Evidence map›Paper›PMID 41909661›Full record

ReviewFrontiers in immunology2026

Super responders to biologic therapy in psoriasis: definitions, predictors, and implications for precision medicine.

Xiya Peng, Zhenhua Wang, Kun Han

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Xiya PengDepartment of Dermatology and Venerology, Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China.
Zhenhua WangDepartment of Dermatology, Weifang People's Hospital, Weifang, China.
Kun HanDepartment of Nuclear Medicine, Weifang People's Hospital, Weifang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biologic therapies have revolutionized psoriasis management, yet treatment responses remain highly heterogeneous. A distinct subgroup of patients, termed super responders (SRs), achieves exceptionally rapid, complete, and sustained responses to biologic therapies. Understanding this phenotype is critical for advancing precision medicine in psoriasis. This review summarizes the latest advances in SRs, focusing on definitions, predictors, and therapeutic implications. Definitions of SRs vary widely across studies, differing in both temporal criteria and efficacy endpoints. Meanwhile, emerging evidence suggests a convergent trend toward multidimensional definitions that combine rapid and complete skin clearance (typically PASI 100 within 3-6 months) and sustained low disease activity during long-term follow-up (often ≥1 year). Convergent predictors of SRs include a lower body mass index (BMI), a favorable metabolic profile, biologic-naïve status and emerging genetic and immunological markers. As a potentially biologically distinct subgroup, SRs present a unique opportunity for treatment optimization, including dosing-interval extension and treatment-free remission in selected patients, offering the potential to sustain efficacy while reducing drug exposure, cost, and patient burden. Key research priorities include establishing consensus definitions, developing validated predictive models, and generating long-term safety data to guide treatment optimization. Integrating the SRs concept into practice may transform psoriasis care from fixed, lifelong regimens to adaptive, evidence-based management grounded in precision medicine.

Indexed as

Biological TherapyPrecision MedicinePsoriasisBiomarkersHumansTreatment Effect HeterogeneityTreatment OutcomeBiomarkersbiologicprecision medicinepredictive factorpsoriasissuper responder

Identifiers

PMID41909661
PMCPMC13021661

What OpenQuestion holds

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Registered trials

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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.