METHODS: Study samples were collected from 22 participants; 9 from patients with AKU, 9 from individuals who were AKU carriers, and 4 people served as control. Confirmation of AKU diagnosis was established by the ferric chloride test and quantitative determination of urinary homogentisic acid (HGA) levels.
RESULTS: In the ferric chloride test, the urine samples of AKU patients showed a characteristic black ring upon addition of few drops of ferric chloride solution. During urinary HGA determination, patients with AKU had increased levels of urinary HGA as compared to carriers and controls. The following 10 bacterial species were isolated from the urinary tract of AKU patients, carriers and controls: Sphingomonas paucimobilis, Escherichia coli, Francisella tularensis, Staphylococcus hominis, Staphylococcus haemolyticus, Leuconostoc mesenteroides, Dermacoccus nishinomiyaensis, Kytococcus sedentarius, Serratia fonticola and Granulicatella adiacens. The presence of S. paucimobilis was found in three male patients, and one female each from the carrier and control groups. Almost all study samples were positive for D. nishinomiyaensis and K. sedentarius. S. fonticola and G. adiacens were found only in AKU carrier females.
CONCLUSIONS: The results deduced that males show symptoms of arthritis early and more severely than females and by this it appears that there is an association between these symptoms and the percentage of bacterial infection in males that requires more accurate diagnosis and treatment to clarify such relationship. In the current study, males (patients, carriers, and controls) were more likely to have bacterial infections than females (64% vs. 36%). The 16 and 2 bacterial isolates, detected in 7 males and 2 females AKU patients, respectively, revealed that male AKU patients had a 2.3-fold greater rate of bacterial infection than female AKU patients. Therefore, further studies are warranted to investigate if there's any relationship between higher incidence of bacterial infections and development of AKU-related clinical symptoms in the male population.
METHODS: Urine samples were collected from pesticide applicators in Malaysia, Uganda, and the UK during mixing/application days (and also during non-application days in Uganda). Samples were collected pre- and post-activity on the same day and analysed for biomarkers of active ingredients (AIs), including synthetic pyrethroids (via the metabolite 3-phenoxybenzoic acid [3-PBA]) and glyphosate, as well as creatinine. We performed multilevel Tobit regression models for each study to assess the relationship between exposure modifying factors (e.g., mixing/application of AI, duration of activity, personal protective equipment [PPE]) and urinary biomarkers of exposure.
RESULTS: From the Malaysia, Uganda, and UK studies, 81, 84, and 106 study participants provided 162, 384 and 212 urine samples, respectively. Pyrethroid use on the sampling day was most common in Malaysia (n = 38; 47%), and glyphosate use was most prevalent in the UK (n = 93; 88%). Median pre- and post-activity 3-PBA concentrations were similar, with higher median concentrations post-compared to pre-activity for glyphosate samples in the UK (1.7 to 0.5 μg/L) and Uganda (7.6 to 0.8 μg/L) (glyphosate was not used in the Malaysia study). There was evidence from individual studies that higher urinary biomarker concentrations were associated with mixing/application of the AI on the day of urine sampling, longer duration of mixing/application, lower PPE protection, and less education/literacy, but no factor was consistently associated with exposure across biomarkers in the three studies.
CONCLUSIONS: Our results suggest a need for AI-specific interpretation of exposure modifying factors as the relevance of exposure routes, levels of detection, and farming systems/practices may be very context and AI-specific.
SUBJECTS/METHODS: A taste database including 467 foods' sweet, sour, bitter, salt, umami and fat sensation values was combined with food intake data to assess dietary taste patterns: the contribution to energy intake of 6 taste clusters. The FFQ's reliability was assessed against 3-d 24hR and urinary biomarkers for sodium (Na) and protein intake (N) in Dutch men (n = 449) and women (n = 397) from the NQplus validation study (mean age 53 ± 11 y, BMI 26 ± 4 kg/m2).
RESULTS: Correlations of dietary taste patterns ranged from 0.39-0.68 between FFQ and 24hR (p
MATERIAL AND METHODS: Urine samples were collected from plastic factory workers and from control subjects after their shift. Air samples were collected using gas analyzers from 5 sampling positions in the injection molding unit work area and from ambient air. The level of BPA in airborne and urine samples was quantified by the gas chromatography mass spectrometry - selected ion monitoring (GCMS-SIM) analysis.
RESULTS: Bisphenol A was detected in the median range of 8-28.3 ng/m³ and 2.4-3.59 ng/m³ for the 5 sampling points in the plastic molding factory and in the ambient air respectively. The median urinary BPA concentration was significantly higher in the workers (3.81 ng/ml) than in control subjects (0.73 ng/ml). The urinary BPA concentration was significantly associated with airborne BPA levels (ρ = 0.55, p < 0.01).
CONCLUSIONS: Our findings provide the first evidence that workers in a molding factory in Malaysia are occupationally exposed to BPA. Int J Occup Med Environ Health 2017;30(5):743-750.
OBJECTIVES: To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics.
METHOD: Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC).
RESULTS: The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0).
CONCLUSION: This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy.