Relationship between Metal Exposures, Dietary Macronutrient Intake, and Blood Glucose Levels of Informal Electronic Waste Recyclers in Ghana.
Fayizatu DawudSylvia Akpene TakyiJohn Arko-MensahNiladri BasuGodfred EgbiEbenezer Ofori-AttahSerwaa Akoto BawuahJulius Najah FobilPublished in: International journal of environmental research and public health (2022)
While metal exposures are generally high among informal electronic waste (e-waste) recyclers, the joint effect of metals and dietary macronutrients on their metabolic health is unknown. Therefore, we investigated the relationship between metal exposures, dietary macronutrients intake, and blood glucose levels of e-waste recyclers at Agbogbloshie using dietary information (48-h recall survey), blood metals (Pb & Cd), and HbA1C levels of 151 participants (100 e-waste recyclers and 51 controls from the Accra, Ghana) in March 2017. A linear regression model was used to estimate the joint relationship between metal exposures, dietary macronutrient intake, and blood glucose levels. Except for dietary proteins, both groups had macronutrient deficiencies. Diabetes prevalence was significantly higher among controls. Saturated fat, OMEGA-3, and cholesterol intake were associated with significant increases in blood glucose levels of recyclers. In a joint model, while 1 mg of cholesterol consumed was associated with a 0.7% increase in blood glucose, 1 g/L of Pb was found to significantly increase blood glucose levels by 0.9% among recyclers. Although the dietary consumption of cholesterol and fat was not high, it is still possible that exposure to Pb and Cd may still increase the risk of diabetes among both e-waste recyclers and the general population.
Keyphrases
- blood glucose
- glycemic control
- heavy metals
- type diabetes
- blood pressure
- sewage sludge
- health risk assessment
- cardiovascular disease
- healthcare
- municipal solid waste
- public health
- health risk
- life cycle
- adipose tissue
- mental health
- low density lipoprotein
- mass spectrometry
- insulin resistance
- cross sectional
- risk factors
- nk cells
- neural network