OBJECTIVES: To examine: (i) the relationships between sleep characteristics, including social jetlag, and obesity-related outcomes during childhood, and (ii) whether these relationships are moderated by sex.
METHODS: This cross-sectional study included 381 children aged 9-11 years (49.6% female). Average sleep duration, social jetlag, and physical activity were assessed via wrist-worn accelerometry. Sleep disturbances were quantified from the Children's Sleep Habits Questionnaire. Obesity-related outcomes included age-specific body mass index Z-scores (zBMI) and waist-to-height ratio. Additionally % fat, total fat mass, and fat mass index were assessed via bioelectrical impedance analysis. Linear mixed models that nested children within schools were used to identify relationships among sleep characteristics and obesity-related outcomes.
RESULTS: Positive associations between social jetlag with zBMI, % fat, and fat mass index were seen in univariable and unadjusted multivariable analyses. Following adjustments for known confounders, social jetlag remained significantly associated with zBMI (β = 0.12, p = 0.013). Simple slopes suggested a positive association in girls (β = 0.19, p = 0.006) but not in boys (β = 0.03, p = 0.703).
CONCLUSIONS: Obesity prevention efforts, particularly in girls, may benefit from targeted approaches to improving the consistency of sleep timing in youth.
OBJECTIVE: The Improving Exposure Assessment Methodologies for Epidemiological Studies on Pesticides (IMPRESS) project aims to assess the reliability and external validity of the surrogate measures used to assign exposure within individuals or groups of individuals, which are frequently based on self-reported data on exposure determinants. IMPRESS will also evaluate the size of recall bias on the misclassification of exposure to pesticides; this in turn will affect epidemiological estimates of the effect of pesticides on human health.
METHODS: The IMPRESS project will recruit existing cohort participants from previous and ongoing research studies primarily of epidemiological origin from Malaysia, Uganda, and the United Kingdom. Consenting participants of each cohort will be reinterviewed using an amended version of the original questionnaire addressing pesticide use characteristics administered to that cohort. The format and relevant questions will be retained but some extraneous questions from the original (eg, relating to health) will be excluded for ethical and practical reasons. The reliability of pesticide exposure recall over different time periods (<2 years, 6-12 years, and >15 years) will then be evaluated. Where the original cohort study is still ongoing, participants will also be asked if they wish to take part in a new exposure biomonitoring survey, which involves them providing urine samples for pesticide metabolite analysis and completing questionnaire information regarding their work activities at the time of sampling. The participant's level of exposure to pesticides will be determined by analyzing the collected urine samples for selected pesticide metabolites. The biomonitoring measurement results will be used to assess the performance of algorithm-based exposure assessment methods used in epidemiological studies to estimate individual exposures during application and re-entry work.
RESULTS: The project was funded in September 2017. Enrollment and sample collection was completed for Malaysia in 2019 and is on-going for Uganda and the United Kingdom. Sample and data analysis will proceed in 2020 and the first results are expected to be submitted for publication in 2021.
CONCLUSIONS: The study will evaluate the consistency of questionnaire data and accuracy of current algorithms in assessing pesticide exposures. It will indicate where amendments can be made to better capture exposure data for future epidemiology studies and thus improve the reliability of exposure-disease associations.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/16448.
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.
METHODS: Germline DNA from 467 breast cancer patients in Sarawak General Hospital, Malaysia, where 93% of the breast cancer patients in Sarawak are treated, was sequenced for the entire coding region of BRCA1; BRCA2; PALB2; Exons 6, 7, and 8 of TP53; and Exons 7 and 8 of PTEN. Pathogenic variants included known pathogenic variants in ClinVar, loss of function variants, and variants that disrupt splice site.
RESULTS: We found 27 pathogenic variants (11 BRCA1, 10 BRCA2, 4 PALB2, and 2 TP53) in 34 patients, which gave a prevalence of germline mutations of 2.8, 3.23, and 0.86% for BRCA1, BRCA2, and PALB2, respectively. Compared to mutation non-carriers, BRCA1 mutation carriers were more likely to have an earlier age at onset, triple-negative subtype, and lower body mass index, whereas BRCA2 mutation carriers were more likely to have a positive family history. Mutation carrier cases had worse survival compared to non-carriers; however, the association was mostly driven by stage and tumor subtype. We also identified 19 variants of unknown significance, and some of them were predicted to alter splicing or transcription factor binding sites.
CONCLUSION: Our data provide insight into the genetics of breast cancer in this understudied group and suggest the need for modifying genetic testing guidelines for this population with a much younger age at diagnosis and more limited resources compared with Caucasian populations.