Design and Rationale of the Biomarker Center of the Household Air Pollution Intervention Network (HAPIN) Trial
2020; National Institute of Environmental Health Sciences; Volume: 128; Issue: 4 Linguagem: Inglês
10.1289/ehp5751
ISSN1552-9924
AutoresDana Boyd Barr, Naveen Puttaswamy, Lindsay M. Jaacks, Kyle Steenland, Sarah Rajkumar, Savannah Gupton, P. Barry Ryan, Kalpana Balakrishnan, Jennifer L. Peel, William Checkley, Thomas Clasen, Maggie L. Clark,
Tópico(s)Climate Change and Health Impacts
ResumoVol. 128, No. 4 ResearchOpen AccessDesign and Rationale of the Biomarker Center of the Household Air Pollution Intervention Network (HAPIN) Trialis accompanied byAir Pollutant Exposure and Stove Use Assessment Methods for the Household Air Pollution Intervention Network (HAPIN) TrialDesign and Rationale of the HAPIN Study: A Multicountry Randomized Controlled Trial to Assess the Effect of Liquefied Petroleum Gas Stove and Continuous Fuel Distribution Dana Boyd Barr, Naveen Puttaswamy, Lindsay M. Jaacks, Kyle Steenland, Sarah Rajkumar, Savannah Gupton, P. Barry Ryan, Kalpana Balakrishnan, Jennifer L. Peel, William Checkley, Thomas Clasen, and Maggie L. Clark (HAPIN Investigative Team) Dana Boyd Barr Address correspondence to Dana Boyd Barr, Emory University, RSPH, 1518 Clifton Rd., CNR2007, Atlanta, GA 30322 USA. Telephone: (404) 727-9605. Email: E-mail Address: [email protected] Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , Naveen Puttaswamy Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai, India Search for more papers by this author , Lindsay M. Jaacks Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA Search for more papers by this author , Kyle Steenland Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , Sarah Rajkumar Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai, India Search for more papers by this author , Savannah Gupton Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , P. Barry Ryan Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , Kalpana Balakrishnan Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai, India Search for more papers by this author , Jennifer L. Peel Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA Search for more papers by this author , William Checkley Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA Center for Global Non-Communicable Disease Research and Training, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA Search for more papers by this author , Thomas Clasen Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Search for more papers by this author , and Maggie L. Clark Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA Search for more papers by this author (HAPIN Investigative Team) Search for more papers by this author Published:29 April 2020CID: 047010https://doi.org/10.1289/EHP5751Cited by:2AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Biomarkers of exposure, susceptibility, and effect are fundamental for understanding environmental exposures, mechanistic pathways of effect, and monitoring early adverse outcomes. To date, no study has comprehensively evaluated a large suite and variety of biomarkers in household air pollution (HAP) studies in concert with exposure and outcome data. The Household Air Pollution Intervention Network (HAPIN) trial is a liquified petroleum gas (LPG) fuel/stove randomized intervention trial enrolling 800 pregnant women in each of four countries (i.e., Peru, Guatemala, Rwanda, and India). Their offspring will be followed from birth through 12 months of age to evaluate the role of pre- and postnatal exposure to HAP from biomass burning cookstoves in the control arm and LPG stoves in the intervention arm on growth and respiratory outcomes. In addition, up to 200 older adult women per site are being recruited in the same households to evaluate indicators of cardiopulmonary, metabolic, and cancer outcomes.Objectives:Here we describe the rationale and ultimate design of a comprehensive biomarker plan to enable us to explore more fully how exposure is related to disease outcome.Methods:HAPIN enrollment and data collection began in May 2018 and will continue through August 2021. As a part of data collection, dried blood spot (DBS) and urine samples are being collected three times during pregnancy in pregnant women and older adult women. DBS are collected at birth for the child. DBS and urine samples are being collected from the older adult women and children three times throughout the child’s first year of life. Exposure biomarkers that will be longitudinally measured in all participants include urinary hydroxy-polycyclic aromatic hydrocarbons, volatile organic chemical metabolites, metals/metalloids, levoglucosan, and cotinine. Biomarkers of effect, including inflammation, endothelial and oxidative stress biomarkers, lung cancer markers, and other clinically relevant measures will be analyzed in urine, DBS, or blood products from the older adult women. Similarly, genomic/epigenetic markers, microbiome, and metabolomics will be measured in older adult women samples.Discussion:Our study design will yield a wealth of biomarker data to evaluate, in great detail, the link between exposures and health outcomes. In addition, our design is comprehensive and innovative by including cutting-edge measures such as metabolomics and epigenetics. https://doi.org/10.1289/EHP5751IntroductionUnderstanding individual exposures and effects is critical in successful epidemiologic investigations to avoid misclassification of exposures or outcomes (Antó et al. 2000; Kogevinas 2011); however, intra- and inter-person variation in predictors of exposures (e.g., behaviors, microactivity patterns, work- and home-related tasks), genetic susceptibility, and toxicokinetics make quantitative assessment difficult without individual-level data (Cohen Hubal et al. 2019; Lioy 1999; Lioy and Smith 2013; Vincent 2012). Biomarkers are useful tools for understanding environmental exposures, susceptibility, and biological responses leading to disease outcomes (Barr et al. 2005; Needham et al. 2005b). Further, the collection of temporally resolved biological samples enables the individual analysis of markers of exposure and disease accounting for this intra- and inter-person variation (Barr et al. 2005).This manuscript describes a comprehensive biomarker approach to enable us to evaluate household air pollution (HAP) exposures, susceptibility, and early effects for a variety of health outcomes as a part of a large randomized controlled trial called the Household Air Pollution Intervention Network (HAPIN) trial. HAPIN was designed to evaluate the effect of a randomized liquified petroleum gas (LPG) stove and fuel intervention on health among family members in 800 households in each of four diverse biomass-using low- and middle-income countries (LMICs; Guatemala, India, Peru and Rwanda) populations using exposure–response (i.e., evaluation of how exposure relates to biomarker or disease outcome) analyses and comparisons between the study arms to which participants were assigned, regardless of their actual adherence to the intended condition. These LMIC locations were purposefully selected to be diverse in characteristics such as altitude, population density, cooking practices, fuel types, and baseline levels of pollution to improve the study’s generalizability. Briefly, HAPIN is enrolling 800 eligible pregnant women (at 9 to<20 weeks gestation) at each of the LMIC countries referred to as International Research Collaboration (IRCs) sites and following these women through pregnancy and their child to 1 year of age. In approximately one-fourth of these households, up to 200 older adult women (OAW) are also being enrolled. Households are randomized into control and intervention arms (1:1) with the intervention arm receiving an LPG stove and gas supply for the duration of the study. The primary health outcomes of HAPIN are birth weight, severe pneumonia in the first 12 months of life, stunting at 12 months of age, and blood pressure in the OAW. The study protocol has been reviewed and approved by institutional review boards (IRBs) or ethics committees of all participating institutions. The study has been registered with ClinicalTrials.gov and is overseen by an independent Data Safety Monitoring Board (DSMB). Recruitment and data collection began in May 2018 and expected completion is August 2021. The HAPIN trial is described in more detail elsewhere (Clasen et al. 2020).The HAPIN trial is composed of many core components, including exposure assessment [e.g., particulate matter ≤2.5μm in aerodynamic diameter (PM2.5), black carbon measures], biomarker measurements, stove use monitoring, surveillance, and data management. In particular, one of the aims of HAPIN is to evaluate associations between targeted and untargeted/exploratory biomarkers of exposure and effect with intervention status or exposure defined from personal or household air pollution measurements (detailed in Johnson et al. 2020). To successfully achieve this aim, we developed a comprehensive biomarker center (BMC) comprising scientists from each IRC and collaborative institutions involved with HAPIN, including experts in exposure science, HAP, analytical chemistry, epidemiology, and toxicology to ensure the most appropriate biomarkers are measured in the most viable and logistically feasible matrix to provide maximum exposure and health information in the HAPIN cohort. In addition, the BMC is supported by two analytical biomarker laboratories: a) the Laboratory for Exposure Analysis and Development in Environmental Research (LEADER) at Rollins School of Public Health, Emory University, Atlanta, Georgia; and b) the laboratory at Sri Ramachandra Institute of Higher Education and Research (SRIHER), Chennai, India. The overall goal of our BMC is to provide high-capacity, high-quality, and high-throughput analysis of a wide range of biomarkers in samples collected from participants. This entails four primary objectives: a) to provide training and monitoring compliance of sample collection, handling, and storage including developing collection and aliquoting protocols, ensuring sample integrity throughout the process and developing a short- and long-term archival system; b) to identify, prioritize, and measure specific biomarkers of HAP exposure and effect in urine and dried blood spots (DBS); c) to develop local laboratory capacity and harmonize biomarker measures across IRCs; and d) to develop and validate novel biomarkers that will provide insight into the broader mechanistic questions linking HAP exposure to disease development.Rationale for the HAPIN Biomarker DesignHAPIN is a complex and costly study funded by multiple agencies with different missions. Because of this, we sought to collect as many biological matrices as were feasible from both a cost and participant and field staff burden perspective. Thus, we wanted to evaluate biomarkers related to outcomes including cardiovascular, metabolic, cancer, and respiratory disease, as well as birth outcome and child development. This included a detailed prioritization scheme that included strength of evidence linking exposure or outcome and biomarker, our ability to measure the biomarker, and the stability/validation of the biomarker in the specified matrix. With this in mind, we sought out the most viable biomarkers that could comprehensively evaluate the components of HAP exposure and these health outcomes. Furthermore, we wanted to allow for innovation in our design by incorporating cutting-edge biomarkers, including epigenetic alterations and metabolic alterations. Recognizing the field is ever changing and new biomarkers are discovered each year, we also implemented a Biomarker Nomination process whereby newly emerging biomarkers such as fibrinogen and telomeres could be continually included throughout the duration of the study, given financial and laboratory limitations. Although all of the newly nominated biomarkers may not be discussed in this paper, we are working toward formalizing plans for their eventual measurement. Finally, we also had to consider the reality that a minimum of two laboratories would be involved in these measurements because biological samples from India cannot be shipped to another country or analyzed by a non-Indian laboratory (with the exception of small subsets of samples used for cross-validation purposes). This required that we evaluate both the capabilities and capacities of the laboratories, including instrumentation that differed widely. In an ideal scenario, one laboratory would perform all analyses to allow for easy comparison of data between countries. To ensure we would be able to compare data, we developed a thorough quality assurance/quality control scheme that is described in detail below. This scheme extended beyond the laboratories, however, and included metrics of quality and success in field activities that involved collecting the biological specimens. This multifaceted biomarker design required careful planning and coordination to enable successful execution that was continually evaluated for quality and completeness.Biological Matrix SelectionBecause of the large number of participants and the already burdensome exposure, behavioral, and intervention measures in place in the HAPIN trial, we opted for a biospecimen approach that would enable us to maximize the number of appropriate samples collected while minimizing participant burden and risk (Table 1). For participating pregnant women, children, and OAW, we chose to collect routine blood samples as DBS on a five-spot Guthrie filter paper card, a method that overcomes collection, transportation, and storage limitations of venipuncture sampling (McDade et al. 2007), and to collect a convenience or spot urine sample. The use of DBS is a novel aspect of our study because it also allows us to collect blood from the children, which is often not feasible when venipuncture is required. As such, the measurements of biomarkers that are traditionally measured in whole blood or serum will be validated against DBS measurements. We will use venous blood/DBS pairs collected in the formative pilot phases of our research to ensure these measurements are stable and interpretable. All biomarkers will be similarly validated in one or both of the BMC laboratories. Ideally, serum and DBS measurements are highly correlated. In instances where they are not, we may still have internal validity to use the markers as pre-intervention and post-intervention measures, although the concentrations may not be comparable to clinically interpretable values.Table 1 Biosample collection timeline.Table 1 has seven columns, namely, child age (study time point); less than 20 weeks gestation (baseline); 24 to 28 weeks gestation (1 to 3 months post-randomization); 32 to 36 weeks gestation or birth (3 to 5 months post-randomization); approximately 3 to 7 months of age (approximately 9 months post-randomization); approximately 6 months of age (approximately 12 months post-randomization); approximately 12 months of age (approximately 18 months post-randomization).Child age (study time point) 75% of all chronic disease deaths globally, share common pathophysiological mechanisms (e.g., inflammation and oxidative stress) (Jha et al. 2012), these markers were considered important to measure.Table 2 Target biomarkers for the HAPIN trial.Table 2 has six columns, namely, biomarker, biomarker number for Table 2, reason for selection, matrix, method, and method reference.BiomarkerBiomarker no. for Table 2Reason for selectionMatrixMethodMethod referenceIntercellular adhesion molecule 1 (ICAM-1)a1Endothelial marker of cardiovascular functionDBSImmunoassayBarnett and Ware 2011; Hecht et al. 2011; McElrath et al. 2011, 2013Vascular cellular adhesion molecule 1 (VCAM-1)a2Endothelial marker of cardiovascular functionDBSImmunoassayMcElrath et al. 2011Endothelin-1a3Endothelial marker of cardiovascular functionDBSImmunoassayGoddard and Webb 2000E-selectina4Endothelial marker of cardiovascular functionDBSImmunoassayBarnett and Ware 2011; McElrath et al. 2011C-reactive protein (CRP)a5Inflammation markerDBSImmunoassayBarnett and Ware 2011; McElrath et al. 2011, 2013Interleukin 6 (IL-6)a6Inflammation markerDBSImmunoassayBarnett and Ware 2011; McElrath et al. 2011von Willebrand factor antigen (vWF)a7Blood coagulation proteinDBSImmunoassayBarnett and Ware 2011; Mannucci 1998Hemoglobin A1c (HbA1c)a8Marker of glycemic controlDBS, capillary bloodPOC, LC-MS/MSDubach et al. 2019; Jeppsson et al. 2002Hemoglobin (Hb)9Clinical biomarkerDBS, capillary bloodPOC, LC-MS/MSJeppsson et al. 2002; Osborn et al. 2019Lipidsa10Clinical biomarkersDBSImmunoassayAkins et al. 1989P53 Tumor-associated antigen antibodies (p53 TAA antibodies)a11Lung and other cancer biomarkerDBSArray assayXu et al. 2019F2-Isoprostanesa12Inflammation markersDBSImmunoassaySoffler et al. 2010Clara cell protein (CC16)a13Lung insult markerDBSImmunoassayBroeckaert and Bernard 20008-OH-deoxyguanosine (8OHdG)a14Oxidative stress markerUrineLC-MS/MSMarrocco et al. 2017Myeloperoxidase (MPO)a15Oxidative stress markerDBSImmunoassayMarrocco et al. 2017Malondialdehyde (MDA)a16Oxidative stress markerUrineLC-MS/MSKartavenka et al. 2019aCytochrome P450 (Cyp450)a17Enzyme inductionDBSImmunoassayLake et al. 20093-OH cotinine; cotinine, and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL)b18Short-term and longer-term (∼6 weeks) tobacco smoke biomarkersUrineLC-MS/MSAvila-Tang et al. 2013; Braun et al. 2010; Carmella et al. 2003; Needham et al. 2005a; Sexton et al. 2011; Yuan et al. 2014Polycyclic aromatic hydrocarbons (1-OH pyrene,1-/2-naphthol, 2-/3-hydroxyfluorine, 2-/3-/4-hydroxyphenanthrene, phenanthrene tetrol)b19Carcinogen exposure biomarkerUrineGC-MS/MSAquilina et al. 2010; Perera et al. 2005Volatile organic chemicals [mercapturate metabolites including S-phenylmercaptuate (benzene metabolite), S-benzylmercapturate (toluene metabolite), S-1-phenyl-2-hydroxyethylmercapturate (styrene metabolite), and S-2-hydroxyethylmercapturate (acrylonitrile, vinyl chloride, ethylene oxide metabolite)]b20Carcinogen exposure biomarkerUrineLC-MS/MSAlwis et al. 2012; Barr and Ashley 1998; Calafat et al. 1999Heavy metals/metalloids (lead, mercury, cadmium, arsenic)b21Air pollution exposure, neurotoxicantsDBS, urineICP-MSBuck Louis et al. 2012; Jones et al. 2010; Needham et al. 2005a; Rubin et al. 2007; Sexton et al. 2011Levoglucosanb22Wood exposure biomarkerUrineLC-MS/MSNaeher et al. 2013Metabolomec23Biomarker discoveryDBS, serumHRMSBurgess et al. 2015; Frediani et al. 2014; Go et al. 2015; Roede et al. 2013microRNAc24Biomarker discoveryNT, plasmaRT-PCRHarrison et al. 2000; Ponnusamy et al. 2015mRNAc25Biomarker discoveryNTRT-PCRHarrison et al. 2000DNA methylationc26Biomarker discoveryNT, BC, BuccalBead chipPaquette et al. 2016Oral microbiomec27Biomarker discoveryOral rinse16S rDNAJovel et al. 2016Novel inflammation cancer markers [serum amyloid A, soluble tumor necrosis factor receptor 2, chemokine (C-X-C motif) ligand 9 or monokine induced by γ-interferon]c28Cancer risk evaluationPlasmaImmunoassayShiels et al. 2017Note: BC, buffy coat; buccal, buccal cells; DBS, dried blood spots; GC-MS/MS, gas chromatography-tandem mass spectrometry; HAPIN, Household Air Pollution Intervention Network; HRMS, high resolution mass spectrometry; ICP-MS, inductively coupled plasma-mass spectrometry; LC-MS/MS, liquid chromatography-tandem mass spectrometry; NT, nasal turbinate swab; POC, point-of-care; RT-PCR, reverse transcription–polymerase chain reaction.aMeasured in all other adult women.bMeasured in all participants including pregnant women, children, and other adult women.cMeasured in a subset of samples.Biomarkers of exposure [polycyclic aromatic hydrocarbons (PAHs), volatile organic chemicals (VOCs), and levoglucosan] will be measured in all or a subset of all longitudinally collected urine samples from all participants (Table 1). In the OAW, we will measure a suite of biomarkers of endothelial function [intercellular adhesion molecule 1 (ICAM-1), vascular cellular adhesion molecule 1 (VCAM-1), endothelin-1, E-selectin, von Willebrand factor antigen (vWF)] (Poggesi et al. 2016), inflammation [C-reactive protein (CRP), interleukin 6 (IL-6), F2-Isoprostanes] (Ghezzi et al. 2018), blood coagulation (vWF) (Wiseman et al. 2014), oxidative stress {8-hydroxy-deoxyguanosine (8OHdG), peroxidation [i.e., myeloperoxidase (MPO)]} (Marrocco et al. 2017), glycemic control/diabetes [hemoglobin A1C (HbA1c)] (Jia 2016), a marker with specific relevance to lung cancer [P53 tumor-associated antigen (TAA) antibodies] (Fortner et al. 2017; Shi et al. 2015), enzyme induction [cytochrome P450 (Cyp450)], and a marker of lung insult/inflammation [Clara cell protein (CC16)] (Broeckaert and Bernard 2000; Wong et al. 2009).Because this is a large randomized controlled trial, it provides an ideal mechanism for discovery of novel biomarkers of exposure and effects associated with HAP. In collaboration with the National Cancer Institute of the National Institutes of Health, an ancillary study was incorporated that involves additional sample collections in the OAW participants of Peru and Guatemala. These additional samples will be collected among all OAW at baseline and at the visit occurring approximately 12 months after the intervention (n=400 samples; Table 1). Novel inflammatory cancer markers will be measured and epigenetic and omics techniques will be used for biomarker discovery. In venous blood, the inflammatory markers serum amyloid A, soluble tumor necrosis factor receptor 2, chemokine (C-X-C motif) ligand 9 or monokine induced by γ-interferon will be evaluated, along with CRP to evaluate lung cancer risk (Shiels et al. 2015, 2017). In addition, measurement of mRNA, microRNA (miRNA), DNA methylation, the metabolome, and the microbiome in complementary samples will enable us to gain a better understanding of the response of these measures to exposure and intervention (Robles and Harris 2017; Vargas and Harris 2016).Biomarker MeasurementsAcross the course of the study, over 55,000 samples will be collected from participants, so it is not logistically feasible to analyze every biomarker in every sample. Thus, we have developed a biomarker analysis scheme that will enable us to maximize the data collected while still keeping the costs and human resource needs within budgetary constraints (Table 3). Our rationale for this measurement scheme relates to the health outcomes evaluated in each participant subset. For example, because cardiovascular outcomes will be assessed in the OAW, most clinical markers of cardiovascular disease will only be measured in those samples. Exposure markers will be measured in all participants, including children for whom direct exposure measurements will not be available. Further, we will measure all analytes in all longitudinal samples of a 5% subsample of the population to finalize our biomarker prioritization scheme. These data will provide information on within- and between-person variability in biomarker concentrations and on estimates for exposure–response that will inform the most efficient analysis scheme (i.e., to maximize the information gained for each sample type and aliquot by determining the number of longitudinal measures needed to efficiently answer our research questions). At minimum, this will include longitudinal measurements collected at baseline and after randomization. This process is currently underway as sample collection continues.Table 3 Biomarker class to be measured in each participant.Table 3 has five columns, namely, biomarker, biomarker number from Table 2, mother, OAW, and child.BiomarkerBiomarker no. from Table 2MotherOAWChildCardiovascular/endothelial markers1–4—X—Oxidative stress markers14–16—X—Lipids10—X—HbA1c8XXXHb9X—XOther clinical biomarkers7, 11, 13, 17—X—Exposure biomarkers18–22XXXInflammation markers5, 6, 12—X—Metabolome (subset)23XXXMicrobiome27—X—miRNA/mRNA24–25—X—DNA methylation26—X—Novel inflammation markers (subset)28———Note: —, not applicable; Hb, hemoglobin; HbA1c, hemoglobin A1c; OAW, older adult woman.Clinical Biomarkers in DBSTo more efficiently use DBS samples for clinical markers, a full spot will be sampled and eluted in 1.5mL phosphate buffered saline (PBS). Portions of this stock extract will be used in each of the biomarker measurements, which will require two aliquots from this same stock to allow for a replicate analysis.Endothelial, cardiovascular, inflammation, and oxidative stress (i.e., MPO) markers.These biomarkers are measured using commercially available multiplexed immunoassays (Meso Scale Discovery Multiplex Immunoassay Reader) with customized V-Plex kits. The PBS extracts are placed in 96-well plates, in duplicate, and prepared according to the standard assay protocol. The resulting reaction products are analyzed on a multiplex plate reader with a full set of calibrants and quality control samples. Values are averaged before reporting. Ca
Referência(s)