Related Experiment Video
Updated: Jan 26, 2026
10:40
CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
Published on: April 25, 2022
2.8K
Predicting therapeutic response in IgG4-related disease based on cluster analysis
Motohisa Yamamoto1, Ken-Ichi Takano2, Ryuta Kamekura3
1a Department of Rheumatology and Clinical Immunology , Sapporo Medical University School of Medicine , Sapporo , Japan.
Immunological Medicine
|April 3, 2019
Summary
This study classified patients with immunoglobulin (Ig)G4-related disease (IgG4-RD) into four groups using cluster analysis. Different patient clusters showed distinct therapeutic responses and prognoses, paving the way for personalized IgG4-RD treatment.
Area of Science:
- Immunology
- Rheumatology
- Clinical Medicine
Background:
- Immunoglobulin (Ig)G4-related disease (IgG4-RD) management lacks personalization.
- Classifying IgG4-RD patients is crucial for tailored therapeutic strategies.
Purpose of the Study:
- To classify IgG4-RD patients into distinct groups using cluster analysis.
- To elucidate the therapeutic responses and prognoses of each identified patient group.
- To advance personalized medicine approaches in IgG4-RD clinical practice.
Main Methods:
- Cluster analysis of 147 IgG4-RD patients from a registry.
- Classification into four distinct groups based on clinical and serological features.
- Examination of therapeutic responses, including glucocorticoid and immunosuppressant use, and relapse rates.
Main Results:
- Four patient clusters were identified based on hypergammaglobulinemia, IgG4 levels, hypocomplementemia, eosinophilia, age of onset, and CRP levels.
- Cluster 1 (hypergammaglobulinemia, elevated IgG4, hypocomplementemia) required higher glucocorticoid doses.
- Cluster 4 (elderly onset, low eosinophils) exhibited lower relapse rates and better steroid discontinuation potential, while Clusters 1 and 3 showed higher relapse frequencies.
Conclusions:
- Patient classification based on clinical features enables personalized medicine in IgG4-RD.
- Distinct clusters demonstrate varying treatment needs and prognoses.
- This stratification approach can optimize therapeutic strategies and improve patient outcomes in IgG4-RD.
Related Concept Videos
02:27Predicting Molecular Geometry
45.6K
VSEPR Theory for Determination of Electron Pair Geometries
45.6K
01:18Cardiovascular Drugs: Classification based on Therapeutic Indications
4.1K
Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
4.1K
01:26Therapeutic Drug Monitoring: Drug Analysis Methods
188
Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
188
01:20Cluster Sampling Method
14.3K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.3K
01:45Vesicular Tubular Clusters
3.1K
After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
With the help of motor proteins such...
3.1K
01:03Prediction Intervals
3.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.3K