Psoriasis, Cardiovascular Risk, and Artificial Intelligence: Bridging | JIR | Dove Medical Press

Introduction

Psoriasis has definitively transitioned from a skin-limited disease to a systemic inflammatory disorder. It functions as an “inflammatory pump” driving multi-organ comorbidities via the Skin–Vascular Axis—a crucial immunovascular conduit translating persistent cutaneous inflammation into systemic vascular injury.^1–3 This paradigm shift has catalyzed the field of cardio-dermatology, focusing on the critical interface between immune-mediated skin disease and cardiovascular (CV) health.^1,2 Mechanistically, the IL-23/IL-17 pathway, alongside elevations in TNF-α and IL-6, sustains moderate-to-severe psoriasis.^4,5 These circulating mediators are hypothesized to drive endothelial dysfunction and vascular smooth muscle cell osteogenic transdifferentiation; establishing a direct, isolated causal link in human psoriatic disease remains complex due to shared confounders such as obesity, metabolic syndrome, and smoking.^4,6 Clinically, this cumulative burden translates to a 25–50% higher risk of major adverse cardiovascular events (MACE) compared to the general population.^7,8 However, a critical “risk-prediction gap” hampers preventive care. Conventional tools, such as SCORE2 and the ASCVD Risk Estimator, are calibrated for general populations and consistently fail to capture the prolonged, compounding impact of chronic systemic inflammation.^6,9 Consequently, CV risk is systematically underestimated in psoriatic patients, particularly among the young or those with highly inflammatory endotypes.^3,9 To bridge this gap, multimodal Artificial Intelligence (AI) has emerged as a transformative strategy. By integrating clinical phenotyping, digital skin biomarkers, and advanced imaging radiomics (eg, automated coronary plaque assessment from CCTA), AI has the potential to facilitate precise risk stratification.^10,11 Accordingly, this review delineates the molecular drivers of the skin-vascular axis, evaluates the vasculo-protective potential of biologics and the safety landscape of small-molecule inhibitors, and explores how AI-augmented integration will redefine future CV risk management in psoriasis. A recent SWOT analysis of Artificial Intelligence in clinical medicine further supports this emerging role, highlighting its considerable potential to enhance diagnostic accuracy, risk prediction, and personalized clinical decision-making.¹² Accordingly, this review delineates the molecular drivers of the skin-vascular axis, evaluates the vasculo-protective potential of systemic therapies, and explores how AI-augmented integration will redefine future CV risk management in psoriasis.

Methods

We conducted a structured narrative review with a defined search strategy. PubMed, Embase, and IEEE Xplore were searched from inception through June 2025 using the following combined terms: (“psoriasis” OR “psoriatic”) AND (“cardiovascular” OR “atherosclerosis” OR “vascular”) AND (“artificial intelligence” OR “machine learning” OR “deep learning” OR “radiomics”) AND (“IL-17” OR “IL-23” OR “biologic therapy”). Additional searches targeted specific interventions (guselkumab, bimekizumab, deucravacitinib, JAK inhibitors) combined with cardiovascular outcomes. We prioritized peer-reviewed Phase III clinical trials, prospective cohort studies, and externally validated AI model development studies. Reference lists of included articles were hand-searched for additional relevant publications. Studies were categorized as providing either (a) direct evidence in psoriatic populations or (b) validated cardiovascular AI methodologies with translational relevance to inflammation-driven atherosclerosis. The detailed study selection flow is summarized in Supplementary Figure 1.

Pathophysiology of the Skin-Vascular Axis: From Inflammation to Vascular Remodeling

Molecular Mechanisms of the Skin-Vascular Axis

The Skin-Vascular Axis describes a continuum from acute systemic inflammation to chronic structural vascular remodeling (Figure 1). It begins with systemic spillover of pro-inflammatory cytokines—primarily TNF-α, IL-17A, and IL-23—from psoriatic plaques into the circulation (Figure 1A and B). As summarized in Table 1, this systemic inflammatory burden affects both the vascular endothelium and the myocardium. In the vasculature, these cytokines promote oxidative stress through NADPH oxidase upregulation, increasing reactive oxygen species (ROS) and reducing nitric oxide (NO) bioavailability.^13,14 These early changes are consistent with endothelial dysfunction and can be accompanied by subclinical myocardial deformation. Recent evidence suggests that speckle-tracking echocardiographic analysis of longitudinal strain provides incremental prognostic information beyond SCORE2 in patients with psoriasis.⁹ Inflammation also involves perivascular adipose tissue (PVAT), with adipocyte remodeling and localized vascular inflammation (Figure 1C). PVAT imaging biomarkers, particularly the fat attenuation index (FAI) derived from coronary CT angiography, enable quantification of this perivascular inflammatory state.¹⁵ As noted in Table 1, these metrics may capture active coronary inflammation before the appearance of calcified plaques.

Table 1 Molecular Drivers and AI-Based Quantification of the Skin-Vascular Axis

AI-Based Quantification and Vulnerable Phenotypes

With sustained inflammatory signaling, the chronic stage is characterized by vascular smooth muscle cell phenotypic switching toward an osteogenic program. This process is linked to the IL-17/RANKL axis and increased expression of transcriptional regulators such as Runx2,^16,17 contributing to vascular calcification. Crucially, this remodeling involves a “defense failure” characterized by the downregulation of osteoprotegerin (OPG)—a natural inhibitor of calcification.¹⁷ The resulting imbalance in the RANKL/OPG axis accelerates the formation of spotty, unstable calcium deposits that are prone to rupture.^16,17

Traditional imaging captures the structural aftermath but may miss this active molecular destabilization. In general cardiovascular populations, deep learning-based tools have demonstrated the capacity to detect such subclinical vulnerability. Zeleznik et al developed deep convolutional neural networks that automatically quantify coronary artery calcium from routine, non-gated chest CT scans, achieving performance comparable to expert manual scoring and independently predicting cardiovascular events.¹⁰ Lin et al further demonstrated that deep learning-enabled coronary CT angiography (CCTA) can automatically quantify plaque composition and stenosis severity, with AI-derived features predicting major adverse cardiovascular events in a multicentre international cohort.¹¹ Separately, the CRISP CT study established that the perivascular fat attenuation index (FAI)—a CT-derived biomarker of coronary inflammation—independently predicts cardiac mortality beyond traditional risk factors.¹²

Critically, Elnabawi et al provided the first direct evidence linking these imaging biomarkers to psoriasis treatment, demonstrating that one year of biologic therapy significantly reduced coronary inflammation as measured by FAI.¹⁵ This finding establishes FAI as a quantifiable surrogate endpoint for vascular benefit in psoriatic disease. However, no study has yet validated integrated multimodal AI models—combining radiomics with clinical and proteomic data—specifically in psoriasis cohorts. We propose that such integration represents a critical translational opportunity: fusing AI-derived anatomical features (eg, automated plaque characterization, FAI) with circulating biomarkers of systemic inflammation (eg, GlycA¹⁹) could enable identification of high-risk “vulnerable” phenotypes that exceed the detection limits of any single modality alone (Figure 1D).

Figure 1 The Skin-Vascular Axis in Psoriasis: From Molecular Pathogenesis to AI-Augmented Clinical Management. (A) (Cutaneous Origin): The psoriatic plaque acts as a systemic “inflammatory pump”, actively releasing key pro-inflammatory cytokines, primarily IL-23 and IL-17. (B) (Systemic Conduit): These cytokines enter the systemic circulation, establishing the crucial immunovascular link between localized skin inflammation and remote vascular beds. (C) (Cardiovascular Target): Chronic inflammatory exposure drives endothelial dysfunction, perivascular adipose tissue (PVAT) remodeling, and structural vascular remodeling via osteogenic transdifferentiation (Runx2+) of vascular smooth muscle cells. (D) (The AI-Augmented Lens): Multimodal AI integrates advanced imaging radiomics (eg, FAI from CCTA) and novel circulating biomarkers (eg, GlycA) to unmask subclinical vascular vulnerability often missed by traditional risk scores.15 (E) (Dermatologist’s Actionable Insight): AI-derived personalized cardiovascular risk profiling empowers the dermatologist to optimize systemic therapy (eg, prioritizing biologic agents with potential vascular benefit) and initiate timely cardio-metabolic referrals, effectively closing the clinical loop.

Pharmacological Interventions and AI-Augmented Systemic Monitoring

Biologic and targeted therapies have profoundly shifted the psoriasis treatment paradigm from mere cutaneous clearance toward comprehensive systemic inflammation control. By targeting key nodes of the Skin-Vascular Axis, these agents attenuate the circulating mediators driving endothelial dysfunction. However, assessing their true cardiovascular (CV) benefit requires moving beyond traditional clinical observation.

The Cardiovascular Protective Potential of Biologics

Targeting the IL-23/IL-17 pathway not only resolves psoriatic plaques but also disrupts the inflammatory cascade responsible for vascular remodeling. IL-23 inhibitors (eg, guselkumab) disrupt the upstream maintenance of the Th17 program. Evidence from trials like APEX and DISCOVER-2 demonstrates robust inhibition of structural damage progression.^20,21 While primarily assessed in musculoskeletal endpoints, this sustained suppression of tissue remodeling pathways is mechanistically relevant to vascular homeostasis. Similarly, dual neutralization of IL-17A and IL-17F with bimekizumab (BE OPTIMAL and BE COMPLETE trials) maintains long-term systemic disease control and prevents pathological mineralization—pathways directly implicated in vascular calcification biology.^22–25 However, as these trials evaluated joint and cutaneous endpoints rather than vascular outcomes, translating musculoskeletal structural preservation to vascular calcification prevention remains theoretical.

Importantly, Elnabawi et al provided direct imaging evidence that biologic therapy reduces coronary inflammation in psoriasis, as measured by perivascular FAI on CCTA.¹⁵ This landmark finding suggests that effective biologic treatment may confer vascular benefit beyond skin clearance. However, whether this translates to a reduction in hard cardiovascular endpoints (MACE) remains unconfirmed in prospective trials. Although prospective MACE trials are lacking, extensive registry and observational data show that TNF, IL-17, and IL-23 inhibitors are associated with neutral-to-reduced MACE risk compared to conventional therapies, offering real-world support for their systemic vascular safety.

For dermatologists, the critical unmet need is a tool to identify which patients harbor residual vascular inflammation despite adequate skin control—and thus who would benefit most from intensified systemic therapy or cardio-metabolic co-intervention. AI-augmented imaging biomarkers, such as serial FAI monitoring, represent a promising strategy to address this gap.

Cardiovascular Safety Profile of Small-Molecule Inhibitors

When prescribing oral targeted therapies, CV safety remains a paramount clinical consideration. While safety signals from rheumatoid arthritis trials (eg, ORAL Surveillance) prompted regulatory warnings for JAK inhibitors,²⁶ comparative evidence in psoriatic disease suggests that rates of MACE and venous thromboembolism (VTE) with agents like tofacitinib and upadacitinib are low and broadly comparable to TNF inhibitors.^27,28 Furthermore, deucravacitinib, a selective allosteric TYK2 inhibitor, demonstrates a distinct mechanism from JAK1/2/3 inhibition.²⁹ Across Phase 3 and long-term extension trials in plaque psoriasis, deucravacitinib has shown stable adjudicated MACE rates without consistent adverse lipid signals.^30–32 Nevertheless, individualized baseline risk appraisal—factoring in age, smoking status, and metabolic syndrome—remains essential before initiating these therapies. The European Medicines Agency (EMA) has maintained a class-wide precaution for JAK inhibitors in patients aged ≥65 years or with established cardiovascular risk factors, reinforcing the need for individualized risk-benefit assessment in European clinical practice. Beyond JAK/TYK2 inhibitors, PDE4 inhibitors such as apremilast offer a favorable cardiometabolic profile. Apremilast frequently induces modest weight loss and exhibits neutral-to-beneficial effects on vascular inflammation, making it a suitable oral option for patients with cardiovascular risk.

Overcoming “Cutaneous-Vascular Dissociation”: An AI-Driven Framework

A crucial caveat in cardio-dermatology is the phenomenon of cutaneous-vascular dissociation: patients may achieve PASI 100 (complete skin clearance) while subclinical vascular inflammation persists.³³ Traditional surrogate endpoints, such as the aortic target-to-background ratio on 18F-FDG PET/CT, are semi-quantitative and may not fully capture complex structural vascular remodeling. To bridge this gap, advances in cardiovascular AI offer tools with direct translational relevance. In general populations, three validated approaches have demonstrated superiority over conventional risk assessment:

First, deep learning-based coronary artery calcium (CAC) quantification from non-gated chest CT enables fully automated, opportunistic cardiovascular screening without additional radiation or cost.¹⁰ For psoriasis patients undergoing chest imaging for other indications, this approach could provide incidental cardiovascular risk data.

Second, the perivascular fat attenuation index (FAI), derived from standard CCTA, detects active coronary inflammation by quantifying changes in perivascular adipose tissue composition. The CRISP CT study demonstrated that FAI independently predicts cardiac mortality with a hazard ratio exceeding traditional risk factors.¹² Elnabawi et al subsequently showed that FAI is responsive to biologic therapy in psoriasis,¹⁵ establishing it as a dynamic biomarker of treatment-related vascular benefit.

Third, machine learning models integrating multiple data streams—clinical variables, imaging features, and biomarkers—have shown improved discrimination for cardiovascular events compared to traditional linear calculators in large cohorts.^11,18 The incorporation of GlycA, a nuclear magnetic resonance-derived composite biomarker of systemic inflammation validated in psoriasis,^19,34 represents a particularly promising input for such models given its association with both disease activity and subclinical atherosclerosis.

We propose that integrating these validated components into a unified multimodal framework—specifically designed for and validated in psoriasis populations—represents the next critical step in cardio-dermatology (Figure 2). Such a framework would enable clinicians to evaluate treatment efficacy “beyond the skin”, guiding decisions on whether to adjust systemic therapies or initiate early cardio-metabolic interventions.

Figure 2 AI-Powered Multimodal Integration Framework for Cardiovascular Risk Stratification in Psoriasis. Schematic of the AI-driven pipeline for CV risk assessment. (A) Multimodal Data Acquisition: (1) Imaging Data, encompassing CCTA (for FAI and plaque characterization), non-gated CT (for CAC scoring), and 18F-FDG PET/CT (for vascular inflammation); (2) Molecular Biomarkers (GlycA); (3) Clinical Metadata (PASI, metabolic status). (B) Feature Extraction: Radiomics and Biochemical tracks. (C) Multimodal Fusion Layer: Integrates multi-source data to capture non-linear interactions. (D) Clinical Output: Generates a proposed Psoriatic Cardiovascular Risk Index (PCR-i) to predict 10-year MACE.

Several methodological considerations are essential for clinical translation. Predictive models must be trained on longitudinal cardiovascular outcomes (eg, 10-year MACE) rather than cutaneous metrics, ensuring they capture vascular trajectories independently of skin-level remission. Moreover, extrapolating AI models validated in general populations to psoriasis requires caution, as disease-specific systemic inflammation and immunomodulatory therapies can shift baseline biomarker distributions (eg, FAI, GlycA), necessitating cohort-specific model calibration. Algorithm transparency must also be ensured through explainability tools (eg, SHAP values, saliency maps) to mitigate “black-box” limitations. Furthermore, the clinical translation of radiomic features (eg, CCTA-derived FAI) is constrained by imaging data heterogeneity caused by variations in scanner hardware, tube voltage, and reconstruction algorithms; implementing standardized imaging acquisition and data harmonization across clinical centers represents a mandatory future direction.^35,36 Ultimately, prospective validation in psoriasis-specific cohorts—ideally embedded within ongoing biologic therapy trials—remains essential prior to clinical adoption.

Beyond Traditional Pathways: Integrated Risk Assessment Strategies

Limitations of Traditional Risk Calculators

Traditional cardiovascular risk calculators, including QRISK3 and SCORE2, were developed in general populations and may not fully reflect inflammation-related risk in psoriatic disease.^37–39 External validation studies, such as the UK Biobank analysis by Hughes et al,³⁷ evaluated conventional cardiovascular risk tools (QRISK3, SCORE2, ASCVD) in psoriatic disease, revealing variable performance and consistent underestimation of risk in younger patients and those with high inflammatory burden. Cohort studies also report gaps in cardiovascular risk assessment and prevention in routine care for psoriatic arthritis,⁴⁰ and psoriasis-specific data suggest that performance may differ across calculators and cohorts.⁴¹ Conventional risk scores largely depend on linear models that evaluate variables independently, often missing the interactive effects of chronic inflammation. Multimodal machine learning frameworks offer a methodological advantage by jointly processing diverse inputs, such as coronary radiomics and GlycA. Through late-fusion techniques, these models can evaluate the non-linear synergy between structural vascular changes and systemic inflammatory burden, identifying high-risk profiles that standard linear tools typically miss. This is particularly relevant in the European context, where SCORE2 is the recommended first-line tool for cardiovascular risk estimation,⁴² yet lacks any adjustment for chronic inflammatory disease burden. The clinical trial evidence for targeted therapies is summarized in Table 2(Part 1), while proposed AI-enhanced monitoring strategies are outlined in Table 2(Part 2).

Table 2 Systemic Efficacy of Biologic Agents and AI-Enhanced Risk Monitoring

Clinical Guidelines and Integrated Management

An integrated assessment that combines clinical risk factors with evidence of subclinical atherosclerosis, such as coronary artery calcium scoring or carotid ultrasound, and selected biomarkers may better support individualized decision making. GlycA has been associated with systemic inflammatory burden and subclinical atherosclerosis in psoriasis and psoriatic arthritis.^19,42 Both European (EuroGuiDerm) and American (AAD-NPF) clinical guidelines emphasize systematic assessment and management of cardiometabolic comorbidities as part of psoriasis care.^43,44 For patients with obesity or type 2 diabetes, early observational signals suggest potential cardiovascular benefit with GLP-1 receptor agonists in psoriasis, but prospective confirmation is needed.⁴⁵

Conclusions

The management of psoriasis must evolve from prioritizing isolated cutaneous clearance to addressing systemic cardiovascular risk intrinsic to the Skin-Vascular Axis. Traditional risk calculators often underestimate inflammation-driven risk, and cutaneous-vascular dissociation confirms that skin clearance alone is an insufficient marker of internal vascular homeostasis. AI tools—such as deep learning CAC quantification, plaque characterization, and perivascular FAI—offer promising diagnostic precision; however, clinicians must recognize that most AI applications are currently extrapolated from general cardiovascular populations and lack prospective validation in psoriasis cohorts. Importantly, Artificial Intelligence should complement, rather than replace, clinical judgment and guideline-directed risk assessment. Future progress requires disease-specific prospective AI validation, multi-center imaging standardization, and interdisciplinary collaboration to safely translate these emerging technologies into improved patient outcomes.

Data Sharing Statement

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Author Contributions

Langhuan Yang and Huimei Zeng: Conceptualization, Methodology, Investigation, Writing – Original Draft, Writing – Review & Editing.

Yan Yang, Jin Gao, Bo Wang, and Yongkang Zhang: Formal Analysis, Validation, Writing – Review & Editing.

Mo Chen, Yourui Chen, and Shiyu Zhang: Data Curation, Validation, Writing – Review & Editing.

Jikun Zheng and Xuesong Yang: Conceptualization, Supervision, Resources, Funding Acquisition, Writing – Review & Editing.

All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Basic Research Program of the Department of Science and Technology of Yunnan Province (No. 202501AS070158 and 202401AZ070001-014); the Engineering Research Center of Traditional Chinese Medicine for the Treatment of Skin Diseases of the Department of Education of Yunnan Province (No. 2022(J)10-A05-03); the Key Joint Special Project of Yunnan Province (No. 202101AZ070001-168); and the Joint Special Project of Traditional Chinese Medicine (Basic Research Program) of the Department of Science and Technology of Yunnan Province (No. 202101A2070001-022).

Disclosure

The authors declare no competing interests.

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