Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0311277 (abdominal obesity)
 2,792 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Exposure to chemical contaminants is considered as one of risk factors to the current epidemic of obesity. Acrylamide (AA) is a ubiquitous chemical contaminant in environmental waste, mainstream cigarette smoke and carbohydrate-rich foods, and widely used in industrial manufacturers and cosmetics. Few studies have highlighted the association of daily exposure to AA with obesity-related outcomes. We analyzed data from 8364 participants who aged 20-85years and were recruited in National Health and Nutrition Examination Surveys (NHANES) 2003-2006. We established the model of PROC Survey Logistic regressions via using AA biomarkers in blood, hemoglobin adducts of acrylamide and glycidamide (HbAA and HbGA), as the measure of internal exposure to AA, and assessing obesity, abdominal obesity and overweight with body mass index (BMI) or waist circumference (WC). After the adjustment of sociodemographic variables, lifestyle behaviors, and health-related factors, the ratio of HbGA to HbAA (HbGA/HbAA) was significantly associated with obesity (p for trend<0.0001). The odd ratios (ORs) with 95% confidence intervals (CIs) of HbGA/HbAA across increasing quartiles were 1.740 (1.413-2.144), 2.604 (2.157-3.144), and 2.863 (2.425-3.380) compared with the lowest quartile. HbGA was positively associated with obesity [OR (95% CI): 1.226 (1.041-1.443), 1.283 (1.121-1.468), and 1.398 (1.165-1.679); p for trend=0.0004], while HbAA was inversely associated with obesity [OR (95% CI): 0.839 (0.718-0.980), 0.713 (0.600-0.848), and 0.671 (0.554-0.811); p for trend<0.0001]. Negative associations were found between the sum of HbAA and HbGA (HbAA+HbGA) and the body weight outcomes. Similar associations were also observed between the hemoglobin biomarkers of AA and abdominal obesity as well as overweight. Thus, the hemoglobin adducts of AA as long-term internal exposure biomarkers are strongly associated with obesity-related outcomes in a population of US adults.
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PMID:Association of acrylamide hemoglobin biomarkers with obesity, abdominal obesity and overweight in general US population: NHANES 2003-2006. 2953 95

Background: The prevalence of metabolic syndrome (MS) is rapidly increasing in the world. Thus, the aim of the present study was to identify the latent subgroups of Iranian male adults based on MS components and investigate the effect of abnormal alanine aminotransferase (ALT) and aspartate aminotransferase (AST), high total cholesterol (TC), and low-density lipoprotein (LDL) on the odds of membership in each class. Methods: In the present study, we used the data of a population-based screening program conducted on 823 urban adult men aged 25 years and older in city of Qom in 2014. Abdominal obesity, fasting blood sugar (FBS), blood pressure, and serum lipid profile were measured in participants after for at least 8 hours. MS was defined according to the Adults Treatment Panel III criteria. Latent class analysis was used to achieve the aims of study. Analyses were conducted using PROC LCA in SAS 9.2 software. In all analysis, p value < 0.05 was considered statistically significant. Results: There were 3 different latent classes among participants. Latent class 1, non-MS, 55.1%; latent lass 2, at risk, 21.3%; and finally latent class 3, MS, with 23.6% of the participants. Age (OR=0.98, 95% CI: 0.98-0.99, high LDL (OR=0.27, 95% CI: 0.13-0.56), high TC (OR=8.12, 95% CI: 4.40-15.00), and abnormal ALT (OR=2.25, 95% CI 1.49-3.41) were associated with at risk class. Also, only age (OR=1.02, 95% CI: 1.01-1.04) was associated with MS class. The most prevalent components among the participants were having low HDL (34.0%) and high WC (33.9%). Conclusion: Notable percent of samples fell in "at risk" and "MS" classes, which stress the necessity of designing preventive interventions for these specific stratums of population.
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PMID:Comorbidity of metabolic syndrome components in a population-based screening program: A latent class analysis. 3297 35