Investigation of the risk factors associated with prediabetes in normal-weight Qatari adults: a cross-sectional study

Abstract

Type 2 diabetes is one of the most prevalent chronic diseases in the world, and more people than ever before have impaired glucose tolereance, or prediabetes. Many patients with impaired glucose tolerance and undiagnosed diabetes do not know that their glucose metabolism system has been in a state of disorder. Every year, about 5-10% of prediabetics develop diabetes. One of the important achieving factors may be the increase in blood lipids. However, it is not clear whether triglyceride is associated with impaired glucose tolerance and prediabetes in the Qatari population. Therefore, we investigated the relationship between the first several clinical variables and prediabetes status in normal and overweight populations. We conducted a cross-sectional study using data from the Qatar Biobank program. The study included 5,996 participants who were adults over the age of 20. We collected information about participants' fasting blood glucose levels with other clinical measurements and used various machine learning models and logistic regression to study the association between the clinical measurements and prediabetes for normal and overobese weight groups. The use of several machine learning models showed that, after adjusting the potential confounding factors such as age and sex, Triglyceride has been demonstrated to be positively correlated with prediabetes, and there was a special population dependence phenomenon. Among them, nonobese people (p < 0.05). The effect value and 95% confidence interval and OR of triglyceride on prediabetes was 2.79 and (e0.78, e1.28), respectively.

Keywords: Diabetes; Normal-weight; Obesity; Prediabetes; Qatar; Triglyceride-glucose-related.

Fig. 1

Flowchart of data processing.

Fig. 2

Stepwise logistic regression model: NW versus OWO.

Fig. 3

ROC curve of the triglyceride-based model with an AUC equal to 86.27%.

Fig. 4

ROC curve of the different 8 nested models.

Fig. 5

ROC curve of the different 4 machine learning models using only triglyceride.