Evidence map›Paper›PMID 42890461›Full record

ReviewJournal of inflammation research2026

Psoriasis, Cardiovascular Risk, and Artificial Intelligence: Precision Risk Stratification Along the Skin-Vascular Axis-A Narrative Review.

Langhuan Yang, Huimei Zeng, Yan Yang, Jin Gao, Bo Wang, Yongkang Zhang, Mo Chen, Yourui Chen, Shiyu Zhang, Jikun Zheng and 1 more

Abstract readReview
In one paragraph

Review in Journal of inflammation research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Langhuan Yang *Department of Radiology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.ORCID 0009-0008-6622-3705
Huimei Zeng *Department of Dermatology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.
Yan YangDepartment of Dermatology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.
Jin GaoDepartment of Dermatology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.
Bo WangDepartment of Dermatology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.
Yongkang ZhangDepartment of Dermatology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.
Mo ChenDepartment of Dermatology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.
Yourui ChenDepartment of Dermatology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.
Shiyu ZhangDepartment of Dermatology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.
Jikun ZhengDepartment of Radiology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.
Xuesong YangDepartment of Dermatology, The First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Traditional Chinese Medicine), Kunming, Yunnan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Psoriasis functions as a systemic inflammatory disorder associated with accelerated atherosclerosis via the Skin-Vascular Axis, conferring a cardiovascular (CV) risk frequently underestimated by conventional calculators. In this narrative review, we elucidate the mechanistic role of the IL-23/IL-17 pathway in vascular remodeling and highlight a critical clinical challenge: cutaneous-vascular dissociation, wherein profound skin clearance may not reflect internal vascular homeostasis. We evaluate the systemic CV impact of targeted therapies, highlighting the vasculo-protective potential and real-world observational evidence of biologics, the neutral-to-favorable cardiometabolic profile of oral PDE4 inhibitors, and the safety landscape of TYK2/JAK inhibitors. Furthermore, we assess the emerging clinical potential of Artificial Intelligence (AI) in cardio-dermatology, detailing how deep learning radiomics and novel multimodal fusion models incorporating biomarkers like GlycA can identify subclinical, vulnerable phenotypes. However, current evidence is largely derived from general cardiovascular cohorts, underscoring the need for disease-specific validation. Ultimately, we propose that AI-augmented tools should complement clinical judgment within an interdisciplinary framework to precisely stratify risk and potentially guide interventions to improve cardiovascular outcomes in psoriasis.

Indexed as

artificial intelligencebiologicscardiovascular riskmultimodal fusionpsoriasisradiomicsskin–vascular axis

Identifiers

PMID42890461
PMCPMC13673235

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.