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Abstract Background Polycyclic aromatic hydrocarbons (PAHs) have been linked to adverse birth outcomes that have been reported to be induced by oxidative stress, but few epidemiological studies to date have evaluated associations between urinary PAH metabolites and oxidative stress biomarkers in pregnancy and identified critical periods for these outcomes and PAH exposures in pregnancy. Methods A cohort of pregnant women was recruited early in pregnancy from antenatal clinics at the University of California Los Angeles during 2016–2019. We collected urine samples up to three times during pregnancy in a total of 159 women enrolled in the cohort. A total of 7 PAH metabolites and 2 oxidative stress biomarkers [malondialdehyde (MDA), 8-hydroxy-2’-deoxyguanosine (8-OHdG)] were measured in all available urine samples. Using multiple linear regression models, we estimated the percentage change (%) and 95% confidence interval (CI) in 8-OHdG and MDA measured at each sample collection time per doubling of PAH metabolite concentrations. Furthermore, we used linear mixed models with a random intercept for participant to estimate the associations between PAH metabolite and oxidative stress biomarker concentrations across multiple time points in pregnancy. Results Most PAH metabolites were positively associated with both urinary oxidative stress biomarkers, MDA and 8-OHdG, with stronger associations in early and late pregnancy. A doubling of each urinary PAH metabolite concentration increased MDA concentrations by 5.8-41.1% and 8-OHdG concentrations by 13.8-49.7%. Linear mixed model results were consistent with those from linear regression models for each gestational sampling period. Conclusion Urinary PAH metabolites are associated with increases in oxidative stress biomarkers during pregnancy, especially in early and late pregnancy.

Abstract An under-recognised aspect of the current humanitarian catastrophe in Gaza is the impact of the war on the environment and the associated risks for human health. This commentary contextualises these impacts against the background of human suffering produced by the overwhelming violence associated with the use of military force against the general population of Gaza. In calling for an immediate cessation to the violence, the authors draw attention to the urgent need to rebuild the health care system and restore the physical and human infrastructure that makes a liveable environment possible and promotes human health and well-being, especially for the most vulnerable in the population. Environmental remediation should therefore form one of the most important parts of international efforts to assist reconstruction, through which we hope Palestinians and Israelis will achieve lasting peace, health, and sustainable development, all as part of accepted international human rights obligations.

Abstract Type 2 diabetes mellitus (T2DM) is a common comorbidity among Parkinson’s disease (PD) patients. Yet, little is known about dysregulated pathways that are unique in PD patients with T2DM. We applied high-resolution metabolomic profiling in serum samples of 636 PD and 253 non-PD participants recruited from Central California. We conducted an initial discovery metabolome-wide association and pathway enrichment analysis. After adjusting for multiple testing, in positive (or negative) ion mode, 30 (25) metabolic features were associated with T2DM in both PD and non-PD participants, 162 (108) only in PD participants, and 32 (7) only in non-PD participants. Pathway enrichment analysis identified 17 enriched pathways associated with T2DM in both the PD and non-PD participants, 26 pathways only in PD participants, and 5 pathways only in non-PD participants. Several amino acid, nucleic acids, and fatty acid metabolisms were associated with T2DM only in the PD patient group suggesting a possible link between PD and T2DM.

Abstract Parkinson’s disease (PD), the second most common neurodegenerative disorder, develops sporadically, likely through a combination of polygenic and environmental factors. Previous studies associate pesticide exposure and genes involved in lysosomal function with PD risk. We evaluated the frequency of variants in lysosomal function genes among patients from the Parkinson’s, Environment, and Genes (PEG) study with ambient pesticide exposure from agricultural sources. 757 PD patients, primarily of White European/non-Hispanic ancestry (75%), were screened for variants in 85 genes using a custom amplicon panel. Variant enrichment was calculated against the Genome Aggregation Database (gnomAD). Enriched exonic variants were prioritized by exposure to a cluster of pesticides used on cotton and severity of disease progression in a subset of 386 patients subdivided by race/ethnicity. Gene enrichment analysis identified 36 variants in 26 genes in PEG PD patients. Twelve of the identified genes (12/26, 46%) had multiple enriched variants and/or a single enriched variant present in multiple individuals, representing 61% (22/36) of the observed variation in the cohort. The majority of enriched variants (26/36, 72%) were found in genes contributing to lysosomal function, particularly autophagy, and were bioinformatically deemed functionally deleterious (31/36, 86%). We conclude that, in this study, variants in genes associated with lysosomal function, notably autophagy, were enriched in PD patients exposed to agricultural pesticides suggesting that altered lysosomal function may generate an underlying susceptibility for developing PD with pesticide exposure. Further study of gene-environment interactions targeting lysosomal function may improve understanding of PD risk in individuals exposed to pesticides.

Abstract It has been suggested that gut microbiota influence Parkinson’s disease (PD) via the gut–brain axis. Here, we examine associations between diet and gut microbiome composition and its predicted functional pathways in patients with PD. We assessed gut microbiota in fecal samples from 85 PD patients in central California using 16S rRNA gene sequencing. Diet quality was assessed by calculating the Healthy Eating Index 2015 (HEI-2015) based on the Diet History Questionnaire II. We examined associations of diet quality, fiber, and added sugar intake with microbial diversity, composition, taxon abundance, and predicted metagenomic profiles, adjusting for age, sex, race/ethnicity, and sequencing platform. Higher HEI scores and fiber intake were associated with an increase in putative anti-inflammatory butyrate-producing bacteria, such as the genera Butyricicoccus and Coprococcus 1. Conversely, higher added sugar intake was associated with an increase in putative pro-inflammatory bacteria, such as the genera Klebsiella. Predictive metagenomics suggested that bacterial genes involved in the biosynthesis of lipopolysaccharide decreased with higher HEI scores, whereas a simultaneous decrease in genes involved in taurine degradation indicates less neuroinflammation. We found that a healthy diet, fiber, and added sugar intake affect the gut microbiome composition and its predicted metagenomic function in PD patients. This suggests that a healthy diet may support gut microbiome that has a positive influence on PD risk and progression.

Abstract Background Organophosphorus pesticides (OP) have been associated with various human health conditions. Animal experiments and in-vitro models suggested that OP may also affect the gut microbiota. We examined associations between ambient chronic exposure to OP and gut microbial changes in humans. Methods We recruited 190 participants from a community-based epidemiologic study of Parkinson’s disease living in a region known for heavy agricultural pesticide use in California. Of these, 61% of participants had Parkinson’s disease and their mean age was 72 years. Microbiome and predicted metagenome data were generated by 16S rRNA gene sequencing of fecal samples. Ambient long-term OP exposures were assessed using pesticide application records combined with residential addresses in a geographic information system. We examined gut microbiome differences due to OP exposures, specifically differences in microbial diversity based on the Shannon index and Bray–Curtis dissimilarities, and differential taxa abundance and predicted Metacyc pathway expression relying on regression models and adjusting for potential confounders. Results OP exposure was not associated with alpha or beta diversity of the gut microbiome. However, the predicted metagenome was sparser and less evenly expressed among those highly exposed to OP (p = 0.04). Additionally, we found that the abundance of two bacterial families, 22 genera, and the predicted expression of 34 Metacyc pathways were associated with long-term OP exposure. These pathways included perturbed processes related to cellular respiration, increased biosynthesis and degradation of compounds related to bacterial wall structure, increased biosynthesis of RNA/DNA precursors, and decreased synthesis of Vitamin B1 and B6. Conclusion In support of previous animal studies and in-vitro findings, our results suggest that ambient chronic OP pesticide exposure alters gut microbiome composition and its predicted metabolism in humans.

Background: Autoimmune diseases have been linked to an increased risk of pregnancy-related complications. A family history of autoimmune diseases may be related to the risk of childhood cancer based on similar histocompatibility antigens. We utilized data from national registries in Denmark to examine associations between maternal autoimmune disease and cancer in their offspring. Methods: We linked data from several national registries in Denmark to identify childhood cancer cases in children

Abstract Background Untargeted high-resolution metabolomic profiling provides simultaneous measurement of thousands of metabolites. Metabolic networks based on these data can help uncover disease-related perturbations across interconnected pathways. Objective Identify metabolic disturbances associated with Parkinson’s disease (PD) in two population-based studies using untargeted metabolomics. Methods We performed a metabolome-wide association study (MWAS) of PD using serum-based untargeted metabolomics data derived from liquid chromatography with high-resolution mass spectrometry (LC-HRMS) using two distinct population-based case-control populations. We also combined our results with a previous publication of 34 metabolites linked to PD in a large-scale, untargeted MWAS to assess external validation. Results LC-HRMS detected 4,762 metabolites for analysis (HILIC: 2716 metabolites; C18: 2046 metabolites). We identified 296 features associated with PD at FDR

Background: Air pollution is a global health concern, with fine particulate matter (PM2.5) constituents posing potential risks to human health, including children's neurodevelopment. Here we investigated associations between exposure during pregnancy and infancy to specific traffic-related PM2.5 components with Autism Spectrum Disorder (ASD) diagnosis. Methods: For exposure assessment, we estimated PM2.5 components related to traffic exposure (Barium [Ba] as a marker of brake dust and Zinc [Zn] as a tire wear marker, Black Carbon [BC]) and oxidative stress potential (OSP) markers (Hydroxyl Radical [OPOH] formation, Dithiothreitol activity [OPDTT], reactive oxygen species [ROS]) modeled with land use regression with co-kriging based on an intensive air monitoring campaign. We assigned exposures to a cohort of 444,651 children born in Southern California between 2016 and 2019, among whom 11,466 ASD cases were diagnosed between 2018 and 2022, Odds ratios (ORs) and 95% confidence intervals (CIs) were obtained with logistic regression for single pollutant and PM2.5 mass co-adjusted models, also adjusting for sociodemographic characteristics. Results: Among PM2.5 components, we found the strongest positive association with ASD for our brake wear marker Ba (ORper IQR = 1.29, 95 % CI: 1.24, 1.34). This was followed by an increased risk for all PM2.5 oxidative stress potential markers; the strongest association was with ROS formation (ORper IQR = 1.22, 95 % CI: 1.18, 1.25). PM2.5 mass was linked to ASD in Hispanic and Black children, but not White children, while traffic-related PM2.5 and OSP markers increased ASD risk across all groups. In neighborhoods with the lowest socioeconomic status (SES), associations with ASD were stronger for all examined pollutants compared to higher SES areas. Conclusions: Our findings suggest that brake wear-related PM2.5 and PM2.5 OSP are associated with ASD diagnosis in Southern California. These results suggest that strategies aimed at reducing the public health impacts of PM2.5 need to consider specific sources.

Background: Although traffic-related air pollution is largely regulated at the federal level, congestion reduction projects may reduce local traffic and air pollution to levels that create positive co-benefits for population health. In recent years, many urban areas have implemented electronic tolling systems to improve traffic conditions. Objective: Quantify associations between implementing electronic tolling and local changes in traffic and infant health. Methods: Using a population-based birth cohort (Texas, 1999–2016), we calculated residential proximity to the nearest tolled road segment within 5 km (n = 625,279) and examined changes in local traffic before and after toll implementation. Using a difference-in-differences design, we compared four markers of adverse birth outcomes (term birth weight, term low birth weight, preterm birth, very preterm birth) among infants from pregnant people residing

Abstract Parkinson’s disease (PD) is a complex neurodegenerative disease with etiology rooted in genetic vulnerability and environmental factors. Here we combine quantitative epidemiologic study of pesticide exposures and PD with toxicity screening in dopaminergic neurons derived from PD patient induced pluripotent stem cells (iPSCs) to identify Parkinson’s-relevant pesticides. Agricultural records enable investigation of 288 specific pesticides and PD risk in a comprehensive, pesticide-wide association study. We associate long-term exposure to 53 pesticides with PD and identify co-exposure profiles. We then employ a live-cell imaging screening paradigm exposing dopaminergic neurons to 39 PD-associated pesticides. We find that 10 pesticides are directly toxic to these neurons. Further, we analyze pesticides typically used in combinations in cotton farming, demonstrating that co-exposures result in greater toxicity than any single pesticide. We find trifluralin is a driver of toxicity to dopaminergic neurons and leads to mitochondrial dysfunction. Our paradigm may prove useful to mechanistically dissect pesticide exposures implicated in PD risk and guide agricultural policy.

Road traffic is a significant source of particulate matter pollution, whose exposure is a significant risk factor in pregnancy-related health outcomes. The exact mechanisms behind the relationship between traffic-related air pollution (TRAP) exposure and adverse pregnancy outcomes remain unclear. We aim to assess the relationship between exposure to brake and tire wear-associated metals and oxidative potential and ischemic placental disease (IPD). Data were assembled from a final population of 178 women who sought specialized prenatal care at UCLA between 2016 and 2019 in Los Angeles, CA. Modeled first trimester exposures to chemical constituents and oxidative stress potential of PM _2.5 , black carbon, and PM _2.5 mass concentration. Speciated measurements included tracers of brake wear (barium), tire wear (zinc), and oxidative potential markers based on metal concentrations (KM-SUB-ELF ROS) or laboratory assays (DTT loss, OH radical formation). Exposures were modeled by integrating data from filter samples, a low-cost PM _2.5 sensor network, and land-use data. We used logistic regression to estimate the associations between air pollution exposures and IPD, adjusting for covariates assessed through medical records and interviews. Scaled to the interquartile range, odds ratios (95% CI) were as follows: barium OR: 1.7 (1.1, 2.7), zinc OR: 1.4 (.86, 2.4), and oxidative potential markers, both modeled as well as measured through DTT loss and OH formation assays (ORs ranging from 1.1-2.0). Point estimates of effect sizes for PM _2.5 and black carbon were lower than most measurements (ORs: 1.3-1.4). mass and black carbon. Our findings suggest two key points: (i) metals associated with brake and tire wear, currently unregulated, may play a role in the relationship between TRAP and adverse pregnancy outcomes, and (ii) reducing tailpipe emissions may not be sufficient to protect pregnant women from TRAP.

Background: Both air pollution and noise exposures have separately been shown to affect cognitive impairment. Here, we examine how air pollution and noise exposures interact to influence the development of incident dementia or cognitive impairment without dementia (CIND). Methods: We used 1,612 Mexican American participants from the Sacramento Area Latino Study on Aging conducted from 1998 to 2007. Air pollution (nitrogen dioxides, particulate matter, ozone) and noise exposure levels were modeled with a land-use regression and via the SoundPLAN software package implemented with the Traffic Noise Model applied to the greater Sacramento area, respectively. Using Cox proportional hazard models, we estimated the hazard of incident dementia or CIND from air pollution exposure at the residence up to 5-years prior to diagnosis for the members of each risk set at event time. Further, we investigated whether noise exposure modified the association between air pollution exposure and dementia or CIND. Results: In total, 104 incident dementia and 159 incident dementia/CIND cases were identified during the 10 years of follow-up. For each ∼2 µg/m3 increase in time-varying 1- and 5-year average PM2.5 exposure, the hazard of dementia increased 33% (HR = 1.33, 95%CI: 1.00, 1.76). The hazard ratios for NO2-related dementia/CIND and PM2.5-related dementia were stronger in high-noise (≥65 dB) exposed than low-noise (

Background: Pesticides have been widely used in agriculture for more than half a century. However, with thousands currently in use, most have not been adequately assessed for influence Parkinson’s disease (PD). Objectives: Here we aimed to assess biologic plausibility of 70 pesticides implicated with PD through an agnostic pesticide-wide association study using a data mining approach linking toxicology and toxicogenomics databases. Methods: We linked the 70 targeted pesticides to quantitative high-throughput screening assay findings from the Toxicology in the 21st Century (Tox21) program and pesticide-related genetic/disease information with the Comparative Toxicogenomics Database (CTD). We used the CTD to determine networks of genes each pesticide has been linked to and assess enrichment of relevant gene ontology (GO) annotations. With Tox21, we evaluated pesticide induced activity on a series of 43 nuclear receptor and stress response assays and two cytotoxicity assays. Results: Overall, 59 % of the 70 pesticides had chemical-gene networks including at least one PD gene/gene product. In total, 41 % of the pesticides had chemical-gene networks enriched for ≥ 1 high-priority PD GO terms. For instance, 23 pesticides had chemical-gene networks enriched for response to oxidative stress, 21 for regulation of neuron death, and twelve for autophagy, including copper sulfate, endosulfan and chlorpyrifos. Of the pesticides tested against the Tox21 assays, 79 % showed activity on ≥ 1 assay and 11 were toxic to the two human cell lines. The set of PD-associated pesticides showed more activity than expected on assays testing for xenobiotic homeostasis, mitochondrial membrane permeability, and genotoxic stress. Conclusions: Overall, cross-database queries allowed us to connect a targeted set of pesticides implicated in PD via epidemiology to specific biologic targets relevant to PD etiology. This knowledge can be used to help prioritize targets for future experimental studies and improve our understanding of the role of pesticides in PD etiology.

Although Parkinson's Disease (PD) is typically described in terms of motor symptoms, depression is a common feature. We explored whether depression influences blood-based genome-wide DNA methylation (DNAm) in 692 subjects from a population-based PD case-control study, using both a history of clinically diagnosed depression and current depressive symptoms measured by the geriatric depression scale (GDS). While PD patients in general had more immune activation and more accelerated epigenetic immune system aging than controls, the patients experiencing current depressive symptoms (GDS≥5) showed even higher levels of both markers than patients without current depressive symptoms (GDS

Due to regulations and technological advancements reducing tailpipe emissions, an increasing proportion of emissions arise from brake and tire wear particulate matter (PM). PM from these non-tailpipe sources contains heavy metals capable of generating oxidative stress in the lung. Although important, these particles remain understudied because the high cost of actively collecting filter samples. Improvements in electrical engineering, internet connectivity, and an increased public concern over air pollution have led to a proliferation of dense low-cost air sensor networks such as the PurpleAir monitors, which primarily measure unspeciated fine particulate matter (PM2.5). In this study, we model the concentrations of Ba, Zn, black carbon, reactive oxygen species concentration in the epithelial lining fluid, dithiothreitol (DTT) loss, and OH formation. We use a cokriging approach, incorporating data from the PurpleAir network as a secondary predictor variable and a land-use regression (LUR) as an external drift. For most pollutant species, cokriging models produced more accurate predictions than an LUR model, which did not incorporate data from the PurpleAir monitors. This finding suggests that low-cost sensors can enhance predictions of pollutants that are costly to measure extensively in the field.

Abstract Background Aging and inflammation are important components of Parkinson’s disease (PD) pathogenesis and both are associated with changes in hematopoiesis and blood cell composition. DNA methylation (DNAm) presents a mechanism to investigate inflammation, aging, and hematopoiesis in PD, using epigenetic mitotic aging and aging clocks. Here, we aimed to define the influence of blood cell lineage on epigenetic mitotic age and then investigate mitotic age acceleration with PD, while considering epigenetic age acceleration biomarkers. Results We estimated epigenetic mitotic age using the “epiTOC” epigenetic mitotic clock in 10 different blood cell populations and in a population-based study of PD with whole-blood. Within subject analysis of the flow-sorted purified blood cell types DNAm showed a clear separation of epigenetic mitotic age by cell lineage, with the mitotic age significantly lower in myeloid versus lymphoid cells (p = 2.1e-11). PD status was strongly associated with accelerated epigenetic mitotic aging (AccelEpiTOC) after controlling for cell composition (OR = 2.11, 95 % CI = 1.56, 2.86, p = 1.6e-6). AccelEpiTOC was also positively correlated with extrinsic epigenetic age acceleration, a DNAm aging biomarker related to immune system aging (with cell composition adjustment: R = 0.27, p = 6.5e-14), and both were independently associated with PD. Among PD patients, AccelEpiTOC measured at baseline was also associated with longitudinal motor and cognitive symptom decline. Conclusions The current study presents a first look at epigenetic mitotic aging in PD and our findings suggest accelerated hematopoietic cell mitosis, possibly reflecting immune pathway imbalances, in early PD that may also be related to motor and cognitive progression.

Abstract Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

Background: Within the Multiethnic Cohort (MEC), we examined the association between air pollution and mortality among African American, European American, Japanese American, and Latina American women diagnosed with breast cancer. Methods: We used a land use regression (LUR) model and kriging interpolation to estimate nitrogen oxides (NOx , NO2) and particulate matter (PM2.5, PM10) exposures for 3,089 breast cancer cases in the MEC, who were diagnosed from 1993 through 2013 and resided largely in Los Angeles County, California. Cox proportional hazards models were used to examine the association of time-varying air pollutants with all-cause, breast cancer, cardiovascular disease (CVD), and non-breast cancer/non-CVD mortality, accounting for key covariates. Results: We identified 1,125 deaths from all causes (474 breast cancer, 272 CVD, 379 non-breast cancer/non-CVD deaths) among the 3,089 breast cancer cases with 8.1 years of average follow-up. LUR and kriged NOX (per 50 ppb) and NO2 (per 20 ppb), PM2.5 (per 10 µg/m3), and PM10 (per 10 µg/m3) were positively associated with risks of all-cause (Hazard Ratio (HR) range = 1.13–1.25), breast cancer (HR range = 1.19–1.45), and CVD mortality (HR range = 1.37–1.60). Associations were statistically significant for LUR NOX and CVD mortality (HR = 1.60; 95% CI: 1.08–2.37) and kriged NO2 and breast cancer mortality (HR = 1.45; 95% CI 1.02–2.07). Gaseous and PM pollutants were positively associated with breast cancer mortality across racial/ethnic group. Conclusion: In this study, air pollutants have a harmful impact on breast cancer survival. Additional studies should evaluate potential confounding by socioeconomic factors. These data support maintaining clean air laws to improve survival for women with breast cancer.

Parkinson’s disease (PD) is a common neurodegenerative disorder with a complex etiology involving genetics and the environment. Here, Vallerga et al. identify two CpG probes associated with PD in a blood cell type-corrected epigenome-wide meta-analysis, implicating the SLC7A11 gene as a plausible biological target.

Abstract While the spatial resolution of exposure surfaces has greatly improved, our ability to locate people in space remains a limiting factor in accurate exposure assessment. In this case‐control study, two approaches to geocoding participant locations were used to study the impact of geocoding uncertainty on the estimation of ambient pesticide exposure and breast cancer risk among women living in California's Central Valley. Residential and occupational histories were collected and geocoded using a traditional point‐based method along with a novel area‐based method. The standard approach to geocoding uses centroid points to represent all geocoded locations, and is unable to adapt exposure areas based on geocode quality, except through the exclusion of low‐certainty locations. In contrast, area‐based geocoding retains the complete area to which an address matched (the same area from which the centroid is returned), and therefore maintains the appropriate level of precision when it comes to assessing exposure by geography. Incorporating the total potential exposure area for each geocoded location resulted in different exposure classifications and resulting odds ratio estimates than estimates derived from the centroids of those same areas (using a traditional point‐based geocoder). The direction and magnitude of these differences varied by pesticide, but in all cases odds ratios differed by at least 6% and up to 35%. These findings demonstrate the importance of geocoding in exposure estimation and suggest it is important to consider geocode certainty and quality throughout exposure assessment, rather than simply using the best available point geocodes.

Background: Determinants of changes in cognitive function during aging are not well-understood. We aimed to estimate the effects of depression-, anxiety- and anger symptoms on cognition and on cognition changes, especially on changes in episodic memory (EM) and executive functioning (EF).Methods: We analyze data from the Mid-Life in the Midlife in the United States Biomarker study at two time points including n = 710 women, and n = 542 men (1996/1997) at the first assessment and n = 669 women, and n = 514 men at the second assessment (2013/2014). To assess cognition we used the Brief Test of Adult Cognition (BTACT). To measure depression-, anxiety- and anger symptoms we used the Mood and Anxiety Symptom Questionnaire (MASQ), the Center for Epidemiologic Studies Depression Scale (CES-D) and the State-Trait Anger Expression Inventory (STAXI). We used repeated models analyses to explore changes in cognition, and repeated measures linear mixed-effects models to investigate depression, anxiety and anger effects on cognition. All analyses were adjusted for potential confounders (cognition at baseline, age, education, income).Results: At the first assessment, women had significantly better episodic memory functioning than men; men in the oldest age group had significant better executive functioning. At the second assessment, more education, and white ethnicity were associated with less negative changes on episodic memory and executive functioning. Depression- and anger symptoms were associated with declines in episodic memory among women; anxiety symptoms were associated with declines in episodic memory and executive functioning in both gender in men (EF: β: −0.02, (95% CI: −0.03, −0.01; EM: β −0.02 (−0.02, 95% CI: −0.03, −0.01) and in women (EF: β −0.01, 95% CI: −0.02, −0.0004; EM: β −0.013, 95% CI: −0.03, −0.001).Conclusions: Depression-, anxiety- and anger symptoms were associated with changes in episodic memory and executive functioning. Further longitudinal studies are critical in populations in more countries to better understand the impact of depression, anxiety and anger symptoms on cognition changes.

Background: Accurate estimation of nitrogen dioxide (NO2) and nitrogen oxide (NOx) concentrations at high spatiotemporal resolutions is crucial for improving evaluation of their health effects, particularly with respect to short-term exposures and acute health outcomes. For estimation over large regions like California, high spatial density field campaign measurements can be combined with more sparse routine monitoring network measurements to capture spatiotemporal variability of NO2 and NOx concentrations. However, monitors in spatially dense field sampling are often highly clustered and their uneven distribution creates a challenge for such combined use. Furthermore, heterogeneities due to seasonal patterns of meteorology and source mixtures between sub-regions (e.g. southern vs. northern California) need to be addressed. Objectives: In this study, we aim to develop highly accurate and adaptive machine learning models to predict high-resolution NO2 and NOx concentrations over large geographic regions using measurements from different sources that contain samples with heterogeneous spatiotemporal distributions and clustering patterns. Methods: We used a comprehensive Kruskal-K-means method to cluster the measurement samples from multiple heterogeneous sources. Spatiotemporal cluster-based bootstrap aggregating (bagging) of the base mixed-effects models was then applied, leveraging the clusters to obtain balanced and less correlated training samples for less bias and improvement in generalization. Further, we used the machine learning technique of grid search to find the optimal interaction of temporal basis functions and the scale of spatial effects, which, together with spatiotemporal covariates, adequately captured spatiotemporal variability in NO2 and NOx at the state and local levels. Results: We found an optimal combination of four temporal basis functions and 200 m scale spatial effects for the base mixed-effects models. With the cluster-based bagging of the base models, we obtained robust predictions with an ensemble cross validation R2 of 0.88 for both NO2 and NOx [RMSE (RMSEIQR): 3.62 ppb (0.28) and 9.63 ppb (0.37) respectively]. In independent tests of random sampling, our models achieved similarly strong performance (R2 of 0.87–0.90; RMSE of 3.97–9.69 ppb; RMSEIQR of 0.21–0.27), illustrating minimal over-fitting. Conclusions: Our approach has important implications for fusing data from highly clustered and heterogeneous measurement samples from multiple data sources to produce highly accurate concentration estimates of air pollutants such as NO2 and NOx at high resolution over a large region. Keywords: Air pollution, Nitrogen oxides, Spatiotemporal variability, Generalization, Machine learning, Cluster methods

OBJECTIVES: Recent meta-analyses suggest a physical activity health paradox: high levels of occupational physical activity (OPA) increase cardiovascular disease (CVD) risk, while leisure-time physical activity (LTPA) decreases risk. However, studies of women and cerebrovascular disease are limited. This report examines physical activity effects on stroke and transient ischemic attack (TIA) among working women in the United States. METHODS: OPA history, health status, and lifestyle were assessed by baseline interviews of 31 270 employed Sister Study participants aged 35–74 years. OPA was assessed at six intensity levels (lowest: “mostly sitting”); the highest three were combined as “high intensity work.” Independent OPA and LTPA effects on 6-year cerebrovascular disease incidence were estimated in adjusted Cox proportional hazard models. RESULTS: Stroke (N=441) and TIA (N=274) risk increased with more standing and higher intensity work at current and longest held job. Compared with mostly sitting, high intensity work at the current job increased TIA risk by 57% [hazard ratio (HR) 1.57, 95% confidence interval (CI) 1.04–2.38]. High intensity OPA at the longest held job increased risk for stroke by 44% (HR 1.44; 95% CI 1.08–1.93). Among women with CVD, sitting and standing equally, especially at the current job, increased risks up to two-fold (TIA HR 1.98, 95% CI 1.10–3.55) compared with mostly sitting at work. LTPA showed inverse associations. CONCLUSIONS: Higher intensity levels of OPA increased stroke and TIA risks, while LTPA decreased risks; results corroborate the physical activity health paradox for women and cerebrovascular disease. More standing at work increased cerebrovascular disease risks, especially for women with CVD.

Background: Previous studies suggested that air pollutants may increase the incidence of metabolic syndrome, but the potential impact from traffic sources is not well-understood. This study aimed to investigate associations between traffic-related nitrogen oxides (NOx) or noise pollution and risk of incident metabolic syndrome and its components in an elderly Mexican-American population. Methods: A total of 1,554 Mexican-American participants of the Sacramento Area Latino Study on Aging (SALSA) cohort were followed from 1998 to 2007. We used anthropometric measures and biomarkers to define metabolic syndrome according to the recommendations of the Third Adult Treatment Panel of the National Cholesterol Education Program (NCEP ATP III). Based on participants’ residential addresses at baseline, estimates of local traffic-related NOx were generated using the California Line Source Dispersion Model version 4 (CALINE4), and of noise employing the SoundPLAN software package. We used Cox regression models with calendar time as the underlying time scale to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations of air pollution or noise with metabolic syndrome or its components. Results: Each per unit increase of traffic-related NOx (2.29 parts per billion (ppb)) was associated with a 15% (HR = 1.15, 95% CI: 1.04–1.28) lower level of high-density lipoprotein cholesterol (HDL-cholesterol), and each 11.6 decibels (dB) increase in noise increased the risk of developing metabolic syndrome by 17% (HR = 1.17, 95% CI: 1.01–1.35). Conclusion: Policies aiming to reduce traffic-related air pollution and noise might mitigate the risk of metabolic syndrome and its components in vulnerable populations. Keywords: Air pollution, noise, Metabolic syndrome, Mexican-Americans

Abstract The WHO has ranked environmental hazardous exposures in the living and working environment among the top risk factors for chronic disease mortality. Worldwide, about 40 million people die each year from noncommunicable diseases (NCDs) including cancer, diabetes, and chronic cardiovascular, neurological and lung diseases. The exposure to ambient pollution in the living and working environment is exacerbated by individual susceptibilities and lifestyle-driven factors to produce complex and complicated NCD etiologies. Research addressing the links between environmental exposure and disease prevalence is key for prevention of the pandemic increase in NCD morbidity and mortality. However, the long latency, the chronic course of some diseases and the necessity to address cumulative exposures over very long periods does mean that it is often difficult to identify causal environmental exposures. EU-funded COST Action DiMoPEx is developing new concepts for a better understanding of health-environment (including gene-environment) interactions in the etiology of NCDs. The overarching idea is to teach and train scientists and physicians to learn how to include efficient and valid exposure assessments in their research and in their clinical practice in current and future cooperative projects. DiMoPEx partners have identified some of the emerging research needs, which include the lack of evidence-based exposure data and the need for human-equivalent animal models mirroring human lifespan and low-dose cumulative exposures. Utilizing an interdisciplinary approach incorporating seven working groups, DiMoPEx will focus on aspects of air pollution with particulate matter including dust and fibers and on exposure to low doses of solvents and sensitizing agents. Biomarkers of early exposure and their associated effects as indicators of disease-derived information will be tested and standardized within individual projects. Risks arising from some NCDs, like pneumoconioses, cancers and allergies, are predictable and preventable. Consequently, preventative action could lead to decreasing disease morbidity and mortality for many of the NCDs that are of major public concern. DiMoPEx plans to catalyze and stimulate interaction of scientists with policy-makers in attacking these exposure-related diseases.

Abstract Background Several articles suggest that DNA methylation levels in blood relate to Parkinson’s disease (PD) but there is a need for a large-scale study that involves suitable population based controls. The purposes of the study were: (1) to study whether PD status is associated with DNA methylation levels in blood/saliva; (2) to study whether observed associations relate to blood cell types; and (3) to characterize genome-wide significant markers (“CpGs”) and clusters of CpGs (co-methylation modules) in terms of biological pathways. Methods In a population-based case control study of PD, we studied blood samples from 335 PD cases and 237 controls and saliva samples from another 128 cases and 131 controls. DNA methylation data were generated from over 486,000 CpGs using the Illumina Infinium array. We identified modules of CpGs (clusters) using weighted correlation network analysis (WGCNA). Results Our cross-sectional analysis of blood identified 82 genome-wide significant CpGs (including cg02489202 in LARS2 p = 8.3 × 10–11 and cg04772575 in ABCB9 p = 4.3 × 10–10). Three out of six PD related co-methylation modules in blood were significantly enriched with immune system related genes. Our analysis of saliva identified five significant CpGs. PD-related CpGs are located near genes that relate to mitochondrial function, neuronal projection, cytoskeleton organization, systemic immune response, and iron handling. Conclusions This study demonstrates that: (1) PD status has a profound association with DNA methylation levels in blood and saliva; and (2) the most significant PD-related changes reflect changes in blood cell composition. Overall, this study highlights the role of the immune system in PD etiology but future research will need to address the causal structure of these relationships.

OBJECTIVES: This study aimed to examine whether occupational and physical activity (PA) at different ages contribute to Parkinson’s disease (PD) risk in a large population-based case-control study in Denmark. METHODS: We identified 1828 PD patients from the Danish National Hospital Register and recruited 1909 gender and year of birth matched controls from the Danish Central Population Register. Occupational and leisure-time PA were determined from a job exposure matrix based on occupational history and self-reported leisure-time information. RESULTS: No association was found for occupational PA alone in men, but higher leisure-time PA (≥5 hours/week of strenuous activities) in young adulthood (15–25 years) was associated with a lower PD risk (adjusted odds ratio (OR_adj) 0.75, 95% confidence interval (95% CI) 0.62–0.90); men who engaged in high occupational and high leisure-time PA in young adulthood had the lowest PD risk (OR_adj 0.58, 95% CI 0.41–0.81). Among women, inverse associations were found for occupation PA before age 50 (highest vs lowest, OR_adj 0.75, 95% CI 0.55–1.06) and strenuous leisure-time PA after age 50 (OR_adj 0.65, 95% CI 0.87–0.99); no clear pattern was seen for leisure and occupational PA combined. CONCLUSIONS: We observed gender-specific inverse associations between occupational and leisure-time PA and PD risk; however, we cannot preclude reverse causation especially in older ages since PD has a long prodromal stage that might lead to a reduction of PA years before motor symptom onset and PD diagnosis.

Background: Maternal exposure to traffic-related air pollution during pregnancy has been shown to increase the risk of adverse birth outcomes and neurodevelopmental disorders. By utilizing high-resolution metabolomics (HRM), we investigated perturbations of the maternal serum metabolome in response to traffic-related air pollution to identify biological mechanisms. Methods: We retrieved stored mid-pregnancy serum samples from 160 mothers who lived in the Central Valley of California known for high air particulate levels. We estimated prenatal traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter

Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other. Keywords: Air pollution, Bayesian, Clustering, Low birth weight, Pollutant profile, Profile regression

Cerebral palsy (CP) is the most common neuro-motor disability in young children. Disruptions of maternal hormone function during pregnancy have been linked to CP risk. We investigated whether prenatal exposure to pesticide compounds with endocrine-disrupting action affect CP risk. We conducted a case-control study of 3905 CP cases and 39,377 controls born between 1998 and 2010 in California to mothers who lived in proximity (within 2 km) to any agricultural pesticide application recorded in the California Pesticide Use Reporting (PUR) system. We focused on 23 pesticides considered endocrine disruptors that are frequently used, and we found that exposure to any of the 23 pesticides in the first trimester was associated with elevated CP risks in female offspring (OR = 1.19; 95% CI: 1.05–1.35) but not males (OR = 0.99; 95% CI: 0.89–1.09) compared to the unexposed offspring. Positive associations were estimated for 15 pesticides suspected to affect the estrogen and 7 pesticides suspected to affect the thyroid hormone system. Our study suggests that first trimester exposure to pesticides that are suspected endocrine disruptors are associated with CP risk in female offspring. Pesticide exposures in early pregnancy may have sex-specific influences on the neuro-motor development of the fetus by interfering with endocrine systems.

OBJECTIVES: We investigated whether working night shifts was associated with the risk of Parkinson’s disease (PD). METHODS: Between January 2008 and December 2010, we recruited 1808 patients with a confirmed diagnosis of idiopathic PD from Denmark and 1876 population controls matched by year of birth and gender. Information on lifelong occupational history, including information on night work, smoking, caffeine and alcohol consumption habits, and family history of PD was collected through structured telephone interviews. RESULTS: Overall, there was no association between a history of night shift work and PD [odds ratio (OR) for any type of night work (ie, either permanent or rotating night work) 1.01, 95% confidence interval (95% CI) 0.86–1.21]. Compared with persons who never worked night shifts, risks of those with longer durations of night work did not appear to differ (OR_

Pathologic features of Parkinson's disease (PD) include death of dopaminergic neurons in the substantia nigra, presence of α-synuclein containing Lewy bodies, and iron accumulation in PD-related brain regions. The observed iron accumulation may be contributing to PD etiology but it also may be a byproduct of cell death or cellular dysfunction. To elucidate the possible role of iron accumulation in PD, we investigated genetic variation in 16 genes related to iron homeostasis in three case–control studies from the United States, Australia, and France. After screening 90 haplotype tagging single nucleotide polymorphisms (SNPs) within the genes of interest in the US study population, we investigated the five most promising gene regions in two additional independent case–control studies. For the pooled data set (1289 cases, 1391 controls) we observed a protective association (OR = 0.83, 95% CI: 0.71–0.96) between PD and a haplotype composed of the A allele at rs1880669 and the T allele at rs1049296 in transferrin (TF; GeneID: 7018). Additionally, we observed a suggestive protective association (OR = 0.87, 95% CI: 0.74-1.02) between PD and a haplotype composed of the G allele at rs10247962 and the A allele at rs4434553 in transferrin receptor 2 (TFR2; GeneID: 7036). We observed no associations in our pooled sample for haplotypes in SLC40A1, CYB561, or HFE. Taken together with previous findings in model systems, our results suggest that TF or a TF-TFR2 complex may have a role in the etiology of PD, possibly through iron misregulation or mitochondrial dysfunction within dopaminergic neurons.

BackgroundRetinoblastoma is the most frequent tumor of the eye in children and very little is known about the etiology of non-familial (sporadic) retinoblastoma. In this study we examined whether parental tobacco smoking or alcohol consumption (pre- or post-conception) contribute to the two phenotypes (bilateral or unilateral) of sporadic retinoblastoma.MethodsTwo large multicenter case-control studies identified 488 cases through eye referral centers in the United States and Canada or through the Children's Oncology Group. Controls (n = 424) were selected from among friends and relatives of cases and matched by age. Risk factor information was obtained via telephone interview. We employed multivariable logistic regression to estimate the effects of parental tobacco smoking and alcohol consumption on retinoblastoma.FindingsMaternal smoking before and during pregnancy contributed to unilateral retinoblastoma risk in the child: year before pregnancy conditional Odds Ratio (OR), 8.9; 95% confidence interval (CI) 1.5-51, and unconditional OR, 2.4; 95% CI, 1.3-4.7; month before or during pregnancy, conditional OR, 3.3; 95% CI, 0.5-20.8, and unconditional OR, 2.8; 95% CI, 1.1-7.0. No association was found for maternal or paternal alcohol consumption.ConclusionThe results of this study indicate that maternal active smoking during pregnancy may be a risk factor for sporadic retinoblastoma. Our study supports a role for tobacco exposures in embryonal tumors.

Findings from studies of prenatal exposure to pesticides and adverse birth outcomes have been equivocal so far. We examined prenatal exposure to agricultural pesticides in relation to preterm birth and term low birthweight, respectively, in children born between 1998 and 2010, randomly selected from California birth records. We estimated residential exposure to agriculturally applied pesticides within 2 km of residential addresses at birth by pregnancy trimester for 17 individual pesticides and three chemical classes (organophosphates, pyrethroids, and carbamates). Among maternal addresses located within 2 km of any agricultural pesticide application, we identified 24,693 preterm and 220,297 term births, and 4412 term low birthweight and 194,732 term normal birthweight infants. First or second trimester exposure to individual pesticides (e.g., glyphosates, paraquat, imidacloprid) or exposure to 2 or more pesticides in the three chemical classes were associated with a small increase (3–7%) in risk for preterm birth; associations were stronger for female offspring. We did not find associations between term low birthweight and exposure to pesticides other than myclobutanil (OR: 1.11; 95% CI: 1.04–1.20) and possibly the pyrethroids class. Our improved exposure assessment revealed that first and second trimester exposure to pesticides is associated with preterm delivery but is rarely linked with term low birthweight.

Parkinson's disease (PD) is acknowledged as the second most common neurodegenerative disorder after Alzheimer's Disease. Older age may be the only unequivocal risk factor for PD although the male to female ratio is consistently greater than 1 in populations of European ancestry. Characteristic features of PD include dopaminergic neuron death in the substantia nigra (SN) pars compacta, accumulation of α-synuclein inclusions known as Lewy bodies in the SN, and brain iron accumulation beyond that observed in non-PD brains of a similar age. In this review article, we will provide an overview of human and animal studies investigating the contributions of iron in PD, a summary of human studies of iron-related genes in PD, a review of the literature on the genetics of iron metabolism, and some hypotheses on possible roles for iron in the pathogenic processes of PD including potential interactions between iron and other factors associated with Parkinson's disease.

Background. The electronic medical records provide new and unprecedented opportunities for large population-based and clinical studies if valid and reliable diagnoses can be obtained, to determine what information is needed to distinguish idiopathic PD from Parkinsonism in electronic medical records. Methods. Chart review of complete medical records of 2,446 patients with a hospital discharge diagnosis of PD, who, between 1996 and 2009, were registered in the Danish National Hospital Register as idiopathic PD. All patients were examined in neurology departments. Clinical features were abstracted from charts to determine Parkinsonian phenotypes and disease course, using predefined criteria for idiopathic PD. Results. Chart review verified that 2,068 (84.5%) patients met criteria for idiopathic PD. The most distinguishing features of idiopathic PD patients were asymmetric onset, and fewer atypical features at onset or follow-up compared to Parkinsonism, and the area under the curve (AUC) for these items alone is moderate (0.74–0.77) and the highest AUC (0.91) was achieved when using all clinical features recorded in addition to PD medication use and a follow-up of 5 years or more. Conclusion. To reduce disease misclassification, information extracted from medical record review with at least 5 years of follow-up after first diagnosis was key to improve diagnostic accuracy.

Progression of Parkinson's disease (PD) is highly variable, indicating that differences between slow and rapid progression forms could provide valuable information for improved early detection and management. Unfortunately, this represents a complex problem due to the heterogeneous nature of humans in regards to demographic characteristics, genetics, diet, environmental exposures and health behaviors. In this pilot study, we employed high resolution mass spectrometry-based metabolic profiling to investigate the metabolic signatures of slow versus rapidly progressing PD present in human serum. Archival serum samples from PD patients obtained within 3 years of disease onset were analyzed via dual chromatography-high resolution mass spectrometry, with data extraction by xMSanalyzer and used to predict rapid or slow motor progression of these patients during follow-up. Statistical analyses, such as false discovery rate analysis and partial least squares discriminant analysis, yielded a list of statistically significant metabolic features and further investigation revealed potential biomarkers. In particular, N8-acetyl spermidine was found to be significantly elevated in the rapid progressors compared to both control subjects and slow progressors. Our exploratory data indicate that a fast motor progression disease phenotype can be distinguished early in disease using high resolution mass spectrometry-based metabolic profiling and that altered polyamine metabolism may be a predictive marker of rapidly progressing PD.

Currently, there are no reported genetic predictors of motor symptom progression in Parkinson's disease (PD). In familial PD, disease severity is associated with higher α-synuclein (SNCA) expression levels, and in postmortem studies expression varies with SNCA genetic variants. Furthermore, SNCA is a well-known risk factor for PD occurrence. We recruited Parkinson's patients from the communities of three central California counties to investigate the influence of SNCA genetic variants on motor symptom progression in idiopathic PD. We repeatedly assessed this cohort of patients over an average of 5.1 years for motor symptom changes employing the Unified Parkinson's Disease Rating Scale (UPDRS). Of 363 population-based incident PD cases diagnosed less than 3 years from baseline assessment, 242 cases were successfully re-contacted and 233 were re-examined at least once. Of subjects lost to follow-up, 69% were due to death. Adjusting for covariates, risk of faster decline of motor function as measured by annual increase in motor UPDRS exam score was increased 4-fold in carriers of the REP1 263bp promoter variant (OR 4.03, 95%CI:1.57-10.4). Our data also suggest a contribution to increased risk by the G-allele for rs356165 (OR 1.66; 95%CI:0.96-2.88), and we observed a strong trend across categories when both genetic variants were considered (p for trend = 0.002). Our population-based study has demonstrated that SNCA variants are strong predictors of faster motor decline in idiopathic PD. SNCA may be a promising target for therapies and may help identify patients who will benefit most from early interventions. This is the first study to link SNCA to motor symptom decline in a longitudinal progression study.

Our aim was to identify genes that influence the inverse association of coffee with the risk of developing Parkinson's disease (PD). We used genome-wide genotype data and lifetime caffeinated-coffee-consumption data on 1,458 persons with PD and 931 without PD from the NeuroGenetics Research Consortium (NGRC), and we performed a genome-wide association and interaction study (GWAIS), testing each SNP's main-effect plus its interaction with coffee, adjusting for sex, age, and two principal components. We then stratified subjects as heavy or light coffee-drinkers and performed genome-wide association study (GWAS) in each group. We replicated the most significant SNP. Finally, we imputed the NGRC dataset, increasing genomic coverage to examine the region of interest in detail. The primary analyses (GWAIS, GWAS, Replication) were performed using genotyped data. In GWAIS, the most significant signal came from rs4998386 and the neighboring SNPs in GRIN2A. GRIN2A encodes an NMDA-glutamate-receptor subunit and regulates excitatory neurotransmission in the brain. Achieving P(2df) = 10(-6), GRIN2A surpassed all known PD susceptibility genes in significance in the GWAIS. In stratified GWAS, the GRIN2A signal was present in heavy coffee-drinkers (OR = 0.43; P = 6×10(-7)) but not in light coffee-drinkers. The a priori Replication hypothesis that "Among heavy coffee-drinkers, rs4998386_T carriers have lower PD risk than rs4998386_CC carriers" was confirmed: OR(Replication) = 0.59, P(Replication) = 10(-3); OR(Pooled) = 0.51, P(Pooled) = 7×10(-8). Compared to light coffee-drinkers with rs4998386_CC genotype, heavy coffee-drinkers with rs4998386_CC genotype had 18% lower risk (P = 3×10(-3)), whereas heavy coffee-drinkers with rs4998386_TC genotype had 59% lower risk (P = 6×10(-13)). Imputation revealed a block of SNPs that achieved P(2df)