Abstract
Background:
Intra-articular hyaluronic acid (HA) is widely used in the management of knee osteoarthritis (OA); however, treatment responses vary substantially among patients. Cardiometabolic disease (CMD) has been increasingly recognized as a potential modifier of disease progression and treatment outcomes. This study aimed to evaluate the impact of cardiometabolic burden on clinically relevant outcomes in patients with knee OA receiving intra-articular therapy.
Objectives:
To evaluate the impact of cardiometabolic burden on clinically relevant outcomes in patients with knee OA receiving intra-articular therapy.
Design:
Real-world retrospective cohort study.
Methods:
We used the TriNetX Global Network to identify patients with knee OA who received intra-articular HA injections. CMD was defined as the presence of at least one cardiometabolic condition, including type 2 diabetes mellitus (T2DM), hypertension, or hyperlipidemia. Propensity score matching (1:1) was performed to balance baseline characteristics. The primary outcome was incident primary total knee arthroplasty (TKA) within 36 months. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models, and Kaplan–Meier analyses were performed to estimate cumulative incidence.
Results:
Among 50,833 eligible patients, 35,509 were classified into the CMD group and 15,324 into the control group. After propensity score matching, 14,220 patients remained in each cohort. Patients with CMD had a higher risk of TKA than controls at 12 months (HR 1.696, 95% CI 1.537–1.871), 24 months (HR 1.607, 95% CI 1.484–1.740), and 36 months (HR 1.598, 95% CI 1.485–1.720). Among individual cardiometabolic conditions, hypertension (HR 1.597, 95% CI 1.437–1.775) and hyperlipidemia (HR 1.373, 95% CI 1.184–1.594) were associated with increased TKA risk, whereas isolated T2DM was not significantly associated with TKA risk (HR 0.947, 95% CI 0.669–1.342). Kaplan–Meier analysis demonstrated lower TKA-free survival probabilities in the CMD group.
Conclusion:
CMD was associated with poorer clinical trajectories in patients with knee OA receiving intra-articular HA therapy. These findings suggest that metabolic health may be associated with treatment outcomes and may help identify patients at increased risk of progression to total knee arthroplasty
Keywords: cardiometabolic disease, hyperlipidemia, hypertension, knee osteoarthritis, real-world cohort, total knee arthroplasty
Plain language summary
How multiple metabolic conditions affect the risk of knee replacement in people with knee osteoarthritis
This study examined whether cardiometabolic disease was associated with the risk of total knee arthroplasty (TKA) among patients with knee osteoarthritis receiving intra-articular hyaluronic acid therapy. Patients with cardiometabolic disease had a higher likelihood of undergoing TKA than those without these conditions. However, because this was an observational study using real-world data, the findings represent associations and cannot establish that cardiometabolic disease directly caused patients to undergo knee replacement surgery. Importantly, decisions regarding TKA may also be influenced by factors beyond disease severity, including pain intensity, functional limitation, access to healthcare, patient preference, and physician decision-making.
Introduction
Knee osteoarthritis (OA) is a leading cause of pain, disability, and reduced quality of life worldwide, and its prevalence continues to increase with population aging.1,2 As the disease progresses, a substantial proportion of patients ultimately require total knee arthroplasty (TKA), which remains the most effective treatment for end-stage knee OA. 3 Given the growing demand for TKA and its associated healthcare burden, identifying modifiable risk factors that may influence disease progression and delay surgical intervention has become an important clinical priority.
In recent years, increasing attention has been directed toward the role of systemic metabolic conditions in the development and progression of osteoarthritis. Cardiometabolic disease (CMD), characterized by the coexistence of conditions such as type 2 diabetes mellitus (T2DM), hypertension, hyperlipidemia, and obesity, is highly prevalent and has been associated with adverse musculoskeletal outcomes.4,5 Beyond mechanical loading, metabolic and inflammatory pathways, including chronic low-grade inflammation, insulin resistance, and vascular dysfunction, may contribute to cartilage degeneration and joint structural changes, supporting the concept of a metabolic phenotype of osteoarthritis.6,7
Intra-articular hyaluronic acid (HA) injection is commonly used for symptomatic knee osteoarthritis and is considered a treatment option prior to surgical intervention.8,9 However, treatment responses remain heterogeneous, and a proportion of patients receiving HA injections still progress to TKA.10 –12 Although individual cardiometabolic conditions have been linked to osteoarthritis,4,5,7 previous studies have mainly focused on isolated diseases, symptom severity, or radiographic progression rather than clinically meaningful outcomes.4,5 Moreover, little is known about whether overall CMD burden is associated with progression to TKA in patients receiving intra-articular HA therapy. Therefore, evaluating CMD burden in relation to a hard clinical endpoint may provide clinically relevant evidence for risk stratification and treatment planning in routine practice.
Therefore, the aim of this study was to investigate the association between CMD and the risk of TKA in patients with knee osteoarthritis receiving intra-articular HA treatment using a large real-world cohort. We hypothesized that patients with CMD receiving intra-articular HA would have a higher likelihood of undergoing TKA than patients without CMD. We hypothesized that patients with CMD receiving intra-articular HA would have a higher likelihood of undergoing TKA than patients without CMD.
Methods
TriNetX
This retrospective cohort study was conducted using the TriNetX research platform, a global network of deidentified electronic health records (EHRs) contributed by health care organizations. TriNetX aggregates information from more than 200 health care organizations worldwide. It collects data on demographic characteristics, diagnoses (International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)), procedures (International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS) and Current Procedural Terminology (CPT)), laboratory results, medications, and health care resource utilization rates.13,14 For the present analysis, we accessed the Global Collaborative Network, which covers more than 150 health care organizations across multiple countries and includes longitudinal data from millions of patients. The platform integrates EHRs and insurance claims to generate standardized records, and its validity has been demonstrated in numerous publications, with more than 1500 studies using TriNetX data already indexed in PubMed. The present study received approval to use TriNetX data from the Institutional Review Board of Taichung Veterans General Hospital (approval number: CE25855A). The requirement for informed consent was waived because this study used deidentified data obtained from the TriNetX Global Network. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Study population and design
Patients aged 40–85 years who received a diagnosis of knee osteoarthritis (ICD-10-CM code M17) between January 1, 2010, and January 1, 2025, were included in this study. This age range reflects a population of middle-aged and older adults commonly affected by osteoarthritis. To prevent the inclusion of early, untreated cases, we adopted a new-user design, defining the date of the first intra-articular HA injection as the index date. A washout period was required, excluding patients with any HA injections during the preceding 2 years, to ensure that all patients were initiating treatment.
Patients were stratified into two cohorts according to CMD status at the index date. CMD was defined as the presence of at least one cardiometabolic condition, including T2DM, hypertension, or hyperlipidemia. Patients with a CMD (i.e., T2DM, hypertension, or hyperlipidemia) were categorized into a CMD group, and patients without a CMD were categorized into a control group. Patients were excluded if information regarding HA injection or comorbidity status was incomplete or if follow-up was insufficient to allow for a full 3-year observation period (e.g., due to death or data truncation).
To address confounding by indication, we employed 1:1 propensity score matching (PSM), creating a comparison cohort closely aligned with the exposure group across baseline characteristics. Matching variables included demographic characteristics (age, sex, and race), socioeconomic status, body mass index (BMI), comorbidities (including hyperuricemia, chronic kidney disease, and ankylosing spondylitis), medication use, laboratory variables, and healthcare utilization variables available within the TriNetX platform. Detailed baseline characteristics and variables included in PSM are presented in Supplemental Table 1.
The primary outcome was the occurrence of TKA, identified using ICD-10-PCS and CPT codes (Supplemental Table 1) within predefined follow-up periods of 12, 24, and 36 months. Although TKA is an objective and clinically meaningful endpoint, it should be interpreted as progression to surgical intervention rather than a direct measure of structural osteoarthritis progression. Subgroup analyses were conducted on the basis of individual CMD, age, and sex.
Statistical analysis
All analyses were conducted using TriNetX’s built-in statistical tools. PSM was applied to minimize baseline differences, generating 1:1 matched cohorts through greedy nearest-neighbor matching without replacement and using a caliper width of 0.1 pooled standard deviations. This caliper threshold is commonly used to reduce residual imbalance while preserving sample size. Balance was assessed using standardized mean differences (SMDs), with values <0.1 considered acceptable.
Hazard ratios (HRs) and their 95% confidence intervals (CIs) were estimated to evaluate the relative risk of TKA. The proportional hazards assumption was assessed using the generalized Schoenfeld residuals test, which was implemented within the TriNetX environment. Kaplan–Meier survival curves were constructed to estimate survival probabilities (e.g., having avoided TKA at the end of follow-up). Patients were censored at the earliest occurrence of TKA, death, the last available follow-up recorded in the EHR, or the end of the prespecified follow-up period.
To examine the robustness of our findings, analyses were performed using predefined follow-up windows at 12, 24, and 36 months. We also conducted subgroup analyses of age, sex, diabetes status, hypertension status, and hyperlipidemia status. These subgroup analyses were exploratory and descriptive in nature. Formal interaction testing was not performed. TriNetX is a purpose-built research platform that has a proprietary infrastructure and is supported by standard statistical programming environments. Analyses are conducted using R software (version 4.0.2; R Foundation for Statistical Computing, Vienna, Austria) and Python (version 3.7; Python Software Foundation, Wilmington, DE, USA), incorporating commonly used open-source libraries such as survival, Hmisc, lifelines, statsmodels, and scipy.
Results
Patient characteristics
Our initial search identified 3,561,373 patients with knee osteoarthritis, of whom 50,833 were eligible for inclusion in this study. Eligible patients were divided into CMD (n = 35,509) and control (n = 15,324) groups. The patient selection process is depicted in Figure 1.
Figure 1.
Flowchart of the patient selection and cohort construction process for the CMD and non-CMD groups.
CMD, cardiometabolic disease.
Patient demographics are summarized in Table 1. Before PSM was performed, the results indicated that the CMD group was older (65.9 ± 9.8 years vs 61.7 ± 11.0 years, SMD = 0.40) and had higher rates of alcohol dependence or smoking (6.2% vs 1.6%, SMD = 0.24), gout (2.9% vs 0.8%, SMD = 0.21), chronic kidney disease (7.9% vs 0.4%, SMD = 0.38), and rheumatoid arthritis (2.4% vs 1.0%, SMD = 0.11). The CMD group was also more likely to use medications (i.e., diclofenac, triamcinolone, and aspirin) and to be obese (BMI ⩾ 25 kg/m2: 67.8% vs 50.9%, SMD = 0.35). After PSM was performed, the results indicated that the 2 cohorts (n = 14,220 each) were well matched, with SMDs of <0.1 across each variable. The median follow-up duration was 1095 days in the CMD group and 700 days in the control group. Differences in follow-up duration were addressed using time-to-event methods.
Table 1.
Baseline characteristics.
| Before matching | After matching | |||||
|---|---|---|---|---|---|---|
| cardiometabolic disease group (n = 35,509) | Control group (n = 15,324) | SMD | cardiometabolic disease group (n = 14,220) | Control group (n = 14,220) | SMD | |
| Age at the index date (mean ± SD) | 65.9 ± 9.8 | 61.7 ± 11.0 | 0.40 | 62.8 ± 10.4 | 62.6 ± 10.7 | 0.02 |
| Sex | ||||||
| Male | 13,195 (37.2) | 5444 (35.5) | 0.03 | 4971 (35.0) | 5048 (35.5) | 0.01 |
| Female | 20,938 (59.0) | 9351 (61.0) | 0.04 | 8729 (61.4) | 8650 (60.8) | 0.01 |
| Race | ||||||
| White | 26,964 (75.9) | 11,144 (72.7) | 0.07 | 10,575 (74.4) | 10,629 (74.7) | 0.01 |
| Black or African American | 3786 (10.7) | 1286 (8.4) | 0.08 | 1345 (9.5) | 1259 (8.9) | 0.02 |
| Unknown | 2655 (7.5) | 1830 (11.9) | 0.15 | 1418 (10.0) | 1446 (10.2) | 0.01 |
| Asian | 1345 (3.8) | 461 (3.0) | 0.04 | 461 (3.2) | 455 (3.2) | <0.01 |
| Other | 455 (1.3) | 482 (3.1) | 0.13 | 312 (2.2) | 313 (2.2) | <0.01 |
| Socioeconomic status | ||||||
| Socioeconomic or psychosocial problems | 410 (1.2) | 47 (0.3) | 0.10 | 44 (0.3) | 47 (0.3) | <0.01 |
| Lifestyle | ||||||
| Lifestyle-related exposure (alcohol dependence, smoking, and substance use) | 2211 (6.2) | 246 (1.6) | 0.24 | 221 (1.6) | 246 (1.7) | 0.01 |
| Comorbidities | ||||||
| Hyperuricemia without signs of inflammatory arthritis or tophaceous disease | 184 (0.5) | 10 (0.1) | 0.08 | 10 (0.1) | 10 (0.1) | <0.01 |
| Chronic kidney disease | 2788 (7.9) | 66 (0.4) | 0.38 | 59 (0.4) | 66 (0.5) | 0.01 |
| Other types of rheumatoid arthritis | 856 (2.4) | 157 (1.0) | 0.11 | 149 (1.0) | 157 (1.1) | 0.01 |
| Ankylosing spondylitis | 65 (0.2) | 11 (0.1) | 0.03 | 11 (0.1) | 11 (0.1) | <0.01 |
| Gout | 989 (2.8) | 39 (0.3) | 0.21 | 36 (0.3) | 39 (0.3) | <0.01 |
| Medications | ||||||
| Diclofenac | 3866 (10.9) | 1015 (6.6) | 0.2 | 944 (6.6) | 988 (6.9) | 0.01 |
| Triamcinolone | 3387 (9.5) | 749 (4.9) | 0.2 | 733 (5.2) | 739 (5.2) | <0.01 |
| Aspirin | 3531 (9.9) | 508 (3.3) | 0.3 | 468 (3.3) | 507 (3.6) | 0.02 |
| Celecoxib | 1732 (4.9) | 647 (4.2) | 0.03 | 571 (4.0) | 614 (4.3) | 0.02 |
| Anthropometric characteristics | ||||||
| BMI ⩾ 25 kg/m2 | 24,600 (67.8) | 7402 (50.9) | 0.35 | 7168 (52.7) | 7249 (53.3) | 0.01 |
Bold font represents an SMD of >0.1. Categorical variables are expressed as numbers (%), and continuous variables are presented as mean ± SD.
SD, standard deviation; SMD, standardized mean difference.
Primary analysis: HRs
Figure 2 presents a summary of the patients’ HRs. Regardless of the follow-up duration, the risk of TKA was higher in the CMD group than in the control group, with HRs of 1.696 (95% CI: 1.537–1.871) at 12 months, 1.607 (95% CI: 1.484–1.740) at 2 years, and 1.598 (95% CI: 1.485–1.720) at 36 months of follow-up. Subgroup analysis at 36 months revealed significantly higher risks across sex and age strata, with the highest risk observed among men aged 40–64 years (HR: 1.983, 95% CI: 1.548–2.541; Table 2).
Figure 2.
Hazard ratios for total knee arthroplasty in patients with cardiometabolic disease versus controls across different follow-up durations. Hazard ratios were estimated in propensity score-matched cohorts using baseline covariates.
Table 2.
Subgroup analysis results at 36 months (cases of primary TKA).
| Cardiometabolic disease group, no. of events (%) | Control group, no. of events (%) | HR (95% CI) | |
|---|---|---|---|
| Sex | |||
| Male | 780 (15.5) | 358 (7.1) | 1.67 (1.47–1.89) |
| Female | 1340 (15.5) | 680 (7.9) | 1.55 (1.41–1.70) |
| Age at the index date | |||
| 40–64 years | 617 (11.4) | 310 (5.7) | 1.56 (1.36–1.79) |
| 65–85 years | 1390 (17.3) | 711 (8.9) | 1.52 (1.39–1.66) |
| Age × sex strata | |||
| 40–64 Years, male | 223 (11.9) | 87 (4.6) | 1.98 (1.55–2.54) |
| 40–64 Years, female | 364 (11.4) | 199 (6.2) | 1.45 (1.22–1.72) |
| 65–85 Years, male | 498 (18.3) | 236 (8.7) | 1.60 (1.37–1.87) |
| 65–85 Years, female | 873 (17.6) | 455 (9.2) | 1.50 (1.34–1.68) |
CI, confidence interval; HR, hazard ratio; TKA, total knee arthroplasty.
To further elucidate the effect of individual cardiometabolic components, we conducted stratified analyses (Table 3). Among patients with only one cardiometabolic condition, those with hypertension had the highest risk of TKA (HR: 1.597, 95% CI: 1.437–1.775), followed by those with hyperlipidemia (HR: 1.373, 95% CI: 1.184–1.594). By contrast, those with T2DM alone did not have a significantly high risk of TKA (HR: 0.947, 95% CI: 0.669–1.342). Exploratory analyses of combined cardiometabolic conditions demonstrated higher TKA risk across selected disease combinations, with the strongest association observed among patients with concurrent hypertension and hyperlipidemia (Table 3).
Table 3.
Risk of TKA stratified by individual and combined cardiometabolic disease categories.
| Cardiometabolic disease component | Patients with only this condition | Matched control group | HR (95% CI) |
|---|---|---|---|
| Outcome events/N | Outcome events/N | ||
| Individual cardiometabolic conditions | |||
| T2DM | 66/840 | 61/840 | 0.95 (0.67–1.34) |
| Hypertension | 1011/6756 | 520/6756 | 1.60 (1.44–1.78) |
| Hyperlipidemia | 473/3394 | 275/3394 | 1.37 (1.18–1.59) |
| Combined cardiometabolic conditions | |||
| T2DM and hypertension | 281/2237 | 175/2237 | 1.31 (1.0–1.5) |
| T2DM and hyperlipidemia | 86/552 | 45/552 | 1.58 (1.10–2.27) |
| Hypertension and hyperlipidemia | 1448/7693 | 641/7693 | 1.72 (1.56–1.89) |
| Multiple cardiometabolic conditions | |||
| Simultaneous presence of T2DM, hypertension, and hyperlipidemia | 1019/6341 | 534/6341 | 1.41 (1.27–1.56) |
N, total number of patients in that subgroup; The combined cardiometabolic category represents patients with concurrent presence of all three conditions.
CI, confidence interval; HR, hazard ratio; T2DM, type 2 diabetes mellitus; TKA, total knee arthroplasty.
Survival (time-to-event) analysis
Kaplan–Meier survival analysis revealed that patients with all three CMD (i.e., hypertension, hyperlipidemia, and T2DM) had significantly lower TKA-free survival probabilities compared with the control group (log-rank p < .0001; Figure 3(a)). Analysis of individual cardiometabolic components revealed that patients with hypertension or hyperlipidemia had significantly lower TKA-free survival probabilities compared with the control group (log-rank p < .0001 for both; Figure 3(c) and (d)). Patients with T2DM alone did not have a significantly different TKA-free survival probability relative to the control group (log-rank p = .7603; Figure 3(b)).
Figure 3.
Kaplan–Meier survival curves of total knee arthroplasty-free survival over a 3-year follow-up period. (a) Patients with all three components of cardiometabolic disease versus those without (log-rank test, p < .001). (b) Patients with T2DM versus those without (log-rank test, p = .76). (c) Patients with hypertension versus those without (log-rank test, p < .001). (d) Patients with hyperlipidemia versus those without (log-rank test, p < .001).
T2DM, type 2 diabetes mellitus.
Discussion
In this large multicenter real-world cohort study, we found that patients with CMD had a significantly higher risk of undergoing TKA compared with those without such conditions. This association was consistent across age and sex subgroups, suggesting that cardiometabolic burden may play a meaningful role in shaping clinical trajectories in patients with knee osteoarthritis. Importantly, TKA in this context represents a clinically relevant endpoint reflecting both disease severity and treatment decision-making in routine practice rather than a purely structural measure of disease progression.9,15
Our findings extend previous literature linking individual cardiometabolic conditions to osteoarthritis by demonstrating the cumulative impact of CMD on a hard clinical endpoint. Prior studies have often relied on radiographic grading or symptom-based outcomes, which may not fully capture clinically meaningful disease progression.16 –19 In contrast, the use of TKA as an outcome integrates pain, functional limitation, and patient preference, thereby providing a more pragmatic and clinically relevant assessment of disease burden. 15
Several biological mechanisms may explain the observed association. 20 Cardiometabolic conditions are characterized by systemic low-grade inflammation, metabolic dysregulation, and vascular impairment, all of which may contribute to joint degeneration. Hyperglycemia can impair cartilage metabolism and promote advanced glycation end-product accumulation, while dyslipidemia and hypertension may exacerbate inflammatory responses and compromise subchondral bone perfusion.21 –25 Hypertension may also contribute to altered vascular regulation and reduced subchondral perfusion, whereas hyperlipidemia has been associated with abnormal lipid metabolism and pain sensitization, which may further influence functional limitation and progression to surgical intervention.24,26
These processes may accelerate symptom progression and functional decline, thereby increasing the likelihood of surgical intervention. Notably, isolated T2DM was not significantly associated with increased TKA risk in this study. However, this finding should not be interpreted as evidence that diabetes has no impact on osteoarthritis outcomes. 24 Several factors may explain this observation, including heterogeneity in glycemic control, disease duration, treatment effects, underdiagnosis, and limited statistical power within the isolated T2DM subgroup. Therefore, this result should be interpreted cautiously.
Importantly, all patients in this study received intra-articular HA injections, representing a population with symptomatic knee osteoarthritis undergoing non-surgical treatment. Therefore, our findings should be interpreted as reflecting differences in clinical trajectories among HA-treated patients rather than differences in treatment efficacy.
Because all patients received intra-articular HA, the cohort may represent a relatively similar therapeutic stage of symptomatic osteoarthritis management; however, HA treatment should not be interpreted as indicating comparable radiographic severity or functional status across patients. This distinction is critical, as the present study was not designed to evaluate the effectiveness of HA but rather to examine how underlying cardiometabolic conditions influence downstream outcomes in a treated population.8,10 –12
From a clinical perspective, these findings have potential implications for orthopedic practice. Identification of CMD may help clinicians better stratify patients at higher risk of progressing to TKA despite receiving intra-articular therapy. This may facilitate more individualized management strategies, including earlier optimization of metabolic health, closer monitoring, or consideration of alternative treatment pathways. An additional subgroup finding was the relatively higher HR observed among men aged 40–64 years. This observation may reflect differences in physical activity levels, occupational loading, thresholds for surgical referral, healthcare-seeking behavior, or underlying cardiometabolic profiles across demographic groups. However, because subgroup analyses were exploratory in nature, this finding should be interpreted cautiously and requires confirmation in future studies.
Limitations
Several limitations should be acknowledged. First, the use of EHR data limits the availability of detailed clinical variables, including radiographic grading, MRI findings, pain scores, functional outcomes, and detailed surgical indications for TKA. Therefore, TKA should be interpreted as a clinical endpoint reflecting progression to surgical intervention rather than a direct measure of structural osteoarthritis progression. Second, TKA reflects a treatment decision influenced by multiple factors, including patient preference, access to care, and physician practice patterns, and therefore may not represent a direct measure of structural disease progression.15,27 Third, cardiometabolic conditions were identified based on diagnostic codes without detailed information on disease severity, duration, or treatment control. Finally, although PSM was used to reduce confounding, residual confounding cannot be excluded. Although Cox proportional hazards models account for differential follow-up time, differences in median follow-up duration between groups may reflect variations in healthcare utilization, data availability, or longitudinal engagement and may introduce detection bias. Furthermore, the decision to undergo TKA may be linked to factors beyond structural disease severity, including pain intensity, functional limitation, access to care, patient preference, and physician decision-making.
Conclusion
From a clinical perspective, CMD may help identify patients who are less likely to achieve favorable outcomes with intra-articular therapy and are at higher risk of progressing to TKA. These findings highlight the importance of integrating metabolic health assessment into routine osteoarthritis management and may support more individualized treatment strategies in clinical practice assessment into routine musculoskeletal care may improve patient stratification and guide individualized treatment strategies.
Supplemental Material
Supplemental material, sj-docx-1-tab-10.1177_1759720X261467265 for Cardiometabolic disease and risk of total knee arthroplasty among patients with knee osteoarthritis receiving intra-articular hyaluronic acid: a real-world cohort study by Ya-Chi Chuang, Cheng-Chi Wang, I-Chieh Chen, Jun-Fu Lin, Ching-Heng Lin and Chien-Chih Wang in Therapeutic Advances in Musculoskeletal Disease
Acknowledgments
The authors would like to thank the clinical and research staff and Chen, Zhi-Yu, from the participating institutions for their valuable support in data collection.
Footnotes
ORCID iD: Chien-Chih Wang 
https://orcid.org/0000-0002-5992-4991
Supplemental material: Supplemental material for this article is available online.
Generative AI disclosure: During the preparation of this manuscript, the authors used generative artificial intelligence tools to assist with language editing, grammar refinement, and manuscript organization. All scientific content, study design, data analysis, interpretation of results, and final decisions regarding the manuscript were performed and verified by the authors. The authors take full responsibility for the content of this publication.
Contributor Information
Ya-Chi Chuang, Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan.
Cheng-Chi Wang, Department of Public Health, China Medical University, Taichung, Taiwan; Department of Orthopedics, Taichung Veterans General Hospital, Taichung, Taiwan.
I-Chieh Chen, Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan; Master Program in Precision Health, National Chung Hsing University, Taichung, Taiwan.
Jun-Fu Lin, Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
Ching-Heng Lin, Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan; Institute of Public Health and Community Medicine Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Chien-Chih Wang, Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, 1F, No. 1650 Taiwan Boulevard Sect. 4, Taichung, Taiwan 407219; Department of Physical Medicine and Rehabilitation, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
Declarations
Ethics approval and consent to participate: The present study received approval to use TriNetX data from the Institutional Review Board of Taichung Veterans General Hospital (approval number: CE25855A). As this study used de-identified data, informed consent was not required.
Consent for publication: Not applicable.
Author contributions: Ya-Chi Chuang: Conceptualization; Formal analysis; Investigation; Writing – original draft.
Cheng-Chi Wang: Conceptualization.
I-Chieh Chen: Data curation; Software.
Jun-Fu Lin: Data curation; Formal analysis; Methodology.
Ching-Heng Lin: Investigation; Supervision.
Chien-Chih Wang: Conceptualization; Resources; Supervision; Writing – review & editing.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by Taichung Veterans General Hospital (TCVGH-PNYCU1143008).
The authors declare that there is no conflict of interest.
Availability of data and materials: Data were obtained from the TriNetX platform and are available in accordance with institutional and platform policies.
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Supplementary Materials
Supplemental material, sj-docx-1-tab-10.1177_1759720X261467265 for Cardiometabolic disease and risk of total knee arthroplasty among patients with knee osteoarthritis receiving intra-articular hyaluronic acid: a real-world cohort study by Ya-Chi Chuang, Cheng-Chi Wang, I-Chieh Chen, Jun-Fu Lin, Ching-Heng Lin and Chien-Chih Wang in Therapeutic Advances in Musculoskeletal Disease