Endotypes of primary osteoarthritis identified by plasma metabolomics analysis

Werdyani, Salem; Liu, Ming; Zhang, Hongwei; Sun, Guang; Furey, Andrew; Randell, Edward; Rahman, Proton; Zhai, Guangju

doi:10.1093/rheumatology/keaa693

Cited by 34 publications

(37 citation statements)

References 47 publications

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“…Sonh et al showed that several cytokines were elevated in synovial fluid and serum of patients with OA compared with normal samples when looking at an average level; however, it was also obvious that the pattern was very heterogeneous 38. Werdyani et al identified three distinct endotypes using metabolomics 39. One of those clusters showed some association with muscle weakness.…”

Section: Discussionmentioning

confidence: 99%

Osteoarthritis endotype discovery via clustering of biochemical marker data

et al. 2022

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ObjectivesOsteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning.MethodData quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression.ResultsThree dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain.ConclusionsThis work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future.Trial registration numberNCT03883568.

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Section: Discussionmentioning

confidence: 99%

Osteoarthritis endotype discovery via clustering of biochemical marker data

et al. 2022

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“…In the last decade, OA has been recognized as a whole-organ disorder affecting various joint tissues in different degrees depending on the disease stage or the specific patient phenotype and endotype [28]; novel insights are anticipated using high-throughput OMIC technologies [29]. Moreover, OA is not a single entity, and current research focuses on personalized OA management according to OA subtypes and main drivers of disease progression [30].…”

Section: Discussionmentioning

confidence: 99%

Unraveling the Signaling Secretome of Platelet-Rich Plasma: Towards a Better Understanding of Its Therapeutic Potential in Knee Osteoarthritis

Amo

Perez‐Valle

Atilano

et al. 2022

JCM

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Platelets and their secretory products play an important role in determining the balance between tissue repair and tissue damage. To obtain novel insights into the molecular composition of platelet-rich plasma (PRP) and contextualize them in knee osteoarthritis (OA), two different plasma formulations, namely PRP and platelet-poor plasma (PPP), were prepared from six healthy donors following a biobank-automated protocol. Inter-donor differences were analyzed, and pools were created before performing multiplexing protein arrays. In addition, PRP and PPP were prepared from six patients following our in-house protocols. Supernatants from PRP and PPP were harvested one hour after calcium chloride activation. Multiplexing protein arrays were performed in parallel for all plasma formulations. Results were normalized to fold change in relation to PPP and examined using Ingenuity Pathway Analysis Software. Bioinformatic predictions showed that PRPs constitute a signaling system with interrelated networks of inflammatory and angiogenic proteins, including but not limited to interleukin-6 and -8 (IL-6, IL-8), insulin like growth factor 1 (IGF-1), transforming growth factor beta, (TGF-b), and vascular endothelial growth factor (VEGF) signaling, underlying biological actions. Predictions of canonical systems activated with PRP molecules include various inflammatory pathways, including high-mobility group box protein (HMGB1) and interleukin 17 (IL-17) signaling, neuroinflammation, and nuclear factor-kappa b (NF-κB) pathways. Eventually, according to these predictions and OA evolving knowledge, selected PRP formulations should be tailored to modulate different inflammatory phenotypes, i.e., meta-inflammation, inflame-aging or posttraumatic inflammatory osteoarthritis. However, further research to discriminate the peculiarities of autologous versus allogeneic formulations and their effects on the various OA inflammatory phenotypes is needed to foster PRPs.

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“…For validation purposes, fasting plasma samples were derived from the Newfoundland and Labrador cohort in which the controls were from the Complex Diseases in Newfoundland population: Environment and Genetics (CODING) [ 35 ] and the OA samples from the Newfoundland Osteoarthritis Study (NFOAS; https://www.med.mun.ca/NFOAS/Home.aspx ) [ 36 ]. Plasma samples were from 20 controls and 20 OA, the latter equally divided into OA-obese ( n =10) and OA-non-obese ( n =10).…”

Section: Methodsmentioning

confidence: 99%

Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers

et al. 2022

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Background Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers. Methods Serum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography–tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis. Results In total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064). Conclusion Our study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

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Endotypes of primary osteoarthritis identified by plasma metabolomics analysis

Cited by 34 publications

References 47 publications

Osteoarthritis endotype discovery via clustering of biochemical marker data

Osteoarthritis endotype discovery via clustering of biochemical marker data

Unraveling the Signaling Secretome of Platelet-Rich Plasma: Towards a Better Understanding of Its Therapeutic Potential in Knee Osteoarthritis

Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers

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