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Psychological distress and associated factors among asthmatic patients in Southern, Ethiopia, 2021

There is an increased prevalence of psychological distress in adults with asthma. Psychological distress describes unpleasant feelings or emotions that impact the level of functioning. It is a significant exac...

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Retrospective assessment of a collaborative digital asthma program for Medicaid-enrolled children in southwest Detroit: reductions in short-acting beta-agonist (SABA) medication use

Real-world evidence for digitally-supported asthma programs among Medicaid-enrolled children remains limited. Using data from a collaborative quality improvement program, we evaluated the impact of a digital i...

Nonadherence to antiasthmatic medications and its predictors among asthmatic patients in public hospitals of Bahir Dar City, North West Ethiopia: using ASK-12 tool

Globally, adequate asthma control is not yet achieved. The main cause of uncontrollability is nonadherence to prescribed medications.

The hen and the egg question in atopic dermatitis: allergy or eczema comes first

Atopic dermatitis (AD) as a chronic inflammatory systemic condition is far more than skin deep. Co-morbidities such as asthma and allergic rhinitis as well as the psychological impact influence seriously the q...

Medication regimen complexity and its impact on medication adherence and asthma control among patients with asthma in Ethiopian referral hospitals

Various studies have found that medication adherence is generally low among patients with asthma, and that the complexity of the regimen may be a potential factor. However, there is no information on the compl...

Monoclonal antibodies targeting small airways: a new perspective for biological therapies in severe asthma

Small airway dysfunction (SAD) in asthma is characterized by the inflammation and narrowing of airways with less of 2 mm in diameter between generations 8 and 23 of the bronchial tree. It is now widely accepte...

Level of asthma control and its determinants among adults living with asthma attending selected public hospitals in northwestern, Ethiopia: using an ordinal logistic regression model

Asthma is a major public health challenge and is characterized by recurrent attacks of breathlessness and wheezing that vary in severity and frequency from person to person. Asthma control is an important meas...

Static lung volumes and diffusion capacity in adults 30 years after being diagnosed with asthma

Long-term follow-up studies of adults with well-characterized asthma are sparse. We aimed to explore static lung volumes and diffusion capacity after 30 + years with asthma.

Over-prescription of short-acting β 2 -agonists and asthma management in the Gulf region: a multicountry observational study

The overuse of short-acting β 2 -agonists (SABA) is associated with poor asthma control. However, data on SABA use in the Gulf region are limited. Herein, we describe SABA prescription practices and clinical outcom...

A serological biomarker of type I collagen degradation is related to a more severe, high neutrophilic, obese asthma subtype

Asthma is a heterogeneous disease; therefore, biomarkers that can assist in the identification of subtypes and direct therapy are highly desirable. Asthma is a chronic inflammatory disease that leads to change...

Adherence to inhalers and associated factors among adult asthma patients: an outpatient-based study in a tertiary hospital of Rajshahi, Bangladesh

Adherence to inhaler medication is an important contributor to optimum asthma control along with adequate pharmacotherapy. The objective of the present study was to assess self-reported adherence levels and to...

The link between atopic dermatitis and asthma- immunological imbalance and beyond

Atopic diseases are multifactorial chronic disturbances which may evolve one into another and have overlapping pathogenetic mechanisms. Atopic dermatitis is in most cases the first step towards the development...

The effects of nebulized ketamine and intravenous magnesium sulfate on corticosteroid resistant asthma exacerbation; a randomized clinical trial

Asthma exacerbation is defined as an acute attack of shortness of breath with more than 25% decrease in morning peak flow compared to the baseline on 2 consecutive days, which requires immediate standard thera...

Determinants of asthma in Ethiopia: age and sex matched case control study with special reference to household fuel exposure and housing characteristics

Asthma is a chronic inflammatory disorder characterized by airway obstruction and hyper-responsiveness. Studies suggest that household fuel exposure and housing characteristics are associated with air way rela...

Feasibility and acceptability of monitoring personal air pollution exposure with sensors for asthma self-management

Exposure to fine particulate matter (PM 2.5 ) increases the risk of asthma exacerbations, and thus, monitoring personal exposure to PM 2.5 may aid in disease self-management. Low-cost, portable air pollution sensors...

Biological therapy for severe asthma

Around 5–10% of the total asthmatic population suffer from severe or uncontrolled asthma, which is associated with increased mortality and hospitalization, increased health care burden and worse quality of lif...

Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children

Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment...

Positive change in asthma control using therapeutic patient education in severe uncontrolled asthma: a one-year prospective study

Severe asthma is difficult to control. Therapeutic patient education enables patients to better understand their disease and cope with treatment, but the effect of therapeutic patient education in severe uncon...

Asthma and COVID-19: a dangerous liaison?

The coronavirus disease 2019 (COVID-19) pandemic, caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), provoked the most striking international public health crisis of our time. COVI...

Knowledge, attitude, and practice towards COVID-19 among chronic disease patients at Aksum Hospital, Northern Ethiopia, 2020: a cross-sectional study

The Coronavirus disease 2019 outbreak is the first reported case in Wuhan, China in December 2019 and suddenly became a major global health concern. Currently, there is no vaccine and treatment have been repor...

Self-reported vs. objectively assessed adherence to inhaled corticosteroids in asthma

Adherence to inhaled corticosteroids (ICS) in asthma is vital for disease control. However, obtaining reliable and clinically useful measures of adherence remains a major challenge. We investigated the associa...

Association between prevalence of obstructive lung disease and obesity: results from The Vermont Diabetes Information System

The association of obesity with the development of obstructive lung disease, namely asthma and/or chronic obstructive pulmonary disease, has been found to be significant in general population studies, and weig...

Changes in quantifiable breathing pattern components predict asthma control: an observational cross-sectional study

Breathing pattern disorders are frequently reported in uncontrolled asthma. At present, this is primarily assessed by questionnaires, which are subjective. Objective measures of breathing pattern components ma...

The role of leukotriene modifying agent treatment in neuropsychiatric events of elderly asthma patients: a nested case control study

In March 2020, the US Food and Drug Administration decided that the dangers related to neuropsychiatric events (NPEs) of montelukast, one of the leukotriene modifying agents (LTMAs), should be communicated thr...

Asthma and stroke: a narrative review

Asthma is a heterogeneous disease, usually characterized by chronic airway inflammation, bronchial reversible obstruction and hyperresponsiveness to direct or indirect stimuli. It is a severe disease causing a...

Comparison of dental caries (DMFT and DMFS indices) between asthmatic patients and control group in Iran: a meta-analysis

The association between caries index, which is diagnosed by Decayed, Missing, and Filled Teeth (DMFT), and asthma has been assessed in several studies, which yielded contradictory results. Meta-analysis is the...

ICS/formoterol in the management of asthma in the clinical practice of pulmonologists: an international survey on GINA strategy

The treatment with short-acting beta-2 agonists (SABA) alone is no longer recommended due to safety issues. Instead, the current Global Initiative for Asthma (GINA) Report recommends the use of the combination...

Sustainability of residential environmental interventions and health outcomes in the elderly

Research has documented that housing conditions can negatively impact the health of residents. Asthma has many known indoor environmental triggers including dust, pests, smoke and mold, as evidenced by the 25 ...

Non-adherence to inhaled medications among adult asthmatic patients in Ethiopia: a systematic review and meta-analysis

Medication non-adherence is one of a common problem in asthma management and it is the main factor for uncontrolled asthma. It can result in poor asthma control, which leads to decreased quality of life, incre...

The outcome of COVID-19 among the geriatric age group in African countries: protocol for a systematic review and meta-analysis

According to the World Health Organization (WHO), the outbreak of coronavirus disease in 2019 (COVID-19) has been declared as a pandemic and public health emergency that infected more than 5 million people wor...

Correction to: A comparison of biologicals in the treatment of adults with severe asthma – real-life experiences

An amendment to this paper has been published and can be accessed via the original article.

The original article was published in Asthma Research and Practice 2020 6 :2

Disease control in patients with asthma and respiratory symptoms (wheezing, cough) during sleep

The Global Initiative for Asthma ( GINA)-defined criteria for asthma control include questions about daytime symptoms, limitation of activity, nocturnal symptoms, need for reliever treatment and patients’ satisfac...

The burden, admission, and outcomes of COVID-19 among asthmatic patients in Africa: protocol for a systematic review and meta-analysis

Coronavirus disease 2019 outbreak is the first reported case in Wuhan, China in December 2019 and suddenly became a major global health concern. According to the European Centre for Disease Prevention and Cont...

The healthcare seeking behaviour of adult patients with asthma at Chitungwiza Central Hospital, Zimbabwe

Although asthma is a serious public health concern in Zimbabwe, there is lack of information regarding the decision to seek for healthcare services among patients. This study aimed to determine the health care...

Continuous versus intermittent short-acting β2-agonists nebulization as first-line therapy in hospitalized children with severe asthma exacerbation: a propensity score matching analysis

Short-acting β2-agonist (SABA) nebulization is commonly prescribed for children hospitalized with severe asthma exacerbation. Either intermittent or continuous delivery has been considered safe and efficient. ...

Patient perceived barriers to exercise and their clinical associations in difficult asthma

Exercise is recommended in guidelines for asthma management and has beneficial effects on symptom control, inflammation and lung function in patients with sub-optimally controlled asthma. Despite this, physica...

Asthma management with breath-triggered inhalers: innovation through design

Asthma affects the lives of hundred million people around the World. Despite notable progresses in disease management, asthma control remains largely insufficient worldwide, influencing patients’ wellbeing and...

A nationwide study of asthma correlates among adolescents in Saudi Arabia

Asthma is a chronic airway inflammation disease that is frequently found in children and adolescents with an increasing prevalence. Several studies are linking its presence to many lifestyle and health correla...

A comparison of biologicals in the treatment of adults with severe asthma – real-life experiences

Anti-IgE (omalizumab) and anti-IL5/IL5R (reslizumab, mepolizumab and benralizumab) treatments are available for severe allergic and eosinophilic asthma. In these patients, studies have shown beneficial effects...

The Correction to this article has been published in Asthma Research and Practice 2020 6 :10

Determinants of Acute Asthma Attack among adult asthmatic patients visiting hospitals of Tigray, Ethiopia, 2019: case control study

Acute asthma attack is one of the most common causes of visits to hospital emergency departments in all age groups of the population and accounts for the greater part of healthcare burden from the disease. Des...

Determinants of non-adherence to inhaled steroids in adult asthmatic patients on follow up in referral hospital, Ethiopia: cross-sectional study

Asthma is one of the major non-communicable diseases worldwide. The prevalence of asthma has continuously increased over the last five decades, resulting in 235 million people suffering from it. One of the mai...

Development of a framework for increasing asthma awareness in Chitungwiza, Zimbabwe

Asthma accounts for significant global morbidity and health-care costs. It is still poorly understood among health professionals and the general population. Consequently, there are significant morbidity and mo...

Epidemiology and utilization of primary health care services in Qatar by asthmatic children 5–12 years old: secondary data analysis 2016–2017

Childhood asthma is a growing clinical problem and a burden on the health care system due to repetitive visits to children’s emergency departments and frequent hospital admissions where it is poorly controlled...

Is asthma in the elderly different? Functional and clinical characteristics of asthma in individuals aged 65 years and older

The prevalence of chronic diseases in the elderly (> 65 years), including asthma, is growing, yet information available on asthma in this population is scarce.

Factors associated with exacerbations among adults with asthma according to electronic health record data

Asthma is a chronic inflammatory lung disease that affects 18.7 million U.S. adults. Electronic health records (EHRs) are a unique source of information that can be leveraged to understand factors associated w...

What is safe enough - asthma in pregnancy - a review of current literature and recommendations

Although asthma is one of the most serious diseases causing complications during pregnancy, half of the women discontinue therapy thus diminishing the control of the disease, mostly due to the inadequate educa...

Biomarkers in asthma: state of the art

Asthma is a heterogenous disease characterized by multiple phenotypes driven by different mechanisms. The implementation of precision medicine in the management of asthma requires the identification of phenoty...

Exhaled biomarkers in childhood asthma: old and new approaches

Asthma is a chronic condition usually characterized by underlying inflammation. The study of asthmatic inflammation is of the utmost importance for both diagnostic and monitoring purposes. The gold standard fo...

Assessment of predictors for acute asthma attack in asthmatic patients visiting an Ethiopian hospital: are the potential factors still a threat?

Recurrent exacerbations in patients with moderate or severe asthma are the major causes of morbidity, mortality and medical expenditure. Identifying predictors of frequent asthma attack might offer the fertile...

Effect of adjusting the combination of budesonide/formoterol on the alleviation of asthma symptoms

The combination of budesonide + formoterol (BFC) offers the advantages of dose adjustment in a single inhaler according to asthma symptoms. We analyzed the relationship between asthma symptoms in terms of peak...

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  • Systematic review
  • Open access
  • Published: 19 February 2024

‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice

  • Annette Boaz   ORCID: orcid.org/0000-0003-0557-1294 1 ,
  • Juan Baeza 2 ,
  • Alec Fraser   ORCID: orcid.org/0000-0003-1121-1551 2 &
  • Erik Persson 3  

Implementation Science volume  19 , Article number:  15 ( 2024 ) Cite this article

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The gap between research findings and clinical practice is well documented and a range of strategies have been developed to support the implementation of research into clinical practice. The objective of this study was to update and extend two previous reviews of systematic reviews of strategies designed to implement research evidence into clinical practice.

We developed a comprehensive systematic literature search strategy based on the terms used in the previous reviews to identify studies that looked explicitly at interventions designed to turn research evidence into practice. The search was performed in June 2022 in four electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched from January 2010 up to June 2022 and applied no language restrictions. Two independent reviewers appraised the quality of included studies using a quality assessment checklist. To reduce the risk of bias, papers were excluded following discussion between all members of the team. Data were synthesised using descriptive and narrative techniques to identify themes and patterns linked to intervention strategies, targeted behaviours, study settings and study outcomes.

We identified 32 reviews conducted between 2010 and 2022. The reviews are mainly of multi-faceted interventions ( n  = 20) although there are reviews focusing on single strategies (ICT, educational, reminders, local opinion leaders, audit and feedback, social media and toolkits). The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Furthermore, a lot of nuance lies behind these headline findings, and this is increasingly commented upon in the reviews themselves.

Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been identified. We need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of research perspectives (including social science) in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed.

Peer Review reports

Contribution to the literature

Considerable time and money is invested in implementing and evaluating strategies to increase the implementation of research into clinical practice.

The growing body of evidence is not providing the anticipated clear lessons to support improved implementation.

Instead what is needed is better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice.

This would involve a more central role in implementation science for a wider range of perspectives, especially from the social, economic, political and behavioural sciences and for greater use of different types of synthesis, such as realist synthesis.

Introduction

The gap between research findings and clinical practice is well documented and a range of interventions has been developed to increase the implementation of research into clinical practice [ 1 , 2 ]. In recent years researchers have worked to improve the consistency in the ways in which these interventions (often called strategies) are described to support their evaluation. One notable development has been the emergence of Implementation Science as a field focusing explicitly on “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice” ([ 3 ] p. 1). The work of implementation science focuses on closing, or at least narrowing, the gap between research and practice. One contribution has been to map existing interventions, identifying 73 discreet strategies to support research implementation [ 4 ] which have been grouped into 9 clusters [ 5 ]. The authors note that they have not considered the evidence of effectiveness of the individual strategies and that a next step is to understand better which strategies perform best in which combinations and for what purposes [ 4 ]. Other authors have noted that there is also scope to learn more from other related fields of study such as policy implementation [ 6 ] and to draw on methods designed to support the evaluation of complex interventions [ 7 ].

The increase in activity designed to support the implementation of research into practice and improvements in reporting provided the impetus for an update of a review of systematic reviews of the effectiveness of interventions designed to support the use of research in clinical practice [ 8 ] which was itself an update of the review conducted by Grimshaw and colleagues in 2001. The 2001 review [ 9 ] identified 41 reviews considering a range of strategies including educational interventions, audit and feedback, computerised decision support to financial incentives and combined interventions. The authors concluded that all the interventions had the potential to promote the uptake of evidence in practice, although no one intervention seemed to be more effective than the others in all settings. They concluded that combined interventions were more likely to be effective than single interventions. The 2011 review identified a further 13 systematic reviews containing 313 discrete primary studies. Consistent with the previous review, four main strategy types were identified: audit and feedback; computerised decision support; opinion leaders; and multi-faceted interventions (MFIs). Nine of the reviews reported on MFIs. The review highlighted the small effects of single interventions such as audit and feedback, computerised decision support and opinion leaders. MFIs claimed an improvement in effectiveness over single interventions, although effect sizes remained small to moderate and this improvement in effectiveness relating to MFIs has been questioned in a subsequent review [ 10 ]. In updating the review, we anticipated a larger pool of reviews and an opportunity to consolidate learning from more recent systematic reviews of interventions.

This review updates and extends our previous review of systematic reviews of interventions designed to implement research evidence into clinical practice. To identify potentially relevant peer-reviewed research papers, we developed a comprehensive systematic literature search strategy based on the terms used in the Grimshaw et al. [ 9 ] and Boaz, Baeza and Fraser [ 8 ] overview articles. To ensure optimal retrieval, our search strategy was refined with support from an expert university librarian, considering the ongoing improvements in the development of search filters for systematic reviews since our first review [ 11 ]. We also wanted to include technology-related terms (e.g. apps, algorithms, machine learning, artificial intelligence) to find studies that explored interventions based on the use of technological innovations as mechanistic tools for increasing the use of evidence into practice (see Additional file 1 : Appendix A for full search strategy).

The search was performed in June 2022 in the following electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched for articles published since the 2011 review. We searched from January 2010 up to June 2022 and applied no language restrictions. Reference lists of relevant papers were also examined.

We uploaded the results using EPPI-Reviewer, a web-based tool that facilitated semi-automation of the screening process and removal of duplicate studies. We made particular use of a priority screening function to reduce screening workload and avoid ‘data deluge’ [ 12 ]. Through machine learning, one reviewer screened a smaller number of records ( n  = 1200) to train the software to predict whether a given record was more likely to be relevant or irrelevant, thus pulling the relevant studies towards the beginning of the screening process. This automation did not replace manual work but helped the reviewer to identify eligible studies more quickly. During the selection process, we included studies that looked explicitly at interventions designed to turn research evidence into practice. Studies were included if they met the following pre-determined inclusion criteria:

The study was a systematic review

Search terms were included

Focused on the implementation of research evidence into practice

The methodological quality of the included studies was assessed as part of the review

Study populations included healthcare providers and patients. The EPOC taxonomy [ 13 ] was used to categorise the strategies. The EPOC taxonomy has four domains: delivery arrangements, financial arrangements, governance arrangements and implementation strategies. The implementation strategies domain includes 20 strategies targeted at healthcare workers. Numerous EPOC strategies were assessed in the review including educational strategies, local opinion leaders, reminders, ICT-focused approaches and audit and feedback. Some strategies that did not fit easily within the EPOC categories were also included. These were social media strategies and toolkits, and multi-faceted interventions (MFIs) (see Table  2 ). Some systematic reviews included comparisons of different interventions while other reviews compared one type of intervention against a control group. Outcomes related to improvements in health care processes or patient well-being. Numerous individual study types (RCT, CCT, BA, ITS) were included within the systematic reviews.

We excluded papers that:

Focused on changing patient rather than provider behaviour

Had no demonstrable outcomes

Made unclear or no reference to research evidence

The last of these criteria was sometimes difficult to judge, and there was considerable discussion amongst the research team as to whether the link between research evidence and practice was sufficiently explicit in the interventions analysed. As we discussed in the previous review [ 8 ] in the field of healthcare, the principle of evidence-based practice is widely acknowledged and tools to change behaviour such as guidelines are often seen to be an implicit codification of evidence, despite the fact that this is not always the case.

Reviewers employed a two-stage process to select papers for inclusion. First, all titles and abstracts were screened by one reviewer to determine whether the study met the inclusion criteria. Two papers [ 14 , 15 ] were identified that fell just before the 2010 cut-off. As they were not identified in the searches for the first review [ 8 ] they were included and progressed to assessment. Each paper was rated as include, exclude or maybe. The full texts of 111 relevant papers were assessed independently by at least two authors. To reduce the risk of bias, papers were excluded following discussion between all members of the team. 32 papers met the inclusion criteria and proceeded to data extraction. The study selection procedure is documented in a PRISMA literature flow diagram (see Fig.  1 ). We were able to include French, Spanish and Portuguese papers in the selection reflecting the language skills in the study team, but none of the papers identified met the inclusion criteria. Other non- English language papers were excluded.

figure 1

PRISMA flow diagram. Source: authors

One reviewer extracted data on strategy type, number of included studies, local, target population, effectiveness and scope of impact from the included studies. Two reviewers then independently read each paper and noted key findings and broad themes of interest which were then discussed amongst the wider authorial team. Two independent reviewers appraised the quality of included studies using a Quality Assessment Checklist based on Oxman and Guyatt [ 16 ] and Francke et al. [ 17 ]. Each study was rated a quality score ranging from 1 (extensive flaws) to 7 (minimal flaws) (see Additional file 2 : Appendix B). All disagreements were resolved through discussion. Studies were not excluded in this updated overview based on methodological quality as we aimed to reflect the full extent of current research into this topic.

The extracted data were synthesised using descriptive and narrative techniques to identify themes and patterns in the data linked to intervention strategies, targeted behaviours, study settings and study outcomes.

Thirty-two studies were included in the systematic review. Table 1. provides a detailed overview of the included systematic reviews comprising reference, strategy type, quality score, number of included studies, local, target population, effectiveness and scope of impact (see Table  1. at the end of the manuscript). Overall, the quality of the studies was high. Twenty-three studies scored 7, six studies scored 6, one study scored 5, one study scored 4 and one study scored 3. The primary focus of the review was on reviews of effectiveness studies, but a small number of reviews did include data from a wider range of methods including qualitative studies which added to the analysis in the papers [ 18 , 19 , 20 , 21 ]. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. In this section, we discuss the different EPOC-defined implementation strategies in turn. Interestingly, we found only two ‘new’ approaches in this review that did not fit into the existing EPOC approaches. These are a review focused on the use of social media and a review considering toolkits. In addition to single interventions, we also discuss multi-faceted interventions. These were the most common intervention approach overall. A summary is provided in Table  2 .

Educational strategies

The overview identified three systematic reviews focusing on educational strategies. Grudniewicz et al. [ 22 ] explored the effectiveness of printed educational materials on primary care physician knowledge, behaviour and patient outcomes and concluded they were not effective in any of these aspects. Koota, Kääriäinen and Melender [ 23 ] focused on educational interventions promoting evidence-based practice among emergency room/accident and emergency nurses and found that interventions involving face-to-face contact led to significant or highly significant effects on patient benefits and emergency nurses’ knowledge, skills and behaviour. Interventions using written self-directed learning materials also led to significant improvements in nurses’ knowledge of evidence-based practice. Although the quality of the studies was high, the review primarily included small studies with low response rates, and many of them relied on self-assessed outcomes; consequently, the strength of the evidence for these outcomes is modest. Wu et al. [ 20 ] questioned if educational interventions aimed at nurses to support the implementation of evidence-based practice improve patient outcomes. Although based on evaluation projects and qualitative data, their results also suggest that positive changes on patient outcomes can be made following the implementation of specific evidence-based approaches (or projects). The differing positive outcomes for educational strategies aimed at nurses might indicate that the target audience is important.

Local opinion leaders

Flodgren et al. [ 24 ] was the only systemic review focusing solely on opinion leaders. The review found that local opinion leaders alone, or in combination with other interventions, can be effective in promoting evidence‐based practice, but this varies both within and between studies and the effect on patient outcomes is uncertain. The review found that, overall, any intervention involving opinion leaders probably improves healthcare professionals’ compliance with evidence-based practice but varies within and across studies. However, how opinion leaders had an impact could not be determined because of insufficient details were provided, illustrating that reporting specific details in published studies is important if diffusion of effective methods of increasing evidence-based practice is to be spread across a system. The usefulness of this review is questionable because it cannot provide evidence of what is an effective opinion leader, whether teams of opinion leaders or a single opinion leader are most effective, or the most effective methods used by opinion leaders.

Pantoja et al. [ 26 ] was the only systemic review focusing solely on manually generated reminders delivered on paper included in the overview. The review explored how these affected professional practice and patient outcomes. The review concluded that manually generated reminders delivered on paper as a single intervention probably led to small to moderate increases in adherence to clinical recommendations, and they could be used as a single quality improvement intervention. However, the authors indicated that this intervention would make little or no difference to patient outcomes. The authors state that such a low-tech intervention may be useful in low- and middle-income countries where paper records are more likely to be the norm.

ICT-focused approaches

The three ICT-focused reviews [ 14 , 27 , 28 ] showed mixed results. Jamal, McKenzie and Clark [ 14 ] explored the impact of health information technology on the quality of medical and health care. They examined the impact of electronic health record, computerised provider order-entry, or decision support system. This showed a positive improvement in adherence to evidence-based guidelines but not to patient outcomes. The number of studies included in the review was low and so a conclusive recommendation could not be reached based on this review. Similarly, Brown et al. [ 28 ] found that technology-enabled knowledge translation interventions may improve knowledge of health professionals, but all eight studies raised concerns of bias. The De Angelis et al. [ 27 ] review was more promising, reporting that ICT can be a good way of disseminating clinical practice guidelines but conclude that it is unclear which type of ICT method is the most effective.

Audit and feedback

Sykes, McAnuff and Kolehmainen [ 29 ] examined whether audit and feedback were effective in dementia care and concluded that it remains unclear which ingredients of audit and feedback are successful as the reviewed papers illustrated large variations in the effectiveness of interventions using audit and feedback.

Non-EPOC listed strategies: social media, toolkits

There were two new (non-EPOC listed) intervention types identified in this review compared to the 2011 review — fewer than anticipated. We categorised a third — ‘care bundles’ [ 36 ] as a multi-faceted intervention due to its description in practice and a fourth — ‘Technology Enhanced Knowledge Transfer’ [ 28 ] was classified as an ICT-focused approach. The first new strategy was identified in Bhatt et al.’s [ 30 ] systematic review of the use of social media for the dissemination of clinical practice guidelines. They reported that the use of social media resulted in a significant improvement in knowledge and compliance with evidence-based guidelines compared with more traditional methods. They noted that a wide selection of different healthcare professionals and patients engaged with this type of social media and its global reach may be significant for low- and middle-income countries. This review was also noteworthy for developing a simple stepwise method for using social media for the dissemination of clinical practice guidelines. However, it is debatable whether social media can be classified as an intervention or just a different way of delivering an intervention. For example, the review discussed involving opinion leaders and patient advocates through social media. However, this was a small review that included only five studies, so further research in this new area is needed. Yamada et al. [ 31 ] draw on 39 studies to explore the application of toolkits, 18 of which had toolkits embedded within larger KT interventions, and 21 of which evaluated toolkits as standalone interventions. The individual component strategies of the toolkits were highly variable though the authors suggest that they align most closely with educational strategies. The authors conclude that toolkits as either standalone strategies or as part of MFIs hold some promise for facilitating evidence use in practice but caution that the quality of many of the primary studies included is considered weak limiting these findings.

Multi-faceted interventions

The majority of the systematic reviews ( n  = 20) reported on more than one intervention type. Some of these systematic reviews focus exclusively on multi-faceted interventions, whilst others compare different single or combined interventions aimed at achieving similar outcomes in particular settings. While these two approaches are often described in a similar way, they are actually quite distinct from each other as the former report how multiple strategies may be strategically combined in pursuance of an agreed goal, whilst the latter report how different strategies may be incidentally used in sometimes contrasting settings in the pursuance of similar goals. Ariyo et al. [ 35 ] helpfully summarise five key elements often found in effective MFI strategies in LMICs — but which may also be transferrable to HICs. First, effective MFIs encourage a multi-disciplinary approach acknowledging the roles played by different professional groups to collectively incorporate evidence-informed practice. Second, they utilise leadership drawing on a wide set of clinical and non-clinical actors including managers and even government officials. Third, multiple types of educational practices are utilised — including input from patients as stakeholders in some cases. Fourth, protocols, checklists and bundles are used — most effectively when local ownership is encouraged. Finally, most MFIs included an emphasis on monitoring and evaluation [ 35 ]. In contrast, other studies offer little information about the nature of the different MFI components of included studies which makes it difficult to extrapolate much learning from them in relation to why or how MFIs might affect practice (e.g. [ 28 , 38 ]). Ultimately, context matters, which some review authors argue makes it difficult to say with real certainty whether single or MFI strategies are superior (e.g. [ 21 , 27 ]). Taking all the systematic reviews together we may conclude that MFIs appear to be more likely to generate positive results than single interventions (e.g. [ 34 , 45 ]) though other reviews should make us cautious (e.g. [ 32 , 43 ]).

While multi-faceted interventions still seem to be more effective than single-strategy interventions, there were important distinctions between how the results of reviews of MFIs are interpreted in this review as compared to the previous reviews [ 8 , 9 ], reflecting greater nuance and debate in the literature. This was particularly noticeable where the effectiveness of MFIs was compared to single strategies, reflecting developments widely discussed in previous studies [ 10 ]. We found that most systematic reviews are bounded by their clinical, professional, spatial, system, or setting criteria and often seek to draw out implications for the implementation of evidence in their areas of specific interest (such as nursing or acute care). Frequently this means combining all relevant studies to explore the respective foci of each systematic review. Therefore, most reviews we categorised as MFIs actually include highly variable numbers and combinations of intervention strategies and highly heterogeneous original study designs. This makes statistical analyses of the type used by Squires et al. [ 10 ] on the three reviews in their paper not possible. Further, it also makes extrapolating findings and commenting on broad themes complex and difficult. This may suggest that future research should shift its focus from merely examining ‘what works’ to ‘what works where and what works for whom’ — perhaps pointing to the value of realist approaches to these complex review topics [ 48 , 49 ] and other more theory-informed approaches [ 50 ].

Some reviews have a relatively small number of studies (i.e. fewer than 10) and the authors are often understandably reluctant to engage with wider debates about the implications of their findings. Other larger studies do engage in deeper discussions about internal comparisons of findings across included studies and also contextualise these in wider debates. Some of the most informative studies (e.g. [ 35 , 40 ]) move beyond EPOC categories and contextualise MFIs within wider systems thinking and implementation theory. This distinction between MFIs and single interventions can actually be very useful as it offers lessons about the contexts in which individual interventions might have bounded effectiveness (i.e. educational interventions for individual change). Taken as a whole, this may also then help in terms of how and when to conjoin single interventions into effective MFIs.

In the two previous reviews, a consistent finding was that MFIs were more effective than single interventions [ 8 , 9 ]. However, like Squires et al. [ 10 ] this overview is more equivocal on this important issue. There are four points which may help account for the differences in findings in this regard. Firstly, the diversity of the systematic reviews in terms of clinical topic or setting is an important factor. Secondly, there is heterogeneity of the studies within the included systematic reviews themselves. Thirdly, there is a lack of consistency with regards to the definition and strategies included within of MFIs. Finally, there are epistemological differences across the papers and the reviews. This means that the results that are presented depend on the methods used to measure, report, and synthesise them. For instance, some reviews highlight that education strategies can be useful to improve provider understanding — but without wider organisational or system-level change, they may struggle to deliver sustained transformation [ 19 , 44 ].

It is also worth highlighting the importance of the theory of change underlying the different interventions. Where authors of the systematic reviews draw on theory, there is space to discuss/explain findings. We note a distinction between theoretical and atheoretical systematic review discussion sections. Atheoretical reviews tend to present acontextual findings (for instance, one study found very positive results for one intervention, and this gets highlighted in the abstract) whilst theoretically informed reviews attempt to contextualise and explain patterns within the included studies. Theory-informed systematic reviews seem more likely to offer more profound and useful insights (see [ 19 , 35 , 40 , 43 , 45 ]). We find that the most insightful systematic reviews of MFIs engage in theoretical generalisation — they attempt to go beyond the data of individual studies and discuss the wider implications of the findings of the studies within their reviews drawing on implementation theory. At the same time, they highlight the active role of context and the wider relational and system-wide issues linked to implementation. It is these types of investigations that can help providers further develop evidence-based practice.

This overview has identified a small, but insightful set of papers that interrogate and help theorise why, how, for whom, and in which circumstances it might be the case that MFIs are superior (see [ 19 , 35 , 40 ] once more). At the level of this overview — and in most of the systematic reviews included — it appears to be the case that MFIs struggle with the question of attribution. In addition, there are other important elements that are often unmeasured, or unreported (e.g. costs of the intervention — see [ 40 ]). Finally, the stronger systematic reviews [ 19 , 35 , 40 , 43 , 45 ] engage with systems issues, human agency and context [ 18 ] in a way that was not evident in the systematic reviews identified in the previous reviews [ 8 , 9 ]. The earlier reviews lacked any theory of change that might explain why MFIs might be more effective than single ones — whereas now some systematic reviews do this, which enables them to conclude that sometimes single interventions can still be more effective.

As Nilsen et al. ([ 6 ] p. 7) note ‘Study findings concerning the effectiveness of various approaches are continuously synthesized and assembled in systematic reviews’. We may have gone as far as we can in understanding the implementation of evidence through systematic reviews of single and multi-faceted interventions and the next step would be to conduct more research exploring the complex and situated nature of evidence used in clinical practice and by particular professional groups. This would further build on the nuanced discussion and conclusion sections in a subset of the papers we reviewed. This might also support the field to move away from isolating individual implementation strategies [ 6 ] to explore the complex processes involving a range of actors with differing capacities [ 51 ] working in diverse organisational cultures. Taxonomies of implementation strategies do not fully account for the complex process of implementation, which involves a range of different actors with different capacities and skills across multiple system levels. There is plenty of work to build on, particularly in the social sciences, which currently sits at the margins of debates about evidence implementation (see for example, Normalisation Process Theory [ 52 ]).

There are several changes that we have identified in this overview of systematic reviews in comparison to the review we published in 2011 [ 8 ]. A consistent and welcome finding is that the overall quality of the systematic reviews themselves appears to have improved between the two reviews, although this is not reflected upon in the papers. This is exhibited through better, clearer reporting mechanisms in relation to the mechanics of the reviews, alongside a greater attention to, and deeper description of, how potential biases in included papers are discussed. Additionally, there is an increased, but still limited, inclusion of original studies conducted in low- and middle-income countries as opposed to just high-income countries. Importantly, we found that many of these systematic reviews are attuned to, and comment upon the contextual distinctions of pursuing evidence-informed interventions in health care settings in different economic settings. Furthermore, systematic reviews included in this updated article cover a wider set of clinical specialities (both within and beyond hospital settings) and have a focus on a wider set of healthcare professions — discussing both similarities, differences and inter-professional challenges faced therein, compared to the earlier reviews. These wider ranges of studies highlight that a particular intervention or group of interventions may work well for one professional group but be ineffective for another. This diversity of study settings allows us to consider the important role context (in its many forms) plays on implementing evidence into practice. Examining the complex and varied context of health care will help us address what Nilsen et al. ([ 6 ] p. 1) described as, ‘society’s health problems [that] require research-based knowledge acted on by healthcare practitioners together with implementation of political measures from governmental agencies’. This will help us shift implementation science to move, ‘beyond a success or failure perspective towards improved analysis of variables that could explain the impact of the implementation process’ ([ 6 ] p. 2).

This review brings together 32 papers considering individual and multi-faceted interventions designed to support the use of evidence in clinical practice. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been conducted. As a whole, this substantial body of knowledge struggles to tell us more about the use of individual and MFIs than: ‘it depends’. To really move forwards in addressing the gap between research evidence and practice, we may need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of perspectives, especially from the social, economic, political and behavioural sciences in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed. Harvey et al. [ 53 ] suggest that when context is likely to be critical to implementation success there are a range of primary research approaches (participatory research, realist evaluation, developmental evaluation, ethnography, quality/ rapid cycle improvement) that are likely to be appropriate and insightful. While these approaches often form part of implementation studies in the form of process evaluations, they are usually relatively small scale in relation to implementation research as a whole. As a result, the findings often do not make it into the subsequent systematic reviews. This review provides further evidence that we need to bring qualitative approaches in from the periphery to play a central role in many implementation studies and subsequent evidence syntheses. It would be helpful for systematic reviews, at the very least, to include more detail about the interventions and their implementation in terms of how and why they worked.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Before and after study

Controlled clinical trial

Effective Practice and Organisation of Care

High-income countries

Information and Communications Technology

Interrupted time series

Knowledge translation

Low- and middle-income countries

Randomised controlled trial

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Acknowledgements

The authors would like to thank Professor Kathryn Oliver for her support in the planning the review, Professor Steve Hanney for reading and commenting on the final manuscript and the staff at LSHTM library for their support in planning and conducting the literature search.

This study was supported by LSHTM’s Research England QR strategic priorities funding allocation and the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. Grant number NIHR200152. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or Research England.

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Boaz, A., Baeza, J., Fraser, A. et al. ‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice. Implementation Sci 19 , 15 (2024). https://doi.org/10.1186/s13012-024-01337-z

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  • Genetic variation
  • Genome-wide association studies

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics 1 , 2 , 3 , 4 . The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health 5 , 6 . Here we describe the programme’s genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

Comprehensively identifying genetic variation and cataloguing its contribution to health and disease, in conjunction with environmental and lifestyle factors, is a central goal of human health research 1 , 2 . A key limitation in efforts to build this catalogue has been the historic under-representation of large subsets of individuals in biomedical research including individuals from diverse ancestries, individuals with disabilities and individuals from disadvantaged backgrounds 3 , 4 . The All of Us Research Program (All of Us) aims to address this gap by enrolling and collecting comprehensive health data on at least one million individuals who reflect the diversity across the USA 5 , 6 . An essential component of All of Us is the generation of whole-genome sequence (WGS) and genotyping data on one million participants. All of Us is committed to making this dataset broadly useful—not only by democratizing access to this dataset across the scientific community but also to return value to the participants themselves by returning individual DNA results, such as genetic ancestry, hereditary disease risk and pharmacogenetics according to clinical standards, to those who wish to receive these research results.

Here we describe the release of WGS data from 245,388 All of Us participants and demonstrate the impact of this high-quality data in genetic and health studies. We carried out a series of data harmonization and quality control (QC) procedures and conducted analyses characterizing the properties of the dataset including genetic ancestry and relatedness. We validated the data by replicating well-established genotype–phenotype associations including low-density lipoprotein cholesterol (LDL-C) and 117 additional diseases. These data are available through the All of Us Researcher Workbench, a cloud platform that embodies and enables programme priorities, facilitating equitable data and compute access while ensuring responsible conduct of research and protecting participant privacy through a passport data access model.

The All of Us Research Program

To accelerate health research, All of Us is committed to curating and releasing research data early and often 6 . Less than five years after national enrolment began in 2018, this fifth data release includes data from more than 413,000 All of Us participants. Summary data are made available through a public Data Browser, and individual-level participant data are made available to researchers through the Researcher Workbench (Fig. 1a and Data availability).

figure 1

a , The All of Us Research Hub contains a publicly accessible Data Browser for exploration of summary phenotypic and genomic data. The Researcher Workbench is a secure cloud-based environment of participant-level data in a Controlled Tier that is widely accessible to researchers. b , All of Us participants have rich phenotype data from a combination of physical measurements, survey responses, EHRs, wearables and genomic data. Dots indicate the presence of the specific data type for the given number of participants. c , Overall summary of participants under-represented in biomedical research (UBR) with data available in the Controlled Tier. The All of Us logo in a is reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Participant data include a rich combination of phenotypic and genomic data (Fig. 1b ). Participants are asked to complete consent for research use of data, sharing of electronic health records (EHRs), donation of biospecimens (blood or saliva, and urine), in-person provision of physical measurements (height, weight and blood pressure) and surveys initially covering demographics, lifestyle and overall health 7 . Participants are also consented for recontact. EHR data, harmonized using the Observational Medical Outcomes Partnership Common Data Model 8 ( Methods ), are available for more than 287,000 participants (69.42%) from more than 50 health care provider organizations. The EHR dataset is longitudinal, with a quarter of participants having 10 years of EHR data (Extended Data Fig. 1 ). Data include 245,388 WGSs and genome-wide genotyping on 312,925 participants. Sequenced and genotyped individuals in this data release were not prioritized on the basis of any clinical or phenotypic feature. Notably, 99% of participants with WGS data also have survey data and physical measurements, and 84% also have EHR data. In this data release, 77% of individuals with genomic data identify with groups historically under-represented in biomedical research, including 46% who self-identify with a racial or ethnic minority group (Fig. 1c , Supplementary Table 1 and Supplementary Note ).

Scaling the All of Us infrastructure

The genomic dataset generated from All of Us participants is a resource for research and discovery and serves as the basis for return of individual health-related DNA results to participants. Consequently, the US Food and Drug Administration determined that All of Us met the criteria for a significant risk device study. As such, the entire All of Us genomics effort from sample acquisition to sequencing meets clinical laboratory standards 9 .

All of Us participants were recruited through a national network of partners, starting in 2018, as previously described 5 . Participants may enrol through All of Us - funded health care provider organizations or direct volunteer pathways and all biospecimens, including blood and saliva, are sent to the central All of Us Biobank for processing and storage. Genomics data for this release were generated from blood-derived DNA. The programme began return of actionable genomic results in December 2022. As of April 2023, approximately 51,000 individuals were sent notifications asking whether they wanted to view their results, and approximately half have accepted. Return continues on an ongoing basis.

The All of Us Data and Research Center maintains all participant information and biospecimen ID linkage to ensure that participant confidentiality and coded identifiers (participant and aliquot level) are used to track each sample through the All of Us genomics workflow. This workflow facilitates weekly automated aliquot and plating requests to the Biobank, supplies relevant metadata for the sample shipments to the Genome Centers, and contains a feedback loop to inform action on samples that fail QC at any stage. Further, the consent status of each participant is checked before sample shipment to confirm that they are still active. Although all participants with genomic data are consented for the same general research use category, the programme accommodates different preferences for the return of genomic data to participants and only data for those individuals who have consented for return of individual health-related DNA results are distributed to the All of Us Clinical Validation Labs for further evaluation and health-related clinical reporting. All participants in All of Us that choose to get health-related DNA results have the option to schedule a genetic counselling appointment to discuss their results. Individuals with positive findings who choose to obtain results are required to schedule an appointment with a genetic counsellor to receive those findings.

Genome sequencing

To satisfy the requirements for clinical accuracy, precision and consistency across DNA sample extraction and sequencing, the All of Us Genome Centers and Biobank harmonized laboratory protocols, established standard QC methodologies and metrics, and conducted a series of validation experiments using previously characterized clinical samples and commercially available reference standards 9 . Briefly, PCR-free barcoded WGS libraries were constructed with the Illumina Kapa HyperPrep kit. Libraries were pooled and sequenced on the Illumina NovaSeq 6000 instrument. After demultiplexing, initial QC analysis is performed with the Illumina DRAGEN pipeline (Supplementary Table 2 ) leveraging lane, library, flow cell, barcode and sample level metrics as well as assessing contamination, mapping quality and concordance to genotyping array data independently processed from a different aliquot of DNA. The Genome Centers use these metrics to determine whether each sample meets programme specifications and then submits sequencing data to the Data and Research Center for further QC, joint calling and distribution to the research community ( Methods ).

This effort to harmonize sequencing methods, multi-level QC and use of identical data processing protocols mitigated the variability in sequencing location and protocols that often leads to batch effects in large genomic datasets 9 . As a result, the data are not only of clinical-grade quality, but also consistent in coverage (≥30× mean) and uniformity across Genome Centers (Supplementary Figs. 1 – 5 ).

Joint calling and variant discovery

We carried out joint calling across the entire All of Us WGS dataset (Extended Data Fig. 2 ). Joint calling leverages information across samples to prune artefact variants, which increases sensitivity, and enables flagging samples with potential issues that were missed during single-sample QC 10 (Supplementary Table 3 ). Scaling conventional approaches to whole-genome joint calling beyond 50,000 individuals is a notable computational challenge 11 , 12 . To address this, we developed a new cloud variant storage solution, the Genomic Variant Store (GVS), which is based on a schema designed for querying and rendering variants in which the variants are stored in GVS and rendered to an analysable variant file, as opposed to the variant file being the primary storage mechanism (Code availability). We carried out QC on the joint call set on the basis of the approach developed for gnomAD 3.1 (ref.  13 ). This included flagging samples with outlying values in eight metrics (Supplementary Table 4 , Supplementary Fig. 2 and Methods ).

To calculate the sensitivity and precision of the joint call dataset, we included four well-characterized samples. We sequenced the National Institute of Standards and Technology reference materials (DNA samples) from the Genome in a Bottle consortium 13 and carried out variant calling as described above. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations 14 . The overall sensitivity for single-nucleotide variants was over 98.7% and precision was more than 99.9%. For short insertions or deletions, the sensitivity was over 97% and precision was more than 99.6% (Supplementary Table 5 and Methods ).

The joint call set included more than 1 billion genetic variants. We annotated the joint call dataset on the basis of functional annotation (for example, gene symbol and protein change) using Illumina Nirvana 15 . We defined coding variants as those inducing an amino acid change on a canonical ENSEMBL transcript and found 272,051,104 non-coding and 3,913,722 coding variants that have not been described previously in dbSNP 16 v153 (Extended Data Table 1 ). A total of 3,912,832 (99.98%) of the coding variants are rare (allelic frequency < 0.01) and the remaining 883 (0.02%) are common (allelic frequency > 0.01). Of the coding variants, 454 (0.01%) are common in one or more of the non-European computed ancestries in All of Us, rare among participants of European ancestry, and have an allelic number greater than 1,000 (Extended Data Table 2 and Extended Data Fig. 3 ). The distributions of pathogenic, or likely pathogenic, ClinVar variant counts per participant, stratified by computed ancestry, filtered to only those variants that are found in individuals with an allele count of <40 are shown in Extended Data Fig. 4 . The potential medical implications of these known and new variants with respect to variant pathogenicity by ancestry are highlighted in a companion paper 17 . In particular, we find that the European ancestry subset has the highest rate of pathogenic variation (2.1%), which was twice the rate of pathogenic variation in individuals of East Asian ancestry 17 .The lower frequency of variants in East Asian individuals may be partially explained by the fact the sample size in that group is small and there may be knowledge bias in the variant databases that is reducing the number of findings in some of the less-studied ancestry groups.

Genetic ancestry and relatedness

Genetic ancestry inference confirmed that 51.1% of the All of Us WGS dataset is derived from individuals of non-European ancestry. Briefly, the ancestry categories are based on the same labels used in gnomAD 18 . We trained a classifier on a 16-dimensional principal component analysis (PCA) space of a diverse reference based on 3,202 samples and 151,159 autosomal single-nucleotide polymorphisms. We projected the All of Us samples into the PCA space of the training data, based on the same single-nucleotide polymorphisms from the WGS data, and generated categorical ancestry predictions from the trained classifier ( Methods ). Continuous genetic ancestry fractions for All of Us samples were inferred using the same PCA data, and participants’ patterns of ancestry and admixture were compared to their self-identified race and ethnicity (Fig. 2 and Methods ). Continuous ancestry inference carried out using genome-wide genotypes yields highly concordant estimates.

figure 2

a , b , Uniform manifold approximation and projection (UMAP) representations of All of Us WGS PCA data with self-described race ( a ) and ethnicity ( b ) labels. c , Proportion of genetic ancestry per individual in six distinct and coherent ancestry groups defined by Human Genome Diversity Project and 1000 Genomes samples.

Kinship estimation confirmed that All of Us WGS data consist largely of unrelated individuals with about 85% (215,107) having no first- or second-degree relatives in the dataset (Supplementary Fig. 6 ). As many genomic analyses leverage unrelated individuals, we identified the smallest set of samples that are required to be removed from the remaining individuals that had first- or second-degree relatives and retained one individual from each kindred. This procedure yielded a maximal independent set of 231,442 individuals (about 94%) with genome sequence data in the current release ( Methods ).

Genetic determinants of LDL-C

As a measure of data quality and utility, we carried out a single-variant genome-wide association study (GWAS) for LDL-C, a trait with well-established genomic architecture ( Methods ). Of the 245,388 WGS participants, 91,749 had one or more LDL-C measurements. The All of Us LDL-C GWAS identified 20 well-established genome-wide significant loci, with minimal genomic inflation (Fig. 3 , Extended Data Table 3 and Supplementary Fig. 7 ). We compared the results to those of a recent multi-ethnic LDL-C GWAS in the National Heart, Lung, and Blood Institute (NHLBI) TOPMed study that included 66,329 ancestrally diverse (56% non-European ancestry) individuals 19 . We found a strong correlation between the effect estimates for NHLBI TOPMed genome-wide significant loci and those of All of Us ( R 2  = 0.98, P  < 1.61 × 10 −45 ; Fig. 3 , inset). Notably, the per-locus effect sizes observed in All of Us are decreased compared to those in TOPMed, which is in part due to differences in the underlying statistical model, differences in the ancestral composition of these datasets and differences in laboratory value ascertainment between EHR-derived data and epidemiology studies. A companion manuscript extended this work to identify common and rare genetic associations for three diseases (atrial fibrillation, coronary artery disease and type 2 diabetes) and two quantitative traits (height and LDL-C) in the All of Us dataset and identified very high concordance with previous efforts across all of these diseases and traits 20 .

figure 3

Manhattan plot demonstrating robust replication of 20 well-established LDL-C genetic loci among 91,749 individuals with 1 or more LDL-C measurements. The red horizontal line denotes the genome wide significance threshold of P = 5 × 10 –8 . Inset, effect estimate ( β ) comparison between NHLBI TOPMed LDL-C GWAS ( x  axis) and All of Us LDL-C GWAS ( y  axis) for the subset of 194 independent variants clumped (window 250 kb, r2 0.5) that reached genome-wide significance in NHLBI TOPMed.

Genotype-by-phenotype associations

As another measure of data quality and utility, we tested replication rates of previously reported phenotype–genotype associations in the five predicted genetic ancestry populations present in the Phenotype/Genotype Reference Map (PGRM): AFR, African ancestry; AMR, Latino/admixed American ancestry; EAS, East Asian ancestry; EUR, European ancestry; SAS, South Asian ancestry. The PGRM contains published associations in the GWAS catalogue in these ancestry populations that map to International Classification of Diseases-based phenotype codes 21 . This replication study specifically looked across 4,947 variants, calculating replication rates for powered associations in each ancestry population. The overall replication rates for associations powered at 80% were: 72.0% (18/25) in AFR, 100% (13/13) in AMR, 46.6% (7/15) in EAS, 74.9% (1,064/1,421) in EUR, and 100% (1/1) in SAS. With the exception of the EAS ancestry results, these powered replication rates are comparable to those of the published PGRM analysis where the replication rates of several single-site EHR-linked biobanks ranges from 76% to 85%. These results demonstrate the utility of the data and also highlight opportunities for further work understanding the specifics of the All of Us population and the potential contribution of gene–environment interactions to genotype–phenotype mapping and motivates the development of methods for multi-site EHR phenotype data extraction, harmonization and genetic association studies.

More broadly, the All of Us resource highlights the opportunities to identify genotype–phenotype associations that differ across diverse populations 22 . For example, the Duffy blood group locus ( ACKR1 ) is more prevalent in individuals of AFR ancestry and individuals of AMR ancestry than in individuals of EUR ancestry. Although the phenome-wide association study of this locus highlights the well-established association of the Duffy blood group with lower white blood cell counts both in individuals of AFR and AMR ancestry 23 , 24 , it also revealed genetic-ancestry-specific phenotype patterns, with minimal phenotypic associations in individuals of EAS ancestry and individuals of EUR ancestry (Fig. 4 and Extended Data Table 4 ). Conversely, rs9273363 in the HLA-DQB1 locus is associated with increased risk of type 1 diabetes 25 , 26 and diabetic complications across ancestries, but only associates with increased risk of coeliac disease in individuals of EUR ancestry (Extended Data Fig. 5 ). Similarly, the TCF7L2 locus 27 strongly associates with increased risk of type 2 diabetes and associated complications across several ancestries (Extended Data Fig. 6 ). Association testing results are available in Supplementary Dataset 1 .

figure 4

Results of genetic-ancestry-stratified phenome-wide association analysis among unrelated individuals highlighting ancestry-specific disease associations across the four most common genetic ancestries of participant. Bonferroni-adjusted phenome-wide significance threshold (<2.88 × 10 −5 ) is plotted as a red horizontal line. AFR ( n  = 34,037, minor allele fraction (MAF) 0.82); AMR ( n  = 28,901, MAF 0.10); EAS ( n  = 32,55, MAF 0.003); EUR ( n  = 101,613, MAF 0.007).

The cloud-based Researcher Workbench

All of Us genomic data are available in a secure, access-controlled cloud-based analysis environment: the All of Us Researcher Workbench. Unlike traditional data access models that require per-project approval, access in the Researcher Workbench is governed by a data passport model based on a researcher’s authenticated identity, institutional affiliation, and completion of self-service training and compliance attestation 28 . After gaining access, a researcher may create a new workspace at any time to conduct a study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is regularly audited and made accessible publicly on the All of Us Research Projects Directory. This streamlined access model is guided by the principles that: participants are research partners and maintaining their privacy and data security is paramount; their data should be made as accessible as possible for authorized researchers; and we should continually seek to remove unnecessary barriers to accessing and using All of Us data.

For researchers at institutions with an existing institutional data use agreement, access can be gained as soon as they complete the required verification and compliance steps. As of August 2023, 556 institutions have agreements in place, allowing more than 5,000 approved researchers to actively work on more than 4,400 projects. The median time for a researcher from initial registration to completion of these requirements is 28.6 h (10th percentile: 48 min, 90th percentile: 14.9 days), a fraction of the weeks to months it can take to assemble a project-specific application and have it reviewed by an access board with conventional access models.

Given that the size of the project’s phenotypic and genomic dataset is expected to reach 4.75 PB in 2023, the use of a central data store and cloud analysis tools will save funders an estimated US$16.5 million per year when compared to the typical approach of allowing researchers to download genomic data. Storing one copy per institution of this data at 556 registered institutions would cost about US$1.16 billion per year. By contrast, storing a central cloud copy costs about US$1.14 million per year, a 99.9% saving. Importantly, cloud infrastructure also democratizes data access particularly for researchers who do not have high-performance local compute resources.

Here we present the All of Us Research Program’s approach to generating diverse clinical-grade genomic data at an unprecedented scale. We present the data release of about 245,000 genome sequences as part of a scalable framework that will grow to include genetic information and health data for one million or more people living across the USA. Our observations permit several conclusions.

First, the All of Us programme is making a notable contribution to improving the study of human biology through purposeful inclusion of under-represented individuals at scale 29 , 30 . Of the participants with genomic data in All of Us, 45.92% self-identified as a non-European race or ethnicity. This diversity enabled identification of more than 275 million new genetic variants across the dataset not previously captured by other large-scale genome aggregation efforts with diverse participants that have submitted variation to dbSNP v153, such as NHLBI TOPMed 31 freeze 8 (Extended Data Table 1 ). In contrast to gnomAD, All of Us permits individual-level genotype access with detailed phenotype data for all participants. Furthermore, unlike many genomics resources, All of Us is uniformly consented for general research use and enables researchers to go from initial account creation to individual-level data access in as little as a few hours. The All of Us cohort is significantly more diverse than those of other large contemporary research studies generating WGS data 32 , 33 . This enables a more equitable future for precision medicine (for example, through constructing polygenic risk scores that are appropriately calibrated to diverse populations 34 , 35 as the eMERGE programme has done leveraging All of Us data 36 , 37 ). Developing new tools and regulatory frameworks to enable analyses across multiple biobanks in the cloud to harness the unique strengths of each is an active area of investigation addressed in a companion paper to this work 38 .

Second, the All of Us Researcher Workbench embodies the programme’s design philosophy of open science, reproducible research, equitable access and transparency to researchers and to research participants 26 . Importantly, for research studies, no group of data users should have privileged access to All of Us resources based on anything other than data protection criteria. Although the All of Us Researcher Workbench initially targeted onboarding US academic, health care and non-profit organizations, it has recently expanded to international researchers. We anticipate further genomic and phenotypic data releases at regular intervals with data available to all researcher communities. We also anticipate additional derived data and functionality to be made available, such as reference data, structural variants and a service for array imputation using the All of Us genomic data.

Third, All of Us enables studying human biology at an unprecedented scale. The programmatic goal of sequencing one million or more genomes has required harnessing the output of multiple sequencing centres. Previous work has focused on achieving functional equivalence in data processing and joint calling pipelines 39 . To achieve clinical-grade data equivalence, All of Us required protocol equivalence at both sequencing production level and data processing across the sequencing centres. Furthermore, previous work has demonstrated the value of joint calling at scale 10 , 18 . The new GVS framework developed by the All of Us programme enables joint calling at extreme scales (Code availability). Finally, the provision of data access through cloud-native tools enables scalable and secure access and analysis to researchers while simultaneously enabling the trust of research participants and transparency underlying the All of Us data passport access model.

The clinical-grade sequencing carried out by All of Us enables not only research, but also the return of value to participants through clinically relevant genetic results and health-related traits to those who opt-in to receiving this information. In the years ahead, we anticipate that this partnership with All of Us participants will enable researchers to move beyond large-scale genomic discovery to understanding the consequences of implementing genomic medicine at scale.

The All of Us cohort

All of Us aims to engage a longitudinal cohort of one million or more US participants, with a focus on including populations that have historically been under-represented in biomedical research. Details of the All of Us cohort have been described previously 5 . Briefly, the primary objective is to build a robust research resource that can facilitate the exploration of biological, clinical, social and environmental determinants of health and disease. The programme will collect and curate health-related data and biospecimens, and these data and biospecimens will be made broadly available for research uses. Health data are obtained through the electronic medical record and through participant surveys. Survey templates can be found on our public website: https://www.researchallofus.org/data-tools/survey-explorer/ . Adults 18 years and older who have the capacity to consent and reside in the USA or a US territory at present are eligible. Informed consent for all participants is conducted in person or through an eConsent platform that includes primary consent, HIPAA Authorization for Research use of EHRs and other external health data, and Consent for Return of Genomic Results. The protocol was reviewed by the Institutional Review Board (IRB) of the All of Us Research Program. The All of Us IRB follows the regulations and guidance of the NIH Office for Human Research Protections for all studies, ensuring that the rights and welfare of research participants are overseen and protected uniformly.

Data accessibility through a ‘data passport’

Authorization for access to participant-level data in All of Us is based on a ‘data passport’ model, through which authorized researchers do not need IRB review for each research project. The data passport is required for gaining data access to the Researcher Workbench and for creating workspaces to carry out research projects using All of Us data. At present, data passports are authorized through a six-step process that includes affiliation with an institution that has signed a Data Use and Registration Agreement, account creation, identity verification, completion of ethics training, and attestation to a data user code of conduct. Results reported follow the All of Us Data and Statistics Dissemination Policy disallowing disclosure of group counts under 20 to protect participant privacy without seeking prior approval 40 .

At present, All of Us gathers EHR data from about 50 health care organizations that are funded to recruit and enrol participants as well as transfer EHR data for those participants who have consented to provide them. Data stewards at each provider organization harmonize their local data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, and then submit it to the All of Us Data and Research Center (DRC) so that it can be linked with other participant data and further curated for research use. OMOP is a common data model standardizing health information from disparate EHRs to common vocabularies and organized into tables according to data domains. EHR data are updated from the recruitment sites and sent to the DRC quarterly. Updated data releases to the research community occur approximately once a year. Supplementary Table 6 outlines the OMOP concepts collected by the DRC quarterly from the recruitment sites.

Biospecimen collection and processing

Participants who consented to participate in All of Us donated fresh whole blood (4 ml EDTA and 10 ml EDTA) as a primary source of DNA. The All of Us Biobank managed by the Mayo Clinic extracted DNA from 4 ml EDTA whole blood, and DNA was stored at −80 °C at an average concentration of 150 ng µl −1 . The buffy coat isolated from 10 ml EDTA whole blood has been used for extracting DNA in the case of initial extraction failure or absence of 4 ml EDTA whole blood. The Biobank plated 2.4 µg DNA with a concentration of 60 ng µl −1 in duplicate for array and WGS samples. The samples are distributed to All of Us Genome Centers weekly, and a negative (empty well) control and National Institute of Standards and Technology controls are incorporated every two months for QC purposes.

Genome Center sample receipt, accession and QC

On receipt of DNA sample shipments, the All of Us Genome Centers carry out an inspection of the packaging and sample containers to ensure that sample integrity has not been compromised during transport and to verify that the sample containers correspond to the shipping manifest. QC of the submitted samples also includes DNA quantification, using routine procedures to confirm volume and concentration (Supplementary Table 7 ). Any issues or discrepancies are recorded, and affected samples are put on hold until resolved. Samples that meet quality thresholds are accessioned in the Laboratory Information Management System, and sample aliquots are prepared for library construction processing (for example, normalized with respect to concentration and volume).

WGS library construction, sequencing and primary data QC

The DNA sample is first sheared using a Covaris sonicator and is then size-selected using AMPure XP beads to restrict the range of library insert sizes. Using the PCR Free Kapa HyperPrep library construction kit, enzymatic steps are completed to repair the jagged ends of DNA fragments, add proper A-base segments, and ligate indexed adapter barcode sequences onto samples. Excess adaptors are removed using AMPure XP beads for a final clean-up. Libraries are quantified using quantitative PCR with the Illumina Kapa DNA Quantification Kit and then normalized and pooled for sequencing (Supplementary Table 7 ).

Pooled libraries are loaded on the Illumina NovaSeq 6000 instrument. The data from the initial sequencing run are used to QC individual libraries and to remove non-conforming samples from the pipeline. The data are also used to calibrate the pooling volume of each individual library and re-pool the libraries for additional NovaSeq sequencing to reach an average coverage of 30×.

After demultiplexing, WGS analysis occurs on the Illumina DRAGEN platform. The DRAGEN pipeline consists of highly optimized algorithms for mapping, aligning, sorting, duplicate marking and haplotype variant calling and makes use of platform features such as compression and BCL conversion. Alignment uses the GRCh38dh reference genome. QC data are collected at every stage of the analysis protocol, providing high-resolution metrics required to ensure data consistency for large-scale multiplexing. The DRAGEN pipeline produces a large number of metrics that cover lane, library, flow cell, barcode and sample-level metrics for all runs as well as assessing contamination and mapping quality. The All of Us Genome Centers use these metrics to determine pass or fail for each sample before submitting the CRAM files to the All of Us DRC. For mapping and variant calling, all Genome Centers have harmonized on a set of DRAGEN parameters, which ensures consistency in processing (Supplementary Table 2 ).

Every step through the WGS procedure is rigorously controlled by predefined QC measures. Various control mechanisms and acceptance criteria were established during WGS assay validation. Specific metrics for reviewing and releasing genome data are: mean coverage (threshold of ≥30×), genome coverage (threshold of ≥90% at 20×), coverage of hereditary disease risk genes (threshold of ≥95% at 20×), aligned Q30 bases (threshold of ≥8 × 10 10 ), contamination (threshold of ≤1%) and concordance to independently processed array data.

Array genotyping

Samples are processed for genotyping at three All of Us Genome Centers (Broad, Johns Hopkins University and University of Washington). DNA samples are received from the Biobank and the process is facilitated by the All of Us genomics workflow described above. All three centres used an identical array product, scanners, resource files and genotype calling software for array processing to reduce batch effects. Each centre has its own Laboratory Information Management System that manages workflow control, sample and reagent tracking, and centre-specific liquid handling robotics.

Samples are processed using the Illumina Global Diversity Array (GDA) with Illumina Infinium LCG chemistry using the automated protocol and scanned on Illumina iSCANs with Automated Array Loaders. Illumina IAAP software converts raw data (IDAT files; 2 per sample) into a single GTC file per sample using the BPM file (defines strand, probe sequences and illumicode address) and the EGT file (defines the relationship between intensities and genotype calls). Files used for this data release are: GDA-8v1-0_A5.bpm, GDA-8v1-0_A1_ClusterFile.egt, gentrain v3, reference hg19 and gencall cutoff 0.15. The GDA array assays a total of 1,914,935 variant positions including 1,790,654 single-nucleotide variants, 44,172 indels, 9,935 intensity-only probes for CNV calling, and 70,174 duplicates (same position, different probes). Picard GtcToVcf is used to convert the GTC files to VCF format. Resulting VCF and IDAT files are submitted to the DRC for ingestion and further processing. The VCF file contains assay name, chromosome, position, genotype calls, quality score, raw and normalized intensities, B allele frequency and log R ratio values. Each genome centre is running the GDA array under Clinical Laboratory Improvement Amendments-compliant protocols. The GTC files are parsed and metrics are uploaded to in-house Laboratory Information Management System systems for QC review.

At batch level (each set of 96-well plates run together in the laboratory at one time), each genome centre includes positive control samples that are required to have >98% call rate and >99% concordance to existing data to approve release of the batch of data. At the sample level, the call rate and sex are the key QC determinants 41 . Contamination is also measured using BAFRegress 42 and reported out as metadata. Any sample with a call rate below 98% is repeated one time in the laboratory. Genotyped sex is determined by plotting normalized x versus normalized y intensity values for a batch of samples. Any sample discordant with ‘sex at birth’ reported by the All of Us participant is flagged for further detailed review and repeated one time in the laboratory. If several sex-discordant samples are clustered on an array or on a 96-well plate, the entire array or plate will have data production repeated. Samples identified with sex chromosome aneuploidies are also reported back as metadata (XXX, XXY, XYY and so on). A final processing status of ‘pass’, ‘fail’ or ‘abandon’ is determined before release of data to the All of Us DRC. An array sample will pass if the call rate is >98% and the genotyped sex and sex at birth are concordant (or the sex at birth is not applicable). An array sample will fail if the genotyped sex and the sex at birth are discordant. An array sample will have the status of abandon if the call rate is <98% after at least two attempts at the genome centre.

Data from the arrays are used for participant return of genetic ancestry and non-health-related traits for those who consent, and they are also used to facilitate additional QC of the matched WGS data. Contamination is assessed in the array data to determine whether DNA re-extraction is required before WGS. Re-extraction is prompted by level of contamination combined with consent status for return of results. The arrays are also used to confirm sample identity between the WGS data and the matched array data by assessing concordance at 100 unique sites. To establish concordance, a fingerprint file of these 100 sites is provided to the Genome Centers to assess concordance with the same sites in the WGS data before CRAM submission.

Genomic data curation

As seen in Extended Data Fig. 2 , we generate a joint call set for all WGS samples and make these data available in their entirety and by sample subsets to researchers. A breakdown of the frequencies, stratified by computed ancestries for which we had more than 10,000 participants can be found in Extended Data Fig. 3 . The joint call set process allows us to leverage information across samples to improve QC and increase accuracy.

Single-sample QC

If a sample fails single-sample QC, it is excluded from the release and is not reported in this document. These tests detect sample swaps, cross-individual contamination and sample preparation errors. In some cases, we carry out these tests twice (at both the Genome Center and the DRC), for two reasons: to confirm internal consistency between sites; and to mark samples as passing (or failing) QC on the basis of the research pipeline criteria. The single-sample QC process accepts a higher contamination rate than the clinical pipeline (0.03 for the research pipeline versus 0.01 for the clinical pipeline), but otherwise uses identical thresholds. The list of specific QC processes, passing criteria, error modes addressed and an overview of the results can be found in Supplementary Table 3 .

Joint call set QC

During joint calling, we carry out additional QC steps using information that is available across samples including hard thresholds, population outliers, allele-specific filters, and sensitivity and precision evaluation. Supplementary Table 4 summarizes both the steps that we took and the results obtained for the WGS data. More detailed information about the methods and specific parameters can be found in the All of Us Genomic Research Data Quality Report 36 .

Batch effect analysis

We analysed cross-sequencing centre batch effects in the joint call set. To quantify the batch effect, we calculated Cohen’s d (ref.  43 ) for four metrics (insertion/deletion ratio, single-nucleotide polymorphism count, indel count and single-nucleotide polymorphism transition/transversion ratio) across the three genome sequencing centres (Baylor College of Medicine, Broad Institute and University of Washington), stratified by computed ancestry and seven regions of the genome (whole genome, high-confidence calling, repetitive, GC content of >0.85, GC content of <0.15, low mappability, the ACMG59 genes and regions of large duplications (>1 kb)). Using random batches as a control set, all comparisons had a Cohen’s d of <0.35. Here we report any Cohen’s d results >0.5, which we chose before this analysis and is conventionally the threshold of a medium effect size 44 .

We found that there was an effect size in indel counts (Cohen’s d of 0.53) in the entire genome, between Broad Institute and University of Washington, but this was being driven by repetitive and low-mappability regions. We found no batch effects with Cohen’s d of >0.5 in the ratio metrics or in any metrics in the high-confidence calling, low or high GC content, or ACMG59 regions. A complete list of the batch effects with Cohen’s d of >0.5 are found in Supplementary Table 8 .

Sensitivity and precision evaluation

To determine sensitivity and precision, we included four well-characterized control samples (four National Institute of Standards and Technology Genome in a Bottle samples (HG-001, HG-003, HG-004 and HG-005). The samples were sequenced with the same protocol as All of Us. Of note, these samples were not included in data released to researchers. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations. We use the high-confidence calling region, defined by Genome in a Bottle v4.2.1, as the source of ground truth. To be called a true positive, a variant must match the chromosome, position, reference allele, alternate allele and zygosity. In cases of sites with multiple alternative alleles, each alternative allele is considered separately. Sensitivity and precision results are reported in Supplementary Table 5 .

Genetic ancestry inference

We computed categorical ancestry for all WGS samples in All of Us and made these available to researchers. These predictions are also the basis for population allele frequency calculations in the Genomic Variants section of the public Data Browser. We used the high-quality set of sites to determine an ancestry label for each sample. The ancestry categories are based on the same labels used in gnomAD 18 , the Human Genome Diversity Project (HGDP) 45 and 1000 Genomes 1 : African (AFR); Latino/admixed American (AMR); East Asian (EAS); Middle Eastern (MID); European (EUR), composed of Finnish (FIN) and Non-Finnish European (NFE); Other (OTH), not belonging to one of the other ancestries or is an admixture; South Asian (SAS).

We trained a random forest classifier 46 on a training set of the HGDP and 1000 Genomes samples variants on the autosome, obtained from gnomAD 11 . We generated the first 16 principal components (PCs) of the training sample genotypes (using the hwe_normalized_pca in Hail) at the high-quality variant sites for use as the feature vector for each training sample. We used the truth labels from the sample metadata, which can be found alongside the VCFs. Note that we do not train the classifier on the samples labelled as Other. We use the label probabilities (‘confidence’) of the classifier on the other ancestries to determine ancestry of Other.

To determine the ancestry of All of Us samples, we project the All of Us samples into the PCA space of the training data and apply the classifier. As a proxy for the accuracy of our All of Us predictions, we look at the concordance between the survey results and the predicted ancestry. The concordance between self-reported ethnicity and the ancestry predictions was 87.7%.

PC data from All of Us samples and the HGDP and 1000 Genomes samples were used to compute individual participant genetic ancestry fractions for All of Us samples using the Rye program. Rye uses PC data to carry out rapid and accurate genetic ancestry inference on biobank-scale datasets 47 . HGDP and 1000 Genomes reference samples were used to define a set of six distinct and coherent ancestry groups—African, East Asian, European, Middle Eastern, Latino/admixed American and South Asian—corresponding to participant self-identified race and ethnicity groups. Rye was run on the first 16 PCs, using the defined reference ancestry groups to assign ancestry group fractions to individual All of Us participant samples.

Relatedness

We calculated the kinship score using the Hail pc_relate function and reported any pairs with a kinship score above 0.1. The kinship score is half of the fraction of the genetic material shared (ranges from 0.0 to 0.5). We determined the maximal independent set 41 for related samples. We identified a maximally unrelated set of 231,442 samples (94%) for kinship scored greater than 0.1.

LDL-C common variant GWAS

The phenotypic data were extracted from the Curated Data Repository (CDR, Control Tier Dataset v7) in the All of Us Researcher Workbench. The All of Us Cohort Builder and Dataset Builder were used to extract all LDL cholesterol measurements from the Lab and Measurements criteria in EHR data for all participants who have WGS data. The most recent measurements were selected as the phenotype and adjusted for statin use 19 , age and sex. A rank-based inverse normal transformation was applied for this continuous trait to increase power and deflate type I error. Analysis was carried out on the Hail MatrixTable representation of the All of Us WGS joint-called data including removing monomorphic variants, variants with a call rate of <95% and variants with extreme Hardy–Weinberg equilibrium values ( P  < 10 −15 ). A linear regression was carried out with REGENIE 48 on variants with a minor allele frequency >5%, further adjusting for relatedness to the first five ancestry PCs. The final analysis included 34,924 participants and 8,589,520 variants.

Genotype-by-phenotype replication

We tested replication rates of known phenotype–genotype associations in three of the four largest populations: EUR, AFR and EAS. The AMR population was not included because they have no registered GWAS. This method is a conceptual extension of the original GWAS × phenome-wide association study, which replicated 66% of powered associations in a single EHR-linked biobank 49 . The PGRM is an expansion of this work by Bastarache et al., based on associations in the GWAS catalogue 50 in June 2020 (ref.  51 ). After directly matching the Experimental Factor Ontology terms to phecodes, the authors identified 8,085 unique loci and 170 unique phecodes that compose the PGRM. They showed replication rates in several EHR-linked biobanks ranging from 76% to 85%. For this analysis, we used the EUR-, and AFR-based maps, considering only catalogue associations that were P  < 5 × 10 −8 significant.

The main tools used were the Python package Hail for data extraction, plink for genomic associations, and the R packages PheWAS and pgrm for further analysis and visualization. The phenotypes, participant-reported sex at birth, and year of birth were extracted from the All of Us CDR (Controlled Tier Dataset v7). These phenotypes were then loaded into a plink-compatible format using the PheWAS package, and related samples were removed by sub-setting to the maximally unrelated dataset ( n  = 231,442). Only samples with EHR data were kept, filtered by selected loci, annotated with demographic and phenotypic information extracted from the CDR and ancestry prediction information provided by All of Us, ultimately resulting in 181,345 participants for downstream analysis. The variants in the PGRM were filtered by a minimum population-specific allele frequency of >1% or population-specific allele count of >100, leaving 4,986 variants. Results for which there were at least 20 cases in the ancestry group were included. Then, a series of Firth logistic regression tests with phecodes as the outcome and variants as the predictor were carried out, adjusting for age, sex (for non-sex-specific phenotypes) and the first three genomic PC features as covariates. The PGRM was annotated with power calculations based on the case counts and reported allele frequencies. Power of 80% or greater was considered powered for this analysis.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The All of Us Research Hub has a tiered data access data passport model with three data access tiers. The Public Tier dataset contains only aggregate data with identifiers removed. These data are available to the public through Data Snapshots ( https://www.researchallofus.org/data-tools/data-snapshots/ ) and the public Data Browser ( https://databrowser.researchallofus.org/ ). The Registered Tier curated dataset contains individual-level data, available only to approved researchers on the Researcher Workbench. At present, the Registered Tier includes data from EHRs, wearables and surveys, as well as physical measurements taken at the time of participant enrolment. The Controlled Tier dataset contains all data in the Registered Tier and additionally genomic data in the form of WGS and genotyping arrays, previously suppressed demographic data fields from EHRs and surveys, and unshifted dates of events. At present, Registered Tier and Controlled Tier data are available to researchers at academic institutions, non-profit institutions, and both non-profit and for-profit health care institutions. Work is underway to begin extending access to additional audiences, including industry-affiliated researchers. Researchers have the option to register for Registered Tier and/or Controlled Tier access by completing the All of Us Researcher Workbench access process, which includes identity verification and All of Us-specific training in research involving human participants ( https://www.researchallofus.org/register/ ). Researchers may create a new workspace at any time to conduct any research study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is made accessible publicly on the All of Us Research Projects Directory at https://allofus.nih.gov/protecting-data-and-privacy/research-projects-all-us-data .

Code availability

The GVS code is available at https://github.com/broadinstitute/gatk/tree/ah_var_store/scripts/variantstore . The LDL GWAS pipeline is available as a demonstration project in the Featured Workspace Library on the Researcher Workbench ( https://workbench.researchallofus.org/workspaces/aou-rw-5981f9dc/aouldlgwasregeniedsubctv6duplicate/notebooks ).

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Acknowledgements

The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers (OT2 OD026549; OT2 OD026554; OT2 OD026557; OT2 OD026556; OT2 OD026550; OT2 OD 026552; OT2 OD026553; OT2 OD026548; OT2 OD026551; OT2 OD026555); Inter agency agreement AOD 16037; Federally Qualified Health Centers HHSN 263201600085U; Data and Research Center: U2C OD023196; Genome Centers (OT2 OD002748; OT2 OD002750; OT2 OD002751); Biobank: U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: U24 OD023163; Communications and Engagement: OT2 OD023205; OT2 OD023206; and Community Partners (OT2 OD025277; OT2 OD025315; OT2 OD025337; OT2 OD025276). In addition, the All of Us Research Program would not be possible without the partnership of its participants. All of Us and the All of Us logo are service marks of the US Department of Health and Human Services. E.E.E. is an investigator of the Howard Hughes Medical Institute. We acknowledge the foundational contributions of our friend and colleague, the late Deborah A. Nickerson. Debbie’s years of insightful contributions throughout the formation of the All of Us genomics programme are permanently imprinted, and she shares credit for all of the successes of this programme.

Author information

Authors and affiliations.

Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Alexander G. Bick & Henry R. Condon

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

Ginger A. Metcalf, Eric Boerwinkle, Richard A. Gibbs, Donna M. Muzny, Eric Venner, Kimberly Walker, Jianhong Hu, Harsha Doddapaneni, Christie L. Kovar, Mullai Murugan, Shannon Dugan, Ziad Khan & Eric Boerwinkle

Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA

Kelsey R. Mayo, Jodell E. Linder, Melissa Basford, Ashley Able, Ashley E. Green, Robert J. Carroll, Jennifer Zhang & Yuanyuan Wang

Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Lee Lichtenstein, Anthony Philippakis, Sophie Schwartz, M. Morgan T. Aster, Kristian Cibulskis, Andrea Haessly, Rebecca Asch, Aurora Cremer, Kylee Degatano, Akum Shergill, Laura D. Gauthier, Samuel K. Lee, Aaron Hatcher, George B. Grant, Genevieve R. Brandt, Miguel Covarrubias, Eric Banks & Wail Baalawi

Verily, South San Francisco, CA, USA

Shimon Rura, David Glazer, Moira K. Dillon & C. H. Albach

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

Robert J. Carroll, Paul A. Harris & Dan M. Roden

All of Us Research Program, National Institutes of Health, Bethesda, MD, USA

Anjene Musick, Andrea H. Ramirez, Sokny Lim, Siddhartha Nambiar, Bradley Ozenberger, Anastasia L. Wise, Chris Lunt, Geoffrey S. Ginsburg & Joshua C. Denny

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

I. King Jordan, Shashwat Deepali Nagar & Shivam Sharma

Neuroscience Institute, Institute of Translational Genomic Medicine, Morehouse School of Medicine, Atlanta, GA, USA

Robert Meller

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

Mine S. Cicek, Stephen N. Thibodeau & Mine S. Cicek

Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Kimberly F. Doheny, Michelle Z. Mawhinney, Sean M. L. Griffith, Elvin Hsu, Hua Ling & Marcia K. Adams

Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA

Evan E. Eichler, Joshua D. Smith, Christian D. Frazar, Colleen P. Davis, Karynne E. Patterson, Marsha M. Wheeler, Sean McGee, Mitzi L. Murray, Valeria Vasta, Dru Leistritz, Matthew A. Richardson, Aparna Radhakrishnan & Brenna W. Ehmen

Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

Evan E. Eichler

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Stacey Gabriel, Heidi L. Rehm, Niall J. Lennon, Christina Austin-Tse, Eric Banks, Michael Gatzen, Namrata Gupta, Katie Larsson, Sheli McDonough, Steven M. Harrison, Christopher Kachulis, Matthew S. Lebo, Seung Hoan Choi & Xin Wang

Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA

Gail P. Jarvik & Elisabeth A. Rosenthal

Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Dan M. Roden

Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA

Center for Individualized Medicine, Biorepository Program, Mayo Clinic, Rochester, MN, USA

Stephen N. Thibodeau, Ashley L. Blegen, Samantha J. Wirkus, Victoria A. Wagner, Jeffrey G. Meyer & Mine S. Cicek

Color Health, Burlingame, CA, USA

Scott Topper, Cynthia L. Neben, Marcie Steeves & Alicia Y. Zhou

School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

Eric Boerwinkle

Laboratory for Molecular Medicine, Massachusetts General Brigham Personalized Medicine, Cambridge, MA, USA

Christina Austin-Tse, Emma Henricks & Matthew S. Lebo

Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA

Christina M. Lockwood, Brian H. Shirts, Colin C. Pritchard, Jillian G. Buchan & Niklas Krumm

Manuscript Writing Group

  • Alexander G. Bick
  • , Ginger A. Metcalf
  • , Kelsey R. Mayo
  • , Lee Lichtenstein
  • , Shimon Rura
  • , Robert J. Carroll
  • , Anjene Musick
  • , Jodell E. Linder
  • , I. King Jordan
  • , Shashwat Deepali Nagar
  • , Shivam Sharma
  •  & Robert Meller

All of Us Research Program Genomics Principal Investigators

  • Melissa Basford
  • , Eric Boerwinkle
  • , Mine S. Cicek
  • , Kimberly F. Doheny
  • , Evan E. Eichler
  • , Stacey Gabriel
  • , Richard A. Gibbs
  • , David Glazer
  • , Paul A. Harris
  • , Gail P. Jarvik
  • , Anthony Philippakis
  • , Heidi L. Rehm
  • , Dan M. Roden
  • , Stephen N. Thibodeau
  •  & Scott Topper

Biobank, Mayo

  • Ashley L. Blegen
  • , Samantha J. Wirkus
  • , Victoria A. Wagner
  • , Jeffrey G. Meyer
  •  & Stephen N. Thibodeau

Genome Center: Baylor-Hopkins Clinical Genome Center

  • Donna M. Muzny
  • , Eric Venner
  • , Michelle Z. Mawhinney
  • , Sean M. L. Griffith
  • , Elvin Hsu
  • , Marcia K. Adams
  • , Kimberly Walker
  • , Jianhong Hu
  • , Harsha Doddapaneni
  • , Christie L. Kovar
  • , Mullai Murugan
  • , Shannon Dugan
  • , Ziad Khan
  •  & Richard A. Gibbs

Genome Center: Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine

  • Niall J. Lennon
  • , Christina Austin-Tse
  • , Eric Banks
  • , Michael Gatzen
  • , Namrata Gupta
  • , Emma Henricks
  • , Katie Larsson
  • , Sheli McDonough
  • , Steven M. Harrison
  • , Christopher Kachulis
  • , Matthew S. Lebo
  • , Cynthia L. Neben
  • , Marcie Steeves
  • , Alicia Y. Zhou
  • , Scott Topper
  •  & Stacey Gabriel

Genome Center: University of Washington

  • Gail P. Jarvik
  • , Joshua D. Smith
  • , Christian D. Frazar
  • , Colleen P. Davis
  • , Karynne E. Patterson
  • , Marsha M. Wheeler
  • , Sean McGee
  • , Christina M. Lockwood
  • , Brian H. Shirts
  • , Colin C. Pritchard
  • , Mitzi L. Murray
  • , Valeria Vasta
  • , Dru Leistritz
  • , Matthew A. Richardson
  • , Jillian G. Buchan
  • , Aparna Radhakrishnan
  • , Niklas Krumm
  •  & Brenna W. Ehmen

Data and Research Center

  • Lee Lichtenstein
  • , Sophie Schwartz
  • , M. Morgan T. Aster
  • , Kristian Cibulskis
  • , Andrea Haessly
  • , Rebecca Asch
  • , Aurora Cremer
  • , Kylee Degatano
  • , Akum Shergill
  • , Laura D. Gauthier
  • , Samuel K. Lee
  • , Aaron Hatcher
  • , George B. Grant
  • , Genevieve R. Brandt
  • , Miguel Covarrubias
  • , Melissa Basford
  • , Alexander G. Bick
  • , Ashley Able
  • , Ashley E. Green
  • , Jennifer Zhang
  • , Henry R. Condon
  • , Yuanyuan Wang
  • , Moira K. Dillon
  • , C. H. Albach
  • , Wail Baalawi
  •  & Dan M. Roden

All of Us Research Demonstration Project Teams

  • Seung Hoan Choi
  • , Elisabeth A. Rosenthal

NIH All of Us Research Program Staff

  • Andrea H. Ramirez
  • , Sokny Lim
  • , Siddhartha Nambiar
  • , Bradley Ozenberger
  • , Anastasia L. Wise
  • , Chris Lunt
  • , Geoffrey S. Ginsburg
  •  & Joshua C. Denny

Contributions

The All of Us Biobank (Mayo Clinic) collected, stored and plated participant biospecimens. The All of Us Genome Centers (Baylor-Hopkins Clinical Genome Center; Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine; and University of Washington School of Medicine) generated and QCed the whole-genomic data. The All of Us Data and Research Center (Vanderbilt University Medical Center, Broad Institute of MIT and Harvard, and Verily) generated the WGS joint call set, carried out quality assurance and QC analyses and developed the Researcher Workbench. All of Us Research Demonstration Project Teams contributed analyses. The other All of Us Genomics Investigators and NIH All of Us Research Program Staff provided crucial programmatic support. Members of the manuscript writing group (A.G.B., G.A.M., K.R.M., L.L., S.R., R.J.C. and A.M.) wrote the first draft of this manuscript, which was revised with contributions and feedback from all authors.

Corresponding author

Correspondence to Alexander G. Bick .

Ethics declarations

Competing interests.

D.M.M., G.A.M., E.V., K.W., J.H., H.D., C.L.K., M.M., S.D., Z.K., E. Boerwinkle and R.A.G. declare that Baylor Genetics is a Baylor College of Medicine affiliate that derives revenue from genetic testing. Eric Venner is affiliated with Codified Genomics, a provider of genetic interpretation. E.E.E. is a scientific advisory board member of Variant Bio, Inc. A.G.B. is a scientific advisory board member of TenSixteen Bio. The remaining authors declare no competing interests.

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Extended data figures and tables

Extended data fig. 1 historic availability of ehr records in all of us v7 controlled tier curated data repository (n = 413,457)..

For better visibility, the plot shows growth starting in 2010.

Extended Data Fig. 2 Overview of the Genomic Data Curation Pipeline for WGS samples.

The Data and Research Center (DRC) performs additional single sample quality control (QC) on the data as it arrives from the Genome Centers. The variants from samples that pass this QC are loaded into the Genomic Variant Store (GVS), where we jointly call the variants and apply additional QC. We apply a joint call set QC process, which is stored with the call set. The entire joint call set is rendered as a Hail Variant Dataset (VDS), which can be accessed from the analysis notebooks in the Researcher Workbench. Subsections of the genome are extracted from the VDS and rendered in different formats with all participants. Auxiliary data can also be accessed through the Researcher Workbench. This includes variant functional annotations, joint call set QC results, predicted ancestry, and relatedness. Auxiliary data are derived from GVS (arrow not shown) and the VDS. The Cohort Builder directly queries GVS when researchers request genomic data for subsets of samples. Aligned reads, as cram files, are available in the Researcher Workbench (not shown). The graphics of the dish, gene and computer and the All of Us logo are reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Extended Data Fig. 3 Proportion of allelic frequencies (AF), stratified by computed ancestry with over 10,000 participants.

Bar counts are not cumulative (eg, “pop AF < 0.01” does not include “pop AF < 0.001”).

Extended Data Fig. 4 Distribution of pathogenic, and likely pathogenic ClinVar variants.

Stratified by ancestry filtered to only those variants that are found in allele count (AC) < 40 individuals for 245,388 short read WGS samples.

Extended Data Fig. 5 Ancestry specific HLA-DQB1 ( rs9273363 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight ancestry specific consequences across ancestries.

Extended Data Fig. 6 Ancestry specific TCF7L2 ( rs7903146 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight diabetic consequences across ancestries.

Supplementary information

Supplementary information.

Supplementary Figs. 1–7, Tables 1–8 and Note.

Reporting Summary

Supplementary dataset 1.

Associations of ACKR1, HLA-DQB1 and TCF7L2 loci with all Phecodes stratified by genetic ancestry.

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The All of Us Research Program Genomics Investigators. Genomic data in the All of Us Research Program. Nature (2024). https://doi.org/10.1038/s41586-023-06957-x

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Published : 19 February 2024

DOI : https://doi.org/10.1038/s41586-023-06957-x

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Targeting inflammatory protein could help treat severe asthma

26 February 2024

asthma attack_500x500.jpg

Australian researchers have found that a family of proinflammatory molecules called beta common cytokines control inflammation and scarring of the airways (fibrosis) in severe and steroid-resistant asthma.

They believe that a human therapeutic antibody called trabikihart could be the key to effectively blocking the inflammation and scarring.

The findings , published in the Journal of Allergy and Clinical Immunology, are a result of a joint study led by researchers from the University of South Australia (UniSA) and the Royal Melbourne Institute of Technology ( RMIT ), in collaboration with researchers from CSL and SA Pathology .

Joint study leader Dr Damon Tumes , Head of the Allergy and Cancer Immunology Laboratory in the Centre for Cancer Biology *, says the findings are significant.

“Inflammation and tissue damage in severe asthma is caused by several types of immune cells that enter the lungs due to allergens, viruses and other microbes that interact with the airways,” Dr Tumes says.

“In some people, the inflammation is resistant to steroids – the first treatment option for controlling severe asthma.

“Currently, limited treatment options are available for severe asthma. New and existing drugs often only target single molecules when multiple overlapping cells and inflammatory pathways are responsible for asthma.

“Targeting multiple inflammatory cytokines with a single drug may be the key to treat and control complex and severe chronic airway disease.”

The most recent statistics show a 30% rise in asthma-related deaths (467 people) nationally in 2022, with South Australia recording the most drastic increase at 88%.

According to experts, most of the deaths were preventable and were linked to people not having treatment on hand, or using it as prescribed, especially inhaled corticosteroids.

2022 marked the highest asthma deaths since 2017, partly driven by the post-Covid return of viral respiratory infections which are associated with increases in asthma hospitalisations.

Widespread rainfall, triggering an increase in fungal spores and pollen, is also a factor.

Notes to editors

“Dual inhibition of airway inflammation and fibrosis by commonβcytokine receptor blockade” is published in the Journal of Allergy and Clinical Immunology. DOI:  10.1016/j.jaci.2023.10.021

*The Centre for Cancer Biology is an alliance between the University of South Australia (UniSA) and CAHLN / SA Pathology .

…………………………………………………………………………………………………………………………

Media contact: Candy Gibson M:  0434 605 142 E: [email protected]

Lead researcher: Dr Damon Tumes E: [email protected]

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Affiliations

  • 1 Department of Respiratory and Infectious Diseases, Bispebjerg Hospital, Copenhagen, Denmark; Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. Electronic address: [email protected].
  • 2 Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet and Sachs' Children and Youth Hospital, Stockholm, Sweden.
  • 3 Allergy Centre, Tampere University Hospital, Tampere, Finland; Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
  • 4 National Institute for Health and Care Research Nottingham Biomedical Research Centre, Division of Respiratory Medicine, School of Medicine, University of Nottingham, Nottingham, UK.
  • PMID: 36682372
  • DOI: 10.1016/S0140-6736(22)02125-0

Asthma is one of the most common chronic non-communicable diseases worldwide and is characterised by variable airflow obstruction, causing dyspnoea and wheezing. Highly effective therapies are available; asthma morbidity and mortality have vastly improved in the past 15 years, and most patients can attain good asthma control. However, undertreatment is still common, and improving patient and health-care provider understanding of when and how to adjust treatment is crucial. Asthma management consists of a cycle of assessment of asthma control and risk factors and adjustment of medications accordingly. With the introduction of biological therapies, management of severe asthma has entered the precision medicine era-a shift that is driving clinical ambitions towards disease remission. Patients with severe asthma often have co-existing conditions contributing to their symptoms, mandating a multidimensional management approach. In this Seminar, we provide a clinically focused overview of asthma; epidemiology, pathophysiology, diagnosis, and management in children and adults.

Copyright © 2023 Elsevier Ltd. All rights reserved.

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  • Asthma* / drug therapy
  • Respiratory Sounds / etiology

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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  • Continuing Education Activity

Asthma is a chronic disease of the air passages characterized by inflammation and narrowing of the airways. Symptoms of asthma include shortness of breath, cough, and wheezing. It commonly presents in childhood and is usually associated with conditions such as eczema and hay fever. This activity outlines the evaluation and treatment of asthma and explains the role of the interprofessional team in managing patients with this condition.

  • Review the epidemiology of asthma.
  • Identify the typical patient history of asthma.
  • Summarize the use of pulse oximetry and peak flow measures in the bedside evaluation of asthma.
  • Outline the importance of collaboration and communication among the interprofessional team members to improve outcomes in patients affected by asthma.
  • Introduction

Asthma is a common disease and has a range of severity, from a very mild, occasional wheeze to acute, life-threatening airway closure. It usually presents in childhood and is associated with other features of atopy, such as eczema and hayfever. [1] [2] [3]

Asthma is a very common childhood illness leading to multiple hospital admissions and increased healthcare costs. The key feature is airway hyper-responsiveness, which can be triggered by many factors. If not treated promptly, asthma has a high mortality. [4]

Asthma comprises a range of diseases and has a variety of heterogeneous phenotypes. The recognized factors that are associated with asthma are a genetic predisposition, specifically a personal or family history of atopy (propensity to allergy, usually seen as eczema, hay fever, and asthma). [5] [6]

Asthma also is associated with exposure to tobacco smoke and other inflammatory gases or particulate matter.

The overall etiology is complex and still not fully understood, especially when it comes to being able to say which children with pediatric asthma will carry on to have asthma as adults (up to 40% of children have a wheeze, only 1% of adults have asthma), but it is agreed that it is a multifactorial pathology, influenced by both genetics and environmental exposure.

Triggers for asthma include:

  • Viral respiratory tract infections
  • Gastroesophageal reflux disease
  • Chronic sinusitis
  • Environmental allergens
  • Use of aspirin, beta-blockers
  • Tobacco smoke
  • Insects, plants, chemical fumes
  • Emotional factors or stress
  • Epidemiology

Asthma is a common pathology, affecting around 15% to 20% of people in developed countries and around 2% to 4% in less developed countries. It is significantly more common in children. Up to 40% of children will have a wheeze at some point, which, if reversible by beta-2 agonists, is termed asthma, regardless of lung function tests. Asthma is associated with exposure to tobacco smoke and inhaled particulates and is thus more common in groups with these environmental exposures. [7] [8]

In childhood, asthma is more common in boys with a male to female ratio of 2:1 until puberty when the ratio becomes 1:1. After puberty, the prevalence of asthma is greater in females, and adult-onset cases after the age of 40 years are mostly females. Asthma prevalence is greater in extreme of ages due to airway responsiveness and lower levels of lung function. [9]

Of all the asthma cases, about 66% are diagnosed before the age of 18 years. almost 50% of children with asthma have a decrease in severity or disappearance of symptoms during early adulthood. [10]

  • Pathophysiology

Asthma is a condition of acute, fully reversible airway inflammation, often following exposure to an environmental trigger. The pathological process begins with the inhalation of an irritant (e.g., cold air) or an allergen (e.g., pollen), which then, due to bronchial hypersensitivity, leads to airway inflammation and an increase in mucus production. This leads to a significant increase in airway resistance, which is most pronounced on expiration.

Airway obstruction occurs due to the combination of:

  • Inflammatory cell infiltration.
  • Mucus hypersecretion with mucus plug formation.
  • Smooth muscle contraction.

These irreversible changes may become irreversible over time due to

  • Basement membrane thickening, collagen deposition, and epithelial desquamation.
  • Airway remodeling occurs in chronic disease with smooth muscle hypertrophy and hyperplasia.

If not corrected rapidly, asthma may become more difficult to treat, as the mucus production prevents the inhaled medication from reaching the mucosa. The inflammation also becomes more edematous. This process is resolved (in theory complete resolution is required in asthma, but in practice, this is not checked or tested) with beta-2 agonists (e.g., salbutamol, salmeterol, albuterol) and can be aided by muscarinic receptor antagonists (e.g., ipratropium bromide), which act to reduce the inflammation and relax the bronchial musculature, as well as reducing mucus production. [11]

  • Toxicokinetics

The only relevant toxicokinetics in asthma relates to its management as the absorption and systemic side effects of the beta-2 agonists must be monitored. Typically these will be removed from the body in 2 to 4 hours if salbutamol and albuterol, 18 to 24 hours if salmeterol, or 48 to 72 hours if clenbuterol, which is no longer used in the management of asthma.

The side effects of the beta-2 agonists include tachycardia, flushing, sweating, and other signs of sympathetic system overdrive. There is also the chance of iatrogenic hypokalaemia, which must be monitored.

  • History and Physical

Patients will usually give a history of a wheeze or a cough, exacerbated by allergies, exercise, and cold. There is often diurnal variation, with symptoms being worse at night. Patients may give a history of other forms of atopy, such as eczema and hay fever. There may be some mild chest pain associated with acute exacerbations. Many asthmatics have nocturnal coughing spells but appear normal in the day time

Physical exam findings will depend on whether the patient is currently experiencing an acute exacerbation.

During an acute exacerbation, there may be a fine tremor in the hands due to salbutamol use, and mild tachycardia. Patients will show some respiratory distress, often sitting forward to splint open their airways. On auscultation, a bilateral, expiratory wheeze will be heard. In life-threatening asthma, the chest may be silent, as air cannot enter or leave the lungs, and there may be signs of systemic hypoxia.

Children with imminent arrest may appear drowsy, unresponsive, cyanotic, and confused. Wheezing may be absent, and bradycardia may occur, indicating severe respiratory muscle fatigue.

Life-threatening asthma is a type of asthma that does not respond to systemic steroids and beta 2 agonist nebulization. It is necessary to identify it early as it may lead to high mortality. It has the following characteristic findings on examination

  • Peak expiratory flow less than 33% of personal best
  • Oxygen saturation less than 92%
  • The normal partial pressure of carbon dioxide
  • Silent chest
  • Feeble respiratory effort
  • Bradycardia
  • Arrhythmias
  • Hypotension
  • Confusion, coma

In near-fatal asthma, the partial pressure of carbon dioxide is raised, or mechanical ventilation is required with raised inflation pressures.

Pulse oximetry can be useful in assessing the severity of an asthma attack or monitoring for deterioration. Note that pulse oximetry lag, and the physiological reserve of many patients means that a falling pO2 on pulse oximetry is a late finding, indicating a severely unwell or peri-arrest patient.

Peak flow measures also can be used to assess asthma and should always be checked against a nomogram as well as the individual patient's normal baseline function. The different severities of acute asthma attacks have an associated peak flow measurement, recorded as a certain percentage of expected peak flow.

Urea and electrolytes (kidney function) should be taken if the patient has a high dose or repeat salbutamol, as one of the side effects of salbutamol is to cause potassium to shift into the intracellular space transiently, which can induce a transient, iatrogenic hypokalaemia. Eosinophilia is common but is not specific for asthma. Recent studies show that levels of sputum eosinophils may guide therapy. In addition, some patients may have an elevation of serum IgE.

Arterial blood gas may reveal hypoxemia and respiratory acidosis. Studies indicate that periostin may be a marker for asthma, but its clinical role remains unsettled.

An ECG will reveal sinus tachycardia, which may be due to asthma, albuterol, or theophylline.

A chest x-ray is an important test, especially if patients have a history of risk of the potential foreign body or possible infection. A Chest CT scan is done in patients with recurrent symptoms who do not respond to therapy.

Special Tests

Spirometry is the diagnostic method of choice and will show an obstructive pattern that is partially or completely resolved by salbutamol. Spirometry should be done before treatment to determine the severity of the disorder. A reduced ratio of FEV1 to FVC is indicative of airway obstruction, which is reversible with treatment. Reversibility testing is done by giving the patient inhaled short-acting beta 2 agonists, and after that, the spirometry test is repeated. If there is a 12% or 200ml improvement in FEV1 from the previous value, then it shows reversibility and diagnostic for bronchial asthma. Peak expiratory flow measurement is common today and allows one to document response to therapy. A limitation of this test is that it is effort dependent.

In some patients, a methacholine/histamine challenge may be required to determine if airway hyper-reactivity is present. This test should only be done by trained individuals.

Exercise spirometry may help identify patients with exercise-induced bronchoconstriction.

  • Treatment / Management

Conservative Measures

Measures to take include calming the patient to get them to relax, moving outside or away from the likely source of allergen, and cooling the person. Removing clothing and washing the face and mouth to remove allergens is sometimes done, but it is not evidence-based. [12] [13] [14]

Environmental control is vital if one wants to avoid recurrent attacks. Allergen avoidance can significantly improve the quality of life. This means avoiding tobacco, dust mites, animals, and pollen.

Weight reduction in obese asthmatics leads to improved control.

Allergen immunotherapy remains controversial. Large studies have not shown any significant benefit, and the technique is prohibitively expensive.

Monoclonal antibody therapy is indicated for patients with moderate to severe asthma who have a positive skin test. The treatment can lower IgE levels, which in turn decreases histamine production. However, the cost of the injections is high.

Bronchial thermoplasty is a relatively new technique that delivers thermal energy to the airway wall and reduces the narrowing of the airways. Several studies show that it can reduce emergency visits and days missed from school.

Medical management includes bronchodilators like beta-2 agonists and muscarinic antagonists (salbutamol and ipratropium bromide respectively) and anti-inflammatories such as inhaled steroids (usually beclometasone but steroids via any route will be helpful).

There are five steps in the management of chronic asthma; treatment is started depending on the severity and then escalated or de-escalated depending on the response to treatment. [15]

Step 1: The Preferred controller is as needed low dose inhaled corticosteroid and formoterol.

Step 2: The preferred controllers are daily low dose inhaled corticosteroid plus as-needed short-acting beta 2 agonists.

Step 3: The preferred controllers are low dose inhaled corticosteroid and long-acting beta 2 agonists plus as-needed short-acting beta 2 agonists.

Step 4: The preferred controller is a medium-dose inhaled corticosteroid and long-acting beta 2 agonist plus as-needed short-acting beta 2 agonists.

Step 5: High dose inhaled corticosteroid and long-acting beta 2 agonist plus long-acting muscarinic antagonist/anti-IgE.

Indications for admission

If a patient has received three doses of an inhaled bronchodilator and shows no response, the following factors should be used to determine admission:

  • The severity of airflow obstruction
  • Duration of asthma
  • Response to medications
  • Adequacy of home support
  • Any mental illness

Patients with life-threatening asthma are managed with high flow oxygen inhalation, systemic steroids, back to back nebulizations with short-acting beta 2 agonists, and short-acting muscarinic antagonists and intravenous magnesium sulfate. Early involvement of the intensive care team consultation helps to reduce mortality. In the case of near-fatal asthma, early intubation and mechanical ventilation are needed.

There is no surgical input into the management of typical asthma.

Other/Long Term

Weight loss, smoking cessation, occupational change, and self-monitoring are all important in preventing disease progression and reducing the number of acute attacks.

  • Differential Diagnosis

The main differential for an acute, life-threatening asthma attack is an anaphylactic reaction. In this case, the patient may also present with orofacial swelling, a rash, and itching. The patient will partially respond to salbutamol and steroids, but intramuscular adrenaline is the lifesaving medication needed to manage these patients.

Other differentials include vocal cord dysfunction, tracheal or bronchial obstruction due to foreign body or tumor, heart failure, gastroesophageal reflux, chronic sinusitis, and chronic obstructive pulmonary disease.

Chronic asthma is usually classified as follows:

  • Intermittent
  • Mild persistent
  • Moderate persistent
  • Severe persistent

Acute asthma is classified as below: 

  • Acute severe asthma
  • Life-threatening asthma
  • Near-fatal asthma

Asthma is not a benign illness and accounts for 1 death per 100,000 people in some countries. The mortality is related to lung function and is exacerbated by smoking. Factors that affect mortality include age more than 40 years, cigarette smoking more than 20 pack-years, blood eosinophilia, FEV1 of 40-70% of predicted, and greater reversibility. [16] Asthma leads to loss of time from work and school; it also leads to multiple hospital admissions increasing the cost of healthcare. Poorly controlled asthma can be disabling and leads to poor quality of life.

  • Postoperative and Rehabilitation Care

Patients with asthma need life-long follow up for monitoring of the disease, quality of life, and functional status. At each visit, compliance with medications should be emphasized.

Asthma is not a curable disorder, and patients need life long monitoring. Patients should be educated about the need for monitoring of the disease and compliance with medications. The patient should be given a written asthma action plan.

  • Consultations
  • Pulmonology consultation.
  • Involvement of the intensive care unit early in cases of severe persistent asthma and life-threatening asthma.
  • Deterrence and Patient Education

Patient education about the disease and modifying behavior is vital. The patient should also be encouraged to change lifestyle and control the environmental trigger factors.

Patients should be asked to maintain healthy body weight as evidence reveals that the disorder is more difficult to control in overweight individuals.

Patients should avoid tobacco and use of beta-blockers, aspirin, and sulfites.

  • Pearls and Other Issues

Disposition

If the patient requires nebulized salbutamol and is not ordinarily on home nebulizers, he or she should be admitted. Anyone who has presented with severe or life-threatening asthma should usually be monitored to ensure that the disease does not return when the medication has worn off.

Issues include forgetting to remove the nebulizer mask once the nebulizer is done (thus leaving the patient on only 6L of 02/min, rather than changing them to 15 L/min via a non-rebreather mask), not assessing inhaler technique, and neglecting to stress the importance of maintenance therapy with inhaled steroids even when the patient is well.

  • Enhancing Healthcare Team Outcomes

In many countries, including the US, asthma kills one out of every 100,000 persons. The worse the lung function, the higher the mortality. In addition, mortality has also been linked to poor management and lack of medication compliance, especially in young people. Other factors that increase the risk of death include smoking and use of illicit drugs.

Asthma also results in millions of school and workdays lost. In the US alone, close to 2 million asthmatics seek regular care in the emergency department, which also increases the costs of healthcare.

Even though asthma is a reversible disorder, poor lifestyle and lack of management can lead to airway remodeling that leads to chronic symptoms, which are disabling. [17]

The disorder has no cure, and thus life long monitoring is necessary. For best outcomes, an interprofessional approach is recommended.

Evidence-based Medicine

Many guidelines have been published for the diagnosis and management of asthma, but the most critical feature is patient education. The nurses are the last professionals to see the patient before discharge from the emergency department or the floors. Similarly, since most asthmatics are treated as outpatients, pharmacists encounter them regularly. Evidence shows that teaching patients about this disorder and the importance of compliance are critical for good outcomes. The patient should be taught about monitoring techniques, inhaler use, and modifying the environment. A social worker should be involved in the care to ensure that the patient has adequate home support and facilities.

Many evidence-based asthma plans are available for the management of asthma and should be handed out to patients. Finally, nurses also play a vital role in school-based asthma education programs that can help improve self-esteem, knowledge, and self-management behaviors. [18] [19] [20]  (Level II)

Management of asthma requires an interprofessional approach. Nurses work with the clinician in providing patient and family education regarding avoiding triggers, regular use of medications and being prepared with rescue inhalers. The pharmacist should assist with the appropriate use of inhalers and encouraging daily medication administration. The pharmacist should carefully examine the current medications and make sure the patient is not taking any medications that may trigger an attack, working with the prescriber to modify the treatment. an interprofessional approach will result in the best outcomes. [Level V]

Despite great awareness of the disease, asthma still results in high morbidity and even mortality. There are universal guidelines on managing the disorder, but patient compliance with medications remains a big problem. Hence, all healthcare workers have a responsibility to encourage medication compliance and close follow up with the primary care physician. [21] [22] (Level V)

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Asthma Pathology. Figure A shows the location of the lungs and airways in the body. Figure B shows a cross-section of a normal airway. Figure C shows a cross-section of an airway during asthma symptoms. Contributed by United States-National Institute (more...)

X-ray, COPD, Chronic Obstructive Disease, Asthma, Anterior, Lateral Contributed by chestatlas.com (H. Shulman MD)

Allergic Bronchopulmonary Aspergillosis on Computed Tomography. This image shows bronchiectasis in both upper lobes in a patient with bronchial asthma, which are findings consistent with allergic bronchopulmonary aspergillosis. Contributed by (more...)

Asthma Classification Table Contributed by Rina Chabra, DO

Disclosure: Muhammad Hashmi declares no relevant financial relationships with ineligible companies.

Disclosure: Maryam Tariq declares no relevant financial relationships with ineligible companies.

Disclosure: Mary Cataletto declares no relevant financial relationships with ineligible companies.

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Asthma drug sharply cuts danger from food allergens: US study

what is asthma research paper

WASHINGTON - A drug long used to treat asthma can help protect people from dangerous – even fatal – food allergies, a study published on Feb 25 in the respected New England Journal of Medicine found.

The randomised study, funded partly by the National Institute of Allergy and Infectious Disease, tested the drug Xolair (chemical name omalizumab) on 118 children known to be allergic to peanuts and at least one other food, like milk or eggs.

The survey, carried out at 10 US medical centres, found that after treatment 67 per cent of the children were able to tolerate a small amount of peanut protein without symptoms. Of 59 other children given a placebo, only 7 per cent were able to do so.

The US Food and Drug Administration (FDA) approved the drug’s use for food allergies in adults and children as young as one earlier in February. It was approved more than 20 years ago for use against allergic asthma.

Scientists cautioned, however, that the drug does not mean the allergy-prone can completely drop their guard; they must still try to avoid known allergens. But the drug should reduce dangerous reactions.

Xolair is administered, by injection, every two to four weeks – not easy for the needle-averse.

Still, for people who have had to live in constant fear that unwittingly consuming even a trace of an allergen could result in hospitalisation – or worse – the treatment could be “life-changing,” said one of the study’s leaders, Dr Robert Wood of Johns Hopkins University School of Medicine.

Severe allergic reactions account for an estimated 30,000 emergency-room visits a year in the US.

Xolair is sold by drug companies Roche and Novartis. AFP

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Our Clinical Trials

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We conduct many clinical trials with the goal of developing therapies for allergic disorders and asthma.

Our studies include a wide range of patients, representing a diverse group of ages, ethnicities and socioeconomic backgrounds. Our Center offers equal opportunity to all people with allergies, so long as they are eligible based on study parameters.

PRE-SCREENING

Participate in Prescreening at the Sean N. Parker Center for Allergy & Asthma at Stanford University

Condition:  Allergies, Food Allergies, Asthma, Eczema, Atopic Dermatitis, Allergic Rhinitis, Eosinophilic Esophagitis (EOE) Age Group:  0-90 y/o Duration:  1 Time Event Description:  We invite people with doctor-diagnosed food allergies, asthma, eczema, allergic rhinitis, eosinophilic esophagitis (EOE), and other allergic conditions to participate in prescreening for allergy and asthma research at our center. Contact:  If you are interested in learning more about the study or scheduling a prescreening visit, you can call us at (650) 521-7237 or contact us at  [email protected]

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Your new family can help us learn how about the progression of allergic diseases!

We hope to learn how environmental and genetic factors play a role in food allergy and eczema development.

To Participate in this study you must:

  • Be a Pregnant Mother at any point during your pregnancy and willing to enroll your child at birth
  • Participation for you and your child lasts until your child is three years old
  • Biological Fathers may also participate, but their participation is not required  

Participation will involve:

  • Clinic visits several times over three years to monitor the development of allergies
  • Sample collection (blood, urine, hair, stool, skin tapes and swabs, saliva, cord blood and nasal swabs; mother only: vaginal swabs and breast milk)
  • Questionnaires related to your child’s health, diet, and environment

Contact: Email: [email protected] Clinic Phone: (650) 521-7237

View full flyer HERE .

View our SUNBEAM Website HERE.

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SEAL (Stopping Eczema and ALlergy) Study

Study Objective:  To test if proactive use of topical skin creams is more effective than common atopic dermatitis (eczema) treatment methods at preventing eczema and food allergies.

Who is Eligible: Any child up to 12 weeks of age is welcomed. Our physicians will determine the participant's eligibility based on the participant's skin and other criteria in the clinic. Parental consent is required prior to enrollment.

Study Duration:  36 months (5 clinic visits)

Study Principal Investigators:  Sayantani B. Sindher, MD

Study Sponsor:  Stanford University School of Medicine 

Compensation for participation is provided. Contact for more information and to schedule a pre-screening visit: Clinic Email: [email protected]

Clinic Phone: (650) 521-7237

MAGIC

Our Center is looking for milk allergy participants aged 4 to 50 years old for a study led by Dr. Sayantani (Tina) Sindher. Qualified participants will receive the treatments of oral immunotherapy and a biologic. There is no cost to your participation and your participation is entirely voluntary. Call (650) 521-7237 or email [email protected] to learn more about the study.

View Full Flyer HERE

MAGIC

SNP Center is seeking participants with peanut and 1-2 other food allergies for a clinical trial. 

Description: 

Our goal is to determine if using biologics and multi oral immunotherapy (mOIT) will increase tolerance to multiple food allergens.

Who Can Participate:

  • Children, adolescents and adults between 4-55 years old.
  • History of reaction to peanut and diagnosed with allergy to 1-2 additional foods from the following list: almond, cashew, hazelnut, egg, walnut, sesame seed, soy, shellfish, fish, wheat and milk  

No cost to participate. Reimbursement will be provided.

Have questions or want to learn more? Please contact: [email protected], 650-521-7237

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Visit our  StudyPages  platform to learn more about our clinical trials and studies!

The Sean N. Parker Center is proud to be a Verified Partner with  Spokin , a leading resource in the food allergy community. Spokin’s free  iOS app  connects the food allergy community in a fast and fun way to resources including food, restaurants, bakeries, travel and more. Download the app to explore over 65,000 user reviews in over 80 countries, top rated product and travel guides, recipes and more. Visit  spokin.com  to learn more.

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Join the FARE Patient Registry! Answer survey questions about your food allergy experiences and take an important step that benefits both you and the entire food allergy community!

FARE Patient Registry

Your answers will help food allergy researchers understand the disease and search for new treatments.

You’ll have the option to learn about clinical trials and connect with researchers.

Sean N. Parker Center is proud to be a part of the FARE Clinical Network conducting novel research on treatments, diagnostics, prevention, and improvements to care.

Click HERE to get started!

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FDA expands use of asthma drug Xolair to treat severe food allergies

FILE - This Feb. 20, 2015 photo shows an arrangement of peanuts in New York. Xolair, the brand name for the drug omalizumab, used to treat asthma can now be used to help people with food allergies avoid severe reactions, the U.S. Food and Drug Administration said Friday, Feb. 16, 2024. (AP Photo/Patrick Sison, File)

FILE - This Feb. 20, 2015 photo shows an arrangement of peanuts in New York. Xolair, the brand name for the drug omalizumab, used to treat asthma can now be used to help people with food allergies avoid severe reactions, the U.S. Food and Drug Administration said Friday, Feb. 16, 2024. (AP Photo/Patrick Sison, File)

what is asthma research paper

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A medication used to treat asthma can now be used to help people with food allergies avoid severe reactions, the U.S. Food and Drug Administration said Friday .

Xolair, the brand name for the drug omalizumab, became the first medication approved to reduce allergic reactions caused by accidental exposure to food triggers. Patients as young as age 1 with allergies can take the drug by injection every two to four weeks, depending on their weight and their body’s response to allergens.

An estimated 17 million people in the U.S. have the type of food allergies that can cause rapid, serious symptoms, including severe, whole-body reactions that are potentially deadly.

People who use Xolair must continue to avoid the foods that cause them reactions, such as peanuts, cashews, hazelnuts, walnuts, milk products and eggs. The medication allows them to tolerate higher amounts of such foods without causing major reactions.

CORRECTS NAME TO GABE AYALA FROM LIAM LAZO - Gabe Ayala boards a diesel school bus near his home, Tuesday, Feb. 6, 2024, in Virginia Beach, Va. Diesel exhaust from school buses affects one-third of U.S. students, their parents and educators each day. (AP Photo/Tom Brenner)

Many people with allergies — and their families — live with constant anxiety about exposure to allergens and often avoid dining out and other social situations.

“To have this protection is going to be life-changing,” said Dr. Robert Wood, director of the pediatric allergy division at Johns Hopkins Children’s Center.

The FDA decision is based on a study led by Wood and funded by the National Institutes of Health. It showed that Xolair allowed about 68% of participants with peanut allergies to tolerate about 600 milligrams, or about 1/2 teaspoon, of peanut protein, compared with about 6% of those who received dummy injections.

The results were similar for other allergens such as tree nuts, milk, egg and wheat, a study abstract reported. Full results are expected to be presented at a meeting and published in a peer-reviewed journal later this month.

Wood estimated that 25% to 50% of people with food allergies, particularly children and young adults, would elect to use Xolair.

The drug has been used “off-label” to treat food allergies, said Dr. Ruchi Gupta, director of the Center for Food Allergy & Asthma Research at Northwestern University. She welcomed full approval of the product.

Xolair is a monoclonal antibody, a type of treatment that works by blocking the body’s natural response to allergens. It was first approved in 2003 and has been used to treat asthma, nasal polyps and chronic hives. It is produced by drugmakers Novartis and Roche and is distributed by a Roche subsidiary, Genentech.

The most common side effects of Xolair are injection site reactions and fever, but the FDA noted that the drug has also been associated with joint pain, rash, parasitic infections, malignancies and abnormal laboratory tests. Xolair comes with a warning saying the treatment itself can cause anaphylaxis and must be started in a health care setting equipped to manage the reaction.

The medication is not approved for emergency treatment of allergic reactions.

The list price for Xolair ranges from about $2,900 a month for children to $5,000 a month for adults, according to Genentech. Most insured patients typically pay less out of pocket, the company said.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

JONEL ALECCIA

what is asthma research paper

Analytical Methods

A paper-based chromogenic strip and electrochemical sensor for the detection of 4-(dimethylamino)azobenzene †.

ORCID logo

* Corresponding authors

a Food Toxicology Group, CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India E-mail: [email protected] , [email protected] Tel: +91-7080541888

b Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India

Natural and synthetic dyes are added to different food commodities to enhance their appearance and acceptance by consumers. Acute and chronic exposure owing to the consumption of non-permitted dyes may lead to health concerns such as allergic reactions, eczema, and asthma. 4-(Dimethylamino)azobenzene (4-DMAAB) is a non-permitted dye that has been reported in adulterated mustard oil. Consumption of 4-DMAAB poses severe risks due to its mutagenic and carcinogenic properties. Several sensitive methods such as FT-NIR, FT-MIR and SERS are available for the detection of 4-DMAAB. Here, a spectrophotometric method was developed for the detection of 4-DMAAB. The developed method was translated to a point-of-test paper-based, chromogenic strip which showed a detection limit of 0.025 mM for 4-DMAAB. Also, an electrochemical sensor was developed by electro-depositing the test solution on a screen-printed electrode. The electrochemical sensor showed an LOD of 0.027 ± 0.008 mM with recovery in the range of 91–107% of 4-DMAAB. Oil samples collected from the market were processed by liquid–liquid extraction and the content of 4-DMAAB was assessed. The developed point-of-use sensors for the detection of 4-DMAAB have potential for use by the consumers, food industry and regulatory agencies for on-site analysis and assuring the quality of edible oils.

Graphical abstract: A paper-based chromogenic strip and electrochemical sensor for the detection of 4-(dimethylamino)azobenzene

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what is asthma research paper

A paper-based chromogenic strip and electrochemical sensor for the detection of 4-(dimethylamino)azobenzene

P. Rai, S. Mehrotra, S. Verma and S. K. Sharma, Anal. Methods , 2024, Advance Article , DOI: 10.1039/D3AY01928D

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What to Know About Xolair and Food Allergies

The drug does not completely prevent reactions, but it can reduce the risks posed by trace amounts of food allergens.

A close-up view of peanuts in shells.

By Roni Caryn Rabin

The Food and Drug Administration approved a drug this month that cuts the risk of severe reactions in children and adults exposed to trace amounts of peanuts, tree nuts, milk, dairy and other food allergens — a move that could dramatically improve quality of life for people coping with these risks. The results of the clinical trial supporting the decision were published on Sunday.

While the drug, Xolair, offers a new layer of protection to people who may have life-threatening reactions to common foods, and especially to those who are allergic to several foods, its use comes with important caveats.

Does Xolair cure food allergies?

No. Xolair is not a cure for food allergies, nor can it be used to treat acute reactions. People who take Xolair must continue to avoid foods that they are allergic to.

But Xolair can significantly reduce the odds that people with severe food allergies will develop acute reactions if they ingest minute amounts of allergens, like peanuts or eggs in prepared foods, or are exposed to trace amounts in some other way.

People must take the drug continuously in order to benefit from its protection. Even then, the drug does not entirely eliminate the risk.

How does it work?

Xolair is a synthetic antibody that works by binding to immunoglobulin-E, a component of the immune system, preventing it from arming key immune cells that are responsible for setting off allergic reactions.

Who benefits the most from taking Xolair?

Xolair helps protect against most severe allergic reactions — specifically those that are driven by immunoglobulin-E. The drug may be most beneficial for people who have allergies to multiple foods and must constantly avoid all of them, which can be challenging, and for those who eat a lot of food prepared by others (like college students on meal plans).

It is approved for adults and for children ages 1 and up. (Babies younger than 1 were not included in the clinical trial.)

How well does it work?

Xolair reduces, but does not eliminate, the risk of having a severe reaction to a certain food. People with these allergies must continue to exercise vigilance and avoid the foods they are allergic to; they should read food labels and inform others of their allergies. They or their caregivers should continue to carry epinephrine, a drug that can reverse symptoms of anaphylaxis, at all times.

How is Xolair administered?

One downside of the drug is that it has to be administered by injection, usually in the arm. The shots are given every two or four weeks, in dosing intervals tailored to the patient.

Participants in the clinical trial were found to have benefited after 16 to 20 weeks of treatment. But the protection against severe reactions appears to last only as long as patients continue the treatment.

Does Xolair have side effects? Is it safe for long-term use?

Even though the drug was shown only recently to reduce risks from food allergies, it has been on the market for 20 years for other uses, including asthma caused by allergies and chronic hives. So its safety profile is fairly well known.

The most common side effects that participants experienced in the recent trial were reactions at the injection site and fever. In rare cases, the drug itself may cause life-threatening anaphylaxis: It has been shown to occur after the first dose of Xolair, according to the F.D.A., as well as a year or longer after starting treatment.

For that reason, Xolair should be administered in a health care setting equipped to treat anaphylaxis. The label also warns of rare side effects like joint pain, rash and parasitic infection.

How much does Xolair cost? Is it covered by insurance?

Even though Xolair has been used for other conditions since 2003 and the National Institutes of Health helped fund the new trial, Xolair carries a hefty list price: it is roughly $2,900 a month for children with food allergies and around $5,000 for adults, according to Genentech, the manufacturer.

But now that it is approved for severe food allergies, insurance plans are expected at least partly to cover it. Patient assistance programs are available through Genentech Access Solutions.

For eligible patients who have commercial health insurance, Genentech also offers the Xolair Co-Pay Program , which may help cover the cost of treatment.

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