Submitted:
07 April 2026
Posted:
08 April 2026
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Abstract
Background: Cardiovascular risk (CVR) prediction using SCORE2 may underestimate subclinical atherosclerosis in patients with chronic inflammatory conditions. Identify-ing simple, accessible markers to refine risk stratification remains an unmet need. Objective: To evaluate whether serum urate improves detection of subclinical athero-sclerosis beyond SCORE2 in a high-risk inflammatory cohort. Methods: We conducted a cross-sectional study including 250 patients with psoriatic arthritis fulfilling CASPAR criteria. Vascular assessment comprised carotid and femoral ultrasound and ab-dominal radiography. Atherosclerotic plaque was defined according to Mannheim criteria. The main outcomes were global plaque (≥1 vascular territory) and extended plaque (≥2 territories). Multivariable logistic regression adjusted for SCORE2 categories assessed independent associations. Incremental value was evaluated using decision curve analysis (DCA), category-free net reclassification improvement (cfNRI), and in-tegrated discrimination improvement (IDI). Results: Hyperuricemia prevalence was 21.6%. Patients with hyperuricemia showed a higher prevalence of global plaque (88.9% vs. 62.8%, p< 0.001). After adjustment for SCORE2, serum urate independently predicted global plaque (OR 4.23, 95% CI 1.26–14.2). Notably, 64.3% of patients classi-fied as low–moderate risk already exhibited plaque. In the 50–69-year subgroup, adding serum urate improved reclassification (cfNRI +0.60; IDI +0.031) and was asso-ciated with higher net clinical benefit across decision thresholds. The combined model (SCORE2+HU+cIMT) achieved the highest curves, although with limited incremental gain over HU alone. Conclusions: SCORE2 substantially underestimates subclinical atherosclerosis in this population. Serum urate, an inexpensive and widely available marker, may help refine cardiovascular risk stratification and identify patients who could benefit from further vascular assessment.
Keywords:
hyperuricemia
; urate
; cardiometabolic burden
; cardiovascular risk
; SCORE2
; psoriatic arthritis
Introduction
Psoriatic arthritis (PsA) is a chronic immune-mediated inflammatory disease associated with a well-documented excess of cardiovascular (CV) morbidity and mortality. Beyond traditional risk factors, persistent systemic inflammation, metabolic comorbidities, and endothelial dysfunction converge to accelerate atherosclerosis in this population [1]. Patients with PsA frequently exhibit obesity, hypertension, dyslipidemia, insulin resistance, and features of metabolic syndrome, creating a cardiometabolic milieu that differs substantially from that of the general population [1]. Accurate cardiovascular risk (CVR) stratification in PsA therefore remains a major unmet clinical need.
The SCORE2 algorithm, endorsed by the European Society of Cardiology for estimating 10-year CVR in apparently healthy individuals, represents the current standard in Europe [2]. However, SCORE2 was derived from general population cohorts and does not incorporate PsA as a systemic inflammatory disease or disease-specific metabolic abnormalities. Consequently, its performance in chronic inflammatory conditions, such as PsA, may be suboptimal. Several studies in psoriatic disease and other immune-mediated inflammatory disorders have shown discordance between calculated risk and imaging-detected subclinical atherosclerosis, suggesting systematic underestimation in selected subgroups [3,4,5,6,7,8].
Hyperuricemia (HU) is a frequent metabolic abnormality in psoriatic disease. Elevated serum uric acid levels have been linked to obesity, hypertension, insulin resistance, and dyslipidemia, and experimental data suggest mechanistic connections with oxidative stress, endothelial dysfunction, and activation of inflammatory pathways such as the NLRP3 inflammasome [9,10]. On the other hand, prospective longitudinal evidence from the Spanish CARMA cohort indicates that elevated serum urate levels independently predict cardiovascular events in PsA patients beyond traditional risk scores, reinforcing the clinical relevance of uricemia in CVR stratification [11]. The emerging concept of ‘psout’, a clinical spectrum reflecting the coexistence of HU/gout and PsA, highlights shared pathophysiological mechanisms and clinical phenotypes that may further complicate CVR stratification [9,12].
Imaging-based detection of subclinical atherosclerosis has important prognostic implications. Carotid plaque presence approximately doubles the risk of future CV events in population-based studies, and involvement of multiple vascular territories confers additional risk [13,14]. In this context, risk “enhancers” — factors not included in standard algorithms but capable of refining classification in borderline or intermediate-risk patients — have gained attention. Whether HU could function as such a pragmatic risk enhancer in PsA has not been formally tested.
We hypothesized that HU identifies PsA patients with a greater burden of subclinical atherosclerosis beyond that predicted by SCORE2 and may improve risk stratification, particularly in the clinically challenging 50–69-year subgroup where preventive decisions are most frequently considered. To address this question, we evaluated the association between HU and multisite vascular plaque in a well-characterized PsA cohort and examined its incremental value beyond SCORE2 using multivariable modeling, decision curve analysis, and reclassification metrics.
Materials and Methods
2.1. Study Design and Population
We conducted a cross-sectional observational study including 250 consecutive adult patients fulfilling the CASPAR criteria for PsA, attending a tertiary rheumatology center. Patients were enrolled consecutively during routine clinical visits to minimize selection bias. Exclusion criteria included prior clinically overt cardiovascular disease (for SCORE2 applicability analyses), severe renal impairment (eGFR <30 mL/min/1.73 m²), active infection, or incomplete vascular imaging.
Demographic data, CVR factors, and PsA-related variables were collected at the time of vascular assessment. Traditional CVR factors included hypertension, diabetes mellitus, dyslipidemia, obesity (BMI ≥30 kg/m²), and smoking status (current or former). Disease-related variables comprised psoriasis duration, PsA duration, joint phenotype, current biologic therapy, and disease activity measures. Hyperuricemia was defined as serum uric acid >6.0 mg/dL in women or >7.0 mg/dL in men, consistent with commonly accepted clinical thresholds, or current use of urate-lowering therapy. Serum uric acid was measured in fasting blood samples using standardized enzymatic methods in the hospital central laboratory. The study was approved by the Ethics Committee of the Principality of Asturias (protocol 2020.423), and all participants provided written informed consent.
2.2. Cardiovascular Risk Assessment
The 2021 European Society of Cardiology guidelines [2] recommend using SCORE2 to estimate ten-year CVR. Patients were categorized into SCORE2 risk strata based on age-specific thresholds (<50 years, 50–69 years, ≥70 years). Analyses focusing on incremental value were prespecified in the 50–69-year subgroup, as this group represents the clinically most actionable range for preventive therapy decisions.
2.3. Vascular Imaging
All participants underwent standardized vascular imaging by experienced sonographers blinded to serum urate status. Carotid and femoral arteries were assessed using high-resolution B-mode ultrasound. Atherosclerotic plaque was defined according to Mannheim consensus criteria as a focal structure encroaching into the arterial lumen of at least 0.5 mm or 50% of the surrounding intima–media thickness, or with thickness >1.5 mm measured from media–adventitia interface to intima–lumen interface [15]. Carotid intima–media thickness (cIMT) was measured bilaterally in the common carotid artery and averaged. A threshold of >0.9 mm was considered abnormal. Abdominal radiography was performed to detect calcified aortic plaques. Composite vascular outcomes were defined as global plaque: presence of plaque in at least one vascular territory (carotid, femoral, or aortic); extended plaque: plaque in two or more vascular territories.
2.4. Statistical Analysis
Continuous variables were expressed as mean ± standard deviation (SD) or median with interquartile range (IQR) as appropriate. Categorical variables were expressed as frequencies and percentages. Between-group comparisons (HU vs non-HU) used Student’s t-test or Mann–Whitney U test for continuous variables and chi-square or Fisher’s exact test for categorical variables. To evaluate the independent association between HU and plaque outcomes, multivariable logistic regression models were constructed adjusting for SCORE2 risk category (entered as ordinal variable) to reflect baseline predicted risk. Additional sensitivity models included traditional CVR factors individually to explore robustness. Collinearity was assessed using variance inflation factors (VIF), and model discrimination was evaluated using area under the receiver operating characteristic curve (AUC). Incremental predictive value beyond SCORE2 was assessed using category-free Net Reclassification Improvement (cfNRI): quantifying correct upward reclassification among patients with plaque and correct downward reclassification among those without plaque; Integrated Discrimination Improvement (IDI): measuring change in mean predicted probabilities between events and non-events; Categorical NRI: calculated using predefined 10%, 20%, and 30% risk thresholds. These metrics were selected because high plaque prevalence limits the interpretability of AUC alone in this setting. Clinical utility was evaluated using Decision Curve Analysis (DCA), which estimates net clinical benefit across a range of threshold probabilities representing potential treatment decision cut-offs (10–30%). Models compared included: SCORE2 alone, SCORE2 + HU, SCORE2 + cIMT, SCORE2 + HU + cIMT. Net benefit curves were interpreted relative to “treat-all” and “treat-none” strategies. All analyses were performed using Python 3.11 (pandas, statsmodels, scikit-learn) and R 4.3 (rmda package for DCA). Two-sided p-values <0.05 were considered statistically significant.
Results
3.1. Study Population
A total of 250 patients with PsA were included. Median age was 63.2 years (IQR 52.6–69.6), and 119 (47.6%) were women. Median psoriasis and PsA duration were 24.0 and 11.0 years, respectively. Traditional CVR factors were frequent: hypertension in 35.6%, dyslipidemia in 67.6%, obesity in 22.4%, diabetes in 10.0%, and 50.8% were ever-smokers. Overall, 70.8% were receiving biologic therapy at the time of vascular evaluation. Detailed baseline characteristics and sex-specific differences are shown Table 1.
Hyperuricemia prevalence was 21.6% (n=54), with marked sex disparity (32.8% in men vs 9.2% in women). Hyperuricemia patients had significantly higher BMI (29.2±4.1 vs 27.3±3.8 kg/m², p=0.02), higher systolic blood pressure (127.8±12.0 vs 122.1±11.4 mmHg, p=0.03), and lower HDL cholesterol (48.3±12.5 vs 57.0±13.2 mg/dL, p=0.01) compared with non-HU individuals. Other traditional risk factors did not differ significantly.
3.2. Association Between Hyperuricemia and Plaque Burden
Plaque prevalence was high across the cohort but consistently greater among HU patients. Global plaque was present in 88.9% of HU versus 62.8% of non-HU patients (p<0.001). Similar patterns were observed across vascular territories: carotid plaque (53.7% vs 31.1%, p=0.004), femoral plaque (85.2% vs 56.6%, p<0.001), aortic plaque (55.6% vs 25.0%, p<0.001), and extended plaque (64.8% vs 37.2%, p=0.001).
In multivariable logistic regression adjusting for SCORE2 categories, HU independently predicted global plaque (OR 4.23, 95% CI 1.26–14.2, p=0.02) and aortic plaque (OR 2.60, 95% CI 1.20–5.62, p=0.015). Associations with femoral and extended plaque showed similar directionality but did not reach statistical significance (Table 2).
In a sensitivity analysis adjusting for DAPSA and traditional CVR factors, HU remained independently associated with global plaque (aOR 2.80, 95% CI 1.02–7.64; p<0.05) (Table 3). Although DAPSA was associated with plaque in univariable analysis, its effect attenuated after accounting for cardiometabolic factors, whereas HU retained independent significance.
3.3. Plaque Prevalence Across SCORE2 Categories
Global plaque prevalence increased stepwise across SCORE2 categories (p<0.001): 17.4% in category 0 (low risk), 64.3% in category 1 (low–moderate), 94.1% in category 2 (high), and 100% in category 3 (very high). Pairwise comparisons showed significant differences between categories 0 and 1 and between 1 and 2 (both p<0.001), whereas no difference was observed between categories 2 and 3 (p=0.57), indicating a ceiling effect at higher risk strata (Figure 1).
Notably, 64.3% of patients classified as low–moderate risk already had plaque, highlighting substantial discordance between algorithm-estimated risk and structural vascular burden (Table 4)
3.4. Incremental Value in the 50–69-Year Subgroup
In the prespecified 50–69-year subgroup, HU further stratified plaque risk within each SCORE2 category. Addition of HU to SCORE2 improved reclassification, with a category-free net reclassification improvement (cfNRI) of +0.60 and integrated discrimination improvement (IDI) of +0.031. The improvement was primarily driven by correct downward reclassification of non-events while maintaining acceptable event sensitivity. In contrast, adding cIMT >0.9 mm to SCORE2 yielded minimal incremental discrimination (IDI ≈ 0; cfNRI +0.12), with loss in the event component. Combining HU and cIMT preserved the benefit attributable to HU, without meaningful additional gain (Table 5).
Decision curve analysis demonstrated that models incorporating HU provided higher net clinical benefit than SCORE2 alone or SCORE2 combined with cIMT across clinically relevant threshold probabilities (10–30%). The combined model (SCORE2+HU+cIMT) yielded the highest net benefit curves; however, the incremental gain over SCORE2+HU alone was minimal, indicating that most of the clinical utility was driven by HU rather than by cIMT (Figure 2).
Discussion
This study provides clinically meaningful evidence that HU identifies a subgroup of patients with PsA who harbor a disproportionately high burden of subclinical atherosclerosis beyond that estimated by SCORE2. Our findings contribute to the ongoing debate regarding CVR underestimation in immune-mediated inflammatory diseases and suggest that serum urate may function as a pragmatic risk enhancer in PsA. Three principal observations support this interpretation: (1) HU clustered with adverse metabolic traits and marked structural vascular involvement; (2) HU independently predicted global and aortic plaque after adjustment for SCORE2 categories; and (3) addition of HU improved reclassification and clinical net benefit in the clinically actionable 50–69-year subgroup, whereas cIMT yielded limited incremental value.
Psoriatic arthritis is characterized not only by musculoskeletal inflammation but also by a complex cardiometabolic phenotype in which systemic inflammation, adiposity, insulin resistance, and dyslipidemia interact [1,3,16]. HU appears to occupy a central position within this network [17]. In our cohort, HU prevalence reached 21.6%, consistent with prior reports in psoriatic disease [9,12]. The marked sex disparity (higher in men) mirrors known biological differences in urate handling and hormonal modulation of urate renal excretion [18]. Importantly, HU did not occur in isolation. Patients with elevated uric acid levels exhibited higher body mass index, elevated systolic blood pressure, and lower HDL cholesterol, reinforcing its integration within a pro-atherogenic metabolic profile. This clustering raises the question of whether HU is merely a surrogate of metabolic burden or whether it contributes independently to vascular pathology. Experimental and translational data support biological plausibility for a contributory role. Uric acid has been implicated in endothelial dysfunction through reduced nitric oxide bioavailability, induction of oxidative stress, activation of the renin–angiotensin system, and stimulation of inflammatory pathways including NLRP3 inflammasome activation [9,10]. In PsA, where systemic inflammation is persistent and often accompanied by adipose tissue–derived cytokines, urate-mediated endothelial injury may synergize with immune activation to accelerate vascular remodeling. While our cross-sectional design does not permit causal inference, the persistence of associations after adjustment for SCORE2 suggests that HU captures vascular risk not fully encompassed by traditional variables.
A major insight from this study is the discordance between algorithm-estimated CVR and structural vascular damage detected by imaging. Although plaque prevalence increased stepwise across SCORE2 categories, a striking 64.3% of patients classified as low–moderate risk already had detectable plaque. This finding supports the hypothesis that general-population algorithms underestimate subclinical atherosclerosis in PsA [6]. Such discordance has been reported in other immune-mediated inflammatory diseases where inflammation-related mechanisms accelerate vascular aging independently of classical risk factors [7,8]. Our data extend this concept to PsA in the context of SCORE2, the current European standard.
Interestingly, at higher SCORE2 categories, plaque prevalence approached universality, resulting in a ceiling effect. This phenomenon illustrates a broader methodological challenge: when outcome prevalence is high, discrimination metrics such as AUC lose interpretability. AUC values near 0.50 in this context do not necessarily reflect absence of risk gradient but rather limited separation between events and non-events when most individuals harbor structural disease. For this reason, we emphasized prevalence gradients, reclassification indices, and decision curve analysis, which better capture clinical utility under conditions of high baseline prevalence.
Demonstrating association alone is insufficient to justify clinical integration. Therefore, we examined whether HU adds incremental predictive information beyond SCORE2. In the 50–69-year subgroup — where preventive strategies such as statin initiation are most debated — adding HU improved category-free net reclassification improvement (cfNRI) and integrated discrimination improvement (IDI). The magnitude of cfNRI (+0.60) suggests meaningful directional reclassification, primarily through more accurate identification of individuals without plaque. In contrast, addition of cIMT >0.9 mm yielded minimal incremental discrimination. This is noteworthy because cIMT has historically been considered a subclinical marker of atherosclerosis [13,14]. However, contemporary evidence increasingly suggests that plaque detection, rather than IMT alone, carries stronger prognostic value [13,14]. Our findings align with this evolving paradigm and suggest that in PsA, serum uric acid may provide at least comparable, if not superior, incremental stratification relative to cIMT.
The DCA findings further support the role of HU as a clinically relevant risk enhancer. Although the combined model (SCORE2+HU+cIMT) achieved the highest net benefit across decision thresholds, the additional contribution of cIMT beyond HU was marginal. This pattern suggests that the improvement in clinical utility is primarily attributable to HU, while cIMT adds limited incremental value in this context. Importantly, DCA complements reclassification metrics by incorporating clinical consequences of decision-making, and the consistency between both approaches reinforces the robustness of our findings.
From a practical standpoint, our results suggest that serum uric acid measurement — inexpensive, widely available, and routinely obtained — may assist in refining CVR discussions in PsA. This is particularly relevant in patients falling into borderline or low–moderate SCORE2 strata, where clinicians face uncertainty regarding initiation of preventive therapies. Importantly, we do not propose that HU replace imaging or established algorithms. Rather, HU may serve as an early signal prompting further evaluation. In resource-limited settings where vascular ultrasound is not readily available, uric acid may offer a pragmatic intermediate step in risk refinement.
Therapeutic implications warrant cautious interpretation. Randomized trials of urate-lowering therapy (ULT) in the general population have yielded mixed cardiovascular results [19,20,21]. However, PsA represents a distinct pathophysiological environment in which urate metabolism intersects with systemic inflammation. Whether normalization of uricemia translates into reduced vascular progression in PsA remains unknown and merits prospective investigation [22]. Our findings support considering HU not merely as a biochemical curiosity but as a potential modifier of cardiometabolic phenotype.
Previous imaging-based studies in PsA have demonstrated high rates of carotid plaque and frequent reclassification when imaging is incorporated into risk assessment [6]. However, most focused on structural detection rather than metabolic enhancers. Studies examining uric acid in psoriatic disease have reported associations with metabolic syndrome and vascular markers, but formal evaluation of incremental value beyond contemporary European algorithms using decision-analytic frameworks has been limited. By integrating multisite plaque assessment, formal reclassification metrics, and DCA, our study provides a more comprehensive evaluation of HU as a risk enhancer. The use of composite global and extended plaque outcomes acknowledges that atherosclerosis is systemic rather than confined to a single vascular bed.
This study benefits from a well-characterized PsA cohort, standardized vascular imaging across multiple arterial territories, and application of advanced analytic methods beyond simple association testing. The prespecified focus on the 50–69-year subgroup enhances relevance to real-world clinical decision-making. Notably, the association between HU and plaque remained significant after adjustment for disease activity and classical CVR factors, whereas the effect of DAPSA attenuated in the multivariable model. This pattern indicates that urate level may capture vascular risk dimensions not fully accounted for by inflammatory burden alone, reinforcing its potential role as a metabolic risk enhancer in PsA.
Nevertheless, limitations must be acknowledged. The cross-sectional design precludes determination of temporal sequence between HU and plaque development. Residual confounding cannot be excluded, particularly given complex interactions between inflammation, adiposity, renal function, and medication exposure. HU was measured at a single time point, and cumulative exposure was not assessed. Additionally, plaque is a surrogate marker, albeit one with recognized prognostic significance. External validation in independent cohorts is essential before widespread adoption. Recent data from the CARMA cohort appear to clearly reinforce the role of HU as a CVR enhancer in psoriatic disease [11]. Moreover, development of PsA-specific risk algorithms incorporating inflammatory and metabolic variables may ultimately be required to address systematic underestimation inherent to general-population tools.
Although the CARMA cohort offers some insight [11], additional prospective studies are required to assess whether HU independently predicts cardiovascular events in PsA. Integration of inflammatory biomarkers, imaging findings, and metabolic markers into composite risk models may offer improved discrimination. Mechanistic investigations exploring the interplay between urate metabolism, adipokines, and immune activation could clarify whether HU is a mediator or marker of vascular risk. Ultimately, refinement of CVR assessment in PsA will likely require disease-adapted frameworks. Our findings position HU as a candidate component of such models.
Conclusions
In this cohort of patients with PsA, HU was common and independently associated with multiterritorial subclinical atherosclerosis beyond SCORE2 classification. A substantial proportion of individuals categorized as low–moderate CVR already exhibited structural plaque, underscoring potential underestimation by general-population algorithms. Hyperuricemia improved risk reclassification and net clinical benefit in the clinically actionable 50–69-year subgroup, whereas cIMT provided limited incremental value. These findings support considering serum uric acid as a pragmatic CVR enhancer in PsA. Prospective studies with hard CV endpoints are required to determine whether incorporation of HU into disease-adapted risk models improves outcome prediction and preventive strategies.
Author Contributions
Conceptualization, L.C.C., M.L., R.Q.; Data curation, L.C.C., M.L., E.P., I.B., S.B., P.A.; Formal analysis, L.C.C., M.L., E.P., I.B., S.B., P.A., R.Q.; Investigation, L.C.C., M.L., E.P., I.B., S.B., P.A.; Methodology, L.C.C., M.L., R.Q.; Resources, R.Q.; Supervision, R.Q.; Validation, L.C.C., M.L., E.P., I.B., S.B., P.A., R.Q.; Visualization, L.C.C., M.L., E.P., I.B., S.B., P.A., R.Q.; Writing—original draft, R.Q.; Writing—review and editing, L.C.C., M.L., E.P., I.B., S.B., P.A., R.Q. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was approved by the Clinical Research Ethics Committee of Principality of Asturias, Spain (protocol 2020.423), and was conducted in accordance with the principles contained in the Declaration of Helsinki for studies in humans.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. Due to patient confidentiality and institutional policies, raw individual-level data cannot be made publicly available.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Prevalence of global plaque by SCORE2 category.
Figure 2.
Decision curve analysis in the 50-69 age group.
Table 1.
Disease features of the study population and sex-related differences.
| Disease feature | Total n: 250 |
Men n: 131 |
Women n: 119 |
p-value |
|---|---|---|---|---|
| Age, yrs, median (IQR) | 63.2 (52.6-69.6) | 62.6 (52.6-71.5) | 63.9 (52.7-69.1) | NS |
| Psoriasis duration, yrs, median (IQR) | 24.0 (14.0-39.5) | 23.0 (12.0-38.0) | 25.0 (14.0-41.5) | NS |
| PsA duration, yrs, median (IQR) | 11.0 (7.0-13.0) | 10.0 (7.0-13.5) | 11.0 (8.0-13.0) | NS |
| BMI, median (IQR) | 27.2 (25.0-29.7) | 27.8 (25.5-29.7) | 26.7 (24.0–29.6) | NS |
| Systolic blood pressure, mm/Hg, median (IQR) | 123.0 (117.0-130.0) | 125.0 (120.0-130.0) | 120.0 [115.0-127.0) | 0.001 |
| Cholesterol mg/dl, median (IQR) | 195.0 (168.0-227.5) | 191.0 (164.5-222.5) | 200.0 [170.0-234.5) | NS |
| HDL-cholesterol, mg/dl, median (IQR) | 54.5 (46.0-63.0) | 51.00 (42.0-61.0) | 57.0 (51.0-66.0) | <0.001 |
| HAQ, median (IQR) | 0.75 (0.25-1.12) | 0.62 (0.00-1.06) | 0.87 (0.50-1.12) | 0.006 |
| PSAID12, median (IQR) | 3.73 (1.21-5.24) | 3.50 (1.07-5.40) | 3.95 (2.03-4.93) | NS |
| cIMT, mm, median (IQR) | 0.76 (0.67-0.90) | 0.78 (0.68-0.95) | 0.76 (0.65-0.90) | NS |
| Psoriasis family history, n (%) | 123 (49.2) | 71 (54.2) | 52 (43.7) | NS |
| Oligoarthritis, n (%) | 115 (46.0) | 59 (45.0) | 56 (47.1) | NS |
| Mixed, n (%) | 80 (32.0) | 55 (42.0) | 25 (21.0) | <0.001 |
| Polyarthritis, n (%) | 47 (18.8) | 15 (11.5) | 32 (26.9) | <0.001 |
| Axial, n (%) | 8 (3.2) | 2 (1.5) | 6 (5.0) | NS |
| DIP disease, n (%) | 37 (14.8) | 21 (16.0) | 16 (13.4) | NS |
| Dactylitis, n (%) | 93 (37.2) | 43 (32.8) | 50 (42.0) | NS |
| Enthesitis, n (%) | 83 (33.2) | 53 (40.5) | 30 (25.2) | 0.015 |
| IBD, n (%) | 9 (3.6) | 5 (3.8) | 4 (3.4) | NS |
| Uveitis, n (%) | 8 (3.2) | 7 (5.3) | 1 (0.8) | NS |
| Former smokers, n (%) | 73 (29.2) | 46 (35.1) | 27 (22.7) | NS |
| Current smokers, n (%) | 54 (21.6) | 27 (20.6) | 27 (22.7) | NS |
| CVD, n (%) | 12 (4.8) | 9 (6.9) | 3 (2.5) | NS |
| Erosive disease, n (%) | 55 (22.0) | 30 (22.9) | 25 (21.0) | NS |
| Mild BSA (≤3%), n (%) | 112 (44.8) | 59 (45.0) | 53 (44.5) | NS |
| Moderate-severe BSA (>3%), n (%) | 15 (6.0) | 13(9.9) | 2 (1.7%) | 0.044 |
| Nail disease, n (%) | 123 (49.2) | 65 (49.6) | 58 (48.7) | NS |
| Biologics, n (%) | 177 (70.8) | 89 (67.9) | 88 (73.9) | NS |
| Rem-low DAPSA, n (%) | 172 (68.8) | 101 (77.1) | 71 (59.7) | 0.007 |
| Moderate-high DAPSA, n (%) | 76 (30.4) | 28 (21.4%) | 48 (40.3) | 0.007 |
| MDA, n (%) | 105 (42.0) | 62 (47.3) | 43 (36.1) | NS |
| SCORE2 <50 low, n (%) |
46 (18.4) | 16 (12.2) | 30 (25.2) | <0.001 |
| SCORE2 <50 moderate, n (%) |
15 (6.0) | 15 (11.5) | 0 (0.0) | <0.001 |
| SCORE2 <50 very high, n (%) |
2 (0.8) | 2 (1.5) | 0 (0.0) | NS |
| SCORE2 50-69 low-mod, n (%) |
75 (30.0) | 28 (21.4) | 47 (39.5) | <0.001 |
| SCORE2 50-69 high, n (%) |
47 (18.8) | 36 (27.5) | 11 (9.2) | <0.001 |
| SCORE2 50-69 very high, n (%) |
9 (3.6) | 6 (4.6) | 3 (2.5) | NS |
| SCORE2 OP moderate, n (%) |
8 (3.2) | 3 (2.3) | 5 (4.2) | NS |
| SCORE2 OP high, n (%) |
21 (8.4) | 14 (10.7) | 7 (5.9) | 0.028 |
| SCORE2 OP very high, n (%) |
8 (3.2) | 8 (6.1) | 0 (0.0) | 0.028 |
| SCORE2 DM high, n (%) |
7 (2.8) | 3 (2.3) | 4 (3.4) | NS |
| SCORE2 DM very high, n (%) |
12 (4.8) | 0 (0.0) | 12 (10.1) | NS |
| Depression, n (%) | 46 (18.4) | 15 (11.5) | 31 (26.1) | 0.005 |
| Fibromyalgia, n (%) | 28 (11.2) | 6 (4.6) | 22 (18.5) | 0.001 |
| Carotid plaque, n (%) | 90 (36.0) | 49 (37.4) | 41 (34.5) | NS |
| Femoral plaque, n (%) | 157 (62.8) | 95 (72.5) | 62 (52.1) | 0.001 |
| Aortic calcification, n (%) | 79 (31.6) | 44 (33.6) | 35 (29.4) | NS |
| Obesity, n (%) | 56 (22.4) | 25 (19.1) | 31 (26.1) | NS |
| Hypertension, n (%) | 89 (35.6) | 52 (39.7) | 37 (31.1) | NS |
| Diabetes, n (%) | 25 (10.0) | 9 (6.9) | 16 (13.4) | NS |
| Dyslipidemia, n (%) | 169 (67.6) | 91 (69.5) | 78 (65.5) | NS |
| Hyperuricemia, n (%) | 54 (21.6) | 43 (32.8) | 11 (9.2) | <0.001 |
Yrs: years; IQR: interquartile range; PsA: psoriatic arthritis; BMI: body mass index; HDL: high-density lipoprotein; HAQ: health assessment questionnaire; PsAID: PsA impact of disease; cIMT: carotid intima-media thickness; mm: millimeter; DIP: distal interphalangeal joint; IBD: inflammatory bowel disease; CVD: cardiovascular disease; BSA: body surface area; rem: remission; MDA: minimal disease activity; SCORE2: systematic coronary risk evaluation 2; OP: older-people; DM: diabetes mellitus; n : number; NS: non-significant.
Table 2.
Adjusted logistic regression for hyperuricemia predicting plaque beyond SCORE2.
| Outcome | OR | 95% CI | p value |
|---|---|---|---|
| Global plaque | 4.23 | 1.26–14.2 | 0.02 |
| Carotid plaque | 1.75 | 0.87–3.52 | 0.11 |
| Femoral plaque | 2.11 | 0.93–4.78 | 0.07 |
| Aortic plaque | 2.60 | 1.20–5.62 | 0.015 |
| Extended plaque | 2.05 | 0.97–4.32 | 0.06 |
OR: odds ratios; CI: confidence interval.
Table 3.
Fully adjusted logistic regression model for global plaque.
| Variable | Adjusted OR | 95% CI | p value |
|---|---|---|---|
| Hyperuricemia | 2.80 | 1.02–7.64 | 0.045 |
| DAPSA | 1.40 | 0.93–2.11 | 0.110 |
| Hypertension | 10.82 | 0.98–118.90 | 0.052 |
| Dyslipidemia | 4.09 | 2.10–7.96 | <0.001 |
| Smoking | 1.87 | 0.37–9.36 | 0.448 |
| Obesity (BMI ≥30 kg/m²) | 0.18 | 0.02–1.72 | 0.136 |
| Diabetes mellitus | 6.49 | 2.76–15.29 | <0.001 |
The outcome was the presence of global atherosclerotic plaque (any carotid, femoral, or aortic). Multicollinearity was assessed using variance inflation factors (all VIF <1.2). Internal stability of the fully adjusted model was examined using non-parametric bootstrap resampling (400 replicates), yielding consistent percentile 95% confidence intervals for the principal predictors (hypertension, dyslipidemia, hyperuricemia, and smoking).
Table 4.
Prevalence of plaque outcomes by SCORE2 category in psoriatic arthritis.
| SCORE2 category | Global plaque % (95% CI) | Carotid % (95% CI) | Femoral % (95% CI) | Aortic % (95% CI) | Extended % (95% CI) |
|---|---|---|---|---|---|
| 0 (<50 y, low), n: 46 | 17.4% (9.1–30.7) |
8.7% (3.2–21.0) |
13.0% (6.1–25.6) |
4.3% (1.2–14.5) |
0% (0.0–7.6) |
| 1 (50–69 y, low–moderate), n: 98 | 64.3% (54.4–73.1) |
33.7% (25.3–43.4) |
49.0% (39.6–58.4) |
18.4% (12.0–27.2) |
33.7% (25.3–43.4) |
| 2 (50–69 or ≥70 y, high), n: 68 | 94.1% (85.8–97.7) |
60.3% (47.9–71.5) |
82.4% (71.1–89.8) |
41.2% (29.9–53.7) |
73.5% (61.4–82.9) |
| 3 (very high risk / prior CV event), n: 19 | 100% (83.2–100) |
73.7% (51.0–88.6) |
84.2% (62.4–94.5) |
57.9% (36.3–76.9) |
78.9% (56.7–91.4) |
Values represent the percentage of patients with each plaque outcome in the corresponding SCORE2 category, with Wilson 95% confidence intervals (CIs). Global plaque = any positive territory (carotid, femoral, or aortic); Extended plaque = ≥2 vascular territories. SCORE2 categories were defined according to ESC 2021 guidelines: <50 years (0=low, 1=moderate, 2=high; 3=very high if prior CV event), 50–69 years (1=low–moderate, 2=high, 3=very high), ≥70 years (1=moderate, 2=high, 3=very high).
Table 5.
Reclassification metrics for SCORE2 models in the 50–69-year subgroup.
| Model comparison |
cfNRI (total) | cfNRI (events / non-events) | IDI | Categorical NRI (10–20–30%) |
|---|---|---|---|---|
| SCORE2 vs +cIMT |
+0.115 | −0.485 / +0.600 | −0.0004 | 0.00 |
| SCORE2 vs +HU |
+0.599 | −0.267 / +0.867 | +0.031 | 0.00 |
| SCORE2 vs +HU+cIMT |
+0.421 | −0.446 / +0.867 | +0.030 | 0.00 |
Compared with SCORE2 alone, adding HU markedly improved reclassification (cfNRI +0.60) and increased discrimination (IDI +0.031), mainly by down-classifying non-events while maintaining acceptable sensitivity. In contrast, adding cIMT >0.9 mm alone did not materially improve discrimination (IDI ≈ 0; cfNRI +0.12 with a loss in the event component). Combining HU and cIMT preserved the benefit provided by HU (IDI +0.030; cfNRI +0.42), but the incremental contribution of cIMT was negligible. Category-based NRI using 10–20–30% thresholds was null across models, consistent with the categorical structure of SCORE2 and the high prevalence of plaque in this cohort. Overall, these analyses indicate that HU may act as a more informative enhancer than cIMT when added to SCORE2 in PsA.
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