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Predictive molecular profiling in blood of healthy vasospastic individuals: clue to targeted prevention as personalised medicine to effective costs.

ABSTRACT: Paradigm change from late interventional approach to predictive diagnostics followed by targeted prevention before manifest pathology, presents innovative concept for advanced healthcare. Preselection of healthy but pathology-predisposed individuals is the primary task in the overall action. Vasospasm is a frequent syndrome defined as an inappropriate constriction or insufficient dilatation in microcirculation. Vasospastic individuals are considered as healthy subpopulation predisposed to several pathologies including neurodegeneration. Clinical observations, subcellular imaging and "gene hunting"-investigations provide evidence for vasospasm as predisposition to glaucoma; development of further related pathologies cannot be excluded. Predictive molecular-profiling in blood can specify individual predisposition for effective prevention.

SUBMITTER: Yeghiazaryan K

PROVIDER: S-EPMC3405317 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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Predictive molecular profiling in blood of healthy vasospastic individuals: clue to targeted prevention as personalised medicine to effective costs.

Yeghiazaryan Kristina K Flammer Josef J Golubnitschaja Olga O

The EPMA journal 20100629 2

Paradigm change from late interventional approach to predictive diagnostics followed by targeted prevention before manifest pathology, presents innovative concept for advanced healthcare. Preselection of healthy but pathology-predisposed individuals is the primary task in the overall action. Vasospasm is a frequent syndrome defined as an inappropriate constriction or insufficient dilatation in microcirculation. Vasospastic individuals are considered as healthy subpopulation predisposed to severa ...[more]

PMID: 23199064

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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

Predictive molecular profiling in blood of healthy vasospastic individuals: clue to targeted prevention as personalised medicine to effective costs.

Publications

Predictive molecular profiling in blood of healthy vasospastic individuals: clue to targeted prevention as personalised medicine to effective costs.

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