Log In Sign up
The largest database of trusted experimental protocols
> Chemicals & Drugs > Biologically Active Substance > Creatinine

Creatinine

Creatine is a nitrogenous organic acid that is found in vertebrate muscles and is important in the supply of energy to all muscles of the body.
Creatinine is a chemical waste product that is created from the breakdown of creatine.
It is filtered out of the body by the kidneys and excreted in urine.
Measuring the level of creatinine in the blood can provide usefull information about kidney function.
Abnormal creatinine levels may be a sign of kidney disease or other health conditions.
This MeSH term provides a comprehensive overview of creatinine and its role in the body.

Most cited protocols related to «Creatinine»

1
CKD-EPI Equations for Estimating GFR
CKD-EPI collaborators provided data from research studies and clinical populations (hereafter referred to as “studies”). Briefly, we identified studies from the Medline database and through investigators' and collaborators' contacts (Appendix Figure 1). Key inclusion criteria were measurement of GFR using exogenous filtration markers and ability to calibrate serum creatinine assay. Studies for development and internal validation of equations were restricted to those using urinary clearance of iothalamate. Studies for external validation included iothalamate and other filtration markers. Ten studies (6 research studies and 4 clinical populations) with a total of 8,254 participants were divided randomly into separate datasets for development (n=5,504) and internal validation (n=2,750) (Appendix Table 1) (3 (link), 9 (link)-15 (link)). Sixteen other studies (6 research studies and 10 clinical populations) with a total of 3,896 participants were used for external validation (Appendix Table 2).(13 (link), 16 (link)-28 (link))
Levey A.S., Stevens L.A., Schmid C.H., Zhang Y.(., Castro AF I.I.I., Feldman H.I., Kusek J.W., Eggers P., Van Lente F., Greene T, & Coresh J. (2009). A New Equation to Estimate Glomerular Filtration Rate. Annals of internal medicine, 150(9), 604-612.
Publication 2009
Biological Assay Creatinine Filtration Iothalamate Population Group Serum Urine
2
NHANES: Estimating CKD Prevalence in U.S.
The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional, multistage, stratified, clustered probability samples of the civilian, non-institutionalized population of the U.S. conducted by the National Center of Health Statistics and appropriate for estimates of prevalence of chronic conditions in the U.S. Data were analyzed from 1999-2000, 2001-2002, 2003-2004, and 2005-2006 surveys. The study population for this analysis was limited to 16,032 participants (3,754 in 1999-2000, 4,297 in 2001-2002, 4,017 in 2003-2004, and 3,964 in 2005-2006), who were 20 years and older, had completed the examination in the mobile examination center, were not pregnant or menstruating, and were not missing serum creatinine measurements and did not have an estimated GFR below 15 ml/min/1.73 m2. Methods are similar to previous reports and are summarized briefly here (7 (link)).
GFR was not measured in NHANES. Serum creatinine was measured using a kinetic rate Jaffe method and re-calibrated to standardized creatinine measurements obtained in at the Cleveland Clinic Research Laboratory (Cleveland, OH) (33 (link)). GFR was estimated using the MDRD Study and the newly developed CKD-EPI equation. Estimates that exceeded 200 mL/min/1.73 m2 were truncated at that level. Methods for collection, analysis, and reporting for albuminuria have been described (7 (link), 34 (link)). Albuminuria was defined as albumin-to-creatinine ratio ≥30 mg/g. Repeated measurements, obtained in a subset of 1,241 NHANES 1988-1994 participants approximately 2 weeks after the original examination were used to estimate the persistence of albuminuria (34 (link)). NHANES does not have accurate diagnoses of causes of kidney disease. CKD was defined as persistent albuminuria or estimated GFR <60 ml/min/1.73 m2 (1 (link)). CKD was classified according to estimated GFR stages as defined above. Distributions of estimated GFR, estimated GFR stages and prevalence of CKD were compared for both equations.
Analyses were performed incorporating the sampling weights to obtain unbiased estimates from the complex NHANES sampling design using Stata (Version 10.0, StataCorp, College Station, TX). Standard errors for all estimates were obtained using the Taylor series (linearization) method following NHANES recommended procedures and weights (35 -37 ). Confidence intervals for prevalence estimates for CKD stages incorporating persistence data on of albuminuria were made using bootstrap methods implemented in Stata. Prevalence estimates were applied to the 2000 U.S. Census to obtain estimates of the number of individuals with CKD in the U.S.
Levey A.S., Stevens L.A., Schmid C.H., Zhang Y.(., Castro AF I.I.I., Feldman H.I., Kusek J.W., Eggers P., Van Lente F., Greene T, & Coresh J. (2009). A New Equation to Estimate Glomerular Filtration Rate. Annals of internal medicine, 150(9), 604-612.
Publication 2009
Albumins Chronic Condition Creatinine Diagnosis Kidney Diseases Kinetics Serum
3
Standardized Creatinine Recalibration Protocol
For all studies, we recalibrated serum creatinine values to the standardized creatinine measurements using the Roche enzymatic method (Roche-Hitachi P-Module instrument with Roche Creatininase Plus assay, Hoffman-La Roche, Ltd., Basel, Switzerland) at the Cleveland Clinic Research Laboratory (Cleveland, OH) as previously described (29 (link), 30 (link)). We compared new equations to the MDRD Study equation, given by: estimated GFR = 175 × standardized Scr −1.154 × age−0.203 × 1.212 [if black] × 0.742 [if female], where GFR is expressed as mL/min/1.73 m2 of body surface area41 and Scr is expressed in mg/dL(4 (link)).
Levey A.S., Stevens L.A., Schmid C.H., Zhang Y.(., Castro AF I.I.I., Feldman H.I., Kusek J.W., Eggers P., Van Lente F., Greene T, & Coresh J. (2009). A New Equation to Estimate Glomerular Filtration Rate. Annals of internal medicine, 150(9), 604-612.
Publication 2009
Biological Assay creatininase Creatinine Enzymes Females Human Body Serum
4
Estimating Glomerular Filtration Rate Equations
We pre-specified a process for developing equations using transformations of continuous variables and inclusion of additional variables and interactions to develop a large number of candidate equations. We used least squares linear regression to relate measured GFR to serum creatinine and clinical characteristics available in all databases. Predictor variables included serum creatinine, age, race (black vs. white and other), and sex in all models, as in the MDRD Study equation, and additional variables [diabetes (yes/no), prior organ transplant (yes/no), and weight, as assigned by the individual studies] in some models. Regression models were fit to all patients in the pooled development dataset, without accounting for study in the models. GFR and serum creatinine were transformed to natural logarithms to reflect their multiplicative (inverse) relationship and to stabilize variance across the range of GFR.
Appropriate transformations of log serum creatinine and age were determined by first fitting non-parametric smoothing splines to characterize the shape of the relationship of these factors with mean log measured GFR and then creating piecewise linear splines to correspond to observed non-linearity (Appendix Table 3) (31 ). Additional variables and pair-wise interactions between them were included if they were significant (p <0.01 for additional variables and <0.001 for interactions) and improved model performance [relative reduction in root mean square error (RMSE) by 2% or more] (Appendix Table 4).
Levey A.S., Stevens L.A., Schmid C.H., Zhang Y.(., Castro AF I.I.I., Feldman H.I., Kusek J.W., Eggers P., Van Lente F., Greene T, & Coresh J. (2009). A New Equation to Estimate Glomerular Filtration Rate. Annals of internal medicine, 150(9), 604-612.
Publication 2009
Creatinine Diabetes Mellitus Patients Plant Roots Serum Transplant, Organ
5
Estimating Glomerular Filtration Rate with Cystatin C and Creatinine
Our goal was to develop two equations for estimating GFR: one using serum cystatin C (hereafter referred to as the cystatin C equation) and another using both serum cystatin C and serum creatinine (hereafter referred to as the creatinine–cystatin C equation). As in our previous work, we prespecified a process for developing and validating equations (described in the Methods section in the Supplementary Appendix). In brief, we used least-squares linear regression to relate logarithm-transformed measured GFR to log serum creatinine, log serum cystatin C, age, and sex. We also used nonparametric smoothing splines to characterize the shape of the relationship of log measured GFR with log creatinine and log cystatin C and then approximated the smoothing splines by means of piecewise linear splines to represent observed nonlinearity. Other candidate variables included the other filtration marker, black race, diabetes status, and weight. The significance threshold for inclusion was P<0.01 for these additional variables and P<0.001 for pairwise interactions among variables. Models that showed improved performance with the use of prespecified criteria were evaluated in the internal validation data set for verification of the statistical significance of predictor variables and interactions. Development and internal-validation data sets were combined into one data set (hereafter referred to as the development data set) to derive final coefficients.
In the external-validation data set, we compared the new equations with each other, with our previous creatinine equation,3 (link) and with our prior equations involving cystatin C that were developed in populations of patients with chronic kidney disease and reexpressed for standardized cystatin C values10 (link),11 (link) (Table S3 in the Supplementary Appendix), as well as with the average of the CKD-EPI creatinine equation and the new cystatin C equation. We compared the performance of equations in the overall data set and in the subgroups, and final models were selected according to the ranking of the root-mean-square error overall and within subgroups, clinically significant differences, and ease of application in clinical practice.
Inker L.A., Schmid C.H., Tighiouart H., Eckfeldt J.H., Feldman H.I., Greene T., Kusek J.W., Manzi J., Van Lente F., Zhang Y.L., Coresh J, & Levey A.S. (2012). Estimating Glomerular Filtration Rate from Serum Creatinine and Cystatin C. The New England journal of medicine, 367(1), 20-29.
Publication 2012
Creatinine Diabetes Mellitus Filtration Negroes Patients Plant Roots Population Group Post-gamma-Globulin Serum

Most recents protocols related to «Creatinine»

1
Synthetic Urine-based Electrochemical Biosensor for Urine Albumin Detection
Not available on PMC !

Example 7

Synthetic urine is prepared by dissolving 14.1 g of NaCl, 2.8 g KCl, 17.3 g of urea, 19 ml ammonia water (25%), 0.60 g CaCl2 and 0.43 g MgSO4 in 0.02 mole/L of HCl. The final pH of synthetic urine is adjusted to 6.04 by using HCl and ammonia water.

40 mg Sigma creatinine is dissolved in 10 ml of synthetic urine solution. 3 mg of human albumin is dissolved in 10 ml of synthetic urine solution to prepare the micro albumin solution.

4 mg Sigma hemin is dissolved in 20 ml of synthetic urine, 20 μL Hemin solution is used as a receptor for urine albumin detection at different creatinine concentration.

A desired volume of the biological sample (synthetic urine) is taken and dispensed on the electrode of the biosensor device and the corresponding cyclic voltammogram is obtained by the CHI-Electrochemical workstation using the potential window, that varies from 0 V to −1 V with scan rate of 0.1 V/sec.

The albumin content in the urine sample binds hemin thereby demonstrates a linear decrease in peak redox current with urine albumin concentration as shown in FIG. 15(a) for different creatinine concentrations. If the concentration of albumin in urine sample is increased, then the albumin increasingly binds with hemin thereby reducing the free hemin concentration on the electrode resulting in the decrease in peak redox current of free hemin. FIG. 16 shows the urine albumin concentrations, urine creatinine concentrations and calculated ACR for different samples.

The values of concentrations of the urine albumin (mg/L) and creatinine for different samples is shown in Table 4.

TABLE 4
SampleUrine albuminUrine CreatinineACR
Number(mg/L)(mg/dL)(mg/g)
1526.719
22026.775
35026.7187
410026.7375
515026.7562
65133.34
720133.315
850133.338
9100133.375
10150133.3113

US11867654B2. Device and method for detecting creatinine and albumin to creatinine ratio (2024-01-09). INDIAN INSTITUTE OF SCIENCE [IN]. Inventors: Vinay Kumar [IN], Navakanta Bhat [IN], Nikhila Kashyap Dhanvantari Madhuresh [IN].
Full text: Click here
Patent 2024
Albumins Ammonium Hydroxide Biopharmaceuticals Biosensors Creatinine Hemin Moles Oxidation-Reduction Radionuclide Imaging Receptors, Albumin Serum Albumin, Human Sodium Chloride Sulfate, Magnesium Urea Urine
2
Rapid Creatinine Quantification via Electrochemistry
Not available on PMC !

Example 2

A sample volume of creatinine sample of 300 uL is placed on the electrode having the FeCl3 receptor of 0.6 mg then the peak reduction current value is noted from cyclic voltammogram specifying a potential window from 0.6 V to −1.0 V with scan rate of 0.1 V/sec in CHI Electrochemical workstation. The value of peak reduction current is measured as 105 μA. The presence of this current value is searched in the values as provided in Table 1 and the corresponding concentration of urine creatinine is retrieved, which is 240 mg/dL.

US11867654B2. Device and method for detecting creatinine and albumin to creatinine ratio (2024-01-09). INDIAN INSTITUTE OF SCIENCE [IN]. Inventors: Vinay Kumar [IN], Navakanta Bhat [IN], Nikhila Kashyap Dhanvantari Madhuresh [IN].
Full text: Click here
Patent 2024
A 300 Creatinine Radionuclide Imaging Urine
3
Electrochemical Creatinine Detection
Not available on PMC !

Example 4

A sample volume of creatinine sample of 300 uL is placed on the electrode having the MB-FeCl3 receptor of 0.6 mg and then the peak reduction current value is observed from cyclic voltammogram by varying a potential window from 0.6 V to −1.0 V, with scan rate of 0.1 V/sec in CHI-Electrochemical workstation. The value of peak reduction current is noted 110 μA. The presence of this current value is searched in the Table 2 and the corresponding concentration of urine creatinine is obtained is 373 mg/dL.

US11867654B2. Device and method for detecting creatinine and albumin to creatinine ratio (2024-01-09). INDIAN INSTITUTE OF SCIENCE [IN]. Inventors: Vinay Kumar [IN], Navakanta Bhat [IN], Nikhila Kashyap Dhanvantari Madhuresh [IN].
Full text: Click here
Patent 2024
A 300 Creatinine Radionuclide Imaging Urine
4
Evaluating DsiRNA Knockdown of HAO1 in Primary Hyperoxaluria
Not available on PMC !

Example 4

The ability of certain, active HAO1-targeting DsiRNAs to reduce HAO1 levels within the liver of a mouse was examined. DsiRNAs employed in the study were: HAO1-1105, HAO1-1171, HAO1-1221, HAO1-1272, HAO1-1273, HAO1-1316, HAO1-1378 and HAO1-1379, each of which were synthesized with passenger (sense) strand modification pattern “SM107” and guide (antisense) strand modification pattern “M48” (patterns described above). To perform the study, a primary hyperoxaluria model was generated through oral gavage of 0.25 mL of 0.5 M glycolate to cause urine oxalate accumulation in C57BL/6 female mice. Animals were randomized and assigned to groups based on body weight. Intravenous dosing of animals with lipid nanoparticles (LNPs; here, an LNP formulation named EnCore-2345 was employed) containing 1 mg/kg or 0.1 mg/kg of DsiRNA was initiated on day 0. Dosing continued BIW for a total of three doses in mice prior to glycolate challenge. Four hour and 24 h urine samples were collected after glycolate challenge for assessment of oxalate/creatinine levels (see FIG. 4 for experimental flow chart). Animals were then sacrificed at 24 hrs after glycolate challenge. Liver was dissected and weighed, and HAO1 levels were assessed using RT-qPCR, ViewRNA, western blot for glycolate oxidase and/or glycolate oxidase immunohistochemistry (ViewRNA, western blot for glycolate oxidase and glycolate oxidase immunohistochemistry data not shown). Serum samples were also subjected to ELISA for detection of glycolate oxidase (data not shown). Notably, all eight DsiRNAs showed robust knockdown of HAO1 when administered at 1 mg/kg (FIG. 5). At least two (HAO1-1171 and HAO1-1378) of the eight DsiRNAs tested in vivo also showed robust knockdown of HAO1 in all treated animals when administered at 0.1 mg/kg. As shown in FIG. 5, administration of the HAO1-1171-M107/M48 DsiRNA at 0.1 mg/kg caused an average knockdown of 70% in liver tissue of treated mice, while administration at 1 mg/kg produced an average knockdown of 97% in liver tissue of treated mice. Similarly, administration of the HAO1-1378-M107/M48 DsiRNA at 0.1 mg/kg caused an average knockdown of 53% in liver tissue of treated mice, while administration at 1 mg/kg produced an average knockdown of 97% in liver tissue of treated mice. HAO-1171-induced knockdown at both 0.1 mg/kg and 1 mg/kg was further confirmed by ViewRNA in situ hybridization assays.

Robust levels of HAO1 mRNA knockdown were observed in liver tissue of mice treated with 1 mg/kg amounts of HAO1-targeting DsiRNAs HAO1-1171 and HAO1-1378 (FIG. 6 and data not shown), and even 0.1 mg/kg amounts of these HAO1-targeting DsiRNAs produced robust HAO1 knockdown. As shown in FIG. 6, single dose HAO1-1171 DsiRNA treatment achieved durable HAO1 mRNA target knockdown for at least 120 hours post-administration in the liver of treated animals. While robust HAO1 knockdown was achieved in liver, initial glycolate challenge experiments yielded inconclusive phenotypic results (data not shown).

In additional in vivo experiments, both HAO1 and oxalate levels were assessed in both control- and DsiRNA-treated genetically engineered PH1 model mice.

US11873493B2. Methods and compositions for the specific inhibition of glycolate oxidase (HAO1) by double-stranded RNA (2024-01-16). Dicerna Pharmaceuticals, Inc. [US]. Inventors: Bob D. Brown [US], Henryk T. Dudek [US].
Full text: Click here
Patent 2024
Animals Biological Assay Body Weight Creatinine Enzyme-Linked Immunosorbent Assay Females Glycolates glycollate oxidase Immunohistochemistry In Situ Hybridization Lipid Nanoparticles Liver Mice, House Mice, Inbred C57BL Oxalates Phenotype Primary Hyperoxaluria RNA, Messenger Serum Tissues Tube Feeding Urine Western Blot
5
Treating OTC Deficiency with mRNA
Not available on PMC !

Example 20

The spfash mutant mice were injected intravenously with a single dose of human OTC mRNA (either construct OTC-07 (SEQ ID NO:35) or OTC-12 (SEQ ID NO:40)) at either 0.5 mg/kg or 1.0 mg/kg, or a control mRNA encoding eGFP at a dose of 1 mg/kg, via tail vein injection. The mRNA was formulated in lipid nanoparticles (Compound II) for delivery into the mice. Urine was collected from mice 48 hours and 24 hours prior to mRNA injection for urinary orotic acid/creatinine analysis. All mice urine was collected for urinary orotic acid/creatinine levels 24 hours, 48 hours, 72 hours, 7 days, 14 days, or 21 days after dosing for each injected human OTC mRNA and for the injected eGFP control. FIG. 5 shows that administering a single dose of mRNA encoding human OTC (either construct OTC-07 or OTC-12) to spfash mice led to a substantial and sustained decrease in orotic acid levels for at least 21 days following the mRNA injection.

US11859215B2. Polynucleotides encoding ornithine transcarbamylase for the treatment of urea cycle disorders (2024-01-02). ModernaTX, Inc. [US]. Inventors: Zhijian Zhuo [US], Andrea Lea Frassetto [US], Paolo G. V. Martini [US], Vladimir Presnyak [US], Patrick Finn [US].
Full text: Click here
Patent 2024
A-A-1 antibiotic Animal Model Creatinine Homo sapiens Lipid Nanoparticles Mus Obstetric Delivery Orotic Acid RNA, Messenger Tail Urinalysis Urine Veins

Top products related to «Creatinine»

1
Cobas 6000 by Roche
Sourced in Switzerland, Germany, United States, Japan, Denmark, Italy, France, United Kingdom, Belgium, Norway, China, Israel, Poland, Netherlands, Sweden
The Cobas 6000 is an automated clinical chemistry and immunoassay analyzer system designed for high-volume laboratory testing. It combines the features of two separate instruments, the Cobas c 501 module for clinical chemistry and the Cobas e 601 module for immunoassays, into a single integrated platform. The Cobas 6000 system is capable of performing a wide range of diagnostic tests, including biochemical, immunological, and specialty assays.
2
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.
3
Cobas 8000 by Roche
Sourced in Switzerland, Germany, United States, Japan, France, Italy, China, United Kingdom, Sweden
The Cobas 8000 is a modular, automated in-vitro diagnostic system designed for high-throughput clinical chemistry and immunochemistry testing. It is used to perform a wide range of laboratory tests, including those for chemistry, immunoassay, and electrolyte analysis. The Cobas 8000 is capable of processing a large volume of samples efficiently and accurately.
4
Quantichrom creatinine assay kit by BioAssay Systems
Sourced in United States, Canada
The QuantiChrom Creatinine Assay Kit is a colorimetric assay that measures creatinine levels in biological samples. The kit provides a simple, direct, and accurate method for determining creatinine concentrations.
5
Creatinine by Merck Group
Sourced in United States, Germany, United Kingdom, Italy, Sao Tome and Principe, Australia, Switzerland, France, China, Japan
Creatinine is a lab equipment product used for the measurement of creatinine levels in biological samples. Creatinine is a waste product formed by the breakdown of creatine, which is found in muscle tissue. The measurement of creatinine levels is commonly used to assess kidney function.
6
Advia 1800 by Siemens
Sourced in Germany, United States, United Kingdom, Japan, Denmark
The ADVIA 1800 is a clinical chemistry analyzer designed for use in medical laboratories. It is capable of performing a wide range of common clinical chemistry tests on patient samples. The ADVIA 1800 automates the analysis process, providing efficient and accurate results to support patient diagnosis and treatment.
7
Sas version 9 by SAS Institute
Sourced in United States, Austria, Japan, Cameroon, Germany, United Kingdom, Canada, Belgium, Israel, Denmark, Australia, New Caledonia, France, Argentina, Sweden, Ireland, India
SAS version 9.4 is a statistical software package. It provides tools for data management, analysis, and reporting. The software is designed to help users extract insights from data and make informed decisions.
8
Cobas integra 400 plus by Roche
Sourced in Switzerland, Germany, United States, Australia, China, United Kingdom, Philippines
The Cobas Integra 400 Plus is a clinical chemistry analyzer designed for in-vitro diagnostics. It is capable of performing a variety of tests on biological samples to aid in the diagnosis and monitoring of medical conditions. The core function of the Cobas Integra 400 Plus is to analyze samples and provide quantitative results for clinical parameters.
9
Au680 by Beckman Coulter
Sourced in United States, Germany, Japan, United Kingdom
The AU680 is an automated clinical chemistry analyzer designed for high-throughput clinical laboratory testing. It features a modular design, advanced analytical technologies, and comprehensive test menu capabilities to provide efficient and reliable results.

More about "Creatinine"

Creatinine is a crucial metabolite in the human body, closely linked to the function of the kidneys.
This nitrogenous waste product is generated through the breakdown of creatine, a vital molecule for energy production in muscle tissues.
The level of creatinine in the blood is a reliable biomarker for evaluating kidney health, as it is filtered out and excreted through the urine.
Measuring creatinine levels is a common diagnostic tool used in a variety of clinical settings, including the Cobas 6000, AU5800, Cobas 8000, and ADVIA 1800 analyzer platforms.
The QuantiChrom Creatinine Assay Kit provides a convenient and accurate method for quantifying creatinine concentrations.
Additionally, statistical software like SAS version 9.4 can be utilized to analyze and interpret creatinine data.
Abnormal creatinine levels, whether elevated (hypercreatinemia) or decreased (hypocreatinemia), can be indicative of various health conditions, such as kidney disease, dehydration, or muscle wasting.
Monitoring creatinine levels, often in conjunction with the Cobas Integra 400 Plus or AU680 analyzers, is essential for early detection and proper management of these underlying issues.
By understanding the role of creatine, creatinine, and their associated measurements, healthcare professionals can make informed decisions regarding patient care and treatment plans.
This comprehensive overview of creatinine and its importance in the body aims to provide a solid foundation for further research and clinical applications.