Evidence map›Paper›PMID 41705248›Full record

ArticleFrontiers in immunology2026

Systemic sclerosis-associated pulmonary arterial hypertension and pulmonary fibrosis: exploring biomarker discriminators with advanced omics in a Caucasian cohort.

Nada Mohamed-Ali, Vanessa Acquaah, Maneera Al-Jaber, Rikesh Bhatt, Ibrahim Al-Mohannadi, Konduru Seetharama Sastry, Alka Beotra, Daniel Knight, Christopher Denton, Voon Ong and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article 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. Article
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

15 authors.

Nada Mohamed-Ali *Centre of Metabolism and Inflammation, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.
Vanessa Acquaah *Centre for Rheumatology, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.
Maneera Al-JaberAnti-Doping Lab Qatar, Doha, Qatar.
Rikesh BhattCentre of Metabolism and Inflammation, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.
Ibrahim Al-MohannadiCentre of Metabolism and Inflammation, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.
Konduru Seetharama SastryAnti-Doping Lab Qatar, Doha, Qatar.
Alka BeotraAnti-Doping Lab Qatar, Doha, Qatar.
Daniel KnightDepartment of Cardiac MRI, Royal Free London NHS Foundation Trust, London, United Kingdom.
Christopher DentonCentre for Rheumatology, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.
Voon OngCentre for Rheumatology, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.
Maryam Ali Al-NesfCentre of Metabolism and Inflammation, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.
David AbrahamCentre for Rheumatology, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.
Mohammed Al-MaadheedCentre of Metabolism and Inflammation, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.
Markella PonticosCentre for Rheumatology, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.
Vidya Mohamed-AliCentre of Metabolism and Inflammation, Department of Inflammation and Rare Diseases, Division of Medicine, University College London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Systemic sclerosis (Scleroderma; SSc) is associated with high morbidity and mortality, particularly in patients with pulmonary arterial hypertension (SSc-PAH) and pulmonary fibrosis (SSc-PF). Effective risk stratification and treatment of SSc remains a significant challenge. This proof-of-concept study aimed to identify potential biomarkers capable of distinguishing between three SSc patient groups, defined by no pulmonary involvement (SSc-NLD; n=30), SSc-PAH (n=30), SSc-PF (n=30) compared to healthy controls (HC; n=30). Methods: The study employed Olink-based proteomics using the Cardiovascular II and Immuno-oncology panels, and untargeted metabolomic profiling using Ultra-high Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS), to discover distinct molecular signatures. Results: Proteomics analysis revealed significantly elevated levels of MCP-1, MCP-3, and MCP-4 in SSc-PF compared to all other groups. However, no robust discriminatory cytokines were identified for SSc-PAH or SSc-NLD. Validation of systemic MCP-1 and IL-6 by ELISA supported the proteomics findings. IL-33 levels were found to be reduced in the SSc-PAH group. Increased levels of pro-inflammatory sIL-6R were also identified in SSc-PAH and SSc-PF, indicating shared inflammatory pathways. Protein-protein interaction analyses demonstrated greater network complexity in SSc-PF, with pathway analysis suggesting overlapping biological mechanisms across pulmonary groups. Metabolomics analysis uncovered a unique panel of metabolites altered exclusively in SSc-PAH, including quinolinate, dimethylarginines, hydroxyasparagine and orotidine. In contrast, no metabolites were uniquely discriminatory for SSc-PF or SSc-NLD. Metabolite-metabolite interaction networks revealed nicotinate and nicotinamide metabolism as the more significantly enriched metabolic pathways in SSc-PAH. Correlation analyses identified distinct protein-metabolite profiles across groups. Of note is the loss of IL-33-related metabolic associations specific to SSc-PAH. Discussion: This study identified a candidate biomarker panel comprising three cytokines and ten metabolites capable of differentiating between SSc-PAH, SSc-PF, SSc-NLD, and HC. Biomarkers of SSc-PAH were linked to nicotinate and nicotinamide, as well as tryptophan metabolism, whereas those of SSc-PF reflected immune cell infiltration and fibrosis. These findings highlight the potential biomarker panels for diagnosis and targeted therapeutic development.

Indexed as

BiomarkersPulmonary Arterial HypertensionPulmonary FibrosisScleroderma, SystemicWhite PeopleAdultAgedCohort StudiesCytokinesFemaleHumansMaleMetabolomicsMiddle AgedMultiomicsProteomicsBiomarkersCytokinesbiomarkersmetabolomicsproteomicspulmonary fibrosispulmonary hypertensionsystemic sclerosis

Identifiers

PMID41705248
PMCPMC12907414

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.