Search

Summary Background Mortality risk stratification based on dichotomising a physiological indicator with a cutoff point might not adequately capture increased mortality risk and might not account for non-linear associations. We aimed to characterise the linear and non-linear relationships of 27 physiological indicators with all-cause mortality to evaluate whether the current clinical thresholds are suitable in distinguishing patients at high risk for mortality from those at low risk. Methods For this observational cohort study of the US non-institutionalised population, we used data from adults (≥18 years) included in the 1999–2014 National Health and Nutrition Examination Survey (NHANES) linked with National Death Index mortality data collected from Jan 1, 1999, up until Dec 31, 2015. We used Cox proportional hazards regression models adjusted for age, sex, and race or ethnicity to assess associations of physiological indicators with all-cause mortality. We assessed non-linear associations by discretising the physiological indicator into nine quantiles (termed novemtiles) and by using a weighted sum of cubic polynomials (spline). We used ten-fold cross validation to select the most appropriate model using the concordance index, Nagelkerke R2, and Akaike Information Criterion. We identified the level of each physiological indicator that led to a 10% increase in mortality risk to define our cutoffs used to compare with the current clinical thresholds. Findings We included 47 266 adults of 82 091 assessed for eligibility. 25 (93%) of 27 indicators showed non-linear associations with substantial increases compared with linear models in mortality risk (1·5–2·5-times increase). Height and 60 s pulse were the only physiological indicators to show linear associations. For example, participants with an estimated glomerular filtration rate (GFR) of less than 65 mL/min per 1·73 m2 or between 90–116 mL/min per 1·73 m2 are at moderate (hazard ratio 1–2) mortality risk. Those with a GFR greater than 117 mL/min per 1·73 m2 show substantial (hazard ratio ≥2) mortality risk. Both lower and higher values of cholesterol are associated with increased mortality risk. The current clinical thresholds do not align with our mortality-based cutoffs for fat deposition indices, 60 s pulse, triglycerides, cholesterol-related indicators, alkaline phosphatase, glycohaemoglobin, homoeostatic model assessment of insulin resistance, and GFR. For these indicators, the misalignment suggests the need to consider an additional bound when only one is provided. Interpretation Most clinical indicators were shown to have non-linear associations with all-cause mortality. Furthermore, considering these non-linear associations can help derive reliable cutoffs to complement risk stratification and help inform clinical care delivery. Given the poor alignment with our proposed cutoffs, the current clinical thresholds might not adequately capture mortality risk. Funding Ravitz Family Foundation, Forbes Institute for Cancer Discovery, and National Institutes of Health.

BackgroundAccording to the World Health Organization, occupational exposures to hazardous chemicals are estimated to cause over 370,000 premature annual deaths. The risks due to multiple workplace chemical exposures, and those occupations most susceptible to the resulting health effects, remain poorly characterized.ObjectivesThe aim of this study is to identify occupations with elevated toxicant biomarker concentrations and increased health risk associated with toxicant exposures in a working US population from diverse categories of occupation. More specifically, we aim to 1) define differences in chemical exposures based on occupation description, 2) identify occupational groups with similar chemical exposure profiles, and 3) identify occupational groups with chemical biomarker levels exceeding acceptable health-based biomarker levels.MethodsFor this observational study of 51,008 participants, we used data from the 1999-2014 National Health and Nutrition Examination Survey. We characterized differences in chemical exposures by occupational group for 129 chemicals by applying a series of generalized linear models with the outcome as biomarker concentrations and the main predictor as the occupational groups, adjusting for age, sex, race/ethnicity, poverty income ratio, study period, and biomarker of tobacco use. We identified groups of occupations with similar chemical exposure profiles via hierarchical clustering. For each occupational group, we calculated percentages of participants with chemical biomarker levels exceeding acceptable health-based guidelines.ResultsBlue collar workers from “Construction”, “Professional, Scientific, Technical Services”, “Real Estate, Rental, Leasing”, “Manufacturing”, and “Wholesale Trade” have higher biomarker levels of toxic chemicals such as several heavy metals, acrylamide, glycideamide, and several volatile organic compounds compared to their white-collar counterparts. For these toxicants, 1-58% of blue-collar workers from these industries have toxicant concentrations exceeding acceptable levels.DiscussionBlue collar workers have toxicant levels higher relative to their white-collar counterparts, often exceeding acceptable levels associated with noncancer effects. Our findings identify multiple occupations to prioritize for targeted interventions and health policies to monitor and reduce high toxicant exposures.

CYP3A5 and CYP3A4 are the predominant enzymes responsible for tacrolimus metabolism; however only a proportion of the population expresses CYP3A5 secondary to genetic variation. CYP3A5 is expressed in both the intestine and the liver and has been shown to impact both the bioavailability and metabolism of orally administered tacrolimus. Increasing the initial tacrolimus dose by 50% to 100% is recommended in patients who are known CYP3A5 expressers; however, whether this dose adjustment is appropriate for i.v. tacrolimus administration is unclear. The objective of this study was to evaluate the impact of CYP3A5 genotype as well as other pharmacogenes on i.v. tacrolimus exposure to determine whether the current genotype-guided dosing recommendations are appropriate for this formulation. In addition, this study aimed to investigate dose conversion requirements among CYP3A5 genotypes when converting from i.v. to p.o. tacrolimus. This study is a retrospective chart review of all patients who underwent allogeneic stem cell transplantation at Michigan Medicine between June 1, 2014, and March 1, 2018, who received i.v. tacrolimus at the time of their transplantation. Secondary use samples were obtained for genotyping CYP3A5, CYP3A4, and ABCB1. Patient demographic information, tacrolimus dosing and trough levels, and concomitant medications received at the time of tacrolimus trough were collected retrospectively from the patients’ medical records. The i.v. dose-controlled concentration (C/D) and the i.v.:p.o. exposure ratio was calculated for all tacrolimus doses and patients, respectively. The impact of CYP3A5, CYP3A4, and ABCB1 genotypes on the i.v. C/D were evaluated with linear mixed modeling. The impact of CYP3A5 genotype on the i.v.:p.o. ratio was evaluated while controlling for age and concomitant use of an azole inhibitor. CYP3A5 and CYP3A4 genotypes were significantly associated with the i.v. C/D, with CYP3A5 expressers and CYP3A4 rapid metabolizers having 20% lower tacrolimus exposure. Neither genotype remained significant in the multivariable model, although age, hematocrit, and concomitant use of strong azole inhibitors were associated with increased i.v. C/D. When controlling for patient age and sex, CYP3A5 expressers had significantly higher i.v.:p.o. ratios than CYP3A5 nonexpressers (3.42 versus 2.78; P = .04). Post hoc analysis showed that the i.v.:p.o. ratio may differ among different CYP3A5 genotypes and azole inhibitor combinations. This study demonstrates that the current genotype-guided tacrolimus dose adjustment recommendations are inappropriate for CYP3A5 expressers receiving i.v. tacrolimus. Although CYP3A5 genotype is likely a minor contributor to i.v. tacrolimus exposure, genotype, in addition to capturing concomitant CYP3A inhibitors, would likely improve i.v.:p.o. dose conversion selection. © 2021 American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc.

Background: According to the World Health Organization, occupational exposures to hazardous chemicals are estimated to cause over 370,000 premature annual deaths. The risks due to multiple workplace chemical exposures, and those occupations most susceptible to the resulting health effects, remain poorly characterized. The aim of this study is to identify occupations with elevated toxicant biomarker concentrations and increased health risk associated with exposure to combinations of toxicants in a working US population from diverse categories of occupation. Methods: For this observational study of 51,008 participants, we used data from the 1999-2014 National Health and Nutrition Examination Survey. We characterized differences in chemical exposures by occupational group for 129 chemicals by applying a series of generalized linear models with the outcome as biomarker concentrations and the main predictor as the occupational groups, adjusting for age, sex, race/ethnicity, poverty income ratio, study period, and biomarker of tobacco use. We identified groups of occupations with similar chemical exposure profiles via hierarchical clustering. For each occupational group, we calculated percentages of participants with chemical biomarker levels exceeding acceptable health-based guidelines. Findings: Blue collar workers from “Construction”, “Other Services”, “Professional, Scientific, Technical Services”, “Real Estate, Rental, Leasing”, “Manufacturing”, and “Wholesale Trade” along with several unemployed groups have higher biomarker levels of toxic chemicals such as lead, cadmium, arsenic, mercury, acrylamide, glycideamide, xylenes, 1,4-dichlorobenzene, tetrachloroethane, methyl tert-butyl ether, and benzene compared to their white-collar counterparts. For these toxicants, 1-58% of blue collar workers from these industries have chemical biomarker concentrations exceeding acceptable levels. Interpretation: Blue collar workers and several unemployed groups have toxicant levels higher relative to their white-collar counterparts, often exceeding acceptable levels associated with noncancer effects. Our findings identify multiple occupations to prioritize for targeted interventions and health policies to monitor and reduce high toxicant exposures in susceptible individuals. Funding Information: Ravitz Family Foundation, University of Michigan Forbes Institute for Cancer Discovery, Harvard Data Science Initiative, University of Michigan Center for Occupational Health and Safety Engineering, and National Institutes of Health (R01 ES028802, P30 ES017885, P30 CA046592, UG3 CA267907). Declaration of Interests: Drs. Nguyen, Colacino, Sartor, and Jolliet has nothing to disclose. At the time of the writing of the manuscript, Dr. Patel was a consultant and shareholder of XY.health. Dr. Patel is also paid by J&J.

Background: Chemical biomarker concentrations are driven by complex interactions between chemical use patterns, exposure pathways, and toxicokinetic parameters such as biological half-lives. Criteria to differentiate legacy from current exposures are helpful for interpreting variation in age-based and time trends of chemical exposure and identifying chemicals to which children are highly exposed. A systematic approach is needed to study temporal trends for a wide range of chemicals in the US population. Objectives: Using National Health and Nutrition Examination Survey (NHANES) data on measured biomarker concentrations for 141 chemicals from 1999 to 2014, we aim to 1) understand the influence of temporal determinants, in particular time trends, biological half-lives, and restriction dates on age-based trends, 2) systematically define an age-based pattern to identify chemicals with ongoing and high exposure in children, and 3) characterize how age-based trends for six Per- and Polyfluoroalkyl Substances (PFASs) are changing over time. Methods: We performed an integrated analysis of biological half-lives and restriction dates, compared distributions of chemical biomarker concentrations by age group, and then applied a series of regression models to evaluate the linear (βage) and nonlinear (βage2) relationships between age and chemical biomarker levels. Results: For restricted chemicals, a minimum persistence of 1 year in the human body is needed to observe substantial differences between the less exposed young population and historically exposed adults. We define a metric (βage2/βage>126.9) that identifies several phthalates, brominated flame retardants, pesticides, and metals such as lead and tungsten as elevated and ongoing exposures in children. While a substantial reduction in children's exposures was reflected in PFOS and PFOA, levels of PFNA and PFHxS in children were higher in 2013–2014 compared to those in 1999–2000. Conclusions: Integrating a series of regression models with systemized stratified analyses by age group enabled us to define an age-based pattern to identify chemicals that are of higher levels in children. Keywords: Age-based exposure, Temporal trends, Biomonitoring, Environmental chemicals

Background: Stark racial disparities in disease incidence among American women remain a persistent public health challenge. These disparities likely result from complex interactions between genetic, social, lifestyle, and environmental risk factors. The influence of environmental risk factors, such as chemical exposure, however, may be substantial and is poorly understood. Objectives: We quantitatively evaluated chemical-exposure disparities by race/ethnicity, life stage, and time in United States (US) women (n = 38,080) by using biomarker data for 143 chemicals from the National Health and Nutrition Examination Survey (NHANES) 1999–2014. Methods: We applied a series of survey-weighted, generalized linear models using data from the entire NHANES women population along with cycle and age-group stratified subpopulations. The outcome was chemical biomarker concentration, and the main predictor was race/ethnicity with adjustment for age, socioeconomic status, smoking habits, and NHANES cycle. Results: Compared to non-Hispanic White women, the highest disparities were observed for non-Hispanic Black, Mexican American, Other Hispanic, and Other Race/Multi-Racial women with higher levels of pesticides and their metabolites, including 2,5-dichlorophenol, o,p’-DDE, beta-hexachlorocyclohexane, and 2,4-dichlorophenol, along with personal care and consumer product compounds, including parabens and monoethyl phthalate, as well as several metals, such as mercury and arsenic. Moreover, for Mexican American, Other Hispanic, and non-Hispanic black women, there were several exposure disparities that persisted across age groups, such as higher 2,4- and 2,5-dichlorophenol concentrations. Exposure levels for methyl and propyl parabens, however, were the highest in non-Hispanic black compared to non-Hispanic white children with average differences exceeding 4-fold. Exposure disparities for methyl and propyl parabens are increasing over time in Other Race/Multi-Racial women while fluctuating for non-Hispanic Black, Mexican American, and Other Hispanic. Cotinine levels are among the highest in Non-Hispanic White women compared to Mexican American and Other Hispanic women with disparities plateauing and increasing, respectively. Discussion: We systematically evaluated differences in chemical exposures across women of various race/ethnic groups and across age groups and time. Our findings could help inform chemical prioritization in designing epidemiological and toxicological studies. In addition, they could help guide public health interventions to reduce environmental and health disparities across populations. Keywords: Racial disparities, Age-based trends, Temporal trends, Environmental justice, Biomonitoring, Environmental chemicals

This chapter focuses on the situation in East and South-East Asia, it is important to acknowledge that the circumstances in South Asia, West and Central Asia will also have a bearing on trends in transnational crime. Asia covers a vast area and includes more than half the world’s population, and the People’s Republic of China and India are the two most populous nations in the world. Asia hosts all the major world religions, but also includes socialist market states, like China and Viet Nam. In South-East Asia, crime groups of interest include the Chinese ‘black societies’ or triads, Japanese organized crime and the military-style ethnic groups controlling amphetamine-type stimulants and opium production in the golden triangle. The significance of transnational organized crime as both a global and national threat only has been elevated as a ‘clear and present danger’ for international security.