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bioRxiv2018

Back pain, mental health and substance use are associated in adolescents

BackgroundDuring adolescence, prevalence of pain and health risk factors such as smoking, alcohol use, and poor mental health rise sharply. While these risk factors and mental health are accepted public health concerns, the same is not true for pain. The aim of this study was to describe the relationship between back pain and health risk factors in adolescents.\n\nMethodsCross-sectional data from the Healthy Schools Healthy Futures study, and the Australian Child Wellbeing Project was used. The mean age of participants was 14-15 years. Children were stratified according to the frequency they experienced back pain over the past 6 months. Within each strata, the proportion of children that reported drinking alcohol or smoking in the past month and the proportion that experienced feelings of anxiety or depression was reported. Test-for-trend analyses assessed whether increasing frequency of pain was associated with health risk factors.\n\nResultsData from approximately 2,500 and 3,900 children in the two studies was analysed. Larger proportions of children smoked or drank alcohol within each strata of increasing pain frequency. The trend with report of anxiety and depression was less clear, although there was a marked difference between the children that reported pain rarely or never, and those that experienced back pain more frequently.\n\nConclusionTwo large, independent samples show Australian adolescents that experience back pain more frequently are also more likely to smoke, drink alcohol and report feelings of anxiety and depression. Pain appears to be part of the picture of general health risk in adolescents.\n\nWhat is already known on this subject?The prevalence of back pain rises steeply during the adolescent years, and is responsible for considerable personal impact in a substantial minority. During this time, indicators of adverse health risk such as smoking, alcohol use, anxiety and depression also increase in prevalence. Pain and lifestyle-related health risk factors can have ongoing consequences that stretch into adulthood.\n\nWhat this study adds?This study shows a close relationship between increasing pain frequency, and tendency to engage in health risk behaviours and experience indicators of poor mental health in adolescents. This study shows that pain may be an important consideration in understanding the general health, and health risk in adolescents.

epidemiology↗

bioRxiv2018

What lies beneath: a spatial mosaic of Zika virus transmission in the 2015-2016 epidemic in Colombia

Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015-2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R0. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.

epidemiology↗

bioRxiv2018

An epigenetic biomarker of aging for lifespan and healthspan

Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using a innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimers disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.

epidemiology↗

bioRxiv2018

Interaction of diabetes and smoking on stroke: A population-based cross-sectional survey in China

ObjectivesDiabetes and smoking are known independent risk factors for stroke; however, their interaction concerning stroke is less clear. We aimed to explore such interaction and its influence on stroke in Chinese adults.\n\nDesignCross-sectional study.\n\nSettingCommunity-based investigation in Xuzhou, China.\n\nParticipantsA total of 39,887 Chinese adults who fulfilled the inclusion criteria were included.\n\nMethodsParticipants were selected using a multi-stage stratified cluster method, and completed self-reported questionnaires on stroke and smoking. Type 2 diabetes mellitus (DM2) was assessed by fasting blood glucose or use of antidiabetic medication. Interaction, relative excess risk owing to interaction (RERI), attributable proportion (AP), and synergy index (S) were evaluated using a logistic regression model.\n\nResultsAfter adjustment for age, sex, marital status, educational level, occupation, physical activity, body mass index, hypertension, family history of stroke, alcohol use, and blood lipids, the relationships between DM2 and stroke, and between smoking and stroke, were still significant: odds ratios were 2.75 (95% confidence interval [CI]: 2.03-3.73) and 1.70 (95% CI: 1.38-2.10), respectively. In subjects with DM2 who smoked, the RERI, AP, and S values (and 95% CIs) were 1.80 (1.24-3.83), 0.52 (0.37-0.73), and 1.50 (1.18-1.84), respectively.\n\nConclusionsThe results suggest there are additive interactions between DM2 and smoking and that these affect stroke in Chinese adults.\n\nArticle Summary: Strengths and limitations of this studyO_LIThe strengths of this study were that a large sample population was randomly selected from the general population of Xuzhou and many confounding risk factors were adjusted for.\nC_LIO_LIOwing to the cross-sectional design, we could not determine a causal combined relationship among diabetes, smoking and stroke.\nC_LIO_LIWe were not able to control for some important and well-known risk factors of diabetes, such as heart rate and cardiovascular causes.\nC_LIO_LIWe did not measure fresh fruit consumption, which is causally related to stroke.\nC_LI

epidemiology↗

bioRxiv2018

An epigenetic score for BMI based on DNA methylation correlates with poor physical health and major disease in the Lothian Birth Cohort 1936.

BackgroundThe relationship between obesity and adverse health is well established, but little is known about the contribution of DNA methylation to obesity-related health outcomes. Additionally, it is of interest whether such contributions are independent of those attributed by the most widely used clinical measure of body mass - the Body Mass Index (BMI).\n\nMethodWe tested whether an epigenetic BMI score accounts for inter-individual variation in health-related, cognitive, psychosocial and lifestyle outcomes in the Lothian Birth Cohort 1936 (n=903). Weights for the epigenetic BMI score were derived using penalised regression on methylation data from unrelated Generation Scotland participants (n=2566).\n\nResultsThe Epigenetic BMI score was associated with variables related to poor physical health (R2 ranges from 0.02-0.10), metabolic syndrome (R2 ranges from 0.01-0.09), lower crystallised intelligence (R2=0.01), lower health-related quality of life (R2=0.02), physical inactivity (R2=0.02), and social deprivation (R2=0.02). The epigenetic BMI score (per SD) was also associated with self-reported type 2 diabetes (OR 2.25, 95 % CI 1.74, 2.94), cardiovascular disease (OR 1.44, 95 % CI 1.23, 1.69) and high blood pressure (OR 1.21, 95% CI 1.13, 1.48; all at p<0.0011 after Bonferroni correction).\n\nConclusionsOur results show that regression models with epigenetic and phenotypic BMI scores as predictors account for a greater proportion of all outcome variables than either predictor alone, demonstrating independent and additive effects of epigenetic and phenotypic BMI scores.

epidemiology↗

bioRxiv2018

Optimizing Disease Surveillance by Reporting on the Blockchain

Disease surveillance, especially for infectious diseases, is a complex and inefficient process. Here we propose an optimized, blockchain-based monitoring and reporting process which can achieve all the desired features of an ideal surveillance system while maintaining costs down and being transparent and robust. We describe the technical specifications of such a solution and discuss possibilities for its implementation. Finally, the impact of the adoption of distributed ledger technology for disease surveillance is discussed.

epidemiology↗

bioRxiv2018

Metabolomic consequences of genetic inhibition of PCSK9 compared with statin treatment

BackgroundBoth statins and PCSK9 inhibitors lower blood low-density lipoprotein cholesterol (LDL-C) levels to reduce risk of cardiovascular events. To assess potential differences between metabolic effects of these two lipid-lowering therapies, we performed detailed lipid and metabolite profiling of a large randomized statin trial, and compared the results with the effects of genetic inhibition of PCSK9, acting as a naturally occurring trial.\n\nMethods228 circulating metabolic measures were quantified by nuclear magnetic resonance spectroscopy, including lipoprotein subclass concentrations and their lipid composition, fatty acids, and amino acids, for 5,359 individuals (2,659 on treatment) in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial at 6-months post-randomization. The corresponding metabolic measures were analyzed in eight population cohorts (N=72,185) using PCSK9 rs11591147 as an unconfounded proxy to mimic the therapeutic effects of PCSK9 inhibitors.\n\nResultsScaled to an equivalent lowering of LDL-C, the effects of genetic inhibition of PCSK9 on 228 metabolic markers were generally consistent with those of statin therapy (R2=0.88). Alterations in lipoprotein lipid composition and fatty acid balance were similar. However, discrepancies were observed for very-low-density lipoprotein (VLDL) lipid measures. For instance, genetic inhibition of PCSK9 showed weaker effects on lowering of VLDL-cholesterol compared with statin therapy (54% vs. 77% reduction, relative to the lowering effect on LDL-C; P=2 x 10-7 for heterogeneity). Genetic inhibition of PCSK9 showed no robust effects on amino acids, ketones, and a marker of inflammation (GlycA); in contrast, statin treatment lowered GlycA levels.\n\nConclusionsGenetic inhibition of PCSK9 results in similar metabolic effects as statin therapy across a detailed lipid and metabolite profile. However, for the same lowering of LDL-C, PCSK9 inhibitors are predicted to be less efficacious than statins at lowering VLDL lipids, which could potentially translate into subtle differences in cardiovascular risk reduction.

epidemiology↗

bioRxiv2018

Boosting Diabetes and Pre-Diabetes Screening in Rural Ghana via Mobile Phones Apps

BackgroundDiabetes is a growing worldwide disease with serious consequences to health and high financial burden. Ghana is one of the developing African countries where the prevalence of diabetes is increasing. Moreover, many cases remained undiagnosed, when along with per-diabetic cases they can be easily detected. Pre-diabetes condition occurs when blood sugar levels are higher than normal but are not high enough to be classified as diabetes, and it is still reversible.\n\nMethodsThis study proposes a novel method to increase diabetes and pre-diabetes detection, and to find new behavioral determinants related in rural Ghana. The screening approach was based on tests performed pro-actively by community nurses using glucometers and mobile phone apps. As a pilot for future policies, those glycemic tests were carried out on 101 subjects from rural communities in Ghana deemed at risk and unaware of their diabetic/pre-diabetic status. A comparison of dietary and lifestyle habits of the screened people was conducted in regards to a cohort of 103 diabetic patients from the same rural communities.\n\nResultsThe pilot screening detected 2 diabetic subjects (2% of the cohort) showing WHO diabetic glycemic values, and 20 pre-diabetic subjects (19.8% of the cohort) which showed the effectiveness of the user-friendliness approach. The need of further campaigns on alcohol consumption and physical activities has emerged even for the rural areas.\n\nConclusionsPolicies based on prevention screening as reported in the manuscript have the potential to reduce diabetes incidence and its related health-care costs in the country.\n\nTrial registrationNoguchi Memorial Institute for Medical Research-IRB Study Number: 076/13-14 registered on 20.02.2017

epidemiology↗

bioRxiv2018

Clusters of fatty acids in the serum triacylglyceride fraction associate with the disorders of type 2 diabetes

AimsOur aim was to examine longitudinal associations of triacylglyceride fatty acid (TGFA) composition with insulin sensitivity (IS) and beta-cell function.\n\nMethodsAdults at-risk for T2D (n=477) had glucose and insulin measured from a glucose challenge at 3 time points over 6 years. The outcome variables Matsuda index (ISI), HOMA2-%S, Insulinogenic Index over HOMA-IR (IGI/IR), and Insulin Secretion-Sensitivity Index-2 (ISSI-2) were computed from the glucose challenge. Gas chromatography quantified TGFA composition from the baseline. We used adjusted generalized estimating equations (GEE) models and partial least squares (PLS) regression for the analysis.\n\nResultsIn adjusted GEE models, four TGFA (14:0, 16:0, 14:1n-7, 16:1n-7 as mol%) had strong negative associations with IS while others (e.g. 18:1n-7, 18:1n-9, 20:2n-6, 20:5n-3) had strong positive associations. Few associations were seen for beta-cell function, except for 16:0, 18:1n-7, and 20:2n-6. PLS analysis indicated four TGFA (14:0, 16:0, 14:1n-7, 16:1n-7) that clustered together and strongly related with lower IS. These four TGFA also correlated highly (r>0.4) with clinically measured TG.\n\nConclusionsWe found that higher proportions of a cluster of four TGFA strongly related with lower IS as well as hypertriglyceridemia, suggesting only a few fatty acids within the TGFA composition may primarily explain lipids role in glucose dysregulation.

epidemiology↗

bioRxiv2018

National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB): Outline and Patient-Matching Technique

BackgroundThe National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) is a comprehensive database of health insurance claims data under Japans National Health Insurance system. The NDB uses two types of personal identification variables (referred to in the database as \"ID1\" and \"ID2\") to link the insurance claims of individual patients. However, the information entered against these ID variables is prone to change for several reasons, such as when claimants find or change employment, or due to variations in the spelling of their name. In the present study, we developed a new patient-matching technique that improves upon the existing system of using ID1 and ID2 variables. We also sought to validate a new personal ID variable (ID0) that we propose in order to enhance the efficiency of patient matching in the NDB database.\n\nMethodsOur study targeted data from health insurance claims filed between April 2013 and March 2016 for hospitalization, combined diagnostic procedures, outpatient treatment, and dispensing of prescription medication. We developed a new patient-matching algorithm based on the ID1 and ID2 variables, as well as variables for treatment date and clinical outcome. We then attempted to validate our algorithm by comparing the number of patients identified by patient matching with the current ID1 variable and our proposed ID0 variable against the estimated patient population as of 1 October 2015.\n\nResultsThe numbers of patients in each sex and age group that were identified with the ID0 variable were lower than those identified using the ID1 variable. By using the ID0 variable, we were able to reduce the number of duplicate records for male and female patients by 5.8% and 6.4%, respectively. The numbers of children, adults older than 75 years, and women of reproductive age identified using the ID1 patient-matching variable were all higher than their corresponding estimates. Conversely, the numbers of these patients identified with the ID0 patient-matching variable were all within their corresponding estimates.\n\nConclusionOur findings show that the proposed ID0 variable delivers more precise patient-matching results than the existing ID1 variable. The ID0 variable is currently the best available technique for patient matching in the NDB database. Future patient population estimates should therefore rely on the ID0 variable instead of the ID1 variable.

epidemiology↗

bioRxiv2018

The landscape of incident disease risk for the biomarker GlycA and its mortality stratification in angiography patients

Integration of systems-level biomolecular information with electronic health records has led to the discovery of robust blood-based biomarkers predictive of future health and disease. Of recent intense interest is the GlycA biomarker, a complex nuclear magnetic resonance (NMR) spectroscopy signal reflective of acute and chronic inflammation, which predicts long term risk of diverse outcomes including cardiovascular disease, type 2 diabetes, and all-cause mortality. To systematically explore the specificity of the disease burden indicated by GlycA we analysed the risk for 468 common incident hospitalization and mortality outcomes occurring during an 8-year follow-up of 11,861 adults from Finland. Our analyses of GlycA replicated known associations, identified associations with specific cardiovascular disease outcomes, and uncovered new associations with risk of alcoholic liver disease (meta-analysed hazard ratio 2.94 per 1-SD, P=5x10-6), chronic renal failure (HR=2.47, P=3x10-6), glomerular diseases (HR=1.95, P=1x10-6), chronic obstructive pulmonary disease (HR=1.58, P=3x10-5), inflammatory polyarthropathies (HR=1.46, P=4x10-8), and hypertension (HR=1.21, P=5x10-5). We further evaluated GlycA as a biomarker in secondary prevention of 12-year cardiovascular mortality in 900 angiography patients with suspected coronary artery disease. We observed hazard ratios of 4.87 and 5.00 for 12-year mortality in angiography patients in the fourth and fifth quintiles by GlycA levels demonstrating the prognostic potential of GlycA for identification of high mortality-risk individuals. Both GlycA and C-reactive protein had shared as well as independent contributions to mortality hazard, emphasising the importance of chronic inflammation in secondary prevention of cardiovascular disease.

epidemiology↗

bioRxiv2018

Body mass index and mortality in UK Biobank: revised estimates using Mendelian randomization

ObjectiveObtain estimates of the causal relationship between different levels of body mass index (BMI) and mortality.\n\nMethodsMendelian randomization (MR) was conducted using genotypic variation reliably associated with BMI to test the causal effect of increasing BMI on all-cause and cause-specific mortality in participants of White British ancestry in UK Biobank.\n\nResultsMR analyses supported existing evidence for a causal association between higher levels of BMI and greater risk of all-cause mortality (hazard ratio (HR) per 1kg/m2: 1.02; 95% CI: 0.97,1.06) and mortality from cardiovascular diseases (HR: 1.12; 95% CI: 1.02, 1.23), specifically coronary heart disease (HR: 1.19; 95% CI: 1.05, 1.35) and those other than stroke/aortic aneurysm (HR: 1.13; 95% CI: 0.93, 1.38), stomach cancer (HR: 1.30; 95% CI: 0.91, 1.86) and oesophageal cancer (HR: 1.08; 95% CI: 0.84, 1.38), and with decreased risk of lung cancer mortality (HR: 0.97; 95% CI: 0.84, 1.11). Sex-stratified analyses supported a causal role of higher BMI in increasing the risk of mortality from bladder cancer in males and other causes in females, but in decreasing the risk of respiratory disease mortality in males. The characteristic J-shaped observational association between BMI and mortality was visible with MR analyses but with a smaller value of BMI at which mortality risk was lowest and apparently flatter over a larger range of BMI.\n\nConclusionResults support a causal role of higher BMI in increasing the risk of all-cause mortality and mortality from other causes. However, studies with greater numbers of deaths are needed to confirm the current findings.

epidemiology↗

bioRxiv2018

Mechanisms for European Bat Lyssavirus subtype 1 persistence in non-synanthropic bats: insights from a modeling study

BackgroundLyssaviruses are pathogens of bat origin of considerable zoonotic concern being the causative agent for rabies disease, however our understanding of their persistence in bat populations remains very scarce.\n\nMethodsLeveraging existing data from an extensive ecological field survey characterizing Myotis myotis and Miniopterus schreibersii bat species in the Catalonia region, we develop a data-driven spatially explicit metapopulation model to identify the mechanisms of the empirically observed persistence of European Bat Lyssavirus subtype 1 (EBLV-1), the most common lyssavirus species found in Europe. We consider different disease progressions accounting for lethal infection, immunity waning, and potential cross-species transmission when the two populations share the same refuge along the migratory path of M. schreibersii.\n\nResultsWe find that EBLV-1 persistence relies on host spatial structure through the migratory nature of M. schreibersii bats, on cross-species mixing with M. myotis population, and on a disease progression leading to survival of infected animals followed by temporary immunity. The higher fragmentation along the northern portion of the migratory path is necessary to maintain EBLV-1 sustained circulation in both species, whereas persistence would not be ensured in the single colony of M. myotis. Our study provides first estimates for the EBLV-1 transmission potential in M. schreibersii bats and average duration of immunity in the host species, yielding values compatible with previous empirical observations in M. myotis bats.\n\nConclusionsHabitats sharing and the strong spatial component of EBLV-1 transmission dynamics identified as key drivers in this ecological context may help understanding the observed spatial diffusion of the virus at a larger scale and across a diverse range of host species, through long-range migration and seeding of local populations. Our approach can be readily adapted to other zoonotic pathogens of public health concern.

epidemiology↗

bioRxiv2018

Hierarchical modeling of the effect of pre-exposure prophylaxis on HIV in the US

1.AO_SCPLOWBSTRACTC_SCPLOWIn this paper we present a differential equation model stratified by behavioral risk and sexual activity. Some susceptible individuals have higher rates of risky behavior that increase their chance of contracting the disease. Infected individuals can be considered to be generally sexually active or inactive. The sexually active infected population is at higher risk of transmitting the disease to a susceptible individual. We further divide the sexually active population into diagnosed or undiagnosed infected individuals. We define model parameters for both the national and the urban case. These parameter sets are used to study the predicted population dynamics over the next 5 years. Our results indicate that the undiagnosed high risk infected group is the largest contributor to the epidemic. Finally, we apply a preventative medication protocol to the susceptible population and observe the effective reduction in the infected population. The simulations suggest that preventative medication effectiveness extends outside of the group that is taking the drug (herd immunity). Our models suggest that a strategy targeting the high risk undiagnosed infected group would have the largest impact in the next 5 years. We also find that such a protocol has similar effects for the national as the urban case, despite the smaller sexual network found in rural areas.

epidemiology↗

bioRxiv2018

Appraising the causal relevance of DNA methylation for risk of lung cancer

DNA methylation changes in peripheral blood have been identified in relation to lung cancer risk. However, the causal nature of these associations remains to be fully elucidated. Meta-analysis of four epigenome-wide association studies (918 cases, 918 controls) revealed differential methylation at 16 CpG sites (FDR < 0.05) in relation to lung cancer risk. A two-sample Mendelian randomization analysis, using genetic instruments for methylation at 14 of the 16 CpG sites, and 29,863 cases and 55,586 controls from the TRICL-ILCCO lung cancer consortium, was performed to appraise the causal role of methylation at these sites on lung cancer. This approach provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites play a causal role in lung cancer development, including for cg05575921 AHRR, where methylation is strongly associated with lung cancer risk. Further studies are needed to investigate the causal role played by DNA methylation in lung tissue.

epidemiology↗

bioRxiv2018

The median and the mode as robust meta-analysis methods in the presence of small study effects

Meta-analyses based on systematic literature reviews are commonly used to obtain a quantitative summary of the available evidence on a given topic. Despite its attractive simplicity, and its established position at the summit of the evidence-based medicine hierarchy, the reliability of any meta-analysis is largely constrained by the quality of its constituent studies. One major limitation is small study effects, whose presence can often easily be detected, but not so easily adjusted for. Here, robust methods of estimation based on the median and mode are proposed as tools to increase the reliability of findings in a meta-analysis. By re-examining data from published meta-analyses, and by conducting a detailed simulation study, we show that these two simple methods offer notable robustness to a range of plausible bias mechanisms, without making any explicit modelling assumptions. In conclusion, when performing a meta-analysis with suspected small study effects, we recommend reporting the mean, median and modal pooled estimates as a simple but informative sensitivity analyses.

epidemiology↗

bioRxiv2018

Tendency towards being a “Morning person” increases risk of Parkinson’s disease: evidence from Mendelian randomisation

BackgroundCircadian rhythm may play a role in neurodegenerative diseases such as Parkinsons disease (PD). Chronotype is the behavioural manifestation of circadian rhythm and Mendelian randomisation (MR) involves the use of genetic variants to explore causal effects of exposures on outcomes. This study aimed to explore a causal relationship between chronotype and coffee consumption on risk of PD.\n\nMethodsTwo-sample MR was undertaken using publicly available GWAS data. Associations between genetic instrumental variables (IV) and \"morning person\" (one extreme of chronotype) were obtained from the personal genetics company 23andMe, Inc., and UK Biobank, and consisted of the per-allele odds ratio of being a \"morning person\" for 15 independent variants. The per-allele difference in log-odds of PD for each variant was estimated from a recent meta-analysis. The inverse variance weight method was used to estimate an odds ratio (OR) for the effect of being a \"morning person\" on PD. Additional MR methods were used to check for bias in the IVW estimate, arising through violation of MR assumptions. The results were compared to analyses employing a genetic instrument of coffee consumption, because coffee consumption has been previously inversely linked to PD.\n\nFindingsBeing a \"morning person\" was causally linked with risk of PD (OR 1*27; 95% confidence interval 1*06-1*51; p=0*012). Sensitivity analyses did not suggest that invalid instruments were biasing the effect estimate and there was no evidence for a reverse causal relationship between liability for PD and chronotype. There was no robust evidence for a causal effect of high coffee consumption using IV analysis, but the effect was imprecisely estimated (OR 1*12; 95% CI 0*89-1*42; p=0*22).\n\nInterpretationWe observed causal evidence to support the notion that being a \"morning person\", a phenotype driven by the circadian clock, is associated with a higher risk of PD. Further work on the mechanisms is warranted and may lead to novel therapeutic targets.\n\nFundingNo specific funding source.

epidemiology↗

bioRxiv2018

Estimating the proportion of bystander selection for antibiotic resistance in the US

Bystander selection -- the selective pressures exerted by antibiotics on microbial flora that are not the target pathogen of treatment -- is critical to understanding the total impact of broad-spectrum antibiotic use; however, to our knowledge, this effect has never been quantified. Using the 2010-2011 National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey (NAMCS/NHAMCS), the Human Microbiome Project, and additional carriage and etiological data from existing literature, we estimate the magnitude of bystander selection for a range of clinically relevant antibiotic-species pairs as the proportion of all exposures of an antibiotic experienced by a species for conditions in which that species was not the causative pathogen (\"proportion of bystander exposures\"). For outpatient prescribing in the United States, we find that this proportion over all included antibiotics is over 80% for 8 out of 9 organisms of interest. Low proportions of bystander exposure are often associated with infrequent bacterial carriage or a high proportion of antibiotic prescribing focused on conditions caused by the species of interest. Using the proportion of bystander exposures, we roughly estimate that S. aureus and E. coli may benefit from 90.7% and 99.7%, respectively, of the estimated reduction in antibiotic use due to pneumococcal conjugate vaccination, despite not being the pathogen targeted by the vaccine. These results underscore the importance of considering antibiotic exposures to bystanders, in addition to the targeted pathogen, in measuring the impact of antibiotic resistance interventions.\n\nSignificance StatementThe forces that contribute to changing population prevalence of antibiotic resistance are not well understood. Bystander selection -- the inadvertent pressures imposed by antibiotics on the microbial flora other than the pathogen targeted by treatment -- is hypothesized to be a major factor in the propagation of antibiotic resistance, but its extent has not been characterized. We estimate the proportion of bystander exposures across a range of antibiotics and organisms and describe factors driving variability of these proportions. Impact estimates for antibiotic resistance interventions, including vaccination, are often limited to effects on a target pathogen. However, the reduction of antibiotic treatment for illnesses caused by the target pathogen may have the broader potential to decrease bystander selection pressures for resistance on many other organisms.

epidemiology↗