Urinary metabolomics as a potentially novel diagnostic and stratification tool for knee osteoarthritis

Li, Xin; Yang, Songbing; Qiu, Yunping; Zhao, Tengteng; Chen, Tianlu; Su, Mingming; Chu, Lixi; Ai-ping, LV; Liu, Ping; Wang, Jia

doi:10.1007/s11306-009-0184-0

Cited by 41 publications

(41 citation statements)

References 46 publications

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“…All prior metabolomic studies in humans have been cross-sectional in nature comparing individuals with or without OA using either urine 18, 40 , serum 19 , synovial fluid 41 or plasma 20 . An NMR-based urine metabolomics study that used samples from the Johnston County Osteoarthritis project found differences in participants with radiographic hip or knee OA from controls and noted a correlation of a metabolite profile with radiographic OA severity 18 .…”

Section: Discussionmentioning

confidence: 99%

Association of urinary metabolites with radiographic progression of knee osteoarthritis in overweight and obese adults: an exploratory study

Loeser

Pathmasiri

Sumner

et al. 2016

Osteoarthritis and Cartilage

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Summary Introduction Metabolic factors may contribute to osteoarthritis (OA).This study employed metabolomics analyses to determine if differences in metabolite profiles could distinguish people with knee OA who exhibited radiographic progression. Methods Urine samples obtained at baseline and 18 months from overweight and obese adults in the Intensive Diet and Exercise for Arthritis (IDEA) trial were selected from two subgroups (n=22 each) for metabolomics analysis: a group that exhibited radiographic progression (≥ 0.7mm decrease in joint space width, JSW) and an age, gender, and BMI matched group who did not progress (≤0.35mm decrease in JSW). Multivariate analysis methods, including orthogonal partial least square discriminate analysis, were used to identify metabolite profiles that separated progressors and non-progressors. Plasma levels of IL-6 and C-reactive protein were evaluated as inflammatory markers. Results Multivariate analysis of the binned metabolomics data distinguished progressors from non-progressors. Library matching revealed that glycolate, hippurate, and trigonelline were among the important metabolites for distinguishing progressors from non-progressors at baseline whereas alanine, N,N-dimethyglycine, glycolate, hippurate, histidine, and trigonelline, were among the metabolites that were important for the discrimination at 18 months. In non-progressors, IL-6 decreased from baseline to 18 months while IL-6 was unchanged in progressors; the change over time in IL-6 was significantly different between groups. Conclusion These findings support a role for metabolic factors in the progression of knee OA and suggest that measurement of metabolites could be useful to predict progression. Further investigation in a larger sample that would include targeted investigation of specific metabolites is warranted.

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Section: Discussionmentioning

confidence: 99%

Association of urinary metabolites with radiographic progression of knee osteoarthritis in overweight and obese adults: an exploratory study

Loeser

Pathmasiri

Sumner

et al. 2016

Osteoarthritis and Cartilage

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“…Up to now, metabolomics has found applications in many chronic metabolic disorders, such as diabetes, lung disease, neurodegenerative disease, cancer, hypertension, and cardiovascular diseases [3,4,5]. In recent years, metabolomics have gained popularity in the orthopedic field, including osteonecrosis, osteoarthritis, ankylosing spondylitis, bone tumors and osteoporosis [6,7,8,9]. However, the application of metabolomics in osteoporosis has just begun.…”

Section: Introductionmentioning

confidence: 99%

Metabolomics and Its Application in the Development of Discovering Biomarkers for Osteoporosis Research

Jiang

Guan

et al. 2016

IJMS

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Osteoporosis is a progressive skeletal disorder characterized by low bone mass and increased risk of fracture in later life. The incidence and costs associated with treating osteoporosis cause heavy socio-economic burden. Currently, the diagnosis of osteoporosis mainly depends on bone mineral density and bone turnover markers. However, these indexes are not sensitive and accurate enough to reflect the osteoporosis progression. Metabolomics offers the potential for a holistic approach for clinical diagnoses and treatment, as well as understanding of the pathological mechanism of osteoporosis. In this review, we firstly describe the study subjects of osteoporosis and bio-sample preparation procedures for different analytic purposes, followed by illustrating the biomarkers with potentially predictive, diagnosis and pharmaceutical values when applied in osteoporosis research. Then, we summarize the published metabolic pathways related to osteoporosis. Furthermore, we discuss the importance of chronological data and combination of multi-omics in fully understanding osteoporosis. The application of metabolomics in osteoporosis could provide researchers the opportunity to gain new insight into the metabolic profiling and pathophysiological mechanisms. However, there is still much to be done to validate the potential biomarkers responsible for the progression of osteoporosis and there are still many details needed to be further elucidated.

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“…Recent studies have shown the use of mass spectrometry-based (MS-based) metabolomics as a reliable tool for detecting biomarker patterns for the purpose of finding differences among samples. This information has proven effective for disease diagnosis (Cortes et al 2010;Li et al 2010;Pasikanti et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool

et al. 2011

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An MS-based metabolomics strategy including variable selection and PLSDA analysis has been assessed as a tool to discriminate between non-steatotic and steatotic human liver profiles. Different chemometric approaches for uninformative variable elimination were performed by using two of the most common software packages employed in the field of metabolomics (i.e., MATLAB and SIMCA-P). The first considered approach was performed with MATLAB where the PLS regression vector coefficient values were used to classify variables as informative or not. The second approach was run under SIMCA-P, where variable selection was performed according to both the PLS regression vector coefficients and VIP scores. PLSDA models performance features, such as model validation, variable selection criteria, and potential biomarker output, were assessed for comparison purposes. One interesting finding is that variable selection improved the classification predictiveness of all the models by facilitating metabolite identification and providing enhanced insight into the metabolic information acquired by the UPLC-MS method. The results prove that the proposed strategy is a potentially straightforward approach to improve model performance. Among others, GSH, lysophospholipids and bile acids were found to be the most important altered metabolites in the metabolomic profiles studied. However, further research and more in-depth biochemical interpretations are needed to unambiguously propose them as disease biomarkers.

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Urinary metabolomics as a potentially novel diagnostic and stratification tool for knee osteoarthritis

Cited by 41 publications

References 46 publications

Association of urinary metabolites with radiographic progression of knee osteoarthritis in overweight and obese adults: an exploratory study

Association of urinary metabolites with radiographic progression of knee osteoarthritis in overweight and obese adults: an exploratory study

Metabolomics and Its Application in the Development of Discovering Biomarkers for Osteoporosis Research

Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool

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