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Personalised medicine in asthma: from curative to preventive medicine.

ABSTRACT: The concept of asthma has changed substantially in recent years. Asthma is now recognised as a heterogeneous entity that is complex to treat. The subdivision of asthma, provided by "cluster" analyses, has revealed various groups of asthma patients who share phenotypic features. These phenotypes underlie the need for personalised asthma therapy because, in contrast to the previous approach, treatment must be tailored to the individual patient. Determination of the patient's asthma phenotype is therefore essential but sometimes challenging, particularly in elderly patients with a multitude of comorbidities and a complex exposure history. This review first describes the various asthma phenotypes, some of which were defined empirically and others through cluster analysis, and then discusses personalisation of the patient's diagnosis and therapy, addressing in particular biological therapies and patient education. This personalised approach to curative medicine should make way in the coming years for personalised preventive and predictive medicine, focused on subjects at risk who are not yet ill, with the aim of preventing asthma before it occurs. The concept of personalised preventive medicine may seem a long way off, but is it really?

SUBMITTER: Guilleminault L

PROVIDER: S-EPMC9489193 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Personalised medicine in asthma: from curative to preventive medicine.

Guilleminault Laurent L Ouksel Hakima H Belleguic Chantal C Le Guen Yannick Y Germaud Patrick P Desfleurs Emilie E Leroyer Christophe C Magnan Antoine A

European respiratory review : an official journal of the European Respiratory Society 20170103 143

The concept of asthma has changed substantially in recent years. Asthma is now recognised as a heterogeneous entity that is complex to treat. The subdivision of asthma, provided by "cluster" analyses, has revealed various groups of asthma patients who share phenotypic features. These phenotypes underlie the need for personalised asthma therapy because, in contrast to the previous approach, treatment must be tailored to the individual patient. Determination of the patient's asthma phenotype is th ...[more]

PMID: 28049124

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Project description:BackgroundRecognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing.MethodsThis investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used to inform the models. Model performance was mainly evaluated by the area under the receiver operating characteristic (AUC) curve. Additional techniques-permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP)-were applied for explaining the black-box ML models.ResultsFifteen routine haematological and biochemical features were selected to establish ML-based models for the prediction of IS. The XGBoost-based model achieved the highest predictive performance, reaching AUCs of 0.91 (0.90-0.92) and 0.92 (0.91-0.93) in the internal and external datasets respectively. PFI globally revealed that demographic feature age, routine haematological parameters, haemoglobin and neutrophil count, and biochemical analytes total protein and high-density lipoprotein cholesterol were more influential on the model's prediction. LIME and SHAP showed similar local feature attribution explanations.ConclusionIn the context of PPPM/3PM, we used the selected predictors obtained from the results of common blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction. By incorporating the individualised patient profile, this prediction tool is simple and quick to administer. This is promising to support subjective decision making in resource-limited settings or primary care, thereby shortening the time window for the treatment, and improving outcomes after IS.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-022-00283-4.

Project description:Background/aimsExposure to bioactive compounds from nutrition, pharmaceuticals, environmental contaminants or other lifestyle habits may affect the human organism. To gain insight into the effects of these influences, as well as the fundamental biochemical mechanisms behind them, individual molecular profiling seems to be a promising tool and may support the further development of predictive, preventive and personalised medicine.MethodsWe developed an assay, called metabo-tip for the analysis of sweat, collected from fingertips, using mass spectrometry-by far the most comprehensive and sensitive method for such analyses. To evaluate this assay, we exposed volunteers to various xenobiotics using standardised protocols and investigated their metabolic response.ResultsAs early as 15 min after the consumption of a cup of coffee, 50 g of dark chocolate or a serving of citrus fruits, significant changes in the sweat composition of the fingertips were observed, providing relevant information in regard to the ingested substances. This included not only health-promoting bioactive compounds but also potential hazardous substances. Furthermore, the identification of metabolites from orally ingested medications such as metamizole indicated the applicability of this assay to observe specific enzymatic processes in a personalised fashion. Remarkably, we found that the sweat composition fluctuated in a diurnal rhythm, supporting the hypothesis that the composition of sweat can be influenced by endogenous metabolic activities. This was further corroborated by the finding that histamine was significantly increased in the metabo-tip assay in individuals with allergic reactions.ConclusionMetabo-tip analysis may have a large number of practical applications due to its analytical power, non-invasive character and the potential of frequent sampling, especially regarding the individualised monitoring of specific lifestyle and influencing factors. The extraordinarily rich individualised metabolomics data provided by metabo-tip offer direct access to individual metabolic activities and will thus support predictive preventive personalised medicine.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-021-00241-6.

Dataset Information

Personalised medicine in asthma: from curative to preventive medicine.

Publications

Personalised medicine in asthma: from curative to preventive medicine.

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