Epma Journal最新文献

A natural “medicine and food” plant, Rhodiola rosea (RR) is primarily made up of organic acids, phenolic compounds, sterols, glycosides, vitamins, lipids, proteins, amino acids, trace elements, and other physiologically active substances. In vitro, non-clinical and clinical studies confirmed that it exerts anti-inflammatory, antioxidant, and immune regulatory effects, balances the gut microbiota, and alleviates vascular circulatory disorders. RR can prolong life and has great application potential in preventing and treating suboptimal health, non-communicable diseases, and COVID-19. This narrative review discusses the effects of RR in preventing organ damage (such as the liver, lung, heart, brain, kidneys, intestines, and blood vessels) in non-communicable diseases from the perspective of predictive, preventive, and personalised medicine (PPPM/3PM). In conclusion, as an adaptogen, RR can provide personalised health strategies to improve the quality of life and overall health status.

Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide.

Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs.

Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large.

DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.

Background

Suboptimal health is identified as a reversible phase occurring before chronic diseases manifest, emphasizing the significance of early detection and intervention in predictive, preventive, and personalized medicine (PPPM/3PM). While the biological and genetic factors associated with suboptimal health have received considerable attention, the influence of social determinants of health (SDH) remains relatively understudied. By comprehensively understanding the SDH influencing suboptimal health, healthcare providers can tailor interventions to address individual needs, improving health outcomes and facilitating the transition to optimal well-being. This study aimed to identify distinct profiles within SDH indicators and examine their association with suboptimal health status.

Method

This cross-sectional study was conducted from June 16 to September 23, 2023, in five regions of China. Various SDH indicators, such as family health, economic status, eHealth literacy, mental disorder, social support, health behavior, and sleep quality, were examined in this study. Latent profile analysis was employed to identify distinct profiles based on these SDH indicators. Logistic regression analysis by profile was used to investigate the association between these profiles and suboptimal health status.

Results

The analysis included 4918 individuals. Latent profile analysis revealed three distinct profiles (prevalence): the Adversely Burdened Vulnerability Group (37.6%), the Adversity-Driven Struggle Group (11.7%), and the Advantaged Resilience Group (50.7%). These profiles exhibited significant differences in suboptimal health status (p < 0.001). The Adversely Burdened Vulnerability Group had the highest risk of suboptimal health, followed by the Adversity-Driven Struggle Group, while the Advantaged Resilience Group had the lowest risk.

Conclusions and relevance

Distinct profiles based on SDH indicators are associated with suboptimal health status. Healthcare providers should integrate SDH assessment into routine clinical practice to customize interventions and address specific needs. This study reveals that the group with the highest risk of suboptimal health stands out as the youngest among all the groups, underscoring the critical importance of early intervention and targeted prevention strategies within the framework of 3PM. Tailored interventions for the Adversely Burdened Vulnerability Group should focus on economic opportunities, healthcare access, healthy food options, and social support. Leveraging their higher eHealth literacy and resourcefulness, interventions empower the Adversity-Driven Struggle Group. By addressing healthcare utilization, substance use, and social support, targeted interventions effectively reduce suboptimal health risks and improv

Purpose

Retinopathy of prematurity (ROP) is a retinal vascular proliferative disease common in low birth weight and premature infants and is one of the main causes of blindness in children.

In the context of predictive, preventive and personalized medicine (PPPM/3PM), early screening, identification and treatment of ROP will directly contribute to improve patients’ long-term visual prognosis and reduce the risk of blindness. Thus, our objective is to establish an artificial intelligence (AI) algorithm combined with clinical demographics to create a risk model for ROP including treatment-requiring retinopathy of prematurity (TR-ROP) infants.

Methods

A total of 22,569 infants who underwent routine ROP screening in Shenzhen Eye Hospital from March 2003 to September 2023 were collected, including 3335 infants with ROP and 1234 infants with TR-ROP among ROP infants. Two machine learning methods of logistic regression and decision tree and a deep learning method of multi-layer perceptron were trained by using the relevant combination of risk factors such as birth weight (BW), gestational age (GA), gender, whether multiple births (MB) and mode of delivery (MD) to achieve the risk prediction of ROP and TR-ROP. We used five evaluation metrics to evaluate the performance of the risk prediction model. The area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) were the main measurement metrics.

Results

In the risk prediction for ROP, the BW + GA demonstrated the optimal performance (mean ± SD, AUCPR: 0.4849 ± 0.0175, AUC: 0.8124 ± 0.0033). In the risk prediction of TR-ROP, reasonable performance can be achieved by using GA + BW + Gender + MD + MB (AUCPR: 0.2713 ± 0.0214, AUC: 0.8328 ± 0.0088).

Conclusions

Combining risk factors with AI in screening programs for ROP could achieve risk prediction of ROP and TR-ROP, detect TR-ROP earlier and reduce the number of ROP examinations and unnecessary physiological stress in low-risk infants. Therefore, combining ROP-related biometric information with AI is a cost-effective strategy for predictive diagnostic, targeted prevention, and personalization of medical services in early screening and treatment of ROP.

Background

Cancer cell growth, metastasis, and drug resistance are major challenges in treating liver hepatocellular carcinoma (LIHC). However, the lack of comprehensive and reliable models hamper the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) strategy in managing LIHC.

Methods

Leveraging seven distinct patterns of mitochondrial cell death (MCD), we conducted a multi-omic screening of MCD-related genes. A novel machine learning framework was developed, integrating 10 machine learning algorithms with 67 different combinations to establish a consensus mitochondrial cell death index (MCDI). This index underwent rigorous evaluation across training, validation, and in-house clinical cohorts. A comprehensive multi-omics analysis encompassing bulk, single-cell, and spatial transcriptomics was employed to achieve a deeper insight into the constructed signature. The response of risk subgroups to immunotherapy and targeted therapy was evaluated and validated. RT-qPCR, western blotting, and immunohistochemical staining were utilized for findings validation.

Results

Nine critical differentially expressed MCD-related genes were identified in LIHC. A consensus MCDI was constructed based on a 67-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. MCDI correlated with immune infiltration, Tumor Immune Dysfunction and Exclusion (TIDE) score and sorafenib sensitivity. Findings were validated experimentally. Moreover, we identified PAK1IP1 as the most important gene for predicting LIHC prognosis and validated its potential as an indicator of prognosis and sorafenib response in our in-house clinical cohorts.

Conclusion

This study developed a novel predictive model for LIHC, namely MCDI. Incorporating MCDI into the PPPM framework will enhance clinical decision-making processes and optimize individualized treatment strategies for LIHC patients.

Graphical Abstract

Background

DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC.

Method

Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified.

Results

A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC.

Conclusions

This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling p

In times where sudden-onset disasters (SODs) present challenges to global health systems, the integration of predictive, preventive, and personalized medicine (PPPM / 3PM) into emergency medical responses has manifested as a critical necessity. We introduce a modern electronic patient record system designed specifically for emergency medical teams (EMTs), which will serve as a novel approach in how digital healthcare management can be optimized in crisis situations. This research is based on the principle that advanced information technology (IT) systems are key to transforming humanitarian aid by offering predictive insights, preventive strategies, and personalized care in disaster scenarios. We aim to address the critical gaps in current emergency medical response strategies, particularly in the context of SODs. Building upon a collaborative effort with European emergency medical teams, we have developed a comprehensive and scalable electronic patient record system. It not only enhances patient management during emergencies but also enables predictive analytics to anticipate patient needs, preventive guidelines to reduce the impact of potential health threats, and personalized treatment plans for the individual needs of patients. Furthermore, our study examines the possibilities of adopting PPPM-oriented IT solutions in disaster relief. By integrating predictive models for patient triage, preventive measures to mitigate health risks, and personalized care protocols, potential improvements to patient health or work efficiency could be established. This system was evaluated with clinical experts and shall be used to establish digital solutions and new forms of assistance for humanitarian aid in the future. In conclusion, to really achieve PPPM-related efforts more investment will need to be put into research and development of electronic patient records as the foundation as well as into the clinical processes along all pathways of stakeholders in disaster medicine.

Despite their subordination in humans, to a great extent, mitochondria maintain their independent status but tightly cooperate with the “host” on protecting the joint life quality and minimizing health risks. Under oxidative stress conditions, healthy mitochondria promptly increase mitophagy level to remove damaged “fellows” rejuvenating the mitochondrial population and sending fragments of mtDNA as SOS signals to all systems in the human body. As long as metabolic pathways are under systemic control and well-concerted together, adaptive mechanisms become triggered increasing systemic protection, activating antioxidant defense and repair machinery. Contextually, all attributes of mitochondrial patho-/physiology are instrumental for predictive medical approach and cost-effective treatments tailored to individualized patient profiles in primary (to protect vulnerable individuals again the health-to-disease transition) and secondary (to protect affected individuals again disease progression) care. Nutraceuticals are naturally occurring bioactive compounds demonstrating health-promoting, illness-preventing, and other health-related benefits. Keeping in mind health-promoting properties of nutraceuticals along with their great therapeutic potential and safety profile, there is a permanently growing demand on the application of mitochondria-relevant nutraceuticals. Application of nutraceuticals is beneficial only if meeting needs at individual level. Therefore, health risk assessment and creation of individualized patient profiles are of pivotal importance followed by adapted nutraceutical sets meeting individual needs. Based on the scientific evidence available for mitochondria-relevant nutraceuticals, this article presents examples of frequent medical conditions, which require protective measures targeted on mitochondria as a holistic approach following advanced concepts of predictive, preventive, and personalized medicine (PPPM/3PM) in primary and secondary care.

The prevalence of chronic diseases is currently a major public health issue worldwide and is exploding with the population growth and aging. Dietary patterns are well known to play a important role in our overall health and well-being, and therefore, poor diet and malnutrition are among the most critical risk factors for chronic disease. Thus, dietary recommendation and nutritional supplementation have significant clinical implications for the targeted treatment of some of these diseases. Multiple dietary patterns have been proposed to prevent chronic disease incidence, like Dietary Approaches to Stop Hypertension (DASH) and Diabetes Risk Reduction Diet (DRRD). Among them, the MedDiet, which is one of the most well-known and studied dietary patterns in the world, has been related to a wide extent of health benefits. Substantial evidence has supported an important reverse association between higher compliance to MedDiet and the risk of chronic disease. Innovative strategies within the healthcare framework of predictive, preventive, and personalized medicine (PPPM/3PM) view personalized dietary customization as a predictive medical approach, cost-effective preventive measures, and the optimal dietary treatment tailored to the characteristics of patients with chronic diseases in primary and secondary care. Through a comprehensive collection and review of available evidence, this review summarizes health benefits of MedDiet in the context of PPPM/3PM for chronic diseases, including cardiovascular disease, hypertension, type 2 diabetes, obesity, metabolic syndrome, osteoporosis, and cancer, thereby a working hypothesis that MedDiet can personalize the prevention and treatment of chronic diseases was derived.