Editorial: Road trip from mild to severe asthmatic inflammation: the traffic lights of biomarkers in asthma management, volume II
Bibliographic record
Abstract
There is still a clinical need to delineate complex endotypes of asthma and to identify novel biomarkers with high predictive and prognostic value to achieve an optimal personalized approach. This Research Topic, describes current topics in asthma biomarker research, providing a better understanding of the utility of the currently available biomarkers and the current biomarker research regarding asthma remission, and suggesting new approaches to use biomarkers in everyday clinical practice for optimal management of patients with asthma. Our issue assembles six high-quality articles describing the benefits of using biomarkers for asthma management.Within this context, Jingcheng Dong et al. explored the association between baseline Th2 biomarker levels and clinical manifestations in pediatric asthma and identified predictors of clinical remission. The study included 172 children and the authors evaluated a number of clinical parameters, incuding FeNO, blood eosinophils, and serum biomarkers (TSLP, IL-4/5/13, TARC, Periostin, IgE). The authors concluded that serum TSLP is independently associated with clinical remission in Th2-high pediatric asthma and integration with lung function and IgE may form a composite biomarker panel for remission evaluation. This stratification tool may guide asthma risk stratification and personalized disease management, but longitudinal studies are warranted to validate its prognostic utility.In a slightly different approach, given the important role of cytokines in asthma pathophysiology, Yansen Zheng et al. investigated the causal effects between cytokines and asthma, using the inverse variance weighted Mendelian randomization (MR) method. The MR analysis showed that levels of IL-5 and IL-9 were increased in asthma, indicating the downstream effects of IL-5 and IL-9 on asthma. Besides, they concluded that there was no evidence that cytokines increased or decreased the risk of asthma. Using similar methodology, Roan Eltigani Zaied et al. investigated the distinct and shared genetic risk factors contributing to the development of unspecified asthma (no age-specific), childhood onset asthma (COA) and adult-onset asthma (AOA). They employed a two-sample MR analysis to elucidate the causal association between genes within lung and whole-blood-specific gene regulatory networks (GRNs) and the development of unspecified asthma, COA, and AOA using the Wald ratio method. They identified genes (including ORMDL3, PEBP1P3) whose altered expression in lung or blood is putatively causally associated with unspecified asthma and two age-specific asthma presentations, proposing that the causal genes identified in this analysis hold promise as potential drug targets, emphasizing the need to consider the asthma subtype in the development of asthma drugs.Feng Xu et al. explored the relationship between the systemic immuneinflammation index (SII) and mortality in patients with asthma. The study included 6,156 participants from the National Health and Nutrition Examination Survey (NHANES) for US adults from 2001 to 2018. Subgroup analyses revealed SII's association with all-cause mortality across various demographics, including age, sex, race, education levels, smoking status, and marital status suggesting that SII may potentially serve as a predictive tool for evaluating asthma mortality rates. Similarly, Tulei Tian et al. analyzed data from 40,664 participants from NHANES to assess the relationship between SII and asthma and asthma-related events. They found that SII is positively correlated with the persistence of asthma, yet has limited predictive power for asthma recurrence, highlighting SII's potential as a tool for assessing asthma risk and formulating targeted management strategies.Both studies, by analyzing participants from NHANES revealed the potential use of SII in asthma management.In a broader population, Celeste M Porsbjerg et al. aimed to elucidate the association between individual biomarker levels or levels of biomarker combinations before initiation of a biologic with changes in asthma outcomes after therapy with a biologic in real-life. This was a registry-based, cohort study using data from 23 countries, which participate in the International Severe Asthma Registry (May 2017-February 2023); results from 3,751 patients that initiated biologics were included. They concluded that since higher baseline blood eosinophil count, FeNO and their combination can predict biologic-associated lung function improvement, earlier intervention in patients with impaired lung function or at risk of accelerated lung function decline with biologics may be beneficial.We believe that this Research Topic adds to the current literature and advances our understanding of the role of biomarkers in asthma management given the big cohorts analyzed. It is with great pleasure that we are presenting the articles included in this research Topic to the asthma research community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".