Weighting approaches for a genetic risk score and an oxidative stress score for predicting the incidence of obesity

Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score

We aimed to use a genetic risk score (GRS) constructed with prediabetes and type 2 diabetes-related single nucleotide polymorphisms (SNPs) and an oxidative stress score (OSS) to construct an early-prediction model for prediabetes and type 2 diabetes (T2DM) incidence in a Korean population. The study population included 549 prediabetes and T2DM patients and 1036 normal subjects. The GRS was constructed using six prediabetes and T2DM-related SNPs, and the OSS was composed of three recognized oxidative stress biomarkers. Among the nine SNPs, six showed significant associations with the incidence of prediabetes and T2DM. The GRS was profoundly associated with increased prediabetes and T2DM (OR = 1.946) compared with individual SNPs after adjusting for age, sex, and BMI. Each of the three oxidative stress biomarkers was markedly higher in the prediabetes and T2DM group than in the normal group, and the OSS was significantly associated with increased prediabetes and T2DM (OR = 2.270). When BMI was introduced to the model with the OSS and GRS, the area under the ROC curve improved (from 69.3% to 70.5%). We found that the prediction model composed of the OSS, GRS, and BMI showed a significant prediction ability for the incidence of prediabetes and T2DM.

A Machine Learning Algorithm for Quantitatively Diagnosing Oxidative Stress Risks in Healthy Adult Individuals Based on Health Space Methodology: A Proof-of-Concept Study Using Korean Cross-Sectional Cohort Data

Oxidative stress aggravates the progression of lifestyle-related chronic diseases. However, knowledge and practices that enable quantifying oxidative stress are still lacking. Here, we performed a proof-of-concept study to predict the oxidative stress status in a healthy population using retrospective cohort data from Boramae medical center in Korea (n = 1328). To obtain binary performance measures, we selected healthy controls versus oxidative disease cases based on the "health space" statistical methodology. We then developed a machine learning algorithm for discrimination of oxidative stress status using least absolute shrinkage and selection operator (LASSO)/elastic net regression with 10-fold cross-validation. A proposed fine-tune model included 16 features out of the full spectrum of diverse and complex data. The predictive performance was externally evaluated by generating receiver operating characteristic curves with area under the curve of 0.949 (CI 0.925 to 0.974), sensitivity of 0.923 (CI 0.879 to 0.967), and specificity of 0.855 (CI 0.795 to 0.915). Moreover, the discrimination power was confirmed by applying the proposed diagnostic model to the full dataset consisting of subjects with various degrees of oxidative stress. The results provide a feasible approach for stratifying the oxidative stress risks in the healthy population and selecting appropriate strategies for individual subjects toward implementing data-driven precision nutrition.

Non-alcoholic fatty liver disease and lipotoxicity

Non-alcoholic fatty liver disease (NAFLD) has become the most common liver pathology worldwide due to the rising prevalence of obesity. This term includes changes from simple steatosis to steatohepatitis and fibrosis. It was previously thought to be a hepatic manifestation of metabolic syndrome, but recent literature describes this relation as much more complex and bi-directional. Development of NAFLD is associated with other metabolic syndrome components but it can also exacerbate insulin resistance and increase cardiovascular risk. Recently a lot of attention is brought to the role of lipids and lipotoxicity in pathogenesis and progression of non-alcoholic fatty disease. It seems that some lipid classes can be protective against liver injury while others are harmful in excessive amounts. This study presents an overview of the main lipids involved in the pathogenesis of non-alcoholic fatty liver disease and summarizes their association with lipotoxicity, insulin resistance, oxidative stress and other processes responsible for its progression.

Identification of a TMEM182 rs141764639 polymorphism associated with central obesity by regulating tumor necrosis factor-α in a Korean population

AIMS

To investigate the effect of a single nucleotide polymorphism (SNP) in transmembrane protein 182 (TMEM182) on the risk of having central obesity and the related phenotype.

METHODS

In total, 2141 subjects with central obesity (n = 827) and normal controls (n = 1314) were included. The most strongly associated SNPs were related to waist circumference, and one SNP, rs141764639, was identified in TMEM182 (p = 7.30E-06, q = 0.0326).

RESULTS

The TC genotype was associated with more central obesity; higher levels of blood pressure, glucose-related parameters, and inflammatory markers; abnormal lipid profiles; and smaller LDL particle sizes than the major allele homozygotes in the total population. TNF-α in the TC genotype showed extremely high levels compared to the TT genotype. There were significant interactions between the genotypes and waist circumference in relation to LDL particle size, TNF-α level, and IL-6 level. Compared with the reference group, the odds ratio for central obesity in C allele carriers was significantly increased by 2-fold.

CONCLUSIONS

The polymorphism of TMEM182 rs141764639 might have an effect on the incidence of central obesity in the Korean population by interacting with the upregulation of TNF-α, a proinflammatory cytokine. Moreover, LDL particle size, which is an atherogenic lipid profile trait, was associated with the TMEM182 rs141764639 genotype.