Comment on: Comorbidity burden in axial spondyloarthritis: a cluster analysis: reply

Abstract Objectives To examine how comorbidities cluster in axial spondyloarthritis (axSpA) and whether these clusters are associated with quality of life, global health and other outcome measures. Methods We conducted a cross-sectional study of consecutive patients meeting ASAS criteria for axSpA in Liverpool, UK. Outcome measures included quality of life (EQ5D), global health and disease activity (BASDAI). We used hierarchical cluster analysis to group patients according to 38 pre-specified comorbidities. In multivariable linear models, the associations between distinct comorbidity clusters and each outcome measure were compared, using axSpA patients with no comorbidities as the reference group. Analyses were adjusted for age, gender, symptom duration, BMI, deprivation, NSAID-use and smoking. Results We studied 419 patients (69% male, mean age 46 years). 255 patients (61%) had at least one comorbidity, among whom the median number was 1 (range 1–6). Common comorbidities were hypertension (19%) and depression (16%). Of 15 clusters identified, the most prevalent clusters were hypertension-coronary heart disease and depression-anxiety. Compared with patients with no comorbidities, the fibromyalgia-irritable bowel syndrome cluster was associated with adverse patient-reported outcome measures; these patients reported 1.5-unit poorer global health (95%CI 0.01, 2.9), reduced quality of life (0.25-unit lower EQ5D; 95%CI −0.37, −0.12) and 1.8-unit higher BASDAI (95% CI 0.4, 3.3). Similar effect estimates were found for patients in the depression-anxiety cluster. Conclusion Comorbidity is common among axSpA patients. The two most common comorbidities were hypertension and depression. Patients in the depression-anxiety and fibromyalgia-IBS clusters reported poorer health and increased axSpA severity.

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Assessment of the relationship between estimated cardiovascular risk and structural damage in patients with axial spondyloarthritis

Therapeutic Advances in Musculoskeletal Disease ◽

10.1177/1759720x20982837 ◽

2020 ◽

Vol 12 ◽

pp. 1759720X2098283

Author(s):

María Lourdes Ladehesa-Pineda ◽

Iván Arias de la Rosa ◽

Clementina López Medina ◽

María del Carmen Castro-Villegas ◽

María del Carmen Ábalos-Aguilera ◽

...

Keyword(s):

Risk Factors ◽

Cluster Analysis ◽

Structural Damage ◽

Disease Duration ◽

Linear Models ◽

Axial Spondyloarthritis ◽

Subclinical Atherosclerosis ◽

Intima Media Thickness ◽

Atherosclerotic Plaques ◽

Cross Sectional

Aims: To evaluate the association of estimated cardiovascular (CV) risk and subclinical atherosclerosis with radiographic structural damage in patients with axial spondyloarthritis (axSpA). Methods: Cross-sectional study including 114 patients axSpA from the SpA registry of Córdoba (CASTRO) and 132 age- and sex-matched healthy controls (HCs). Disease activity and the presence of traditional CV risk factors were recorded. The presence of atherosclerotic plaques and carotid intima media thickness (cIMT) were evaluated through carotid ultrasound and the SCORE index was calculated. Radiographic damage was measured though modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS). The association between mSASSS and SCORE was tested using generalized linear models (GLM), and an age-adjusted cluster analysis was performed to identify different phenotypes dependent on the subclinical CV risk. Results: Increased traditional CV risk factors, SCORE, and the presence of carotid plaques were found in axSpA patients compared with HCs. The presence of atherosclerotic plaques and SCORE were associated with radiographic structural damage. The GLM showed that the total mSASSS was associated independently with the SCORE [β coefficient 0.24; 95% confidence interval (CI) 0.10–0.38] adjusted for disease duration, age, tobacco, C-reactive protein, and non-steroidal anti-inflammatory drugs (NSAID) intake. Hard cluster analysis identified two phenotypes of patients. Patients from cluster 1, characterized by the presence of plaques and increased cIMT, had a higher prevalence of CV risk factors and SCORE, and more structural damage than cluster two patients. Conclusion: Radiographic structural damage is associated closely with increased estimated CV risk: higher SCORE levels in axSpA patients were found to be associated independently with mSASSS after adjusting for age, disease duration, CRP, tobacco and NSAID intake.

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POS0399 TMT-BASED QUANTITATIVE PROTEOMICS ANALYSIS OF SYNOVIAL FLUID-DERIVED EXOSOMES IN RHEUMATOID ARTHRITIS, AXIAL SPONDYLOARTHRITIS, GOUT AND OSTEOARTHRITIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3607 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 428.3-429

Author(s):

Y. Liu ◽

Y. Huang ◽

Q. Huang ◽

Z. Huang ◽

Z. Li ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Cluster Analysis ◽

Synovial Fluid ◽

Pathway Analysis ◽

Quantitative Proteomics ◽

Axial Spondyloarthritis ◽

Kegg Pathway ◽

Hierarchical Cluster ◽

Kegg Pathway Analysis ◽

Differential Proteins

Background:The pathogeneses of the joint diseases rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), gout, and osteoarthritis (OA) are still not fully elucidated. Exosomes in synovial fluid (SF) has a critical role in the pathogenesis of arthritis. None of study has compared the proteomics of SF-derived exosomes in RA, axSpA, gout and OA.Objectives:To compare the proteomics of SF-derived exosomes in RA, axSpA, gout and OA based on tandem mass tags (TMT) labeled quantitative proteomics technique.Methods:SF-derived exosomes was isolated from RA, axSpA, gout and OA patients by the Exoquick kit combined ultracentrifugation method. TMT labeled quantitative proteomics technique was used to compare the proteomics of SF-derived exosomes. Volcano plot, hierarchical cluster, Gene Ontologies (GO), Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted.Results:A total of 1678 credible proteins were detected. With the cut off criteria of |log2 (fold-change)| ≥1.2 and p-value <0.05, 267 (140 up-regulated and 127 down-regulated)differential proteins were found in OA vs gout, 291 (179 and 112) in axSpA vs OA, 515 (109 and 406) in RA vs axSpA, 298 (191 and 107) in axSpA vs gout, 462 (160 and 302) in RA vs gout, 536 (170 and 366) in RA vs OA. GO analysis showed that the biological progress of differential proteins were mainly enriched in the “immune response”. Regarding the molecular function, the differential proteins mainly mediated “antigen binding”. GO analysis of the cellular components indicated that most proteins were annotated as “extracellular exosomes”. KEGG pathway analysis demonstrated differential proteins were significantly enriched in “complement and coagulation cascades”. The hierarchical cluster analysis of the differential proteins in the four groups showed that Lysozyme C and Keratin were more abundant in gout, Hemoglobin and Actin-related protein 2/3 complex subunit 3 in OA, Sodium/potassium-transporting ATPase subunit alpha-1 and Immunoglobulin heavy constant delta in axSpA, Pregnancy zone protein and Stromelysin-1 in RA.Conclusion:The protein profiles of SF-derived exosomes in RA, axSpA, gout and OA patients were different. The differential proteins were the potential biomarkers of RA, axSpA, gout and OA.References:[1]Cretu D, Diamandis E P, Chandran V. Delineating the synovial fluid proteome: recent advancements and ongoing challenges in biomarker research.[J]. Critical reviews in clinical laboratory sciences, 2013,50(2):51-63.[2]McArdle A J, Menikou S. What is proteomics?[J]. Archives of disease in childhood. Education and practice edition, 2020.Figure 1.The hierarchical cluster analysis of differential proteins in axSpA, OA, Gout and RA.Disclosure of Interests:None declared

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Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

Diagnostics ◽

10.3390/diagnostics11112119 ◽

2021 ◽

Vol 11 (11) ◽

pp. 2119

Author(s):

Charat Thongprayoon ◽

Janina Paula T. Sy-Go ◽

Voravech Nissaisorakarn ◽

Carissa Y. Dumancas ◽

Mira T. Keddis ◽

...

Keyword(s):

Cluster Analysis ◽

Kidney Function ◽

Serum Magnesium ◽

Hospitalized Patients ◽

Consensus Clustering ◽

Comorbidity Burden ◽

Clustering Approach ◽

One Year ◽

Cluster 2 ◽

Function Cluster

Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.

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Evaluating the relationship between clinical and demographic characteristics of insulin-using people with diabetes and their health outcomes: a cluster analysis application

BMC Health Services Research ◽

10.1186/s12913-021-06603-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Elizabeth L. Eby ◽

Alison Edwards ◽

Eric Meadows ◽

Ilya Lipkovich ◽

Brian D. Benneyworth ◽

...

Keyword(s):

Cluster Analysis ◽

Health Outcomes ◽

Healthcare Utilization ◽

Claims Data ◽

Diabetes Management ◽

Hospital Admissions ◽

Healthcare Resource Utilization ◽

Administrative Claims ◽

Clustering Methods ◽

Comorbidity Burden

Abstract Background The aim of this study was to determine how clusters or subgroups of insulin-treated people with diabetes, based upon healthcare resource utilization, select social demographic and clinical characteristics, and diabetes management parameters, are related to health outcomes including acute care visits and hospital admissions. Methods This was a non-experimental, retrospective cluster analysis. We utilized Aetna administrative claims data to identify insulin-using people with diabetes with service dates from 01 January 2015 to 30 June 2018. The study included adults over the age of 18 years who had a diagnosis of type 1 (T1DM) or type 2 diabetes mellitus (T2DM) on insulin therapy and had Aetna medical and pharmacy coverage for at least 18 months (6 months prior and 12 months after their index date, defined as either their first insulin prescription fill date or their earliest date allowing for 6 months’ prior coverage). We used K-means clustering methods to identify relevant subgroups of people with diabetes based on 13 primary outcome variables. Results A total of 100,650 insulin-using people with diabetes were identified in the Aetna administrative claims database and met study criteria, including 11,826 (11.7%) with T1DM and 88,824 (88.3%) with T2DM. Of these 79,053 (78.5%) people were existing insulin users. Seven distinct clusters were identified with different characteristics and potential risks of diabetes complications. Overall, clusters were significantly associated with differences in healthcare utilization (emergency room visits, inpatient admissions, and total inpatient days) after multivariable adjustment. Conclusions This analysis of healthcare claims data using clustering methodologies identified meaningful subgroups of patients with diabetes using insulin. The subgroups differed in comorbidity burden, healthcare utilization, and demographic factors which could be used to identify higher risk patients and/or guide the management and treatment of diabetes.

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O35 Multimorbidity in patients with axial spondyloarthritis: a cluster analysis

Rheumatology ◽

10.1093/rheumatology/kez105.034 ◽

2019 ◽

Vol 58 (Supplement_3) ◽

Author(s):

Sizheng Zhao ◽

Helga Radner ◽

Daniel Thong ◽

Stephen J Duffield ◽

David M Hughes ◽

...

Keyword(s):

Cluster Analysis ◽

Axial Spondyloarthritis

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Prevalence and impact of comorbidities in axial spondyloarthritis: systematic review and meta-analysis

Rheumatology ◽

10.1093/rheumatology/keaa246 ◽

2020 ◽

Vol 59 (Supplement_4) ◽

pp. iv47-iv57 ◽

Cited By ~ 2

Author(s):

Sizheng Steven Zhao ◽

Selina Robertson ◽

Tzvi Reich ◽

Nicolas L Harrison ◽

Robert J Moots ◽

...

Keyword(s):

Systematic Review ◽

Axial Spondyloarthritis ◽

Meta Analysis ◽

Work Productivity ◽

Comorbid Condition ◽

Random Effects Models ◽

Pooled Prevalence ◽

Comorbidity Burden ◽

Meta Analyses ◽

The Impact

Abstract Objectives Comorbidities are common in people with axial spondyloarthritis (axSpA). In this systematic review and meta-analysis, we aimed to: (i) describe the prevalence of commonly reported comorbidities, (ii) compare comorbidities between axSpA and control populations, and (iii) examine the impact of comorbidity burden on axSpA outcomes. Methods We systematically searched Medline, PubMed, Scopus and Web of Science using a predefined protocol in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. We excluded studies of only one comorbid condition or a few closely related diseases within one organ system. Where possible, meta-analysis was performed using random-effects models. Results A total of 40 studies were included for analysis. 36 studies reported prevalence of comorbidities, amounting to a combined sample size of 119 427 patients. The number of comorbidities studied ranged from 3 to 43. The most prevalent individual comorbidities were hypertension (pooled prevalence 23%), hyperlipidaemia (17%) and obesity (14%). Eleven studies consistently showed higher prevalence of comorbidities in axSpA than controls, particularly large differences were seen for depression [pooled odds ratio (OR) 1.80] and heart failure (OR 1.84). Comorbidities (total number of and individual conditions) were also associated with axSpA disease activity, functional impairment, quality of life, work productivity and mortality. Conclusions Comorbidities are common in axSpA, particularly cardiovascular diseases and risk factors. Most comorbidities were more prevalent in axSpA patients than in control populations. Overall comorbidity burden, and many individual conditions, were associated with axSpA outcomes including worse disease severity, work productivity and mortality.

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

Arthritis Research & Therapy ◽

10.1186/s13075-021-02647-z ◽

2021 ◽

Vol 23 (1) ◽

Author(s):

Seulkee Lee ◽

Seonyoung Kang ◽

Yeonghee Eun ◽

Hong-Hee Won ◽

Hyungjin Kim ◽

...

Keyword(s):

Cluster Analysis ◽

Disease Activity ◽

Clinical Characteristics ◽

Axial Spondyloarthritis ◽

Tumour Necrosis Factor Alpha ◽

Necrosis Factor Alpha ◽

Drug Survival ◽

Axial Symptoms ◽

Factor Alpha ◽

Necrosis Factor

Abstract 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.

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Comorbidity and response to TNF inhibitors in axial spondyloarthritis: longitudinal analysis of the BSRBR-AS

Rheumatology ◽

10.1093/rheumatology/keaa900 ◽

2020 ◽

Author(s):

Sizheng Steven Zhao ◽

Gareth T Jones ◽

Gary J Macfarlane ◽

David M Hughes ◽

Robert J Moots ◽

...

Keyword(s):

Quality Of Life ◽

Axial Spondyloarthritis ◽

Tnf Inhibitors ◽

Treatment Discontinuation ◽

Disease Assessment ◽

Cox Models ◽

Time To Treatment ◽

Comorbidity Burden ◽

Over Time

Abstract Objective Comorbidities influence disease assessment in axial spondyloarthritis (axSpA), but their association with response to TNF inhibitors (TNFi) is unclear. We examined associations between comorbidity history at TNFi initiation and: 1) change in disease indices over time; 2) binary response definitions; 3) time-to-treatment-discontinuation. Methods We studied participants starting their first TNFi from a national axSpA register. Comorbidity categories were created from 14 physician-diagnosed conditions and compared against: change in disease indices over time using linear mixed effects models; BASDAI50/2 (50% or 2-unit reduction) and BASDAI<4 at 6 months using logistic models; and time-to-treatment-discontinuation using Cox models. Models were adjusted for age, gender, BMI, deprivation and education. Results 994 were eligible for analysis (68% male, mean age 45 years); 21% had one comorbidity and 11% had ≥2. Baseline disease severity was higher in those with comorbidities across all indices, but absolute improvement over time was comparable for BASDAI and spinal pain. Participants with ≥2 comorbidities had smaller absolute improvement in BASFI and quality of life. This group also had numerically reduced odds of achieving BASDAI50/2 (OR 0.81; 95%CI 0.45, 1.45) and BASDAI<4 (OR 0.57; 95%CI 0.32, 1.04). Treatment discontinuation was increased in those with 2 comorbidities (HR 1.32; 95%CI 0.88, 2.00) and ≥3 comorbidities (HR 2.18; 95%CI 1.20, 3.93) compared to none. Conclusions Participants with multiple comorbidities had poorer treatment outcomes, particularly increased treatment discontinuation and poorer improvements in function and quality of life. These results inform clinicians and educate patients about response to the first TNFi given comorbidity burden.

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