Influencing factors of coexistence PreDM and PreHTN in occupational population of state grid corporation of Chinese

Arch Environ Occup Health. 2020;75(6):365-370. doi: 10.1080/19338244.2019.1703623. Epub 2019 Dec 17.

Abstract

Background: Early intervention of coexisting prediabetes (PreDM) and prehypertension (PreHTN) has great significance for the prevention and treatment of cardiovascular diseases. Therefore, the influencing factors of the coexisting PreDM and PreHTN has been widely concerned by human beings. The State Grid Corporation occupational population as a special group, who are often exposed to a certain amount of voltage. Earlier studies have shown that exposure to a certain level of voltage can cause cardiovascular disease. The aim of the present study was to explore the risk factors of coexisting PreDM and PreHTN, and to provide theoretical basis for early intervention.

Methods: A stratified random sampling method was used to randomly select Occupational population from the five power supply regions of China in 2012 for questionnaire surveys and clinical examinations. Respondents were divided into Normal blood glucose group, PreDM group, Diabetes group, Normal blood pressure group, PreHTN group, Hypertension group.

Results: The prevalence of coexisting PreDM and PreHTN in the study population was 1.9%. The binary Logistic regression results showed that region, gender, age, BMI, triglyceride (TG), and low density lipoprotein cholesterol (LDL-C) were the effects of factor coexisting PreDM and PreHTN.

Conclusion: It is important to pay attention to the early stage of hypertension and diabetes, control the transition from PreHTN and PreDM to hypertension and diabetes, and improve the health of Power Supply Enterprise Population.

Keywords: PreDM; PreHTN; influencing factors; power supply enterprise.

MeSH terms

  • Adult
  • Blood Glucose
  • Blood Pressure
  • Body Mass Index
  • China / epidemiology
  • Diabetes Mellitus / epidemiology
  • Female
  • Humans
  • Hypertension / epidemiology
  • Lipids / blood
  • Logistic Models
  • Male
  • Middle Aged
  • Occupational Health
  • Power Plants / statistics & numerical data*
  • Prediabetic State / epidemiology*
  • Prehypertension / epidemiology*
  • Risk Factors

Substances

  • Blood Glucose
  • Lipids