The EPMA journal最新文献

Metformin has recently garnered attention for its unique role in cellular metabolism regulation, prompting investigations into its potential applications in skin wound healing. Predictive, preventive, and personalized medicine (PPPM/3PM) is the most advantageous healthcare framework and is appropriate for managing chronic illnesses. This review systematically outlines the key signaling pathways through which metformin modulates inflammatory responses via metabolic reprogramming. Integrating the 3PM concept, it explores innovative application strategies for metformin in wound management, thereby proposing a working hypothesis: metformin may be utilized for personalized prevention and treatment of skin wounds. It focuses on the latest research advances in the field of wound healing, particularly the potential of metformin in local applications, as well as studies combining it with innovative carriers such as hydrogels, nanofibers, and microneedles. These studies not only expand the therapeutic scope of metformin but also provide novel perspectives and solutions to the management of chronic wounds.

Background: In the era of shifting healthcare, a "reactive" approach to colorectal cancer (CRC) management-that is, initiating treatment only after the onset of symptoms-remains a major global health challenge. This study uses the Global Burden of Disease (GBD) 2021 data to quantify CRC burden and provide a foundation for developing targeted Predictive, Preventive, and Personalized Medicine (PPPM/3PM) strategies worldwide, especially in China.

Methods: We conducted a comprehensive analysis using data from the Global Burden of Disease GBD 2021 study, obtained through VizHub and GBD Foresight Visualization tools. Statistical analyses were performed using R (4.4.2; available from: https://cloud.r-project.org/) and Biowinford (Available from: http://biowinford.site:3838/trial/), incorporating key parameters including age, sex, disease-specific factors, disability-adjusted life years (DALYs), and socio-demographic index (SDI) and modifiable risk factors, such as behavioral and dietary factors. This methodological framework is designed to identify high-risk populations and regions, thereby enabling predictive diagnostics and targeted prevention strategies as core tenets of the PPPM model.

Results: From 1990 to 2021, the global age-standardized rate of CRC prevalence increased by 24.6%, with the number of prevalent cases rising from 4.26 million to 11.7 million. During the same period, China experienced an increase of 141.2% in its ASR, as its prevalent cases increased from 0.6 million to 3.6 million. While age-standardized death rates declined globally (-20.7%), regional disparities persisted, with men bearing a higher burden and DALYs rising in parts of Africa and Asia. For instance, the number of deaths in East Asia, North Africa and the Middle East was 287,900 and 37,400, respectively; the corresponding DALYs were 7,149,000 and 1,012,700. Major modifiable risks were high BMI, diet high in red meat, and low physical activity. Projections to 2050 indicate a continued rise in cases in China and Africa.

Conclusion: Our study provides evidence to support the shift towards PPPM in CRC care. With rising urbanization, dietary shifts, and aging populations, predictive diagnostics using Artificial Intelligence and big data, targeted prevention of modifiable risks, and personalized treatments based on genetic and tumor data could be essential. Detailed burden mapping forms the foundation for this proactive approach, especially in high-burden regions like China.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-025-00431-6.

Background: Glaucoma remains the leading cause of irreversible blindness worldwide. The development of predictive, preventive, and personalized medicine (3PM) strategies in the area is essential to address high inter-individual heterogeneity in glaucoma progression, in order to effectively protect stratified patients against disease progression.

Aim: This study aims to develop and validate a personalized, multimodal predictive modeling framework that integrates structural, functional, and vascular biomarkers for individualized risk stratification of progression rates in primary open-angle glaucoma (POAG).

Methods: Patients with POAG at varying stages were monitored for at least 36 months and underwent comprehensive multimodal evaluation, including structural optical coherence tomography (OCT), OCT angiography (OCT-A), automated perimetry, and biomechanical assessments. Predictive modeling was performed using Ranked Partial Least Squares Discriminant Analysis (Ranked PLS-DA). Model performance and variable importance were established through Procrustes Cross-Validation and optimization procedures.

Results and data interpretation in the framework of 3pm: The final models included up to 27 parameters in early-stage POAG and 20 in advanced disease, leading to high prognostic accuracy (AUC up to 0.90) for classifying slow, moderate, and rapid rates of glaucoma progression. Feature importance analysis demonstrated that different biomarkers dominate at different disease stages: RNFL thickness, peripapillary microvascular dropout, parafoveal vascular density and corneal hysteresis in early POAG, while age, ganglion cell complex thickness, specific macular thickness measures, and peripapillary perfusion parameters were most predictive in advanced stages.

Conclusions and 3pm-relevant outlook: The proposed innovation utilizes multimodal predictive disease modeling that supports accurate risk stratification, personalized glaucoma management and individualized protection against disease progression. Successful clinical application requires initial profiling, regular model recalibration, and adaptive treatment strategies - altogether leading to improved visual outcomes in stratified patients and leveraging resources used.

Protein phosphorylation is an important molecular event in tumor angiogenesis that is a canonical hallmark in glioma. We hypothesize that the phosphoproteome and phosphorylation-mediated signaling networks are significantly different in glioma neovascular tissues compared to controls, which aimed to identify glioma angiogenesis phosphoproteomic landscape, phosphorylation-mediated signaling pathways, kinase-substrate networks, and phosphorylation biomarkers with integration of phosphoprotein data and multiomics data, for deep understanding of molecular mechanisms of glioma angiogenesis, discovery of effective antiangiogenesis therapeutic targets, and establishment of angiogenesis-related phosphorylation biomarker signature for patient stratification, early-stage diagnosis, and effective prognostic assessment, in the framework of predictive, preventive, and personalized medicine (PPPM, 3PM) approaches. This study used laser capture microdissection to isolate neovascular tissues from gliomas, followed by quantitative phosphoproteomics analysis, which identified 195 differentially phosphorylated proteins (DPPs) with 635 phosphosites and 58 hub DPPs. Pathway analysis of 195 DPPs found that cell adhesion-related pathways and HIF-1 signaling pathway were significantly regulated by phosphorylation to associate with glioma angiogenesis. Upstream kinase analysis found 321 upstream kinases to regulate the intratumoral neovascular tissue-associated phosphorylation, including 12 kinases that were differentially expressed in glioma neovascular tissues and 2 kinases (CAMK2D and MYLK) that were also DPPs, and 48 chemotherapeutic agents as kinase inhibitors such as staurosporine that had antiangiogenesis effects in glioma. Integrated analysis of DPPs and DEGs (differentially expressed genes) revealed 82 overlapped molecules; of them, SYN1, STX1A, PRKAR2B, PACSIN1, LSP1, HSPB1, and DMTN were associated with overall survival of glioma, and ANK1, L1CAM, and LSP1 were constructed as glioma prognosis signature. Immunohistochemistry confirmed hypophosphorylation at PDHA1-Ser293/300 in glioma angiogenesis. This study provided the first phosphoproteome landscape, kinase profile, phosphorylation-mediated signaling pathway network alterations in human glioma neovascular tissues, and effective tumor angiogenesis-based biomarkers for patient stratification, prognostic assessment, and targeted therapy in glioma. These findings provide concrete molecular targets for antiangiogenic therapy and establish clinically actionable biomarkers for glioma patient stratification in the 3PM framework.

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Supplementary information: The online version contains supplementary material available at 10.1007/s13167-025-00428-1.

Background: Hypertension, a major modifiable risk factor for cardiovascular disease, exhibits significant heterogeneity due to genetic, metabolic, and environmental influences. Traditional one-size-fits-all management is inadequate, necessitating predictive, preventive, and personalized medicine (3PM) approaches.

Study objective: This review critically evaluates 3PM's application in hypertension, focusing on leveraging biomarkers, artificial intelligence (AI), and digital health for risk stratification, early intervention, and tailored therapies.

Key discussion: The 3PM framework leverages AI-driven integration of multi-omics, retinal imaging like ViT models, and hemodynamic profiling for risk prediction and treatment response forecasting; genetic profiling such as MTHFR, UMOD variants, urinary proteomics (CKD273 classifier), and microbiome-guided nutrition for early intervention; and pharmacogenomics, digital phenotyping like smartphone-guided dosing, and novel therapies such as aprocitentan and finerenone for personalized efficacy. Specific findings include aprocitentan reducing systolic BP by -15.3 mmHg in resistant hypertension, UMOD-guided torasemide use lowering BP by 8.5 mmHg in carriers, and microbiome-based nutrition reducing systolic BP by 14% in hyperglycemic patients. Key challenges include limited biomarker validation, "black box" AI algorithms, high costs, interoperability gaps, and equity barriers.

Conclusion: 3PM transforms hypertension management by enabling proactive, individualized care. However, rigorous validation, affordable diagnostics, pragmatic trials, and equitable access are essential to bridge translational gaps and achieve personalized cardiology's full potential.

Objective: Suboptimal health status (SHS) is a reversible predisease stage and represents a key "window of opportunity" for predictive, preventive, and personalized medicine (3PM/PPPM). However, current screening methods still rely mainly on subjective questionnaires and lack objective, interpretable, and actionable tools for timely intervention. We aimed to develop an exploratory prototype system that combines multiomic signals with explainable artificial intelligence to apply 3PM in young adults.

Methods and results: Transcriptomic, metabolomic, and gut microbiome data from 30 SHS patients and 35 healthy controls were analyzed. Seven machine learning algorithms were compared, with elastic net selected for its balance of accuracy, stability, and interpretability. Calibration and decision curve analyses were performed to test robustness and clinical utility. Shapley additive explanations (SHAP) were applied for both global and individual interpretations. The multiomic elastic net prototype achieved high and stable discrimination (accuracy 0.941, ROC-AUC 0.999), with strong calibration and net benefit. Beyond statistical performance, the system identified biologically plausible and modifiable molecular targets-such as reduced vitamin K and elevated glycerophosphocholine-that are directly amenable to preventive strategies. SHAP further provided individual-level profiles, revealing the specific biological drivers of SHS risk for each participant and offering a template for personalized recommendations.

Conclusions: This study proposes an innovative 3PM-guided prototype system for predicting suboptimal health status on the basis of multiomics data. We suggest embedding this tool into preventive healthcare to enable early risk prediction, applying personalized interventions to delay or reverse the progression of SHS, and providing individualized follow-up to support long-term health management. From a public health perspective, this approach may substantially reduce the future burden of chronic diseases by addressing risks at a reversible stage.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-025-00426-3.

Background: Non-communicable diseases (NCDs), including cardiovascular diseases, respiratory disorders, And age-related degenerative diseases, account for 74% of global mortality, imposing heavy burdens on healthcare systems and global sustainable development. Current management strategies face challenges such as poor long-term drug adherence and complex comorbidities. Functional foods and nutraceuticals, particularly those rooted in traditional Chinese medicine (TCM) that embody the "food-medicine homology" principle, have emerged as promising adjuncts for NCD prevention and treatment. Schisandra chinensis (SC) is an edible and medicinal plant with a long history. Rich in bioactive components such as lignans, polysaccharides, and terpenoids, it exhibits multi-targeted pharmacological activities and holds great potential in improving clinical diseases and progressing suboptimal health to optimal, making it a promising candidate for integrated health strategies.

Aims: This review aims to synthesize the mechanisms underlying SC's actions in NCD prevention and management, clarify its therapeutic and preventive roles across clinical populations and suboptimal health states, and explore its applications within the framework of predictive, preventive, and personalized medicine (PPPM/3PM).

Results: Preclinical evidence highlights SC's multi-target pharmacological activities, including anti-inflammatory, antioxidant and adaptogenic effects, which are key pathological drivers in the treatment of non-communicable diseases such as cardiovascular diseases, respiratory diseases and age-related degenerative diseases. For clinical populations, SC supports therapeutic goals through liver protection in liver disease management, cancer-assisted immune regulation, neuroprotection, reduction of lung damage, and blood sugar regulation. In suboptimal health individuals characterized by stress-induced fatigue, subclinical inflammation, or impaired elasticity, the adaptive properties of SC offer specific benefits, helping the body resist stress and maintain homeostasis.

Conclusion: SC bridges therapeutic and preventive applications via its multifunctional properties, aligning with PPPM principles by enabling tailored interventions for NCDs and proactive health management. Its safety, multi-target activities, and compatibility with lifestyle medicine highlight its potential as a natural resource for integrated NCD care.

Recommendations: Further clinical trials are needed to validate dosage regimens and efficacy in diverse populations. Standardization of SC quality and exploration of synergistic effects with conventional therapies will facilitate its translation into personalized PPPM strategies.

Objective: Hypertension management remains challenging due to coexisting insulin resistance (IR) and arterial stiffness-two silent yet synergistic drivers of atherosclerotic cardiovascular disease (ASCVD). This study aimed to evaluate their joint impact on ASCVD risk and assess their utility in predictive, preventive, and personalized strategies.

Methods: In this prospective cohort study of 30,094 adults with hypertension, IR was assessed using the triglyceride-glucose (TyG) index (calculated as ln [TG (mg/dL) × FBG (mg/dL)/2]) and arterial stiffness via brachial-ankle pulse wave velocity (baPWV). Time-to-event analyses examined their individual and combined associations with ASCVD incidence.

Results: Over a median 5.6-year follow-up, 1655 ASCVD cases occurred. TyG exhibited a dose-response relationship with ASCVD across baPWV strata. Each 1-SD increase in TyG was associated with a higher ASCVD risk in the elevated baPWV subgroup (HR: 1.17, 95% CI: 1.07-1.27) than in the normal baPWV subgroup (HR: 1.11, 95% CI: 1.02-1.21). A significant additive interaction was observed: individuals with both elevated TyG and baPWV had the highest ASCVD risk (HR: 1.93, 95% CI: 1.66-2.25), with 33.4% of the joint risk attributable to their interaction. Adding both biomarkers to traditional models improved discrimination (C-index: 0.66 to 0.68) and reclassification (NRI: 18.63%, P < 0.001).

Conclusions: This study reveals a synergistic effect of IR and arterial stiffness on ASCVD risk and provides strong support for their integration into predictive models. This dual-biomarker approach enables early identification of high-risk hypertensive phenotypes and aligns with the predictive, preventive, and personalized medicine (PPPM) paradigm to optimize cardiovascular outcomes through tailored intervention.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-025-00421-8.

Introduction: Atrial fibrillation (AF) presents a significant challenge in patients with obstructive sleep apnea (OSA), as traditional risk factors often fail to accurately predict individual risk levels. This study exemplifies the paradigm of predictive, preventive, and personalized medicine (PPPM/3PM) by developing a comprehensive predictive nomogram for atrial fibrillation (AF) risk in patients with diabetes mellitus and obstructive sleep apnea (OSA).

Methods: This cohort study included 797 patients with new-onset OSA but without AF from January 2017 to December 2019. A total of 53 baseline clinical features were systematically collected and analyzed. Clinical feature pre-screening was conducted using the Boruta algorithm. The best predictive model was selected from nine machine learning algorithms based on area under the curve (AUC), calibration curve, and decision curve analysis (DCA). The SHapley Additive exPlanations (SHAP) tool was used to explain model decisions.

Results: With a median follow-up period of 56.78 months, the development cohort consisted of 494 participants, whereas the time-series independent verification cohort comprised 303 participants. Key predictors identified included age, fasting blood glucose, very low-density lipoprotein cholesterol, triglycerides, triglyceride-glucose index, autonomic nervous system indicators, apnea-hypopnea index, and average blood oxygen saturation. The XGBoost model was chosen for its superior performance, achieving an AUC of 0.922 and a Brier score of 0.071 in the time-series independent verification cohort. Calibration curve and DCA demonstrated that the XGBoost model exhibited the closest alignment with the ideal calibration line and provided the highest net clinical benefit across various threshold probabilities. SHAP analysis revealed significant contributions of individual variables to long-term AF risk.

Conclusions: This study proposes a comprehensive XGBoost-based PPPM/3PM framework that demonstrates superior clinical performance in predicting long-term atrial fibrillation risk among patients with new-onset OSA.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-025-00422-7.