Metabolic Signatures of Oxidative Stress and Their Relationship with Erythrocyte Membrane Surface Roughness Among Workers of Manual Materials Handling (MMH)

Abstract

Background: Brickfield workers in India perform manual materials handling (MMH) and as a result, are at a high risk of developing oxidative stress. This results in an alteration of the various markers of metabolic oxidative stress at the cellular level. Since red blood cell (RBC) is the central point where oxygen, glucose-6-phosphate dehydrogenase (G-6-PD), and glutathione (GSH) are involved, the surface roughness and its alteration and modeling with respect to workers exposed to MMH may be considered as helpful determinants in predicting early damage to the cell and restoring better health to the exposed population, that is, the worker exposed to stress. Hence, nanometric analysis of the surface roughness of the RBC may serve as an early indicator of the stress-related damage in these individuals. Aims: The purpose of the study was to identify early red blood corpuscular surface damage profile in terms of linear modeling correlating various biochemical parameters. Linear modeling has been aimed to be developed in order to demonstrate how individual oxidative stress markers such as malondialdehyde (MDA), G-6-PD, and reduced GSH are related to the RBC surface roughness [root mean square (RMS)]. Materials and Methods: Conventional analysis of these biochemical responses were evaluated in MMH laborers (age varying between 18 years and 21 years) and a comparable control group of the same age group (with sedentary lifestyles). Peak expiratory flow rate (PEFR) and RBC surface analysis by atomic-force microscopy (AFM) and correlated scanning probe microscopy (SPM-analytical software) with corresponding image analysis were performed immediately after completion of standardized exercise (MMH) at the brickfield. Results: A number of correlated significances and regressive linear models were developed among MDA, G-6-PD, GSH, and RBC surface roughness. Conclusion: It appears that these linear models might be instrumental in predicting early oxidative damages related to specific occupational hazards.