Unraveling progression subtypes in people with Huntington's disease.

Background: Huntington's disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient's quality of life. Despite this clear genetic course, high variability of HD patients' symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care. Methods: Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits. Results: Results demonstrate two distinct subtypes, one large cluster (n = 7122) showing a relative stable disease progression and a second, smaller cluster (n = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients' first visit only. Conclusion: In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients' disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals' treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. This greatly aligns with the goal of shifting towards 3P medicine. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00368-2.

Digital biomarkers: 3PM approach revolutionizing chronic disease management - EPMA 2024 position.

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.

Clinically relevant immune subtypes based on alternative splicing landscape of immune-related genes for lung cancer advanced PPPM approach.

Background: Alternative splicing (AS) occurs in the process of gene post-transcriptional process, which is very important for the correct synthesis and function of protein. The change of AS pattern may lead to the change of expression level or function of lung cancer-related genes, and then affect the occurrence and development of lung cancers. The specific AS pattern might be used as a biomarker for early warning and prognostic assessment of a cancer in the framework of predictive, preventive, and personalized medicine (PPPM; 3PM). AS events of immune-related genes (IRGs) were closely associated with tumor progression and immunotherapy. We hypothesize that IRG-AS events are significantly different in lung adenocarcinomas (LUADs) vs. controls or in lung squamous cell carcinomas (LUSCs) vs. controls. IRG-AS alteration profiling was identified to construct IRG-differentially expressed AS (IRG-DEAS) signature models. Study on the selective AS events of specific IRGs in lung cancer patients might be of great significance for further exploring the pathogenesis of lung cancer, realizing early detection and effective monitoring of lung cancer, finding new therapeutic targets, overcoming drug resistance, and developing more effective therapeutic strategies, and better used for the prediction, diagnosis, prevention, and personalized medicine of lung cancer. Methods: The transcriptomic, clinical, and AS data of LUADs and LUSCs were downloaded from TCGA and its SpliceSeq databases. IRG-DEAS events were identified in LUAD and LUSC, followed by their functional characteristics, and overall survival (OS) analyses. OS-related IRG-DEAS prognostic models were constructed for LUAD and LUSC with Lasso regression, which were used to classify LUADs and LUSCs into low- and high-risk score groups. Furthermore, the immune cell distribution, immune-related scores, drug sensitivity, mutation status, and GSEA/GSVA status were analyzed between low- and high-risk score groups. Also, low- and high-immunity clusters and AS factor (SF)-OS-related-AS co-expression network and verification of cell function of CELF6 were analyzed in LUAD and LUSC. Results: Comprehensive analysis of transcriptomic, clinical, and AS data of LUADs and LUSCs identified IRG-AS events in LUAD (n = 1607) and LUSC (n = 1656), including OS-related IRG-AS events in LUAD (n = 127) and LUSC (n = 105). A total of 66 IRG-DEAS events in LUAD and 89 IRG-DEAS events in LUSC were identified compared to controls. The overlapping analysis between IRG-DEASs and OS-related IRG-AS events revealed 14 OS-related IRG-DEAS events for LUAD and 16 OS-related IRG-DEAS events for LUSC, which were used to identify and optimize a 12-OS-related-IRG-DEAS signature prognostic model for LUAD and an 11-OS-related-IRG-DEAS signature prognostic model for LUSC. These two prognostic models effectively divided LUAD or LUSC samples into low- and high-risk score groups that were closely associated with OS, clinical characteristics, and tumor immune microenvironment, with significant gene sets and pathways enriched in the two groups. Moreover, weighted gene co-expression network (WGCNA) and nonnegative matrix factorization method (NMF) analyses identified four OS-relevant subtypes of LUAD and six OS-relevant subtypes of LUSC, and ssGSEA identified five immunity-relevant subtypes of LUAD and five immunity-relevant subtypes of LUSC. Interestingly, splicing factors-OS-related-AS network revealed hub molecule CELF6 was significantly related to the malignant phenotype in lung cancer cells. Conclusions: This study established two reliable IRG-DEAS signature prognostic models and constructed interesting splicing factor-splicing event networks in LUAD and LUSC, which can be used to construct clinically relevant immune subtypes, patient stratification, prognostic prediction, and personalized medical services in the PPPM practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00366-4.

The Emergency Medical Team Operating System - a vision for field hospital data management in following the concepts of predictive, preventive, and personalized medicine.

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.

Mediterranean diet in the targeted prevention and personalized treatment of chronic diseases: evidence, potential mechanisms, and prospects.

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.

Energy metabolism as the hub of advanced non-small cell lung cancer management: a comprehensive view in the framework of predictive, preventive, and personalized medicine.

Energy metabolism is a hub of governing all processes at cellular and organismal levels such as, on one hand, reparable vs. irreparable cell damage, cell fate (proliferation, survival, apoptosis, malignant transformation etc.), and, on the other hand, carcinogenesis, tumor development, progression and metastazing versus anti-cancer protection and cure. The orchestrator is the mitochondria who produce, store and invest energy, conduct intracellular and systemically relevant signals decisive for internal and environmental stress adaptation, and coordinate corresponding processes at cellular and organismal levels. Consequently, the quality of mitochondrial health and homeostasis is a reliable target for health risk assessment at the stage of reversible damage to the health followed by cost-effective personalized protection against health-to-disease transition as well as for targeted protection against the disease progression (secondary care of cancer patients against growing primary tumors and metastatic disease). The energy reprogramming of non-small cell lung cancer (NSCLC) attracts particular attention as clinically relevant and instrumental for the paradigm change from reactive medical services to predictive, preventive and personalized medicine (3PM). This article provides a detailed overview towards mechanisms and biological pathways involving metabolic reprogramming (MR) with respect to inhibiting the synthesis of biomolecules and blocking common NSCLC metabolic pathways as anti-NSCLC therapeutic strategies. For instance, mitophagy recycles macromolecules to yield mitochondrial substrates for energy homeostasis and nucleotide synthesis. Histone modification and DNA methylation can predict the onset of diseases, and plasma C7 analysis is an efficient medical service potentially resulting in an optimized healthcare economy in corresponding areas. The MEMP scoring provides the guidance for immunotherapy, prognostic assessment, and anti-cancer drug development. Metabolite sensing mechanisms of nutrients and their derivatives are potential MR-related therapy in NSCLC. Moreover, miR-495-3p reprogramming of sphingolipid rheostat by targeting Sphk1, 22/FOXM1 axis regulation, and A2 receptor antagonist are highly promising therapy strategies. TFEB as a biomarker in predicting immune checkpoint blockade and redox-related lncRNA prognostic signature (redox-LPS) are considered reliable predictive approaches. Finally, exemplified in this article metabolic phenotyping is instrumental for innovative population screening, health risk assessment, predictive multi-level diagnostics, targeted prevention, and treatment algorithms tailored to personalized patient profiles-all are essential pillars in the paradigm change from reactive medical services to 3PM approach in overall management of lung cancers. This article highlights the 3PM relevant innovation focused on energy metabolism as the hub to advance NSCLC management benefiting vulnerable subpopulations, affected patients, and healthcare at large. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00357-5.

Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach.

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 pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00359-3.

Multi-omic profiling reveals potential biomarkers of hepatocellular carcinoma prognosis and therapy response among mitochondria-associated cell death genes in the context of 3P medicine.

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00362-8.

Social determinants of health and their relation to suboptimal health status in the context of 3PM: a latent profile analysis.

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 improve well-being in vulnerable populations. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00365-5.

Mini-encyclopedia of mitochondria-relevant nutraceuticals protecting health in primary and secondary care-clinically relevant 3PM innovation.

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.

Promoting advanced medical services in the framework of 3PM-a proof-of-concept by the "Centro" Region of Portugal.

Multidisciplinary team from three universities based in the "Centro" Region of Portugal developed diverse approaches as parts of a project dedicated to enhancing and expanding Predictive, Preventive, and Personalized Medicine (3PM) in the Region. In a sense, outcomes acted as a proof-of-concept, in that they demonstrated the feasibility, but also the relevance of the approaches. The accomplishments comprise defining a new regional strategy for implementing 3PM within the Region, training of human resources in genomic sequencing, and generating good practices handbooks dedicated to diagnostic testing via next-generation sequencing, to legal and ethical concerns, and to knowledge transfer and entrepreneurship, aimed at increasing literacy on 3PM approaches. Further approaches also included support for entrepreneurship development and start-ups, and diverse and relevant initiatives aimed at increasing literacy relevant to 3PM. Efforts to enhance literacy encompassed citizens across the board, from patients and high school students to health professionals and health students. This focus on empowerment through literacy involved a variety of initiatives, including the creation of an illustrated book on genomics and the production of two theater plays centered on genetics. Additionally, authors stressed that genomic tools are relevant, but they are not the only resources 3PM is based on. Thus, they defend that other initiatives intended to enable citizens to take 3PM should include multi-omics and, having in mind the socio-economic burden of chronic diseases, suboptimal health status approaches in the 3PM framework should also be considered, in order to anticipate medical intervention in the subclinical phase. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00353-9.

Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system.

Purpose: We developed an Infant Retinal Intelligent Diagnosis System (IRIDS), an automated system to aid early diagnosis and monitoring of infantile fundus diseases and health conditions to satisfy urgent needs of ophthalmologists. Methods: We developed IRIDS by combining convolutional neural networks and transformer structures, using a dataset of 7697 retinal images (1089 infants) from four hospitals. It identifies nine fundus diseases and conditions, namely, retinopathy of prematurity (ROP) (mild ROP, moderate ROP, and severe ROP), retinoblastoma (RB), retinitis pigmentosa (RP), Coats disease, coloboma of the choroid, congenital retinal fold (CRF), and normal. IRIDS also includes depth attention modules, ResNet-18 (Res-18), and Multi-Axis Vision Transformer (MaxViT). Performance was compared to that of ophthalmologists using 450 retinal images. The IRIDS employed a five-fold cross-validation approach to generate the classification results. Results: Several baseline models achieved the following metrics: accuracy, precision, recall, F1-score (F1), kappa, and area under the receiver operating characteristic curve (AUC) with best values of 94.62% (95% CI, 94.34%-94.90%), 94.07% (95% CI, 93.32%-94.82%), 90.56% (95% CI, 88.64%-92.48%), 92.34% (95% CI, 91.87%-92.81%), 91.15% (95% CI, 90.37%-91.93%), and 99.08% (95% CI, 99.07%-99.09%), respectively. In comparison, IRIDS showed promising results compared to ophthalmologists, demonstrating an average accuracy, precision, recall, F1, kappa, and AUC of 96.45% (95% CI, 96.37%-96.53%), 95.86% (95% CI, 94.56%-97.16%), 94.37% (95% CI, 93.95%-94.79%), 95.03% (95% CI, 94.45%-95.61%), 94.43% (95% CI, 93.96%-94.90%), and 99.51% (95% CI, 99.51%-99.51%), respectively, in multi-label classification on the test dataset, utilizing the Res-18 and MaxViT models. These results suggest that, particularly in terms of AUC, IRIDS achieved performance that warrants further investigation for the detection of retinal abnormalities. Conclusions: IRIDS identifies nine infantile fundus diseases and conditions accurately. It may aid non-ophthalmologist personnel in underserved areas in infantile fundus disease screening. Thus, preventing severe complications. The IRIDS serves as an example of artificial intelligence integration into ophthalmology to achieve better outcomes in predictive, preventive, and personalized medicine (PPPM / 3PM) in the treatment of infantile fundus diseases. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00350-y.

Frailty mediating the causality between leucocyte telomere length and mortality: a cohort study of 440,551 UK Biobank participants.

Introduction: Previous studies reported leucocyte telomere length (LTL) and frailty were associated with mortality, but it remains unclear whether frailty serves as a mediator in the relationship between leucocyte telomere length and mortality risk. This study aimed to evaluate how measuring LTL and frailty can support early monitoring and prevention of risk of mortality from the prospective of predictive, preventive, and personalized medicine (PPPM/3PM). Methods: We included 440,551 participants from the UK Biobank between the baseline visit (2006-2010) and November 30, 2022. The time-dependent Cox proportional hazards model was conducted to assess the association between LTL and frailty index with the risk of mortality. Furthermore, we conducted causal mediation analyses to examine the extent to which frailty mediated the association between LTL and mortality. Results: During a median follow-up of 13.74 years, each SD increase in LTL significantly decreased the risk of all-cause [hazard ratio (HR): 0.94, 95% confidence interval (CI): 0.93-0.95] and CVD-specific mortality (HR: 0.92, 95% CI: 0.90-0.95). The SD increase in FI elevated the risk of all-cause (HR: 1.35, 95% CI: 1.34-1.36), CVD-specific (HR: 1.47, 95% CI: 1.44-1.50), and cancer-specific mortality (HR: 1.22, 95% CI: 1.20-1.24). Frailty mediated approximately 10% of the association between LTL and all-cause and CVD-specific mortality. Conclusions: Our results indicate that frailty mediates the effect of LTL on all-cause and CVD-specific mortality. There findings might be valuable to predict, prevent, and reduce mortality through primary prevention and healthcare in context of PPPM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00355-7.

Cancer screening in hospitalized ischemic stroke patients: a multicenter study focused on multiparametric analysis to improve management of occult cancers.

Background/aims: The reciprocal promotion of cancer and stroke occurs due to changes in shared risk factors, such as metabolic pathways and molecular targets, creating a "vicious cycle." Cancer plays a direct or indirect role in the pathogenesis of ischemic stroke (IS), along with the reactive medical approach used in the treatment and clinical management of IS patients, resulting in clinical challenges associated with occult cancer in these patients. The lack of reliable and simple tools hinders the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) approach. Therefore, we conducted a multicenter study that focused on multiparametric analysis to facilitate early diagnosis of occult cancer and personalized treatment for stroke associated with cancer. Methods: Admission routine clinical examination indicators of IS patients were retrospectively collated from the electronic medical records. The training dataset comprised 136 IS patients with concurrent cancer, matched at a 1:1 ratio with a control group. The risk of occult cancer in IS patients was assessed through logistic regression and five alternative machine-learning models. Subsequently, select the model with the highest predictive efficacy to create a nomogram, which is a quantitative tool for predicting diagnosis in clinical practice. Internal validation employed a ten-fold cross-validation, while external validation involved 239 IS patients from six centers. Validation encompassed receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and comparison with models from prior research. Results: The ultimate prediction model was based on logistic regression and incorporated the following variables: regions of ischemic lesions, multiple vascular territories, hypertension, D-dimer, fibrinogen (FIB), and hemoglobin (Hb). The area under the ROC curve (AUC) for the nomogram was 0.871 in the training dataset and 0.834 in the external test dataset. Both calibration curves and DCA underscored the nomogram's strong performance. Conclusions: The nomogram enables early occult cancer diagnosis in hospitalized IS patients and helps to accurately identify the cause of IS, while the promotion of IS stratification makes personalized treatment feasible. The online nomogram based on routine clinical examination indicators of IS patients offered a cost-effective platform for secondary care in the framework of PPPM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00354-8.

Significance of flavonoids targeting PI3K/Akt/HIF-1α signaling pathway in therapy-resistant cancer cells - A potential contribution to the predictive, preventive, and personalized medicine.

BACKGROUND: Cancer management faces multiple obstacles, including resistance to current therapeutic approaches. In the face of challenging microenvironments, cancer cells adapt metabolically to maintain their supply of energy and precursor molecules for biosynthesis and thus sustain rapid proliferation and tumor growth. Among the various metabolic adaptations observed in cancer cells, the altered glucose metabolism is the most widely studied. The aberrant glycolytic modification in cancer cells has been associated with rapid cell division, tumor growth, cancer progression, and drug resistance. The higher rates of glycolysis in cancer cells, as a hallmark of cancer progression, is modulated by the transcription factor hypoxia inducible factor 1 alpha (HIF-1α), a downstream target of the PI3K/Akt signaling, the most deregulated pathway in cancer. AIM OF REVIEW: We provide a detailed overview of current, primarily experimental, evidence on the potential effectiveness of flavonoids to combat aberrant glycolysis-induced resistance of cancer cells to conventional and targeted therapies. The manuscript focuses primarily on flavonoids reducing cancer resistance via affecting PI3K/Akt, HIF-1α (as the transcription factor critical for glucose metabolism of cancer cells that is regulated by PI3K/Akt pathway), and key glycolytic mediators downstream of PI3K/Akt/HIF-1α signaling (glucose transporters and key glycolytic enzymes). KEY SCIENTIFIC CONCEPTS OF REVIEW: The working hypothesis of the manuscript proposes HIF-1α - the transcription factor critical for glucose metabolism of cancer cells regulated by PI3K/Akt pathway as an attractive target for application of flavonoids to mitigate cancer resistance. Phytochemicals represent a source of promising substances for cancer management applicable to primary, secondary, and tertiary care. However, accurate patient stratification and individualized patient profiling represent crucial steps in the paradigm shift from reactive to predictive, preventive, and personalized medicine (PPPM / 3PM). The article is focused on targeting molecular patterns by natural substances and provides evidence-based recommendations for the 3PM relevant implementation.

The predictive, preventive, and personalized medicine of insomnia: gut microbiota and inflammation.

Background: The human gut microbiota (GM) has been recognized as a significant factor in the development of insomnia, primarily through inflammatory pathways, making it a promising target for therapeutic interventions. Considering the principles of primary prediction, targeted prevention, and personalized treatment medicine (PPPM), identifying specific gut microbiota associated with insomnia and exploring the underlying mechanisms comprehensively are crucial steps towards achieving primary prediction, targeted prevention, and personalized treatment of insomnia. Working hypothesis and methodology: We hypothesized that alterations in the composition of specific GM could induce insomnia through an inflammatory response, which postulates the existence of a GM-inflammation-insomnia pathway. Mendelian randomization (MR) analyses were employed to examine this pathway and explore the mediative effects of inflammation. We utilized genetic proxies representing GM, insomnia, and inflammatory indicators (including 41 circulating cytokines and C-reactive protein (CRP)), specifically identified from European ancestry. The primary method used to identify insomnia-related GM and examine the medicative effect of inflammation was the inverse variance weighted method, supplemented by the MR-Egger and weighted median methods. Our findings have the potential to identify individuals at risk of insomnia through screening for GM imbalances, leading to the development of targeted prevention and personalized treatment strategies for the condition. Results: Nine genera and three circulating cytokines were identified to be associated with insomnia; only the associations of Clostridium (innocuum group) and ß-NGF on insomnia remained significant after the FDR test, OR = 1.08 (95% CI = 1.04-1.12, P = 1.45 × 10-4, q = 0.02) and OR = 1.06 (95% CI = 1.02-1.10, P = 1.06 × 10-3, q = 0.04), respectively. CRP was associated with an increased risk of insomnia, OR = 1.05 (95% CI = 1.01-1.10, P = 6.42 × 10-3). CRP mediated the association of Coprococcus 1, Holdemania, and Rikenellaceae (RC9gut group) with insomnia. No heterogeneity or pleiotropy were detected. Conclusions: Our study highlights the role of specific GM alterations in the development of insomnia and provides insights into the mediating effects of inflammation. Targeting these specific GM alterations presents a promising avenue for advancing the transition from reactive medicine to PPPM in managing insomnia, potentially leading to significant clinical benefits. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00345-1.

Nitroproteomics is instrumental for stratification and targeted treatments of astrocytoma patients: expert recommendations for advanced 3PM approach with improved individual outcomes.

Protein tyrosine nitration is a selectively and reversible important post-translational modification, which is closely related to oxidative stress. Astrocytoma is the most common neuroepithelial tumor with heterogeneity and complexity. In the past, the diagnosis of astrocytoma was based on the histological and clinical features, and the treatment methods were nothing more than surgery-assisted radiotherapy and chemotherapy. Obviously, traditional methods short falls an effective treatment for astrocytoma. In late 2021, the World Health Organization (WHO) adopted molecular biomarkers in the comprehensive diagnosis of astrocytoma, such as IDH-mutant and DNA methylation, which enabled the risk stratification, classification, and clinical prognosis prediction of astrocytoma to be more correct. Protein tyrosine nitration is closely related to the pathogenesis of astrocytoma. We hypothesize that nitroproteome is significantly different in astrocytoma relative to controls, which leads to establishment of nitroprotein biomarkers for patient stratification, diagnostics, and prediction of disease stages and severity grade, targeted prevention in secondary care, treatment algorithms tailored to individualized patient profile in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). Nitroproteomics based on gel electrophoresis and tandem mass spectrometry is an effective tool to identify the nitroproteins and effective biomarkers in human astrocytomas, clarifying the biological roles of oxidative/nitrative stress in the pathophysiology of astrocytomas, functional characteristics of nitroproteins in astrocytomas, nitration-mediated signal pathway network, and early diagnosis and treatment of astrocytomas. The results finds that these nitroproteins are enriched in mitotic cell components, which are related to transcription regulation, signal transduction, controlling subcellular organelle events, cell perception, maintaining cell homeostasis, and immune activity. Eleven statistically significant signal pathways are identified in astrocytoma, including remodeling of epithelial adherens junctions, germ cell-sertoli cell junction signaling, 14-3-3-mediated signaling, phagosome maturation, gap junction signaling, axonal guidance signaling, assembly of RNA polymerase III complex, and TREM1 signaling. Furthermore, protein tyrosine nitration is closely associated with the therapeutic effects of protein drugs, and molecular mechanism and drug targets of cancer. It provides valuable data for studying the protein nitration biomarkers, molecular mechanisms, and therapeutic targets of astrocytoma towards PPPM (3P medicine) practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00348-y.

Emerging frontiers of artificial intelligence and machine learning in ischemic stroke: a comprehensive investigation of state-of-the-art methodologies, clinical applications, and unraveling challenges.

At present, stroke remains the second highest cause of death globally and a leading cause of disability. From 1990 to 2019, the absolute number of strokes worldwide increased by 70.0%, and the prevalence of stroke increased by 85.0%, causing millions of deaths and disability. Ischemic stroke accounts for the majority of strokes, which is caused by arterial occlusion. Effective primary prevention strategies, early diagnosis, and timely interventions such as rapid reperfusion are in urgent implementation to control ischemic stroke. Otherwise, the stroke burden will probably continue to grow across the world as a result of population aging and an ongoing high prevalence of risk factors. To help with the diagnosis and management of ischemic stroke, newer techniques such as artificial intelligence (AI) are highly anticipated and may bring a new revolution. AI is a recent fast-growing research area which aims to mimic cognitive processes through a number of techniques such as machine learning (ML) methods of random forest learning (RFL) and convolutional neural networks (CNNs). With the help of AI, several momentous milestones have already been attained across diverse dimensions of ischemic stroke. In the context of predictive, preventive, and personalized medicine (PPPM/3PM), we aim to transform stroke care from a reactive to a proactive and individualized paradigm. In this way, AI demonstrates strong clinical utility across all three levels of prevention in ischemic stroke. In this paper, we synoptically illustrated the history and current situation of AI and ML. Then, we summarized their clinical applications and efficacy in the management of stroke. We finally provided an outlook on how AI approaches might contribute to enhancing favorable outcomes after stroke and proposed our suggestions on developing AI-based PPPM strategies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00343-3.

Diabetes, glycemic control and arterial stiffness: a real-world cohort study in the context of predictive, preventive, and personalized medicine.

Background: Arterial stiffness is a major contributor to morbidity and mortality worldwide. Although several metabolic markers associated with arterial stiffness have been developed, there is limited data regarding whether glycemic control modifies the association between diabetes and arterial stiffness. For these reasons, identification of traits around diabetes will directly contribute to arterial stiffness and atherosclerosis management in the context of predictive, preventive, and personalized medicine (PPPM). Thus, this study aimed to explore the relationship of diabetes and glycemic control status with arterial stiffness in a real-world setting. Methods: Data of participants from Beijing Xiaotangshan Examination Center (BXEC) with at least two surveys between 2008 and 2019 were used. Cumulative hazards were presented by inverse probability of treatment weighted (IPTW) Kaplan-Meier curves. Cox models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI). Arterial stiffness was defined as brachial-ankle pulse wave velocity (baPWV) ≥1400 cm/s. Results: Of 5837 participants, the mean baseline age was 46.5±9.3 years, including 3791 (64.9%) males. During a median follow-up of 4.0 years, 1928 (33.0%) cases of incident arterial stiffness were observed. People with diabetes at baseline had a 48.4% (HR: 1.484, 95% CI: 1.250-1.761) excessive risk of arterial stiffness. Adherence to good glycemic control attenuated the relationship between diabetes and arterial stiffness (HR: 1.264, 95% CI: 0.950-1.681); while uncontrolled diabetes was associated with the highest risk of arterial stiffness (HR: 1.629, 95% CI: 1.323-2.005). Results were consistent using IPTW algorithm and multiple imputed data. Conclusion: Our study quantified that diabetes status is closely associated with an increased risk of arterial stiffness and supported that adherence to good glycemic control could attenuate the adverse effect of diabetes on arterial stiffness. Therefore, glucose monitoring and control is a cost-effective strategy for the predictive diagnostics, targeted prevention, patient stratification, and personalization of medical services in early vascular damages and arterial stiffness. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00347-z.

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine.

Background: Population aging is a global public health issue involving increased prevalence of age-related diseases, and concomitant burden on medical resources and the economy. Ninety-two diseases have been identified as age-related, accounting for 51.3% of the global adult disease burden. The economic cost per capita for older people over 60 years is 10 times that of the younger population. From the aspects of predictive, preventive, and personalized medicine (PPPM), developing a risk-prediction model can help identify individuals at high risk for all-cause mortality and provide an opportunity for targeted prevention through personalized intervention at an early stage. However, there is still a lack of predictive models to help community-dwelling older adults do well in healthcare. Objectives: This study aims to develop an accurate 1-, 3-, 5-, and 8-year all-cause mortality risk-prediction model by using clinical multidimensional variables, and investigate risk factors for 1-, 3-, 5-, and 8-year all-cause mortality in community-dwelling older adults to guide primary prevention. Methods: This is a two-center cohort study. Inclusion criteria: (1) community-dwelling adult, (2) resided in the districts of Chaonan or Haojiang for more than 6 months in the past 12 months, and (3) completed a health examination. Exclusion criteria: (1) age less than 60 years, (2) more than 30 incomplete variables, (3) no signed informed consent. The primary outcome of the study was all-cause mortality obtained from face-to-face interviews, telephone interviews, and the medical death database from 2012 to 2021. Finally, we enrolled 5085 community-dwelling adults, 60 years and older, who underwent routine health screening in the Chaonan and Haojiang districts, southern China, from 2012 to 2021. Of them, 3091 participants from Chaonan were recruited as the primary training and internal validation study cohort, while 1994 participants from Haojiang were recruited as the external validation cohort. A total of 95 clinical multidimensional variables, including demographics, lifestyle behaviors, symptoms, medical history, family history, physical examination, laboratory tests, and electrocardiogram (ECG) data were collected to identify candidate risk factors and characteristics. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) models and multivariable Cox proportional hazards regression analysis. A nomogram predictive model for 1-, 3-, 5- and 8-year all-cause mortality was constructed. The accuracy and calibration of the nomogram prediction model were assessed using the concordance index (C-index), integrated Brier score (IBS), receiver operating characteristic (ROC), and calibration curves. The clinical validity of the model was assessed using decision curve analysis (DCA). Results: Nine independent risk factors for 1-, 3-, 5-, and 8-year all-cause mortality were identified, including increased age, male, alcohol status, higher daily liquor consumption, history of cancer, elevated fasting glucose, lower hemoglobin, higher heart rate, and the occurrence of heart block. The acquisition of risk factor criteria is low cost, easily obtained, convenient for clinical application, and provides new insights and targets for the development of personalized prevention and interventions for high-risk individuals. The areas under the curve (AUC) of the nomogram model were 0.767, 0.776, and 0.806, and the C-indexes were 0.765, 0.775, and 0.797, in the training, internal validation, and external validation sets, respectively. The IBS was less than 0.25, which indicates good calibration. Calibration and decision curves showed that the predicted probabilities were in good agreement with the actual probabilities and had good clinical predictive value for PPPM. Conclusion: The personalized risk prediction model can identify individuals at high risk of all-cause mortality, help offer primary care to prevent all-cause mortality, and provide personalized medical treatment for these high-risk individuals from the PPPM perspective. Strict control of daily liquor consumption, lowering fasting glucose, raising hemoglobin, controlling heart rate, and treatment of heart block could be beneficial for improving survival in elderly populations. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00342-4.