Medicine in the early twenty-first century: paradigm and anticipation - EPMA position paper 2016

Identification and Construction of a Disulfidptosis-Mediated Diagnostic Model and Associated Immune Microenvironment of Osteoarthritis from the Perspective of PPPM

Background

Osteoarthritis (OA) is a major cause of human disability. Despite receiving treatment, patients with the middle and late stage of OA have poor survival outcomes. Therefore, within the framework of predictive, preventive, and personalized medicine (PPPM/3PM), early personalized diagnosis of OA is particularly prominent. PPPM aims to accurately identify disease by integrating multiple omic techniques; however, the efficiency of currently available methods and biomarkers in predicting and diagnosing OA should be improved. Disulfidptosis, a novel programmed cell death mechanism and appeared in particular metabolic status, plays a mysterious characteristic in the occurrence and development of OA, which warrants further investigation.

Methods

In this study, we integrated three public datasets from the Gene Expression Omnibus (GEO) database, including 26 OA samples and 20 normal samples. Via a series of bioinformatic analysis and machine learning, we identified the diagnostic biomarkers and several subtypes of OA. Moreover, the expression of these biomarkers were verified in our in-house cohort and the single cell dataset.

Results

Three significant regulators of disulfidptosis (NCKAP1, OXSM, and SLC3A2) were identified through differential expression analysis and machine learning. And a nomogram constructed based on these three regulators exhibited ideal efficiency in predicting early- and late-stage OA. Furthermore, based on the expression of three regulators, we identified two disulfidptosis-related subtypes of OA with different infiltration of immune cells and personalized expression level of immune checkpoints. Notably, the expression of the three regulators was demonstrated in a single-cell RNA profile and verified in the synovial tissue in our in-house cohort including 6 OA patients and 6 normal people. Finally, an efficient disulfidptosis-mediated diagnostic model was constructed for OA, with the AUC value of 97.6923% in the training set and 93.3333% and 100% in two validation sets.

Conclusion

Overall, with regard to PPPM, this study provided novel insights into the role of disulfidptosis regulators in the personalized diagnosis and treatment of OA.

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.

Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity

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.

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.

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.

Personalized medicine and nutrition in hepatology for preventing chronic liver disease in Mexico

Chronic liver disease is a global health issue. Patients with chronic liver disease require a fresh approach that focuses on the genetic and environmental factors that contribute to disease initiation and progression. Emerging knowledge in the fields of Genomic Medicine and Genomic Nutrition demonstrates differences between countries in terms of genetics and lifestyle risk factors such as diet, physical activity, and mental health in chronic liver disease, which serves as the foundation for the implementation of Personalized Medicine and Nutrition (PerMed-Nut) strategies. Most of the world's populations have descended from various ethnic groupings. Mexico's population has a tripartite ancestral background, consisting of Amerindian, European, and African lineages, which is common across Latin America's regional countries. The purpose of this review is to discuss the genetic and environmental components that could be incorporated into a PerMed-Nut model for metabolic-associated liver disease, viral hepatitis B and C, and hepatocellular carcinoma in Mexico. Additionally, the implementation of the PerMed-Nut approach will require updated medicine and nutrition education curricula. Training and equipping future health professionals and researchers with new clinical and investigative abilities focused on preventing liver illnesses in the field of genomic hepatology globally is a vision that clinicians and nutritionists should be concerned about.

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.

The paradigm change from reactive medical services to 3PM in ischemic stroke: a holistic approach utilising tear fluid multi-omics, mitochondria as a vital biosensor and AI-based multi-professional data interpretation

Worldwide stroke is the second leading cause of death and the third leading cause of death and disability combined. The estimated global economic burden by stroke is over US$891 billion per year. Within three decades (1990-2019), the incidence increased by 70%, deaths by 43%, prevalence by 102%, and DALYs by 143%. Of over 100 million people affected by stroke, about 76% are ischemic stroke (IS) patients recorded worldwide. Contextually, ischemic stroke moves into particular focus of multi-professional groups including researchers, healthcare industry, economists, and policy-makers. Risk factors of ischemic stroke demonstrate sufficient space for cost-effective prevention interventions in primary (suboptimal health) and secondary (clinically manifested collateral disorders contributing to stroke risks) care. These risks are interrelated. For example, sedentary lifestyle and toxic environment both cause mitochondrial stress, systemic low-grade inflammation and accelerated ageing; inflammageing is a low-grade inflammation associated with accelerated ageing and poor stroke outcomes. Stress overload, decreased mitochondrial bioenergetics and hypomagnesaemia are associated with systemic vasospasm and ischemic lesions in heart and brain of all age groups including teenagers. Imbalanced dietary patterns poor in folate but rich in red and processed meat, refined grains, and sugary beverages are associated with hyperhomocysteinaemia, systemic inflammation, small vessel disease, and increased IS risks. Ongoing 3PM research towards vulnerable groups in the population promoted by the European Association for Predictive, Preventive and Personalised Medicine (EPMA) demonstrates promising results for the holistic patient-friendly non-invasive approach utilising tear fluid-based health risk assessment, mitochondria as a vital biosensor and AI-based multi-professional data interpretation as reported here by the EPMA expert group. Collected data demonstrate that IS-relevant risks and corresponding molecular pathways are interrelated. For examples, there is an evident overlap between molecular patterns involved in IS and diabetic retinopathy as an early indicator of IS risk in diabetic patients. Just to exemplify some of them such as the 5-aminolevulinic acid/pathway, which are also characteristic for an altered mitophagy patterns, insomnia, stress regulation and modulation of microbiota-gut-brain crosstalk. Further, ceramides are considered mediators of oxidative stress and inflammation in cardiometabolic disease, negatively affecting mitochondrial respiratory chain function and fission/fusion activity, altered sleep-wake behaviour, vascular stiffness and remodelling. Xanthine/pathway regulation is involved in mitochondrial homeostasis and stress-driven anxiety-like behaviour as well as molecular mechanisms of arterial stiffness. In order to assess individual health risks, an application of machine learning (AI tool) is essential for an accurate data interpretation performed by the multiparametric analysis. Aspects presented in the paper include the needs of young populations and elderly, personalised risk assessment in primary and secondary care, cost-efficacy, application of innovative technologies and screening programmes, advanced education measures for professionals and general population-all are essential pillars for the paradigm change from reactive medical services to 3PM in the overall IS management promoted by the EPMA.

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.

Predictive, preventive and personalised approach as a conceptual and technological innovation in primary and secondary care of inflammatory bowel disease benefiting affected individuals and populations

Inflammatory bowel disease (IBD) is a global health burden which carries lifelong morbidity affecting all age groups in populations with the disease-specific peak of the age groups ranging between 15 and 35 years, which are of great economic importance for the society. An accelerating incidence of IBD is reported for newly industrialised countries, whereas stabilising incidence but increasing prevalence is typical for countries with a Westernised lifestyle, such as the European area and the USA. Although the aetiology of IBD is largely unknown, the interplay between the genetic, environmental, immunological, and microbial components is decisive for the disease manifestation, course, severity and individual outcomes. Contextually, the creation of an individualised patient profile is crucial for the cost-effective disease management in primary and secondary care of IBD. The proposed pathomechanisms include intestinal pathoflora and dysbiosis, chronic inflammation and mitochondrial impairments, amongst others, which collectively may reveal individual molecular signatures defining IBD subtypes and leading to clinical phenotypes, patient stratification and cost-effective protection against health-to-disease transition and treatments tailored to individualised patient profiles-all the pillars of an advanced 3PM approach. The paradigm change from reactive medical services to predictive diagnostics, cost-effective targeted prevention and treatments tailored to individualised patient profiles in overall IBD management holds a promise to meet patient needs in primary and secondary care, to increase the life-quality of affected individuals and to improve health economy in the area of IBD management. This article analyses current achievements and provides the roadmap for future developments in the area in the context of 3P medicine benefiting society at large.

Impacts of a multipurpose outpatient hospital pharmacy in the framework of 3P medicine

Challenge in the framework of Predictive Preventive and Personalised Medicine

In recent years, we have been witnessing a change in the performance of hospital pharmacists, aimed at increasing their participation in the pharmacotherapeutic process of patients. The drug cycle, characterised as multidisciplinary, is very complex. It is essential for the multidisciplinary team to have a broad vision of the medication system in order to guarantee safety and quality.Considering the challenges of current healthcare systems and paradigm shift from reactive to predictive medicine, a new professional environment should be created to promote the implementation of Predictive, Preventive and Personalised Medicine in healthcare.

Objectives and study design

To optimise care times in multipurpose outpatient hospital both in the preparation of ready-to-use medications and in the dispensing of medications for home treatment.To increase the confidence and value of hospital pharmacists in the process of patient and family care.The design of the study was carried out by the following:-Coordinating the schedules of the multi-pathology day hospital with the software and records of Medication Preparation in the pharmacy service.-Opening a Pharmacy Outpatient Clinic associated with the multi-pathology day hospital.-Planning and scheduling patient treatments.

Achievements

With the implementation of this programme, the visibility of hospital pharmacists in the multidisciplinary team was increased.This Pharmacy Outpatient Clinic allowed the coordination of the pharmaceutical care process in the day hospital.This project increased the credibility of the Pharmacy Service in the improvement of the integral process of the medicine.

Conclusions and expert recommendations

Predictive approach

The presence of pharmacists in the multi-pathology day hospital has a predictive approach. A change is made in the workflow that allows to generate a speed of intervention by acting before prescribing, dispensing and administering the treatment to the patient.

Targeted prevention

The presence of pharmacists in the multipurpose day hospital unit and their collaboration with other professionals and the patient bring about a selective prevention that decreases the possibility of medication errors occurring.

Personalisation of medical services

With the individualised dispensing of treatments, a step forward is taken in the personalisation of medical services, which avoids medication errors in labelling and administration and improves safety in the overall medication circuit in the hospital.

Sepsis, Management & Advances in Metabolomics

Though there have been developments in clinical care and management, early and accurate diagnosis and risk stratification are still bottlenecks in septic shock patients. Since septic shock is multifactorial with patient-specific underlying co-morbid conditions, early assessment of sepsis becomes challenging due to variable symptoms and clinical manifestations. Moreover, the treatment strategies are traditionally based on their progression and corresponding clinical symptoms, not personalized. The complex pathophysiology assures that a single biomarker cannot identify, stratify, and describe patients affected by septic shock. Traditional biomarkers like CRP, PCT, and cytokines are not sensitive and specific enough to be used entirely for a patient's diagnosis and prognosis. Thus, the need of the hour is a sensitive and specific biomarker after comprehensive analysis that may facilitate an early diagnosis, prognosis, and drug development. Integration of clinical data with metabolomics would provide means to understand the patient's condition, stratify patients better, and predict the clinical outcome.

Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

Colorectal cancer (CRC) is one of the most heterogeneous and deadly diseases, with a global incidence of 1.5 million cases per year. Genomics has revolutionized the clinical management of CRC by enabling comprehensive molecular profiling of cancer. However, a deeper understanding of the molecular factors is needed to identify new prognostic and predictive markers that can assist in designing more effective therapeutic regimens for the improved management of CRC. Recent breakthroughs in single-cell analysis have identified new cell subtypes that play a critical role in tumor progression and could serve as potential therapeutic targets. Spatial analysis of the transcriptome and proteome holds the key to unlocking pathogenic cellular interactions, while liquid biopsy profiling of molecular variables from serum holds great potential for monitoring therapy resistance. Furthermore, gene expression signatures from various pathways have emerged as promising prognostic indicators in colorectal cancer and have the potential to enhance the development of equitable medicine. The advancement of these technologies for identifying new markers, particularly in the domain of predictive and personalized medicine, has the potential to improve the management of patients with CRC. Further investigations utilizing similar methods could uncover molecular subtypes specific to emerging therapies, potentially strengthening the development of personalized medicine for CRC patients.

Application of a Radiomics Machine Learning Model for Differentiating Aldosterone-Producing Adenoma from Non-Functioning Adrenal Adenoma

To evaluate the secretory function of adrenal incidentaloma, this study explored the usefulness of a contrast-enhanced computed tomography (CECT)-based radiomics model for distinguishing aldosterone-producing adenoma (APA) from non-functioning adrenal adenoma (NAA). Overall, 68 APA and 60 NAA patients were randomly assigned (8:2 ratio) to either a training or a test cohort. In the training cohort, univariate and least absolute shrinkage and selection operator regression analyses were conducted to select the significant features. A logistic regression machine learning (ML) model was then constructed based on the radiomics score and clinical features. Model effectiveness was evaluated according to the receiver operating characteristic, accuracy, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis. In the test cohort, the area under the curve (AUC) of the Radscore model was 0.869 [95% confidence interval (CI), 0.734-1.000], and the accuracy, sensitivity, specificity, and F1 score were 0.731, 1.000, 0.583, and 0.900, respectively. The Clinic-Radscore model had an AUC of 0.994 [95% CI, 0.978-1.000], and the accuracy, sensitivity, specificity, and F1 score values were 0.962, 0.929, 1.000, and 0.931, respectively. In conclusion, the CECT-based radiomics and clinical radiomics ML model exhibited good diagnostic efficacy in differentiating APAs from NAAs; this non-invasive, cost-effective, and efficient method is important for the management of adrenal incidentaloma.

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.

Comprehensive analysis of mitochondrial dysfunction and necroptosis in intracranial aneurysms from the perspective of predictive, preventative, and personalized medicine

Mitochondrial dysfunction and necroptosis are closely associated, and play vital roles in the medical strategy of multiple cardiovascular diseases. However, their implications in intracranial aneurysms (IAs) remain unclear. In this study, we aimed to explore whether mitochondrial dysfunction and necroptosis could be identified as valuable starting points for predictive, preventive, and personalized medicine for IAs. The transcriptional profiles of 75 IAs and 37 control samples were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene co-expression network analysis, and least absolute shrinkage and selection operator (LASSO) regression were used to screen key genes. The ssGSEA algorithm was performed to establish phenotype scores. The correlation between mitochondrial dysfunction and necroptosis was evaluated using functional enrichment crossover, phenotype score correlation, immune infiltration, and interaction network construction. The IA diagnostic values of key genes were identified using machine learning. Finally, we performed the single-cell sequencing (scRNA-seq) analysis to explore mitochondrial dysfunction and necroptosis at the cellular level. In total, 42 IA-mitochondrial DEGs and 15 IA-necroptosis DEGs were identified. Screening revealed seven  key genes invovled in mitochondrial dysfunction (KMO, HADH, BAX, AADAT, SDSL, PYCR1, and MAOA) and five genes involved in necroptosis (IL1B, CAMK2G, STAT1, NLRP3, and BAX). Machine learning confirmed the high diagnostic value of these key genes for IA. The IA samples showed  higher expression of mitochondrial dysfunction and necroptosis. Mitochondrial dysfunction and necroptosis exhibited a close association. Furthermore, scRNA-seq indicated that mitochondrial dysfunction and necroptosis were preferentially up-regulated in monocytes/macrophages and vascular smooth muscle cells (VSMCs) within IA lesions. In conclusion, mitochondria-induced necroptosis was involved in IA formation, and was mainly up-regulated in monocytes/macrophages and VSMCs within IA lesions. Mitochondria-induced necroptosis may be a novel potential target for diagnosis, prevention, and treatment of IA.

Individualisation, personalisation and person-centredness in mental healthcare: a scoping review of concepts and linguistic network visualisation

BACKGROUND

Targeted mental health interventions are increasingly described as individualised, personalised or person-centred approaches. However, the definitions for these terms vary significantly. Their interchangeable use prevents operationalisations and measures.

OBJECTIVE

This scoping review provides a synthesis of key concepts, definitions and the language used in the context of these terms in an effort to delineate their use for future research.

STUDY SELECTION AND ANALYSIS

Our search on PubMed, EBSCO and Cochrane provided 2835 relevant titles. A total of 176 titles were found eligible for extracting data. A thematic analysis was conducted to synthesise the underlying aspects of individualisation, personalisation and person-centredness. Network visualisations of co-occurring words in 2625 abstracts were performed using VOSViewer.

FINDINGS

Overall, 106 out of 176 (60.2%) articles provided concepts for individualisation, personalisation and person-centredness. Studies using person-centredness provided a conceptualisation more often than the others. A thematic analysis revealed medical, psychological, sociocultural, biological, behavioural, economic and environmental dimensions of the concepts. Practical frameworks were mostly found related to person-centredness, while theoretical frameworks emerged in studies on personalisation. Word co-occurrences showed common psychiatric words in all three network visualisations, but differences in further contexts.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The use of individualisation, personalisation and person-centredness in mental healthcare is multifaceted. While individualisation was the most generic term, personalisation was often used in biomedical or technological studies. Person-centredness emerged as the most well-defined concept, with many frameworks often related to dementia care. We recommend that the use of these terms follows a clear definition within the context of their respective disorders, treatments or medical settings.

SCOPING REVIEW REGISTRATION

Open Science Framework: osf.io/uatsc.

Royal jelly: a predictive, preventive and personalised strategy for novel treatment options in non-communicable diseases

Royal jelly (RJ) is a bee product produced by young adult worker bees, composed of water, proteins, carbohydrates and lipids, rich in bioactive components with therapeutic properties, such as free fatty acids, mainly 10-hydroxy-trans-2-decenoic acid (10-H2DA) and 10-hydroxydecanoic acid (10-HDA), and major royal jelly proteins (MRJPs), as well as flavonoids, most flavones and flavonols, hormones, vitamins and minerals. In vitro, non-clinical and clinical studies have confirmed its vital role as an antioxidant and anti-inflammatory. This narrative review discusses the possible effects of royal jelly on preventing common complications of non-communicable diseases (NCDs), such as inflammation, oxidative stress and intestinal dysbiosis, from the viewpoint of predictive, preventive and personalised medicine (PPPM/3PM). It is concluded that RJ, predictively, can be used as a non-pharmacological therapy to prevent and mitigate complications related to NCDs, and the treatment must be personalised.

Genetically predicted the causal relationship between gut microbiota and infertility: bidirectional Mendelian randomization analysis in the framework of predictive, preventive, and personalized medicine

Objective

Several studies have reported the association between gut microbiota and infertility; however, the causal association between them remains unclear. This study aimed to explore the causal relationship between gut microbiota and infertility and evaluate how specific gut microbiota can support early monitoring and prevention of infertility in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

The gut microbiota GWAS data included 18,340 individuals. Female infertility (6481 cases and 68,969 controls) and male infertility data (680 cases and 72,799 controls) were obtained from the FinnGen consortium. The inverse variance weighting (IVW), MR-Egger, weighted median (WM), Cochran Q tests, MR-PRESSO, and leave-one-out were used as a supplement to Mendelian randomization (MR) results and sensitivity analysis.

Results

The results of MR analysis indicated a significant causal association between Eubacterium oxidoreducens (OR = 2.048, P = 0.008), Lactococcus (OR = 1.445, P = 0.042), Eubacterium ventriosum (OR = 0.436, P = 0.018), Eubacterium rectale (OR = 0.306, P = 0.002), and Ruminococcaceae NK4A214 (OR = 0.537, P = 0.045) and male infertility. Genetically predicted Eubacterium ventriosum (OR = 0.809, P = 0.018), Holdemania (OR = 0.836, P = 0.037), Lactococcus (OR = 0.867, P = 0.020), Ruminococcaceae NK4A214 (OR = 0.830, P < 0.050), Ruminococcus torques (OR = 0.739, P = 0.022), and Faecalibacterium (OR = 1.311, P = 0.007) were associated with female infertility. Sensitivity analysis did not detect heterogeneity and pleiotropy (P > 0.05).

Conclusions

Our results provided evidence for the causal relationship between some gut microbiota and male and female infertility. These findings might be valuable in providing personalized treatment options for preventing infertility and improving reproductive function by monitoring and regulating the gut microbiota of infertility patients in the context of PPPM. Moreover, detecting the abundance of microbiota in feces can support preventive and personalized strategies, which may benefit more infertility patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00332-6.

Anatomic characteristics of shoulder based on MRI accurately predict incomplete rotator cuff injuries in patients: relevance for predictive, preventive, and personalized healthcare strategies

Background and PPPM-related working hypothesis

In the diagnosis of incomplete rotator cuff injuries (IRCI), magnetic resonance imaging (MRI) and ultrasound examination often have false-positive and false-negative results, while arthroscopy is expensive, invasive, and complex. From the strategy of predictive, preventive, and personalized medicine (PPPM), shoulder anatomical characteristics based on MRI have been demonstrated to accurately predict IRCI and their clinical applicability for personalized prediction of IRCI.

Aims

This study aimed to develop and validate a nomogram based on anatomical features of the shoulder on MRI to identify IRCI for PPPM healthcare strategies.

Methods

The medical information of 257 patients undergoing preoperative MRI examination was retrospectively reviewed and served as the primary cohort. Partial-thickness rotator cuff tears (RCTs) and tendinopathy observed under arthroscopy were considered IRCI. Using logistic regression analyses and least absolute shrinkage and selection operator (LASSO), IRCI was identified among various preoperative factors containing shoulder MRI and clinical features. A nomogram was constructed and subjected to internal and external validations (80 patients).

Results

The following eight independent risk factors for IRCI were identified:AgeThe left injured sidesThe Goutallier classification of supraspinatus in oblique coronal positionThe Goutallier classification of supraspinatus in the axial positionAcromial thicknessAcromiohumeral distanceCoracohumeral distanceAbnormal acromioclavicular joint signalsThe nomogram accurately predicted IRCI in the development (C-index, 0.932 (95% CI, 0.891, 0.973)) and validation (C-index, 0.955 (95% CI, 0.918, 0.992)) cohorts. The calibration curve was consistent between the predicted IRCI probability and the actual IRCI ratio of the nomogram. The decision curve analysis and clinical impact curves demonstrated that the model had high clinical applicability.

Conclusions

Eight independent factors that accurately predicted IRCI were determined using MRI anatomical findings. These personalized factors can prevent unnecessary diagnostic interventions (e.g., arthroscopy) and can assist surgeons in implementing individualized clinical decisions in medical practice, thus addressing the goals of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00333-5.

Machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy

Background

Primary angle closure glaucoma (PACG) is still one of the leading causes of irreversible blindness, with a trend towards an increase in the number of patients to 32.04 million by 2040, an increase of 58.4% compared with 2013. Health risk assessment based on multi-level diagnostics and machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure are considered essential tools to reverse the trend and protect vulnerable subpopulations against health-to-disease progression.

Aim

To develop a methodology for personalized choice of an effective method of primary angle closure (PAC) treatment based on comparing the prognosis of intraocular pressure (IOP) changes due to laser peripheral iridotomy (LPI) or lens extraction (LE).

Methods

The multi-parametric data analysis was used to develop models predicting individual outcomes of the primary angle closure (PAC) treatment with LPI and LE. For doing this, we suggested a positive dynamics in the intraocular pressure (IOP) after treatment, as the objective measure of a successful treatment. Thirty-seven anatomical parameters have been considered by applying artificial intelligence to the prospective study on 30 (LE) + 30 (LPI) patients with PAC.

Results and data interpretation in the framework of 3P medicine

Based on the anatomical and topographic features of the patients with PAC, mathematical models have been developed that provide a personalized choice of LE or LPI in the treatment. Multi-level diagnostics is the key tool in the overall advanced approach. To this end, for the future application of AI in the area, it is strongly recommended to consider the following:Clinically relevant phenotyping applicable to advanced population screeningSystemic effects causing suboptimal health conditions considered in order to cost-effectively protect affected individuals against health-to-disease transitionClinically relevant health risk assessment utilizing health/disease-specific molecular patterns detectable in body fluids with high predictive power such as a comprehensive tear fluid analysis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00337-1.

COVID-19 lessons to protect populations against future pandemics by implementing PPPM principles in healthcare

The coronavirus disease 2019 (COVID-19) pandemic has continued for more than 3 years, placing a huge burden on society worldwide. Although the World Health Organization (WHO) has declared an end to COVID-19 as a Public Health Emergency of International Concern (PHEIC), it is still considered a global threat. Previously, there has been a long debate as to whether the COVID-19 emergency will eventually end or transform into a more common infectious disease from a PHEIC, and how should countries respond to similar pandemics in the future more time-efficiently and cost-effectively. We reviewed the past, middle and current situation of COVID-19 based on bibliometric analysis and epidemiological data. Thereby, the necessity is indicated to change the paradigm from reactive healthcare services to predictive, preventive and personalised medicine (PPPM) approach, in order to effectively protect populations against COVID-19 and any future pandemics. Corresponding measures are detailed in the article including the involvement of multi-professional expertise, application of artificial intelligence, rapid diagnostics and patient stratification, and effective protection, amongst other to be considered by advanced health policy.

Impact of patient nationality on the severity of early side effects after radiotherapy

BACKGROUND

Major demographical changes in Germany commenced in the 1960s. Ongoing humanitarian crises in the Ukraine with subsequent immigration will have also long-range effects on national provision of cancer treatment. Ensuring the best possible outcomes for each cancer patient undergoing radiotherapy requires the prediction and prevention of unfavorable side effects. Given that recent research has primarily focused on clinical outcome indicators solely, less is known regarding sociodemographic predictors of therapeutic outcomes, such as patient nationality. Here, we investigated whether the severity of early side effects after radiotherapy are associated with patient nationality and other sociodemographic and clinical characteristics.

METHODS

Out of 9187 patients treated at a German university medical center between 2017 and 2021, 178 German and 178 non-German patients were selected for matched-pair analysis based on diagnostic and demographic criteria. For all 356 patients, data on side effects from follow-up care after radiotherapy were collected.

RESULTS

Non-German patients were more likely to have severe side effects than German patients. Side effect severity was also associated with tumor entity, concomitant therapy, body mass index, and age.

CONCLUSION

Foreign cancer patients are at higher risk of experiencing severe side effects of radiotherapy, suggesting a need to develop and implement targeted preventive measures for these patients. Further research investigating factors predicting the occurrence of radiotherapy side effects, including other sociodemographic characteristics, is needed to better personalize therapy regimens for cancer.

An Assessment of Personal Hygiene Practices Among Young Adults: A Cross-Sectional, Descriptive Study

Background Personal Hygiene behavior and practices play a major role in health promotion and disease prevention. Socio-demographic, behavioral, and psychological factors sway a person's overall adaptation of good practices. Disease burden leading to loss of productivity and its influence on the economy cannot be overstated. As medical professionals come in contact with a large number of people, they carry the potential to be super-spreaders in disease outbreaks. Thus, it is of utmost importance that medics and paramedics maintain hygiene to the highest standards. Method This cross-sectional study comprised 323 young adult participants from Muslim Town, Faisalabad. A structured questionnaire containing close-ended questions was used for data collection regarding personal hygiene behavior and practices. Dependency between various qualitative categorical variables and hygiene practices was analyzed using Chi-squared tests. Results It was found that although the majority of the participants followed good hygiene for most of the practices; laziness, lack of relevant education, improper time management, and unreliable water supply posed a significant barrier to good practices. Females had demonstrably better practices compared to males. Urbanity did not have a significant correlation with the results.  Conclusion We conclude that a rigorous program of awareness and education regarding this subject is the need of the hour to facilitate an improvement in predictive and preventive health care and reduce morbidity and mortality. Steps should be taken to ease the barriers that are obstructing optimal hygienic practices.

Mechanical power normalized to aerated lung predicts noninvasive ventilation failure and death and contributes to the benefits of proning in COVID-19 hypoxemic respiratory failure

Background

Concern exists that noninvasive ventilation (NIV) may promote ventilation-induced lung injury(VILI) and worsen outcome in acute hypoxemic respiratory failure (AHRF). Different individual ventilatory variables have been proposed to predict clinical outcomes, with inconsistent results.Mechanical power (MP), a measure of the energy transfer rate from the ventilator to the respiratory system during mechanical ventilation, might provide solutions for this issue in the framework of predictive, preventive and personalized medicine (PPPM). We explored (1) the impact of ventilator-delivered MP normalized to well-aerated lung (MP_WAL) on physio-anatomical and clinical responses to NIV in COVID-19-related AHRF and (2) the effect of prone position(PP) on MP_WAL.

Methods

We analyzed 216 noninvasively ventilated COVID-19 patients (108 patients receiving PP + NIV and 108 propensity score-matched patients receiving supine NIV) with moderate-to-severe(paO2/FiO2 ratio < 200) AHRF enrolled in the PRO-NIV controlled non-randomized study (ISRCTN23016116).Quantification of differentially aerated lung volumes by lung ultrasonography (LUS) was validated against CT scans. Respiratory parameters were hourly recorded, ABG were performed 1 h after each postural change. Time-weighed average values of ventilatory variables, including MP_WAL, and gas exchange parameters (paO2/FiO2 ratio, dead space indices) were calculated for each ventilatory session. LUS and circulating biomarkers were assessed daily.

Results

Compared with supine position, PP was associated with a 34% MP_WAL reduction, attributable largely to an absolute MP reduction and secondly to an enhanced lung reaeration.Patients receiving a high MP_WAL during the 1^st 24 h of NIV [MP_WAL(day 1)] had higher 28-d NIV failure (HR = 4.33,95%CI:3.09 - 5.98) and death (HR = 5.17,95%CI: 3.01 - 7.35) risks than those receiving a low MP_WAL(day 1).In Cox multivariate analyses, MP_WAL(day 1) remained independently associated with 28-d NIV failure (HR = 1.68,95%CI:1.15-2.41) and death (HR = 1.69,95%CI:1.22-2.32).MP_WAL(day 1) outperformed other power measures and ventilatory variables as predictor of 28-d NIV failure (AUROC = 0.89;95%CI:0.85-0.93) and death (AUROC = 0.89;95%CI:0.85-0.94).MP_WAL(day 1) predicted also gas exchange, ultrasonographic and inflammatory biomarker responses, as markers of VILI, on linear multivariate analysis.

Conclusions

In the framework of PPPM, early bedside MP_WAL calculation may provide added value to predict response to NIV and guide subsequent therapeutic choices i.e. prone position adoption during NIV or upgrading to invasive ventilation, to reduce hazardous MP_WAL delivery, prevent VILI progression and improve clinical outcomes in COVID-19-related AHRF.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00325-5.

Clarifying the effect of gut microbiota on allergic conjunctivitis risk is instrumental for predictive, preventive, and personalized medicine: a Mendelian randomization analysis

Background

Allergic conjunctivitis is an ocular immune disease which affects the conjunctiva, eyelids, and cornea. Growing evidence implicates the gut microbiota in balancing and modulating immunity response, and in the pathogenesis of allergic disease. As a result, gut microbial imbalance could be a useful indicator for allergic conjunctivitis. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of gut microbial imbalance in the development of allergic conjunctivitis could provide a window of opportunity for primary prediction, targeted prevention, and personalized treatment of the disease.

Working hypothesis and methodology

In our study, we hypothesized that individuals with microbial dysbiosis may be more susceptible to allergic conjunctivitis due to an increased inflammatory response. To verify the working hypothesis, our analysis selected genetic variants linked with gut microbiota features (N = 18,340) and allergic conjunctivitis (4513 cases, 649,376 controls) from genome-wide association studies. The inverse-variance weighted (IVW) estimate, Mendelian randomization (MR)-Egger, weighted median estimator, maximum likelihood estimator (MLE), and MR robust adjusted profile score (MR.RAPS) were employed to analyze the impact of gut microbiota on the risk of allergic conjunctivitis and identify allergic conjunctivitis-related gut microbes. Ultimately, these findings may enable the identification of individuals at risk of allergic conjunctivitis through screening of gut microbial imbalances, and allow for new targeted prevention and personalized treatment strategies.

Results

Genetic liability to Ruminococcaceae_UCG_002 (OR, 0.83; 95% CI, 0.70-0.99; P = 4.04×10^-2), Holdemanella (OR, 0.78; 95% CI, 0.64-0.96; P = 2.04×10^-2), Catenibacterium (OR, 0.69; 95% CI, 0.56-0.86; P = 1.09×10^-3), Senegalimassilia (OR, 0.71; 95% CI, 0.55-0.93; P = 1.23×10^-2) genus were associated with a low risk of allergic conjunctivitis with IVW. Besides, we found suggestive associations of a genetic-driven increase in the Oscillospira (OR, 1.41; 95% CI, 1.00-2.00; P = 4.63×10^-2) genus with a higher risk of allergic conjunctivitis. Moreover, MLE and MR.RAPS show consistent results with IVW after further validation and strengthened confidence in the true causal associations. No heterogeneity and pleiotropy was detected.

Conclusions

Our study suggests that gut microbiota may play a causal role in the development of allergic conjunctivitis and provides new insights into the microbiota-mediated mechanism of the disease. Gut microbiota may serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in allergic conjunctivitis, facilitating the transition from reactive medical services to PPPM in the management of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00321-9.

Identification of potential necroinflammation-associated necroptosis-related biomarkers for delayed graft function and renal allograft failure: a machine learning-based exploration in the framework of predictive, preventive, and personalized medicine

Delayed graft function (DGF) is one of the key post-operative challenges for a subset of kidney transplantation (KTx) patients. Graft survival is significantly lower in recipients who have experienced DGF than in those who have not. Assessing the risk of chronic graft injury, predicting graft rejection, providing personalized treatment, and improving graft survival are major strategies for predictive, preventive, and personalized medicine (PPPM/3PM) to promote the development of transplant medicine. However, since PPPM aims to accurately identify disease by integrating multiple omics, current methods to predict DGF and graft survival can still be improved. Renal ischemia/reperfusion injury (IRI) is a pathological process experienced by all KTx recipients that can result in varying occurrences of DGF, chronic rejection, and allograft failure depending on its severity. During this process, a necroinflammation-mediated necroptosis-dependent secondary wave of cell death significantly contributes to post-IRI tubular cell loss. In this article, we obtained the expression matrices and corresponding clinical data from the GEO database. Subsequently, nine differentially expressed necroinflammation-associated necroptosis-related genes (NiNRGs) were identified by correlation and differential expression analysis. The subtyping of post-KTx IRI samples relied on consensus clustering; the grouping of prognostic risks and the construction of predictive models for DGF (the area under the receiver operating characteristic curve (AUC) of the internal validation set and the external validation set were 0.730 and 0.773, respectively) and expected graft survival after a biopsy (the internal validation set's 1-year AUC: 0.770; 2-year AUC: 0.702; and 3-year AUC: 0.735) were based on the least absolute shrinkage and selection operator regression algorithms. The results of the immune infiltration analysis showed a higher infiltration abundance of myeloid immune cells, especially neutrophils, macrophages, and dendritic cells, in the cluster A subtype and prognostic high-risk groups. Therefore, in the framework of PPPM, this work provides a comprehensive exploration of the early expression landscape, related pathways, immune features, and prognostic impact of NiNRGs in post-KTx patients and assesses their capabilities as.predictors of post-KTx DGF and graft loss,targets of the vicious loop between regulated tubular cell necrosis and necroinflammation for targeted secondary and tertiary prevention, andreferences for personalized immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00320-w.

Multi-faceted CRISPR/Cas technological innovation aspects in the framework of 3P medicine

Since 2009, the European Association for Predictive, Preventive and Personalised Medicine (EPMA, Brussels) promotes the paradigm change from reactive approach to predictive, preventive, and personalized medicine (PPPM/3PM) to protect individuals in sub-optimal health conditions from the health-to-disease transition, to increase life-quality of the affected patient cohorts improving, therefore, ethical standards and cost-efficacy of healthcare to great benefits of the society at large. The gene-editing technology utilizing CRISPR/Cas gene-editing approach has demonstrated its enormous value as a powerful tool in a broad spectrum of bio/medical research areas. Further, CRISPR/Cas gene-editing system is considered applicable to primary and secondary healthcare, in order to prevent disease spread and to treat clinically manifested disorders, involving diagnostics of SARS-Cov-2 infection and experimental treatment of COVID-19. Although the principle of the proposed gene editing is simple and elegant, there are a lot of technological challenges and ethical considerations to be solved prior to its broadly scaled clinical implementation. This article highlights technological innovation beyond the state of the art, exemplifies current achievements, discusses unsolved technological and ethical problems, and provides clinically relevant outlook in the framework of 3PM.

Multi-omics identification of an immunogenic cell death-related signature for clear cell renal cell carcinoma in the context of 3P medicine and based on a 101-combination machine learning computational framework

Background

Clear cell renal cell carcinoma (ccRCC) is a prevalent urological malignancy associated with a high mortality rate. The lack of a reliable prognostic biomarker undermines the efficacy of its predictive, preventive, and personalized medicine (PPPM/3PM) approach. Immunogenic cell death (ICD) is a specific type of programmed cell death that is tightly associated with anti-cancer immunity. However, the role of ICD in ccRCC remains unclear.

Methods

Based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network (WGCNA) analyses, ICD-related genes were screened at both the single-cell and bulk transcriptome levels. We developed a novel machine learning framework that incorporated 10 machine learning algorithms and their 101 combinations to construct a consensus immunogenic cell death-related signature (ICDRS). ICDRS was evaluated in the training, internal validation, and external validation sets. An ICDRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Multi-omics analysis was performed, including genome, single-cell transcriptome, and bulk transcriptome, to gain a more comprehensive understanding of the prognosis signature. We evaluated the response of risk subgroups to immunotherapy and screened drugs that target specific risk subgroups for personalized medicine. Finally, the expression of ICD-related genes was validated by qRT-PCR.

Results

We identified 131 ICD-related genes at both the single-cell and bulk transcriptome levels, of which 39 were associated with overall survival (OS). A consensus ICDRS was constructed based on a 101-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. ICDRS can also be used to predict the occurrence, development, and metastasis of ccRCC. Multivariate analysis verified it as an independent prognostic factor for OS, progression-free survival (PFS), and disease-specific survival (DSS) of ccRCC. The ICDRS-integrated nomogram provided a quantitative tool in clinical practice. Moreover, we observed distinct biological functions, mutation landscapes, and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. Notably, the immunophenoscore (IPS) score showed a significant difference between risk subgroups, suggesting a better response to immunotherapy in the high-risk group. Potential drugs targeting specific risk subgroups were also identified.

Conclusion

Our study constructed an immunogenic cell death-related signature that can serve as a promising tool for prognosis prediction, targeted prevention, and personalized medicine in ccRCC. Incorporating ICD into the PPPM framework will provide a unique opportunity for clinical intelligence and new management approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00327-3.

Ideal cardiovascular health metrics and life expectancy free of cardiovascular diseases: a prospective cohort study

Objectives

Whether cardiovascular health (CVH) metrics impact longevity with and without cardiovascular diseases (CVDs) has not been well established. This study aimed to investigate the association between CVH metrics and life expectancy in participants free of CVD events. We hypothesized that ideal CVH status was associated with increased life expectancy and assessed the effect of CVH status as a prevention target of longevity in the framework of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

A total of 92,795 participants in the Kailuan study were examined and thereafter followed up until 2020. We considered three transitions (from non-CVD events to incident CVD events, from non-CVD events to mortality, and from CVD events to mortality). The multistate lifetable method was applied to estimate the life expectancy.

Results

During a median follow-up of 13 years, 12,541 (13.51%) deaths occurred. Compared with poor CVH, ideal CVH attenuated the risk of incident CVD events and mortality without CVD events by approximately 58% and 27%, respectively. Women with ideal CVH at age 35 had a 5.00 (3.23-6.77) year longer life expectancy free of CVD events than did women with poor CVH metrics. Among men, ideal CVH was associated with a 6.74 (5.55-7.93) year longer life expectancy free of CVD events.

Conclusion

An ideal CVH status is associated with a lower risk of premature mortality and a longer life expectancy, either in the general population or in CVD patients, which are cost-effective ways for personalized medicine of potential CVD patients. Our findings suggest that the promotion of a higher CVH score or ideal CVH status would result in reduced burdens of CVD events and extended disease-free life expectancy, which offered an accurate prediction for primary care following the concept of PPPM/3PM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00322-8.

Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation

Background

Gastric cancer (GC) represents a malignancy with a multi-factorial combination of genetic, environmental, and microbial factors. Targeting lysosomes presents significant potential in the treatment of numerous diseases, while lysosome-related genetic markers for early GC detection have not yet been established, despite implementing this process by assembling artificial intelligence algorithms would greatly break through its value in translational medicine, particularly for immunotherapy.

Methods

To this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. We optimized an AI-based predictor for GC diagnosis. Then, the reliability of the model was initially confirmed by the results of enrichment analyses currently in use. And the immunological implications of the genes comprising the predictor was explored and response of GC patients were evaluated to immunotherapy and chemotherapy. Further, we performed systematic laboratory work to evaluate the build-up of the central genes, both at the expression stage and at the functional aspect, by which we could also demonstrate the reliability of the model to guide cancer immunotherapy.

Results

Eight lysosomal-related genes were selected for predictive model construction based on the inclusion of RMSE as a reference standard and RF algorithm for ranking, namely ADRB2, KCNE2, MYO7A, IFI30, LAMP3, TPP1, HPS4, and NEU4. Taking into account accuracy, precision, recall, and F1 measurements, a preliminary determination of our study was carried out by means of applying the extra tree and random forest algorithms, incorporating the ROC-AUC value as a consideration, the Extra Tree model seems to be the optimal option with the AUC value of 0.92. The superiority of diagnostic signature is also reflected in the analysis of immune features.

Conclusion

In summary, this study is the first to integrate around 20 mainstream ML algorithms to construct an AI-based diagnostic predictor for gastric cancer based on lysosomal-related genes. This model will facilitate the accurate prediction of early gastric cancer incidence and the subsequent risk assessment or precise individualized immunotherapy, thus improving the survival prognosis of GC patients.

Development and validation of a routine blood parameters-based model for screening the occurrence of retinal detachment in high myopia in the context of PPPM

Background/aims

Timely detection and treatment of retinal detachment (RD) could effectively save vision and reduce the risk of progressing visual field defects. High myopia (HM) is known to be associated with an increased risk of RD. Evidently, it should be clearly discriminated the individuals with high or low risk of RD in patients with HM. By using multi-parametric analysis, risk assessment, and other techniques, it is crucial to create cutting-edge screening programs that may be utilized to improve population eye health and develop person-specific, cost-effective preventative, and targeted therapeutic measures. Therefore, we propose a novel, routine blood parameters-based prediction model as a screening program to help distinguish who should offer detailed ophthalmic examinations for RD diagnosis, prevent visual field defect progression, and provide personalized, serial monitoring in the context of predictive, preventive, and personalized medicine (PPPM/3 PM).

Methods

This population-based study included 20,870 subjects (HM = 19,284, HMRD = 1586) who underwent detailed routine blood tests and ophthalmic evaluations. HMRD cases and HM controls were matched using a nested case-control design. Then, the HMRD cases and HM controls were randomly assigned to the discovery cohort, validation cohort 1, and validation cohort 2 maintaining a 6:2:2 ratio, and other subjects were assigned to the HM validation cohort. Receiver operating characteristic curve analysis was performed to select feature indexes. Feature indexes were integrated into seven algorithm models, and an optimal model was selected based on the highest area under the curve (AUC) and accuracy.

Results

Six feature indexes were selected: lymphocyte, basophil, mean platelet volume, platelet distribution width, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. Among the algorithm models, the algorithm of conditional probability (ACP) showed the best performance achieving an AUC of 0.79, a diagnostic accuracy of 0.72, a sensitivity of 0.71, and a specificity of 0.74 in the discovery cohort. A good performance of the ACP model was also observed in the validation cohort 1 (AUC = 0.81, accuracy = 0.72, sensitivity = 0.71, specificity = 0.73) and validation cohort 2 (AUC = 0.77, accuracy = 0.71, sensitivity = 0.70, specificity = 0.72). In addition, ACP model calibration was found to be good across three cohorts. In the HM validation cohort, the ACP model achieved a diagnostic accuracy of 0.81 for negative classification.

Conclusion

We have developed a routine blood parameters-based model with an ACP algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring and predicting the occurrence of RD in HM and can facilitate the prevention of HM progression to RD. According to the current study, routine blood measures are essential in patient risk classification, predictive diagnosis, and targeted therapy. Therefore, for high-risk RD persons, novel screening programs and prompt treatment plans are essential to enhance individual outcomes and healthcare offered to the community with HM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00319-3.

J-shape relationship between normal fasting plasma glucose and risk of type 2 diabetes in the general population: results from two cohort studies

BACKGROUND

Previous studies have reported that high fasting plasma glucose (FPG), even that within the normal range, is associated with the risk of type 2 diabetes (T2D). Nevertheless, these findings are limited to specific populations. Thus, studies in the general population are imperative.

METHODS

This study included two cohorts comprising 204 640 individuals who underwent physical examinations at the Rich Healthcare Group present at 32 locations in 11 cities of China from 2010 to 2016 and 15 464 individuals who underwent physical tests at the Murakami Memorial Hospital in Japan. Cox regression, restricted cubic spline (RCS), Kaplan-Meier (KM) curves, and subgroup analysis were used to determine the relationship between FPG and T2D. Receiver operating characteristic (ROC) curves were used to evaluate the predictive power of FPG for T2D.

RESULTS

The mean age of the 220 104 participants (204 640 Chinese and 15 464 Japanese participants) was 41.8 years (41.7 years for the Chinese and 43.7 years for the Japanese participants). During follow-up, 2611 individuals developed T2D (2238 Chinese and 373 Japanese participants). The RCS demonstrated a J-shaped relationship between FPG and T2D risk, with inflexion points of 4.5 and 5.2 for the Chinese and Japanese populations, respectively. Multivariate-adjusted hazard ratio (HR) was 7.75 for FPG and T2D risk after the inflexion point (7.3 for Chinese and 21.13 for Japanese participants).

CONCLUSIONS

In general Chinese and Japanese populations, the normal baseline FPG range showed a J-shaped relationship with the risk of T2D. Baseline FPG levels help identify individuals at high risk of T2D and may enable early primary prevention to improve their outcomes.

Short communication: unique metabolic signature of proliferative retinopathy in the tear fluid of diabetic patients with comorbidities - preliminary data for PPPM validation

Type 2 diabetes (T2DM) defined as the adult-onset type that is primarily not insulin-dependent, comprises over 95% of all diabetes mellitus (DM) cases. According to global records, 537 million adults aged 20-79 years are affected by DM that means at least 1 out of 15 persons. This number is projected to grow by 51% by the year 2045. One of the most common complications of T2DM is diabetic retinopathy (DR) with an overall prevalence over 30%. The total number of the DR-related visual impairments is on the rise, due to the growing T2DM population. Proliferative diabetic retinopathy (PDR) is the progressing DR and leading cause of preventable blindness in working-age adults. Moreover, PDR with characteristic systemic attributes including mitochondrial impairment, increased cell death and chronic inflammation, is an independent predictor of the cascading DM-complications such as ischemic stroke. Therefore, early DR is a reliable predictor appearing upstream of this "domino effect". Global screening, leading to timely identification of DM-related complications, is insufficiently implemented by currently applied reactive medicine. A personalised predictive approach and cost-effective targeted prevention shortly - predictive, preventive and personalised medicine (PPPM / 3PM) could make a good use of the accumulated knowledge, preventing blindness and other severe DM complications. In order to reach this goal, reliable stage- and disease-specific biomarker panels are needed characterised by an easy way of the sample collection, high sensitivity and specificity of analyses. In the current study, we tested the hypothesis that non-invasively collected tear fluid is a robust source for the analysis of ocular and systemic (DM-related complications) biomarker patterns suitable for differential diagnosis of stable DR versus PDR. Here, we report the first results of the comprehensive ongoing study, in which we correlate individualised patient profiles (healthy controls versus patients with stable D as well as patients with PDR with and without co-morbidities) with their metabolic profiles in the tear fluid. Comparative mass spectrometric analysis performed has identified following metabolic clusters which are differentially expressed in the groups of comparison: acylcarnitines, amino acid & related compounds, bile acids, ceramides, lysophosphatidyl-choline, nucleobases & related compounds, phosphatidyl-cholines, triglycerides, cholesterol esters, and fatty acids. Our preliminary data strongly support potential clinical utility of metabolic patterns in the tear fluid indicating a unique metabolic signature characteristic for the DR stages and PDR progression. This pilot study creates a platform for validating the tear fluid biomarker patterns to stratify T2DM-patients predisposed to the PDR. Moreover, since PDR is an independent predictor of severe T2DM-related complications such as ischemic stroke, our international project aims to create an analytical prototype for the "diagnostic tree" (yes/no) applicable to healthrisk assessment in diabetes care.

Association of IgG N-glycomics with prevalent and incident type 2 diabetes mellitus from the paradigm of predictive, preventive, and personalized medicine standpoint

Objectives

Type 2 diabetes mellitus (T2DM), a major metabolic disorder, is expanding at a rapidly rising worldwide prevalence and has emerged as one of the most common chronic diseases. Suboptimal health status (SHS) is considered a reversible intermediate state between health and diagnosable disease. We hypothesized that the time frame between the onset of SHS and the clinical manifestation of T2DM is the operational area for the application of reliable risk assessment tools, such as immunoglobulin G (IgG) N-glycans. From the viewpoint of predictive, preventive, and personalized medicine (PPPM/3PM), the early detection of SHS and dynamic monitoring by glycan biomarkers could provide a window of opportunity for targeted prevention and personalized treatment of T2DM.

Methods

Case-control and nested case-control studies were performed and consisted of 138 and 308 participants, respectively. The IgG N-glycan profiles of all plasma samples were detected by an ultra-performance liquid chromatography instrument.

Results

After adjustment for confounders, 22, five, and three IgG N-glycan traits were significantly associated with T2DM in the case-control setting, baseline SHS, and baseline optimal health participants from the nested case-control setting, respectively. Adding the IgG N-glycans to the clinical trait models, the average area under the receiver operating characteristic curves (AUCs) of the combined models based on repeated 400 times fivefold cross-validation differentiating T2DM from healthy individuals were 0.807 in the case-control setting and 0.563, 0.645, and 0.604 in the pooled samples, baseline SHS, and baseline optimal health samples of nested case-control setting, respectively, which presented moderate discriminative ability and were generally better than models with either glycans or clinical features alone.

Conclusions

This study comprehensively illustrated that the observed altered IgG N-glycosylation, i.e., decreased galactosylation and fucosylation/sialylation without bisecting GlcNAc, as well as increased galactosylation and fucosylation/sialylation with bisecting GlcNAc, reflects a pro-inflammatory state of T2DM. SHS is an important window period of early intervention for individuals at risk for T2DM; glycomic biosignatures as dynamic biomarkers have the ability to identify populations at risk for T2DM early, and the combination of evidence could provide suggestive ideas and valuable insight for the PPPM of T2DM.

Supplementary information

The online version contains supplementary material available at 10.1007/s13167-022-00311-3.

Global burden of cancers attributable to tobacco smoking, 1990-2019: an ecological study

Aim and background

Identifying risk factors for cancer initiation and progression is the cornerstone of the preventive approach to cancer management and control (EPMA J. 4(1):6, 2013). Tobacco smoking is a well-recognized risk factor for initiation and spread of several cancers. The predictive, preventive, and personalized medicine (PPPM) approach to cancer management and control focuses on smoking cessation as an essential cancer prevention strategy. Towards this end, this study examines the temporal patterns of cancer burden due to tobacco smoking in the last three decades at global, regional, and national levels.

Data and methods

The data pertaining to the burden of 16 cancers attributable to tobacco smoking at global, regional, and national levels were procured from the Global Burden of Disease 2019 Study. Two main indicators, deaths and disability-adjusted life years (DALYs), were used to describe the burden of cancers attributable to tobacco smoking. The socio-economic development of countries was measured using the socio-demographic index (SDI).

Results

Globally, deaths due to neoplasms caused by tobacco smoking increased from 1.5 million in 1990 to 2.5 million in 2019, whereas the age-standardized mortality rate (ASMR) decreased from 39.8/100,000 to 30.6/100,000 and the age-standardized DALY rate (ASDALR) decreased from 948.9/100,000 to 677.3/100,000 between 1990 and 2019. Males accounted for approximately 80% of global deaths and DALYs in 2019. Populous regions of Asia and a few regions of Europe account for the largest absolute burden, whereas countries in Europe and America have the highest age-standardized rates of cancers due to tobacco smoking. In 8 out of 21 regions, there were more than 100,000 deaths due to cancers attributable to tobacco smoking led by East Asia, followed by Western Europe in 2019. The regions of Sub-Saharan Africa (except southern region) had one of the lowest absolute counts of deaths, DALYs, and age-standardized rates. In 2019, tracheal, bronchus, and lung (TBL), esophageal, stomach, colorectal, and pancreatic cancer were the top 5 neoplasms attributable to tobacco smoking, with different burdens in regions as per their development status. The ASMR and ASDALR of neoplasms due to tobacco smoking were positively correlated with SDI, with pairwise correlation coefficient of 0.55 and 0.52, respectively.

Conclusion

As a preventive tool, tobacco smoking cessation has the biggest potential among all risk factors for preventing millions of cancer deaths every year. Cancer burden due to tobacco smoking is found to be higher in males and is positively associated with socio-economic development of countries. As tobacco smoking begins mostly at younger ages and the epidemic is unfolding in several parts of the world, more accelerated efforts are required towards tobacco cessation and preventing youth from entering this addiction. The PPPM approach to medicine suggests that not only personalized and precision medicine must be provided to cancer patients afflicted by tobacco smoking but personalized and targeted preventive solutions must be provided to prevent initiation and progression of smoking.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00308-y.

Diagnostic accuracy of autoverification and guidance system for COVID-19 RT-PCR results

BACKGROUND

To date, most countries worldwide have declared that the pandemic of COVID-19 is over, while the WHO has not officially ended the COVID-19 pandemic, and China still insists on the personalized dynamic COVID-free policy. Large-scale nucleic acid testing in Chinese communities and the manual interpretation for SARS-CoV-2 nucleic acid detection results pose a huge challenge for labour, quality and turnaround time (TAT) requirements. To solve this specific issue while increase the efficiency and accuracy of interpretation, we created an autoverification and guidance system (AGS) that can automatically interpret and report the COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) results relaying on computer-based autoverification procedure and then validated its performance in real-world environments. This would be conductive to transmission risk prediction, COVID-19 prevention and control and timely medical treatment for positive patients in the context of the predictive, preventive and personalized medicine (PPPM).

METHODS

A diagnostic accuracy test was conducted with 380,693 participants from two COVID-19 test sites in China, the Hong Kong Hybribio Medical Laboratory (n = 266,035) and the mobile medical shelter at a Shanghai airport (n = 114,658). These participants underwent SARS-CoV-2 RT-PCR from March 28 to April 10, 2022. All RT-PCR results were interpreted by laboratorians and by using AGS simultaneously. Considering the manual interpretation as gold standard, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were applied to evaluate the diagnostic value of the AGS on the interpretation of RT-PCR results.

RESULTS

Among the 266,035 samples in Hong Kong, there were 16,356 (6.15%) positive, 231,073 (86.86%) negative, 18,606 (6.99%) indefinite, 231,073 (86.86%, negative) no retest required and 34,962 (13.14%, positive and indefinite) retest required; the 114,658 samples in Shanghai consisted of 76 (0.07%) positive, 109,956 (95.90%) negative, 4626 (4.03%) indefinite, 109,956 (95.90%, negative) no retest required and 4702 (4.10%, positive and indefinite) retest required. Compared to the fashioned manual interpretation, the AGS is a procedure of high accuracy [99.96% (95%CI, 99.95-99.97%) in Hong Kong and 100% (95%CI, 100-100%) in Shanghai] with perfect sensitivity [99.98% (95%CI, 99.97-99.98%) in Hong Kong and 100% (95%CI, 100-100%) in Shanghai], specificity [99.87% (95%CI, 99.82-99.90%) in Hong Kong and 100% (95%CI, 99.92-100%) in Shanghai], PPV [99.98% (95%CI, 99.97-99.99%) in Hong Kong and 100% (95%CI, 99.99-100%) in Shanghai] and NPV [99.85% (95%CI, 99.80-99.88%) in Hong Kong and 100% (95%CI, 99.90-100%) in Shanghai]. The need for manual interpretation of total samples was dramatically reduced from 100% to 13.1% and the interpretation time fell from 53 h to 26 min in Hong Kong; while the manual interpretation of total samples was decreased from 100% to 4.1% and the interpretation time dropped from 20 h to 16 min at Shanghai.

CONCLUSIONS

The AGS is a procedure of high accuracy and significantly relieves both labour and time from the challenge of large-scale screening of SARS-CoV-2 using RT-PCR. It should be recommended as a powerful screening, diagnostic and predictive system for SARS-CoV-2 to contribute timely the ending of the COVID-19 pandemic following the concept of PPPM.

A Transcriptomic and Reverse-Engineering Strategy Reveals Molecular Signatures of Arachidonic Acid Metabolism in 12 Cancers

Cancer and arachidonic acid (AA) have important linkages. For example, AA metabolites regulate several critical biological functions associated with carcinogenesis: angiogenesis, apoptosis, and cancer invasion. However, little is known about the comparative changes in metabolite expression of the arachidonic acid pathway (AAP) in carcinogenesis. In this study, we examined transcriptome data from 12 cancers, such as breast invasive carcinoma, colon adenocarcinoma, lung adenocarcinoma, and prostate adenocarcinoma. We also report here a reverse-engineering strategy wherein we estimated metabolic signatures associated with AAP by (1) making deductive inferences through transcriptome-level data extraction, (2) remodeling AA metabolism, and (3) performing a comparative analysis of cancer types to determine the similarities and differences between different cancer types with respect to AA metabolic alterations. We identified 77 AAP gene signatures differentially expressed in cancers and 37 AAP metabolites associated with them. Importantly, the metabolite 15(S)-HETE was identified in almost all cancers, while arachidonate, 5-HETE, PGF2α, 14,15-EET, 8,9-EET, 5,6-EET, and 20-HETE were discovered as other most regulated metabolites. This study shows that the 12 cancers studied herein, although in different branches of the AAP, have altered expression of AAP gene signatures. Going forward, AA related-cancer research generally, and the molecular signatures and their estimated metabolites reported herein specifically, hold broad promise for precision/personalized medicine in oncology as potential therapeutic and diagnostic targets.

Multi-omics and immune cells' profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine

Background

Intensive care unit admission (ICUA) triage has been urgent need for solving the shortage of ICU beds, during the coronavirus disease 2019 (COVID-19) surge. In silico analysis and integrated machine learning (ML) approach, based on multi-omics and immune cells (ICs) profiling, might provide solutions for this issue in the framework of predictive, preventive, and personalized medicine (PPPM).

Methods

Multi-omics was used to screen the synchronous differentially expressed protein-coding genes (SDEpcGs), and an integrated ML approach to develop and validate a nomogram for prediction of ICUA. Finally, the independent risk factor (IRF) with ICs profiling of the ICUA was identified.

Results

Colony-stimulating factor 1 receptor (CSF1R) and peptidase inhibitor 16 (PI16) were identified as SDEpcGs, and each fold change (FC_ij) of CSF1R and PI16 was selected to develop and validate a nomogram to predict ICUA. The area under curve (AUC) of the nomogram was 0.872 (95% confidence interval (CI): 0.707 to 0.950) on the training set, and 0.822 (95% CI: 0.659 to 0.917) on the testing set. CSF1R was identified as an IRF of ICUA, expressed in and positively correlated with monocytes which had a lower fraction in COVID-19 ICU patients.

Conclusion

The nomogram and monocytes could provide added value to ICUA prediction and targeted prevention, which are cost-effective platform for personalized medicine of COVID-19 patients. The log₂fold change (log₂FC) of the fraction of monocytes could be monitored simply and economically in primary care, and the nomogram offered an accurate prediction for secondary care in the framework of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00317-5.

New insight of metabolomics in ocular diseases in the context of 3P medicine

Metabolomics refers to the high-through untargeted or targeted screening of metabolites in biofluids, cells, and tissues. Metabolome reflects the functional states of cells and organs of an individual, influenced by genes, RNA, proteins, and environment. Metabolomic analyses help to understand the interaction between metabolism and phenotype and reveal biomarkers for diseases. Advanced ocular diseases can lead to vision loss and blindness, reducing patients' quality of life and aggravating socio-economic burden. Contextually, the transition from reactive medicine to the predictive, preventive, and personalized (PPPM / 3P) medicine is needed. Clinicians and researchers dedicate a lot of efforts to explore effective ways for disease prevention, biomarkers for disease prediction, and personalized treatments, by taking advantages of metabolomics. In this way, metabolomics has great clinical utility in the primary and secondary care. In this review, we summarized much progress achieved by applying metabolomics to ocular diseases and pointed out potential biomarkers and metabolic pathways involved to promote 3P medicine approach in healthcare.

Multi-probiotic consumption sex-dependently interferes with MSG-induced obesity and concomitant phagocyte pro-inflammatory polarization in rats: Food for thought about personalized nutrition

Epidemic scope which obesity has reached in many countries necessitates shifting the emphasis in medicine from traditional reaction to individualized and personalized prevention. Numerous trials convincingly prove sexual dimorphism of obesity in morbidity, pathophysiology, comorbidity, outcomes and prophylaxis efficacy. Obesity is characterized by chronic systemic low-grade inflammation that creates the preconditions for the emergence of numerous comorbidities. Leading role in the initiation, propagation and resolution of inflammation belongs to tissue resident and circulating phagocytes. The outcome of inflammation largely depends on phagocyte functional polarization, which in turn is governed by environmental stimuli. Gut microbiota (GM), whose disturbances are one of the key pathogenetic features in obesity, substantially affect phagocyte functions and can either aggravate or calm obesity-associated inflammation. Probiotics possess promising physiological functions, including microbiota-restoring and anti-inflammatory traits, that may possibly help prevent obesity. However, sex-specific effects of probiotic supplementation for targeted obesity prevention remain unknown. The aim of the current study was aimed to compare the effect of multi-probiotic preparation used in prophylactic regimen on the adiposity, profile of culturable GM and its short-chain fatty acids as well as on functional profile of phagocytes from different locations in male and female rats with monosodium glutamate (MSG)-induced obesity. Obesity was induced by neonatal MSG injections in male and female rats, who were given the multi-species probiotic during juvenile and adult developmental stages. Culturable fecal and mucosa-associated microbiota of the intestine were examined using selective diagnostic media. Short-chain fatty acid profile in fecal samples was determined by GC-MS. Phagocyte functional profile was evaluated using flow cytometry and colorimetric methods. Probiotic supplementation after the administration of MSG prevented weight gain and fat accumulation, inflammatory phagocyte activation and alterations in GM in female rats. In male MSG-injected rats, probiotic supplementation restricted but did not prevent weight gain and fat deposition, alleviated but did not prevent systemic inflammation, prevented the alterations in GM, but with residual imbalance in the ratio of obligate anaerobic to facultative anaerobic bacteria. Our findings emphasize the necessity of sex-centered approaches to the prophylactic use of probiotics in obesity in the context of predictive preventive and personalized medicine.

Alternatively Spliced Isoforms of MUC4 and ADAM12 as Biomarkers for Colorectal Cancer Metastasis

There is a pertinent need to develop prognostic biomarkers for practicing predictive, preventive and personalized medicine (PPPM) in colorectal cancer metastasis. The analysis of isoform expression data governed by alternative splicing provides a high-resolution picture of mRNAs in a defined condition. This information would not be available by studying gene expression changes alone. Hence, we utilized our prior data from an exon microarray and found ADAM12 and MUC4 to be strong biomarker candidates based on their alternative splicing scores and pattern. In this study, we characterized their isoform expression in a cell line model of metastatic colorectal cancer (SW480 & SW620). These two genes were found to be good prognostic indicators in two cohorts from The Cancer Genome Atlas database. We studied their exon structure using sequence information in the NCBI and ENSEMBL genome databases to amplify and validate six isoforms each for the ADAM12 and MUC4 genes. The differential expression of these isoforms was observed between normal, primary and metastatic colorectal cancer cell lines. RNA-Seq analysis further proved the differential expression of the gene isoforms. The isoforms of MUC4 and ADAM12 were found to change expression levels in response to 5-Fluorouracil (5-FU) treatment in a dose-, time- and cell line-dependent manner. Furthermore, we successfully detected the protein isoforms of ADAM12 and MUC4 in cell lysates, reflecting the differential expression at the protein level. The change in the mRNA and protein expression of MUC4 and ADAM12 in primary and metastatic cells and in response to 5-FU qualifies them to be studied as potential biomarkers. This comprehensive study underscores the importance of studying alternatively spliced isoforms and their potential use as prognostic and/or predictive biomarkers in the PPPM approach towards cancer.

Clarifying sleep characteristics and analyzing risk factors of sleep disorders to promote a predictive, preventive, and personalized medicine in patients with burn scars

Purpose

This study assessed sleep quality in patients with burn scars and investigated risk factors of sleep disorders to guide clinical therapy. From the strategy of predictive, preventive, and personalized medicine (PPPM/3PM), we proposed that risk assessment based on clinical indicators could prompt primary prediction, targeted prevention, and personalized interventions to improve the management of sleep disorders present in patients with burn scars.

Methods

This retrospective study recruited patients with burn scars and healthy volunteers from the Shanghai Burn Treatment Center between 2017 and 2022. Relevant information and data, including demographic characteristics, scar evaluation, and sleep quality, were obtained through the hospital information system, classical scar scale, and self-report questionnaires. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and monitored using a cardiopulmonary-coupled electrocardiograph. Pain and pruritus were assessed using the visual analog scale (VAS). Scar appearance was assessed using the modified Vancouver scar scale (mVSS).

Results

The sample was comprised of 128 hypertrophic scar (HS) patients, with 61.7% males, a mean age of 41.1 ± 11.6 years, and burn area of 46.2 ± 27.9% total body surface area (TBSA). Patients with PSQI ≥ 7 accounted for 76.6%, and the global PSQI score was 9.4 ± 4.1. Objective sleep data showed that initial enter deep sleep time, light sleep time, awakening time, light sleep efficiency, and sleep apnea index were higher but deep sleep time, sleep efficiency, and deep sleep efficiency were lower in HS patients than that in healthy controls. Preliminary univariate analysis showed that age, hyperplasia time of scar, narrow airway, microstomia, VAS for pain and pruritus, and mVSS total (comprised of pigmentation, vascularity, height and pliability) were associated with the PSQI score (p < 0.1). Multivariable linear regression showed narrow airway, VAS for pain and pruritus, and mVSS specifically height, were the risk factors for PSQI score (p < 0.1).

Conclusions

This study model identified that narrow airway, pain, pruritus and scar appearance specifically height may provide excellent predictors for sleep disorders in HS patients. Our results provided a basis for the predictive diagnostics, targeted prevention, and individualized therapy of somnipathy predisposition and progression of HS patients in the setting of PPPM/3PM health care system, which contributed to a paradigm shift from reactive cure to advanced therapy.

Identification of Critical Molecular Factors and Side Effects Underlying the Response to Thalicthuberine in Prostate Cancer: A Systems Biology Approach

Background

Uncontrolled mitosis of cancer cells and resistance cells to chemotherapy drugs are the challenges of prostate cancer. Thalicthuberine causes a mitotic arrest and a reduction of the effects of drug resistance, resulting in cell death. In this study, we applied bioinformatics and computational biology methods to identify functional pathways and side effects in response to Thalicthuberine in prostate cancer patients.

Methods

Microarray data were retrieved from Gene Expression Omnibus (GEO), and protein-protein interactions and gene regulatory networks were constructed, using the Cytoscape software. The critical genes and molecular mechanisms in response to Thalicthuberine and its side effects were identified, using the Cytoscape software and WebGestalt server, respectively. Finally, GEPIA2 was used to predict the relationship between critical genes and prostate cancer.

Results

The POLQ, EGR1, CDKN1A, FOS, MDM2, CDC20, CCNB1, and CCNB2 were identified as critical genes in response to this drug. The functional mechanisms of Thalicthuberine include a response to oxygen levels, toxic substances and immobilization stress, cell cycle regulation, regeneration, the p53 signaling pathway, the action of the parathyroid hormone, and the FoxO signaling pathway. Besides, the drug has side effects including muscle cramping, abdominal pains, paresthesia, and metabolic diseases.

Conclusion

Our model suggested newly predicted crucial genes, molecular mechanisms, and possible side effects of this drug. However, further studies are required.

In vitro effect of low-fluoride toothpaste supplemented with sodium trimetaphosphate, xylitol, and erythritol on enamel demineralization

OBJECTIVE

Regular use of toothpaste with fluoride (F) concentrations of ≥ 1000 ppm has been shown to contribute to reducing caries increment. However, when used by children during the period of dental development, it can lead to dental fluorosis. In this study, we aimed to evaluate the in vitro effect of a toothpaste formulation with reduced fluoride (F) concentration (200 ppm) supplemented with sodium trimetaphosphate (TMP: 0.2%), Xylitol (X:16%), and Erythritol (E: 4%) on dental enamel demineralization.

METHODOLOGY

Bovine enamel blocks were selected according to initial surface hardness (SHi) and then divided into seven experimental toothpaste groups (n=12). These groups included 1) no F-TMP-X-E (Placebo); 2) 16% Xylitol and 4% Erythritol (X-E); 3) 16% Xylitol, 4% Erythritol and 0.2%TMP (X-E-TMP); 4) 200 ppm F (no X-E-TMP: (200F)); 5) 200 ppm F and 0.2% TMP (200F-TMP); 200 ppm F, 16% Xylitol, 4% Erythritol, and 0.2% TMP (200F-X-E-TMP); and 7) 1,100 ppm F (1100F). Blocks were individually treated 2×/day with slurries of toothpastes and subjected to a pH cycling regimen for five days (DES: 6 hours and RE: 18 hours). Then, the percentage of surface hardness loss (%SH), integrated loss of subsurface hardness (ΔKHN), fluoride (F), calcium (Ca), and phosphorus (P) in enamel were determined. The data were analyzed by ANOVA (1-criterion) and the Student-Newman-Keuls test (p<0.001).

RESULTS

We found that the 200F-X-E-TMP treatment reduced %SH by 43% compared to the 1100F treatments (p<0.001). The ΔKHN was ~ 65% higher with 200F-X-E-TMP compared to 1100F (p<0.001). The highest concentration of F in enamel was observed on the 1100F treatment (p<0.001). The 200F-X-E-TMP treatment promote higher increase of Ca and P concentration in the enamel (p<0.001).

CONCLUSION

The association of 200F-X-E-TMP led to a significant increase of the protective effect on enamel demineralization compared to the 1100F toothpaste.

Identification and characterization of a novel molecular classification incorporating oxidative stress and metabolism-related genes for stomach adenocarcinoma in the framework of predictive, preventive, and personalized medicine

BACKGROUND

Stomach adenocarcinoma (STAD) is one of the primary contributors to deaths that are due to cancer globally. At the moment, STAD does not have any universally acknowledged biological markers, and its predictive, preventive, and personalized medicine (PPPM) remains sufficient. Oxidative stress can promote cancer by increasing mutagenicity, genomic instability, cell survival, proliferation, and stress resistance pathways. As a direct and indirect result of oncogenic mutations, cancer depends on cellular metabolic reprogramming. However, their roles in STAD remain unclear.

METHOD

743 STAD samples from GEO and TCGA platforms were selected. Oxidative stress and metabolism-related genes (OMRGs) were acquired from the GeneCard Database. A pan-cancer analysis of 22 OMRGs was first performed. We categorized STAD samples by OMRG mRNA levels. Additionally, we explored the link between oxidative metabolism scores and prognosis, immune checkpoints, immune cell infiltration, and sensitivity to targeted drugs. A series of bioinformatics technologies were employed to further construct the OMRG-based prognostic model and clinical-associated nomogram.

RESULTS

We identified 22 OMRGs that could evaluate the prognoses of patients with STAD. Pan-cancer analysis concluded and highlighted the crucial part of OMRGs in the appearance and development of STAD. Subsequently, 743 STAD samples were categorized into three clusters with the enrichment scores being C2 (upregulated) > C3 (normal) > C1 (downregulated). Patients in C2 had the lowest OS rate, while C1 had the opposite. Oxidative metabolic score significantly correlates with immune cells and immune checkpoints. Drug sensitivity results reveal that a more tailored treatment can be designed based on OMRG. The OMRG-based molecular signature and clinical nomogram have good accuracy for predicting the adverse events of patients with STAD. Both transcriptional and translational levels of ANXA5, APOD, and SLC25A15 exhibited significantly higher in STAD samples.

CONCLUSION

The OMRG clusters and risk model accurately predicted prognosis and personalized medicine. Based on this model, high-risk patients might be identified in the early stage so that they can receive specialized care and preventative measures, and choose targeted drug beneficiaries to deliver individualized medical services. Our results showed oxidative metabolism in STAD and led to a new route for improving PPPM for STAD.

Semi-automated socio-anthropologic analysis of the medical discourse on rheumatoid arthritis: Potential impact on public health

BACKGROUND

The debilitating effects of noncommunicable diseases (NCDs) and the accompanying chronic inflammation represent a significant obstacle for the sustainability of our development, with efforts spreading worldwide to counteract the diffusion of NCDs, as per the United Nations Sustainable Development Goals (SDG 3). In fact, despite efforts of varied intensity in numerous directions (from innovations in biotechnology to lifestyle modifications), the incidence of NCDs remains pandemic. The present work wants to contribute to addressing this major concern, with a specific focus on the fragmentation of medical approaches, via an interdisciplinary analysis of the medical discourse, i.e. the heterogenous reporting that biomedical scientific literature uses to describe the anti-inflammatory therapeutic landscape in NCDs. The aim is to better capture the roots of this compartmentalization and the power relations existing among three segregated pharmacological, experimental and unstandardized biomedical approaches to ultimately empower collaboration beyond medical specialties and possibly tap into a more ample and effective reservoir of integrated therapeutic opportunities.

METHOD

Using rheumatoid arthritis (RA) as an exemplar disease, twenty-eight articles were manually translated into a nine-dimensional categorical variable of medical socio-anthropological relevance, relating in particular (but not only) to legitimacy, temporality and spatialization. This digitalized picture (9 x 28 table) of the medical discourse was further analyzed by simple automated learning approaches to identify differences and highlight commonalities among the biomedical categories.

RESULTS

Interpretation of these results provides original insights, including suggestions to: empower scientific communication between unstandardized approaches and basic biology; promote the repurposing of non-pharmacological therapies to enhance robustness of experimental approaches; and align the spatial representation of diseases and therapies in pharmacology to effectively embrace the systemic approach promoted by modern personalized and preventive medicines. We hope this original work can expand and foster interdisciplinarity among public health stakeholders, ultimately contributing to the achievement of SDG3.

mHealth App Barriers, Usability, and Personalization: A Cross-Sectional Study from Egypt and Saudi Arabia

Mobile health (mHealth) has emerged as a substantial segment of eHealth. Herein, we conducted a cross-sectional study to investigate mHealth app barriers, usability, and personalization in Egypt and Saudi Arabia. We used a Google survey to recruit participants from both countries between the 15th of September and the 15th of October 2022. Among 299 participants (247 from Egypt and 52 from Saudi Arabia), aged ≥ 18 years, 27.4% reported mHealth app use. In the age-, sex-, and country-adjusted regression models, age > 25 years: OR (95% CI) = 1.98 (1.11, 3.54), residing in Saudi Arabia: OR (95% CI) = 4.33 (2.22, 8.48), and physical activity: OR (95% CI) = 2.53 (1.44, 4.44) were associated with mHealth app use. The main mHealth app purposes were lifestyle promotion (35.4%), diet and nutrition (30.5%), and administrative services (13.4%). On a scale from 20 to 100, mHealth app usability scores were 46.3% (20−40), 7.3% (41−60), 31.7% (61−80), and 14.7% (81−100). According to 93.9% of users, mHealth app features were modifiable to meet personal health goals, while 37% stated that mHealth apps helped them set new personal health goals. In conclusion, age, residing in Saudi Arabia (compared to Egypt), and physical activity were positively associated with mHealth app use. mHealth app feature personalization and helping users set new personal health goals were largely reported, suggesting that mHealth has the potential to help put personalized healthcare into practice.

Body mass index-based predictions and personalized clinical strategies for colorectal cancer in the context of PPPM

Currently colorectal cancer (CRC) is the third most prevalent cancer worldwide. Body mass index (BMI) is frequently used in CRC screening and risk assessment to quantitatively evaluate weight. However, the impact of BMI on clinical strategies for CRC has received little attention. Within the framework of the predictive, preventive, and personalized medicine (3PM/PPPM), we hypothesized that BMI stratification would affect the primary, secondary, and tertiary care options for CRC and we conducted a critical evidence-based review. BMI dynamically influences CRC outcomes, which helps avoiding adverse treatment effects. The outcome of surgical and radiation treatment is adversely affected by overweight (BMI ≥ 30) or underweight (BMI < 20). A number of interventions, such as enhanced recovery after surgery and robotic surgery, can be applied to CRC at all levels of BMI. BMI-controlling modalities such as exercise, diet control, nutritional therapy, and medications may be potentially beneficial for patients with CRC. Patients with overweight are advised to lose weight through diet, medication, and physical activity while patients suffering of underweight require more focus on nutrition. BMI assists patients with CRC in better managing their weight, which decreases the incidence of adverse prognostic events during treatment. BMI is accessible, noninvasive, and highly predictive of clinical outcomes in CRC. The cost-benefit of the PPPM paradigm in developing countries can be advanced, and the clinical benefit for patients can be improved with the promotion of BMI-based clinical strategy models for CRC.