Synovial tissue metabolomic profiling reveal biomarkers of synovial inflammation in patients with osteoarthritis

Murillo-Saich, Jessica; Coras, Roxana; Meyer, Robert E.; Llorente, Cristina; Lane, Nancy E.; Gumá, Mónica

doi:10.1016/j.ocarto.2022.100295

Cited by 7 publications

(4 citation statements)

References 54 publications

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“…Valine was also predictive for OA and is an essential branched chain amino acid (BCAA). Valine has previously been associated with the severity of in ammation in synovial tissue 51 . BCAAs may play a role in increased in ammation, reduced autophagy, and increased insulin resistance in OA 52 .…”

Section: Discussionmentioning

confidence: 96%

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using an interpretable machine learning framework: a UK biobank study

Gupta,

Nielsen,

Monfeuga

et al. 2023

Preprint

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Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients’ lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA, integrating clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC:0.72 95%CI (0.71 – 0.73)). Higher age, BMI, and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction. 14 sub-groups of OA risk profiles were identified, and validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients uniquely assigned to one of the sub-groups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g. GDF5 and TGF-β signalling) and identified OA-specific biomarkers (e.g. CRTAC1 and COL9A1). In summary, this work identified opportunities for personalised OA prevention and insights into its underlying pathogenesis.

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Section: Discussionmentioning

confidence: 96%

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using an interpretable machine learning framework: a UK biobank study

Gupta,

Nielsen,

Monfeuga

et al. 2023

Preprint

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“…Valine was also predictive for OA and is an essential branched chain amino acid (BCAA). Valine has previously been associated with the severity of inflammation in synovial tissue 60 . BCAAs may play a role in increased inflammation, reduced autophagy, and increased insulin resistance in OA 61 .…”

Section: Discussionmentioning

confidence: 97%

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Nielsen,

Monfeuga,

Kitchen

et al. 2024

Nat Commun

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Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients’ lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71–0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.

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“…5,6 Additionally, associations between various metabolites, for example, lactate and glutamine, and the synovitis scores of osteoarthritic patients were recently noted. 7 Hence, evaluations of various CEST contrasts in tissues such as the synovial fluid or membrane are of interest for noninvasive investigation of arthritic diseases. However, certain challenges complicate the use of knee CEST MRI in clinical research settings.…”

Section: Introductionmentioning

confidence: 99%

“…However, evaluations of synovial fluid and serum showed that the concentrations of other metabolites (e.g., glucose, Cho, lactate, and lipids) also differ across different types of arthritic diseases 5,6 . Additionally, associations between various metabolites, for example, lactate and glutamine, and the synovitis scores of osteoarthritic patients were recently noted 7 . Hence, evaluations of various CEST contrasts in tissues such as the synovial fluid or membrane are of interest for noninvasive investigation of arthritic diseases.…”

Section: Introductionmentioning

confidence: 99%

Multi‐echo–based fat artifact correction for CEST MRI at 7 T

Tkotz,

Liebert,

Gast

et al. 2023

Magnetic Resonance in Med

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PurposeCEST MRI is influenced by fat signal, which can reduce the apparent CEST contrast or lead to pseudo‐CEST effects. Our goal was to develop a fat artifact correction based on multi‐echo fat–water separation that functions stably for 7 T knee MRI data.MethodsOur proposed algorithm utilizes the full complex data and a phase demodulation with an off‐resonance map estimation based on the Z‐spectra prior to fat–water separation to achieve stable fat artifact correction. Our method was validated and compared to multi‐echo–based methods originally proposed for 3 T by Bloch–McConnell simulations and phantom measurements. Moreover, the method was applied to in vivo 7 T knee MRI examinations and compared to Gaussian fat saturation and a published single‐echo Z‐spectrum–based fat artifact correction method.ResultsPhase demodulation prior to fat–water separation reduced the occurrence of fat–water swaps. Utilizing the complex signal data led to more stable correction results than working with magnitude data, as was proposed for 3 T. Our approach reduced pseudo‐nuclear Overhauser effects compared to the other correction methods. Thus, the mean asymmetry contrast at 3.5 ppm in cartilage over five volunteers increased from −9.2% (uncorrected) and −10.6% (Z‐spectrum–based) to −1.5%. Results showed higher spatial stability than with the fat saturation pulse.ConclusionOur work demonstrates the feasibility of multi‐echo–based fat–water separation with an adaptive fat model for fat artifact correction for CEST MRI at 7 T. Our approach provided better fat artifact correction throughout the entire spectrum and image than the fat saturation pulse or Z‐spectrum–based correction method for both phantom and knee imaging results.

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Synovial tissue metabolomic profiling reveal biomarkers of synovial inflammation in patients with osteoarthritis

Cited by 7 publications

References 54 publications

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using an interpretable machine learning framework: a UK biobank study

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using an interpretable machine learning framework: a UK biobank study

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Multi‐echo–based fat artifact correction for CEST MRI at 7 T

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