{
  "abstract": "Introduction SSc is a heterogeneous autoimmune disease with variable clinical presentations and progression rates. Accurate patient stratification is crucial for optimizing treatment strategies. In this study, we performed a comprehensive lipidomic analysis of SSc sera across clinical subsets to characterise lipid signatures associated with distinct disease manifestations and predictive of disease progression.Material and Methods We compared the global lipidomic profiles measured by LC-MS and LC-MS/MS technologies within serological samples from 33 VEDOSS, 47 diffuse cutaneous SSc (DcSSc), and 22 limited cutaneous SSc (LcSSc) collected through the observational study STRIKE (Stratification for Risk of Progression in Systemic Sclerosis). All lipid features corresponding to each clinical subset of SSc were identified using LipidScreener 1.0.0 (Nova Medical Testing Inc.) and classified according to a three-tier identification method based on MS/MS match score. The univariate association of lipids with disease subtypes was assessed using linear models adjusted by age, sex, and race. The multivariate machine learning models were built to predict modified Rodnan skin score (mRSS) progression and forced vital capacity (FVC) decline rate using glmnet and the performance of the final models was evaluated using nested cross-validation.Results We identified 2785 distinct lipid features with 281 significantly dysregulated in progressing DcSSc, 222 in stable DcSSc, and 205 in LcSSc compared with VEDOSS (nominal p-value <0.05). 68 lipids emerged as most important to differentiate disease subtypes. Among these, biologically relevant and significant changes included phosphatidylserine (PS), a hallmark of apoptotic cell death, and phosphatidylcholine (PC), a major component of lung surfactant. PS O-45:0 was the most significantly elevated lipid in both progressing and stable DcSSc compared to VEDOSS. Consistent with these lipid findings, our previous proteomics analysis confirmed elevated levels of inflammation and tissue damage in established disease. Machine learning-based multivariate analysis identified two serum lipid signatures which showed strong predictive accuracy for mRSS and FVC worsening. A 42-lipid-based signature including diacylglycerol 67:9 and lysophosphatidylglycerol O-30:0 predicted mRSS progression (AUC=0.7521), and a 29-lipid-based signature including trimethyl-homoserine 22:6 predicted FVC decline (AUC=0.7828).Conclusions These results highlight distinct lipidomic signatures associated with different SSc clinical subsets and identified specific lipids correlated with key clinical parameters. While our findings suggest potential utility in risk stratification and personalised treatment approach, further validation in independent cohorts are necessary to confirm the reliability and robustness of these lipid biomarkers, and clinical follow up analysis for identifying those VEDOSS lipid signatures linked with progression to SSc.",
  "authors": [
    {
      "affiliations": [
        "AbbVie Precision Medicine Immunology, South San Francisco, USA"
      ],
      "name": "Sunhwa Kim"
    },
    {
      "affiliations": [
        "AbbVie Statistics, Worcester, USA"
      ],
      "name": "Yingtao Bi"
    },
    {
      "affiliations": [
        "Scleroderma Programme, NIHR Leeds Musculoskeletal Biomedical Research Centre, Chapel Allerton Hospital, LEEDS, UK",
        "Leeds Institute of Rheumatic and Musculoskeletal Medicine, Faculty of Medicine and Health, University of Leeds, LEEDS, UK"
      ],
      "name": "Vishal Kakkar"
    },
    {
      "affiliations": [
        "Scleroderma Programme, NIHR Leeds Musculoskeletal Biomedical Research Centre, Chapel Allerton Hospital, LEEDS, UK",
        "Leeds Institute of Rheumatic and Musculoskeletal Medicine, Faculty of Medicine and Health, University of Leeds, LEEDS, UK"
      ],
      "name": "Rebecca Ross"
    },
    {
      "affiliations": [
        "Scleroderma Programme, NIHR Leeds Musculoskeletal Biomedical Research Centre, Chapel Allerton Hospital, LEEDS, UK",
        "Leeds Institute of Rheumatic and Musculoskeletal Medicine, Faculty of Medicine and Health, University of Leeds, LEEDS, UK"
      ],
      "name": "Francesco Del Galdo"
    }
  ],
  "title": "P.227 Identification of lipidomic biomarkers associated with disease progression in systemic sclerosis",
  "uid": "4201e090-c931-5bbe-9796-ff0c30e8f013"
}