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Urate

Urate, also known as uric acid, is a natural chemical compound found in the human body.
It is the end product of purine metabolism and is primarily excreted through the kidneys.
Urate levels can be influenced by various factors, including diet, genetics, and certain medical conditions.
Elevated urate levels can lead to the development of gout, a type of arthritis characterized by painful inflammation in the joints.
Understanding urate metabolism and regulation is crucial for maintaining overall health and preventing urate-related disorders.
This MeSH term provides a concise overview of urate and its importance in clinical and research settings.

Most cited protocols related to «Urate»

1
Causal Effect Estimation Using MR Methods
In all simulation scenarios, causal effect estimates were obtained using established MR methods (multiplicative random effects IVW,7 (link) multiplicative random effects MR-Egger regression7 (link) and weighted median, all implemented using inverse-variance weights calculated under NOME), as well as the simple and the weighted MBEs. Each version of the MBE was evaluated using weights calculated with and without making the NOME assumption, thus yielding four MBEs. Each of these four methods was evaluated for two values of the tuning parameter ϕ ∈{1,0.5} , totalling eight versions of the MBE method. Parametric bootstrap was used to estimate the standard errors of the MBE using the median absolute deviation from the median (multiplied by 1.4826 for asymptotically normal consistency) of the bootstrap distribution of causal effect estimates. These were used to derive symmetrical confidence intervals.
In each scenario, coverage, power and average causal effect estimates, standard errors, F¯GX−1F¯GX and IGX2 statistics (which quantify the magnitude of violation of the NOME assumption in IVW and MR-Egger regression estimates, respectively7 (link),13 (link)) were obtained across 10 000 simulated datasets. Power was defined as the proportion of times that 95% confidence intervals excluded zero, and coverage as the proportion of times that 95% confidence intervals included the true causal effect.
MR methods were also applied to estimate the causal effect of plasma lipid fractions and urate levels on CHD risk. The magnitude of regression dilution bias in IVW and MR-Egger regression was assessed by the F¯GX−1F¯GX and IGX2 statistics, respectively. Cochran’s Q test was used to test for the presence of horizontal pleiotropy (under the assumption that this is the only source of heterogeneity between β^Rj s other than chance).20 (link) All simulations and analyses were performed using R 3.3.1 [www.r-project.org]. R code for implementing the MBE is provided in Supplementary Methods (available as Supplementary data at IJE online).
Hartwig F.P., Davey Smith G, & Bowden J. (2017). Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. International Journal of Epidemiology, 46(6), 1985-1998.
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Publication 2017
Genetic Heterogeneity Lipids Plasma Technique, Dilution Urate
2
Causal Effects of Lipids and Urate on CHD
Do and colleagues16 (link) performed a two-sample MR analysis to evaluate the causal effect of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides on coronary heart disease (CHD) risk, using a total of 185 genetics variants. Summary association results were obtained from the Global Lipids Genetics Consortium17 (link) and the Coronary Artery Disease Genome-Wide Replication and Meta-Analysis Consortium,18 (link) and were downloaded from Do and colleagues’ supplementary material (standard errors were estimated based on the regression coefficients and P-values). Genetic variants were classified as instruments for each lipid fraction using a statistical criterion (P < 1 × 10−8), resulting in 73 instruments for LDL-C, 85 for HDL-C and 31 for triglycerides.
White and colleagues19 (link) performed a similar analysis, but with plasma urate levels rather than lipid fractions. 31 variants associated with urate levels (P < 5 × 10−7) were used as genetic instruments, and the required summary statistics were obtained from the GWAS catalogue [https://www.ebi.ac.uk/gwas/].
Hartwig F.P., Davey Smith G, & Bowden J. (2017). Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. International Journal of Epidemiology, 46(6), 1985-1998.
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Publication 2017
Cholesterol, beta-Lipoprotein Coronary Arteriosclerosis DNA Replication Genetic Diversity Genome Genome-Wide Association Study Heart Disease, Coronary High Density Lipoprotein Cholesterol Lipids Plasma Reproduction Triglycerides Urate
3
Gout Management Case Scenarios
The TFP evaluated scenarios with a broad spectrum of clinical gout, similar to what a clinician might see in a busy practice, and divided into mild, moderate, and severe disease activity in each of three distinct “treatment groups” (Figure 1A–B). In generating these nine fundamental clinical case scenarios, mild disease activity levels in each “treatment group” were meant to represent patients at the lowest disease activity level for which most clinicians would consider initiating or altering a specific medication regimen. Conversely, severe disease activity level was intended to represent patients with disease activity equal or greater to that of the “average” subject studied in a clinical trial. The case scenarios were not intended to serve as classification criteria. To allow the TFP to focus on management decisions, each case scenario had the assumption not only that the diagnosis of gout was correct, and that there was some clinical evidence of gout disease activity. This included intermittent symptoms of variable frequency, specifically presented to the TFP as episodes of acute gouty arthritis of at least moderate to severe pain intensity (17 ). Clinical evidence of gout disease activity, presented to the TFP, also included one or more tophi detected by physical exam, or alternatively, chronic symptomatic arthritis (ie, “chronic arthropathy” or “synovitis”) due to gout, with or without confirmed joint damage (e.g., deformity, erosion due to gout on imaging study). Hyperuricemia was defined here as serum urate >6.8 mg/dL (2 (link)). We determined all aspects of case scenario definitions by a structured iterative process, using regular electronic mail, and teleconferences at least once per month. Multiple revisions to the proposed parameters were carried out, until accepted by the CEP domain leaders.
Khanna D., FitzGerald J.D., Khanna P.P., Bae S., Singh M., Neogi T., Pillinger M.H., Merill J., Lee S., Prakash S., Kaldas M., Gogia M., Perez-Ruiz F., Taylor W., Lioté F., Choi H., Singh J.A., Dalbeth N., Kaplan S., Niyyar V., Jones D., Yarows S.A., Roessler B., Kerr G., King C., Levy G., Furst D.E., Edwards N.L., Mandell B., Schumacher H.R., Robbins M., Wenger N, & Terkeltaub R. (2012). 2012 American College of Rheumatology Guidelines for Management of Gout Part I: Systematic Non-pharmacologic and Pharmacologic Therapeutic Approaches to Hyperuricemia. Arthritis care & research, 64(10), 1431-1446.
Publication 2012
Arthritis Arthritis, Gouty Arthropathy Congenital Abnormality Diagnosis Gout Hyperuricemia Joints Patients Pharmaceutical Preparations Physical Examination Serum Severity, Pain Synovitis Treatment Protocols Urate
4
Genetic Variants Associated with Serum Urate
To further identify genetic variants associated with serum urate concentrations and gout, several additional analyses were carried out. These included X-chromosome analyses, sex-stratified analyses, a gene-based test and urate transporter candidate analyses, as well as secondary analyses to improve the characterization of associated SNPs. To this end, we investigated associations with FEUA, associations in individuals of non-European ancestry, associations with transcript expression, associations with serum metabolite concentrations, risk score analyses and associations with other urate-related phenotypes. Secondary analyses are described in detail in the Supplementary Note.
As a result of the secondary analyses, seven additional SNPs were identified for further replication testing: one from the analysis in men only, five from the analysis in women and one from the urate transporter candidate analysis.
A new part of the secondary analyses was the implementation of functional association networks as described in the Supplementary Note. As detailed there, the approach based on functional associations among the urate genes, mostly protein-protein interactions, led to the identification of 17 additional independent SNPs in newly identified genomic regions that were subjected to replication testing. Altogether, we therefore tested 61 SNPs (37 overall, 6 sex specific, 1 urate transporter candidate and 17 network) for replication in additional study samples.
Köttgen A., Albrecht E., Teumer A., Vitart V., Krumsiek J., Hundertmark C., Pistis G., Ruggiero D., O’Seaghdha C.M., Haller T., Yang Q., Tanaka T., Johnson A.D., Kutalik Z., Smith A.V., Shi J., Struchalin M., Middelberg R.P., Brown M.J., Gaffo A.L., Pirastu N., Li G., Hayward C., Zemunik T., Huffman J., Yengo L., Zhao J.H., Demirkan A., Feitosa M.F., Liu X., Malerba G., Lopez L.M., van der Harst P., Li X., Kleber M.E., Hicks A.A., Nolte I.M., Johansson A., Murgia F., Wild S.H., Bakker S.J., Peden J.F., Dehghan A., Steri M., Tenesa A., Lagou V., Salo P., Mangino M., Rose L.M., Lehtimäki T., Woodward O.M., Okada Y., Tin A., Müller C., Oldmeadow C., Putku M., Czamara D., Kraft P., Frogheri L., Thun G.A., Grotevendt A., Gislason G.K., Harris T.B., Launer L.J., McArdle P., Shuldiner A.R., Boerwinkle E., Coresh J., Schmidt H., Schallert M., Martin N.G., Montgomery G.W., Kubo M., Nakamura Y., Tanaka T., Munroe P.B., Samani N.J., Jacobs DR J.r., Liu K., D’Adamo P., Ulivi S., Rotter J.I., Psaty B.M., Vollenweider P., Waeber G., Campbell S., Devuyst O., Navarro P., Kolcic I., Hastie N., Balkau B., Froguel P., Esko T., Salumets A., Khaw K.T., Langenberg C., Wareham N.J., Isaacs A., Kraja A., Zhang Q., Wild P.S., Scott R.J., Holliday E.G., Org E., Viigimaa M., Bandinelli S., Metter J.E., Lupo A., Trabetti E., Sorice R., Döring A., Lattka E., Strauch K., Theis F., Waldenberger M., Wichmann H.E., Davies G., Gow A.J., Bruinenberg M., Study L.C., Stolk R.P., Kooner J.S., Zhang W., Winkelmann B.R., Boehm B.O., Lucae S., Penninx B.W., Smit J.H., Curhan G., Mudgal P., Plenge R.M., Portas L., Persico I., Kirin M., Wilson J.F., Leach I.M., van Gilst W.H., Goel A., Ongen H., Hofman A., Rivadeneira F., Uitterlinden A.G., Imboden M., von Eckardstein A., Cucca F., Nagaraja R., Piras M.G., Nauck M., Schurmann C., Budde K., Ernst F., Farrington S.M., Theodoratou E., Prokopenko I., Stumvoll M., Jula A., Perola M., Salomaa V., Shin S.Y., Spector T.D., Sala C., Ridker P.M., Kähönen M., Viikari J., Hengstenberg C., Nelson C.P., Consortium C., Consortium D., Consortium I., Consortium M., Meschia J.F., Nalls M.A., Sharma P., Singleton A.B., Kamatani N., Zeller T., Burnier M., Attia J., Laan M., Klopp N., Hillege H.L., Kloiber S., Choi H., Pirastu M., Tore S., Probst-Hensch N.M., Völzke H., Gudnason V., Parsa A., Schmidt R., Whitfield J.B., Fornage M., Gasparini P., Siscovick D.S., Polašek O., Campbell H., Rudan I., Bouatia-Naji N., Metspalu A., Loos R.J., van Duijn C.M., Borecki I.B., Ferrucci L., Gambaro G., Deary I.J., Wolffenbuttel B.H., Chambers J.C., März W., Pramstaller P.P., Snieder H., Gyllensten U., Wright A.F., Navis G., Watkins H., Witteman J.C., Sanna S., Schipf S., Dunlop M.G., Tönjes A., Ripatti S., Soranzo N., Toniolo D., Chasman D.I., Raitakari O., Kao W.H., Ciullo M., Fox C.S., Caulfield M., Bochud M, & Gieger C. (2012). Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nature genetics, 45(2), 145-154.
Publication 2012
DNA Replication Europeans Genes Genetic Diversity Genetic Testing Genome Gout Phenotype Proteins Serum Single Nucleotide Polymorphism Urate urate transporter Woman X Chromosome
5
Genome-wide QC and Meta-analysis
Each file of genome-wide per-SNP summary statistics underwent extensive quality control before meta-analysis. Examination of file formatting, data plausibility and distributions of test statistics and quality measurements was facilitated by the gwasqc() function of the GWAtoolbox package v1.0.0 in R45 (link). Additionally, the direction and magnitude of effect at the known urate concentration–associated SNP rs16890979 in SLC2A9 was investigated, and the minor allele was consistent in frequency and associated with lower serum urate concentrations in all studies.
Before all meta-analyses, monomorphic SNPs were excluded, and all study-specific results were corrected by the genomic inflation factor of the study if it was >1, calculated by dividing the median of the observed χ-square distribution of the GWAS by the median of the expected χ-square distribution under the null hypothesis of no association. Study-specific inflation factors for urate analyses are shown in Supplementary Table 3. For meta-analyses of gout, only cohorts with >50 gout cases were included, and SNPs with an effect size of |β| > 1,000 were excluded to remove only a minimum number of SNPs with implausibly large effects that could systematically influence the results.
Köttgen A., Albrecht E., Teumer A., Vitart V., Krumsiek J., Hundertmark C., Pistis G., Ruggiero D., O’Seaghdha C.M., Haller T., Yang Q., Tanaka T., Johnson A.D., Kutalik Z., Smith A.V., Shi J., Struchalin M., Middelberg R.P., Brown M.J., Gaffo A.L., Pirastu N., Li G., Hayward C., Zemunik T., Huffman J., Yengo L., Zhao J.H., Demirkan A., Feitosa M.F., Liu X., Malerba G., Lopez L.M., van der Harst P., Li X., Kleber M.E., Hicks A.A., Nolte I.M., Johansson A., Murgia F., Wild S.H., Bakker S.J., Peden J.F., Dehghan A., Steri M., Tenesa A., Lagou V., Salo P., Mangino M., Rose L.M., Lehtimäki T., Woodward O.M., Okada Y., Tin A., Müller C., Oldmeadow C., Putku M., Czamara D., Kraft P., Frogheri L., Thun G.A., Grotevendt A., Gislason G.K., Harris T.B., Launer L.J., McArdle P., Shuldiner A.R., Boerwinkle E., Coresh J., Schmidt H., Schallert M., Martin N.G., Montgomery G.W., Kubo M., Nakamura Y., Tanaka T., Munroe P.B., Samani N.J., Jacobs DR J.r., Liu K., D’Adamo P., Ulivi S., Rotter J.I., Psaty B.M., Vollenweider P., Waeber G., Campbell S., Devuyst O., Navarro P., Kolcic I., Hastie N., Balkau B., Froguel P., Esko T., Salumets A., Khaw K.T., Langenberg C., Wareham N.J., Isaacs A., Kraja A., Zhang Q., Wild P.S., Scott R.J., Holliday E.G., Org E., Viigimaa M., Bandinelli S., Metter J.E., Lupo A., Trabetti E., Sorice R., Döring A., Lattka E., Strauch K., Theis F., Waldenberger M., Wichmann H.E., Davies G., Gow A.J., Bruinenberg M., Study L.C., Stolk R.P., Kooner J.S., Zhang W., Winkelmann B.R., Boehm B.O., Lucae S., Penninx B.W., Smit J.H., Curhan G., Mudgal P., Plenge R.M., Portas L., Persico I., Kirin M., Wilson J.F., Leach I.M., van Gilst W.H., Goel A., Ongen H., Hofman A., Rivadeneira F., Uitterlinden A.G., Imboden M., von Eckardstein A., Cucca F., Nagaraja R., Piras M.G., Nauck M., Schurmann C., Budde K., Ernst F., Farrington S.M., Theodoratou E., Prokopenko I., Stumvoll M., Jula A., Perola M., Salomaa V., Shin S.Y., Spector T.D., Sala C., Ridker P.M., Kähönen M., Viikari J., Hengstenberg C., Nelson C.P., Consortium C., Consortium D., Consortium I., Consortium M., Meschia J.F., Nalls M.A., Sharma P., Singleton A.B., Kamatani N., Zeller T., Burnier M., Attia J., Laan M., Klopp N., Hillege H.L., Kloiber S., Choi H., Pirastu M., Tore S., Probst-Hensch N.M., Völzke H., Gudnason V., Parsa A., Schmidt R., Whitfield J.B., Fornage M., Gasparini P., Siscovick D.S., Polašek O., Campbell H., Rudan I., Bouatia-Naji N., Metspalu A., Loos R.J., van Duijn C.M., Borecki I.B., Ferrucci L., Gambaro G., Deary I.J., Wolffenbuttel B.H., Chambers J.C., März W., Pramstaller P.P., Snieder H., Gyllensten U., Wright A.F., Navis G., Watkins H., Witteman J.C., Sanna S., Schipf S., Dunlop M.G., Tönjes A., Ripatti S., Soranzo N., Toniolo D., Chasman D.I., Raitakari O., Kao W.H., Ciullo M., Fox C.S., Caulfield M., Bochud M, & Gieger C. (2012). Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nature genetics, 45(2), 145-154.
Publication 2012
Alleles Genome Genome-Wide Association Study Gout Serum Single Nucleotide Polymorphism Urate

Most recents protocols related to «Urate»

1
Baseline Characteristics of CKD Patients
Baseline characteristics were recorded by clinical research associates from medical files or by interview. Data included age, sex, body mass index (BMI), hypertension (patients having an office blood pressure greater than or equal to 140/90 mmHg or an antihypertensive treatment), cardiovascular history (coronary artery disease, arrhythmic disorders, congestive heart failure, stroke, peripheral vascular disease and/or valvulopathy), diabetes (diabetes history or antidiabetic treatment or glycated hemoglobin ≥ 6.5% or fasting glycemia ≥ 7 mmol/l or non-fasting glycemia ≥ 11), gout history, dyslipidemia, primary kidney disease, time since CKD diagnosis (time elapsed from the date of CKD diagnosis found in the medical record and the cohort entry), number of consultation in the previous year with nephrologist and dietician, treatment (urate-lowering therapy (ULT), diuretics, antiplatelet agents, renin-angiotensin system inhibitors (RASi)), laboratory data (serum creatinine, eGFR estimated by the CKD-EPI equation, serum UA, albuminemia, C-reactive protein and, albuminuria—or equivalent—classified according to the KDIGO 2012 guidelines16 ), salt intake (estimated by 24-h natriuresis) and protein intake (estimated by 24-h urinary urea)17 (link), medication adherence according to the Girerd score in categories (good, minimal and poor)18 (link), health literacy according to their need for help reading medical documents (never need vs always or partly need)19 (link) and type of center (university, non-university hospital, private non-profit and private for-profit clinic).
Prezelin-Reydit M., Combe C., Fouque D., Frimat L., Jacquelinet C., Laville M., Massy Z.A., Lange C., Ayav C., Pecoits-Filho R., Liabeuf S., Stengel B., Harambat J, & Leffondré K. (2023). Longitudinal uric acid has nonlinear association with kidney failure and mortality in chronic kidney disease. Scientific Reports, 13, 3952.
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Publication 2023
Antidiabetics Antihypertensive Agents Antiplatelet Agents Blood Pressure Cardiovascular System Cerebrovascular Accident Congestive Heart Failure Coronary Artery Disease C Reactive Protein Creatinine Diabetes Mellitus Diagnosis Dietitian Diuretics Dyslipidemias EGFR protein, human Gout Health Literacy Hemoglobin, Glycosylated High Blood Pressures Index, Body Mass inhibitors Kidney Diseases Natriuresis Nephrologists Patients Peripheral Vascular Diseases Proteins Serum Sodium Chloride, Dietary System, Renin-Angiotensin Therapeutics Urate Urea Urine Valve Disease, Heart
2
UK Biobank Kidney Phenotypes Analysis
We considered five kidney-related phenotypes: eGFRcrea (based on creatinine, UKB field 30,700, instance 0), eGFRcys (based on cystatin C, UKB field 30720, instance 0), UACR (UKB fields 30,500 and 30,510), serum urate (UKB field 30,880, instance 0), and urea (UKB field 30,670, instance 0). eGFRcrea and eGFRcys were calculated using the CKD-EPI equations41 (link) and winsorized to 15 and 200 ml/min/1.73 m2. To calculate UACR, values for urinary albumin below the detection limit were set to the detection limit value. All phenotypes were inverse-normal transformed.
We fitted linear regression models to the phenotypes, adjusting for sex, age, and the first 40 genetic principal components, as provided by the UK Biobank. For secondary analyses, the same models were additionally adjusted for 639 SNPs for eGFR28 (link), 63 SNPs for UACR9 (link), and 184 SNPs for urate18 (link) to account for the potential effect of common variants.
CKD and gout were defined using ICD10 codes from hospital inpatient records (N18.*, M10.*, UKB field 41270). Microalbuminuria was defined as UACR > 30 mg/g. ExWAS were carried out for these clinically relevant outcomes and used to annotate the findings for continuous kidney markers with respect to the direction and significance of their association with disease. To further characterize the risk allele carriers of selected trait-associated variants, kidney disease was additionally defined by ICD codes for acute kidney injury (N17.9), CKD (N18.3, N18.4, N18.5, N18.9), polycystic kidney disease (Q61.2, Q61.3), and another kidney (N28.1) or ureter (N39.0) disease. Information on allopurinol treatment was obtained from a verbal interview on medication usage.
Wuttke M., König E., Katsara M.A., Kirsten H., Farahani S.K., Teumer A., Li Y., Lang M., Göcmen B., Pattaro C., Günzel D., Köttgen A, & Fuchsberger C. (2023). Imputation-powered whole-exome analysis identifies genes associated with kidney function and disease in the UK Biobank. Nature Communications, 14, 1287.
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Publication 2023
Albumins Alleles Allopurinol Creatinine Gene Components Gout Inpatient Kidney Kidney Diseases Kidney Injury, Acute Pharmaceutical Preparations Phenotype Polycystic Kidney Diseases Post-gamma-Globulin Serum Single Nucleotide Polymorphism Urate Urea Ureter Urine
3
Whole-Exome Association Analysis of Kidney Traits
A single variant, whole-exome linear mixed model association analyses were performed with the REGENIE software package51 (link) v2.0.2 in two steps:
REGENIE Step 1: Whole-genome regression model using the recommended parameter setting. The UK Biobank DNA microarray genotypes (hap_v2) were lifted to build GRCh38 and filtered using the recommended settings (genotype call rate > 0.1; Hardy–Weinberg equilibrium p-value < 1e−15; MAC > 100; MAF > 0.01; sample call rate > 0.90).
REGENIE Step 2: Association analysis was performed on imputed dosage levels (BGEN v1.2 8-bit data format) for 408,511 individuals and 1,844,188 variants. LD was estimated afterward between all pairs of significantly associated variants using PLINK v1.90b252 (link). We restricted the association analysis to variants with MAC ≥ 5, as recommended by the REGENIE developers51 (link).
To calculate association statistics independent of common variants, we extracted 423, 63, and 114 independent, common SNPs previously reported to be associated with eGFR4 (link),28 (link), UACR9 (link), and urate18 (link), respectively, from the imputed UK Biobank data, set. We calculated residuals by regressing eGFR, UACR, and urate on all SNPs for a given trait in one regression analysis, and used these residuals as phenotypes in another ExWAS. Adjusted and unadjusted association results were compared with respect to their effect size estimates. Reported R2 measures originated from a linear regression model.
The PheWeb software (https://github.com/statgen/pheweb) with default settings was used to create a local PheWeb instance that displays the results of the single variant analysis and is available under https://ckdgen-ukbb.gm.eurac.edu/.
Wuttke M., König E., Katsara M.A., Kirsten H., Farahani S.K., Teumer A., Li Y., Lang M., Göcmen B., Pattaro C., Günzel D., Köttgen A, & Fuchsberger C. (2023). Imputation-powered whole-exome analysis identifies genes associated with kidney function and disease in the UK Biobank. Nature Communications, 14, 1287.
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Publication 2023
DNA Chips EGFR protein, human Exome Genome Genotype Phenotype Single Nucleotide Polymorphism Urate
4
Acute Gouty Arthritis Exclusion Criteria
Patients were recruited from the outpatients who visited the dedicated Gout Clinic of the Affiliated Hospital of Qingdao University, and the trial was registered on the Chinese Clinical Trials Registry as #ChiCTR2000038794. The Medical Ethics Committee of the Affiliated Hospital of Qingdao University approved this study. Before the study began, all participants received an adequate explanation of the study’s objectives and provided written informed consent.
All patients were diagnosed with acute gouty arthritis based on clinical criteria, including laboratory and imaging examinations. They had not received any urate-lowering drugs within two weeks before the interview for enrollment in this study. Patients who were taking corticosteroids, anticoagulants, diuretics, or other drugs that alter uric acid excretion were excluded from the study. Other exclusion criteria included patients with hepatic insufficiency (alanine aminotransferase [ALT] or aspartate aminotransferase [AST] > 1.5 times the upper limit of normal; serum total bilirubin [TBIL] > 2 times the upper limit of normal), renal insufficiency (patients with severe renal injury or end-stage renal disease; estimated glomerular filtration rate [eGFR] < 30 mL/min/1.73 m2), active peptic ulcer combined with heart disease, malignant tumors, active tuberculosis or blood disease, and the judgment of the investigator that the candidate was inappropriate for this research.
Zhang J., Sun W., Gao F., Lu J., Li K., Xu Y., Li Y., Li C, & Chen Y. (2023). Changes of serum uric acid level during acute gout flare and related factors. Frontiers in Endocrinology, 14, 1077059.
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Publication 2023
Adrenal Cortex Hormones Anticoagulants Arthritis, Gouty Aspartate Transaminase Bilirubin Chinese D-Alanine Transaminase Diuretics Ethics Committees, Clinical Glomerular Filtration Rate Gout Heart Diseases Hematological Disease Hepatic Insufficiency Injuries Kidney Kidney Failure, Chronic Malignant Neoplasms Outpatients Patients Peptic Ulcer Pharmaceutical Preparations Physical Examination Renal Insufficiency Serum Tuberculosis Urate Uric Acid
5
Adjusting for Gout Risk Factors
Covariates for adjustment were related to gout, and they included demographic factors (age and sex), lifestyle factors, comorbidities, and medication use. Comorbidities included hypertension, DM, CHD, chronic heart failure, urolithiasis, chronic kidney disease, psoriasis, hypothyroidism, hyperthyroidism, anemia, menopause, and obstructive sleep apnea. Medications included thiazide diuretics, loop diuretics, aspirin, cytotoxic agents, pyrazinamide, ethambutol, ciclosporin, and tacrolimus. The use of medications exerting a pleotropic effect, namely metformin (Vazirpanah et al., 2019 (link)), or preventing gout, namely colchicine, urate-lowering therapy (ULT), and losartan (Wolff et al., 2015 (link)), were accounted for. Lifestyle factors, namely obesity, alcohol use, and tobacco use, were also considered. In clinical practice, some asymptomatic patients were prescribed ULT or colchicine for prevention or backup. Thus, the prescription of ULT or colchicine before the index date was used as a surrogate variable for the adjustment of the potential risk of gout. Diagnosis codes of comorbidities were identified if those were recorded ≥2 times during outpatient visits or once during hospitalization in the databases. However, lifestyle factors were rarely recorded as diagnosis in the NHI database. A record of these factors indicates their high severity. Thus, the diagnosis codes of lifestyle factors were identified if recorded once. Medication use was identified on the basis of a prescription of ≥28 days within 1 year before the index date.
Lin G.L., Lin H.C., Lin H.L., Keller J.J, & Wang L.H. (2023). Association between statin use and the risk of gout in patients with hyperlipidemia: A population-based cohort study. Frontiers in Pharmacology, 14, 1096999.
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Publication 2023
A-factor (Streptomyces) Anemia Aspirin Chronic Kidney Diseases Colchicine Congestive Heart Failure Cyclosporins Cytotoxin Diagnosis Ethambutol factor A Gout High Blood Pressures Hospitalization Hyperthyroidism Hypothyroidism Loop Diuretics Losartan Menopause Metformin Obesity Outpatients Patients Pharmaceutical Preparations Psoriasis Pyrazinamide Sleep Apnea, Obstructive Tacrolimus Therapeutics Thiazide Diuretics Urate Urolithiasis

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Uf amino station system by Shimadzu
Sourced in Japan
The UF-Amino Station system is a lab equipment product offered by Shimadzu. It is designed for the analysis of amino acids. The core function of the system is to perform high-performance liquid chromatography (HPLC) analysis to determine the amino acid composition of samples.
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Au5800 by Beckman Coulter
Sourced in United States, Japan, Germany, United Kingdom, China, Italy, Canada
The AU5800 is a chemistry analyzer designed for high-throughput clinical laboratory testing. It features advanced optics and automation to provide reliable and efficient sample processing. The core function of the AU5800 is to perform a variety of clinical chemistry tests, including immunoassays, on patient samples.
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Cobas e801 system by Roche
Sourced in Japan
The Cobas e801 system is an automated immunoassay analyzer designed for use in clinical laboratories. It is capable of performing a wide range of immunoassay tests, including those for hormones, proteins, and other analytes. The system is designed to offer efficient, high-throughput testing with reliable and accurate results.
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Acl top by Mitsubishi
Sourced in Japan
The ACL TOP is an automated coagulation analyzer designed for high-throughput testing of coagulation samples. It performs various coagulation assays to assess the clotting ability of blood samples.

More about "Urate"

Urate, also known as uric acid, is a natural chemical compound found in the human body.
It is the end product of purine metabolism and is primarily excreted through the kidneys.
Urate levels can be influenced by various factors, including diet, genetics, and certain medical conditions.
Elevated urate levels can lead to the development of gout, a type of arthritis characterized by painful inflammation in the joints.
Understanding urate metabolism and regulation is crucial for maintaining overall health and preventing urate-related disorders.
Uric acid, a closely related term, is often used interchangeably with urate.
The Cobas 8000 and Cobas c501 are analytical systems used to measure urate levels in the blood, while the L-7020 and AU5800 are other laboratory instruments that can analyze urate concentrations.
SPSS Statistics for Windows, Version 23.0, is a statistical software package that can be used to analyze data related to urate levels and associated factors.
The UF-Amino Station system and the ACS:180 are additional laboratory instruments that can be employed in the detection and quantification of urate.
Maintaining healthy urate levels is essential for preventing conditions like gout.
The Cobas e801 system is another analytical platform that can be utilized to monitor urate levels.
By understanding the complexities of urate metabolism and regulation, researchers and clinicians can develop more effective strategies for managing urate-related disorders and promoting overall wellbeing.