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Related Concept Videos

Serum Studies: Renal Function Tests01:24

Serum Studies: Renal Function Tests

39
Renal function tests are crucial for assessing kidney health, monitoring disease progression, and evaluating the kidneys' efficiency in waste elimination, fluid balance, and electrolyte regulation. These tests offer critical insights into kidney function, even though routine measurements may appear normal until there is a significant decline in the glomerular filtration rate or GFR. Typically, signs of kidney impairment only become evident when the GFR falls to about 50% of its normal level.
39
One-Compartment Open Model: Urinary Excretion Data and Determination of k01:11

One-Compartment Open Model: Urinary Excretion Data and Determination of k

278
The one-compartment open model leverages urinary excretion data to estimate renal clearance, which gauges the kidney's capacity to expel a drug. This method offers several benefits, including directly measuring drug elimination and assessing the kidney's contribution to overall drug clearance. However, this approach has limitations. It assumes sole renal excretion of the drug, which is not true for all drugs. Accurate urinary excretion and plasma drug concentration measurement can also...
278
Determination of Renal Drug Clearance: Graphical and Midpoint Methods01:07

Determination of Renal Drug Clearance: Graphical and Midpoint Methods

192
Renal clearance, a crucial parameter in pharmacokinetics, can be determined using two different methods: the graphical method and the midpoint method. These methods provide insights into the rate of drug excretion by the kidneys and aid in assessing renal function.
The graphical method involves plotting the rate of drug excretion in urine against the plasma drug concentration. By analyzing the graph, the clearance can be calculated and obtained. Drugs rapidly excreted by the kidneys exhibit a...
192
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

136
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
136
Renal Drug Clearance: Comparison Between Renal Excretion Methods01:08

Renal Drug Clearance: Comparison Between Renal Excretion Methods

261
Renal clearance is a critical parameter encompassing kidney filtration, secretion, and reabsorption processes. It is calculated using a specific equation to determine the rate at which the kidneys clear a drug.
Renal clearance is often associated with the renal glomerular filtration rate (GFR), which represents the rate at which plasma is filtered through the glomeruli in the kidney. When drug reabsorption is minimal and there is no active secretion, renal clearance is closely related to the...
261
Heritability01:06

Heritability

294
Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
294

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Related Experiment Video

Updated: Sep 3, 2025

Unilateral Ureteral Obstruction Model for Investigating Kidney Interstitial Fibrosis
04:37

Unilateral Ureteral Obstruction Model for Investigating Kidney Interstitial Fibrosis

Published on: April 25, 2025

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Local genetic covariance between serum urate and kidney function estimated with Bayesian multitrait models.

Alexa S Lupi1,2, Nicholas A Sumpter3, Megan P Leask3,4

  • 1Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.

G3 (Bethesda, Md.)
|July 25, 2022
PubMed
Summary

This study identifies shared genetic regions contributing to chronic kidney disease and high serum urate levels. These findings advance our understanding of the genetic links between these conditions.

Keywords:
UK Biobankchronic kidney diseaseeGFRgouthyperuricemialocal genetic covariancemultitraitpleiotropyserum creatinineserum urate

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Area of Science:

  • Genetics
  • Nephrology
  • Metabolic Diseases

Background:

  • Hyperuricemia (serum urate >6.8 mg/dl) is linked to cardiometabolic and renal diseases like gout and chronic kidney disease (CKD).
  • Previous research on the shared genetic basis of CKD and hyperuricemia used single-variant tests or whole-genome correlations, lacking power to map specific pleiotropic loci.
  • Whole-genome estimates show a moderate genetic correlation between CKD and hyperuricemia, but do not pinpoint specific genomic regions.

Purpose of the Study:

  • To bridge the gap between single-variant and whole-genome approaches by using local Bayesian multitrait models.
  • To estimate genetic covariance between estimated glomerular filtration rate (a marker for CKD) and serum urate in specific genomic regions.
  • To identify novel pleiotropic genes and biological mechanisms underlying the shared genetic basis of CKD and hyperuricemia.

Main Methods:

  • Applied local Bayesian multitrait models to estimate genetic covariance between CKD and serum urate across genomic regions.
  • Identified statistically significant overlapping linkage disequilibrium (LD) windows with significant covariance estimates.
  • Utilized colocalization analyses to link identified genomic windows with gene expression, exploring biological mechanisms.

Main Results:

  • Identified 134 LD windows with significant genetic covariance between CKD and serum urate.
  • Discovered 64 genetically distinct shared loci, validating 17 known loci and revealing 22 novel pleiotropic genes.
  • Found genomic regions associated with gene expression, providing insights into biological mechanisms.

Conclusions:

  • The local Bayesian multitrait model approach effectively identifies shared genetic loci between CKD and hyperuricemia.
  • The identified novel pleiotropic genes and regions offer new targets for understanding the comorbidity of these conditions.
  • These findings contribute to explaining the association between chronic kidney disease and hyperuricemia through shared genetic underpinnings.