Plasma Proteomic Profiling Reveals Distinct Signatures of Chest CT Phenotypes in Sarcoidosis

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Leach, Brett M. Elicker, Laura L. Koth This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7358697/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sarcoidosis is a granulomatous disease of unknown cause with a highly variable clinical course. The inability to predict progressive inflammation, fibrosis, or both underscores the limited understanding of the underlying molecular mechanisms. This study aimed to identify novel protein signatures associated with distinct pulmonary phenotypes of sarcoidosis, including progressive inflammation, progressive fibrosis, and disease resolution. We performed SomaScan 11K Assay to measure more than 10,000 unique human plasma proteins and compared protein expression between chest CT-defined phenotypes using principal component analysis, differential expression, correlation analysis, and gene set enrichment analysis. We identified distinct proteomic signatures that differentiated progressive fibrosis from progressive nodular inflammation in sarcoidosis. Enrichment and differential expression analyses revealed that progressive fibrosis was associated with epithelial–mesenchymal transition pathways, while progressive nodular disease was linked to mTORC1 and MYC signaling, as well as metabolic activation. Additionally, expression of 44 proteins correlated moderately to strongly with thoracic lymph node enlargement, suggesting immune activity in enlarged lymph node may be reflected in the circulating proteomic signals. This study leverages a unique longitudinal imaging approach to define extreme pulmonary phenotypes based on serial chest CT scoring, enabling the discovery of proteomic signals linked to distinct trajectories of sarcoidosis progression. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Genetics Biological sciences/Immunology Health sciences/Medical research Sarcoidosis Proteomics X-Rays Radiology Lung Diseases Interstitial Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Sarcoidosis is a multi-organ, immune-mediated granulomatous disease of unknown origin that most commonly affects the lungs and exhibits a highly variable clinical course—from spontaneous resolution to chronic inflammation, fibrosis or both. 1 – 6 This heterogeneity complicates clinical management and trial design, as no robust, noninvasive biomarkers currently exist to distinguish these clinical trajectories. Improved patient stratification could improve clinical resource utilization, guide treatment decisions, and reduce clinical trial size. Proteomic profiling is a powerful tool for uncovering circulating biomarkers that reflect disease biology, offering a real-time readout of systemic immune activity, signaling pathways, and post-translational modifications that may be missed by genomic or transcriptomic approaches. 7 – 11 In this study, we applied high-throughput plasma proteomics in pulmonary sarcoidosis to identify biomarkers associated with distinct CT-defined phenotypes. We anchored biomarker discovery to longitudinal CT scans scored for fibrosis, nodular inflammation, and lymphadenopathy. Blood samples were selected from time points reflecting each subject’s most extreme CT phenotype—progressive fibrosis, progressive inflammation, or radiographic resolution. This integrative approach enabled us to link proteomic signatures to meaningful disease trajectories. Our analysis identified distinct protein profiles associated with progressive fibrosis and inflammation. While sampling occurred concurrently with imaging, limiting predictive conclusions, these signatures provide insight into sarcoidosis immunopathology and may serve as prognostic markers pending future validation. Together, these findings underscore the value of linking proteomics with radiographic phenotypes to advance biomarker discovery in sarcoidosis. Methods Cohort Description and CT analysis : The longitudinal cohort study design and procedures have been previously described. 12 Participants with pulmonary sarcoidosis based on ATS/ERS/WASOG diagnostic criteria 3 and healthy controls were recruited from the San Francisco Bay Area. Enrollment occurred between January 1, 2010, to December 31, 2021, with follow-up study visits at 6-to-12-month intervals. All experiments were performed in accordance with relevant guidelines and regulations (including the Declaration of Helsinki) and were approved by the UCSF Institutional Review Board; all participants provided written informed consent. Longitudinal chest CT scans were visually scored by an expert thoracic radiologist who was blinded to clinical data. The mean extent of reticular abnormality and nodular disease was scored to the nearest 5% in three zones in each lung as previously described 13 to produce semiquantitative scores. The severity of traction bronchiectasis was scored as previously described. 14 In brief, traction bronchiectasis severity within each of the five lobes was graded by comparing the diameter of the airway with that of the adjacent pulmonary artery using a 4-point scale (i.e., 0–3), and scores from the five lobes were summed to yield an overall score (range, 0 to 15): score 0, no traction bronchiectasis; score 1, traction bronchiectasis present but mild (comparable diameter with the artery); score 2, moderately severe traction bronchiectasis (up to twice the diameter of the artery); and score 3, severe traction bronchiectasis (more than twice the diameter of the artery). 14 The largest lymph node for each station (paratracheal, subcarinal and AP window) was measured using the short-axis diameter in millimeters. Size of ≤ 10 mm was considered normal. Case Control Study Design of CT Features Longitudinal chest CT scan features were used to create three phenotype groupings based on visual scoring and consisted of sarcoidosis participants with evidence of (1) progressive CT fibrosis over time defined by worsening of extent of reticulation or severity of traction bronchiectasis or any combination of these features; the presence of nodular inflammation was allowed; (2) progressive nodular disease over time with no or very little evidence of fibrosis; (3) remission of all or most of the CT features of disease present at baseline. Healthy controls did not undergo CT scan imaging. The analyzed plasma samples were from a calendar date as close as possible to the chest CT scan that was used to define the CT phenotype groupings. Somalogics Proteomics Assay SomaLogic Inc. applied the SomaScan 11K Assay measuring more than 10,000 unique human proteins to previously unthawed human plasma samples that were stored at -30°C. The SOMAmer-based assay allows for quantitative transformation of the protein epitope amount into a specific SOMAmer-based DNA signal. Fluorescent, single-stranded DNA-based SOMAmer reagents interact and bind to target molecules in the plasma, forming SOMAmer-protein complexes. Unbound proteins are removed, followed by biotinylation of SOMAmer-protein complexes. Addition of polyanionic competitors and wash steps results in specific retention of target proteins, which are then quantified using DNA-hybridization microarrays. Readouts are reported in relative fluorescent units (RFU), which are directly proportional to the amount of target protein epitope in the plasma sample. Plasma RFU data were processed using SomaLogic’s standard multi-step normalization pipeline, which applies hybridization control normalization, intraplate median normalization, plate scaling and calibration, and adaptive normalization to a reference distribution to remove both within-plate and between-run technical variation. Statistical Analyses Continuous variables were summarized using means and standard deviations, and categorical variables as frequencies and percentages. Group comparisons were performed using one-way ANOVA for continuous variables and Pearson’s chi-square test for categorical variables. Principal component analysis (PCA) was performed using the R stats package. The R rstatix package was used to perform Kruskal-Wallis test followed by Dunn’s test on PCA data. The SomaData IO R package was used to calculate the limit of detection per SOMAmer analyte. Differential expression analysis between the progressive fibrosis and nodular CT phenotypes was performed, and false discovery rate (FDR) was controlled using the Benjamini-Hochberg procedure. An FDR of < 0.05 or p-value < 0.05 was considered statistically significant as appropriate for the analysis. Gene Set Enrichment Analysis (GSEA) was conducted using the fgsea R package with the Hallmark gene set collection 15 from MSigDB. Enrichment statistics were calculated using the pre-ranked GSEA method, with normalized enrichment scores and FDR q-values provided for each pathway. Statistical and graphical analyses were carried out using R software version 4.5.0 (4-11-2025) and STATA v18 (StataCorp, College Station, TX, USA). Results Cohort / Sample Description Table 1 summarizes the clinical characteristics of the cohort stratified by CT phenotype group. There were no statistically significant differences across the CT phenotype groups with respect to age, sex, race, tobacco history, body mass index or use of immunosuppression at the blood plasma draw date. There were no statistically significant differences in the levels of serum biomarkers such as C-reactive protein, sedimentation rate, or angiotensin converting enzyme, but there was a difference in the absolute lymphocyte counts with the fibrosis group having the lowest count compared to the other groups (Table 2 ). There were no statistically significant differences in the total number of involved organs with sarcoidosis (Table 3 ). The fibrosis group had the lowest mean values for lung function parameters (Table 3 ). Table 1 Clinical Characteristics at Date of Plasma Sampling* CT Phenotype Healthy Control Resolved CT Features Progressive Nodular Progressive Fibrosis P-Value N 5 5 15 15 Age (yr) 52 (4.4) 47 (8.9) 49 (12.1) 53 (10.1) 0.583 Sex (count, %) Male 2 (40%) 0 (0%) 10 (67%) 6 (40%) 0.068 Female 3 (60%) 5 (100%) 5 (33%) 9 (60%) Race (count, %) White 3 (60%) 3 (60%) 12 (80%) 10 (67%) 0.625 Black 2 (40%) 1 (20%) 1 (7%) 2 (13%) Other 0 (0%) 1 (20%) 2 (13%) 3 (20%) Latino (count, %) Yes 0 (0%) 0 (0%) 3 (20%) 1 (7%) 0.397 Smoking History (count, %) Yes 2 (40%) 1 (20%) 7 (47%) 8 (53%) 0.626 Pack-year Smoking History 0.2 (0.3) 3 (6.7) 3 (6.5) 3 (4.2) 0.792 BMI 29 (5.0) 30 (6.3) 30 (6.5) 26 (2.9) 0.152 Taking Immunosuppression on Blood Draw Date (count, %) Yes 0 (0%) 0 (0%) 3 (20%) 5 (33%) 0.244 *all values presented as mean ± SD unless otherwise indicated Table 2 Clinical Laboratory Blood Test Results at Date of Plasma Sampling* CT Phenotype Healthy Control Resolved CT Features Progressive Nodular Progressive Fibrosis P-Value N 5 5 15 15 Sedimentation Rate (ESR) (mm/h) 9.6 (6.3) 10.3 (5.4) 9.7 (12.4) 13.8 (16.8) 0.869 C-Reactive Protein (mg/L) 3.8 (2.4) 4.1 (2.9) 4.1 (2.5) 11.1 (21.9) 0.499 Angiotensin Converting Enzyme (U/L) 41 (12.5) 28 (2.8) 80 (69.5) 54 (34.9) 0.332 Absolute Lymphocytes (x10 9 /L) 1.96 (0.4) 1.73 (0.7) 1.28 (0.5) 1.11 (0.5) 0.009 *all values presented as mean ± SD Table 3 Clinical Features at Date of Plasma Sampling* CT Phenotype Resolved CT Features Progressive Nodular Progressive Fibrosis P-Value N 5 15 15 4 or more organs involved (count, %) Yes 3 (60%) 6 (40%) 4 (27%) 0.391 FEV1/FVC 0.79 (0.06) 0.74 (0.08) 0.68 (0.07) 0.026 FEV1 z-score 0.347 (0.5) -0.395 (1.3) -1.183 (1.0) 0.033 FEV1%pred 105 (7.8) 95 (19.9) 80 (18.1) 0.024 FVC z-score 0.533 (0.7) 0.168 (1.3) -0.396 (1.0) 0.229 FVC %pred 108 (10.1) 103 (19.7) 93 (16.9) 0.191 DLCO z-score -0.416 (1.552) -1.552 (1.276) -1.662 (2.177) 0.574 DLCO %pred 95 (21.6) 80 (14.9) 79 (25.3) 0.526 *all values presented as mean ± SD unless otherwise indicated Table 4 presents the summary data for the chest CT scan features that were present at the time of plasma collection. The resolved CT feature group had no evidence of reticulation, traction bronchiectasis and had the lowest mean average size of mediastinal lymph nodes. The progressive nodular group was characterized by the highest mean percentage of lung involved with nodular opacities, and the highest mean mediastinal lymph node size. Among participants in the progressive nodular group, only one exhibited lung parenchymal fibrosis, with 10% reticulation and the highest observed traction bronchiectasis score of “3”. Additionally, two other participants in this group had evidence of traction bronchiectasis without associated parenchymal fibrosis. The progressive fibrosis group had the highest average level of reticulation and traction bronchiectasis. There was no difference in the average number of years between the baseline and the last follow-up chest CT. Table 4 Chest CT Scan Features at Date of Plasma Sampling* CT Phenotype Resolved CT Features Progressive Nodular Progressive Fibrosis P-Value N 5 15 15 Percent of Lung Involved with Nodules 4 (6.5) 41 (19.0) 23 (23.8) 0.003 Percent of Lung Involved with Reticulation 0 (0.0) 1 (2.6) 18 (16.4) < 0.001 Traction Bronchiectasis Score (count, %) 0 5 (100%) 12 (80%) 1 (7%) 0.006 2 0 (0%) 2 (13%) 4 (27%) 3 0 (0%) 1 (7%) 1 (7%) 4 0 (0%) 0 (0%) 5 (33%) 8 0 (0%) 0 (0%) 1 (7%) 9 0 (0%) 0 (0%) 3 (20%) Lymph Node Size AP Window (mm) 5 (2.1) 11 (4.0) 7 (2.6) < 0.001 Lymph Node Size Paratracheal (mm) 7 (4.2) 13 (5.4) 9 (4.7) 0.048 Lymph Node Size Subcarinal (mm) 8 (4.3) 15 (4.0) 11 (4.9) 0.007 Years between baseline and last follow-up CT scan 4.6 (2.6) 6.3 (4.2) 5.7 (3.9) 0.686 *all values presented as mean ± SD unless otherwise indicated Global Data Overview / QC/ PCA We used the SomaScan assay to measure 10,760 plasma proteins across 35 patients with sarcoidosis and 5 healthy unaffected volunteers. We performed principal component analysis and identified four sample outliers that were dropped in subsequent analyses. Three out of four of these samples were also flagged by the SomaLogics quality control processing steps. Of the four outliers, three of them belonged to the nodular group, and one belonged to the resolving group. To ensure confidence in selecting high abundance proteins, a filtering parameter was created representing the signal to noise (STN) ratio, obtained by dividing the median RFU per analyte by the estimated limit of detection of the assay buffer controls as recommended by the assay manufacturer. A plot depicting the distribution of STN ratios indicated a clear inflection point around a STN ratio of 31.8, where the signal to noise ratio increased dramatically ( Supplemental Fig. 1 ). For many of our downstream analyses, we used varying numbers of proteins that were observed as having a STN ratio greater than this inflection point. We confirmed that this filtering strategy retained analytes with the highest variance across the samples ( Supplemental Fig. 2 ). Figure 1A depicts the PCA analysis after dropping the four outliers and using the previously mentioned STN filtering parameter. This result suggested reasonable separation of progressive fibrosis and nodular CT phenotypes, although some overlap between the two disease clusters remained. Principal component (PC) 1 explained 27% of the variance in the scaled and centered RFU values, while principal component 2 explained 12%. A plot comparing PC1 scores across all phenotypes showed a statistically significant difference between progressive fibrosis and nodular groups (Figs. 1B). No statistically significant differences in PC1 or PC2 scores between other disease phenotypes were observed (Figs. 1B-C). These results suggested differential regulation between progressive fibrosis and nodular CT phenotypes and motivated additional analyses. Exploratory Analysis via Unsupervised Clustering To further visualize the difference in the range of protein expression across groups, a global comparison of protein expression was performed using unsupervised hierarchal clustering analysis. This approach used the Ward D2 minimum variance clustering method with correlation distance and included the top 2,006 analytes as determined by STN ratio (analytes above the inflection point from Supplemental Fig. 1). Figure 2A displays the clustering analysis with the inclusion of all three CT phenotypes and healthy controls. One cluster contained mostly participants with the progressive fibrosis phenotype (13/20), while another cluster largely contained those with progressive nodular phenotype (8/16). When comparing proteins, three distinct clusters were observed, each containing 554, 869, and 583 analytes respectively. The above observation prompted performing another iteration of cluster analysis, this time with a focus on only the progressive fibrosis and nodular phenotypes. Figure 2B displays this result showing two distinct protein clusters, with 1169 and 869 proteins contained in each cluster. Differential Expression Results Given that the progressive fibrosis and nodular CT phenotypes exhibited potential differences in protein expression based on PCA results and unsupervised hierarchal clustering, a comparison between the fibrosis and nodular CT phenotypes was visualized using a volcano plot (Fig. 3) to depict differentially expressed proteins. The analytes with marker colors represent those with both an FDR cutoff less than 10% and a log 2-fold change (log2FC) greater than 0.6 or less than − 0.6 (i.e., an absolute FC of 1.5). Analytes above the horizontal line have an FDR < 0.05. A greater proportion of down- to up-regulated proteins was observed in the progressive fibrosis phenotype compared to the nodular phenotype. The significantly downregulated proteins in the fibrosis phenotype, ANXA2, MVP, and GBP1, had a log2FC <-1.5 and FDR < 0.05. Another protein, CMPK1, while having FDR -1.5. Sixty-five analytes had FDR = 0.06 ( Supplemental Table 1 ), and when raising the false discovery rate threshold to 0.1 or 10%, the number of differentially expressed proteins increased to 186. Other analytes that were upregulated in the fibrotic CT phenotype compared with the nodular group with an FDR ≤ 0.1 were CYP24A1, FBXO22, BCL9, Matrillin-2, and Fibulin-7 and are notable given their proposed roles in fibrotic pathophysiology. These findings are notable given the modest sample size and highlight the potential of quantitative CT-derived phenotyping as a sensitive approach for uncovering underlying biological mechanisms—an approach that remains underutilized in sarcoidosis research. No differentially expressed proteins were detected when comparing other disease phenotypes, which could be attributed to the relatively smaller number of patients assigned those phenotype groupings. Enrichment Analyses To examine coordinated differences of protein expression between the progressive fibrosis and nodular phenotypes, we performed gene set enrichment analysis (GSEA). The entire set of analytes were mapped to gene identifiers and subsequently ranked by p-value multiplied by the sign of log2 FC. The gene sets that were enriched with an FDR < 0.05 in the nodular phenotype group were pathways associated with mTORC1 signaling, oxidative phosphorylation, adipogenesis, fatty acid metabolism, and MYC signaling which are illustrated in Figs. 4A-4D . The epithelial mesenchymal transition (EMT) gene set with an FDR < 0.05 was enriched in the progressive fibrosis phenotype. (Fig. 4E). Correlation of Protein Expression with Chest CT Features and Blood Biomarkers To understand how the top 69 differentially expressed proteins (with an FDR ≤ 0.06) between progressive fibrosis and nodular CT phenotypes compared to specific chest CT features, we performed unsupervised hierarchal clustering (Fig. 5 ) . The plot recapitulates the strong downregulation of these protein analytes observed when comparing lung reticulation to nodular opacities. Even more striking and unexpected was the moderate to high correlation between lymph node size and protein expression, observed in a cluster of 44 analytes. One implication of this finding is that the profile of circulating plasma proteins may be significantly influenced by immune activity occurring within enlarged lymph nodes. The RFU values of differentially expressed analytes were also correlated with clinically available biomarker levels measured at the same study visit as the plasma sample used in the proteomic analysis. Figure 6 again displays the results of unsupervised hierarchal clustering analysis and reveals weak correlations between serum levels of CD25 (aka soluble Interleukin-2 Receptor α, or IL-2Rα), angiotensin converting enzyme, and lymphocyte count using the same list of differentially expressed analytes between progressive fibrosis vs nodular phenotypes. In contrast, C-Reactive Protein and lysozyme showed weak to moderate negative correlations with most of these analytes. A small group of analytes were positively correlated with C-Reactive Protein (CRP) of which two, UBL7 16 and UBQLN2 17 , have been linked to IL-6 related inflammatory pathways. Discussion This study highlights the power of integrating high-resolution chest CT phenotyping using longitudinal CT imaging with deep plasma proteomics. By leveraging a small but rigorously characterized cohort, we identified biologically plausible protein signatures linked to distinct CT-defined sarcoidosis phenotypes. Specifically, we found enrichment of protein signatures related to metabolic activity in the progressive nodular group, while enrichment of proteins related to epithelial-mesenchymal transition and other fibrotic mechanisms were found in the progressive fibrosis group. From our differential protein analysis, we identified three proteins that were significantly downregulated in the progressive fibrosis group that play plausible mechanistic roles in the development of lung fibrosis. These proteins include ANXA2, a member of the annexin-family of calcium-dependent cytosolic proteins involved in phospholipid binding and plasma membrane repair. Dysregulation of ANXA2 has been previously associated with impaired fibrinolysis 18 and may promote fibrin accumulation, leading to increased collagen and scar formation. Support for ANXA2’s role in sarcoidosis-related fibrosis is strengthened by similarly decreased levels in idiopathic pulmonary fibrosis (IPF). 19 Another highly downregulated protein in the fibrosis group was GBP1, Guanylate-binding protein 1. GBP1 plays a protective role against inflammation-induced cellular damage and dysfunction. It promotes mitophagy, which is a process that clears damaged mitochondria, and helps maintain mitochondrial function. 20 Thus, severe GBP1 downregulation in the fibrotic group may promote fibrosis by driving mitochondrial dysfunction, oxidative stress, inflammation, and cellular senescence. 20 Another downregulated protein was lung resistance protein, or major vault protein (MVP ) , the main component of vaults, which are large ribonucleoprotein particles. While the exact role of vaults and MVP is not fully understood, downregulation could contribute to fibrosis through impaired cell survival and increased apoptosis. Downregulation of MVP has also been found in lung tissues from IPF. 21 Thus, two of the most highly downregulated proteins in the progressive fibrotic group are also downregulated in prototypical progressive fibrotic lung disease, IPF. We also found a group of protein analytes upregulated in the progressive fibrosis group. Many of them have known or putative roles in fibrotic pathophysiology, underscoring their potential relevance in sarcoidosis-related tissue remodeling. For example, Cytochrome P450 24A1 (CYP24A1) promotes the breakdown of active vitamin D (1,25-dihydroxyvitamin D), thereby reducing vitamin D signaling, which is known to exert anti-fibrotic effects. 22 – 24 Elevated expression of CYP24A1 may therefore contribute to fibrosis by limiting this protective signaling pathway. P2X purinoceptor 4 (P2RX4) is an ATP-gated ion channel that contributes to mechano-sensation and inflammation, with growing evidence supporting its role in promoting fibrotic remodeling, particularly in cardiac and pulmonary contexts. 25 , 26 Matrilin-2, a structural extracellular matrix protein, facilitates cell-matrix interactions and has been associated with fibrotic tissue architecture and fibroblast migration. 27 , 28 Lastly, Fibulin-7, a newer member of the fibulin ECM glycoprotein family, binds to the epidermal growth factor receptor and activates downstream signaling pathways, promoting fibroblast-to-myofibroblast trans-differentiation and collagen deposition in cardiac fibrosis. 28 The convergence of these analytes in established fibrosis pathways suggests a shared biological axis that may underlie progressive fibrosis in sarcoidosis and warrants validation and further mechanistic investigation. The pathway analysis offers more insight into the types of molecular drivers underlying the progressive nodular phenotype. The positively enriched pathways—mTORC1 signaling, oxidative phosphorylation, adipogenesis, fatty acid metabolism, and MYC signaling–collectively reflect a coordinated shift toward enhanced cellular metabolism and cellular growth regulation, suggesting that metabolic reprogramming may play a central role in the pathobiology of progressive inflammatory sarcoidosis. The mTORC1 observation aligns with experimental findings showing that chronic mTORC1 activation in macrophages drives granuloma formation and proinflammatory remodeling, providing a mechanistic link between our proteomic signature and persistent granulomatous inflammation. 29 Furthermore, the MYC transcription factor, involved in cell proliferation and growth, has also been associated with greater formation of multinuclear macrophage subsets that comprise granulomas. 30 Pathway analyses results in the fibrosis group point to tissue remodeling pathways, such as the EMT, a key cellular program driving fibroblast accumulation and extracellular matrix production. Relevant EMT proteins that were differentially expressed include (1) F-box only protein 22, a component of the SCF E3 ubiquitin ligase complex, that has been shown to promote EMT; 31 , 32 (2) B-cell CLL/lymphoma 9 functions as a transcriptional co-activator in the Wnt/β-catenin pathway, which is widely implicated in fibrotic diseases through its regulation of mesenchymal cell activation and collagen deposition; 33 , 34 and (3) receptor tyrosine kinase-like orphan receptor 1 (ROR1) which participates in non-canonical Wnt signaling and has been linked to profibrotic EMT in multiple organ systems, including the lung. 35 , 36 Strengths of the analysis include the use of proteomic analysis which offers key advantages by capturing real-time biological activity at the protein level, including post-translational modifications not detected by transcriptomics. It enables discovery of clinically relevant biomarkers and dysregulated pathways to bridge molecular mechanisms with disease manifestations, enhancing translational insight. While these results shed greater light into biological pathways understudied in sarcoidosis, we acknowledge several important limitations including the sample size of our cohort which limits generalizability and the unknown effects of immunosuppressants on protein expression. We attempted to limit the effect of this latter issue by limiting the number of participants taking immunosuppression. In summary, these findings highlight the potential of quantitative CT-derived phenotyping as a sensitive approach for uncovering underlying biological mechanisms—an approach that remains underutilized in sarcoidosis research. Collectively, these peripheral blood protein profile signatures found in the fibrosis and nodular groups appear to distinguish the groups and may reflect the types of underlying mechanisms occurring in tissues. As no biomarkers are currently available for progressive pulmonary fibrosis in sarcoidosis, the proteins highlighted in this study offer exciting candidates for validation studies. Declarations Funding : This research was supported by the National Institutes of Health (grant R01 HL157533, and HL162955), and a grant from the Ann Theodore Foundation Breakthrough Sarcoidosis Initiative. IRB # UCSF 10-02323 Author Contributions: L.L.K. curated the data and conceived of the study. V.S. performed data analysis, statistical modeling and figure generation. V.S., S.M.L., and L.L.K. performed the analyses interpretations. B.M.E. performed the scoring of imaging findings. All authors contributed to writing the manuscript, provided critical revisions, and approved the final version. V.S. and L.L.K. take responsibility for the content of the manuscript, including the data and analysis. Role of sponsors : The sponsors had no input or contributions in the development of the research and manuscript. Conflict of Interest Statement: The authors declare no competing interests Data Availability: The raw RFU data analyzed in the current study are available from the corresponding author on reasonable request. The extended list of differentially expressed analytes between groups of interest are included in Supplemental Data. Acknowledgements: We thank the people living with sarcoidosis who generously contributed samples and clinical information. We also acknowledge the laboratory research staff for their sustained efforts in collection of samples and data curation that enabled this work. References Belperio, J. A. et al. Diagnosis and Treatment of Pulmonary Sarcoidosis: A Review. Jama 327 (9), 856–867 (2022). Comes, A., Sofia, C. & Richeldi, L. Novel insights in fibrotic pulmonary sarcoidosis. Curr. Opin. Pulm Med. 28 (5), 478–484 (2022). Crouser, E. D. et al. Diagnosis and Detection of Sarcoidosis. An Official American Thoracic Society Clinical Practice Guideline. Am. J. Respir Crit. Care Med. 201 (8), e26–e51 (2020). Drent, M., Crouser, E. D. & Grunewald, J. Challenges of Sarcoidosis and Its Management. N Engl. J. Med. 385 (11), 1018–1032 (2021). Patterson, K. C. & Chen, E. S. The Pathogenesis of Pulmonary Sarcoidosis and Implications for Treatment. Chest 153 (6), 1432–1442 (2018). Spagnolo, P. et al. Pulmonary sarcoidosis. Lancet Respir Med. 6 (5), 389–402 (2018). Åkesson, J. et al. Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis. Nat. Commun. 14 (1), 6903 (2023). Cui, M., Cheng, C. & Zhang, L. High-throughput proteomics: a methodological mini-review. Lab. Invest. 102 (11), 1170–1181 (2022). Guerrero, C. R. et al. Application of Proteomics in Sarcoidosis. Am. J. Respir Cell. Mol. Biol. 63 (6), 727–738 (2020). Norman, K. C., Moore, B. B., Arnold, K. B. & O'Dwyer, D. N. Proteomics: Clinical and research applications in respiratory diseases. Respirology. ;23(11):993–1003. (2018). Xie, Y., Chen, X., Xu, M. & Zheng, X. Application of the Human Proteome in Disease, Diagnosis, and Translation into Precision Medicine: Current Status and Future Prospects. Biomedicines ; 13 (3). (2025). Benn, B. S. et al. Clinical and Biological Insights from the University of California San Francisco Prospective and Longitudinal Cohort. Lung 195 (5), 553–561 (2017). Best, A. C. et al. Idiopathic Pulmonary Fibrosis: Physiologic Tests, Quantitative CT Indexes, and CT Visual Scores as Predictors of Mortality. Radiology 246 (3), 935–940 (2008). Arakawa, H. et al. Chronic Interstitial Pneumonia in Silicosis and Mix-Dust Pneumoconiosis: Its Prevalence and Comparison of CT Findings With Idiopathic Pulmonary Fibrosis. Chest 131 (6), 1870–1876 (2007). Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell. Syst. 1 (6), 417–425 (2015). Jiang, W. et al. UBL7 enhances antiviral innate immunity by promoting Lys27-linked polyubiquitination of MAVS. Cell. Rep. 42 (3), 112272 (2023). Chen, T., Zhang, W., Huang, B., Chen, X. & Huang, C. UBQLN2 Promotes the Production of Type I Interferon via the TBK1-IRF3 Pathway. Cells ; 9 (5). (2020). Fassel, H. et al. Reduced expression of annexin A2 is associated with impaired cell surface fibrinolysis and venous thromboembolism. Blood 137 (16), 2221–2230 (2021). Schuliga, M. et al. Annexin A2 contributes to lung injury and fibrosis by augmenting factor Xa fibrogenic activity. Am. J. Physiol. Lung Cell. Mol. Physiol. 312 (5), L772–l782 (2017). Qiu, X. et al. Down-regulation of guanylate binding protein 1 causes mitochondrial dysfunction and cellular senescence in macrophages. Sci. Rep. 8 (1), 1679 (2018). Korfei, M. et al. Comparative proteomic analysis of lung tissue from patients with idiopathic pulmonary fibrosis (IPF) and lung transplant donor lungs. J. Proteome Res. 10 (5), 2185–2205 (2011). Dusso, A. S., Bauerle, K. T. & Bernal-Mizrachi, C. Non-classical Vitamin D Actions for Renal Protection. Front. Med. (Lausanne) . 8 , 790513 (2021). Li, Y., Spataro, B. C., Yang, J., Dai, C. & Liu, Y. 1,25-dihydroxyvitamin D inhibits renal interstitial myofibroblast activation by inducing hepatocyte growth factor expression. Kidney Int. 68 (4), 1500–1510 (2005). Tian, J., Liu, Y., Williams, L. A. & de Zeeuw, D. Potential role of active vitamin D in retarding the progression of chronic kidney disease. Nephrol. Dial Transpl. 22 (2), 321–328 (2007). Reyna-Jeldes, M. et al. Purinergic P2Y2 and P2X4 Receptors Are Involved in the Epithelial-Mesenchymal Transition and Metastatic Potential of Gastric Cancer Derived Cell Lines. Pharmaceutics 13 , 8 (2021). Chadet, S. et al. P2x4 receptor promotes mammary cancer progression by sustaining autophagy and associated mesenchymal transition. Oncogene 41 (21), 2920–2931 (2022). Korpos, É., Deák, F. & Kiss, I. Matrilin-2, an extracellular adaptor protein, is needed for the regeneration of muscle, nerve and other tissues. Neural Regen Res. 10 (6), 866–869 (2015). Luo, J. et al. Matrilin-2 regulates proliferation, apoptosis and cell cycle during radiation-induced injury in HPAEpiC cell. Biochem. Biophys. Res. Commun. 485 (3), 577–583 (2017). Linke, M. et al. Chronic signaling via the metabolic checkpoint kinase mTORC1 induces macrophage granuloma formation and marks sarcoidosis progression. Nat. Immunol. 18 (3), 293–302 (2017). Herrtwich, L. et al. DNA Damage Signaling Instructs Polyploid Macrophage Fate in Granulomas. Cell 167 (5), 1264–1280e1218 (2016). Zheng, Y. et al. Knockdown of FBXO22 inhibits melanoma cell migration, invasion and angiogenesis via the HIF-1α/VEGF pathway. Invest. New. Drugs . 38 (1), 20–28 (2020). Cheng, J. et al. Emerging role of FBXO22 in carcinogenesis. Cell. Death Discov . 6 , 66 (2020). Cohen, M. L. et al. A fibroblast-dependent TGF-β1/sFRP2 noncanonical Wnt signaling axis promotes epithelial metaplasia in idiopathic pulmonary fibrosis. J Clin. Invest ; 134 (18). (2024). Zhao, M. et al. Targeting fibrosis, mechanisms and cilinical trials. Signal. Transduct. Target. Ther. 7 (1), 206 (2022). Cui, B. et al. Targeting ROR1 inhibits epithelial-mesenchymal transition and metastasis. Cancer Res. 73 (12), 3649–3660 (2013). Yamazaki, M. et al. YAP/BRD4-controlled ROR1 promotes tumor-initiating cells and hyperproliferation in pancreatic cancer. Embo j. 42 (14), e112614 (2023). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7358697","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":562001407,"identity":"81c78902-5b7f-4408-b4e4-be35cdcac5ac","order_by":0,"name":"Vibha Shastry","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Vibha","middleName":"","lastName":"Shastry","suffix":""},{"id":562001410,"identity":"bc434334-2a7a-46ad-9e9c-15f03412db87","order_by":1,"name":"Sonia M. Leach","email":"","orcid":"","institution":"Dartmouth College","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"M.","lastName":"Leach","suffix":""},{"id":562001412,"identity":"61305e45-79cb-420d-8db8-f694f5bb52bc","order_by":2,"name":"Brett M. Elicker","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Brett","middleName":"M.","lastName":"Elicker","suffix":""},{"id":562001414,"identity":"8dd92676-0765-44d3-9137-e428ac378197","order_by":3,"name":"Laura L. Koth","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACPgiVwMAgwXwAyJCDcPABNpgWHgk2kFJjkrTwGBCphf2M2YcfDGly9tI93yR+/DFg4GfPMcCvhSfHeGYPQ44xj8zZbZK9bQYMkj1vCGgBqmbgYahI7JHI3SbN2PCHweAGIVv43xgz/gFryXkmzQB0mD1BLRI5xsw8DDkgLWzSDGwGDAYSBLU8K2aWMUgz5rmRZmwJ9AuPxJlnBXi18PMnb2Z8U5Esxz4j+eENYIjJ8bcnb8CrBQKQXMJDhPJRMApGwSgYBYQAAAoqOCvrPF84AAAAAElFTkSuQmCC","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"L.","lastName":"Koth","suffix":""}],"badges":[],"createdAt":"2025-08-12 19:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7358697/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7358697/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98762681,"identity":"d522a99e-7f93-4b98-8daf-a91523fbfc3f","added_by":"auto","created_at":"2025-12-22 10:01:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113155,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptforSRFullv2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/1bd07d095d98f9cd0a319523.docx"},{"id":98762678,"identity":"04d1cb58-e368-4bdf-8129-b6dae0ddafb2","added_by":"auto","created_at":"2025-12-22 10:01:47","extension":"json","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6545,"visible":true,"origin":"","legend":"","description":"","filename":"374182c4f6e442e6986dd2b54ae555f6.json","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/de2d66b5f4e52c53bb56feee.json"},{"id":98778666,"identity":"e8f32bc6-dc4f-4eb1-8995-3460183a03fb","added_by":"auto","created_at":"2025-12-22 12:29:29","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191863,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalDataFiguresandTable.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/1aa768ba5c816645f2a0bb68.pdf"},{"id":98762686,"identity":"9a65ac20-91df-4bf6-9692-23e712e5f07d","added_by":"auto","created_at":"2025-12-22 10:01:47","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107568,"visible":true,"origin":"","legend":"","description":"","filename":"374182c4f6e442e6986dd2b54ae555f61enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/ec6a30557dc092f82c56e5c9.xml"},{"id":98779439,"identity":"55628f1a-08f2-45b9-9c5d-5957b1f3e8ce","added_by":"auto","created_at":"2025-12-22 12:30:21","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3128763,"visible":true,"origin":"","legend":"","description":"","filename":"CombinedFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/b3b1b21a0a3ca231c8b8adff.pdf"},{"id":98762687,"identity":"65723fe1-6448-40de-966c-8a9946d1cdab","added_by":"auto","created_at":"2025-12-22 10:01:47","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105056,"visible":true,"origin":"","legend":"","description":"","filename":"374182c4f6e442e6986dd2b54ae555f61structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/b61e3447c7d835bc1eb75be7.xml"},{"id":98762689,"identity":"57c40499-39a1-4700-b534-86860ae45e32","added_by":"auto","created_at":"2025-12-22 10:01:47","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116831,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/b6432ca3cfa140b314306511.html"},{"id":98762676,"identity":"3c1aa833-93a4-4083-babf-181ef125fd6e","added_by":"auto","created_at":"2025-12-22 10:01:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184553,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) of proteomics data generated from plasma samples. (A) PCA results are displayed after removal of four sample outliers. The proteomics data included in this analysis consisted of analytes with a signal to noise ratio of greater or equal to 31.8 and consisted of 2,006 analytes. Plots in panel B and C show the individual principal component (PC) scores for PC1 and PC2 plotted against the phenotype group. A Kruskal-Wallis rank sum test and post-estimation Dunn’s test with Bonferroni correction was performed to evaluate median PC score differences across the groups. *P-value of \u0026lt;0.05\u003c/p\u003e","description":"","filename":"CombinedFigures1.png","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/3dfb3000447da2c3614a0694.png"},{"id":98779663,"identity":"64ee2f34-9b24-4f75-9933-7ec6bcb2fead","added_by":"auto","created_at":"2025-12-22 12:30:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1666847,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of protein relative expression across samples. Unsupervised hierarchical clustering was applied using Ward’s D2 linkage method and correlation distance as the dissimilarity metric. Rows represent proteins; columns represent individual samples, grouped by cluster membership. Input for analysis included the top 2,006 analytes as determined by signal to noise ratio \u0026gt; 31.8 (analytes above the inflection point from Supplemental Figure 1). Figure 2A displays the clustering analysis with inclusion of all three CT phenotypes and healthy controls. When comparing proteins, three distinct clusters were observed, each containing 554, 869, and 583 analytes, respectively. Figure 2B repeated the same analysis but only included the progressive fibrosis and nodular phenotypes. This result yielded two distinct protein clusters, with 1,169 and 869 proteins contained in each cluster.\u003c/p\u003e","description":"","filename":"CombinedFigures2.png","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/7492a9e210768cadc08379bf.png"},{"id":98762683,"identity":"7d37bbb5-9a31-41d8-8084-d0773f973099","added_by":"auto","created_at":"2025-12-22 10:01:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":221119,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot comparing the protein expression in the progressive fibrosis to nodular CT phenotypes. The top 2,006 analytes by signal to noise ratio were evaluated. Proteins were classified as upregulated (colored red) or downregulated (colored blue) in progressive fibrosis based on having a log2fold change (log2FC) threshold of +/- 0.6 (equivalent to a fold change +/- 1.5 and indicated by the vertical dashed lines) and –log10 false discovery rate (-log10FDR) = 1 (equivalent to FDR=0.1 and indicated by the horizontal dashed line). Four downregulated proteins (GBP, MVP, ANXA2, CMPK1) met a stricter FDR significance threshold of 0.05, though CMPK failed to meet the 1.5-fold-change threshold for significance.\u003c/p\u003e","description":"","filename":"CombinedFigures3.png","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/d3c79d0cc03948887c532717.png"},{"id":98779357,"identity":"9a2ac0e9-47b5-4de5-83dd-42904cc35c6b","added_by":"auto","created_at":"2025-12-22 12:30:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":278519,"visible":true,"origin":"","legend":"\u003cp\u003eGene set enrichment analysis (GSEA) performed on proteins ranked by differential expression between progressive fibrosis and nodular CT phenotypes. Enrichment results using the Hallmark gene set collection for the (A) MYC Targets V1 gene set, and (B) MTORC1 Signaling pathway, (C) Adipogenesis, (D) Oxidative Phosphorylation, and (E) Epithelial Mesenchymal Transition are displayed. Gene sets shown in A-D were enriched in the progressive nodular CT phenotype group compared to progressive fibrosis while the gene set for E was enriched in the progressive fibrosis compared to the nodular group.\u003c/p\u003e","description":"","filename":"CombinedFigures4.png","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/989baecbf9d3d2df4db8a37a.png"},{"id":98762682,"identity":"ed564fba-570d-4d10-ae00-acbaeb5047cd","added_by":"auto","created_at":"2025-12-22 10:01:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":350236,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap with hierarchical clustering of differentially expressed analytes between progressive fibrosis and nodular CT phenotypes. Analytes were clustered based on correlation coefficients calculated between analyte relative fluorescence units and selected chest CT features, which were visually scored by an expert radiologist. CT features, listed top-to-bottom, include the percentage of nodular opacities (%NOD), followed by the size of the largest lymph node in three mediastinal regions—aortopulmonary window (LN-AP), paratracheal (LN-Para), and subcarinal (LN-Sub), severity of traction bronchiectasis (TB) and percentage of lung reticulation (%LR). The FDR threshold used to select these analytes was FDR ≤ 0.06.\u003c/p\u003e","description":"","filename":"CombinedFigures5.png","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/d5e52b274690d4298db46ca6.png"},{"id":98762685,"identity":"02bda3d6-f822-4d6d-a6be-b2280782470d","added_by":"auto","created_at":"2025-12-22 10:01:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":323765,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap with hierarchical clustering of differentially expressed analytes between progressive fibrosis and nodular CT phenotypes. Analytes were clustered based on correlation coefficients calculated between analyte relative fluorescence units and blood biomarker levels that were measured at the same time point used for proteomics measurements. The blood tests presented from top to bottom include absolute lymphocytes (10\u003csup\u003e9\u003c/sup\u003e/L) (Lymph), Angiotensin Converting Enzyme (U/L) (ACE), soluble IL-2Rα (pg/mL) (CD25), C-Reactive Protein (CRP) (mg/L), and lysozyme (ng/mL). The FDR threshold used for these analytes was FDR ≤ 0.06.\u003c/p\u003e","description":"","filename":"CombinedFigures6.png","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/bcaff59f7dc5e70bd0414ee4.png"},{"id":107735595,"identity":"47b00961-af8c-4c96-8c24-d98614bd15e2","added_by":"auto","created_at":"2026-04-24 13:57:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4150065,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/330a40a5-4ff6-4cec-94c0-a55db6084f14.pdf"},{"id":98762680,"identity":"cec83993-1bab-46a3-b98a-5c52e5c17908","added_by":"auto","created_at":"2025-12-22 10:01:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":191863,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalDataFiguresandTable.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7358697/v1/30b8f9686d79089718a81dcb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Plasma Proteomic Profiling Reveals Distinct Signatures of Chest CT Phenotypes in Sarcoidosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcoidosis is a multi-organ, immune-mediated granulomatous disease of unknown origin that most commonly affects the lungs and exhibits a highly variable clinical course\u0026mdash;from spontaneous resolution to chronic inflammation, fibrosis or both.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e This heterogeneity complicates clinical management and trial design, as no robust, noninvasive biomarkers currently exist to distinguish these clinical trajectories. Improved patient stratification could improve clinical resource utilization, guide treatment decisions, and reduce clinical trial size.\u003c/p\u003e \u003cp\u003eProteomic profiling is a powerful tool for uncovering circulating biomarkers that reflect disease biology, offering a real-time readout of systemic immune activity, signaling pathways, and post-translational modifications that may be missed by genomic or transcriptomic approaches.\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e In this study, we applied high-throughput plasma proteomics in pulmonary sarcoidosis to identify biomarkers associated with distinct CT-defined phenotypes.\u003c/p\u003e \u003cp\u003eWe anchored biomarker discovery to longitudinal CT scans scored for fibrosis, nodular inflammation, and lymphadenopathy. Blood samples were selected from time points reflecting each subject\u0026rsquo;s most extreme CT phenotype\u0026mdash;progressive fibrosis, progressive inflammation, or radiographic resolution. This integrative approach enabled us to link proteomic signatures to meaningful disease trajectories.\u003c/p\u003e \u003cp\u003eOur analysis identified distinct protein profiles associated with progressive fibrosis and inflammation. While sampling occurred concurrently with imaging, limiting predictive conclusions, these signatures provide insight into sarcoidosis immunopathology and may serve as prognostic markers pending future validation. Together, these findings underscore the value of linking proteomics with radiographic phenotypes to advance biomarker discovery in sarcoidosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eCohort Description and CT analysis\u003c/em\u003e: The longitudinal cohort study design and procedures have been previously described.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Participants with pulmonary sarcoidosis based on ATS/ERS/WASOG diagnostic criteria\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and healthy controls were recruited from the San Francisco Bay Area. Enrollment occurred between January 1, 2010, to December 31, 2021, with follow-up study visits at 6-to-12-month intervals. All experiments were performed in accordance with relevant guidelines and regulations (including the Declaration of Helsinki) and were approved by the UCSF Institutional Review Board; all participants provided written informed consent. Longitudinal chest CT scans were visually scored by an expert thoracic radiologist who was blinded to clinical data. The mean extent of reticular abnormality and nodular disease was scored to the nearest 5% in three zones in each lung as previously described\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e to produce semiquantitative scores. The severity of traction bronchiectasis was scored as previously described.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e In brief, traction bronchiectasis severity within each of the five lobes was graded by comparing the diameter of the airway with that of the adjacent pulmonary artery using a 4-point scale (i.e., 0\u0026ndash;3), and scores from the five lobes were summed to yield an overall score (range, 0 to 15): score 0, no traction bronchiectasis; score 1, traction bronchiectasis present but mild (comparable diameter with the artery); score 2, moderately severe traction bronchiectasis (up to twice the diameter of the artery); and score 3, severe traction bronchiectasis (more than twice the diameter of the artery).\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e The largest lymph node for each station (paratracheal, subcarinal and AP window) was measured using the short-axis diameter in millimeters. Size of \u0026le;\u0026thinsp;10 mm was considered normal.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCase Control Study Design of CT Features\u003c/strong\u003e \u003cp\u003e Longitudinal chest CT scan features were used to create three phenotype groupings based on visual scoring and consisted of sarcoidosis participants with evidence of (1) progressive CT fibrosis over time defined by worsening of extent of reticulation or severity of traction bronchiectasis or any combination of these features; the presence of nodular inflammation was allowed; (2) progressive nodular disease over time with no or very little evidence of fibrosis; (3) remission of all or most of the CT features of disease present at baseline. Healthy controls did not undergo CT scan imaging. The analyzed plasma samples were from a calendar date as close as possible to the chest CT scan that was used to define the CT phenotype groupings.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSomalogics Proteomics Assay\u003c/strong\u003e \u003cp\u003eSomaLogic Inc. applied the SomaScan 11K Assay measuring more than 10,000 unique human proteins to previously unthawed human plasma samples that were stored at -30\u0026deg;C. The SOMAmer-based assay allows for quantitative transformation of the protein epitope amount into a specific SOMAmer-based DNA signal. Fluorescent, single-stranded DNA-based SOMAmer reagents interact and bind to target molecules in the plasma, forming SOMAmer-protein complexes. Unbound proteins are removed, followed by biotinylation of SOMAmer-protein complexes. Addition of polyanionic competitors and wash steps results in specific retention of target proteins, which are then quantified using DNA-hybridization microarrays. Readouts are reported in relative fluorescent units (RFU), which are directly proportional to the amount of target protein epitope in the plasma sample. Plasma RFU data were processed using SomaLogic\u0026rsquo;s standard multi-step normalization pipeline, which applies hybridization control normalization, intraplate median normalization, plate scaling and calibration, and adaptive normalization to a reference distribution to remove both within-plate and between-run technical variation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical Analyses\u003c/strong\u003e \u003cp\u003eContinuous variables were summarized using means and standard deviations, and categorical variables as frequencies and percentages. Group comparisons were performed using one-way ANOVA for continuous variables and Pearson\u0026rsquo;s chi-square test for categorical variables. Principal component analysis (PCA) was performed using the R stats package. The R rstatix package was used to perform Kruskal-Wallis test followed by Dunn\u0026rsquo;s test on PCA data. The SomaData IO R package was used to calculate the limit of detection per SOMAmer analyte. Differential expression analysis between the progressive fibrosis and nodular CT phenotypes was performed, and false discovery rate (FDR) was controlled using the Benjamini-Hochberg procedure. An FDR of \u0026lt;\u0026thinsp;0.05 or p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant as appropriate for the analysis. Gene Set Enrichment Analysis (GSEA) was conducted using the fgsea R package with the Hallmark gene set collection \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e from MSigDB. Enrichment statistics were calculated using the pre-ranked GSEA method, with normalized enrichment scores and FDR q-values provided for each pathway. Statistical and graphical analyses were carried out using R software version 4.5.0 (4-11-2025) and STATA v18 (StataCorp, College Station, TX, USA).\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCohort / Sample Description\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the clinical characteristics of the cohort stratified by CT phenotype group. There were no statistically significant differences across the CT phenotype groups with respect to age, sex, race, tobacco history, body mass index or use of immunosuppression at the blood plasma draw date. There were no statistically significant differences in the levels of serum biomarkers such as C-reactive protein, sedimentation rate, or angiotensin converting enzyme, but there was a difference in the absolute lymphocyte counts with the fibrosis group having the lowest count compared to the other groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There were no statistically significant differences in the total number of involved organs with sarcoidosis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The fibrosis group had the lowest mean values for lung function parameters (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Characteristics at Date of Plasma Sampling*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eCT Phenotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResolved CT Features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive Nodular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProgressive Fibrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (yr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (count, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (count, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatino (count, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking History (count, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year Smoking History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaking Immunosuppression on Blood Draw Date (count, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*all values presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD unless otherwise indicated\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Laboratory Blood Test Results at Date of Plasma Sampling*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eCT Phenotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResolved CT Features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive Nodular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProgressive Fibrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedimentation Rate (ESR) (mm/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.7 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.8 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Reactive Protein (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.1 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngiotensin Converting Enzyme (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute Lymphocytes (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.96 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*all values presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Features at Date of Plasma Sampling*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCT Phenotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResolved CT Features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProgressive Nodular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive Fibrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 or more organs involved (count, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1 z-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.347 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.395 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.183 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1%pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC z-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.533 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.168 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.396 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC %pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLCO z-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.416 (1.552)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.552 (1.276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.662 (2.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLCO %pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*all values presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD unless otherwise indicated\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the summary data for the chest CT scan features that were present at the time of plasma collection. The resolved CT feature group had no evidence of reticulation, traction bronchiectasis and had the lowest mean average size of mediastinal lymph nodes. The progressive nodular group was characterized by the highest mean percentage of lung involved with nodular opacities, and the highest mean mediastinal lymph node size. Among participants in the progressive nodular group, only one exhibited lung parenchymal fibrosis, with 10% reticulation and the highest observed traction bronchiectasis score of \u0026ldquo;3\u0026rdquo;. Additionally, two other participants in this group had evidence of traction bronchiectasis without associated parenchymal fibrosis. The progressive fibrosis group had the highest average level of reticulation and traction bronchiectasis. There was no difference in the average number of years between the baseline and the last follow-up chest CT.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChest CT Scan Features at Date of Plasma Sampling*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCT Phenotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResolved CT Features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProgressive Nodular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive Fibrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent of Lung Involved with Nodules\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent of Lung Involved with Reticulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraction Bronchiectasis Score (count, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph Node Size AP Window (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph Node Size Paratracheal (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph Node Size Subcarinal (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears between baseline and last follow-up CT scan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*all values presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD unless otherwise indicated\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGlobal Data Overview / QC/ PCA\u003c/h3\u003e\n\u003cp\u003eWe used the SomaScan assay to measure 10,760 plasma proteins across 35 patients with sarcoidosis and 5 healthy unaffected volunteers. We performed principal component analysis and identified four sample outliers that were dropped in subsequent analyses. Three out of four of these samples were also flagged by the SomaLogics quality control processing steps. Of the four outliers, three of them belonged to the nodular group, and one belonged to the resolving group. To ensure confidence in selecting high abundance proteins, a filtering parameter was created representing the signal to noise (STN) ratio, obtained by dividing the median RFU per analyte by the estimated limit of detection of the assay buffer controls as recommended by the assay manufacturer. A plot depicting the distribution of STN ratios indicated a clear inflection point around a STN ratio of 31.8, where the signal to noise ratio increased dramatically (\u003cb\u003eSupplemental Fig.\u0026nbsp;1\u003c/b\u003e). For many of our downstream analyses, we used varying numbers of proteins that were observed as having a STN ratio greater than this inflection point. We confirmed that this filtering strategy retained analytes with the highest variance across the samples (\u003cb\u003eSupplemental Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1A\u003c/b\u003e depicts the PCA analysis after dropping the four outliers and using the previously mentioned STN filtering parameter. This result suggested reasonable separation of progressive fibrosis and nodular CT phenotypes, although some overlap between the two disease clusters remained. Principal component (PC) 1 explained 27% of the variance in the scaled and centered RFU values, while principal component 2 explained 12%. A plot comparing PC1 scores across all phenotypes showed a statistically significant difference between progressive fibrosis and nodular groups (Figs.\u0026nbsp;1B). No statistically significant differences in PC1 or PC2 scores between other disease phenotypes were observed (Figs.\u0026nbsp;1B-C). These results suggested differential regulation between progressive fibrosis and nodular CT phenotypes and motivated additional analyses.\u003c/p\u003e\n\u003ch3\u003eExploratory Analysis via Unsupervised Clustering\u003c/h3\u003e\n\u003cp\u003eTo further visualize the difference in the range of protein expression across groups, a global comparison of protein expression was performed using unsupervised hierarchal clustering analysis. This approach used the Ward D2 minimum variance clustering method with correlation distance and included the top 2,006 analytes as determined by STN ratio (analytes above the inflection point from Supplemental Fig.\u0026nbsp;1). Figure\u0026nbsp;2A displays the clustering analysis with the inclusion of all three CT phenotypes and healthy controls. One cluster contained mostly participants with the progressive fibrosis phenotype (13/20), while another cluster largely contained those with progressive nodular phenotype (8/16). When comparing proteins, three distinct clusters were observed, each containing 554, 869, and 583 analytes respectively. The above observation prompted performing another iteration of cluster analysis, this time with a focus on only the progressive fibrosis and nodular phenotypes. Figure\u0026nbsp;2B displays this result showing two distinct protein clusters, with 1169 and 869 proteins contained in each cluster.\u003c/p\u003e\n\u003ch3\u003eDifferential Expression Results\u003c/h3\u003e\n\u003cp\u003eGiven that the progressive fibrosis and nodular CT phenotypes exhibited potential differences in protein expression based on PCA results and unsupervised hierarchal clustering, a comparison between the fibrosis and nodular CT phenotypes was visualized using a volcano plot (Fig.\u0026nbsp;3) to depict differentially expressed proteins. The analytes with marker colors represent those with both an FDR cutoff less than 10% and a log 2-fold change (log2FC) greater than 0.6 or less than \u0026minus;\u0026thinsp;0.6 (i.e., an absolute FC of 1.5). Analytes above the horizontal line have an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A greater proportion of down- to up-regulated proteins was observed in the progressive fibrosis phenotype compared to the nodular phenotype. The significantly downregulated proteins in the fibrosis phenotype, ANXA2, MVP, and GBP1, had a log2FC \u0026lt;-1.5 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Another protein, CMPK1, while having FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, had a log2FC \u0026gt;-1.5. Sixty-five analytes had FDR\u0026thinsp;=\u0026thinsp;0.06 (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e), and when raising the false discovery rate threshold to 0.1 or 10%, the number of differentially expressed proteins increased to 186. Other analytes that were upregulated in the fibrotic CT phenotype compared with the nodular group with an FDR\u0026thinsp;\u0026le;\u0026thinsp;0.1 were CYP24A1, FBXO22, BCL9, Matrillin-2, and Fibulin-7 and are notable given their proposed roles in fibrotic pathophysiology. These findings are notable given the modest sample size and highlight the potential of quantitative CT-derived phenotyping as a sensitive approach for uncovering underlying biological mechanisms\u0026mdash;an approach that remains underutilized in sarcoidosis research. No differentially expressed proteins were detected when comparing other disease phenotypes, which could be attributed to the relatively smaller number of patients assigned those phenotype groupings.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment Analyses\u003c/h2\u003e \u003cp\u003eTo examine coordinated differences of protein expression between the progressive fibrosis and nodular phenotypes, we performed gene set enrichment analysis (GSEA). The entire set of analytes were mapped to gene identifiers and subsequently ranked by p-value multiplied by the sign of log2 FC. The gene sets that were enriched with an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the nodular phenotype group were pathways associated with mTORC1 signaling, oxidative phosphorylation, adipogenesis, fatty acid metabolism, and MYC signaling which are illustrated in \u003cb\u003eFigs.\u0026nbsp;4A-4D\u003c/b\u003e. The epithelial mesenchymal transition (EMT) gene set with an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was enriched in the progressive fibrosis phenotype. (Fig.\u0026nbsp;4E).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation of Protein Expression with Chest CT Features and Blood Biomarkers\u003c/h3\u003e\n\u003cp\u003eTo understand how the top 69 differentially expressed proteins (with an FDR\u0026thinsp;\u0026le;\u0026thinsp;0.06) between progressive fibrosis and nodular CT phenotypes compared to specific chest CT features, we performed unsupervised hierarchal clustering (Fig.\u0026nbsp;5\u003cb\u003e)\u003c/b\u003e. The plot recapitulates the strong downregulation of these protein analytes observed when comparing lung reticulation to nodular opacities. Even more striking and unexpected was the moderate to high correlation between lymph node size and protein expression, observed in a cluster of 44 analytes. One implication of this finding is that the profile of circulating plasma proteins may be significantly influenced by immune activity occurring within enlarged lymph nodes.\u003c/p\u003e \u003cp\u003eThe RFU values of differentially expressed analytes were also correlated with clinically available biomarker levels measured at the same study visit as the plasma sample used in the proteomic analysis. Figure\u0026nbsp;6 again displays the results of unsupervised hierarchal clustering analysis and reveals weak correlations between serum levels of CD25 (aka soluble Interleukin-2 Receptor α, or IL-2Rα), angiotensin converting enzyme, and lymphocyte count using the same list of differentially expressed analytes between progressive fibrosis vs nodular phenotypes. In contrast, C-Reactive Protein and lysozyme showed weak to moderate negative correlations with most of these analytes. A small group of analytes were positively correlated with C-Reactive Protein (CRP) of which two, UBL7\u003csup\u003e16\u003c/sup\u003e and UBQLN2\u003csup\u003e17\u003c/sup\u003e, have been linked to IL-6 related inflammatory pathways.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights the power of integrating high-resolution chest CT phenotyping using longitudinal CT imaging with deep plasma proteomics. By leveraging a small but rigorously characterized cohort, we identified biologically plausible protein signatures linked to distinct CT-defined sarcoidosis phenotypes. Specifically, we found enrichment of protein signatures related to metabolic activity in the progressive nodular group, while enrichment of proteins related to epithelial-mesenchymal transition and other fibrotic mechanisms were found in the progressive fibrosis group.\u003c/p\u003e \u003cp\u003eFrom our differential protein analysis, we identified three proteins that were significantly downregulated in the progressive fibrosis group that play plausible mechanistic roles in the development of lung fibrosis. These proteins include ANXA2, a member of the annexin-family of calcium-dependent cytosolic proteins involved in phospholipid binding and plasma membrane repair. Dysregulation of ANXA2 has been previously associated with impaired fibrinolysis\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and may promote fibrin accumulation, leading to increased collagen and scar formation. Support for ANXA2\u0026rsquo;s role in sarcoidosis-related fibrosis is strengthened by similarly decreased levels in idiopathic pulmonary fibrosis (IPF).\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Another highly downregulated protein in the fibrosis group was GBP1, Guanylate-binding protein 1. GBP1 plays a protective role against inflammation-induced cellular damage and dysfunction. It promotes mitophagy, which is a process that clears damaged mitochondria, and helps maintain mitochondrial function.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Thus, severe GBP1 downregulation in the fibrotic group may promote fibrosis by driving mitochondrial dysfunction, oxidative stress, inflammation, and cellular senescence.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Another downregulated protein was lung resistance protein, or major vault protein (MVP\u003cem\u003e)\u003c/em\u003e, the main component of vaults, which are large ribonucleoprotein particles. While the exact role of vaults and MVP is not fully understood, downregulation could contribute to fibrosis through impaired cell survival and increased apoptosis. Downregulation of MVP has also been found in lung tissues from IPF.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Thus, two of the most highly downregulated proteins in the progressive fibrotic group are also downregulated in prototypical progressive fibrotic lung disease, IPF.\u003c/p\u003e \u003cp\u003eWe also found a group of protein analytes upregulated in the progressive fibrosis group. Many of them have known or putative roles in fibrotic pathophysiology, underscoring their potential relevance in sarcoidosis-related tissue remodeling. For example, Cytochrome P450 24A1 (CYP24A1) promotes the breakdown of active vitamin D (1,25-dihydroxyvitamin D), thereby reducing vitamin D signaling, which is known to exert anti-fibrotic effects.\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Elevated expression of CYP24A1 may therefore contribute to fibrosis by limiting this protective signaling pathway. P2X purinoceptor 4 (P2RX4) is an ATP-gated ion channel that contributes to mechano-sensation and inflammation, with growing evidence supporting its role in promoting fibrotic remodeling, particularly in cardiac and pulmonary contexts.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Matrilin-2, a structural extracellular matrix protein, facilitates cell-matrix interactions and has been associated with fibrotic tissue architecture and fibroblast migration.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Lastly, Fibulin-7, a newer member of the fibulin ECM glycoprotein family, binds to the epidermal growth factor receptor and activates downstream signaling pathways, promoting fibroblast-to-myofibroblast trans-differentiation and collagen deposition in cardiac fibrosis.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e The convergence of these analytes in established fibrosis pathways suggests a shared biological axis that may underlie progressive fibrosis in sarcoidosis and warrants validation and further mechanistic investigation.\u003c/p\u003e \u003cp\u003eThe pathway analysis offers more insight into the types of molecular drivers underlying the progressive nodular phenotype. The positively enriched pathways\u0026mdash;mTORC1 signaling, oxidative phosphorylation, adipogenesis, fatty acid metabolism, and MYC signaling\u0026ndash;collectively reflect a coordinated shift toward enhanced cellular metabolism and cellular growth regulation, suggesting that metabolic reprogramming may play a central role in the pathobiology of progressive inflammatory sarcoidosis. The mTORC1 observation aligns with experimental findings showing that chronic mTORC1 activation in macrophages drives granuloma formation and proinflammatory remodeling, providing a mechanistic link between our proteomic signature and persistent granulomatous inflammation.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Furthermore, the MYC transcription factor, involved in cell proliferation and growth, has also been associated with greater formation of multinuclear macrophage subsets that comprise granulomas.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePathway analyses results in the fibrosis group point to tissue remodeling pathways, such as the EMT, a key cellular program driving fibroblast accumulation and extracellular matrix production. Relevant EMT proteins that were differentially expressed include (1) F-box only protein 22, a component of the SCF E3 ubiquitin ligase complex, that has been shown to promote EMT;\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e (2) B-cell CLL/lymphoma 9 functions as a transcriptional co-activator in the Wnt/β-catenin pathway, which is widely implicated in fibrotic diseases through its regulation of mesenchymal cell activation and collagen deposition;\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and (3) receptor tyrosine kinase-like orphan receptor 1 (ROR1) which participates in non-canonical Wnt signaling and has been linked to profibrotic EMT in multiple organ systems, including the lung.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eStrengths of the analysis include the use of proteomic analysis which offers key advantages by capturing real-time biological activity at the protein level, including post-translational modifications not detected by transcriptomics. It enables discovery of clinically relevant biomarkers and dysregulated pathways to bridge molecular mechanisms with disease manifestations, enhancing translational insight. While these results shed greater light into biological pathways understudied in sarcoidosis, we acknowledge several important limitations including the sample size of our cohort which limits generalizability and the unknown effects of immunosuppressants on protein expression. We attempted to limit the effect of this latter issue by limiting the number of participants taking immunosuppression.\u003c/p\u003e \u003cp\u003eIn summary, these findings highlight the potential of quantitative CT-derived phenotyping as a sensitive approach for uncovering underlying biological mechanisms\u0026mdash;an approach that remains underutilized in sarcoidosis research. Collectively, these peripheral blood protein profile signatures found in the fibrosis and nodular groups appear to distinguish the groups and may reflect the types of underlying mechanisms occurring in tissues. As no biomarkers are currently available for progressive pulmonary fibrosis in sarcoidosis, the proteins highlighted in this study offer exciting candidates for validation studies.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research was supported by the National Institutes of Health (grant R01 HL157533, and HL162955), and a grant from the Ann Theodore Foundation Breakthrough Sarcoidosis Initiative.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRB\u003c/strong\u003e # UCSF 10-02323\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eL.L.K. curated the data and conceived of the study. V.S. performed data analysis, statistical modeling and figure generation. \u0026nbsp;V.S., S.M.L., and L.L.K. performed the analyses interpretations. B.M.E. performed the scoring of imaging findings. All authors contributed to writing the manuscript, provided critical revisions, and approved the final version. V.S. and L.L.K. take responsibility for the content of the manuscript, including the data and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of sponsors\u003c/strong\u003e: The sponsors had no input or contributions in the development of the research and manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003eData Availability: The raw RFU data analyzed in the current study are available from the corresponding author on reasonable request. The extended list of differentially expressed analytes between groups of interest are included in Supplemental Data.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: We thank the people living with sarcoidosis who generously contributed samples and clinical information. We also acknowledge the laboratory research staff for their sustained efforts in collection of samples and data curation that enabled this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBelperio, J. A. et al. Diagnosis and Treatment of Pulmonary Sarcoidosis: A Review. \u003cem\u003eJama\u003c/em\u003e \u003cb\u003e327\u003c/b\u003e (9), 856\u0026ndash;867 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComes, A., Sofia, C. \u0026amp; Richeldi, L. Novel insights in fibrotic pulmonary sarcoidosis. \u003cem\u003eCurr. Opin. Pulm Med.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (5), 478\u0026ndash;484 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrouser, E. D. et al. Diagnosis and Detection of Sarcoidosis. An Official American Thoracic Society Clinical Practice Guideline. \u003cem\u003eAm. J. Respir Crit. Care Med.\u003c/em\u003e \u003cb\u003e201\u003c/b\u003e (8), e26\u0026ndash;e51 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrent, M., Crouser, E. D. \u0026amp; Grunewald, J. Challenges of Sarcoidosis and Its Management. \u003cem\u003eN Engl. J. Med.\u003c/em\u003e \u003cb\u003e385\u003c/b\u003e (11), 1018\u0026ndash;1032 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatterson, K. C. \u0026amp; Chen, E. S. The Pathogenesis of Pulmonary Sarcoidosis and Implications for Treatment. \u003cem\u003eChest\u003c/em\u003e \u003cb\u003e153\u003c/b\u003e (6), 1432\u0026ndash;1442 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpagnolo, P. et al. Pulmonary sarcoidosis. \u003cem\u003eLancet Respir Med.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (5), 389\u0026ndash;402 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Aring;kesson, J. et al. Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (1), 6903 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui, M., Cheng, C. \u0026amp; Zhang, L. High-throughput proteomics: a methodological mini-review. \u003cem\u003eLab. Invest.\u003c/em\u003e \u003cb\u003e102\u003c/b\u003e (11), 1170\u0026ndash;1181 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerrero, C. R. et al. Application of Proteomics in Sarcoidosis. \u003cem\u003eAm. J. Respir Cell. Mol. Biol.\u003c/em\u003e \u003cb\u003e63\u003c/b\u003e (6), 727\u0026ndash;738 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorman, K. C., Moore, B. B., Arnold, K. B. \u0026amp; O'Dwyer, D. N. Proteomics: Clinical and research applications in respiratory diseases. \u003cem\u003eRespirology.\u003c/em\u003e ;23(11):993\u0026ndash;1003. (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, Y., Chen, X., Xu, M. \u0026amp; Zheng, X. Application of the Human Proteome in Disease, Diagnosis, and Translation into Precision Medicine: Current Status and Future Prospects. \u003cem\u003eBiomedicines\u003c/em\u003e ;\u003cb\u003e13\u003c/b\u003e(3). (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenn, B. S. et al. Clinical and Biological Insights from the University of California San Francisco Prospective and Longitudinal Cohort. \u003cem\u003eLung\u003c/em\u003e \u003cb\u003e195\u003c/b\u003e (5), 553\u0026ndash;561 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBest, A. C. et al. Idiopathic Pulmonary Fibrosis: Physiologic Tests, Quantitative CT Indexes, and CT Visual Scores as Predictors of Mortality. \u003cem\u003eRadiology\u003c/em\u003e \u003cb\u003e246\u003c/b\u003e (3), 935\u0026ndash;940 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArakawa, H. et al. Chronic Interstitial Pneumonia in Silicosis and Mix-Dust Pneumoconiosis: Its Prevalence and Comparison of CT Findings With Idiopathic Pulmonary Fibrosis. \u003cem\u003eChest\u003c/em\u003e \u003cb\u003e131\u003c/b\u003e (6), 1870\u0026ndash;1876 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. \u003cem\u003eCell. Syst.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e (6), 417\u0026ndash;425 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, W. et al. UBL7 enhances antiviral innate immunity by promoting Lys27-linked polyubiquitination of MAVS. \u003cem\u003eCell. Rep.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (3), 112272 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, T., Zhang, W., Huang, B., Chen, X. \u0026amp; Huang, C. UBQLN2 Promotes the Production of Type I Interferon via the TBK1-IRF3 Pathway. \u003cem\u003eCells\u003c/em\u003e ;\u003cb\u003e9\u003c/b\u003e(5). (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFassel, H. et al. Reduced expression of annexin A2 is associated with impaired cell surface fibrinolysis and venous thromboembolism. \u003cem\u003eBlood\u003c/em\u003e \u003cb\u003e137\u003c/b\u003e (16), 2221\u0026ndash;2230 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuliga, M. et al. Annexin A2 contributes to lung injury and fibrosis by augmenting factor Xa fibrogenic activity. \u003cem\u003eAm. J. Physiol. Lung Cell. Mol. Physiol.\u003c/em\u003e \u003cb\u003e312\u003c/b\u003e (5), L772\u0026ndash;l782 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiu, X. et al. Down-regulation of guanylate binding protein 1 causes mitochondrial dysfunction and cellular senescence in macrophages. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (1), 1679 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorfei, M. et al. Comparative proteomic analysis of lung tissue from patients with idiopathic pulmonary fibrosis (IPF) and lung transplant donor lungs. \u003cem\u003eJ. Proteome Res.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (5), 2185\u0026ndash;2205 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDusso, A. S., Bauerle, K. T. \u0026amp; Bernal-Mizrachi, C. Non-classical Vitamin D Actions for Renal Protection. \u003cem\u003eFront. Med. (Lausanne)\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 790513 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y., Spataro, B. C., Yang, J., Dai, C. \u0026amp; Liu, Y. 1,25-dihydroxyvitamin D inhibits renal interstitial myofibroblast activation by inducing hepatocyte growth factor expression. \u003cem\u003eKidney Int.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e (4), 1500\u0026ndash;1510 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian, J., Liu, Y., Williams, L. A. \u0026amp; de Zeeuw, D. Potential role of active vitamin D in retarding the progression of chronic kidney disease. \u003cem\u003eNephrol. Dial Transpl.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (2), 321\u0026ndash;328 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReyna-Jeldes, M. et al. Purinergic P2Y2 and P2X4 Receptors Are Involved in the Epithelial-Mesenchymal Transition and Metastatic Potential of Gastric Cancer Derived Cell Lines. \u003cem\u003ePharmaceutics\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 8 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChadet, S. et al. P2x4 receptor promotes mammary cancer progression by sustaining autophagy and associated mesenchymal transition. \u003cem\u003eOncogene\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e (21), 2920\u0026ndash;2931 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorpos, \u0026Eacute;., De\u0026aacute;k, F. \u0026amp; Kiss, I. Matrilin-2, an extracellular adaptor protein, is needed for the regeneration of muscle, nerve and other tissues. \u003cem\u003eNeural Regen Res.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (6), 866\u0026ndash;869 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, J. et al. Matrilin-2 regulates proliferation, apoptosis and cell cycle during radiation-induced injury in HPAEpiC cell. \u003cem\u003eBiochem. Biophys. Res. Commun.\u003c/em\u003e \u003cb\u003e485\u003c/b\u003e (3), 577\u0026ndash;583 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinke, M. et al. Chronic signaling via the metabolic checkpoint kinase mTORC1 induces macrophage granuloma formation and marks sarcoidosis progression. \u003cem\u003eNat. Immunol.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (3), 293\u0026ndash;302 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrtwich, L. et al. DNA Damage Signaling Instructs Polyploid Macrophage Fate in Granulomas. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e167\u003c/b\u003e (5), 1264\u0026ndash;1280e1218 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, Y. et al. Knockdown of FBXO22 inhibits melanoma cell migration, invasion and angiogenesis via the HIF-1α/VEGF pathway. \u003cem\u003eInvest. New. Drugs\u003c/em\u003e. \u003cb\u003e38\u003c/b\u003e (1), 20\u0026ndash;28 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, J. et al. Emerging role of FBXO22 in carcinogenesis. \u003cem\u003eCell. Death Discov\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e, 66 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, M. L. et al. A fibroblast-dependent TGF-β1/sFRP2 noncanonical Wnt signaling axis promotes epithelial metaplasia in idiopathic pulmonary fibrosis. \u003cem\u003eJ Clin. Invest\u003c/em\u003e ;\u003cb\u003e134\u003c/b\u003e(18). (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, M. et al. Targeting fibrosis, mechanisms and cilinical trials. \u003cem\u003eSignal. Transduct. Target. Ther.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (1), 206 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui, B. et al. Targeting ROR1 inhibits epithelial-mesenchymal transition and metastasis. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cb\u003e73\u003c/b\u003e (12), 3649\u0026ndash;3660 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamazaki, M. et al. YAP/BRD4-controlled ROR1 promotes tumor-initiating cells and hyperproliferation in pancreatic cancer. \u003cem\u003eEmbo j.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (14), e112614 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sarcoidosis, Proteomics, X-Rays, Radiology, Lung Diseases, Interstitial","lastPublishedDoi":"10.21203/rs.3.rs-7358697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7358697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSarcoidosis is a granulomatous disease of unknown cause with a highly variable clinical course. The inability to predict progressive inflammation, fibrosis, or both underscores the limited understanding of the underlying molecular mechanisms. This study aimed to identify novel protein signatures associated with distinct pulmonary phenotypes of sarcoidosis, including progressive inflammation, progressive fibrosis, and disease resolution. We performed SomaScan 11K Assay to measure more than 10,000 unique human plasma proteins and compared protein expression between chest CT-defined phenotypes using principal component analysis, differential expression, correlation analysis, and gene set enrichment analysis. We identified distinct proteomic signatures that differentiated progressive fibrosis from progressive nodular inflammation in sarcoidosis. Enrichment and differential expression analyses revealed that progressive fibrosis was associated with epithelial\u0026ndash;mesenchymal transition pathways, while progressive nodular disease was linked to mTORC1 and MYC signaling, as well as metabolic activation. Additionally, expression of 44 proteins correlated moderately to strongly with thoracic lymph node enlargement, suggesting immune activity in enlarged lymph node may be reflected in the circulating proteomic signals. This study leverages a unique longitudinal imaging approach to define extreme pulmonary phenotypes based on serial chest CT scoring, enabling the discovery of proteomic signals linked to distinct trajectories of sarcoidosis progression.\u003c/p\u003e","manuscriptTitle":"Plasma Proteomic Profiling Reveals Distinct Signatures of Chest CT Phenotypes in Sarcoidosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:01:42","doi":"10.21203/rs.3.rs-7358697/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcd85108-709f-427c-ae70-7fceb9d990fe","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59847149,"name":"Health sciences/Biomarkers"},{"id":59847150,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":59847151,"name":"Health sciences/Diseases"},{"id":59847152,"name":"Biological sciences/Genetics"},{"id":59847153,"name":"Biological sciences/Immunology"},{"id":59847154,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-04-24T13:56:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:01:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7358697","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7358697","identity":"rs-7358697","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}