Mediation of an association between neighborhood socioeconomic environment and type 2 diabetes through the leisure-time physical activity environment in an analysis of three independent samples.

INTRODUCTION: Inequitable access to leisure-time physical activity (LTPA) resources may explain geographic disparities in type 2 diabetes (T2D). We evaluated whether the neighborhood socioeconomic environment (NSEE) affects T2D through the LTPA environment. RESEARCH DESIGN AND METHODS: We conducted analyses in three study samples: the national Veterans Administration Diabetes Risk (VADR) cohort comprising electronic health records (EHR) of 4.1 million T2D-free veterans, the national prospective cohort REasons for Geographic and Racial Differences in Stroke (REGARDS) (11 208 T2D free), and a case-control study of Geisinger EHR in Pennsylvania (15 888 T2D cases). New-onset T2D was defined using diagnoses, laboratory and medication data. We harmonized neighborhood-level variables, including exposure, confounders, and effect modifiers. We measured NSEE with a summary index of six census tract indicators. The LTPA environment was measured by physical activity (PA) facility (gyms and other commercial facilities) density within street network buffers and population-weighted distance to parks. We estimated natural direct and indirect effects for each mediator stratified by community type. RESULTS: The magnitudes of the indirect effects were generally small, and the direction of the indirect effects differed by community type and study sample. The most consistent findings were for mediation via PA facility density in rural communities, where we observed positive indirect effects (differences in T2D incidence rates (95% CI) comparing the highest versus lowest quartiles of NSEE, multiplied by 100) of 1.53 (0.25, 3.05) in REGARDS and 0.0066 (0.0038, 0.0099) in VADR. No mediation was evident in Geisinger. CONCLUSIONS: PA facility density and distance to parks did not substantially mediate the relation between NSEE and T2D. Our heterogeneous results suggest that approaches to reduce T2D through changes to the LTPA environment require local tailoring.

Associations of four indexes of social determinants of health and two community typologies with new onset type 2 diabetes across a diverse geography in Pennsylvania.

Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008-2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients' residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region's census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated with lower odds of T2D (0.92 [0.87, 0.97]). With the modified typology, the strongest association (1.37 [1.15, 1.63]) was observed in cities in the suburban affluent-like category (vs. rural affordable-like-township), followed by cities in the rural affordable-like category (1.20 [1.05, 1.36]). We conclude that in evaluating geographic disparities in T2D onset, it is beneficial to conduct simultaneous evaluation of SDOH in multiple dimensions. Associations with the modified typology showed the importance of incorporating governmentally, behaviorally, and experientially relevant community definitions when evaluating geographic health disparities.

Urban-Rural Differences in Health Care Utilization and COVID-19 Outcomes in Patients With Type 2 Diabetes.

INTRODUCTION: Two studies in Pennsylvania aimed to determine whether community type and community socioeconomic deprivation (CSD) 1) modified associations between type 2 diabetes (hereinafter, diabetes) and COVID-19 hospitalization outcomes, and 2) influenced health care utilization among individuals with diabetes during the COVID-19 pandemic. METHODS: The hospitalization study evaluated a retrospective cohort of patients hospitalized with COVID-19 through 2020 for COVID-19 outcomes: death, intensive care unit (ICU) admission, mechanical ventilation, elevated D-dimer, and elevated troponin level. We used adjusted logistic regression models, adding interaction terms to evaluate effect modification by community type (township, borough, or city census tract) and CSD. The utilization study included patients with diabetes and a clinical encounter between 2017 and 2020. Autoregressive integrated moving average time-series models evaluated changes in weekly rates of emergency department and outpatient visits, hemoglobin A1c (HbA1c) laboratory tests, and antihyperglycemic medication orders from 2018 to 2020. RESULTS: In the hospitalization study, of 2,751 patients hospitalized for COVID-19, 1,020 had diabetes, which was associated with ICU admission and elevated troponin. Associations did not differ by community type or CSD. In the utilization study, among 93,401 patients with diabetes, utilization measures decreased in March 2020. Utilization increased in July, and then began to stabilize or decline through the end of 2020. Changes in HbA1c tests and medication order trends during the pandemic differed by community type and CSD. CONCLUSION: Diabetes was associated with selected outcomes among individuals hospitalized for COVID-19, but these did not differ by community features. Utilization trajectories among individuals with diabetes during the pandemic were influenced by community type and CSD and could be used to identify individuals at risk of gaps in diabetes care.

Arsenic Exposure, Blood DNA Methylation, and Cardiovascular Disease.

BACKGROUND: Epigenetic dysregulation has been proposed as a key mechanism for arsenic-related cardiovascular disease (CVD). We evaluated differentially methylated positions (DMPs) as potential mediators on the association between arsenic and CVD. METHODS: Blood DNA methylation was measured in 2321 participants (mean age 56.2, 58.6% women) of the Strong Heart Study, a prospective cohort of American Indians. Urinary arsenic species were measured using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. We identified DMPs that are potential mediators between arsenic and CVD. In a cross-species analysis, we compared those DMPs with differential liver DNA methylation following early-life arsenic exposure in the apoE knockout (apoE-/-) mouse model of atherosclerosis. RESULTS: A total of 20 and 13 DMPs were potential mediators for CVD incidence and mortality, respectively, several of them annotated to genes related to diabetes. Eleven of these DMPs were similarly associated with incident CVD in 3 diverse prospective cohorts (Framingham Heart Study, Women's Health Initiative, and Multi-Ethnic Study of Atherosclerosis). In the mouse model, differentially methylated regions in 20 of those genes and DMPs in 10 genes were associated with arsenic. CONCLUSIONS: Differential DNA methylation might be part of the biological link between arsenic and CVD. The gene functions suggest that diabetes might represent a relevant mechanism for arsenic-related cardiovascular risk in populations with a high burden of diabetes.

High Level of Selenium Exposure in the Strong Heart Study: A Cause for Incident Cardiovascular Disease?

Increasing evidence suggests that high selenium (Se) exposure is associated with adverse health effects. However, limited evidence exists on the association of Se exposure with cardiovascular disease (CVD), especially in communities affected by high naturally occurring Se in environmental media. We evaluated the prospective association between urinary Se levels and CVD incidence and mortality for 2727 American Indian adults who participated in the Strong Heart Study, with urinary Se levels measured at baseline (1989-1991) and CVD outcomes ascertained through 2017. The median (interquartile range) of urinary Se was 49.0 (36.7-67.4) µg/g creatinine. The multivariable adjusted hazard ratios (95% confidence interval) of incident CVD, coronary heart disease, and stroke comparing the 75th versus 25th percentile of urinary Se distributions were 1.11 (1.01-1.22), 1.05 (0.94-1.17), and 1.08 (0.88-1.33), respectively. In flexible dose-response models, increased risk for CVD incidence was only observed when the urinary Se level exceeded 60 µg/g creatinine. For CVD mortality, a nonstatistically significant U-shaped relationship was found across urinary Se levels. There was no evidence of effect modification by other urinary metal/metalloid levels. Our observation leads to the hypothesis that elevated Se exposure is a risk factor for CVD, especially in Se-replete populations. Antioxid. Redox Signal. 37, 990-997.

Risk factors for Lyme disease stage and manifestation using electronic health records.

BACKGROUND: Little is known about risk factors for early (e.g., erythema migrans) and disseminated Lyme disease manifestations, such as arthritis, neurological complications, and carditis. No study has used both diagnoses and free text to classify Lyme disease by disease stage and manifestation. METHODS: We identified Lyme disease cases in 2012-2016 in the electronic health record (EHR) of a large, integrated health system in Pennsylvania. We developed a rule-based text-matching algorithm using regular expressions to extract clinical data from free text. Lyme disease cases were then classified by stage and manifestation using data from both diagnoses and free text. Among cases classified by stage, we evaluated individual, community, and health care variables as predictors of disseminated stage (vs. early) disease using Poisson regression models with robust errors. Final models adjusted for sociodemographic factors, receipt of Medical Assistance (i.e., Medicaid, a proxy for low socioeconomic status), primary care contact, setting of diagnosis, season of diagnosis, and urban/rural status. RESULTS: Among 7310 cases of Lyme disease, we classified 62% by stage. Overall, 23% were classified using both diagnoses and text, 26% were classified using diagnoses only, and 13% were classified using text only. Among the staged diagnoses (n = 4530), 30% were disseminated stage (762 arthritis, 426 neurological manifestations, 76 carditis, 95 secondary erythema migrans, and 76 other manifestations). In adjusted models, we found that persons on Medical Assistance at least 50% of time under observation, compared to never users, had a higher risk (risk ratio [95% confidence interval]) of disseminated Lyme disease (1.20 [1.05, 1.37]). Primary care contact (0.59 [0.54, 0.64]) and diagnosis in the urgent care (0.22 [0.17, 0.29]), compared to the outpatient setting, were associated with lower risk of disseminated Lyme disease. CONCLUSIONS: The associations between insurance payor, primary care status, and diagnostic setting with disseminated Lyme disease suggest that lower socioeconomic status and less health care access could be linked with disseminated stage Lyme disease. Intervening on these factors could reduce the individual and health care burden of disseminated Lyme disease. Our findings demonstrate the value of both diagnostic and narrative text data to identify Lyme disease manifestations in the EHR.

Association of community socioeconomic deprivation with evidence of reduced kidney function at time of type 2 diabetes diagnosis.

BACKGROUND: While there are known individual-level risk factors for kidney disease at time of type 2 diabetes diagnosis, little is known regarding the role of community context. We evaluated the association of community socioeconomic deprivation (CSD) and community type with estimated glomerular filtration rate (eGFR) when type 2 diabetes is diagnosed. METHODS: This was a retrospective cohort study of 13,144 adults with newly diagnosed type 2 diabetes in Pennsylvania. The outcome was the closest eGFR measurement within one year prior to and two weeks after type 2 diabetes diagnosis, calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equation. We used adjusted multinomial regression models to estimate associations of CSD (quartile 1, least deprivation) and community type (township, borough, city) with eGFR and used adjusted generalized estimating equation models to evaluate whether community features were associated with the absence of diabetes screening in the years prior to type 2 diabetes diagnosis. RESULTS: Of the participants, 1279 (9.7%) had hyperfiltration and 1377 (10.5%) had reduced eGFR. Women were less likely to have hyperfiltration and more likely to have reduced eGFR. Black (versus White) race was positively associated with hyperfiltration when the eGFR calculation was corrected for race but inversely associated without the correction. Medical Assistance (ever versus never) was positively associated with reduced eGFR. Higher CSD and living in a city were each positively associated (odds ratio [95% confidence interval]) with reduced eGFR (CSD quartiles 3 and 4 versus quartile 1, 1.23 [1.04, 1.46], 1.32 [1.11, 1.58], respectively; city versus township, 1.38 [1.15, 1.65]). These features were also positively associated with the absence of a type 2 diabetes screening measure. CONCLUSIONS: In a population-based sample, more than twenty percent had hyperfiltration or reduced eGFR at time of type 2 diabetes diagnosis. Individual- and community-level factors were associated with these outcomes.

Relations of peri-residential temperature and humidity in tick-life-cycle-relevant time periods with human Lyme disease risk in Pennsylvania, USA.

How weather affects tick development and behavior and human Lyme disease remains poorly understood. We evaluated relations of temperature and humidity during critical periods for the tick lifecycle with human Lyme disease. We used electronic health records from 479,344 primary care patients in 38 Pennsylvania counties in 2006-2014. Lyme disease cases (n = 9657) were frequency-matched (5:1) by year, age, and sex. Using daily weather data at ~4 km2 resolution, we created cumulative metrics hypothesized to promote (warm and humid) or inhibit (hot and dry) tick development or host-seeking during nymph development (March 1-May 31), nymph activity (May 1-July 30), and prior year larva activity (Aug 1-Sept 30). We estimated odds ratios (ORs) of Lyme disease by quartiles of each weather variable, adjusting for demographic, clinical, and other weather variables. Exposure-response patterns were observed for higher cumulative same-year temperature, humidity, and hot and dry days (nymph-relevant), and prior year hot and dry days (larva-relevant), with same-year hot and dry days showing the strongest association (4th vs. 1st quartile OR = 0.40; 95% confidence interval [CI] = 0.36, 0.43). Changing temperature and humidity could increase or decrease human Lyme disease risk.

Urinary arsenic and heart disease mortality in NHANES 2003-2014.

BACKGROUND: Evidence evaluating the prospective association between low-to moderate-inorganic arsenic (iAs) exposure and cardiovascular disease in the general US population is limited. We evaluated the association between urinary arsenic concentrations in National Health and Nutrition Examination Survey (NHANES) 2003-2014 and heart disease mortality linked from the National Death Index through 2015. METHODS: We modeled iAs exposure as urinary total arsenic and dimethylarsinate among participants with low seafood intake, based on low arsenobetaine levels (N = 4990). We estimated multivariable adjusted hazard ratios (HRs) for heart disease mortality per interquartile range (IQR) increase in urinary arsenic levels using survey-weighted, Cox proportional hazards models, and evaluated flexible dose-response analyses using restricted quadratic spline models. We updated a previously published relative risk of coronary heart disease mortality from a dose-response meta-analysis per a doubling of water iAs (e.g., from 10 to 20 µg/L) with our results from NHANES 2003-2014, assuming all iAs exposure came from drinking water. RESULTS: A total of 77 fatal heart disease events occurred (median follow-up time 75 months). The adjusted HRs (95% CI) of heart disease mortality for an increase in urinary total arsenic and DMA corresponding to the interquartile range were 1.20 (0.83, 1.74) and 1.18 (0.68, 2.05), respectively. Restricted quadratic splines indicate a significant association between increasing urinary total arsenic and the HR of fatal heart disease for all participants at the lowest exposure levels <4.5 µg/L. The updated pooled relative risk of coronary heart disease mortality per doubling of water iAs (µg/L) was 1.16 (95% CI 1.07, 1.25). CONCLUSIONS: Despite a small number of events, relatively short follow-up time, and high analytical limits of detection for urinary arsenic species, iAs exposure at low-to moderate-levels is consistent with increased heart disease mortality in NHANES 2003-2014 although the associations were only significant in flexible dose-response models.

Risk Factors and Outcomes of Treatment Delays in Lyme Disease: A Population-Based Retrospective Cohort Study.

Background: Longer time between symptom onset and treatment of Lyme disease has been associated with poor outcomes. Reducing time-to-treatment requires knowledge of risks for treatment delays. We conducted a population-based study to evaluate factors associated with delayed treatment of Lyme disease and the relation between delayed treatment and post-treatment Lyme disease syndrome (PTLDS). Methods: We mailed questionnaires to 5,314 individuals with a Lyme disease diagnosis or blood test followed by an antibiotic order in the medical record of a Pennsylvania health system from 2015 to 2017. Analyses were confined to 778 respondents who reported that they were treated for Lyme disease within the past 5 years and reported a rash and/or a positive blood test for Lyme disease. Time-to-treatment was calculated as the sum of two windows before and after seeking care for Lyme disease symptoms: time to first medical contact and time under care. We used logistic regression to evaluate factors associated with delayed time-to-treatment in each time window (>14 days vs. ≤14 days) and the association between total time-to-treatment (>30 days vs. ≤30 days) and PTLDS. We used inverse probability weighting to calculate estimates for the study's source population (5,314 individuals sent questionnaires). Results: In the source population, 25% had time to first contact >14 days, 21% had time under care >14 days, and 31% had a total time-to-treatment >30 days. Being uninsured and attributing initial symptoms to something other than Lyme disease were positively associated with delayed time to first medical contact, while seeking care at an urgent care or emergency setting (vs. primary care) was negatively associated. Diagnoses between November and April, and the absence of rash were positively associated with delays. Individuals whose treatment was delayed, defined as time-to treatment >30 days had 2.26 (95% confidence interval: 1.25, 4.05) times the odds of PTLDS as those who were treated within 30 days of symptom onset. Conclusions: In a population-based study in Pennsylvania, one-third of Lyme disease patients reported delayed treatment, which was associated with PTLDS. To improve Lyme disease outcomes, prevention efforts should aim to reduce the time before and after seeking care.

Peridomestic and community-wide landscape risk factors for Lyme disease across a range of community contexts in Pennsylvania.

Land use and forest fragmentation are thought to be major drivers of Lyme disease incidence and its geographic distribution. We examined the association between landscape composition and configuration and Lyme disease in a population-based case control study in the Geisinger health system in Pennsylvania. Lyme disease cases (n = 9657) were identified using a combination of diagnosis codes, laboratory codes, and antibiotic orders from electronic health records (EHRs). Controls (5:1) were randomly selected and frequency matched on year, age, and sex. We measured six landscape variables based on prior literature, derived from the National Land Cover Database and MODIS satellite imagery: greenness (normalized difference vegetation index), percent forest, percent herbaceous, forest edge density, percent forest-herbaceous edge, and mean forest patch size. We assigned landscape variables within two spatial contexts (community and ½-mile [805 m] Euclidian residential buffer). In models stratified by community type, landscape variables were modeled as tertiles and flexible splines and associations were adjusted for demographic and clinical covariates. In general, we observed positive associations between landscape metrics and Lyme disease, except for percent herbaceous, where associations differed by community type. For example, compared to the lowest tertile, individuals with highest tertile of greenness in residential buffers had higher odds of Lyme disease (odds ratio: 95% confidence interval [CI]) in townships (1.73: 1.55, 1.93), boroughs (1.70: 1.40, 2.07), and cities (3.71: 1.74, 7.92). Similarly, corresponding odds ratios (95% CI) for forest edge density were 1.34 (1.22, 1.47), 1.56 (1.33, 1.82), and 1.90 (1.13, 3.18). Associations were generally higher in residential buffers, compared to community, and in cities, compared to boroughs or townships. Our results reinforce the importance of peridomestic landscape in Lyme disease risk, particularly measures that reflect human interaction with tick habitat. Linkage of EHR data to public data on residential and community context may lead to new health system-based approaches for improving Lyme disease diagnosis, treatment, and prevention.

Epidemiology of Lyme disease in Pennsylvania 2006-2014 using electronic health records.

Lyme disease is the most common vector-borne disease in the United States. Electronic health record (EHR)-based research on Lyme disease is limited. We used Geisinger EHR data from 479,344 primary care patients in 38 Pennsylvania counties in 2006-2014 to compare EHR-based Lyme disease incidence rates to surveillance incidence rates, evaluate individual and community risk factors for incident Lyme disease, and to characterize the proportion of cases with diagnoses consistent with post-treatment Lyme disease syndrome in the EHR (PTLDSEHR). We primarily identified Lyme disease cases using diagnosis codes, serologic testing order codes, and medication orders but also completed subgroup analyses among those with positive serology and those with both diagnosis code and antibiotic treatment. We compared annual incidence rates from the EHR to surveillance by age, sex, and county. In case-control analyses, we compared cases to randomly selected controls (5:1) frequency-matched on year, age, and sex. We identified 9657 cases of Lyme disease, including 1791 cases with positive serology and 4992 cases with both diagnosis code and antibiotic treatment. Annual incidence rates in the EHR were 4.25-7.43 times higher than surveillance. In adjusted analyses, white non-Hispanic race/ethnicity (vs. black, Hispanic, or other) was associated with higher odds of Lyme disease (odds ratio [OR]: 2.06, 95% confidence interval [CI]: 1.73-2.44). Medical Assistance insurance use (always vs. never; OR: 0.77, 95% CI: 0.68-0.88), and higher community-level socioeconomic deprivation (quartile 4 vs. 1 OR: 0.50 (95% CI: 0.42-0.59) were associated with lower odds of Lyme disease. Within 4-52 weeks after Lyme disease diagnosis, 20.8% (n = 735) of cases with a diagnosis code and treatment had a diagnosis of malaise or fatigue, pain, or cognitive difficulties not present in the past 26 weeks. These results highlight the utility of EHR data for epidemiologic research on Lyme disease for case-finding, surveillance, risk factor evaluation, and characterization of PTLDS using EHR data.

Obstacles to diagnosis and treatment of Lyme disease in the USA: a qualitative study.

OBJECTIVE: For many individuals with Lyme disease, prompt treatment leads to rapid resolution of infection. However, severe complications can occur if treatment is delayed. Our objective was to identify themes around belated diagnosis or treatment of Lyme disease using the General Model of Total Patient Delay (GMTPD). DESIGN: We conducted a qualitative interview study using indepth telephone interviews. SETTING: Participants were patients from a large, integrated health system in the state of Pennsylvania, USA. PARTICIPANTS: There were 26 participants. Participants had to have a diagnosis of Lyme disease between 2014 and 2017 and a positive IgG western blot. We used a stratified purposeful sampling design to identify patients with and without late Lyme disease manifestations. To ensure variation in care experiences, we oversampled patients diagnosed outside of primary care. OUTCOME MEASURES: We asked participants about their experience from first Lyme disease symptoms to treatment. We applied an iterative coding process to identify key themes and then synthesised codes into higher order codes representing the GMTPD stages: appraisal delay (symptom to recognition of illness); illness delay (inferring illness to deciding to seek help); behavioural delay (deciding to seek help to the act of seeking help); scheduling delay (seeking help to attending an appointment); and treatment delay (attending appointment to treatment). RESULTS: Appraisal delay themes included symptom misattribution, intermittent symptoms and misperceptions about the necessity of a bull's-eye rash. Health insurance status was a driver of illness and behavioural delays. Scheduling delay was not noted by participants, in part, because 10 of the 26 patients went to urgent care or emergency department settings. Misdiagnoses were more common in these settings, contributing to treatment delay. CONCLUSION: Our study identified potentially modifiable risk factors for belated treatment. Targeting these risk factors may minimise time to treatment and reduce the occurrence of preventable complications.

Association of low-moderate urine arsenic and QT interval: Cross-sectional and longitudinal evidence from the Strong Heart Study.

Epidemiologic studies suggest that chronic exposure to arsenic is related to cardiovascular disease (CVD), but the pathophysiological link remains uncertain. We evaluated the association of chronic low-moderate arsenic exposure and arsenic metabolism with baseline difference and annual change in ECG measures (QT interval, JT interval, PR interval, QRS duration, and QT dispersion) using linear mixed models in the Strong Heart Study main cohort (N = 1174, median age 55 years) and family study (N = 1695 diabetes-free, median age 36 years). At baseline, arsenic exposure was measured as the sum of inorganic and methylated species in urine (ΣAs) and arsenic metabolism was measured as the relative percentage of arsenic species. Median ΣAs and Bazett heart rate-corrected QT interval (QTc) were 8.6 µg/g creatinine and 424 ms in the main cohort and 4.3 µg/g and 414 ms in the family study, respectively. In the main cohort, a comparison of the highest to lowest ΣAs quartile (>14.4 vs. <5.2 µg/g creatinine) was associated with a 5.3 (95% CI: 1.2, 9.5) ms higher mean baseline QTc interval but no difference in annual change in QTc interval. In the family study, a comparison of the highest to lowest quartile (>7.1 vs. <2.9 µg/g creatinine) was associated with a 3.2 (95% CI: 0.6, 5.7) ms higher baseline QTc interval and a 0.6 (95% CI: 0.04, 1.2) ms larger annual increase in QTc interval. Associations with JTc interval were similar but stronger in magnitude compared to QTc interval. Arsenic exposure was largely not associated with PR interval, QRS duration or QT dispersion. Similar to arsenic exposure, a pattern of lower %MMA and higher %DMA was associated with longer baseline QTc interval in both cohorts and with a larger annual change in QTc interval in the family study. Chronic low-moderate arsenic exposure and arsenic metabolism were associated with prolonged ventricular repolarization.

Biomarkers of Secondhand Smoke Exposure in Waterpipe Tobacco Venue Employees in Istanbul, Moscow, and Cairo.

Background: Most smoke-free legislation to reduce secondhand smoke (SHS) exposure exempts waterpipe (hookah) smoking venues. Few studies have examined SHS exposure in waterpipe venues and their employees. Methods: We surveyed 276 employees of 46 waterpipe tobacco venues in Istanbul, Moscow, and Cairo. We interviewed venue managers and employees and collected biological samples from employees to measure exhaled carbon monoxide (CO), hair nicotine, saliva cotinine, urine cotinine, urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), and urine 1-hydroxypyrene glucuronide (1-OHPG). We estimated adjusted geometric mean ratios (GMR) of each SHS biomarker by employee characteristics and indoor air SHS measures. Results: There were 73 nonsmoking employees and 203 current smokers of cigarettes or waterpipe. In nonsmokers, the median (interquartile) range concentrations of SHS biomarkers were 1.1 (0.2, 40.9) µg/g creatinine urine cotinine, 5.5 (2, 15) ng/mL saliva cotinine, 0.95 (0.36, 5.02) ng/mg hair nicotine, 1.48 (0.98, 3.97) pg/mg creatinine urine NNAL, 0.54 (0.25, 0.97) pmol/mg creatinine urine 1-OHPG, and 1.67 (1.33, 2.33) ppm exhaled CO. An 8-hour increase in work hours was associated with higher urine cotinine (GMR: 1.68, 95% CI: 1.20, 2.37) and hair nicotine (GMR: 1.22, 95% CI: 1.05, 1.43). Lighting waterpipes was associated with higher saliva cotinine (GMR: 2.83, 95% CI: 1.05, 7.62). Conclusions: Nonsmoking employees of waterpipe tobacco venues were exposed to high levels of SHS, including measurable levels of carcinogenic biomarkers (tobacco-specific nitrosamines and PAHs). Implications: Smoke-free regulation should be extended to waterpipe venues to protect nonsmoking employees and patrons from the adverse health effects of SHS.

A dose-response meta-analysis of chronic arsenic exposure and incident cardiovascular disease.

Background: Consistent evidence at high levels of water arsenic (≥100 µg/l), and growing evidence at low-moderate levels (<100 µg/l), support a link with cardiovascular disease (CVD). The shape of the dose-response across low-moderate and high levels of arsenic in drinking water is uncertain and critical for risk assessment. Methods: We conducted a systematic review of general population epidemiological studies of arsenic and incident clinical CVD (all CVD, coronary heart disease (CHD) and stroke) with three or more exposure categories. In a dose-response meta-analysis, we estimated the pooled association between log-transformed water arsenic (log-linear) and restricted cubic splines of log-transformed water arsenic (non-linear) and the relative risk of each CVD endpoint. Results: Twelve studies (pooled N = 408 945) conducted at high (N = 7) and low-moderate (N = 5) levels of water arsenic met inclusion criteria, and 11 studies were included in the meta-analysis. Compared with 10 µg/l, the estimated pooled relative risks [95% confidence interval (CI)] for 20 µg/l water arsenic, based on a log-linear model, were 1.09 (1.03, 1.14) (N = 2) for CVD incidence, 1.07 (1.01, 1.14) (N = 6) for CVD mortality, 1.11 (1.05, 1.17) (N = 4) for CHD incidence, 1.16 (1.07, 1.26) (N = 6) for CHD mortality, 1.08 (0.99, 1.17) (N = 2) for stroke incidence and 1.06 (0.93, 1.20) (N = 6) for stroke mortality. We found no evidence of non-linearity, although these tests had low statistical power. Conclusions: Although limited by the small number of studies, this analysis supports quantitatively including CVD in inorganic arsenic risk assessment, and strengthens the evidence for an association between arsenic and CVD across low-moderate to high levels.

Low-moderate urine arsenic and biomarkers of thrombosis and inflammation in the Strong Heart Study.

The underlying pathology of arsenic-related cardiovascular disease (CVD) is unknown. Few studies have evaluated pathways through thrombosis and inflammation for arsenic-related CVD, especially at low-moderate arsenic exposure levels (<100 µg/L in drinking water). We evaluated the association of chronic low-moderate arsenic exposure, measured as the sum of inorganic and methylated arsenic species in urine (ΣAs), with plasma biomarkers of thrombosis and inflammation in American Indian adults (45-74 years) in the Strong Heart Study. We evaluated the cross-sectional and longitudinal associations between baseline ΣAs with fibrinogen at three visits (baseline, 1989-91; Visit 2, 1993-95, Visit 3, 1998-99) using mixed models and the associations between baseline ΣAs and Visit 2 plasminogen activator inhibitor-1 (PAI-1) and high sensitivity C-reactive protein (hsCRP) using linear regression. Median (interquartile range) concentrations of baseline ΣAs and fibrinogen, and Visit 2 hsCRP and PAI-1 were 8.4 (5.1, 14.3) µg/g creatinine, 346 (304, 393) mg/dL, 44 (30, 67) mg/L, and 3.8 (2.0, 7.0) ng/mL, respectively. Comparing the difference between the 75th and the 25th percentile of ΣAs (14.3 vs. 5.1 µg/g creatinine), ΣAs was positively associated with baseline fibrinogen among those with diabetes (adjusted geometric mean ratio (GMR): 1.05, 95% CI: 1.02, 1.07) not associated among those without diabetes (GMR: 1.01, 95% CI: 0.99, 1.02) (p-interaction for diabetes = 0.014), inversely associated with PAI-1 (GMR: 0.94, 95% CI: 0.90, 0.99), and not associated with hsCRP (GMR: 1.00, 95% CI: 0.93, 1.08). We found no evidence for an association between baseline ΣAs and annual change in fibrinogen over follow-up (p-interaction = 0.28 and 0.12 for diabetes and non-diabetes, respectively). Low-moderate arsenic exposure was positively associated with baseline fibrinogen in participants with diabetes and unexpectedly inversely associated with PAI-1. Further research should evaluate the role of prothrombotic factors in arsenic-related cardiovascular disease.

The Association of Arsenic Metabolism with Cancer, Cardiovascular Disease, and Diabetes: A Systematic Review of the Epidemiological Evidence.

BACKGROUND: The available evidence on the role of arsenic metabolism in individual susceptibility to the development of cancer, cardiovascular disease, and diabetes has not been formally and comprehensively reviewed. OBJECTIVES: Our goal was to systematically investigate the association of arsenic metabolism with cancer, cardiovascular disease, and diabetes-related outcomes in epidemiologic studies. As a secondary objective, we characterized the variation of arsenic metabolism in different populations worldwide. METHODS: We searched Medline/PubMed and EMBASE from inception to January 2016 and applied predetermined exclusion criteria. Compositional data analysis was used to describe the distribution of arsenic metabolism biomarkers and evaluate the association between arsenic exposure and metabolism. RESULTS: Twenty-eight studies met the inclusion criteria, 12 on cancer, nine on cardiovascular disease, and seven on diabetes-related outcomes. The median (interquartile range) for mean iAs%, MMA%, and DMA% was 11.2 (7.8-14.9)%, 13.0 (10.4-13.6)%, and 74.9 (69.8-80.0)%, respectively. Findings across studies suggested that higher arsenic exposure levels were associated with higher iAs% and lower DMA% and not associated with MMA%. For cancer, most studies found a pattern of higher MMA% and lower DMA% associated with higher risk of all-site, urothelial, lung, and skin cancers. For cardiovascular disease, higher MMA% was generally associated with higher risk of carotid atherosclerosis and clinical cardiovascular disease but not with hypertension. For diabetes-related outcomes, the pattern of lower MMA% and higher DMA% was associated with higher risk of metabolic syndrome and diabetes. CONCLUSIONS: Population level of iAs% and DMA%, but not MMA%, were associated with arsenic exposure levels. Overall, study findings suggest that higher MMA% was associated with an increased risk of cancer and cardiovascular disease, while lower MMA% was associated with an increased risk of diabetes and metabolic syndrome. Additional population-based studies and experimental studies are needed to further evaluate and understand the role of arsenic exposure in arsenic metabolism and the role of arsenic metabolism in disease development. https://doi.org/10.1289/EHP577.

Chronic arsenic exposure and risk of carotid artery disease: The Strong Heart Study.

BACKGROUND: Inorganic arsenic exposure from naturally contaminated groundwater is related to vascular disease. No prospective studies have evaluated the association between arsenic and carotid atherosclerosis at low-moderate levels. We examined the association of long-term, low-moderate inorganic arsenic exposure with carotid arterial disease. METHODS: American Indians, 45-74 years old, in Arizona, Oklahoma, and North and South Dakota had arsenic concentrations (sum of inorganic and methylated species, µg/g urine creatinine) measured from baseline urine samples (1989-1991). Carotid artery ultrasound was performed in 1998-1999. Vascular disease was assessed by the carotid intima media thickness (CIMT), the presence of atherosclerotic plaque in the carotid, and by the number of segments containing plaque (plaque score). RESULTS: 2402 participants (mean age 55.3 years, 63.1% female, mean body mass index 31.0kg/m2, diabetes 45.7%, hypertension 34.2%) had a median (interquintile range) urine arsenic concentration of 9.2 (5.00, 17.06) µg/g creatinine. The mean CIMT was 0.75mm. 64.7% had carotid artery plaque (3% with >50% stenosis). In fully adjusted models comparing participants in the 80th vs. 20th percentile in arsenic concentrations, the mean difference in CIMT was 0.01 (95% confidence interval (95%CI): 0.00, 0.02) mm, the relative risk of plaque presence was 1.04 (95%CI: 0.99, 1.09), and the geometric mean ratio of plaque score was 1.05 (95%CI: 1.01, 1.09). CONCLUSIONS: Urine arsenic was positively associated with CIMT and increased plaque score later in life although the association was small. The relationship between urinary arsenic and the presence of plaque was not statistically significant when adjusted for other risk factors. Arsenic exposure may play a role in increasing the severity of carotid vascular disease.