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Evaluation of the persistent organic pollutants association with type 2 diabetes: A prospective study from Karachi, Pakistan.

S KhwajaM ZahidA KarimL GurganariZ AzizA Rasheed
Published in: Brazilian journal of biology = Revista brasleira de biologia (2022)
The aim of this study is to determine the association between environmental organic pollutants with type 2 diabetes. This prospective study was conducted in Federal Urdu University of Arts, Science and Technology (FUUAST) Gulshan-e-Iqbal Campus Karachi in duration from January 2016 to June 2017. This study was ethically approved from the Institutional Review Board of FUUAST. The study included 50 male and female convenient subjects with type 2 diabetes. Subject with other type of diabetes was excluded. Consent was obtained by each individual. Self-structured questionnaire was used for data collection. The comparative results suggest that the maximum level of summation polychlorinated biphenyls (PCBs) mean value was found in age group 27-33 as 0.695 mg/kg in 73% having total individual eleven. Median (interquartile range) of pesticides levels among subjects with normal weight, over weight and obesity were 0.49 (0.26-2.13), 1.53 (0.60-2.65), and 1.60 (1.23-2.05) respectively. It was observed that Organochlorine pesticides (OCS) levels of subjects with overweight and obesity were almost similar (P-value > 0.05) but significantly higher as compared to subjects with normal weight (P-value < 0.05). No significant differences were observed between PCB levels of subjects in terms of body mass index (BMI). In present study we trace the important elements involve in the deposition of persistent organic pollutants and established an association between pollutants with etiology of diabetes and associated disorders such as obesity.
Keyphrases
  • body mass index
  • type diabetes
  • weight gain
  • weight loss
  • physical activity
  • metabolic syndrome
  • insulin resistance
  • risk assessment
  • public health
  • machine learning
  • glycemic control
  • heavy metals
  • cross sectional
  • big data