Early detection of critical illnesses using exhaled aldehydes: a non-invasive breath analysis approach

Early recognition of critical illness is crucial for timely intervention and improved prognosis. However, conventional diagnostic methods are often invasive and time-consuming, limiting their utility for rapid screening in critical care. Breath analysis has recently emerged as a promising approach in metabolomics due to its non-invasive, repeatable, and rapid-response characteristics. Volatile organic compounds (VOCs), as byproducts of cellular metabolism, dynamically reflect metabolic status, oxidative stress, and pathophysiological changes, offering broad potential for disease monitoring. Prior studies have linked VOC profiles to conditions such as ventilator-associated pneumonia and cognitive impairment [1, 2], but their application in critical illness remains exploratory. Chen et al. demonstrated that aldehyde metabolism is dysregulated in early and progressive stages of critical illness, resulting in toxic aldehyde accumulation, cellular injury, and organ dysfunction, and suggested that monitoring aldehyde fluctuations could guide treatment decisions [3]. Lipid peroxidation products are established markers of oxidative stress and potential non-invasive biomarkers across diseases. Based on the theory of aldehyde dysregulation, we hypothesize that exhaled aldehyde detection may facilitate early identification of critically ill patients.

From January 2024 to January 2025, we conducted a prospective observational study at Qilu Hospital of Shandong University, enrolling 787 adults (230 ICU patients and 557 healthy controls) and collecting exhaled breath samples. The study was approved by the Ethics Committee of Qilu Hospital of Shandong University (approval number KYLL-202401-047) and adhered to the Declaration of Helsinki. Baseline characteristics are summarized in Table 1. We used photoelectron-induced chemical ionization time-of-flight mass spectrometry (CITOF-MS) to analyze VOCs in exhaled breath; this method requires no preprocessing or enrichment, offers high sensitivity and precision, and acquires each spectrum within one second [4]. Ten aldehyde VOCs were preselected for quantitative analysis and group comparison.

Table 1 Demographic characteristics of participants

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Critically ill patients exhibited significantly elevated exhaled aldehyde levels compared to healthy controls (Fig. 1). The heatmap (Fig. 1A) shows uniformly low, tightly clustered aldehyde expression in controls versus marked heterogeneity in patients, indicating metabolic dysregulation. Analysis of ten aldehydes confirmed this pattern (Fig. 1B). Multivariate analyses (PCA, PLS-DA; Fig. 1C–D) demonstrated clear group separation, highlighting the discriminatory capacity of aldehyde profiles. LASSO regression with stability testing identified four key biomarkers—propionaldehyde, acrolein, pentanal, and benzaldehyde—significantly upregulated in critical illness. An XGBoost model based on these markers achieved excellent performance, with an AUC of 0.968 and PR-AUC of 0.963 in nested cross-validation (Fig. 2), suggesting reliable classification of critical illness using breath metabolite profiles.

Fig. 1

Heatmap and Distribution Differences of Aldehyde Expression Profiles. (A) The heatmap shows the expression differences of aldehyde molecules between the case and control groups; (B) Comparison of the expression of 10 representative aldehyde molecules: the case group (red) shows high and dispersed expression, while the control group (green) clusters in the low expression region; (C) PCA results illustrate the spatial distribution of metabolites between the critically ill group and the control group; (D) PLS-DA discriminant analysis shows clear separation between the two groups

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Fig. 2

Diagnostic Model Performance Evaluation. (A) Receiver operating characteristic (ROC) curve; (B) Precision-recall area under the curve (PR-AUC)

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Our findings suggest that exhaled aldehydes may serve as real-time biomarkers for critical illness. We reason that, in critical illness, disruptions in homeostasis, oxidative stress, and metabolic dysfunction promote lipid peroxidation and increase aldehyde generation. As markers of oxidative stress and cellular injury, these aldehydes can be detected in exhaled breath, offering insights for early diagnosis [5]. The non-invasive nature of breath testing enables safe repeat assessments, making it suitable for screening, early triage, and dynamic monitoring in emergency and intensive care settings.

We acknowledge limitations in our study. Demographic differences between control and patient groups (e.g., age, sex, comorbidity profiles) may confound metabolite levels. Moreover, because we sampled breath after onset of critical illness, our findings reflect diagnostic rather than predictive utility. We plan to validate breath aldehyde testing in the emergency department at initial presentation to predict ICU admission, organ dysfunction, or mortality risk, which would enhance clinical relevance. We will address methodological refinements in future studies.

In conclusion, our study provides preliminary evidence that exhaled aldehydes may serve as screening biomarkers for critical illness, underscoring their potential clinical utility as a non-invasive, rapid diagnostic modality. We recommend that future studies validate these biomarkers in larger, multicenter cohorts, evaluating predictive performance and dynamic monitoring in early disease stages. Integration with portable breath-analysis devices for real-time bedside assessment could establish breath aldehyde profiling as a convenient, scalable early warning tool in emergency and critical care.

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.

VOCs:

Volatile Organic Compounds

CITOF-MS:

Chemical Ionization Time-of-Flight Mass Spectrometry

ICU:

Intensive Care Unit

LASSO:

Least Absolute Shrinkage and Selection Operator

PCA:

Principal Component Analysis

PLS-DA:

Partial Least Squares Discriminant Analysis

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All the authors of this manuscript would like to express their gratitude to the emergency department and emergency intensive care unit.

This work was supported by the Key R&D Program of Shandong Province (2024CXPT089).

Author notes

1. Shuangying Tian, Longxin Li and Yingzhe Guo contributed equally to this work.

Authors and Affiliations

1. Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China

   Shuangying Tian, Xiuting Yang, Jianhua Gu, Hui Lin, Yanzhen Wang, Yuan Bian, Feng Xu & Yuguo Chen

2. Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Chest Pain Center, Institute of Emergency and Critical Care Medicine of Shandong University, Qilu Hospital of Shandong University, Jinan, China

   Shuangying Tian, Xiuting Yang, Jianhua Gu, Hui Lin, Yanzhen Wang, Yuan Bian, Feng Xu & Yuguo Chen

3. Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province, Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Qilu Hospital of Shandong University, Jinan, China

   Shuangying Tian, Xiuting Yang, Jianhua Gu, Hui Lin, Yanzhen Wang, Yuan Bian, Feng Xu & Yuguo Chen

4. The Key Laboratory of Cardiovascular Remodeling and Function Research, The State andShandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Chinese Ministry of Education, Chinese Ministry of Health and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China

   Shuangying Tian, Xiuting Yang, Jianhua Gu, Hui Lin, Yanzhen Wang, Yuan Bian, Feng Xu & Yuguo Chen

5. School of Nursing and Rehabilitation, Shandong University, Jinan, China

   Longxin Li

6. Environment Research Institute, Shandong University, Qingdao, 266237, China

   Yingzhe Guo & Keyong Hou

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Contributions

Shuangying Tian, Longxin Li and Yingzhe Guo contributed equally to this work, collected the data and interpreted the results. Yingzhe Guo, chemical analyze of the exhaled breath. Jianhua Gu analyzed the data. Hui Lin and Yanzhen Wang collected the samples. Yuanbian, Keyong Hou, Feng Xu and Yuguo Chen are the researcher, interpreted the results, and revised the final version of the paper and development of the study concept. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Yuan Bian, Keyong Hou, Feng Xu or Yuguo Chen.

Competing interests

The authors declare no competing interests.

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Tian, S., Li, L., Guo, Y. et al. Early detection of critical illnesses using exhaled aldehydes: a non-invasive breath analysis approach. Crit Care 29, 416 (2025). https://doi.org/10.1186/s13054-025-05529-x

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• Received: 08 June 2025

• Accepted: 24 June 2025

• Published: 01 October 2025

• DOI: https://doi.org/10.1186/s13054-025-05529-x

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