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Glomerular Filtration Rate

Glomerular Filtration Rate (GFR) is a key measure of kidney function, reflecting the volume of fluid filtered from the blood into the Bowman's capsule per unit of time.
GFR can be used to assess kidney health, diagnose kidney disease, and monitor disease progression.
Optimizing GFR research requires reproducible, accurate protocols that can be easily identified and compared.
PubCompare.ai, the leading AI-driven platform, helps researchers streamline this process by locating relevant protocols from literature, pre-prits, and patents, and leveraging AI-powered comparisons to identify the best protocols and products for their GFR research.
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Most cited protocols related to «Glomerular Filtration Rate»

1
Cardiovascular Trait Association Analysis
To determine whether the identified loci were also associated with any of 22 cardio-metabolic traits, we obtained association data from meta-analysis consortia DIAGRAM (T2D)58 (link), CARDIoGRAM-C4D (CAD)59 (link), ICBP (SBP, DBP)60 (link), GIANT (BMI, height)36 ,37 , GLGC (HDL, LDL, and TG)61 (link), MAGIC (fasting glucose, fasting insulin, fasting insulin adjusted for BMI, and two-hour glucose)62 (link)-64 (link), ADIPOGen (BMI-adjusted adiponectin)65 (link), CKDgen (urine albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), and overall CKD)66 (link),67 (link), ReproGen (age at menarche, age at menopause)68 (link),69 (link), and GEFOS (bone mineral density)70 (link); others provided association data for IgA nephropathy71 (link) (also Kiryluk K, Choi M, Lifton RP, Gharavi AG, unpublished data) and for endometriosis (stage B cases only)72 (link). Proxies (r2>0.80 in CEU) were used when an index SNP was unavailable.
We also searched the National Human Genome Research Institute (NHGRI) GWAS Catalog for previous SNP-trait associations near our lead SNPs73 (link). We supplemented the catalog with additional genome-wide significant SNP-trait associations from the literature13 (link),70 (link),74 (link)-80 (link). We used PLINK to identify SNPs within 500 kb of lead SNPs using 1000 Genomes Project Pilot I genotype data and LD (r2) values from CEU81 (link),82 (link); for rs7759742, HapMap release 22 CEU data81 (link),83 (link) were used. All SNPs within the specified regions were compared with the NHGRI GWAS Catalog16 .
Shungin D., Winkler T.W., Croteau-Chonka D.C., Ferreira T., Locke A.E., Mägi R., Strawbridge R.J., Pers T.H., Fischer K., Justice A.E., Workalemahu T., Wu J.M., Buchkovich M.L., Heard-Costa N.L., Roman T.S., Drong A.W., Song C., Gustafsson S., Day F.R., Esko T., Fall T., Kutalik Z., Luan J., Randall J.C., Scherag A., Vedantam S., Wood A.R., Chen J., Fehrmann R., Karjalainen J., Kahali B., Liu C.T., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bragg-Gresham J.L., Buyske S., Demirkan A., Ehret G.B., Feitosa M.F., Goel A., Jackson A.U., Johnson T., Kleber M.E., Kristiansson K., Mangino M., Leach I.M., Medina-Gomez C., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Stančáková A., Sung Y.J., Tanaka T., Teumer A., Van Vliet-Ostaptchouk J.V., Yengo L., Zhang W., Albrecht E., Ärnlöv J., Arscott G.M., Bandinelli S., Barrett A., Bellis C., Bennett A.J., Berne C., Blüher M., Böhringer S., Bonnet F., Böttcher Y., Bruinenberg M., Carba D.B., Caspersen I.H., Clarke R., Daw E.W., Deelen J., Deelman E., Delgado G., Doney A.S., Eklund N., Erdos M.R., Estrada K., Eury E., Friedrich N., Garcia M.E., Giedraitis V., Gigante B., Go A.S., Golay A., Grallert H., Grammer T.B., Gräßler J., Grewal J., Groves C.J., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heikkilä K., Herzig K.H., Helmer Q., Hillege H.L., Holmen O., Hunt S.C., Isaacs A., Ittermann T., James A.L., Johansson I., Juliusdottir T., Kalafati I.P., Kinnunen L., Koenig W., Kooner I.K., Kratzer W., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lobbens S., Lorentzon M., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., Menni C., Merger S., Mihailov E., Milani L., Mills R., Moayyeri A., Monda K.L., Mooijaart S.P., Mühleisen T.W., Mulas A., Müller G., Müller-Nurasyid M., Nagaraja R., Nalls M.A., Narisu N., Glorioso N., Nolte I.M., Olden M., Rayner N.W., Renstrom F., Ried J.S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Sennblad B., Seufferlein T., Sitlani C.M., Smith A.V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tayo B.O., Thorand B., Thorleifsson G., Tomaschitz A., Troffa C., van Oort F.V., Verweij N., Vonk J.M., Waite L.L., Wennauer R., Wilsgaard T., Wojczynski M.K., Wong A., Zhang Q., Zhao J.H., Brennan E.P., Choi M., Eriksson P., Folkersen L., Franco-Cereceda A., Gharavi A.G., Hedman Å.K., Hivert M.F., Huang J., Kanoni S., Karpe F., Keildson S., Kiryluk K., Liang L., Lifton R.P., Ma B., McKnight A.J., McPherson R., Metspalu A., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Olsson C., Perry J.R., Reinmaa E., Salem R.M., Sandholm N., Schadt E.E., Scott R.A., Stolk L., Vallejo E.E., Westra H.J., Zondervan K.T., Amouyel P., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Brown M.J., Burnier M., Campbell H., Chakravarti A., Chines P.S., Claudi-Boehm S., Collins F.S., Crawford D.C., Danesh J., de Faire U., de Geus E.J., Dörr M., Erbel R., Eriksson J.G., Farrall M., Ferrannini E., Ferrières J., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gieger C., Gudnason V., Haiman C.A., Harris T.B., Hattersley A.T., Heliövaara M., Hicks A.A., Hingorani A.D., Hoffmann W., Hofman A., Homuth G., Humphries S.E., Hyppönen E., Illig T., Jarvelin M.R., Johansen B., Jousilahti P., Jula A.M., Kaprio J., Kee F., Keinanen-Kiukaanniemi S.M., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuulasmaa K., Kuusisto J., Lakka T.A., Langenberg C., Le Marchand L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Musk A.W., Möhlenkamp S., Morris A.D., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Palmer L.J., Penninx B.W., Peters A., Pramstaller P.P., Raitakari O.T., Rankinen T., Rao D., Rice T.K., Ridker P.M., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schwarz P.E., Shuldiner A.R., Staessen J.A., Steinthorsdottir V., Stolk R.P., Strauch K., Tönjes A., Tremblay A., Tremoli E., Vohl M.C., Völker U., Vollenweider P., Wilson J.F., Witteman J.C., Adair L.S., Bochud M., Boehm B.O., Bornstein S.R., Bouchard C., Cauchi S., Caulfield M.J., Chambers J.C., Chasman D.I., Cooper R.S., Dedoussis G., Ferrucci L., Froguel P., Grabe H.J., Hamsten A., Hui J., Hveem K., Jöckel K.H., Kivimaki M., Kuh D., Laakso M., Liu Y., März W., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sinisalo J., Slagboom P.E., Snieder H., Spector T.D., Stefansson K., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Veronesi G., Walker M., Wareham N.J., Watkins H., Wichmann H.E., Abecasis G.R., Assimes T.L., Berndt S.I., Boehnke M., Borecki I.B., Deloukas P., Franke L., Frayling T.M., Groop L.C., Hunter D.J., Kaplan R.C., O’Connell J.R., Qi L., Schlessinger D., Strachan D.P., Thorsteinsdottir U., van Duijn C.M., Willer C.J., Visscher P.M., Yang J., Hirschhorn J.N., Zillikens M.C., McCarthy M.I., Speliotes E.K., North K.E., Fox C.S., Barroso I., Franks P.W., Ingelsson E., Heid I.M., Loos R.J., Cupples L.A., Morris A.P., Lindgren C.M, & Mohlke K.L. (2015). New genetic loci link adipose and insulin biology to body fat distribution. Nature, 518(7538), 187-196.
Publication 2014
ADIPOQ protein, human Albumins Bone Density Creatinine Endometriosis Genome Genome-Wide Association Study Genotype Gigantism Glomerular Filtration Rate Glucose HapMap Insulin Menarche Menopause Single Nucleotide Polymorphism Urine
2
Cardiometabolic Traits Genetic Association
To explore the relationship between BMI and an array of cardiometabolic traits and diseases, association results for the 97 BMI index SNPs were requested from 13 GWAS meta-analysis consortia: DIAGRAM (type 2 diabetes)56 (link), CARDIoGRAM-C4D (CAD)57 (link), ICBP (systolic and diastolic blood pressure (SBP, DBP))58 (link), GIANT (waist-to-hip ratio, hip circumference, and waist circumference, each unadjusted and adjusted for BMI)13 (link),59 , GLGC (HDL, low density lipoprotein cholesterol, triglycerides, and total cholesterol)60 (link), MAGIC (fasting glucose, fasting insulin, fasting insulin adjusted for BMI, and two-hour glucose)61 (link)–63 (link), ADIPOGen (BMI-adjusted adiponectin)64 (link), CKDgen (urine albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate, and overall CKD)65 (link),66 (link), ReproGen (age at menarche, age at menopause)67 (link),68 (link), GENIE (diabetic nephropathy)69 (link),70 (link). Proxies (r2 > 0.8 in CEU) were used when an index SNP was unavailable.
Locke A.E., Kahali B., Berndt S.I., Justice A.E., Pers T.H., Day F.R., Powell C., Vedantam S., Buchkovich M.L., Yang J., Croteau-Chonka D.C., Esko T., Fall T., Ferreira T., Gustafsson S., Kutalik Z., Luan J., Mägi R., Randall J.C., Winkler T.W., Wood A.R., Workalemahu T., Faul J.D., Smith J.A., Zhao J.H., Zhao W., Chen J., Fehrmann R., Hedman Å.K., Karjalainen J., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bolton J.L., Bragg-Gresham J.L., Buyske S., Demirkan A., Deng G., Ehret G.B., Feenstra B., Feitosa M.F., Fischer K., Goel A., Gong J., Jackson A.U., Kanoni S., Kleber M.E., Kristiansson K., Lim U., Lotay V., Mangino M., Leach I.M., Medina-Gomez C., Medland S.E., Nalls M.A., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Shungin D., Stančáková A., Strawbridge R.J., Sung Y.J., Tanaka T., Teumer A., Trompet S., van der Laan S.W., van Setten J., Van Vliet-Ostaptchouk J.V., Wang Z., Yengo L., Zhang W., Isaacs A., Albrecht E., Ärnlöv J., Arscott G.M., Attwood A.P., Bandinelli S., Barrett A., Bas I.N., Bellis C., Bennett A.J., Berne C., Blagieva R., Blüher M., Böhringer S., Bonnycastle L.L., Böttcher Y., Boyd H.A., Bruinenberg M., Caspersen I.H., Chen Y.D., Clarke R., Daw E.W., de Craen A.J., Delgado G., Dimitriou M., Doney A.S., Eklund N., Estrada K., Eury E., Folkersen L., Fraser R.M., Garcia M.E., Geller F., Giedraitis V., Gigante B., Go A.S., Golay A., Goodall A.H., Gordon S.D., Gorski M., Grabe H.J., Grallert H., Grammer T.B., Gräßler J., Grönberg H., Groves C.J., Gusto G., Haessler J., Hall P., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heard-Costa N.L., Helmer Q., Hengstenberg C., Holmen O., Hottenga J.J., James A.L., Jeff J.M., Johansson Å., Jolley J., Juliusdottir T., Kinnunen L., Koenig W., Koskenvuo M., Kratzer W., Laitinen J., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lo K.S., Lobbens S., Lorbeer R., Lu Y., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., McLachlan S., Menni C., Merger S., Mihailov E., Milani L., Moayyeri A., Monda K.L., Morken M.A., Mulas A., Müller G., Müller-Nurasyid M., Musk A.W., Nagaraja R., Nöthen M.M., Nolte I.M., Pilz S., Rayner N.W., Renstrom F., Rettig R., Ried J.S., Ripke S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Schumacher F.R., Scott W.R., Seufferlein T., Shi J., Smith A.V., Smolonska J., Stanton A.V., Steinthorsdottir V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tan S.T., Tayo B.O., Thorand B., Thorleifsson G., Tyrer J.P., Uh H.W., Vandenput L., Verhulst F.C., Vermeulen S.H., Verweij N., Vonk J.M., Waite L.L., Warren H.R., Waterworth D., Weedon M.N., Wilkens L.R., Willenborg C., Wilsgaard T., Wojczynski M.K., Wong A., Wright A.F., Zhang Q., Brennan E.P., Choi M., Dastani Z., Drong A.W., Eriksson P., Franco-Cereceda A., Gådin J.R., Gharavi A.G., Goddard M.E., Handsaker R.E., Huang J., Karpe F., Kathiresan S., Keildson S., Kiryluk K., Kubo M., Lee J.Y., Liang L., Lifton R.P., Ma B., McCarroll S.A., McKnight A.J., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Okada Y., Perry J.R., Dorajoo R., Reinmaa E., Salem R.M., Sandholm N., Scott R.A., Stolk L., Takahashi A., Tanaka T., van ’t Hooft F.M., Vinkhuyzen A.A., Westra H.J., Zheng W., Zondervan K.T., Heath A.C., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Bovet P., Campbell H., Caulfield M.J., Cesana G., Chakravarti A., Chasman D.I., Chines P.S., Collins F.S., Crawford D.C., Cupples L.A., Cusi D., Danesh J., de Faire U., den Ruijter H.M., Dominiczak A.F., Erbel R., Erdmann J., Eriksson J.G., Farrall M., Felix S.B., Ferrannini E., Ferrières J., Ford I., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gejman P.V., Gieger C., Gottesman O., Gudnason V., Gyllensten U., Hall A.S., Harris T.B., Hattersley A.T., Hicks A.A., Hindorff L.A., Hingorani A.D., Hofman A., Homuth G., Hovingh G.K., Humphries S.E., Hunt S.C., Hyppönen E., Illig T., Jacobs K.B., Jarvelin M.R., Jöckel K.H., Johansen B., Jousilahti P., Jukema J.W., Jula A.M., Kaprio J., Kastelein J.J., Keinanen-Kiukaanniemi S.M., Kiemeney L.A., Knekt P., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuusisto J., Lakka T.A., Langenberg C., Marchand L.L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Moll F.L., Morris A.D., Morris A.P., Murray J.C., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Madden P.A., Pasterkamp G., Peden J.F., Peters A., Postma D.S., Pramstaller P.P., Price J.F., Qi L., Raitakari O.T., Rankinen T., Rao D.C., Rice T.K., Ridker P.M., Rioux J.D., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schunkert H., Schwarz P.E., Sever P., Shuldiner A.R., Sinisalo J., Stolk R.P., Strauch K., Tönjes A., Trégouët D.A., Tremblay A., Tremoli E., Virtamo J., Vohl M.C., Völker U., Waeber G., Willemsen G., Witteman J.C., Zillikens M.C., Adair L.S., Amouyel P., Asselbergs F.W., Assimes T.L., Bochud M., Boehm B.O., Boerwinkle E., Bornstein S.R., Bottinger E.P., Bouchard C., Cauchi S., Chambers J.C., Chanock S.J., Cooper R.S., de Bakker P.I., Dedoussis G., Ferrucci L., Franks P.W., Froguel P., Groop L.C., Haiman C.A., Hamsten A., Hui J., Hunter D.J., Hveem K., Kaplan R.C., Kivimaki M., Kuh D., Laakso M., Liu Y., Martin N.G., März W., Melbye M., Metspalu A., Moebus S., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sattar N., Schadt E.E., Schlessinger D., Slagboom P.E., Snieder H., Spector T.D., Thorsteinsdottir U., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Walker M., Wallaschofski H., Wareham N.J., Watkins H., Weir D.R., Wichmann H.E., Wilson J.F., Zanen P., Borecki I.B., Deloukas P., Fox C.S., Heid I.M., O’Connell J.R., Strachan D.P., Stefansson K., van Duijn C.M., Abecasis G.R., Franke L., Frayling T.M., McCarthy M.I., Visscher P.M., Scherag A., Willer C.J., Boehnke M., Mohlke K.L., Lindgren C.M., Beckmann J.S., Barroso I., North K.E., Ingelsson E., Hirschhorn J.N., Loos R.J, & Speliotes E.K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197-206.
Publication 2015
ADIPOQ protein, human Albumins Cholesterol Cholesterol, beta-Lipoprotein Creatinine Diabetes Mellitus, Non-Insulin-Dependent Diabetic Nephropathy Genie Genome-Wide Association Study Gigantism Glomerular Filtration Rate Glucose Insulin Menarche Menopause Pressure, Diastolic Systole Triglycerides Urine Waist-Hip Ratio Waist Circumference
3
Predictive Model for Atrial Fibrillation
Means and standard deviation and frequency distribution of relevant covariates were calculated by cohort and race. We initially ran cohort‐ and race‐specific Cox proportional hazard models to assess individual predictors of AF after age‐ and sex‐adjustment in each cohort up to 7 years of follow‐up. Variables considered included age, sex, height, weight, current smoking, systolic and diastolic blood pressure, use of antihypertensive medication, history of diabetes, fasting blood glucose, estimated glomerular filtration rate (eGFR) <60 mL/kg per m2,20 (link) total blood cholesterol, HDL cholesterol, triglycerides, heart rate, electrocardiographic‐derived left ventricular hypertrophy, PR interval, history of coronary artery bypass graft (CABG), history of heart failure, history of myocardial infarction, and history of stroke. We selected as candidate predictors for our pooled model any variable significantly associated with AF (P<0.05) in at least 2 of the 3 cohorts, and ran the final Cox proportional hazards model on our participant‐specific pooled data using backward selection of variables (P<0.05 to remain in the model). Age, sex, and race interactions were tested, as was the assumption of proportional hazards. Model‐based individual 5‐year risk of AF was calculated. We evaluated model performance using the C‐statistic,21 (link) discrimination slopes,22 (link) and Nam and D'Agostino's modified Hosmer‐Lemeshow chi‐square statistic for survival analysis.23 To facilitate the use of our score in those clinical settings with limited access to electrocardiograms or blood tests, we first developed a predictive model that did not require information from electrocardiogram and blood tests (which we labeled “simple model”). We then developed a more complex model adding electrocardiographic variables and blood tests (labeled “augmented model”). Variables were retained in the models if they were significantly associated with AF incidence (P<0.05). We calculated the added predicted value of the augmented model versus the simple model with the increment in the C‐statistic and the categorical net reclassification improvement (NRI) using the following risk categories: <2.5%, 2.5% to 5%, >5%.22 (link)
Alonso A., Krijthe B.P., Aspelund T., Stepas K.A., Pencina M.J., Moser C.B., Sinner M.F., Sotoodehnia N., Fontes J.D., Janssens A.C., Kronmal R.A., Magnani J.W., Witteman J.C., Chamberlain A.M., Lubitz S.A., Schnabel R.B., Agarwal S.K., McManus D.D., Ellinor P.T., Larson M.G., Burke G.L., Launer L.J., Hofman A., Levy D., Gottdiener J.S., Kääb S., Couper D., Harris T.B., Soliman E.Z., Stricker B.H., Gudnason V., Heckbert S.R, & Benjamin E.J. (2013). Simple Risk Model Predicts Incidence of Atrial Fibrillation in a Racially and Geographically Diverse Population: the CHARGE‐AF Consortium. Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 2(2), e000102.
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Publication 2013
Antihypertensive Agents BLOOD Blood Glucose Cerebrovascular Accident Cholesterol Congestive Heart Failure Coronary Artery Bypass Surgery Diabetes Mellitus Discrimination, Psychology Electrocardiogram Electrocardiography Glomerular Filtration Rate Hematologic Tests High Density Lipoprotein Cholesterol Left Ventricular Hypertrophy Myocardial Infarction Pressure, Diastolic Rate, Heart Systole Triglycerides
4
Atrial Fibrillation Anticoagulation Risk Factors

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Publication 2011
Adult Anemia Anti-Inflammatory Agents, Non-Steroidal Aspirin Atrial Fibrillation Clinical Laboratory Services Clopidogrel Creatinine Diagnosis Genotype Glomerular Filtration Rate Heart Atrium Hemoglobin Hospitalization Inpatient Outpatients Patient Discharge Patients Pharmaceutical Preparations Platelet Counts, Blood Pulmonary Embolism Renal Insufficiency Serum Thrombocytopenia Ticlopidine Transients Warfarin Woman
5
Assessing Comorbidities and Outcomes in COVID-19
Demographic, clinical, and outcome data were collected by using a prespecified case report form. Comorbidities were defined according to a modified Charlson comorbidity index.10 (link) Comorbidities collected were chronic cardiac disease, chronic respiratory disease (excluding asthma), chronic renal disease (estimated glomerular filtration rate ≤30), mild to severe liver disease, dementia, chronic neurological conditions, connective tissue disease, diabetes mellitus (diet, tablet, or insulin controlled), HIV or AIDS, and malignancy. These conditions were selected a priori by a global consortium to provide rapid, coordinated clinical investigation of patients presenting with any severe or potentially severe acute infection of public interest and enabled standardisation.
Clinician defined obesity was also included as a comorbidity owing to its probable association with adverse outcomes in patients with covid-19.11 (link)
12 (link) The clinical information used to calculate prognostic scores was taken from the day of admission to hospital.13 (link) A practical approach was taken to sample size requirements.14 We used all available data to maximise the power and generalisability of our results. Model reliability was assessed by using a temporally distinct validation cohort with geographical subsetting, together with sensitivity analyses.
Knight S.R., Ho A., Pius R., Buchan I., Carson G., Drake T.M., Dunning J., Fairfield C.J., Gamble C., Green C.A., Gupta R., Halpin S., Hardwick H.E., Holden K.A., Horby P.W., Jackson C., Mclean K.A., Merson L., Nguyen-Van-Tam J.S., Norman L., Noursadeghi M., Olliaro P.L., Pritchard M.G., Russell C.D., Shaw C.A., Sheikh A., Solomon T., Sudlow C., Swann O.V., Turtle L.C., Openshaw P.J., Baillie J.K., Semple M.G., Docherty A.B., Harrison E.M., Baillie J.K., Semple M.G., Openshaw P.J., Carson G., Alex B., Bach B., Barclay W.S., Bogaert D., Chand M., Cooke G.S., Docherty A.B., Dunning J., da Silva Filipe A., Fletcher T., Green C.A., Harrison E.M., Hiscox J.A., Ho A.Y., Horby P.W., Ijaz S., Khoo S., Klenerman P., Law A., Lim W.S., Mentzer A.J., Merson L., Meynert A.M., Noursadeghi M., Moore S.C., Palmarini M., Paxton W.A., Pollakis G., Price N., Rambaut A., Robertson D.L., Russell C.D., Sancho-Shimizu V., Scott J.T., Sigfrid L., Solomon T., Sriskandan S., Stuart D., Summers C., Tedder R.S., Thomson E.C., Thwaites R.S., Turtle L.C., Zambon M., Hardwick H., Donohue C., Ewins J., Oosthuyzen W., Griffiths F., Norman L., Pius R., Drake T.M., Fairfield C.J., Knight S., Mclean K.A., Murphy D., Shaw C.A., Dalton J., Girvan M., Saviciute E., Roberts S., Harrison J., Marsh L., Connor M., Halpin S., Jackson C., Gamble C., Leeming G., Law A., Hendry R., Scott-Brown J., Greenhalf W., Shaw V., McDonald S., Ahmed K.A., Armstrong J.A., Ashworth M., Asiimwe I.G., Bakshi S., Barlow S.L., Booth L., Brennan B., Bullock K., Catterall B.W., Clark J.J., Clarke E.A., Cole S., Cooper L., Cox H., Davis C., Dincarslan O., Dunn C., Dyer P., Elliott A., Evans A., Fisher L.W., Foster T., Garcia-Dorival I., Greenhalf W., Gunning P., Hartley C., Ho A., Jensen R.L., Jones C.B., Jones T.R., Khandaker S., King K., Kiy R.T., Koukorava C., Lake A., Lant S., Latawiec D., Lavelle-Langham L., Lefteri D., Lett L., Livoti L.A., Mancini M., McDonald S., McEvoy L., McLauchlan J., Metelmann S., Miah N.S., Middleton J., Mitchell J., Moore S.C., Murphy E.G., Penrice-Randal R., Pilgrim J., Prince T., Reynolds W., Ridley P.M., Sales D., Shaw V.E., Shears R.K., Small B., Subramaniam K.S., Szemiel A., Taggart A., Tanianis-Hughes J., Thomas J., Trochu E., van Tonder L., Wilcock E., Zhang J.E., Adeniji K., Agranoff D., Agwuh K., Ail D., Alegria A., Angus B., Ashish A., Atkinson D., Bari S., Barlow G., Barnass S., Barrett N., Bassford C., Baxter D., Beadsworth M., Bernatoniene J., Berridge J., Best N., Bothma P., Brealey D., Brittain-Long R., Bulteel N., Burden T., Burtenshaw A., Caruth V., Chadwick D., Chambler D., Chee N., Child J., Chukkambotla S., Clark T., Collini P., Cosgrove C., Cupitt J., Cutino-Moguel M.T., Dark P., Dawson C., Dervisevic S., Donnison P., Douthwaite S., DuRand I., Dushianthan A., Dyer T., Evans C., Eziefula C., Fegan C., Finn A., Fullerton D., Garg S., Garg S., Garg A., Godden J., Goldsmith A., Graham C., Hardy E., Hartshorn S., Harvey D., Havalda P., Hawcutt D.B., Hobrok M., Hodgson L., Holme A., Hormis A., Jacobs M., Jain S., Jennings P., Kaliappan A., Kasipandian V., Kegg S., Kelsey M., Kendall J., Kerrison C., Kerslake I., Koch O., Koduri G., Koshy G., Laha S., Larkin S., Leiner T., Lillie P., Limb J., Linnett V., Little J., MacMahon M., MacNaughton E., Mankregod R., Masson H., Matovu E., McCullough K., McEwen R., Meda M., Mills G., Minton J., Mirfenderesky M., Mohandas K., Mok Q., Moon J., Moore E., Morgan P., Morris C., Mortimore K., Moses S., Mpenge M., Mulla R., Murphy M., Nagel M., Nagarajan T., Nelson M., Otahal I., Pais M., Panchatsharam S., Paraiso H., Patel B., Pepperell J., Peters M., Phull M., Pintus S., Pooni J.S., Post F., Price D., Prout R., Rae N., Reschreiter H., Reynolds T., Richardson N., Roberts M., Roberts D., Rose A., Rousseau G., Ryan B., Saluja T., Shah A., Shanmuga P., Sharma A., Shawcross A., Sizer J., Smith R., Snelson C., Spittle N., Staines N., Stambach T., Stewart R., Subudhi P., Szakmany T., Tatham K., Thomas J., Thompson C., Thompson R., Tridente A., Tupper-Carey D., Twagira M., Ustianowski A., Vallotton N., Vincent-Smith L., Visuvanathan S., Vuylsteke A., Waddy S., Wake R., Walden A., Welters I., Whitehouse T., Whittaker P., Whittington A., Wijesinghe M., Williams M., Wilson L., Wilson S., Winchester S., Wiselka M., Wolverson A., Wooton D.G., Workman A., Yates B, & Young P. (2020). Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. The BMJ, 370, m3339.
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Publication 2020
Acquired Immunodeficiency Syndrome Asthma Chronic Condition Chronic Kidney Diseases Connective Tissue Diseases COVID 19 Dementia Diabetes Mellitus Diet Disease, Chronic Glomerular Filtration Rate Heart Heart Diseases Hypersensitivity Infection Insulin Liver Diseases Malignant Neoplasms Obesity Patients Respiration Disorders Respiratory Rate Tablet

Most recents protocols related to «Glomerular Filtration Rate»

1
Cardiometabolic Risk Factors in NHANES
The following covariates were considered in the study: age, sex, race/ethnicity, family poverty income ratio (PIR), education level, marital status, the complication of hypertension, and diabetes mellitus (DM), smoker, drinker, body mass index (BMI), waist circumference, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean energy intake, hemoglobin (Hb), fast glucose (FBG), glycosylated hemoglobin (HbA1c), alanine transaminase (Alt), aspartate aminotransferase (Ast), albumin, total cholesterol (TC), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), uric acid (UA), blood urea nitrogen (BUN), serum creatinine (Scr), and estimated glomerular filtration rate (eGFR). Individuals who have smoked less than 100 cigarettes in their lifetime/smoked less than 100 cigarettes in their lifetime, do not smoke at all at present/smoked more than 100 cigarettes in their lifetime, and smoke some days or every day were defined as never smoke, former smokers, and now smokers, respectively. There are three categories of drinkers: current heavy alcohol consumption were defined as ≥3 drinks per day for females, ≥4 drinks per day for males, or binge drinking [≥4 drinks on same occasion for females, ≥5 drinks on same occasion for males] on 5 or more days per month; current moderate alcohol consumption were defined as ≥2 drinks per day for females, ≥3 drinks per day for males, or binge drinking ≥2 days per month. Those who did not meet the above criteria were classified as current mild alcohol user.21 (link) Hypertension was defined as an average systolic blood pressure more than 140 mmHg/diastolic blood pressure greater than 90 mmHg or self-reported use of antihypertensive medication. DM will be assessed by measures of blood glycohemoglobin, fasting plasma glucose, 2-hour glucose (Oral Glucose Tolerance Test), serum insulin in participants aged 12 years and over. Hb, FBG, HbA1c, Alt, Ast, albumin, TC, TG, HDL-C, UA, BUN, Scr, and eGFR were all determined in the laboratory. More information regarding the variables used is available at https://www.cdc.gov/nchs/nhanes/index.htm.
Xiao S., Wang Z., Zuo R., Zhou Y., Yang Y., Chen T, & Liu N. (2023). Association of Systemic Immune Inflammation Index with All-Cause, Cardiovascular Disease, and Cancer-Related Mortality in Patients with Cardiovascular Disease: A Cross-Sectional Study. Journal of Inflammation Research, 16, 941-961.
Publication 2023
Alanine Transaminase Albumins Alcohols Antihypertensive Agents BLOOD Cholesterol Creatinine Diabetes Mellitus Ethnicity Females Glomerular Filtration Rate Glucose Hemoglobin Hemoglobin, Glycosylated High Blood Pressures High Density Lipoprotein Cholesterol Index, Body Mass Insulin Males Oral Glucose Tolerance Test Plasma Pressure, Diastolic Serum Smoke Systolic Pressure Transaminase, Serum Glutamic-Oxaloacetic Triglycerides Urea Nitrogen, Blood Uric Acid Waist Circumference
2
Comprehensive Cardiometabolic Risk Assessment
A case report form was developed to record general characteristics, clinical diagnosis, and biochemical examination. Waist circumference (WC) was measured at the middle point between the costal margin and iliac crest. BMI was calculated as body weight in kilograms divided by body height in meters squared (kg/m2). Smoking habit was categorized as current smoking, ever smoking, or no smoking. Current smoking was determined when subjects were smoking currently and more than one cigarette daily in at least one year continuously. Ever smoking was determined when subjects smoked more than one cigarette daily, but had quitted smoking at least one year before. Drinking habit was categorized as current drinking, ever drinking, or no drinking. Current drinking was determined when subjects were drinking liquor, beer or wine currently in at least one year. Ever drinking was determined when subjects drank previously, but had quitted drinking at least one year before. History of lipid disorders included that plasma total cholesterol was ≥ 5.7 mmol/l, or low-density lipoprotein cholesterol (LDL-C) was ≥ 3.6 mmol/l, or high-density lipoprotein cholesterol (HDL-C) < 1.04 mmol/l, triglyceride was ≥ 1.7 mmol/l, or treatment with antihyperlipidemic agents due to hyperlipidemia. Hypertension was diagnosed by systolic blood pressure (SBP) ≥ 140 mmHg, or diastolic blood pressure (DBP) ≥ 90 mmHg, or being actively treated with anti-hypertension drugs. Diabetes mellitus was diagnosed by a fasting plasma glucose ≥ 7.0 mmol/l, or by a random plasma glucose ≥ 11.1 mmol/l, or when they were actively receiving therapy using insulin or oral medications for diabetes. Chronic kidney disease was defined as an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2.
Wang Y., Guo X., Zhang Y., Zhang R, & Li J. (2023). Different associations of general and abdominal obesity with upper and lower extremity artery disease among a community population in China. Nutrition & Metabolism, 20, 14.
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Publication 2023
Amniotic Fluid Antihypertensive Agents Beer Body Height Body Weight Cholesterol Cholesterol, beta-Lipoprotein Chronic Kidney Diseases Costal Arch Diabetes Mellitus Glomerular Filtration Rate Glucose High Blood Pressures High Density Lipoprotein Cholesterol Hyperlipidemia Hypolipidemic Agents Iliac Crest Insulin Lipid Metabolism Disorders Pharmaceutical Preparations Plasma Pressure, Diastolic Systolic Pressure Therapeutics Triglycerides Waist Circumference Wine
3
Evaluating Exercise Tolerance in Stable Heart Failure Patients
The research was carried out according to the Declaration of Helsinki. The author’s institutional review board approved the study, and the Clinical Trial Registry number is NCT04038723. All participants provided their written informed consent after understanding the experimental procedure. HF patients, diagnosed according to the Framingham HF diagnostic criteria [24 (link)], who had stable clinical presentations ≥ 4 weeks and received individualized patient education under optimized guideline-based management [25 (link)], were initially surveyed. Individuals who were > 80 years old and < 20 years old, were unable to perform exercise due to other noncardiac diseases, were pregnant, would have future cardiac transplantation within 6 months, had uncompensated HF, and had an estimated glomerular filtration rate < 30 ml/min/1.73 m2 were not enrolled in the study. We also excluded individuals with absolute contraindications for exercise suggested by the American College of Sports Medicine (ACSM) [26 ].
Hsu C.C., Wang J.S., Shyu Y.C., Fu T.C., Juan Y.H., Yuan S.S., Wang C.H., Yeh C.H., Liao P.C., Wu H.Y, & Hsu P.H. (2023). Hypermethylation of ACADVL is involved in the high-intensity interval training-associated reduction of cardiac fibrosis in heart failure patients. Journal of Translational Medicine, 21, 187.
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Publication 2023
Diagnosis Education of Patients Ethics Committees, Research Glomerular Filtration Rate Heart Transplantation Patients
4
Dementia Risk Factors Among Korean Adults
The primary outcome of this study was the incidence of all-cause dementia, including Alzheimer’s disease (AD), vascular dementia (VaD), and other types of dementia (ICD-10 diagnostic codes: F00, F01, F02, F03, G30, or G31). Outcome events were defined when both ICD-10 codes and the prescription records of anti-dementia drugs, including donepezil, galantamine, rivastigmine, and memantine were used. This definition has been widely accepted with high accuracy, having 94.7% positive predictive value in K-NHID21 (link). The secondary outcomes included AD (F00 or G30) and VaD (F01). The date of the second health checkup was defined as the index date, and the participants were followed up until December 31, 2019 or until the development of the primary outcome, whichever came first.
We obtained information from the second general health checkup based on previous studies related to risk factors for dementia. Specifically, we obtained demographic data, including age, sex, height, body weight, and waist circumference, as well as information regarding health-related lifestyles, including smoking status, categorized as a current smoker or not, and alcohol consumption status, categorized as alcohol users (any alcohol consumption) or not. Baseline comorbidities including hypertension, dyslipidemia, diabetes mellitus, and chronic kidney disease were also obtained. The operational definition of covariates and outcomes in cardiovascular research is well documented in several studies based on K-NHID12 (link). CKD was defined as an estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m2 calculated using the CKD Epidemiology Collaboration (CKD-EPI) equation. Low-income level was defined when the participants were medical benefit beneficiaries and were included in the lowest quartile of income levels. Further, laboratory data, including levels of random glucose, total cholesterol, glomerular filtration rate, and systolic/diastolic blood pressure, were also obtained at the ambulatory health exam visits after index stroke. Among these variables, age, sex, smoking status, alcohol consumption, income level, history of diabetes mellitus, hypertension, dyslipidemia, and chronic kidney disease were used as covariates.
Cheon D.Y., Han K.D., Kim C.H., Oh M.S., Lee B.C., Kim Y., Lee S.H., Kim C., Lim J.S., Lee M, & Yu K.H. (2023). Association between exercise habit changes and incident dementia after ischemic stroke. Scientific Reports, 13, 3959.
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Publication 2023
Body Weight Cardiovascular System Cerebrovascular Accident Cholesterol Chronic Kidney Diseases Dementia, Vascular Diabetes Mellitus Diagnosis Diastole Donepezil Dyslipidemias Ethanol Galantamine Glomerular Filtration Rate Glucose High Blood Pressures Memantine Pharmaceutical Preparations Presenile Dementia Pressure, Diastolic Rivastigmine Systole Systolic Pressure Waist Circumference
5
Propensity-Matched Comparison of ACEI and ARB
For continuous variables, data were expressed as mean ± standard deviation or median (interquartile range) and differences between the two groups were evaluated using the unpaired t-test or Mann-Whitney U test. For discrete variables, differences were expressed as counts and percentages and were analyzed with the χ2 test between the two groups. To adjust for any potential confounders, propensity score-matching (PSM) analysis was performed using the logistic regression model with all available variables that could be of potential relevance: age, gender, body mass index (BMI), history of smoking, Killip class on admission, BP, heart rate, LV ejection fraction (LVEF), CV risk factors or co-morbidity (hypertension, diabetes mellitus, hyperlipidemia, prior HF, prior stroke, prior MI, and prior angina), initial estimated glomerular filtration rate (eGFR) by Modification of Diet in Renal Disease (MDRD) equation, co-medications (aspirin, P2Y12 inhibitors, CCB, beta-blockers and statins) at discharge and types of MI (STEMI or NSTEMI). Patients in the ARB group were 1:1 matched to those in the ACEI group according to propensity score with nearest neighbor matching algorithm. Subjects were matched with a caliper width equal to 0.1 of the standard deviation of the propensity score. The efficacy of the propensity score model was assessed by estimating standardized differences for each covariate between groups. Survival curves for clinical endpoints and cumulative event rates with incidence rates per 100 patient-years up to 2-year were generated using Kaplan–Meier estimates. Cox-proportional hazard models were used to assess the adjusted hazard ratio (HR) comparing the two groups and their 95% confidence interval (CI) for each clinical endpoint. Subgroups that were defined post-hoc according to demographic and clinical characteristics included age (<75 & ≥75 years), gender, diabetes mellitus, Killip class, LVEF (<50% & ≥50%), beta-blockers at discharge, type of MI, multi-vessel disease and infarct-related artery.
All data were processed with SPSS version 23 (IBM Co, Armonk, NY, US) and R version 3.1.3 (R Foundation for Statistical Computing, Vienna, Austria). For all analyses, a two-sided p < 0.05 was considered to be statistically significant.
Lee J.G., Joo S.J., Kim S.Y., Choi J.H., Boo K.Y., Hwang J.Y., Hur S.H, & Jeong M.H. (2023). Impact of angiotensin-converting enzyme inhibitors versus angiotensin receptor blockers on clinical outcomes in hypertensive patients with acute myocardial infarction. PLOS ONE, 18(3), e0281460.
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Publication 2023
Adrenergic beta-Antagonists Angina Pectoris Arteries Aspirin Cerebrovascular Accident Diabetes Mellitus Dietary Modification Gender Glomerular Filtration Rate High Blood Pressures Hydroxymethylglutaryl-CoA Reductase Inhibitors Hyperlipidemia Index, Body Mass Infarction inhibitors Kidney Diseases Non-ST Elevated Myocardial Infarction Patient Discharge Patients Pharmaceutical Preparations Rate, Heart ST Segment Elevation Myocardial Infarction Vascular Diseases

Top products related to «Glomerular Filtration Rate»

1
Cobas integra 800 by Roche
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The Cobas Integra 800 is a fully automated, high-throughput clinical chemistry analyzer designed for use in medical laboratories. It is capable of performing a wide range of clinical chemistry tests on a variety of sample types. The Cobas Integra 800 utilizes advanced technology to provide accurate and reliable results, with the ability to handle a high volume of samples efficiently.
2
Cobas 6000 by Roche
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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.
3
Advia 1800 by Siemens
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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.
4
Cobas 8000 by Roche
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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.
5
Automatic clinical analyzer by Beckman Coulter
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Automatic clinical analyzers are laboratory instruments designed to perform a wide range of biochemical and immunochemical tests on biological samples, such as blood, serum, or urine. These analyzers automate the analytical processes, enabling efficient and consistent testing, and providing reliable results to support clinical decision-making.
6
Elecsys 2010 by Roche
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The Elecsys 2010 is an automated immunoassay analyzer used for the in vitro determination of various analytes in biological samples. It is designed for high-throughput testing and offers precise and reliable results.
7
Sas version 9 by SAS Institute
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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
Advia 2400 by Siemens
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The ADVIA 2400 is a fully automated clinical chemistry analyzer designed for high-volume laboratory testing. It features advanced technology for efficient sample processing and accurate analysis of a wide range of clinical chemistry parameters.
9
Automatic analyzer 7600 by Hitachi
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The Hitachi Automatic Analyzer 7600 is a versatile laboratory instrument designed for the automated analysis of various samples. It is capable of performing a wide range of clinical chemistry tests, including quantitative determination of analytes in biological fluids.
10
Sas 9 by SAS Institute
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SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.

More about "Glomerular Filtration Rate"

Glomerular filtration rate (GFR) is a crucial metric that measures the kidney's ability to filter waste and excess fluid from the blood.
It serves as a valuable indicator of overall kidney health, allowing for the diagnosis and monitoring of kidney disease progression.
Optimizing GFR research is essential, as it requires the use of reproducible and accurate protocols that can be easily identified and compared.
PubCompare.ai, the leading AI-driven platform, is designed to streamline this process.
By leveraging artificial intelligence, the platform helps researchers locate relevant protocols from literature, pre-prints, and patents, and provides AI-powered comparisons to identify the best protocols and products for their GFR research.
This can be particularly useful when working with automated clinical analyzers, such as the Cobas Integra 800, Cobas 6000, ADVIA 1800, Cobas 8000, and Hitachi Automatic Analyzer 7600, which are commonly used for GFR measurements.
By accessing a curated database of protocols, researchers can ensure their workflow is efficient and their results are reliable.
Additionally, the use of statistical software like SAS version 9.4 and SAS 9.4 can be integrated into the research process, allowing for the analysis and interpretation of GFR data.
The Elecsys 2010 and ADVIA 2400 analyzers are also often employed in GFR studies, further highlighting the diverse range of tools and technologies available for this important area of kidney research.
By utilizing the insights and capabilities of PubCompare.ai, researchers can streamline their GFR studies, optimize their workflow, and achieve more reliable and reproducible results, ultimately contributing to a better understanding of kidney function and the advancement of nephrology research.