Endothelin-1 axes in the framework of predictive, preventive and personalised (3P) medicine.
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ABSTRACT:
SUBMITTER: Torres Crigna A
PROVIDER: S-EPMC8334338 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In 2019, the EPMA celebrated its 10th anniversary at the 5th World Congress in Pilsen, Czech Republic. The history of the International Professional Network dedicated to Predictive, Preventive and Personalised Medicine (PPPM / 3PM) is rich in achievements. Facing the coronavirus COVID-19 pandemic it is getting evident globally that the predictive approach, targeted prevention and personalisation of medical services is the optimal paradigm in healthcare demonstrating the high potential to save lives and to benefit the society as a whole. The EPMA World Congress Supplement 2020 highlights advances in 3P medicine.
| S-EPMC7435254 | biostudies-literature
Project description: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?
| S-EPMC9489193 | biostudies-literature
Project description:In the field of predictive, preventive and personalised medicine, researchers are keen to identify novel and reliable ways to predict and diagnose disease, as well as to monitor patient response to therapeutic agents. In the last decade alone, the sensitivity of profiling technologies has undergone huge improvements in detection sensitivity, thus allowing quantification of minute samples, for example body fluids that were previously difficult to assay. As a consequence, there has been a huge increase in tear fluid investigation, predominantly in the field of ocular surface disease. As tears are a more accessible and less complex body fluid (than serum or plasma) and sampling is much less invasive, research is starting to focus on how disease processes affect the proteomic, lipidomic and metabolomic composition of the tear film. By determining compositional changes to tear profiles, crucial pathways in disease progression may be identified, allowing for more predictive and personalised therapy of the individual. This article will provide an overview of the various putative tear fluid biomarkers that have been identified to date, ranging from ocular surface disease and retinopathies to cancer and multiple sclerosis. Putative tear fluid biomarkers of ocular disorders, as well as the more recent field of systemic disease biomarkers, will be shown.
| S-EPMC4942926 | biostudies-other
Project description:According to the GLOBOCAN 2020, prostate cancer (PCa) is the most often diagnosed male cancer in 112 countries and the leading cancer-related death in 48 countries. Moreover, PCa incidence permanently increases in adolescents and young adults. Also, the rates of metastasising PCa continuously grow up in young populations. Corresponding socio-economic burden is enormous: PCa treatment costs increase more rapidly than for any other cancer. In order to reverse current trends in exploding PCa cases and treatment costs, pragmatic decisions should be made, in favour of advanced populational screening programmes and effective anti-PCa protection at the level of the health-to-disease transition (sub-optimal health conditions) demonstrating the highest cost-efficacy of treatments. For doing this, the paradigm change from reactive treatments of the clinically manifested PCa to the predictive approach and personalised prevention is essential. Phytochemicals are associated with potent anti-cancer activity targeting each stage of carcinogenesis including cell apoptosis and proliferation, cancer invasiveness and metastatic disease. For example, their positive effects are demonstrated for stabilising and restoring mitochondrial health quality, which if compromised is strongly associated with sub-optimal health conditions and strong predisposition to aggressive PCa sub-types. Further, phytochemicals significantly enhance response of cancer cells to anti-cancer therapies including radio- and chemotherapy. Evident plant-based mitigation of negative side-effects frequently observed for conventional anti-cancer therapies has been reported. Finally, dual anti-cancer and anti-viral effects of phytochemicals such as these of silibinin have been demonstrated as being highly relevant for improved PCa management at the level of secondary and tertiary care, for example, under pandemic conditions, since PCa-affected individuals per evidence are highly vulnerable towards COVID-19 infection. Here, we present a comprehensive data analysis towards clinically relevant anti-cancer effects of phytochemicals to be considered for personalised anti-PCa protection in primary care as well as for an advanced disease management at the level of secondary and tertiary care in the framework of predictive, preventive and personalised medicine.
| S-EPMC9263437 | biostudies-literature
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.
| S-EPMC9203613 | biostudies-literature
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.
| S-EPMC8192631 | biostudies-literature
Project description:The Warburg effect is characterised by increased glucose uptake and lactate secretion in cancer cells resulting from metabolic transformation in tumour tissue. The corresponding molecular pathways switch from oxidative phosphorylation to aerobic glycolysis, due to changes in glucose degradation mechanisms known as the 'Warburg reprogramming' of cancer cells. Key glycolytic enzymes, glucose transporters and transcription factors involved in the Warburg transformation are frequently dysregulated during carcinogenesis considered as promising diagnostic and prognostic markers as well as treatment targets. Flavonoids are molecules with pleiotropic activities. The metabolism-regulating anticancer effects of flavonoids are broadly demonstrated in preclinical studies. Flavonoids modulate key pathways involved in the Warburg phenotype including but not limited to PKM2, HK2, GLUT1 and HIF-1. The corresponding molecular mechanisms and clinical relevance of 'anti-Warburg' effects of flavonoids are discussed in this review article. The most prominent examples are provided for the potential application of targeted 'anti-Warburg' measures in cancer management. Individualised profiling and patient stratification are presented as powerful tools for implementing targeted 'anti-Warburg' measures in the context of predictive, preventive and personalised medicine.
| S-EPMC7429635 | biostudies-literature
Project description:Tuberculosis is a major global health issue, with approximately 10 million people falling ill and 1.4 million dying yearly. One of the most significant challenges to public health is the emergence of drug-resistant tuberculosis. For the last half-century, treating tuberculosis has adhered to a uniform management strategy in most patients. However, treatment ineffectiveness in some individuals with pulmonary tuberculosis presents a major challenge to the global tuberculosis control initiative. Unfavorable outcomes of tuberculosis treatment (including mortality, treatment failure, loss of follow-up, and unevaluated cases) may result in increased transmission of tuberculosis and the emergence of drug-resistant strains. Treatment failure may occur due to drug-resistant strains, non-adherence to medication, inadequate absorption of drugs, or low-quality healthcare. Identifying the underlying cause and adjusting the treatment accordingly to address treatment failure is important. This is where approaches such as artificial intelligence, genetic screening, and whole genome sequencing can play a critical role. In this review, we suggest a set of particular clinical applications of these approaches, which might have the potential to influence decisions regarding the clinical management of tuberculosis patients.
| S-EPMC10582268 | biostudies-literature
Project description:Prevalences of non-communicable diseases such as depression and a range of somatic diseases are continuously increasing requiring simple and inexpensive ways to identify high-risk individuals to target with predictive and preventive approaches. Using k-mean cluster analytics, in study 1, we identified biochemical clusters (based on C-reactive protein, interleukin-6, fibrinogen, cortisol, and creatinine) and examined their link to diseases. Analyses were conducted in a US American sample (from the Midlife in the US study, N = 1234) and validated in a Japanese sample (from the Midlife in Japan study, N = 378). In study 2, we investigated the link of the biochemical clusters from study 1 to childhood maltreatment (CM). The three identified biochemical clusters included one cluster (with high inflammatory signaling and low cortisol and creatinine concentrations) indicating the highest disease burden. This high-risk cluster also reported the highest CM exposure. The current study demonstrates how biomarkers can be utilized to identify individuals with a high disease burden and thus, may help to target these high-risk individuals with tailored prevention/intervention, towards personalized medicine. Furthermore, our findings raise the question whether the found biochemical clusters have predictive character, as a tool to identify high-risk individuals enabling targeted prevention. The finding that CM was mostly prevalent in the high-risk cluster provides first hints that the clusters could indeed have predictive character and highlight CM as a central disease susceptibility factor and possibly as a leverage point for disease prevention/intervention.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-021-00255-0.
| S-EPMC8648886 | biostudies-literature
Project description:BackgroundSuboptimal health status (SHS) is an intermediate health status between health and illness, a syndrome characterised by the perception of health complaints, general weakness and low energy. This study aimed to investigate the prevalence of SHS and the correlation between SHS and psychological symptoms among Chinese college students and to identify the SHS-related risk factors from the perspective of predictive, preventive and personalised medicine (PPPM).MethodsA cross-sectional study was conducted among 4119 college students who were enrolled from Taishan Medical University and Baoji Vocational and Technical College in the eastern and western areas of China. SHS levels of the participants were measured by an established self-reporting Suboptimal Health Status Questionnaire-25 (SHSQ-25). Psychosomatic conditions were estimated by the self-rating Symptom Checklist-90 (SCL-90) scale. Spearman correlation analysis was applied to analyse the relationship between SHSQ-25 scores and SCL-90 estimates. Logistic regression analysis was applied for multivariate analysis.ResultsThe prevalence of SHS was 21.0% (864/4119), with 23.3% (701/3005) for female students and 14.6% (163/1114) for male students. The prevalence of general positive psychological symptom was 14.2% (586/4119), with 15.6% (470/3005) for female students and 10.4% (116/1114) for male students. A strong correlation was identified between SHS score and SCL-90 estimates, with the correlation coefficient (r) of 0.719. Logistic regression showed that variables significantly associated with SHS were somatisation (adjusted odds ratio (aOR) = 3.185, 95% confidence interval [CI] = 2.048-4.953), obsessive-compulsive (aOR = 3.518, 95% CI = 2.834-4.368), interpersonal sensitivity (aOR = 1.883, 95% CI = 1.439-2.463) and depression (aOR = 1.847, 95% CI = 1.335-2.554).ConclusionsOur findings confirm that there is a high prevalence of SHS among college students and there is a strong association between SHS and psychological symptoms among Chinese college students. High susceptibility of SHS occurs particularly in vulnerable groups: female students, sophomore students, medical students and students from rural area. Identification of SHS and prompt application of personalised psychological health-supporting activities will promote college students' health status.
| S-EPMC6261912 | biostudies-literature