We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ZeptoMetrix an Antylia scientific company

Download Mobile App




AI Algorithm Predicts Diabetic Kidney Disease through Blood Tests

By LabMedica International staff writers
Posted on 01 Jun 2023

Diabetes is globally recognized as the main contributor to kidney failure. More...

Notable advancements have been made in devising treatments for kidney disease in diabetic patients. Yet, evaluating an individual's risk for kidney disease based solely on clinical factors can be challenging. Consequently, identifying who is most susceptible to developing diabetic kidney disease is a vital clinical need. Now, scientists have created a computational method that predicts the likelihood of a person with type 2 diabetes developing kidney disease, a common yet severe diabetes complication. This could aid physicians in preventing or improving the management of kidney disease in type 2 diabetes patients.

The new algorithm developed by researchers from Sanford Burnham Prebys (La Jolla, CA, USA) and the Chinese University of Hong Kong (CUHK, Hong Kong) relies on measuring a process known as DNA methylation, which is the accumulation of subtle changes in the DNA. DNA methylation can provide essential insights into gene activation and deactivation and can be easily measured via blood tests.

Utilizing comprehensive data from over 1,200 type 2 diabetes patients registered in the Hong Kong Diabetes Register, the researchers constructed their model which they also tested on an independent group of 326 Native Americans with type 2 diabetes. This confirmed the model's predictive power for kidney disease across diverse populations. The researchers are presently fine-tuning their model and extending its application to address other health and disease-related inquiries, such as why some cancer patients do not respond favorably to certain treatments.

“This study provides a glimpse into the powerful future of predictive diagnostics,” said co-senior author Kevin Yip, Ph.D., a professor and director of Bioinformatics at Sanford Burnham Prebys. “Our team has demonstrated that by combining clinical data with cutting-edge technology, it’s possible to develop computational models to help clinicians optimize the treatment of type 2 diabetes to prevent kidney disease.”

“Our computational model can use methylation markers from a blood sample to predict both current kidney function and how the kidneys will function years in the future, which means it could be easily implemented alongside current methods for evaluating a patient’s risk for kidney disease,” added Yip.

Related Links:
Sanford Burnham Prebys
CUHK 


Gold Member
Fully Automated Cell Density/Viability Analyzer
BioProfile FAST CDV
Verification Panels for Assay Development & QC
Seroconversion Panels
New
Nutating Mixer
Enduro MiniMix
New
Mumps Test
ReQuest MUMPS IgM Assay
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to LabMedica.com and get access to news and events that shape the world of Clinical Laboratory Medicine.
  • Free digital version edition of LabMedica International sent by email on regular basis
  • Free print version of LabMedica International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of LabMedica International in digital format
  • Free LabMedica International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








Channels

Clinical Chemistry

view channel
Image: The POC device rapidly predicts neonatal respiratory disease at birth in the NICU (Photo courtesy of SIME Diagnostics)

AI-Powered Lung Maturity Test Identifies Newborns at Higher Risk of Respiratory Distress

Each year, approximately 300,000 babies in the United States are born between 32 and 36 weeks' gestation, according to national health data. This group is at an elevated risk for respiratory distress,... Read more

Hematology

view channel
Image: CitoCBC is the world first cartridge-based CBC to be granted CLIA Waived status by FDA (Photo courtesy of CytoChip)

Disposable Cartridge-Based Test Delivers Rapid and Accurate CBC Results

Complete Blood Count (CBC) is one of the most commonly ordered lab tests, crucial for diagnosing diseases, monitoring therapies, and conducting routine health screenings. However, more than 90% of physician... Read more

Immunology

view channel
Image: The cancer stem cell test can accurately choose more effective treatments (Photo courtesy of University of Cincinnati)

Stem Cell Test Predicts Treatment Outcome for Patients with Platinum-Resistant Ovarian Cancer

Epithelial ovarian cancer frequently responds to chemotherapy initially, but eventually, the tumor develops resistance to the therapy, leading to regrowth. This resistance is partially due to the activation... Read more

Pathology

view channel
Image: A biomarker discovery pipeline has shown promise as a noninvasive method of diagnosing CRC (Photo courtesy of NCI Center for Cancer Research)

Machine Learning Tool Enables Noninvasive Diagnosis and Monitoring of Colorectal Cancer

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States when considering both genders. Colonoscopy remains the gold standard for CRC diagnosis, but it is invasive,... Read more

Technology

view channel
Image: Scanning electron microscopy images showing 3D micro-printed Limacon-shaped whispering-gallery-mode microcavities with different amounts of deformation (Photo courtesy of A. Ping Zhang/PolyU)

Tiny Microlaser Sensors with Supercharged Biosensing Ability to Enable Early Disease Diagnosis

Optical whispering-gallery-mode microlaser sensors function by trapping light within tiny microcavities. When target molecules bind to the cavity, they induce subtle changes in the laser’s frequency, allowing... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.