Real-world and Genomic data-based Asthma Insights through Network Analysis (REGAIN study): protocol for a novel retrospective and prospective longitudinal asthma cohort study - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. 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Learn more: PMC Disclaimer | PMC Copyright Notice BMJ Open . 2026 Apr 15;16(4):e104380. doi: 10.1136/bmjopen-2025-104380 Search in PMC Search in PubMed View in NLM Catalog Add to search Real-world and Genomic data-based Asthma Insights through Network Analysis (REGAIN study): protocol for a novel retrospective and prospective longitudinal asthma cohort study Andrew Kasarskis Andrew Kasarskis 1 Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA Find articles by Andrew Kasarskis 1 , Eileen Wang Eileen Wang 2 National Jewish Health, Denver, Colorado, USA Find articles by Eileen Wang 2 , Paul J Bryce Paul J Bryce 3 Sanofi SA, Cambridge, Massachusetts, USA Find articles by Paul J Bryce 3 , Radoslav Savic Radoslav Savic 1 Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA Find articles by Radoslav Savic 1 , Qi Pan Qi Pan 4 GeneDx LLC, Stamford, Connecticut, USA Find articles by Qi Pan 4 , Rachelle Weisman Rachelle Weisman 5 Pulmonary, Icahn School of Medicine at Mount Sinai, New York City, New York, USA Find articles by Rachelle Weisman 5 , Ellen Barnhart Ellen Barnhart 5 Pulmonary, Icahn School of Medicine at Mount Sinai, New York City, New York, USA Find articles by Ellen Barnhart 5 , Andrea Sifuentes Andrea Sifuentes 5 Pulmonary, Icahn School of Medicine at Mount Sinai, New York City, New York, USA Find articles by Andrea Sifuentes 5 , Eliseo Mosso Eliseo Mosso 5 Pulmonary, Icahn School of Medicine at Mount Sinai, New York City, New York, USA Find articles by Eliseo Mosso 5 , Lu Zhang Lu Zhang 3 Sanofi SA, Cambridge, Massachusetts, USA Find articles by Lu Zhang 3 , Eunjee Lee Eunjee Lee 4 GeneDx LLC, Stamford, Connecticut, USA Find articles by Eunjee Lee 4 , Zhongyang Zhang Zhongyang Zhang 1 Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA Find articles by Zhongyang Zhang 1 , Ke Hao Ke Hao 1 Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA Find articles by Ke Hao 1 , Eric Schadt Eric Schadt 1 Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA 4 GeneDx LLC, Stamford, Connecticut, USA Find articles by Eric Schadt 1, 4, 0 , Frank Nestle Frank Nestle 3 Sanofi SA, Cambridge, Massachusetts, USA Find articles by Frank Nestle 3, 0 , Emanuele de Rinaldis Emanuele de Rinaldis 3 Sanofi SA, Cambridge, Massachusetts, USA Find articles by Emanuele de Rinaldis 3, 0 , Linda Rogers Linda Rogers 5 Pulmonary, Icahn School of Medicine at Mount Sinai, New York City, New York, USA Find articles by Linda Rogers 5, ✉, 0 Author information Article notes Copyright and License information 1 Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA 2 National Jewish Health, Denver, Colorado, USA 3 Sanofi SA, Cambridge, Massachusetts, USA 4 GeneDx LLC, Stamford, Connecticut, USA 5 Pulmonary, Icahn School of Medicine at Mount Sinai, New York City, New York, USA Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise. AK, RS, QP, EL, KH and ES are former employees of GeneDx and may hold stock and/or stock options in the company. LZ, EdR, PB and FN are employees or former employees of Sanofi and may hold stock and/or stock options in the company. AK, RS, RW, EB, AS, EM, ZZ, KH, ES and LR are employees, or former employees, of Mount Sinai. AK is also a consultant to Sanofi. RS is also a contractor at Sanofi. LR is an employee of Mount Sinai Health System. She has received honouraria compensation as a consultant for Sanofi and AstraZeneca. She has been an investigator in studies sponsored by AstraZeneca and Sanofi, for which her institution has received funding. EW is an employee of National Jewish Health and has received honouraria from AstraZeneca, GlaxoSmithKline, Amgen, Apogee and Genentech. She has been an investigator on studies sponsored by AstraZeneca, GlaxoSmithKline, Genentech, Sanofi, Novartis and Teva, for which her institution has received funding. ✉ Dr Linda Rogers; [email protected] 0 ES, FN, EdR and LR are joint senior authors. Received 2025 Apr 29; Accepted 2026 Mar 2; Collection date 2026. Copyright © Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: https://creativecommons.org/licenses/by-nc/4.0/ . PMC Copyright notice PMCID: PMC13084774 PMID: 41985945 Abstract Introduction Although important learnings come from traditionally designed large prospective asthma cohorts, highly restrictive inclusion and exclusion criteria limit generalisability to clinical practice. Moreover, small sample sizes for important disease subtypes, narrow scope of clinical data collection and limited biomarker assessments reduce the power of some studies to detect important and diverse longitudinal disease courses. The Real-world and Genomic data-based Asthma Insights through Network Analysis (REGAIN) study takes a novel approach to asthma cohort development by employing a pragmatic definition of asthma and simplified study procedures for biospecimen and data collection. REGAIN will produce a large scale, real-world, longitudinal clinical and molecular description of asthma powered to characterise and compare clinically relevant asthma subtypes. This design will provide insights on distinct longitudinal trajectories of disease, predictors of response to therapies and likelihood of clinical remission, all of which should help guide asthma management. Methods and analysis REGAIN is a clinical observational retrospective and prospective cohort study designed to determine large scale, real-world longitudinal clinical and molecular descriptions of asthma according to types of treatment, level of asthma control and inflammatory biology based on clinical biomarkers. Key questions include predictors of change in asthma control as well as timing and durability of clinical remission on biological therapy. To complement these clinical insights, REGAIN will produce one of the largest multiscale data sets in asthma that will include demographic and clinical features, inflammatory biomarkers, responses to therapy with inhaled steroids and other inhaled controllers with or without asthma biologics, and serial airway epithelium and peripheral blood transcriptomics and proteomics. REGAIN targets enrolment of 780 participants with asthma fitting one of five prespecified asthma subtypes with the aim of better characterising under-studied groups and allowing comparative analyses to elucidate important differential therapeutic responses and clinical trajectories. We target enrolment of 400 healthy controls to provide a healthy state molecular description of the tissues sampled in REGAIN participants with asthma. Participants with asthma are followed prospectively for 18 months with assessment of longitudinal clinical status including prospective clinical data collection, integration of electronic medical record data and serial biospecimen collection at 6 and 18 months. Participants with asthma starting treatment with asthma biologics undergo additional clinical assessment and biospecimen sampling at 3 months to track early clinical and molecular response to therapy. Healthy participants without asthma are evaluated cross-sectionally on enrolment without longitudinal follow-up in order to compare molecular profiles for airway epithelium and blood. An optional study component for participants with asthma employs a mobile phone application, digital inhaler monitors and home digital peak flow measurements and contributes data on real-time medication use, serial lung function and geolocated environmental data relevant to asthma. Ethics and dissemination The REGAIN protocol and all amendments were approved by The Icahn School of Medicine at Mount Sinai Program for Protection of Human Subjects (PPHS19-0358), and all participants provided written informed consent. Enrolment began in November 2019 and was completed in February 2024. Results will be presented at local, national and international meetings, and results will be submitted to peer-reviewed journals for consideration for publication. Trial registration number NCT06623435 . Keywords: Asthma, Genomic Medicine, Clinical Protocols, Clinical Trial, Pulmonary Disease, Observational Study STRENGTHS AND LIMITATIONS OF THIS STUDY. Streamlined study procedures, including the use of electronic medical record (EMR) data and conducting research visits at clinic visits or home, allowed accrual of a large cohort representative of clinical practice at a small number of study sites. Use of nasal brushing is a minimally invasive, validated, efficient and inexpensive methodology to sample upper airway epithelial cells as a surrogate for lower airway cells serially over time. The protocol obtains demographic, clinical, electronic health records, digital and molecular data in REGAIN participants with asthma and a large cross-sectional healthy control population. Use of EMR data requires effort to harmonise data between sites and may result in some heterogeneity and missingness of clinical study data. The impact of the COVID-19 pandemic lasting into 2022 necessitated the addition of a study site and some modifications of the original protocol. Introduction Large cohort studies and clinical trials have helped illuminate the clinical and mechanistic heterogeneity of asthma. Research identified two main endotypes of airway inflammation in asthma, reflecting groups of patients with distinct inflammatory pathophysiology. Patients with type 2 high asthma have commonly been identified in clinical practice by blood eosinophil counts (BECs) >150 cells/µL and/or elevated levels of fractional exhaled nitric oxide (FeNO). Less commonly, type 2 low asthma presents with low or absent levels of type 2 biomarkers, and a neutrophilic immune response may predominate or coexist with type 2 inflammation. 1 These endotypes identify long-term disease-associated risk and likelihood of response to asthma therapies, including asthma biologics. 1 These endotypes were at least partly identified by observation of differential response to clinical treatments, suggesting that investigations focused on differential response to asthma therapies may reveal important insights into disease. 2 3 Historically, asthma cohort studies varied substantively in their design. Some studies used prospective enrolment and eligibility criteria akin to stringent criteria used for asthma treatment clinical trials. 4 5 These cohorts typically conducted extensive visits with the administration of many validated questionnaires, physiological testing and collection of biospecimens for clinical and translational investigations. Strengths of this type of prospective cohort design included rigorous validation of diagnosis and comprehensive, uniform data collection, but they came at the expense of complex study visits and accrual of a cohort that may be less generalisable to clinical practice. Important prospective and retrospective real-world registry studies have also been conducted to understand severe asthma. 6 7 Strengths of retrospective cohorts included size, scope and diversity of data, but they came at the cost of less diagnostic rigour, need for significant effort to validate and harmonise data, and missing and irregular data, complicating analysis. To date, it has been difficult to obtain the benefits of both study designs and simultaneously obtain molecular data at scale in a setting representative of contemporary specialist practices and biologics prescribed for asthma. We designed the Real-world and Genomic data-based Asthma Insights through Network Analysis (REGAIN) study to address these challenges. REGAIN is a retrospective and prospective longitudinal observational cohort study of asthma structured to provide a real-world clinical and molecular description of asthma according to treatment, level of control and inflammatory endotype. In the REGAIN protocol, we seek to leverage strengths of the different observational study designs to efficiently assemble a large asthma cohort that represents the diverse spectrum of inflammatory endotypes of asthma by adopting prospective data collection complemented by electronic medical records (EMRs) and the use of minimally invasive biospecimen collection. Our design facilitates the participation of a broad range of individuals by using a pragmatic clinical diagnosis of asthma to accrue large numbers of participants at few study sites. REGAIN also targets enrolment of specific clinically relevant asthma subtypes at numbers sufficient to power comprehensive longitudinal molecular analysis to elucidate potential predictors and mechanisms behind clinical outcomes over time. Our primary objective is to produce a multiscale integrated clinical and molecular data set of asthma including specific clinical subtypes previously understudied or underpowered for comparative analyses. We propose that these analyses will help identify high-risk patients that may require earlier interventions to avoid poor outcomes in terms of both asthma and comorbid conditions. Moreover, we expect these analyses will uncover critical disease mechanisms, biomarkers of disease trajectory and therapeutic response, and novel treatment targets. Our secondary objective is to use mobile health and digital tools to collect high-fidelity real-world data with continuous asthma monitoring and geographically identified environmental drivers of asthma events. We believe that multimodal longitudinal evaluation of clinically relevant and under-studied asthma subtypes in REGAIN directly addresses clinical knowledge gaps regarding predictors of treatment response, changes in asthma control and clinical remission and has high potential to support development of future biomarkers and therapeutic targets. Methods and analysis Study overview REGAIN is a retrospective and prospective longitudinal cohort study conducted at asthma specialty clinics and designed to (1) characterise and compare the longitudinal clinical courses of moderate-to-severe asthma in this setting and (2) understand the molecular underpinnings of that clinical course. We identify and enrol participants with asthma meeting specific inclusion criteria by screening the EMR from pulmonary and allergy/immunology specialty clinics. Targeted participants include 780 persons with asthma fitting five subtypes of asthma representing a spectrum of asthma organised according to treatment type, level of asthma control and inflammatory biology based on clinical biomarkers as outlined in table 1 and defined more precisely below. We recruit healthy participants without any history of asthma (n=400) in the New York and Denver metropolitan areas by community advertising. Healthy participants undergo one assessment cross-sectionally at enrolment and are not followed longitudinally. In contrast, participants with asthma are followed prospectively for 18 months after enrolment, and retrospective data is collected by survey at enrolment and both retrospectively and prospectively for the 18 month duration of the study from EMRs of consented participants with asthma ( figure 1 ). The full study protocol is available as online supplemental file 1 . Table 1. REGAIN asthma group comparison table. Type 2 high step therapy group (n=200) Likely type 2 low step therapy group (n=200) Stable biologic group (n=200) De novo biologic group (n=120) Failed multiple biologics group (n=60) FeNO ≥50 ppb at any clinical care time point OR BEC ≥300 /µL at any clinical care time point OR Eosinophilia by induced sputum or bronchoscopy (≥3%) if available historically. ACT score ≥20. FeNO ≤25 ppb AND BEC ≤200 /µL AND No eosinophilia by induced sputum or bronchoscopy. Any ACT score. Either type 2 high or likely type 2 low. ACT ≥20. OR ACT 16–19 and GINA Symptom 1–2. On biological therapy for at least 4 months. Either type 2 high or likely type 2 low. Any ACT. Starting a biological therapy for asthma. Not on another biological therapy for at least 4 months. Either type 2 high or likely type 2 low. Any ACT. Failed two different biological therapies. May or may not be on current biological therapy. Open in a new tab ACT, Asthma Control Test; BEC, blood eosinophil count; FeNO, fractional exhaled nitric oxide; GINA, Global Initiative for Asthma; REGAIN, Real-world and Genomic data-based Asthma Insights through Network Analysis. Figure 1. REGAIN study design. This table outlines the specific clinical criteria used to assign participants to the five asthma study groups: type 2 high step therapy, de novo biologic, stable biologic, failed multiple biologics and likely 2 low step therapy. Clinical data is collected through EMR, standard clinical workups and MP. Most study visits occur during scheduled clinical care to optimise participant convenience. Healthy control participants complete only the Day 0 baseline visit. The 3-month follow-up visit is exclusive to the de novo biologic group. EMR, electronic medical record; MP, molecular profiling. Open in a new tab Because all participants with asthma recruited for REGAIN contribute longitudinal molecular data from upper airway and blood, our asthma groups are intended to enable direct comparison of groups and learn the molecular features that distinguish them. For example, to understand differences in control achieved by biologics and control achieved by traditional inhaled therapy, we recruit the type 2 high step therapy group and the stable biologic group. Similarly, to understand the difference between those who experience benefit from biologics and those who do not, we recruit the failed multiple biologics group. To better understand the dynamics of establishing control and remission, we recruit participants not achieving adequate control on traditional inhaled therapies into the de novo biologic group at the time of their first biological therapy dose and follow them closely over time. Although type 2 high and low biology are already well understood molecularly, the gene expression associated with these endotypes in airway epithelium and blood over time in a real-world setting is not known, so to understand the differences between type 2 high and low biology, we recruit participants into a likely type 2 low step therapy group. We include presumptive type 2 low patients irrespective of asthma control on inhaled therapy alone in this last group to maximise number of participants with this type of asthma as they are less common than the type 2 high endotype in our clinic populations. Taken together, we feel that these groups balance the need to recruit asthma groups that have been challenging to study and pragmatic inclusion/exclusion criteria that facilitate recruitment. Study setting Specialty asthma clinics at the Mount Sinai Health System, New York, New York, USA, and National Jewish Health (NJH), Denver, Colorado, USA. Study enrolment period Enrolment began in November 2019 and closed in February 2024. Inclusion criteria for participants with asthma REGAIN uses a pragmatic, clinically oriented definition of asthma for study eligibility, with the goal of representing patients with asthma typically seen in clinical practice. We recruit participants 18 years of age or older with a clinical diagnosis of asthma verified by a specialist physician (allergist or pulmonologist) and at least one of the following: Historical variability of airflow limitation via standard reversibility criteria (≥12%, 200 mL within past 10 years). Historical methacholine challenge PC20 ≤8 mg/mL (within past 10 years). Forced expiratory volume in 1 sec (FEV1) variability between two clinic visits of 20% or more within the past 5 years either with improvement according to appropriate therapy or decrease in lung function on withdrawal or decrease of therapy. Elevated FeNO ≥50 ppb at least once historically (within the past 10 years). At least partial response of presenting symptoms to Global Initiative for Asthma (GINA) 2018 Step 1–5 asthma inhaled treatment Exclusion criteria for participants with asthma We exclude individuals with asthma from REGAIN if they have a history of ≥10 pack per year tobacco smoking, are current cigarette smokers, are users of e-cigarettes or other inhalants, have clinically significant lung disease other than asthma based on imaging or clinical history as determined by investigators and EMR, are currently pregnant or breast-feeding, or have anaemia at the time of enrolment. Inclusion and exclusion criteria for healthy participants We enrol healthy individuals without asthma to provide control data for mechanistic studies of nasal brushings and peripheral blood in comparison with asthma participants. Healthy individuals 18 years of age or older with <10 pack-years tobacco use who are not current smokers and not using other inhalants (cessation >6 months earlier) are eligible to participate in the study. We confirm smoking status by saliva cotinine level ≤30 ng/mL (The Abbott iScreen OFD Cotinine Test) and exclude individuals with a salivary cotinine level >30 ng/mL at the time of their study visit. We exclude healthy participants with current respiratory symptoms (cough, wheeze, dyspnoea) or any history of diagnosis of asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis or other significant lung diseases. We also exclude those with a current malignancy or a history of malignancy within the past 5 years and those with current renal, hepatic, cardiovascular, endocrine/metabolic, neurological, haematological, ophthalmological, gastrointestinal, inflammatory or autoimmune disease, HIV infection or other relevant chronic health conditions by judgement of the investigator. Finally, we exclude individuals currently treated with immune-modulating medications other than asthma biologics, individuals who received any vaccination within 4 weeks of the study visit, and individuals who are currently pregnant or breastfeeding. Study eligibility and enrolment target Our enrolment target is 780 participants with asthma and 400 healthy controls. We verify inclusion/exclusion criteria by EMR review and confirm diagnosis with a treating specialist clinician and/or study investigator. The five prespecified asthma groups are included based on the definitions outlined below and illustrated in table 1 . Definition of clinical type 2 high asthma status Participants with type 2 high asthma have a diagnosis of asthma as outlined above and a history of at least one of the following: FeNO ≥50 ppb at any clinical care time point. at least one BEC ≥300 /µL at any clinical care time point. History of eosinophilia by induced sputum or bronchoscopy (≥3%) if performed as part of clinical care. Definition of clinical likely type 2 low asthma Participants with likely type 2 low asthma have a diagnosis of asthma as outlined above and a history of all the following: FeNO ≤25 ppb and BEC ≤200 /µL and no eosinophilia by induced sputum or bronchoscopy if available historically or as obtained during clinical care. Type 2 high step therapy group (enrolment target = 200) Participants in this group have to meet the criteria for asthma diagnosis and type 2 asthma status and be managed with inhaled step therapy according to GINA 2018 Steps 1–5. They cannot be on chronic oral corticosteroids or biological therapy and must have an Asthma Control Test (ACT) score of 20 or greater at the time of enrolment. Likely type 2 low step therapy group (enrolment target = 200) Participants in this group have to meet the criteria for asthma diagnosis and type 2 low asthma status and be managed with inhaled step therapy according to GINA 2018 Steps 1–5. They cannot be on chronic daily oral corticosteroids or biological therapy and are permitted to have any level of asthma control as defined by ACT in order to facilitate enrolment of this relatively rare asthma group. Stable biologic group (enrolment target = 200) Participants included in the stable biologic group must meet the criteria for asthma diagnosis. They must be on biological therapy for at least 4 months at enrolment and have an ACT score of 20 or greater at the time of enrolment, indicating asthma control. Alternatively, if their ACT score is 16–19, but their GINA symptom control level is 1–2, then they are considered partly controlled and can also be included. 8 Failed multiple biologics group (enrolment target = 60) Participants included in this group can be on or off biological therapy at enrolment and can have any level of asthma control as defined by ACT and GINA scores at the time of enrolment. They have to report having prior biological treatment and stopping treatment with at least two asthma biologics. The prior biological therapies can be in the same class (for instance, two anti-IL5 agents) or in two different classes (for instance, anti-IgE and anti-IL-5). If members of this group are on biological therapy at enrolment, it must be with a biologic other than one of the two or more biologics they had previously stopped. De novo biologic group (enrolment target = 120) Participants starting a new asthma biological at the time of enrolment can be included in this group. They cannot be on a current biological at the time of the baseline visit and have to be off treatment with a prior asthma biological for at least 4 months prior to the baseline visit to be included in this group. Participants in this group have to complete their baseline biospecimen collection before administration of their first dose of the new asthma biological. Duration of study participation We enrol asthma participants in the study for 18 consecutive months ( figure 1 ). All asthma participant groups undergo follow-up visits at 6 and 18 months. De novo biologics participants undergo an additional study visit at 3 months. We assess healthy participants cross-sectionally at one baseline visit where data and biospecimen samples are collected after consent, but they are not followed prospectively and do not provide samples longitudinally. Patient and public involvement We did not directly involve patients and members of the public in setting the research question, the outcome measures or the study design. Participants with asthma in REGAIN have a ‘visit choice’ option that allows them to choose the time and site of their visit within the designated study visit time window and an option to participate in the digital asthma monitoring arm that allows them to document their asthma medications and symptoms during everyday life. Additionally, non-asthma members of the public who participated as healthy volunteers are essential to the dissemination of the eligibility criteria and opportunity to their local community, which is vital to our healthy cohort recruitment. We intend to share the main results with study participants and will seek patient and public involvement in the development of the most appropriate method of dissemination. Study flow overview Flow of participants in the study is outlined in figure 1 . Screening and consent The study obtained a waiver of authorisation so that EMRs for all patients with an asthma diagnosis and scheduled for a clinical visit at pulmonary and allergy/immunology clinics at the Mount Sinai Hospital, Mount Sinai Beth Israel Philips Ambulatory Care Center, Mount Sinai West Hospital and Mount Sinai Morningside Hospital in New York City and NJH in Denver, Colorado, USA could be evaluated for verification of asthma diagnosis and inclusion in the study. We prescreen provider schedules for eligible participants, and clinical coordinators contact the treating provider for permission to approach potential participants. We contact potential participants by email or phone about their interest or approach them in person at their clinical visit. We obtain informed consent prior to study procedures, typically on the day of the participant’s clinical visit or at a scheduled time before the visit. We collect qualifying study data by reviewing the participants’ EMR after obtaining informed consent. We recruit healthy control participants using paper and electronic flyers posted throughout health system sites, electronic billboards (ResearchMatch) and social media (Craigslist, Facebook). Healthy volunteers who are interested and believe they fit the criteria contact the study email. A study coordinator screens the potential participant by phone regarding their medical history using a phone script. We record this data only when the participant is eligible and after consent is obtained. Baseline visit For participants with asthma, following consent, we administer the ACT and prospective questionnaires regarding demographics, asthma history and other medical history. Coordinators then collect biospecimens and onboard interested participants into the optional digital health component. For healthy control participants, we collect the same biospecimens plus an additional saliva sample for cotinine testing and administer a standard medical history questionnaire. For all participants, the baseline study visit can occur in one of several ways according to participant preference: (1) on the same day and either before or after their clinical visit with their provider, (2) on a separate day as a dedicated study visit or (3) at a home visit with a research nurse (New York sites only). Follow-up visits For all asthma participant groups, we conduct follow-up study visits at 6 months and 18 months. Participants scheduled to start a new asthma biological therapy (the de novo biologic group) undergo an additional study visit at 3 months to capture potential early clinical and molecular responses to their new biological therapy ( figure 1 ). We coordinate follow-up study visits with the same options as baseline visits to either align with routine clinical follow-up appointments or schedule them separately. Participant stipends Participants receive stipends for time and effort for each study visit completed. Additional small stipends are provided for those who participate in the digital health component of the study at sign-up and on an incremental scale based on the frequency of completion of home digital peak flow monitoring to incentivise at least weekly use of the peak flow monitor between study visits. Study procedures Questionnaires We collect prospective data by questionnaire at each study visit to complement information collected from the EMR. We summarise types of data we collect and the source of the data in table 2 . This includes demographic information such as birthdate, self-reported race and/or ethnicity, educational attainment, number of adults and children in household, family household income, country of birth, height, weight and age of asthma diagnosis. We also record data from the EMR confirming asthma diagnosis and type two high inflammation status and document current asthma therapies and comorbid health conditions via questionnaire. We record asthma symptom control as assessed by the ACT and GINA Symptom Control. 8 At follow-up visits, we administer questionnaires including interval history of exacerbations and acute care and changes in asthma treatment, medications and medical history. Table 2. Clinical variables related to asthma severity and control collected from REGAIN participants with asthma at study visits and electronic medical records. Variable Measured at study visit Collected from EMR ACT Score Yes Yes GINA Score Yes Yes FeNO Yes Yes Blood eosinophil count Yes Yes Skin prick and blood allergy test results No Yes Medications Yes Yes Spirometry No Yes Comorbidities Yes Yes Exacerbations Yes Yes Demographics Yes Yes Open in a new tab ACT, Asthma Control Test; EMR, electronic medical record; FeNO, fractional exhaled nitric oxide; GINA, Global Initiative for Asthma; REGAIN, Real-world and Genomic data-based Asthma Insights through Network Analysis. Electronic medical record data Consent allowed for the collection of retrospective EMR data prior to enrolment and prospective EMR data for the 18-month study period. Collected EMR data includes medical history, including acute care and ambulatory visit history, diagnosis codes, medications, pulmonary function testing, FeNO values, allergy test results, BEC differential values and other blood test results, and imaging data ( table 2 ). Biospecimens Blood The blood biospecimens collected from study participants are used for DNA isolation and genotyping, RNA-Seq, serum, plasma and isolation of peripheral blood mononuclear cells separated by Ficoll according to standard protocols. 9 Nasal brushings We sample airway epithelial cells via nasal brushing at every study visit for asthma participants and at baseline for healthy participants. To collect the nasal brushings, we sequentially insert a cytology brush 1–2 cm into each nostril and then rotate it for 3–5 s behind the inferior turbinate. We immediately place the brushes in RNALater, preserving RNA for processing (Thermo Fisher Scientific, Waltham, Massachusetts, USA). 10 Optional digital health study component We offer participants with asthma the digital health component of the study at the baseline visit. This includes the use of the BreatheSmart application, digital inhaler monitors and home digital peak flow metre device. 11 We give participants who did not initially participate in the digital health component another opportunity to join at their 6-month follow-up visit. The mobile application connects remotely to digital inhaler monitors that record use of maintenance and relief inhaled therapy. The mobile application also administers symptom surveys and collects information about weather and pollen counts via Global Positioning System (GPS) location. The BreatheSmart app prompts participants joining the digital health component to perform peak flow monitoring weekly, and participants can also take additional measurements as frequently as they wish for self-monitoring of their asthma. We provide incentive payments at enrolment, 6 months and 18 months for participants who complete at least 80% of BreatheSmart app surveys and 80% of weekly peak flow measurements over the course of the study. The app collects location data at the time of use if the participant opts to allow location tracking. In these participants, the app collects longitude and latitude at least once daily when the app is open if location services are turned on in their mobile phone settings. These data are used to identify location-specific environmental information with high geographical resolution (air quality such as PM2.5 levels, weather, pollen counts, etc) including up to 250 m (roughly two city blocks) resolution of pollution levels, pollen counts, temperature, humidity and related variables, providing a far more granular context than lower resolution maps based on zip codes for participant home addresses. Protocol modifications We modified the original study protocol several times in response to (1) inability to identify sufficient type 2 low participants eligible for the study based on initial inclusion criteria early in the study, (2) impacts of the SARS-CoV-2 pandemic and (3) clinical introduction of new biological treatments for patients with type 2 low asthma. Clinical type 2 low asthma criteria modification In the initial version of the protocol, clinical type 2 low status had a stringent definition, including negative allergy testing, FeNO <19 ppb and BEC <150 cells/µL. However, following enrolment of the initial 71 participants, we noted that it was unlikely that we would reach an enrolment target of 200 type 2 low participants using these criteria. Of 432 patients initially screened, only 6 patients met type 2 low or potential type 2 low criteria, and we noted that potential participants often did not have allergy testing recorded, severely limiting enrolment. Moreover, laboratory values for BEC in the Mount Sinai EMR were often reported in increments of 100, forcing us to use a de facto cut-off for BEC to qualify for type 2 low status of 100 cells/µL. To support enrolment goals for a group enriched in type 2 low status, we modified the protocol to remove allergy status as a criterion, increase the FeNO threshold from 19 ppb to 25 ppb and increase the BEC threshold to 200 cells/µL. This created an asthma group referred to as ‘likely type 2 low’, anticipating that the group would be enriched in those without significant type 2 inflammation but that might potentially also include some participants with lower levels of type 2 inflammation. Modifications related to the SARS-CoV-2 pandemic We paused all study activities from 9 March 2020 through 25 June 2020 due to research restrictions implemented for the SARS-CoV-2 pandemic. We added an additional study site (NJH, Denver, Colorado, USA) in September 2021 and home visits in December 2021 in the New York City Metro area to accelerate enrolment. In the initial protocol, enrolled participants with asthma were to contact the study team within 24 hours of an asthma exacerbation requiring initiation of systemic corticosteroids so they could present for a visit to undergo biospecimen sampling within 72 hours of onset of the exacerbation. Several such exacerbation visits were performed in the initial 3 months of enrolment between November 2019 and March 2020. However, after March 2020, institutionally mandated SARS-CoV-2 pandemic safety precautions in New York City limited our ability to conduct these in-person visits during a meaningful window that would allow for mechanistic evaluation. This made ad hoc exacerbation study visits difficult to coordinate, and we therefore no longer prioritised them, although the protocol remained unchanged to leave open the possibility that this aspect of the study could be reintroduced later. After March 2020 until late 2021, pulmonary function testing could not be performed due to SARS-CoV-2 pandemic infection control measures, and we modified the protocol to remove this as an inclusion criterion for healthy participants. We continue to capture all pulmonary function test results for REGAIN participants with asthma present in the EMR for data analysis, including longitudinal data analysis where possible. Stable biologic and de novo biologic group modification At the start of the study, participants included in the stable biologic group and de novo biologic group were required to meet the criteria for asthma diagnosis and type 2 high endotype described above in the type 2 high asthma criteria. However, in December 2021, tezepelumab was approved in the USA for severe asthma irrespective of type 2 inflammation status, so we amended the study protocol to remove the type 2 biomarker requirements for the stable and de novo biologic groups. Study objectives The primary objective for REGAIN is to identify a clinical and molecular description of asthma disease according to type 2 high and low asthma biology and level of asthma control across a range of treatments. Secondary objectives for REGAIN include the following: To identify new biomarkers or biomarker combinations that could classify endotypes of asthma, differentiate clinical trajectory in different patients and identify responders versus non-responders to currently available asthma biological therapies. To identify novel mechanisms and treatment targets relevant to treating patients with otherwise poorly controlled subtypes of asthma. To assess molecular patterns associated with asthma exacerbations related to current therapy dependent on the spectrum of asthma severity. To use digital devices such as inhaler monitors and spirometers to monitor continuous community-based asthma and analyse specific environmental drivers of asthma events that may be location-specific using high-resolution geographical location data. We intend to achieve these objectives by pursuing the following analytical goals: To establish a comprehensive prospective and retrospective clinical assessment of asthma using primary data collection, electronic health records and community-based data using digital inhalers, spirometry and environmental data collected using the BreatheSmart mobile application. To identify predictors of treatment response and differential disease trajectories between groups. To identify correlations between clinical findings and molecular data to gain a comprehensive understanding of therapeutic response and disease risk. To identify a correlation between clinical measures of asthma control and asthma exacerbations and specific high-resolution environmental data by geolocation via digital monitoring control tools on the BreatheSmart application. To determine genetic profiles for asthma according to clinical inflammatory type, treatment regimen and level of control. To identify a nasal and blood transcriptome for asthma according to clinical inflammatory type, treatment regimen and level of control. To identify a nasal and blood transcriptome for asthma exacerbations according to clinical inflammatory type. Analytical plan We will review and clean data per standard protocols to create a locked, analysis-ready version of clinical data, including measures collected by questionnaires and from the EMR. We will store and process all biospecimens including DNA and RNA samples in batches to minimise batch effects. We will evaluate the nasal transcriptome as a surrogate for the bronchial transcriptome as described by others, and because nasal biospecimen acquisition is far less invasive than bronchoscopy, we anticipate that we will have nasal epithelial biospecimens from almost all REGAIN participants and time points, ensuring reliable power for molecular analysis of the airway. 12 , 15 We will examine clinical and molecular profiling data for procedural, demographic and other sources of variation, and account for this variation in subsequent analyses. In addition, we will randomise sample processing and data generation steps to the extent possible. All analyses using identifiable data, including birthdates and geolocation, will be conducted at academic sites and no identifiable data is shared with industry partners or anyone not an Institutional Review Board (IRB)-approved REGAIN study investigator. To meet the study objectives, we will rely on the following as primary endpoints that the modelling of molecular profiling and other data from this study would be used to predict: (1) time to exacerbation, (2) frequency of exacerbation, (3) pulmonary function results via clinic-measured or home-measured spirometry (FEV 1 ), (4) fraction of individuals experiencing an exacerbation and (5) asthma control by ACT or GINA on a specific level of therapy. Asthma exacerbations are defined by at least 3 days of oral corticosteroids use, and severity of exacerbations is measured by whether or not acute care of any type or hospitalisation occurred based on EMR data and patient report in questionnaires. The analytical team will model each type of data separately to build network models of the biology measured by those data in the tissue of origin. Following adjustment for covariates, network modelling of each data type will be performed (eg, blood RNA-Seq, nasal brush RNA-Seq), augmented by reference to public data such as Genome-wide Association Studies (GWAS) and relevant gene, tissue and drug-specific gene expression signatures in public repositories. A combination of coexpression, Bayesian and other network models will be used to evaluate heterogeneity of asthma at the molecular level and to identify network modules that relate molecular information to clinical outcomes and response to treatment in asthma. All aspects of disease treatment aim to return the state of the molecular networks in an individual to a state as close as possible to the normal healthy state. Accordingly, we will take advantage of the abundance of data from healthy controls to build maximally rich network models of this goal state from 400 healthy individuals. We will match molecular asthma endotypes to clinical asthma subtypes and attempt to predict response to therapy based on joint analysis of all data. We will also perform longitudinal data analysis to investigate the correlation of asthma control, exacerbations and impact of therapies. We will assess potential asthma triggers according to GPS and correlate environmental triggers, including pollen, weather and PM2.5 with clinical course and inflammatory profiles. The analytical team will work using an iterative ‘team science’ approach and include representation by disease expert clinicians, immunologists, computational biologists and statisticians to achieve the scientific objectives of this study as stated. Study power The overall sample size of this study and the sample sizes for each of the clinical subgroups were determined by a multidisciplinary group including clinicians and computational biologists. The sample size was based on feasibility according to clinical investigator assessment and subgroup sample size needed to power network analysis according to the computational team as described below. In general, there is a progression in complexity of the type of model that can be achieved from gene expression profiling or other molecular profiling of tissues. With ∼15–50 samples per group or more, signatures of differentially expressed transcripts and genes can be identified. This analysis provides a list of genes related to the disease, but not necessarily which of the many differentially expressed genes are most causally associated with the disease state. With ∼50–100 samples per group or more, coexpression network models of the molecular state of disease in the group can be created. 16 These models identify nodes central to the network and allow for ranking genes by their likelihood of influencing the network’s behaviour. However, these models are not suitable for key driver analysis or precise simulations of network behaviour in response to therapeutic or environmental intervention. With ∼100–300 samples per group, Bayesian network models of the molecular state of disease in the group can be developed. 17 18 These models are optimal for key driver analysis and simulation of the effect of ablating or increasing the activity of a network node (gene or other). 19 These models can identify the network genes with the most significant overall effect on the network if perturbed and can begin to identify how therapeutics might impact the disease network toward a healthy state, provided that appropriately detailed network models of both disease and healthy states are available. This last point was the rationale for including a large population of healthy individuals, n=400, another distinguishing feature of this study. The recommendation of asthma participant subgroup numbers in each group followed the modelling requirements described above for the type 2 high step therapy group and stable biologic group. In the case of the failed multiple biologics group and the de novo biologic group, sample size was influenced by these factors but also limited by recruitment feasibility determined by asthma experts on the research team. Networks of all sizes and types provide excellent frameworks for organising and integrating clinical and gene-related data. 20 21 Ethics and dissemination We adhere to ethical principles and standards as described in Guidance for Industry: E6 Good Clinical Practice Consolidated Guidance for the REGAIN study. 22 Academic partners developed the REGAIN study protocol with input from industry partners; patients with asthma were not formally involved in protocol development. This study involved human participants and was approved by the Mount Sinai Program for Protection of Human Subjects (PPHS19-0358), and they served as the IRB of record for clinical sites in both New York and Colorado and reviewed and approved the protocol and informed consent documents for REGAIN. Before any study procedures, all participants provided written informed consent, and participants could withdraw at any time. Participants could opt to participate in digital monitoring via the Aptar BreathSmart app but could enrol in the study without any obligation to participate in digital monitoring. Digital study participants could also choose to exclude or permit geolocation tracking from their participation. Data analysis and manuscript preparation will involve input from all partners, with final versions approved by academic site principal investigators. The primary results of this study will be presented at academic conferences and disseminated via peer-reviewed journals. Discussion The REGAIN study takes a novel approach to asthma cohort development to produce a large scale, diverse, real-world, longitudinal clinical and molecular description of asthma according to treatment, level of asthma control, degree of remission and airway inflammatory subtype. We focus enrolment on five clinically relevant groups of patients on treatment with the full scope of inhaled and biological asthma therapies used in current practice. The goal is to leverage heterogeneity of airway inflammation, differential responses to therapies and diversity of clinical trajectories to identify novel clinical and mechanistic observations. The five asthma groups capture early and later stage responses across a diverse range of biologics and include those clinically well controlled on inhaled therapy, those refractory despite trials of multiple biologics, those with T2 high versus T2 low inflammation and a key group of those starting on a new biologic. We anticipate that these design features will help generate a unique, comprehensive multimodal data set to provide novel insights on asthma, response to asthma therapy and disease-associated risk. We anticipate achieving the desired sample size using fewer study sites and enrolling a more generalisable group of participants with asthma than many other studies of asthma. Specific strategies include (1) use of a pragmatic criteria for asthma diagnosis, (2) use of EMR data to streamline data collection and limit the need for prospective questionnaires (3), use of ‘visit choice’ allowing participants to choose time and site of visit within the designated study visit time window and (4) use of minimally invasive biospecimen collection. Most large asthma cohort studies adopt one of two approaches for establishing asthma diagnosis. Some mandate demonstration of bronchodilator reversibility or bronchial hyperreactivity based on inhalational challenge testing, while others accept physician or medical record diagnosis. Increasingly, data suggests that a single demonstration of bronchodilator response using various criteria has low sensitivity for asthma diagnosis and discriminates poorly between asthma and COPD. 23 24 Inhalational challenge is costly and challenging to implement in large studies. Moreover, the use of bronchodilator response or inhalational challenge as inclusion criteria reduces external validity of studies. 25 Use of physician diagnosis of asthma as an inclusion criterion, particularly diagnoses from primary care, is associated with significant misdiagnosis. 26 We combine multiple clinical criteria and specialist diagnosis in our study as a pragmatic ‘middle way’ between these two approaches. To reduce bias in the representation of people with asthma in REGAIN and thereby enhance the generalisability of our conclusions, we seek to increase broad participation by reducing the study burden for participants. Conducting research visits at the time of clinical appointments reduces the number of patients who decline participation due to concerns about missing work or school. Similarly, adding home visits in response to the impact of the SARS-CoV-2 pandemic helps sustain enrolment and maximise the retention of participants in the study. To streamline visits and reduce questionnaire burden, we limit prospective questionnaires to the ACT to assess symptom control and limit other questions to verify key demographics and asthma-specific information. We then leverage EMR data to acquire a broader range of clinical data and simultaneously reduce questionnaire burden and time needed to complete study visits. This streamlines visits such that the initial study visit can be conducted in under 1 hour and follow-up visits in 30 min or less. The primary goal of the study is to construct a data set integrating clinical parameters, treatment response, electronic health record and digital disease monitoring data, DNA variation and gene and protein expression. This will allow construction of multiscale causal networks of disease. To collect biospecimens for this study, we use nasal brushing as a minimally invasive methodology to sample airway epithelial cells as a surrogate for lower airway cells. This efficient, low-risk, inexpensive, minimally invasive approach allows repeated sampling of large numbers of patients over time. Prior work has shown 90% concordance in the expression of non-ubiquitously expressed genes between nasal airway epithelial cells sampled using this approach and those obtained by bronchoscopy. 12 , 15 Studies that used sputum induction and/or bronchoscopy to obtain airway samples often failed to obtain specimens in many participants due to participant preference and/or concern for risk, as well as technical failure. For example, in Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (UBIOPRED) cohort, sputum induction and bronchoscopy were completed on 44% and 22% of asthma participants, respectively, and less than half of healthy participants for both procedures. 5 Sputum samples were obtained on approximately 78% of adult participants in Severe Asthma Research Program (SARP) III. 27 Bronchoscopy and sputum induction are technically more difficult and costly to perform on a large scale. Following the SARS-CoV-2 pandemic, most potential participants are accustomed to nasal sampling, which is quick and inexpensive. We propose that using nasal brushing as an approach to sample airway epithelial cells could offer advantages in terms of study power by achieving more complete sampling free from bias toward those able and willing to undergo bronchoscopy and sputum induction, as well as larger sample sizes. Moreover, we hypothesise that by selecting patient groups of specific interest, particularly around their response to different therapies and targeting sample sizes of subgroups to enable network analysis, we will have sufficient power to identify key pathways determining therapeutic response to treatments in both peripheral blood and airway tissue samples. There are some limitations to the study approach undertaken in the REGAIN cohort. First, early in the study, we needed to adjust our study protocol definition of type 2 high and type 2 low asthma due to challenges identifying type 2 low participants when using extremely stringent criteria. At the time of the study design, we set a goal to include 200 participants meeting this criterion based on data suggesting that type 2 low asthma, by this definition, constituted 50% of those with asthma. 28 Subsequent data suggested that these prevalence rates of type 2 low asthma were 5% or less. 29 Thus, early in the study, we realised that true type 2 low patients were much less prevalent than anticipated, and we adjusted our inclusion criteria accordingly to have a group of sufficient size to perform meaningful analyses that we define as likely type 2 low. We recognise this group may contain participants with type 2 inflammation and plan to analyse the data accordingly, for instance by considering BEC and FeNO as covariables in the analysis of the molecular data and conducting sensitivity analysis with different definitions of type 2 low asthma. Shortly after the initiation of the study in November 2019, the study was severely impacted by the SARS-CoV-2 pandemic. To mitigate this impact, we added an additional study site (NJH Denver) and home visits to improve recruitment time. This resulted in the addition of a site (NJH) using a different EMR system, which could create challenges during analysis in harmonising and analysing EMR data from the study. Although the original protocol intended to perform a study visit for participants within 72 hours of the onset of an exacerbation, infection control protocols that lasted well into 2022 made it infeasible to perform this aspect of the study. Additional limitations that we anticipated include gaps in data/irregular data structure due to the use of clinical practice/EMR data and limitations of nasal brushing data to approximate the distal lung epithelium. 12 Supplementary material online supplemental file 1 bmjopen-16-4-s001.pdf (2.5MB, pdf) DOI: 10.1136/bmjopen-2025-104380 Acknowledgements This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Award (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. We would like to thank our clinical coordinators and study staff: Joseph Smith, Juno Pak, Kevin Smilor, Lily Vesel, Teishalee Hemmings, Nicholas Roscoe, Wil Robinson, Asher Leviton, Nicole Lewis, Kara Hebbe, Robert Cahn, Madeline Ng, Caresse Opoku and Tuhina Bhat. Footnotes Funding: This work was supported by a grant from Sanofi to GeneDx (formerly Sema4) with a subcontract to Mount Sinai Health System, New York, New York, USA and National Jewish Health, Denver, Colorado, USA. Several authors are or have been affiliated with Sanofi, the study sponsor, and have made contributions to this manuscript as described in the contributions section. Specifically, Sanofi authors agreed with the decision to submit this paper for publication and contributed to study conception and design, protocol implementation and study execution, oversight of data collection and quality control of data, statistical support and development of analytical plan, manuscript writing, revision and preparation for submission, and manuscript review. While all author contributions improved the quality of the REGAIN study, Sanofi did not influence the results, outcomes or interpretations of the study despite author affiliations with the funder. Prepub: Prepublication history and additional supplemental material for this paper are available online. To view these files, please visit the journal online ( https://doi.org/10.1136/bmjopen-2025-104380 ). Provenance and peer review: Not commissioned; externally peer reviewed. Patient consent for publication: Not applicable. Patient and public involvement: Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details. References 1. Hammad H, Lambrecht BN. The basic immunology of asthma. Cell. 2021;184:1469–85. doi: 10.1016/j.cell.2021.02.016. [ DOI ] [ PubMed ] [ Google Scholar ] 2. Flood-Page P, Swenson C, Faiferman I, et al. A study to evaluate safety and efficacy of mepolizumab in patients with moderate persistent asthma. Am J Respir Crit Care Med. 2007;176:1062–71. doi: 10.1164/rccm.200701-085OC. [ DOI ] [ PubMed ] [ Google Scholar ] 3. 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