Background/Purpose: Systemic sclerosis (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 characterize lipid signatures associated with distinct disease manifestations and predictive of disease progression.

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 a total of 2785 distinct lipid features. Among these, 281 were significantly dysregulated in progressing DcSSc, 222 in stable DcSSc, and 205 in LcSSc as compared to VEDOSS (nominal p-value < 0.05). Additionally, we identified 68 most important lipids to differentiate disease subtypes. Phosphatidylserine (PS) O-45:0 was the most significantly elevated lipid in both progressing and stable DcSSc compared to VEDOSS. Notably, PS O-45:0 showed a weak positive correlation with baseline mRSS (r=0.2759). Additionally, glycerophospholipids 40:6, lysophosphatidic acid (LPA) O-27:5, and LPA 27:0 demonstrated weak correlations with baseline FVC (r=0.2876, r=-0.2583, and r=-0.2482, respectively). Machine learning-based multivariate analysis identified two serum lipid signatures which showed strong predictive accuracy for mRSS and FVC worsening compared to each biomarker used alone. A 42-lipid-based signature including diacylglycerol 67:9 and lysophosphatidylglycerol O-30:0 predicted mRSS progression with an AUC=0.7521, and a 29-lipid-based signature including trimethyl-homoserine 22:6 predicted FVC decline with an AUC=0.7828.

Conclusion: Lipidomic profiling revealed unique signatures associated with different SSc clinical subsets and identified specific lipids correlated with key clinical parameters. The identification of lipids predictive of disease progression underscored their potential utility in risk stratification and personalized treatment approaches. However, it is important to note that while our results are promising, further validation studies in an independent cohort 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.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/deconvolution-of-the-lipidomic-signature-of-very-early-diagnosis-of-systemic-sclerosis-vedoss-and-established-disease-lipid-biomarker-features-that-predict-disease-progression-in-skin-and-lung/