Metabolomics approach by 1H NMR spectroscopy of serum reveals progression axes for asymptomatic hyperuricemia and gout

Zhang, Yannan; Zhang, Huanzhen; Dong, Chaofang; Guo, Fuchuan; Pan, Hongzhi; Yang, Yuexin

doi:10.1186/s13075-018-1600-5

Cited by 65 publications

(64 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the importance of using glucose in the profiling of a pre-disease state was stablished in another clinical trial, where healthy people and patients with different levels of T2D presented an increase on glucose concentration depending on the disease state (T2D and its complications) [28]. In addition, Zhang et al [19] aimed to identify the biomarker signature of pre-states in metabolic diseases by serum profiling with NMR. Principal components analysis and orthogonal partial least squares-discriminant analysis were used to distinguish between samples from patients and healthy controls.…”

Section: Glucosementioning

confidence: 99%

“…Principal components analysis and orthogonal partial least squares-discriminant analysis were used to distinguish between samples from patients and healthy controls. In this study, glucose was highly expressed and included in the suggested metabolic profile for the early prediction [19]. However, taking into account the wide and easy extended use of glucometers to measure glycaemia, measure glucose levels with NMR analysis will be not justified unless additional parameters would be obtained in the same NMR profile.…”

Section: Glucosementioning

confidence: 99%

“…Then we will discuss the involvement of new biomarkers in the early stages of MetS distributed in the following list of molecular clusters: carbohydrate metabolism, dyslipidemia, inflammation, oxidative stress, and gut microbiota dysbiosis. [25,26] [ 19,27,28] Lactate Increased Serum, urine Carbohydrate disruption Gluconeogenesis, Pyruvate metabolism [29,30] [27, 31,32] Increased Urine Gut microbiota metabolism [33] Uric [81,82] Branched chain aminoacids (BCAAs); aromatic aminoacids (AAAs); trimethylamine N-oxide (TMAO); trimethylamine (TMA); dimethylamine (DMA); lysophospholipids (LPCs); N-acetylglycoproteins (NAG); saturated fatty acids (SFA); monounsaturated fatty acids (MUFAs); polyunsaturated fatty acids (PUFAs); eicosapentaenoic acid (EPA); docosahexaenoic acid (DHA); arachidonic acid (ARA); arachidonic acid (AA).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

et al. 2020

View full text Add to dashboard Cite

The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular markers of pre-disease stages. A promising source of new molecular markers are metabolomics standing out the research of biomarkers in NMR approaches. An increasing number of nutritionists integrate metabolomics into their study design, making nutrimetabolomics one of the most promising avenues for improving personalized nutrition. This review highlight the major five risk factors associated with metabolic syndrome and related diseases including carbohydrate dysfunction, dyslipidemia, oxidative stress, inflammation, and gut microbiota dysbiosis. Together, it is proposed a profile of metabolites of each risk factor obtained from NMR approaches to target them using personalized nutrition, which will improve the quality of life for these patients.

show abstract

Section: Glucosementioning

confidence: 99%

Section: Glucosementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Most of the metabolic differences found in this study were amino acids, which were different from previous studies. Many of the previous studies were lipid metabolites [7,9], which might be related to the inclusion criteria of our enrolled population. Adenosine is a nucleoside that is composed of adenine and D-ribose.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, several studies have reported the preliminary application of metabolomics in gout. Most of these studies were based on gas chromatography-mass spectrometer (GC-MS) or nuclear magnetic resonance (NMR) spectroscopy platforms [6][7][8][9]. Very few studies have applied a more sensitive metabolomics platform, ultraperformance liquid chromatography-mass spectrometry (UPLC-MS).…”

Section: Introductionmentioning

confidence: 99%

Plasma Metabolomics Signature of Gout and Asymptomatic Hyperuricemia

Zhang

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Gout is a metabolic disease and is the most common form of inflammatory arthritis affecting men. However, the pathogenesis of gout is still uncertain, and novel biomarkers are needed for early prediction and diagnosis of gout. To reveal the metabolic alterations in plasma of gout patients and hyperuricemia patients and discover novel molecular biomarkers for early diagnosis. Metabonomics was employed to screen and identify novel biomarkers of gout based on human plasma. High performance liquid chromatography-diode array detector (HPLC-DAD) and orthogonal signal correction partial least squares discriminate analysis (OPLS-DA) were also used for metabonomics study.Results: 80 and 62 features were selected as remarkable significant variables in the two modes between gout and control group, 90 and 50 features between hyperuricemia (HUA) and control group, 63 and 60 features between gout and HUA group, respectively. 25 potential metabolic biomarkers which at least in two comparison groups were remained. Among 25 metabolites, 34% presented high area under the curve (AUC) values (AUC >0.75). Four metabolites including Lys-Ser, L-Pipecolic acid, glycine, arecoline were screened out. They were used to distinguish gout from hyperuricemia with AUC>0.75, which was greater than the AUC of uric acid.Conclusion: The differential metabolites of gout screened out were involved in amino acid metabolism, including glycine, serine and threonine metabolism, arginine biosynthesis, glutathione metabolism. Lys-Ser, L-pipecolic acid, glycine, arecoline were down-regulated in gout patients compared with hyperuricemia patients. The metabolomics signatures could serve as an efficient tool for early diagnosis and provide novel insights into the pathogenesis of gout.

show abstract

Prediagnostic Amino Acid Metabolites and Risk of Gout, Accounting for Serum Urate: Prospective Cohort Study and Mendelian Randomization

McCormick,

Joshi,

Yokose

et al. 2024

Arthritis Care & Research

View full text Add to dashboard Cite

ObjectivesOur objective was to prospectively investigate pre‐diagnostic population‐based metabolome for risk of hospitalized gout (i.e., most accurate, severe, and costly cases), accounting for serum urate.MethodsWe conducted pre‐diagnostic metabolome‐wide analyses among 249,677 UK Biobank participants with NMR metabolomic profiling (N=168 metabolites, including eight amino acids) from baseline blood samples (2006‐2010), without a history of gout. We calculated multivariable hazard ratios (HRs) for incident hospitalized gout, before and after adjusting for serum urate levels; we included non‐hospitalised incident gout cases in a sensitivity analysis. Potential causal effects were evaluated with two‐sample Mendelian randomization.ResultsCorrecting for multiple testing, 107 metabolites were associated with incidence of hospitalized gout (N=2735) before urate adjustment, including glycine and glutamine (inversely; HR=0.64 [95% CI: 0.54, 0.75], P=8.3x10‐8 and HR=0.69 [0.61, 0.78], P=3.3x10‐9 between extreme quintiles, respectively), and glycoprotein acetyls (GlycA; HR=2.48 [2.15, 2.87], P=1.96x10‐34). Associations remained significant and directionally‐consistent following urate adjustment (HR=0.83 [0.70, 0.98], 0.86 [0.76, 0.98], 1.41 [1.21, 1.63] between extreme quintiles), respectively; corresponding HR per SD were 0.91 (0.86, 0.97), 0.94 (0.91, 0.98), and 1.10 (1.06, 1.14). Findings persisted when including non‐hospitalised incident gout cases. Mendelian randomization corroborated their potential causal role on hyperuricemia or gout risk; with change in urate levels of ‐0.05 mg/dL (‐0.08, ‐0.01), and ‐0.12 mg/dL (‐0.22, ‐0.03), per SD of glycine and glutamine, respectively, and ORs 0.94 (0.88, 1.00), and 0.81 (0.67, 0.97), for gout.ConclusionThese prospective findings with causal implications could lead to biomarker‐based risk prediction and potential supplementation‐based interventions with glycine or glutamine.

show abstract

Metabolomics approach by 1H NMR spectroscopy of serum reveals progression axes for asymptomatic hyperuricemia and gout

Cited by 65 publications

References 58 publications

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

Plasma Metabolomics Signature of Gout and Asymptomatic Hyperuricemia

Prediagnostic Amino Acid Metabolites and Risk of Gout, Accounting for Serum Urate: Prospective Cohort Study and Mendelian Randomization

Contact Info

Product

Resources

About