ObjectivePatients with gout are at elevated risk of multiple vascular and metabolic comorbidities. Whether they are also at risk of sarcopenia, which is known to affect patients with other rheumatic diseases, has not been previously assessed. We examined whether patients with gout have decreased lumbar muscle quality and quantity, indicating an association between gout and sarcopenia. MethodsFifty gout subjects and 25 controls, ages 45–80, underwent computed tomography imaging of the lumbosacral spine. We measured muscle quantity (skeletal muscle area [SMA] and index [SMI]) and quality (skeletal muscle radiation attenuation [SMRA] and intermuscular adipose tissue [IMAT] area and index [IMATI]) of the psoas and erector spinae muscles at the L3 level. ResultsSeventy subjects (45 gout and 25 controls) were included in the analysis. Gout subjects had higher BMI, more kidney disease and hypertension, lower exercise frequency, and higher mean serum urate and creatinine vs. controls. Lumbar SMRA was significantly lower in gout subjects vs. controls, indicating reduced muscle quality. Lumbar IMAT area was significantly higher in gout subjects vs. controls, as was lumbar IMATI, indicating increased muscle adiposity. These differences persisted after adjusting for potential confounders. In contrast, there was no significant difference between gout and control groups in lumbar SMA or lumbar SMI, suggesting that muscle quantity may not be routinely affected by the diagnosis of gout. ConclusionsGout patients exhibit decreased lumbar muscle quality compared with controls, consistent with an association between gout and sarcopenia.

Health literacy in patients with gout: A latent profile analysis

Objectives Understanding the health literacy status of patients with gout diagnosis is essential for improving the health of this population. Our study aimed to investigate the latent profiles of health literacy in patients with gout and to analyze differences in characteristics across potential profiles. Methods This was a cross-sectional study. Eligible participants attended the Shandong Gout Medical Center, from March 2023 to May 2023 and self-reported gout diagnosis. We used the Health Literacy Scale for Patients with Gout designed and validated by our team. The scale had good reliability and validity among patients with gout. 243 patients completed the Demographic Information Questionnaire and the Health Literacy Scale for Patients with Gout. We used latent profile analysis to identify the latent profiles of gout patients’ health literacy. We used Chi-square tests with Bonferroni correction to analyze differences in demographics and illness characteristics across identified profiles. Results Three profiles of patients with gout emerged (prevalence): the low literacy-low critical group (21.81%), the moderate literacy group (42.79%), and the high literacy-stable group (35.39%). The three groups differed in age, education level, monthly income, disease duration, and place of residence (P<0.01). Conclusions The health literacy of patients with gout was heterogeneous. Healthcare professionals should adopt targeted interventions based on the characteristics of each latent health literacy profile to improve the health literacy level of patients with gout.