A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey

Abstract

The National Health and Nutrition Examination Survey (NHANES) is a population survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of the United States whose data is publicly available in hundreds of files. This Data Descriptor describes a single unified and universally accessible data file, merging across 255 separate files and stitching data across 4 surveys, encompassing 41,474 individuals and 1,191 variables. The variables consist of phenotype and environmental exposure information on each individual, specifically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and (4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts find variables by category and human-readable description. The datasets are available on DataDryad and a hands-on analytics tutorial is available on GitHub. Through a new big data platform, BD2K Patient Centered Information Commons (http://pic-sure.org), we provide a new way to browse the dataset via a web browser (https://nhanes.hms.harvard.edu) and provide application programming interface for programmatic access.

Data Descriptor: A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey

OPEN

Received: 06 June 2016

Accepted: 26 September 2016

Published: 25 October 2016

Chirag J. Patel1, Nam Pho1, Michael McDufe1, Jeremy Easton-Marks1, Cartik Kothari1, Isaac S. Kohane1 & Paul Avillach1

The National Health and Nutrition Examination Survey (NHANES) is a population survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of the United States whose data is publicly available in hundreds of les. This Data Descriptor describes a single unied and universally accessible data le, merging across 255 separate les and stitching data across 4 surveys, encompassing 41,474 individuals and 1,191 variables. The variables consist of phenotype and environmental exposure information on each individual, specically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and(4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts nd variables by category and human-readable description. The datasets are available on DataDryad and a hands-on analytics tutorial is available on GitHub. Through a new big data platform, BD2K Patient Centered Information Commons (http://pic-sure.org

Web End =http://pic-sure.org), we provide a new way to browse the dataset via a web browser (https://nhanes.hms.harvard.edu

Web End =https://nhanes.hms.harvard.edu) and provide application programming interface for programmatic access.

Design Type(s) source-based data transformation objective

Measurement Type(s) database creation objectiveTechnology Type(s) computational methodFactor Type(s)

Sample Characteristic(s) Homo sapiens United States of America

1Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St., Boston, Massachusetts 02115,

USA. Correspondence and requests for materials should be addressed to C.J.P. (email: mailto:[email protected]

Web End =chirag_patel@hms.

mailto:[email protected]

Web End =harvard.edu ).

SCIENTIFIC DATA | 3:160096 | DOI: 10.1038/sdata.2016.96 1

Background & Summary

United States health agencies, including the United States Centers for Disease Control and Prevention (CDC), have made a signicant investment in monitoring the health of the population through surveys such as the National Health and Nutrition Examination Survey (NHANES). These datasets provide individual-level health-related measures in a large and representative sample of the United States (e.g., from 19992006, N = 41,474). For example, these data are instrumental in providing prevalence of disease-related factors, such as diabetes and obesity (e.g., refs 13), drug use4, and present reference intervals for child growth, such as head circumference. These data have helped to shape public health policy. For example, these data were used to demonstrate the effect of removal of lead from gasoline (a gross decrease since legislation). Many have used these data to create hypotheses regarding associations between biomarkers of environmental chemical factors and disease, such as diabetes and heart disease. We have used these data to perform the rst environment-wide association studies (EWAS)5, linking >250 environmental biomarkers with disease phenotypes, such as diabetes6,7, self-reported preterm birth8, serum cholesterol levels9, blood pressure10, all-cause mortality11, telomere length12, and income13.

The NHANES is a CDC program that began in the 1960 s and in the current day, bi-annually samples 15 counties of United States population (N ~ 5 K per year). Each year, the counties that are sampled change, ensuring a representative and diverse sampling. Specically, NHANES uses a multistage and probability sampling design. To provide reliable statistics, the NHANES over-samples persons 60 and older, African Americans, and Hispanics and analysts.

The NHANES is designed to estimate major disease prevalence, such as diabetes, obesity, and cardiovascular disease in the United States. It is one of the only studies that combines simultaneously assessed self-reported questionnaires and physical measurements. Self-reported instruments include dietary questionnaires the estimate nutrient content of foods consumed around the time of survey and health and disease-related questionnaire. Second, the NHANES contains phenotypes such as blood pressure, pulse rate, respiratory capacity, height, weight, and tooth count in an effort to estimate the range and prevalence of these phenotypic measures. They are not used for medical diagnoses for the participants.

The exposome has been touted as the comprehensive battery of environmental exposures encountered in humans14. The CDC NHANES is one of the rst population survey programs to have exposome measurements. The CDC samples urine, blood, and other human tissue to measure environmental exposure indicators of the exposome using gold standard mass spectrometry and immunological assays. Environmental exposure assays include, for example, lead, mercury, arsenic, pesticide metabolites, air pollution indicators, and plasticizing agents, all hypothesized to have some relationship with health. The NHANES has been instrumental in providing what and how many environmental chemicals are found in human tissue (e.g., ref. 15). Clinical and physiological phenotypes of the phenome include cholesterol (e.g., HDL-cholesterol, LDL-cholesterol, triglycerides), glucose, insulin, C-reactive protein (CRP), white blood counts, and other blood or urine based measures. All of the measures are taken simultaneously.

The NHANES raw datasets for surveys currently exist in >250 number of separate proprietary SAS-formatted les (e.g.: https://wwwn.cdc.gov/Nchs/Nhanes/1999-2000/DEMO.XPT

Web End =https://wwwn.cdc.gov/Nchs/Nhanes/1999-2000/DEMO.XPT ). Description of each variable (e.g., a human-readable variable name and units of measurement) exist in a separate table embedded in an.html webpage (e.g.: https://wwwn.cdc.gov/nchs/nhanes/search/variablelist.aspx?Component=Demographics

Web End =https://wwwn.cdc.gov/nchs/nhanes/search/variablelist.aspx? https://wwwn.cdc.gov/nchs/nhanes/search/variablelist.aspx?Component=Demographics

Web End =Component = Demographics ). All technical information about each variable, such as way it was measured, are also available on the NHANES website as a.html page. The NHANES has variables of many types, including biomarkers of environmental exposures, clinical markers, physiological measures, questionnaire items, that are continuous or categorical. Next, the NHANES consists of multiple survey waves that represent a sampling for a 2-year period (e.g., 19992000 to 20052006 and beyond). Our data resource allows investigators move beyond examining a handful of variables to one that takes advantage of the multiple variables across a number of NHANES survey waves (e.g., akin to refs 11,16,17). Second, our data resource allows for quick evaluation of hypotheses before executing a formal scientic investigation. We are offering this integrated resource ready to analyze for free of cost, leveraging our previous experience.

We also offer a way to access the dataset programmatically through an application programming interface (API). We utilize i2b2/tranSMART, a data repository software platform used to implement BD2K Patient Information Commons-Standardized Unication of Research Elements (PIC-SURE) (http://pic-sure.org

Web End =http:// http://pic-sure.org

Web End =pic-sure.org ). The Informatics for Integrating Biology and the Bedside (i2b2) open-source software was developed to provide a federated informatics infrastructure to house/store, maintain, and analyze cohort data emerging from population-level datasets from around the nation for the purpose of driving biological discovery1820. i2b2 enables the cohesive analysis of heterogeneous phenotypic data. tranSMART is an open-sourced application layer for i2b2 (refs 21,22), providing a software add-ons to i2b2 for user interfaces, data mapping, and loading cohort data. This software provides a means to assemble, query, and analyze disparate and heterogeneous cohort datasets, such as the NHANES. The PIC-SURE software technology provides an accessible representation of NHANES, facilitating ad hoc querying of the health measures of the US while providing an application programming interface (API) for consumption by external applications and scripts, such as statistical tools such as R.

In this data descriptor, we provide (1) a data descriptor for unied raw NHANES data, (2) sample starter analytic code, analytic compute environment in a Docker container, and guide to conduct analysis

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with the NHANES data, and (3) introduce the PIC-SURE enabled web application to browse and download the data through an application programming interface (API). Further, we have provided a web video tutorial on the web application located here: https://vimeo.com/182576739

Web End =https://vimeo.com/182576739 .

We emphasize our data descriptor is an introduction for use of the NHANES dataset and that all analyses must be veried with data from CDC/NHANES directly. Furthermore, we also emphasize that the derived variables we include were suitable for our own analyses of NHANES and may not be suitable for hypotheses specic to other investigators. Therefore, we include all raw variables in our integrated dataset for investigators.

Methods

National Health and Nutrition Examination Surveys (NHANES) data

NHANES datasets are publicly accessible through the United States Centers of Disease Control and Prevention (US CDC)2326. All NHANES participants have consented for their information to be used in research.

Figure 1 shows our procedure. We downloaded 255 total data les, encoded in proprietary SAS .xpt format, corresponding to participants surveyed in 19992000 (52 les), 20012002 (57 les), 20032004 (77 les), 20052006 (69 les) from the CDC NHANES website (Fig. 1a,b) which are hyperlinked to a CDC website in January 2014. We chose to focus on these surveys as they had the greatest number of variables available at the time of download. We will make future instances of merged NHANES available via DataDryad with additional Data Descriptors.

Each participant of the NHANES has a unique identier; in other words, there is no overlap in participants in the 19992000, 20012002, 20032004, and 20052006 surveys. In total, these 255 les contain information on 41,474 distinct individuals representative of the United States population and 1,191 unique variables.

Each.xpt formatted data le consists of information structured in a N M form, in which N number of individuals make up every row and M number of columns of variables for each individual (Fig. 1a,b) and a participant identier (called SEQN), the primary key that joins the data les together (shown as a gray column, Fig. 1a). The CDC/NHANES have binned each le in 4 separate modules that corresponded to(1) whether they contain demographic information (e.g., age, race/ethnicity, survey characteristics, income [Fig. 1a, red folder]), (2) laboratory measures (e.g., biomarker measurements assayed in biological tissue, such as serum or urine, depicted in orange [Fig. 1a, orange folder]), (3) physical examination (e.g., measurements such as body mass index, weight, height; [Fig. 1a, green folder]), or questionnaire (e.g., food-frequency questionnaire or health status questionnaire [Fig. 1a, blue folder]). Each of these categories, or modules, are called Demographics, Laboratory, Examination, and/or Questionnaire modules respectively.

In total, we downloaded 4 Demographics data les, 163 Laboratory data les, 19 Examination les, and 69 Questionnaire data les. Fig. 1b and Table 1 depicts the total les for each NHANES module for the 19992000, 20012002, 20032004, and 20052006 datasets.

After downloading all 255.xpt les, we executed a number of data processing steps. First, all.xpt les were converted into.csv les using using the foreign R package27, preserving the original N M form of the data. Next, we created some derived variables to ease potential downstream analyses, including(1) occupation (1 variable), (2) chronic disease (40 variables), and (3) pharmaceutical drug use (100 variables) (Fig. 1c).

We coded occupation as variables that correspond to (1) white-collar and professional jobs that are coded as white-collar and semi-routine (e.g., technicians), blue-collar and high-skill (e.g., mechanics, construction trades, and military), blue-collar and semi-routine (e.g., personal services, farm workers) as previously described in our previous EWAS28. Labor force participation was dened as working at a job or business or having a job or business within the last two weeks, not including work around the house.

We dened presence of 6 types of chronic diseases, including diabetes (1 variable), coronary disease (1 variable), hypertension (1 variable), asthma (1 variable), rheumatoid arthritis, osteoarthritis, and 30 site-specic cancers. We coded diabetes as present (as an integer 1) if the participant had a fasting blood glucose greater than 125 mg/dl (as per American Diabetes Association [ADA]) threshold for diabetes diagnosis or if the participant answered yes to the question, Other than during pregnancy, have you ever been told by a doctor or health professional that {you have/{he/she/SP} has} diabetes or sugar diabetes?. If the participant did not have both of those characteristics, he/she were coded as 0 (ref. 29). Similarly, we dened presence of hypertension as 1 if the participant had a systolic over diastolic blood pressure greater than 130 over 90 or answered yes to the question, Have you ever been told by a doctor or other health professional that you had hypertension, also called high blood pressure and 0 otherwise. We dened presence of coronary disease as 1 if the participant answered yes to the question, Has a doctor or other health professional ever told you that you had coronary (kor-o-nare-ee) heart disease? and 0 otherwise. The NHANES also contains coding for site-specic cancers. First, participants were asked whether a doctor has ever told you you have cancer?. If the participant replies yes to a question, a followup question is administered, what type of cancer do you have and the participant can answer from a set of 27 cancers, such as breast, skin, lung, colon, bladder, kidney, and other type of cancers. We turned these into 27 separate variables that are coded 1 if the site-specic cancer is present, 0 otherwise.

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Figure 1. Methods overview for creating the unied NHANES dataset. (a) Each SAS-formatted (.xpt) data le provided by the CDC/NHANES are binned by module (represented by folders), including Demographics (4 les), Laboratory (163 les), Examination (19 les), and Questionnaire (69 les). Participant identiers to merge data les across modules are depicted as gray colums. (b) File number breakdown by survey year and module. (c) We processed the data to create new variables, added pharmaceutical drug information, and added mortality information. (d) We merged all 255 les by the patient identier to create a large unied table (MainTable) consisting of 41 K participants and 1191 unique variables. (e) We created a data dictionary that contains human readable variable descriptions and other meta-data, such as variable category and the levels of the variable if categorical. (f) Data is accessible via DataDryad and browsable through the PIC-SURE website (https://nhanes.hms.harvard.edu

Web End =https://nhanes.hms.harvard.edu). Data and a Usage Guide is available on GitHub. Rstudio analytics environment with dataset, xwas R library, and user guides packaged as a Docker hub container (chiragjp/ nhanes_scidata).

Third, we extracted pharmaceutical drug use for each participant. The CDC used a Master Drug Database (MDDB), a proprietary but comprehensive database of all prescription and some nonprescription drug products available in the U.S. drug market. The CDC NHANES interviewer asked participants whether they were taking a drug in the past month, and if they were, what drugs they were taking. The CDC NHANES interviewer matched each drug to an MDDB identier and drug description (e.g., METFORMIN or ALBUTEROL). Second, the CDC NHANES interviewerif the interview was

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Description NHANES module Number of variables Number of les Physical examination Examination 134 19 Laboratory assay (serum and urine) results Laboratory 1,246 163 Self-reported questionnaire items Questionnaire 1,794 69 Demographic attributes and cause of death in 2006 Demographics and mortality 28 4

Table 1. Number of variables and les per NHANES module.

occurring at the participants homeveried possession of the prescription drug container. Each participant could report taking more than one drug. There were 626, 668, 667, and 692 unique drugs found by the CDC interviewers in the 19992000, 20012002, 2003200, and 20052006 cohorts respectively. To keep the merged data table (Fig. 1d) of tractable size, we chose to focus on the top 100 drugs that were most prevalent in the population. We coded a participant was on a drug if (1) they reported use of a drug and (2) whether the interviewer veried the container was present.

The CDC also ascertained cause and time of death (mortality) information for a subset of the participants in 2006 by linking eligible participants to the National Death Index. We incorporated this data into our data merge (n = 11,429 participants). The variables that describe the mortality information include ELIGSTAT (whether the person was eligible for death linkage), MORTSTAT (whether the participant was deceased), PERMTH_INT (time to death from interview or time to linkage if participant is living [censored data]), PERMTH_EXM (time to death from examination or time to linkage if participant if living), DIABETES (if the cause of death was diabetes), HYPERTEN (if the cause of death was hypertension, and HIPFRACT (if the cause of death was hip fracture).

Finally, we combined the 255 les together into single data le by merging by the patient identier (SEQN) (Fig. 1d). This merge resulted in one consolidated and analysis-ready data le representing a grand total of 1,191 variables on 41,474 participants.

Creation of a digital handbook: annotating and categorizing the NHANES datasetsThe CDC NHANES have provided a.html formatted codebook (e.g.: https://wwwn.cdc.gov/Nchs/Nhanes/Search/variablelist.aspx?Component=Laboratory&CycleBeginYear=1999

Web End =https://wwwn.cdc.gov/Nchs/ https://wwwn.cdc.gov/Nchs/Nhanes/Search/variablelist.aspx?Component=Laboratory&CycleBeginYear=1999

Web End =Nhanes/Search/variablelist.aspx?Component = Laboratory&CycleBeginYear = 1999 ) that consists of variable name (column in the.xpt le) and a human-readable description of each variable. For example, the variable with names RIDAGEYR or LBXGLU is described as Age in Years and fasting serum glucose [mg ul1] respectively. These descriptions include the variable units, such as ug/mL (inferred as a continuous variable), or positive/negative (a binary variable) of each variable.

We have extended the CDC NHANES data description methodology in the following ways (Fig. 1e) to facilitate analysis and data browsing. Specically, we have created a data dictionary that contains the name of the variable, a human readable description of the variable, what module a variable belongs to, what survey the variable was measured (e.g., 19992000). Second, we have binned each variable into categories that offer more specicity than the CDC NHANES module characterization. We make available the data dictionary (Fig. 1e) along with the data set (Data Citation 1). A summary of the number of variables per category, the median sample size for the variables in the category, and the demographic representation (percent female and race/ethnicity available for each variable) in Table 2. The entire data dictionary is available as Table 3 (available online only) (Data Citation 1 and Table 3 (available online only)).

These categories aide in the ltering and querying of variables with common types, such as nutrients, body measures, pharmaceutical drug, viral infection, and pesticides. Second, we have created a column that denotes the categorical levels for variables that are categorical or binary. For example, Are you a past, current, or never smoker? is a variable that has three levels, one representing never smoker, current smoker, and past smoker; these categories are captured in a column called categorical levels.

Browsing and accessing the data through BD2K Patient-Centered Information

Commons (PIC)

We leveraged the Patient-Centered Information Commons (PIC, for an overview, see: http://pic-sure.org

Web End =http://pic-sure. http://pic-sure.org

Web End =org )) platform is leveraged to (1) enable interactive web browsing of the NHANES data (see: https://nhanes.hms.harvard.edu

Web End =https:// https://nhanes.hms.harvard.edu

Web End =nhanes.hms.harvard.edu ) and (2) access data through an application programming interface (API). PICs are built using the i2b2/tranSMART software stack. Data is organized into a hierarchy resembling a directory structure to facilitate browsing (Figs 2 and 3). Raw data can be also queried using a drag and drop interface (Fig. 3). With the NHANES, we organized each of the 1171 variables into a multi-level hierarchy that was ordered by the module (i.e., Laboratory, Examination, Demographics, and Questionnaire) and category (i.e., pesticides, body measures, etc, see Table 2). To display this NHANES data hierarchy in our user interface we created a Metadata mapping le located here: https://github.com/hms-dbmi/public-data-deployments/blob/master/NHANES/nhanes_9906.map

Web End =https:// https://github.com/hms-dbmi/public-data-deployments/blob/master/NHANES/nhanes_9906.map

Web End =github.com/hms-dbmi/public-data-deployments/blob/master/NHANES/nhanes_9906.map and used this mapping le to integrate the data le.

The merged dataset (MainTable) and data dictionary (VarDescription) (Fig. 1d,e) are made available in DataDryad (Fig. 1f). A Usage Guide and.Rdata les are provided for download in GitHub (Fig. 1f). Finally, all data are browsable at https://nhanes.hms.harvard.edu

Web End =https://nhanes.hms.harvard.edu .

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Category Number of variables Surveys Median (N) Female (%) White (%) Black (%) Mexican (%) Other his (%) Other eth (%) acrylamide 2 3 7189.50 0.51 0.41 0.26 0.25 0.03 0.04 aging 1 1;2 7827.00 0.52 0.51 0.17 0.24 0.05 0.03 alcohol use 4 1;2;3;4 11141.50 0.46 0.54 0.17 0.21 0.04 0.03 allergen test 20 4 7796.50 0.51 0.40 0.26 0.26 0.03 0.05 bacterial infection 48 1;2;3;4 742.00 0.50 0.43 0.26 0.24 0.04 0.04 biochemistry 56 1;2;3;4 26038.00 0.51 0.43 0.23 0.26 0.04 0.04 blood 20 1;2;3;4 33661.00 0.51 0.39 0.25 0.28 0.04 0.04 blood pressure 4 1;2;3;4 26036.00 0.51 0.39 0.25 0.28 0.04 0.04 body measures 19 1;2;3;4 27259.00 0.48 0.40 0.25 0.27 0.04 0.04 cognitive functioning 2 1;2 2975.00 0.52 0.61 0.14 0.19 0.04 0.02 cotinine 1 1;2;3;4 31136.00 0.51 0.40 0.25 0.27 0.04 0.04 diakyl 7 1;2;3 7422.00 0.52 0.39 0.25 0.27 0.04 0.04 dioxins 7 1;2;3 4988.00 0.52 0.44 0.21 0.26 0.04 0.04 disease 40 1;2;3;4 18526.50 0.53 0.48 0.21 0.23 0.04 0.04 food component recall 162 1;2;3;4 16412.00 0.51 0.39 0.25 0.26 0.04 0.04 furans 10 1;2;3 4980.00 0.52 0.44 0.21 0.26 0.04 0.04 heavy metals 31 1;2;3;4 10081.00 0.50 0.39 0.26 0.27 0.04 0.04 hormone 8 1;2;3;4 9473.00 0.52 0.42 0.22 0.26 0.04 0.04 housing 9 1;2;3;4 35087.00 0.51 0.39 0.25 0.28 0.04 0.05 hydrocarbons 23 1;2;3 7209.00 0.52 0.41 0.25 0.27 0.04 0.04 immunization 3 1;2;3;4 35305.00 0.52 0.40 0.25 0.27 0.04 0.04 melamine 1 3 492.00 0.53 0.42 0.27 0.23 0.04 0.04 nutrients 31 1;2;3;4 22880.00 0.51 0.42 0.25 0.25 0.04 0.04 occupation 21 1;2;3;4 769.00 0.27 0.46 0.20 0.27 0.05 0.04 pcbs 38 1;2;3 6049.00 0.52 0.43 0.22 0.27 0.04 0.04 perchlorate 7 3;4 5479.50 0.51 0.41 0.26 0.25 0.03 0.05 pesticides 66 1;2;3;4 4999.00 0.52 0.39 0.25 0.27 0.04 0.04 pharmaceutical 221 1;2;3;4 20456.00 0.51 0.39 0.25 0.28 0.04 0.04 phenols 7 3;4 5065.00 0.51 0.42 0.26 0.25 0.03 0.05 phthalates 15 1;2;3;4 10476.00 0.51 0.40 0.25 0.27 0.04 0.04 physical tness 15 1;2;3;4 8688.00 0.48 0.34 0.26 0.32 0.04 0.04 phytoestrogens 6 1;2;3;4 10453.50 0.51 0.40 0.25 0.27 0.04 0.04 polybrominated ethers 12 3 1999.50 0.51 0.45 0.24 0.24 0.03 0.04 polyourochemicals 12 1;3;4 5805.00 0.51 0.42 0.24 0.27 0.04 0.03 sexual behavior 2 1;2;3;4 5178.00 0.00 0.48 0.21 0.22 0.04 0.04 smoking behavior 30 1;2;3;4 7479.50 0.48 0.51 0.20 0.20 0.04 0.04 smoking family 8 1;2;3;4 7668.00 0.49 0.43 0.35 0.15 0.04 0.04 social support 3 1;2;3;4 9937.00 0.51 0.56 0.19 0.18 0.03 0.03 street drug 24 1;2;3;4 600.00 0.39 0.54 0.20 0.20 0.04 0.04sun exposure 1 3;4 2444.00 0.50 0.70 0.06 0.17 0.02 0.04 supplement use 85 1;2;3;4 41366.00 0.51 0.39 0.25 0.28 0.04 0.04 viral infection 18 1;2;3;4 15400.00 0.51 0.40 0.24 0.27 0.04 0.04 volatile compounds 51 1;2;3;4 5573.00 0.53 0.45 0.24 0.23 0.04 0.05

Table 2. Categories of variables, the number of variables, surveys represented (1 = 19992000, 2 = 20012002,3 = 20032004,4 = 20052006) number of raw data les, sample size, and demographic distribution. Sample sizes are the median participants available for the variables in the respective categories (e.g., the median sample size available for all the alcohol use variables is 11,141.5). Other His denotes Other Hispanic. Other Eth denotes Other race/ethnicity.

We have provided two additional resources for individuals to learn about the resource. The rst is a tutorial of the web application located at Vimeo (https://vimeo.com/182576739

Web End =https://vimeo.com/182576739). This web application shows users how to count the number of variables and number of participants (by age, sex, and

SCIENTIFIC DATA | 3:160096 | DOI: 10.1038/sdata.2016.96 6

Figure 2. Screenshot of NHANES data hierarchy displayed in the PIC data browser tool. Variables are shown with sample sizes. Highlighted in the screen shot are all laboratory measures of dioxins, a type of environmental exposure assayed in serum.

SCIENTIFIC DATA | 3:160096 | DOI: 10.1038/sdata.2016.96 7

Figure 3. Screenshot of Drag-and-drop example to explore the NHANES datasets (left) using the PIC cohort browser tool. A comparison of raw Dioxin (1-9-ocdd) levels by age groups. Red, age o25 y; blue, age 25 y.

race/ethnicity) that we believe will aid in planning analyses of the data. Second, we have built an online course (http://www.chiragjpgroup.org/exposome-analytics-course/

Web End =http://www.chiragjpgroup.org/exposome-analytics-course/) to guide users step-by-step through an investigation our group recently published (Patel et al., 2016).

We plan to assess how frequently our data descriptor and data resources are being utilized by the scientic community through traditional means (e.g., number of citations to this descriptor), but also through by counting the number of unique visitors to the Vimeo video website, the web application (http://nhanes.hms.harvard.edu

Web End =http://nhanes.hms.harvard.edu), and through feedback from course materials.

Code availability

We demonstrate 3 use-cases in using the integrated NHANES datasets in a R markdown source le (see Usage Notes). Code is available on GitHub here: https://github.com/chiragjp/nhanes_scidata

Web End =https://github.com/chiragjp/nhanes_scidata . One other example using our API access is available here: https://github.com/hms-dbmi/R-IRCT/blob/master/Example_NHANES.Rmd

Web End =https://github.com/hms-dbmi/R-IRCT/blob/master/ https://github.com/hms-dbmi/R-IRCT/blob/master/Example_NHANES.Rmd

Web End =Example_NHANES.Rmd

I2b2/TranSMART software stack. Code to implement a PIC is open-sourced and available here: https://github.com/hms-dbmi/HMS-DBMI-transmartApp

Web End =https://github.com/hms-dbmi/HMS-DBMI-transmartApp

Data Records

Data record 1: Integrated NHANES dataset and data dictionary in.csv format.

The integrated NHANES dataset and a data dictionary is available online at Dryad (Data Citation 1) as a .zip le which includes 3 .csv formatted les. The rst le (data le) contains each individual (as rows) surveyed in 19992006 with all of their measurements (as columns) (MainTable, Fig. 1d). The second le contains a data dictionary le which contains the name of the variable as represented in the data le, a human readable description of the variable, the categories that the variable belongs to), and the levels of the categories (if a categorical variable) (Fig. 1e). The third le is a dictionary specically for demographic information, such as describing the columns for age, sex, race/ethnicity, whether the participant was born in the US, education level, income level, and mortality information. Also, to facilitate analyses using the R programming language, we have provided a 4th le that contains all the les described above as a R data object in.Rdata format.

Technical Validation

The raw data contained herein are from the CDC NHANES. The CDC NHANES have performed extensive technical validation of their data described elsewhere (e.g., refs 30,31).

Usage Notes

The NHANES utilizes a multistage survey sampled study design to ensure minority subgroups (e.g., Blacks, Mexican-American, elderly, pre-adolescents) of the population are appropriately represented in the dataset32 and to optimize sampling resources. Therefore, statistical analyses need to take into account the structure of the sampling into account to provide accurate estimates of the population, such as means, standard errors, and correlations33.

To demonstrate how to properly analyse NHANES data, we provide a R markdown les in our GitHub repository (https://github.com/chiragjp/nhanes_scidata

Web End =https://github.com/chiragjp/nhanes_scidata) to re-create several relevant analyses.

SCIENTIFIC DATA | 3:160096 | DOI: 10.1038/sdata.2016.96 8

Conducting an environment-wide association analysis in all-cause mortality in NHANES Previously, we conducted a data-driven search of environmental exposure factors associated with all-cause mortality known as an environment-wide association study28. In the guide (https://github.com/chiragjp/nhanes_scidata/blob/master/User_Guide.Rmd

Web End =https://github.com/ https://github.com/chiragjp/nhanes_scidata/blob/master/User_Guide.Rmd

Web End =chiragjp/nhanes_scidata/blob/master/User_Guide.Rmd ), we describe how to associate one of the top ndings, serum cadmium, with all-cause mortality using survey-weighted Cox proportional hazards regression.

Distribution of serum lead in in children: Accessing the NHANES in PIC-SURE APIIn this guide (https://github.com/chiragjp/nhanes_scidata/blob/master/User_Guide_PIC.Rmd

Web End =https://github.com/chiragjp/nhanes_scidata/blob/master/User_Guide_PIC.Rmd), we demonstrate how to access the NHANES data programmatically through the PIC-SURE API. In our example, we show how to query the API to estimate the quartiles of serum lead in the US population of all ages and aged under 18.

Redistributable analytics environment in Docker

The issue of reproducibility, replicability, and scalability in computational scientic research has been raised on multiple occasions34,35. We promote a reproducible practice by packaging the curated NHANES data (Data Citation 1) with an analytics environment comprised of R-3.3.0 (ref. 36) and the Rstudio-0.99.902 (ref. 37) web interface in addition to a custom R library for regression studies in a Docker container38. The packaged environment is publically available on Docker hub (https://hub.docker.com/r/chiragjp/nhanes_scidata/

Web End =https://hub. https://hub.docker.com/r/chiragjp/nhanes_scidata/

Web End =docker.com/r/chiragjp/nhanes_scidata/ ) and can be consistently deployed across local or cloud-based environments. We have provided these materials as a hands-on short course available here: http://www.chiragjpgroup.org/exposome-analytics-course/

Web End =http://www. http://www.chiragjpgroup.org/exposome-analytics-course/

Web End =chiragjpgroup.org/exposome-analytics-course/

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Data Citation

1. Patel, C. J. Dryad Digital Repository http://dx.doi.org/10.5061/dryad.d5h62 (2016).

Acknowledgements

This project is conducted with support from NIH Big Data to Knowledge (BD2K) grant (U54 HG007963). We thank Cassandra Perry of Harvard DBMI for producing and narrating the video tutorial. C.J.P. is additionally supported by a NIH/NIEHS R00 ES23054 R21 ES025052 and a gift from Agilent Technologies, Inc.

Author Contributions

C.J.P. downloaded and processed the NHANES data, wrote the Usage Notes R markdown guide, and wrote the manuscript. N.P. wrote the Usage Notes R markdown manuscript, wrote the manuscript. M.M. participated in developing the i2b2/tranSMART application and loaded NHANES data. J.E.M. developed BD2K PIC-SURE RESTful API. C.K. participated in loading NHANES data in i2b2/tranSMART. I.S.K. architected the i2b2 software infrastructure and wrote the manuscript. P.A. architected the extension to the i2b2/tranSMART platform to accommodate NHANES data, architected BD2K PIC-SURE API, and wrote the manuscript.

Additional Information

Table 3 is only available in the online version of this paper.

Competing nancial interests: The authors declare no competing nancial interests.

How to cite: Patel, C. J. et al. A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey. Sci. Data 3:160096 doi: 10.1038/sdata.2016.96 (2016).

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