detail content area
24th January 2022
New Study by Korea National Institute of Health Demonstrates Pre-emptive Approach to Predicting Prediabetes
Korean researchers develop a new model to predict the development of prediabetes based on metabolic markers.
Prediabetes (PD)—a condition where blood sugar is higher than normal but not high enough to be classified as Type 2 diabetes (T2D)—often progresses to T2D. This progress can be halted with early detection of PD. Researchers at the Korea National Institute of Health have now developed a method to accurately predict PD, giving people a chance to prevent the condition from developing into T2D through medication and lifestyle changes.
New study by KNIH proposes a model that improves chances of diagnosis and treatment monitoring of prediabetes.
Photo courtesy: Shutterstock
While initially considered a “disease of the rich,” Type 2 diabetes (T2D) is a highly prevalent condition today. A chronic disease in which the body either cannot utilize or doesn't produce enough insulin, T2D contributes to high rates of mortality and morbidity. However, like a traffic signal that flashes yellow before flashing red, our bodies produce a state of prediabetes (PD) before T2D.
PD ̶ where blood sugar levels begin to rise, but do not attain the level considered for a diagnosis of diabetes ̶ is a precursor to T2D and can be diagnosed with biochemical methods such as impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and combined IFG/IGT. Clinical risk factors (CRF), such as a person’s age, sex, body mass index (BMI) and cholesterol levels also help in predicting diabetes risk. However, these methods are inconsistent and time-consuming. Additionally, due to its subclinical nature, PD is often diagnosed late, or goes undetected, progressing into T2D with a slew of associated complications like cardiovascular and kidney disease, and nerve and retinal injuries.
A team of researchers at the Korean National Institute of Health (KNIH), led by Dr. Heun‑Sik Lee, decided to tackle the diagnosis of PD with a pre-emptive, targeted metabolomic approach.
Metabolomics is, quite simply put, the study of metabolites—by-products of biological processes in our bodies. Dr. Lee explains, “These are sensitive markers, with prior studies identifying associations between several metabolites and PD. Most of these studies, however, were conducted among Caucasian and European populations, not Koreans.”
In their latest study, supported by intramural grants from the Korea National Institute of Health (2019-NG-053-00;2017-NI73004-00; 2013-NG73001-00) and published in Nature’s Scientific Reports, the team quantified specific metabolites present in 1723 participants in the Korea Association REsource (KARE) cohort, from which 500 normal individuals were followed up for 6 years, and their disease progression was monitored. The levels of these metabolites were measured in comparison to established markers of diabetes and PD, like fasting glucose, 2 h-PPG, HbA1c, and HOMA-IR levels.
Of the initial 39 markers, statistical analyses revealed 12 novel, independent metabolites, which were significantly associated with the development of PD. “We identified five amino acids, four glycerophospholipids, two sphingolipids, and one acylcarnitine. Six of these markers were linked to an increased risk of PD, whereas the others were associated with a decreased risk at baseline,” Dr. Lee elaborates.
These metabolites formed the KARE model, which was more efficient in predicting the future development of PD compared to other metabolite models, CRF, or fasting glucose. How is this useful, though?
Dr. Lee explains, “Our prediction model could help detect PD early. Upon diagnosis, patients can be advised lifestyle and dietary modifications, or medications to manage their blood sugar so that their condition doesn’t progress to T2D.” In fact, combining the KARE model with existing diagnostic criteria (like metabolic markers, CRF, and fasting glucose levels) yielded the best prediction performance, the team found. Moreover, the KARE model could also be used to monitor PD and determine its prognosis (by measuring changes in levels of the 12 metabolic markers it includes), leading to the development of effective medicines for PD.
In other words, the model would give treatment providers a chance to prepare patients early in their fight against T2D, contributing immensely to the global fight against T2D!
Title of original paper
Heun‑Sik Lee, Tae‑Joon Park, Jeong‑Min Kim, Jun Ho Yun, Ho‑Yeong Yu, Yeon‑Jung Kim, Bong‑Jo Kim
Identification of metabolic markers predictive of prediabetes in a Korean population
Division of Genome Research, Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex
About National Institute of Health in Korea
The Korea National Institute of Health (KNIH), one of the major operating components of the Ministry of Health and Welfare, leads the nation’s medical research. Over the past seven decades, the KNIH has made unwavering efforts to enhance the public’s health and innovate biomedical research. The KNIH seeks to eradicate diseases and make people healthier. The KNIH establishes a scientific basis and evidence underlying health policy as well as provides national research infrastructures. We also promote public health research. To this end, we make efforts to enrich a health research environment by granting funds to research projects and keeping our resources, data, and facilities more open and accessible to researchers.
About Dr. Heun‑Sik Lee
Heun‑Sik Lee is a researcher at the Division of Structural and Functional Genomics, Center for Genome Science, Korea National Institute of Health. He has published over 45 papers. His research areas include DNA methylation, gene expression tranion, gene regulation, genomics regulation and Meta-Analysis.