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

IgG N-glycosylation contributes to different severities of insulin resistance: implications for 3P medical approaches

Background

Reliable biomarkers capturing immunometabolic processes in insulin resistance (IR) remain limited. IgG N-glycosylation modulates immune responses and reflects metabolic disorders, yet its role in IR remains unclear. This study investigated its potential for early detection, risk stratification, and targeted prevention within the framework of predictive, preventive, and personalised medicine (PPPM/3PM).

Methods

A total of 313 participants were categorized into three groups based on the homeostatic model assessment for insulin resistance (HOMA-IR): insulin-sensitive (HOMA-IR < 2.69 without diabetes, n = 75), mild IR (HOMA-IR ≥ 2.69 without diabetes, n = 155), and severe IR (HOMA-IR ≥ 2.69 with type 2 diabetes, n = 83). Canonical correlation analysis was conducted to explore the overall relationship between IgG N-glycosylation and IR-related inflammation, indicated by tumour necrosis factor-α, interleukin- 6, C-reactive protein, and adiponectin. Mediation analysis was performed to evaluate the effect of IgG N-glycans on IR. Ordinal logistic regression was used to assess the association between IgG N-glycans and IR severity, with discriminative power evaluated using receiver operating characteristic curves.

Results

Pro-inflammatory IgG N-glycoforms, characterized by reduced sialylation and galactosylation, along with increased bisecting N-acetylglucosamine, were observed as IR severity increased. IgG N-glycosylation significantly correlated with inflammatory markers in the insulin-sensitive (r = 0.599, p < 0.05), mild (r = 0.461, p < 0.05), and severe (r = 0.666, p < 0.01) IR groups. IgG N-glycosylation significantly influenced IR (β = 0.406) partially via modulation of inflammation. Increased glycoforms FA2[6]G1 (OR: 0.86, 95% CI: 0.78-0.96) and A2G2S2 (OR: 0.88, 95% CI: 0.82-0.94) were associated with a lower IR risk, with respective area under the curves (AUCs) of 0.752, 0.683, and 0.764 for the insulin sensitive, mild, and severe IR groups.

Conclusions

IgG N-glycosylation contributes to IR by modulating inflammatory responses. Glycoforms FA2[6]G1 and A2G2S2 emerge as protective biomarkers, offering potential for predicting and preventing IR through primary prevention strategies within the PPPM framework.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-025-00410-x.

Vision transformer-based stratification of pre/diabetic and pre/hypertensive patients from retinal photographs for 3PM applications

Objective

Diabetes and hypertension pose significant health risks, especially when poorly managed. Retinal evaluation though fundus photography can provide non-invasive assessment of these diseases, yet prior studies focused on disease presence, overlooking control statuses. This study evaluated vision transformer (ViT)-based models for assessing the presence and control statuses of diabetes and hypertension from retinal images.

Methods

ViT-based models with ResNet-50 for patch projection were trained on images from the UK Biobank (n = 113,713) and Singapore Epidemiology of Eye Diseases study (n = 17,783), and externally validated on the Singapore Prospective Study Programme (n = 7,793) and the Beijing Eye Study (n = 6064). Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) for multiple tasks: detecting disease, identifying poorly controlled and well-controlled cases, distinguishing between poorly and well-controlled cases, and detecting pre-diabetes or pre-hypertension.

Results

The models demonstrated strong performance in detecting disease presence, with AUROC values of 0.820 for diabetes and 0.781 for hypertension in internal testing. External validation showed AUROCs ranging from 0.635 to 0.755 for diabetes, and 0.727 to 0.832 for hypertension. For identifying poorly controlled cases, the performance remained high with AUROCs of 0.871 (internal) and 0.655-0.851 (external) for diabetes, and 0.853 (internal) and 0.792-0.915 (external) for hypertension. Detection of well-controlled cases also yielded promising results for diabetes (0.802 [internal]; 0.675-0.838 [external]), and hypertension (0.740 [internal] and 0.675-0.807 [external]). In distinguishing between poorly and well-controlled disease, AUROCs were more modest with 0.630 (internal) and 0.512-0.547 (external) for diabetes, and 0.651 (internal) and 0.639-0.683 (external) for hypertension. For pre-disease detection, the models achieved AUROCs of 0.746 (internal) and 0.523-0.590 (external) for pre-diabetes, and 0.669 (internal) and 0.645-0.679 (external) for pre-hypertension.

Conclusion

ViT-based models show promise in classifying the presence and control statuses of diabetes and hypertension from retinal images. These findings support the potential of retinal imaging as a tool in primary care for opportunistic detection of diabetes and hypertension, risk stratification, and individualised treatment planning. Further validation in diverse clinical settings is warranted to confirm practical utility.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-025-00412-9.

Advancing predictive, preventive, and personalized medicine in eyelid diseases: a concerns-based and expandable screening system through structural dissection

Background/aims

Early recognition of eyelid morphological abnormalities was crucial, as untreated conditions could lead to blinding complications. An eyelid screening system that could provide both anatomical and pathological information was essential for formulating personalized treatment strategies. This study aimed to develop a clinically concerns-based framework capable of identifying common eyelid diseases requiring further intervention by evaluating individual anatomical and pathological changes. This approach would enhance individualized and efficient prevention, while supporting targeted treatment strategies.

Methods

The eyelid disorder screening system, Eyetome, was developed based on a morphological atlas and comprised four modules designed to identify 14 common eyelid disorders and pathological changes. A total of 6180 eye patches were analyzed to extract anatomical and pathological features. The performance of Eyetome was evaluated using average accuracy (aACC) and F1 score, with comparisons made against traditional models and ophthalmologists. To assess the system's expandability, an additional test was conducted in a multimorbidity scenario.

Results

Eyetome demonstrated high performance in recognizing single diseases, achieving an aACC of 98.83% and an F1 score of 0.93. The system outperformed classic models, with an aACC of 98.83% compared to 96.72% for Desnet101 and 97.59% for Vit. Additionally, Eyetome's aACC exceeded that of a junior ophthalmologist (JO) (97.11%) and was comparable to a senior ophthalmologist (SO) (98.69%). In the extended multimorbidity dataset, Eyetome maintained robust performance with an accuracy of 97.97%, surpassing JO (95.47%) and closely matching SO (97.81%).

Conclusions

This study developed a clinical concerns-based system for screening and monitoring eyelid disorders, aimed at supporting predictive diagnosis, preventing diseases progression, and facilitating more effective, patient-centered treatment of common eyelid disorders, aligning with the principles of predictive, preventive, and personalized medicine (PPPM/3PM). The system's interpretability, scalability, and user-friendly data acquisition design could further enhance its acceptance among both doctors and patients, facilitating the shift from reactive medicine to proactive precision medicine.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-025-00401-y.

Beyond hormones: 3PM approach to vaginal microbiota dynamics in postmenopausal women

Menopause marks a critical transition characterized by ceased ovarian function and declining estrogen levels, affecting multiple systems with vasomotor symptoms and genitourinary syndrome of menopause (GSM). Recent evidence shows vaginal microbiota undergoes significant alterations during menopause, influencing GSM severity. This comprehensive review examined vaginal microbiota dynamics in postmenopausal women through Predictive, Preventive, and Personalized Medicine (3PM/PPPM), revealing characteristic shifts-increased alpha diversity, reduced Lactobacillus dominance, and transitions toward non-Lactobacillus species-that serve as potential predictive biomarkers for the menopausal state, premature ovarian insufficiency, and GSM symptoms. The analysis evaluated microbiota-based risk stratification strategies for vaginal dysbiosis and demonstrated the effectiveness of both hormonal interventions (systemic/local estrogen, tibolone, ospemifene) and non-hormonal alternatives (probiotics, energy-based devices, pessary) in normalizing microbiota composition and improving vaginal health. The application of PPPM/3PM transformed menopausal healthcare from reactive to proactive precision-based care by establishing microbiota-based biomarkers that predict health risks, enable early targeted interventions tailored to specific microbiota profiles, and guide personalized treatment approaches based on individual microbial compositions. While this paradigm shift significantly advances gynecological medicine, research gaps remain in validating baseline microbiota signatures as predictive biomarkers and establishing standardized screening protocols. Further studies are needed to validate interventions such as probiotics and prebiotics, optimizing strain selection for personalized, evidence-based preventive and therapeutic strategies. Developing standardized yet personalized protocols to restore a balanced vaginal microbiome could help alleviate menopause-related symptoms. Advancing microbiota-based personalized therapeutic approaches is crucial to enhancing the quality of life for postmenopausal women through targeted and individualized vaginal health management.

Virtual reality solution to promote adapted physical activity in older adults: outcomes from VR2Care project exploratory study

Background

Insufficient physical activity is one of the leading risk factors for death worldwide. Regular exercise can improve physical performance and quality of life, reduce the risks of falls and depressive symptoms, and reduce the likelihood of cognitive decline in older adults. Virtual reality (VR) and serious games (SG) are promising tools to improve physical and cognitive functioning. As part of the VR2Care project activities, four pilot sites explored the capabilities of the VR environment in a remote psychomotor training with SG and a hybrid approach with local groups of older adults performing physical activity.

Objective

The present study aimed to explore and measure the impact on older adults' quality of life and physical activity of using VR2Care solution and the level of usability, satisfaction and acceptance.

Methods

The study is a mixed method study, using qualitative and quantitative surveys to evaluate quality of life and physical activity of older users, and usability, satisfaction and acceptance of the solution. The data collection is a mix of investigator site data entry and users' self-reported data through the solutions or through online and paper-based means. Data were collected at baseline and after a follow-up of 6 weeks. Data are expressed as mean ± standard deviation (SD) unless otherwise stated. Within the group, baseline to end of observation differences were assessed by paired sample t-test. A p = 0.05 was considered significant.

Results

No significant improvements in quality of life and physical activity were found. Little improvement, although not significant, in physical activity was found, comparing the Total MET average value of users who participated in phase I and II, therefore using SmartAL and Rehability. Little improvement, although not significant, in physical activity applies in ≥76 population. Users' feedback on usability, satisfaction and acceptance of VR2Care is generally positive. VR2Care was appreciated mostly for its usefulness in managing physical activity and the capacity to influence the consistency of attending physical activity sessions as prescribed by doctors.

Conclusion

Our results suggest that randomized controlled trial will be needed to assess correlations between specific features of the solution and health outcomes.

Multi-omics identification of a polyamine metabolism related signature for hepatocellular carcinoma and revealing tumor microenvironment characteristics

Background

Accumulating evidence indicates that elevated polyamine levels are closely linked to tumor initiation and progression. However, the precise role of polyamine metabolism in hepatocellular carcinoma (HCC) remains poorly understood.

Methods

We conducted differential expression analysis on bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to identify 65 polyamine metabolism-related genes. By employing unsupervised consensus clustering, AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network analysis (WGCNA), we identified polyamine metabolism-related genes at both the bulk RNA-seq and single-cell RNA-seq (scRNA-seq) levels. Utilizing 101 machine learning algorithms, we constructed a polyamine metabolism-related signature (PMRS) and validated its predictive power across training, testing, and external validation cohorts. Additionally, we developed a prognostic nomogram model by integrating PMRS with clinical variables. To explore immune treatment sensitivity, we assessed tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) score, mutation frequency, and immune checkpoint genes expression. Immune cell infiltration was analyzed using the CIBERSORT algorithm. Finally, RT-qPCR experiments were conducted to validate the expression of key genes.

Results

Using 101 machine learning algorithms, we established a polyamine metabolism-related signature comprising 9 genes, which exhibited strong prognostic value for HCC patients. Further analysis revealed significant differences in clinical features, biological functions, mutation profiles, and immune cell infiltration between high-risk and low-risk groups. Notably, TIDE analysis and immune phenotype scoring (IPS) demonstrated distinct immune treatment sensitivities between the two risk groups. RT-qPCR validation confirmed that these 9 genes were highly expressed in normal cells but significantly downregulated in tumor cells.

Conclusions

Our study developed a polyamine metabolism-based prognostic risk signature for HCC, which may provide valuable insights for personalized treatment strategies in HCC patients.

Medical Biorepositories of South Africa: Establishing a Medical Biorepository Network in South Africa to Advance Health Research

Biobanking is crucial for advancing medical research and personalized medicine, offering high-quality biospecimens for studies on biomarkers, drug development, and diagnostics. Despite its global potential, challenges such as fragmented governance and varying standards hinder biorepository collaboration, particularly in South Africa (SA). A unified national biobank network could enhance research and healthcare by improving biospecimen access, ethical governance, and collaboration. Global biobank networks offer models for standardization, data sharing, and international cooperation. SA can benefit from these models by creating a centralized biobank platform, promoting capacity building, and fostering regional and global partnerships. To address the challenges SA faces regarding biobanking, the Medical Biobanks Cluster established a network named Medical Biorepositories of SA (MBirSA), which seeks to build a cohesive network of medical biorepositories in SA. Through this network, it plans to foster an inclusive culture of biospecimen and data protocol harmonization, while encouraging adherence to legal, ethical, and quality best practices and standards. The network aims to bring stakeholders together, increasing visibility and transparency, and encouraging sector-wide collaboration. MBirSA also aims to offer training to build capacity in global best practices, aid in the development of dependable biorepository infrastructure, and promote research partnerships to enhance healthcare advancements.

Physical activity, cardiovascular disease, and mortality across obesity levels

Aims

High physical activity (PA) is associated with decreased risk of cardiovascular disease (CVD) and mortality. However, whether PA can be sufficient to reduce the risk of CVD and mortality contributing to adiposity remains unclear. From the standpoint of predictive, preventive, and personalized medicine (PPPM/3PM), joint assessment of PA and adiposity provides novel insights for individual risk assessment, targeted prevention, and personalized intervention of CVD.

Methods

This prospective cohort study included 92,931 participants in the Kailuan study in Tangshan, followed between the years 2006 and 2020. Adiposity was assessed by body mass index (BMI) and waist circumference (WC). The CVD incidence and all-cause mortality associated with 3 PA levels (low, medium, and high PA) were analyzed by applying Cox regression models to different adiposity subgroups.

Results

After a median follow-up period of 14.02 years, 9997 incident CVD cases and 12,586 deaths occurred. Surprisingly, low PA and lean body mass were at a lower risk for CVD than other phenotypes. Participants with high PA still had a 35% higher CVD risk from obesity (hazard ratio (HR) BMI: 1.35, 95% confidence interval (CI): 1.18-1.54) and a 10% higher CVD risk from central obesity (HRcentral obesity: 1.10, 95% CI: 1.00-1.21) than those with lean. However, only in obese individuals, high PA has a protective effect on CVD (HR: 0.78, 95% CI: 0.64-0.95). Overall obesity and high PA were not associated with increased risk of all-cause mortality, whereas high PA could not attenuate mortality risk associated with central obesity.

Conclusion

High PA did not attenuate the risk of CVD associated with adiposity compared with lean body mass among the Chinese population, whereas the combination of high PA and healthy WC might improve healthy aging and longevity. In addition, this study revealed the importance of maintaining muscle health in obese individuals via PA or other ways. It provides a novel strategy for mitigating the risk of CVD by exercising intervention or maintaining body mass, thereby enhancing effective prevention and targeted intervention.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-025-00397-5.

Identifying Specificity Protein 2 as a key marker for diabetic encephalopathy in the context of predictive, preventive, and personalized medicine

Background

Transcription factor specificity protein (SP2) regulates various cellular functions, including cell division, proliferation, invasion, metastasis, differentiation, and death; however, its role has not been studied in prominent medical conditions including diabetic encephalopathy (DE). Therefore, this study addressed its physiological function in the context of DE to also better characterize its possible use in the context of predictive, preventive, and personalized medicine (PPPM).

Methods

The anti-inflammatory and anti-DE actions of SP2 were investigated using three animal models (SP2-/- mice, streptozocin-treated mice, and db/db mice) and two cell lines (primary cultured hippocampal neurons and N2A cells). The db/db mice were a leptin deficiency model often used to study type 2 diabetes. An equal number of males and females (8-12 weeks of age) was selected. Behavioral changes in mice were determined using both morris water maze (MWM) test and Y-maze (YM) test. The alterations in oxidative stress and inflammation were examined via immunofluorescence assay, flow cytometry, co-immunoprecipitation, and immunoblotting.

Results

Mechanistically, SP2-knockout (SP2-/-) mice showed dysregulation of insulin/glucose homeostasis, neuroinflammation, and cognitive loss. Otherwise, in db/db DE mice and STZ-induced DE mice, neuroinflammation, neuroapoptosis, and cognitive decline were significantly attenuated when SP2 was overexpressed in the brain. On the other hand, SP2 overexpression activates the insulin signaling pathway and improves insulin resistance via targeting X-box binding protein 1 (XBP1) in neurons. Moreover, SP2 overexpression significantly reduces oxidative stress by interacting with XBP1 and nuclear factor erythroid 2-related factor 2 (NRF2) in neurons. Furthermore, SP2 enhances the suppression of inflammatory response triggered by nuclear factor kappa B (NFκB) through the recruitment of XBP1 and NRF2 and by the in vitro inactivation of IκB kinase (IKK) complex.

Conclusions

These findings highlight SP2 as key biological targets for DE and reveal the infammation-related potential molecular mechanism of DE, which is helpful for early risk prediction and targeted prevention of DE. In conclusion, our study provides a new perspective for developing a PPPM method for managing DE patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00394-0.

Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these "omics" studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.

Big data for neuroscience in the context of predictive, preventive, and personalized medicine

Accurate and precise diagnosis made the medicine the hallmark of evidence-based medicine. While attaining absolute patient satisfaction may seem impossible in the aspect of disease recurrent, personalized their mecidal conditions to their responsive treatment approach may save the day. The last generation approaches in medicine require advanced technologies that will lead to evidence-based medicine. One of the trending fields in this is the use of big data in predictive, preventive, and personalized medicine (3PM). This review dwelled through the practical examples in which big data tools harness neuroscience to add more individualized apporahes to the medical conditions in a bid to confer a more personalized treatment strategies. Moreover, the known breakthroughs of big data in 3PM, big data and 3PM in neuroscience, AI and neuroscience, limitations of big data with 3PM in neuroscience, and the challenges are thoroughly discussed. Finally, the prospects of incorporating big data in 3PM are as well discussed. The review could point out that the implications of big data in 3PM are still in their infancy and will require a holistic approach. While there is a need to carefully sensitize the community, convincing them will come under interdisciplinary and, to some extent, inter-professional collaborations, capacity building for professionals, and optimal coordination of the joint systems.

Integrins identified as potential prognostic markers in osteosarcoma through multi-omics and multi-dataset analysis

Osteosarcoma represents 20% of primary malignant bone tumors globally. Assessing its prognosis is challenging due to the complex roles of integrins in tumor development and metastasis. This study utilized 209,268 osteosarcoma cells from the GEO database to identify integrin-associated genes using advanced analysis methods. A novel machine learning framework combining 10 algorithms was developed to construct an Integrin-related Signature (IRS), which demonstrated robust predictive power across multiple datasets. The IRS's utility in predicting overall survival was confirmed using data from The Cancer Genome Atlas, underscoring its potential in personalized cancer management.

Overdiagnosis of dental caries in South Korea: a pseudo-patient study

BACKGROUND

To evaluates the tendency of South Korean dentists to over-diagnose clinically healthy teeth in pseudo-patients.

METHODS

We conducted a pseudo-patient, cross-sectional study in 196 private dental clinics with 58 pseudo-patients in South Korea between August and December 2018. Trained pseudo-patients with no previous oral diseases, including dental caries, diagnosed by two experienced dentists, were sent to each dental clinic. Before visiting each private dental clinic, participants were instructed to state, "I have no symptoms, but I would like to have a dental caries examination". The oral examination was performed using visual and tactile inspection methods only. The interactions between the dental clinic staff and the pseudo-patient were documented on a data collection form shortly after each visit.

RESULTS

In 33.2% (65/196) of these interactions, the pseudo-patients were diagnosed as having no dental caries. 11.7% (23/196), 12.8% (25/196), 10.7% (21/196), and 10.7% (21/196) of the sample were diagnosed with dental caries in one, two, three, and four teeth, respectively. Dentists diagnosed five or more dental caries in 20.9% (41/196) of the sample. 196 dental clinics diagnosed a total of 503 dental caries. Of these, 392 were in molars. Small solo practice dentists diagnosed 3.54 dental caries and large group practice dentists 1.57, but the difference was not significant (p = 0.07). The recommendation rate for dental caries treatment was highest among 43 (55.1%) large solo practices, and lowest in 7 (33.3%) large group practices. However, small solo practices had the lowest rate of preventive care recommendations at 12 (30.8%) and 10 (47.6%) in large group practices. The data shows that preventive care recommendations increased as the practice size increased.

CONCLUSION

The study findings indicate that Korean dentists tend to over-diagnose dental caries, which could pose a threat to public health both in Korea and worldwide. Therefore, it is important to carefully consider strategies to improve the correct diagnosis and standard of care for dental caries by private dentists.

Observational and genetic associations between cardiorespiratory fitness and age-related diseases: longitudinal analyses in the UK Biobank study

Background

Observational studies have indicated that increased cardiorespiratory fitness is associated with a decreased risk of cardiovascular disease (CVD), Alzheimer's disease (AD), and Parkinson's disease (PD). However, the causal mechanisms remain unclear. The objective of this study was to assess the role of fitness in the early detection and reduction of disease risk within the framework of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

The associations of fitness with CVD, AD, and PD were explored in a large cohort of up to 502,486 individuals between the ages of 40 and 69 years from the UK Biobank. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of CVD, AD, and PD among participants who completed a submaximal fitness test. Causality relationships were assessed via two-sample Mendelian randomization (MR).

Results

After a median of 11 years of follow-up, each 3.5 ml of O2⋅min-1⋅kg-1 increase in total body mass (equivalent to 1 metabolic equivalent of task (MET), approximately 0.5 standard deviations (SDs)) was associated with decreased risks of CVD (20.0%, 95% CI 17.6-22.3%), AD (31.9%, 95% CI 26.7-33.6%), and PD (21.2%, 95% CI 11.2-31.8%). After adjusting for obesity, the observational associations were attenuated. According to the MR analyses, fitness was associated with PD (OR IVW 0.937, 95% CI 0.897-0.978) and small vessel stroke (OR IVW 0.964, 95% CI 0.933-0.995).

Conclusion

Our results indicate that fitness has an effect on age-related diseases. Protective associations of higher fitness levels with the risk of CVD, AD, and PD were validated in this cohort study. These findings might be valuable for predicting, preventing, and reducing disease morbidity and mortality through primary prevention and healthcare in the context of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00382-4.

Changes in the triglyceride-glucose-body mass index estimate the risk of hypertension among the middle-aged and older population: a prospective nationwide cohort study in China in the framework of predictive, preventive, and personalized medicine

Background

Hypertension is a major modifiable cause of cardiovascular diseases and premature death worldwide. The triglyceride-glucose-body mass index (TyG-BMI), as a novel indicator, has been proposed for assessing hypertension risk. Nevertheless, a paucity of studies has explored the predictive potential of dynamic TyG-BMI for hypertension. The purpose of this study was to investigate whether cumulative TyG-BMI could better predict hypertension incidence and explore the interplay between TyG and BMI in hypertension development. From the perspective of predictive, preventive, and personalized medicine (PPPM/3PM), we assumed that dynamic monitoring of TyG-BMI level and joint assessment of TyG and BMI provide novel insights for individual risk assessment, targeted prevention, and personalized intervention of cardiovascular diseases.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS), a nationwide cohort conducted between 2011 and 2018, the changes in TyG-BMI between 2012 and 2015 were categorized into four groups by K-means clustering analysis. Cumulative TyG-BMI was also divided into four levels based on quartile cutoffs. Logistic regression and restricted cubic spline analyses were performed to examine the associations of different TyG-BMI classes with hypertension. Mediating and interactive analyses were utilized to discern the mutual effects between TyG and BMI in hypertension development.

Results

A total of 2891 participants were enrolled, among whom 386 (13.4%) developed hypertension during a median 36.5-month follow-up period. Logistic regression analysis revealed that, compared to participants with persistently low TyG‑BMI, an increased risk of hypertension was observed among those with a moderate (odds ratio (OR) = 1.60, 95% confidence interval (CI) 1.15 to 2.22), a higher (OR = 1.93, 95% CI 1.28 to 2.89), and the highest TyG‑BMI (OR = 2.33, 95% CI 1.35 to 4.03). A positive linear association of cumulative TyG-BMI with hypertension was discovered (P for non-linear = 0.343). Furthermore, TyG partially mediated the relationship between BMI and hypertension, accounting for 13.18% of the total effect. The joint effect of BMI and TyG was positively affiliated to hypertension development.

Conclusions

This study demonstrated a significant positive association between dynamic TyG-BMI and hypertension among the Chinese middle-aged and older population. In the context of PPPM/3PM, long-term monitoring of TyG-BMI could assist in identifying individuals at high risk of hypertension, strengthening primary prevention efforts and facilitating prompt intervention strategies. In addition, this study revealed the mutual effect of TyG and BMI on hypertension development, which provides a novel approach for mitigating the risk of cardiovascular diseases via addressing metabolic disorders, thereby enhancing effective prevention and targeted intervention.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00380-6.

Lactobacillaceae-mediated eye-brain-gut axis regulates high myopia-related anxiety: from the perspective of predictive, preventive, and personalized medicine

Background

High myopia has become a major cause of blindness worldwide and can contribute to emotional deficits through its impact on the central nervous system. The potential crosstalk with gut microbiome positions high myopia as a valuable model for studying the eye-brain-gut axis, highlighting the intricate interplay between visual health, neurological function, and the gut microbiome. Understanding these connections is crucial from a predictive, preventive, and personalized medicine (PPPM) perspective, as it may reveal novel intervention targets for managing both visual and mental health.

Working hypothesis and methodology

In our study, we hypothesized that visual stimuli associated with high myopia may lead to gut microecological dysregulation, potentially triggering mood disorders such as anxiety and depression. To test this hypothesis, we assessed genetic associations between high myopia (N = 50,372) and depression (N = 674,452) as well as anxiety (N = 21,761) using inverse variance weighted as the primary analytical method. We also investigated the potential mediating role of the gut microbiome (N = 18,340). The findings were validated in an independent cohort and summarized through meta-analysis.

Results

A genetic causal relationship between high myopia and anxiety was found (odds ratio [OR] = 8.76; 95% confidence interval [CI], 2.69-28.54; p = 3.16 × 10-4), with 20.3% of the effect mediated by the gut microbiome family Lactobacillaceae (β = 0.517; 95% CI, 0.104-1.090; p = 0.037). The analysis also showed a suggestive causal relationship between high myopia and depression (OR = 1.25; 95% CI, 1.00-1.57; p = 0.048).

Conclusions

Our study shows that high myopia causes anxiety via the Lactobacillaceae family of the gut microbiome, supporting the eye-brain-gut axis concept. This underscores the need to shift from reactive to predictive, preventive, and personalized medicine (PPPM). Targeting Lactobacillaceae offers novel insights for early intervention and personalized treatment of high myopia-related anxiety and sheds light on interventions for other vision-related brain disorders.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00387-z.

Pain chronification risk assessment: advanced phenotyping and scoring for prediction and treatments tailored to individualized patient profile

Acute pain is a physiologic, protective life-important warning neurological signal indicating multi-level tissue modulations caused by a broad spectrum of health adverse events such as stress overload, mechanical trauma, ischemia-reperfusion, sterile and infection-triggered inflammation, single- and multi-organ damage, acute and chronic wounds, tissue remodeling and degeneration, amongst others. On the other hand, pain chronification results in a pathologic transformation from the protective pain signaling into persistent debilitative medical condition with severe consequences including but not restricted to phenotype-specific behavioral patterns, reduced quality of life, and cognitive and mood disorders. Who is predisposed to an increased vs. decreased pain sensitivity and to the pain chronification? The motivation of personalized medicine that "same size does not fit all" is getting obvious also for an advanced approach in algesiology. Consequently, an in-depth patient stratification is essential for the paradigm change in overall pain management from currently applied reactive medical services to the cost-effective predictive, preventive, and personalized medicine (PPPM/3PM) in primary (reversible damage to health and targeted protection against health-to-disease transition) and secondary (personalized protection against disease progression) care. To this end, specifically innovative concepts of phenotyping elaborated in this study play a crucial role in patient stratification for predicting pain-associated outcomes, evidence-based targeted prevention of the pain chronification, and creation of treatment algorithms tailored to individualized patient profiles.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00383-3.

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

BACKGROUND

Gut microbiota (GM) is closely associated with the onset of depression, in which inflammation plays an essential role. Identifying specific GM associated with depression and their mechanisms, based on the principles of predictive, preventive, and personalized medicine (PPPM), is a critical step toward achieving targeted prevention and personalized treatment for depression.

WORKING HYPOTHESIS AND METHODOLOGY

We hypothesized that both gut microbiota (GM) and cytokines influence the onset of depression, with cytokines acting as mediators of GM effects on depression. To test this hypothesis, we employed univariate Mendelian Randomization (UVMR) analysis to identify GM taxa associated with depression and cytokines and to determine the potential role of the identified GM taxa on these cytokines. Subsequently, multivariate Mendelian randomization (MVMR) was used to infer the mediating role of cytokines between the identified differential genus of GM and depression. Our results indicate that immune imbalance due to intestinal dysbiosis serves as an early risk indicator for the onset of depression. This provides a basis for utilizing non-invasive stool detection of GM for early screening, timely prevention, and personalized treatment of depression. By combining non-invasive stool detection of GM with existing methods, such as psychological questionnaires, we can jointly predict and assess the risk of developing depression. Additionally, formulating personalized treatment protocols that combine probiotics and medication can help transition depression management from reactive medicine to predictive, preventive, and personalized medicine (PPPM).

RESULTS

UVMR identified 15 GM taxa and 4 cytokines associated with the onset of depression. Specifically, Romboutsia, Intestinimonas, Ruminococcaceae UCG011, and circulating ADA, IL-18R1 were all inferred to be protective factors against the onset of depression. Conversely, Lachnospiraceae FCS020 group, Streptococcus, Marvinbryantia, VEGF_A, and TNFSF14 were inferred as risk factors for the onset of depression. Further, MVMR validated the mediating role of some cytokines in the effects of GM on depression.

CONCLUSIONS

Our study highlights the influence of alterations in GM on depression, revealing a mediating role of inflammation. By regulating these specific GM, it is hoped that the clinical treatment of depression can be transformed from traditional medicine to PPPM. With the help of mendelian randomization (MR) method, this study provides support for the wide application of non-invasive stool detection of GM for early screening of depression in clinical and carries out precise treatment based on the screening results, targeting the supplementation of specific bacteria, correcting the immune imbalance to prevent depression, and mitigating or blocking the disease process of depression.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13167-024-00379-z.

Liver function maximum capacity test during normothermic regional perfusion predicts graft function after transplantation

Purpose

In an effort to reduce waitlist mortality, extended criteria donor organs, including those from donation after circulatory death (DCD), are being used with increasing frequency. These donors carry an increased risk for postoperative complications, and balancing donor-recipient risks is currently based on generalized nomograms. Abdominal normothermic regional perfusion (aNRP) enables individual evaluation of DCD organs, but a gold standard to determine suitability for transplantation is lacking. This study aimed to incorporate individualized and predictive measurements of the liver maximum capacity (LiMAx) test to objectively grade liver function during aNRP and prevent post-op complications.

Methods

aNRP was performed to salvage 18 DCD liver grafts, otherwise discarded. Continuous variables were presented as the median with the interquartile range.

Results

The liver function maximum capacity (LiMAx) test was successfully performed within the aNRP circuit in 17 aNRPs (94%). Donor livers with good lactate clearance during aNRP demonstrated significantly higher LiMAx scores (396 (301-451) µg/kg/h versus those who did not 105 (70-158) µg/kg/h; P = 0.006). This was also true for manifesting stress hyperglycemia > 20 mmol/l (P = 0.032). LiMAx score correlated with alanine aminotransferase (ALT; R =  - 0.755) and aspartate transaminase (AST; R =  - 0.800) levels during perfusion and distinguished livers that were selected for transplantation (397 (346-453) µg/kg/h) from those who were discarded (155 (87-206) µg/kg/h; P < 0.001). Twelve livers were accepted for transplantation, blinded for LiMAx results, and all had LiMAx scores of > 241 µg/kg/h. Postoperatively, LiMAx during aNRP displayed correlation with 24-h lactate levels.

Conclusions

This study shows for the first time the feasibility to assess liver function during aNRP in individual donor livers. LiMAx presents an objective tool to predict donor liver function and risk of complications in the recipient, thus enabling individualized matching of donor livers for an individual recipient. The LiMAx test may present a valuable test for the prediction of donor liver function, preventing post-transplant complication, and personalizing the selection of donor livers for individual recipients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00371-7.

Association of the weight-adjusted waist index with hypertension in the context of predictive, preventive, and personalized medicine

Objective

Hypertension (HTN) is a prevalent global health concern. From the standpoint of preventive and personalized medicine (PPPM/3PM), early detection of HTN offers a crucial opportunity for targeted prevention and personalized treatment. This study aimed to evaluate the association between the weight-adjusted waist index (WWI) and HTN risk.

Methods

A case-control study using data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 was conducted. Logistic regression models assessed the association between WWI and HTN. Subgroup analyses explored differences in age, sex, ethnicity, and diabetes status. Restricted cubic spline (RCS) analyses examined potential nonlinear relationships.

Results

A total of 32,116 participants, with an average age of 49.28 ± 17.56 years, were included in the study. A significant positive association between WWI and the risk of HTN was identified (odds ratio [OR], 2.49; 95% CI, 2.39-2.59; P < 0.001). When WWI was categorized into quartiles (Q1-Q4), the highest quartile (Q4) exhibited a stronger association compared to Q1 (OR, 2.94; 95% CI, 2.65-3.27; P < 0.001). Subgroup analyses indicated that WWI was a risk factor for HTN across different populations, although variations in the magnitude of effect were observed. Furthermore, the findings from the RCS elucidated a nonlinear positive correlation between WWI and HTN.

Conclusion

WWI is independently associated with HTN risk, highlighting its potential as a risk assessment tool in clinical practice. Incorporating WWI into early detection strategies enhances targeted prevention and personalized management of HTN.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00375-3.

Genetic association of lipid traits and lipid-related drug targets with normal tension glaucoma: a Mendelian randomization study for predictive preventive and personalized medicine

Background

Glaucoma is the leading cause of irreversible blindness worldwide. Normal tension glaucoma (NTG) is a distinct subtype characterized by intraocular pressures (IOP) within the normal range (< 21 mm Hg). Due to its insidious onset and optic nerve damage, patients often present with advanced conditions upon diagnosis. NTG poses an additional challenge as it is difficult to identify with normal IOP, complicating its prediction, prevention, and treatment. Observational studies suggest a potential association between NTG and abnormal lipid metabolism, yet conclusive evidence establishing a direct causal relationship is lacking. This study aims to explore the causal link between serum lipids and NTG, while identifying lipid-related therapeutic targets. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of dyslipidemia in the development of NTG could provide a new strategy for primary prediction, targeted prevention, and personalized treatment of the disease.

Working hypothesis and methods

In our study, we hypothesized that individuals with dyslipidemia may be more susceptible to NTG due to a dysregulation of microvasculature in optic nerve head. To verify the working hypothesis, univariable Mendelian randomization (UVMR) and multivariable Mendelian randomization (MVMR) were utilized to estimate the causal effects of lipid traits on NTG. Drug target MR was used to explore possible target genes for NTG treatment. Genetic variants associated with lipid traits and variants of genes encoding seven lipid-related drug targets were extracted from the Global Lipids Genetics Consortium genome-wide association study (GWAS). GWAS data for NTG, primary open angle glaucoma (POAG), and suspected glaucoma (GLAUSUSP) were obtained from FinnGen Consortium. For apolipoproteins, we used summary statistics from a GWAS study by Kettunen et al. in 2016. For metabolic syndrome, summary statistics were extracted from UK Biobank participants. In the end, these findings could help identify individuals at risk of NTG by screening for lipid dyslipidemia, potentially leading to new targeted prevention and personalized treatment approaches.

Results

Genetically assessed high-density cholesterol (HDL) was negatively associated with NTG risk (inverse-variance weighted [IVW] model: OR per SD change of HDL level = 0.64; 95% CI, 0.49-0.85; P = 1.84 × 10-3), and the causal effect was independent of apolipoproteins and metabolic syndrome (IVW model: OR = 0.29; 95% CI, 0.14-0.60; P = 0.001 adjusted by ApoB and ApoA1; OR = 0.70; 95% CI, 0.52-0.95; P = 0.023 adjusted by BMI, HTN, and T2DM). Triglyceride (TG) was positively associated with NTG risk (IVW model: OR = 1.62; 95% CI, 1.15-2.29; P = 6.31 × 10-3), and the causal effect was independent of metabolic syndrome (IVW model: OR = 1.66; 95% CI, 1.18-2.34; P = 0.003 adjusted by BMI, HTN, and T2DM), but not apolipoproteins (IVW model: OR = 1.71; 95% CI, 0.99-2.95; P = 0.050 adjusted by ApoB and ApoA1). Genetic mimicry of apolipoprotein B (APOB) enhancement was associated with lower NTG risks (IVW model: OR = 0.09; 95% CI, 0.03-0.26; P = 9.32 × 10-6).

Conclusions

Our findings supported dyslipidemia as a predictive causal factor for NTG, independent of other factors such as metabolic comorbidities. Among seven lipid-related drug targets, APOB is a potential candidate drug target for preventing NTG. Personalized health profiles can be developed by integrating lipid metabolism with life styles, visual quality of life such as reading, driving, and walking. This comprehensive approach will aid in shifting from reactive medical services to PPPM in the management of NTG.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00373-5.

The caregiving role influences Suboptimal Health Status and psychological symptoms in unpaid carers

BACKGROUND

Suboptimal Health Status (SHS) is the physical state between health and disease. This study aimed to fill in the knowledge gap by investigating the prevalence of SHS and psychological symptoms among unpaid carers and to identify SHS-risk factors from the perspective of predictive, preventive and personalised medicine (PPPM).

METHODS

A cross-sectional study was conducted among 368 participants who were enrolled from Australia, including 203 unpaid carers as cases and 165 individuals from the general population as controls. SHS scores were measured using SHSQ-25 (Suboptimal Health Status Questionnaire-25), whilst psychological symptoms were measured by DASS-21 (Depression, Anxiety and Stress Scale-21). Chi-square was used to measure SHS and psychological symptom prevalence. Spearman correlation analysis was utilised to identify the relationship between SHSQ-25 and DASS-21 scores. Logistic regression analysis was used for multivariate analysis.

RESULTS

The prevalence of SHS in carers was 43.0% (98/203), significantly higher than the prevalence 12.7% (21/165) in the general population (p < 0.001). In addition, suboptimal health prevalence was higher in female carers (50.3%; 95/189) than females in the general population (12.4%; 18/145). Logistic regression showed that the caregiving role influenced SHS, with carers 6.4 times more likely to suffer from SHS than their non-caring counterparts (aOR = 6.400, 95% CI = 3.751-10.919).

CONCLUSIONS

Unpaid carers in Australia have a significantly higher prevalence of SHS than that in the general population and experience poorer health. The SHSQ-25 is a powerful tool that can be utilised to screen at-risk individuals to predict their risk of chronic disease development, an essential pillar for shifting the paradigm change from reactive medicine to that of predictive, preventive and personalised medicine (PPPM).

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13167-024-00370-8.

Identification and Validation of a Necroptosis-Related Prognostic Model in Tumor Recurrence and Tumor Immune Microenvironment in Breast Cancer Management

Background

Breast cancer is the leading cause of cancer-related death in women. Necroptosis, a form of programmed necrotic cell death, occurs in many solid tumors, including breast cancer, and influences anti-tumor immunity. The role of necroptosis in managing breast cancer recurrence remains unclear.

Methods

Gene expression profiles and clinical data of breast cancer patients were obtained from the GEO (GSE20685, GSE21653, GSE25055) and TCGA databases. Data analysis and visualization were performed using R. Unsupervised Consensus Clustering and LASSO-COX regression stratified breast cancer patients. GO, KEGG, GSVA, ESTIMATE, and ROC analyses were used to investigate necroptotic signatures. In vitro and in vivo experiments validated necroptosis's role in breast cancer immunity.

Results

The potential function of necroptotic signature in immunity was first indicated with GO analysis in BRCA cohort. Next, two prognostic models based on the necroptotic profiles both suggested a link between low-risk group with a particular necroptotic immune signature. And a variety of immune cells and immune pathways were shown to be positively associated with a patient's risk score. As an altered immune checkpoint pattern was observed after regulating necroptotic genes, where TIM-3 and LAGLS9 elevated significantly in low-risk group, further validation in vitro and in vivo demonstrated that manipulating a subset of necroptotic gene set could sensitize tumor response to the co-blockade immunotherapy of anti-TIM-3 and anti-PD-1.

Conclusion

We demonstrated two strategies to stratify breast cancer patients based on their necroptotic profiles and showed that necroptotic signature could assign patients with different tumor immune microenvironment patterns and different recurrence-related prognosis. A subset of necroptotic gene set, composed of TLR3, RIPK3, NLRP3, CASP1, ALDH2 and EZH2, was identified as a biomarker set for predicting immunotherapy-response and recurrence-related prognosis. Targeting necroptosis could helpfacilitate the development of novel breast cancer treatments and tailor personalized medical treatment.

Development of an Artificial Intelligence-Based Tailored Mobile Intervention for Nurse Burnout: Single-Arm Trial

BACKGROUND

Nurse burnout leads to an increase in turnover, which is a serious problem in the health care system. Although there is ample evidence of nurse burnout, interventions developed in previous studies were general and did not consider specific burnout dimensions and individual characteristics.

OBJECTIVE

The objectives of this study were to develop and optimize the first tailored mobile intervention for nurse burnout, which recommends programs based on artificial intelligence (AI) algorithms, and to test its usability, effectiveness, and satisfaction.

METHODS

In this study, an AI-based mobile intervention, Nurse Healing Space, was developed to provide tailored programs for nurse burnout. The 4-week program included mindfulness meditation, laughter therapy, storytelling, reflective writing, and acceptance and commitment therapy. The AI algorithm recommended one of these programs to participants by calculating similarity through a pretest consisting of participants' demographics, research variables, and burnout dimension scores measured with the Copenhagen Burnout Inventory. After completing a 4-week program, burnout, job stress, stress response using the Stress Response Inventory Modified Form, the usability of the app, coping strategy by the coping strategy indicator, and program satisfaction (1: very dissatisfied; 5: very satisfied) were measured. The AI recognized the recommended program as effective if the user's burnout score reduced after the 2-week program and updated the algorithm accordingly. After a pilot test (n=10), AI optimization was performed (n=300). A paired 2-tailed t test, ANOVA, and the Spearman correlation were used to test the effect of the intervention and algorithm optimization.

RESULTS

Nurse Healing Space was implemented as a mobile app equipped with a system that recommended 1 program out of 4 based on similarity between users through AI. The AI algorithm worked well in matching the program recommended to participants who were most similar using valid data. Users were satisfied with the convenience and visual quality but were dissatisfied with the absence of notifications and inability to customize the program. The overall usability score of the app was 3.4 out of 5 points. Nurses' burnout scores decreased significantly after the completion of the first 2-week program (t=7.012; P<.001) and reduced further after the second 2-week program (t=2.811; P=.01). After completing the Nurse Healing Space program, job stress (t=6.765; P<.001) and stress responses (t=5.864; P<.001) decreased significantly. During the second 2-week program, the burnout level reduced in the order of participation (r=-0.138; P=.04). User satisfaction increased for both the first (F=3.493; P=.03) and second programs (F=3.911; P=.02).

CONCLUSIONS

This program effectively reduced burnout, job stress, and stress responses. Nurse managers were able to prevent nurses from resigning and maintain the quality of medical services using this AI-based program to provide tailored interventions for nurse burnout. Thus, this app could improve qualitative health care, increase employee satisfaction, reduce costs, and ultimately improve the efficiency of the health care system.

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.

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.

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.

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.

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.

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.

Artificial intelligence to improve ischemia prediction in Rubidium Positron Emission Tomography-a validation study

BACKGROUND

Patients are referred to functional coronary artery disease (CAD) testing based on their pre-test probability (PTP) to search for myocardial ischemia. The recommended prediction tools incorporate three variables (symptoms, age, sex) and are easy to use, but have a limited diagnostic accuracy. Hence, a substantial proportion of non-invasive functional tests reveal no myocardial ischemia, leading to unnecessary radiation exposure and costs. Therefore, preselection of patients before ischemia testing needs to be improved using a more predictive and personalised approach.

AIMS

Using multiple variables (symptoms, vitals, ECG, biomarkers), artificial intelligence-based tools can provide a detailed and individualised profile of each patient. This could improve PTP assessment and provide a more personalised diagnostic approach in the framework of predictive, preventive and personalised medicine (PPPM).

METHODS

Consecutive patients (n = 2417) referred for Rubidium-82 positron emission tomography were evaluated. PTP was calculated using the ESC 2013/2019 and ACC 2012/2021 guidelines, and a memetic pattern-based algorithm (MPA) was applied incorporating symptoms, vitals, ECG and biomarkers. Five PTP categories from very low to very high PTP were defined (i.e., < 5%, 5-15%, 15-50%, 50-85%, > 85%). Ischemia was defined as summed difference score (SDS) ≥ 2.

RESULTS

Ischemia was present in 37.1%. The MPA model was most accurate to predict ischemia (AUC: 0.758, p < 0.001 compared to ESC 2013, 0.661; ESC 2019, 0.673; ACC 2012, 0.585; ACC 2021, 0.667). Using the < 5% threshold, the MPA's sensitivity and negative predictive value to rule out ischemia were 99.1% and 96.4%, respectively. The model allocated patients more evenly across PTP categories, reduced the proportion of patients in the intermediate (15-85%) range by 29% (ACC 2012)-51% (ESC 2019), and was the only tool to correctly predict ischemia prevalence in the very low PTP category.

CONCLUSION

The MPA model enhanced ischemia testing according to the PPPM framework:The MPA model improved individual prediction of ischemia significantly and could safely exclude ischemia based on readily available variables without advanced testing ("predictive").It reduced the proportion of patients in the intermediate PTP range. Therefore, it could be used as a gatekeeper to prevent patients from further unnecessary downstream testing, radiation exposure and costs ("preventive").Consequently, the MPA model could transform ischemia testing towards a more personalised diagnostic algorithm ("personalised").

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13167-023-00341-5.

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

Background

Arterial stiffness is a major contributor to morbidity and mortality worldwide. Although several metabolic markers associated with arterial stiffness have been developed, there is limited data regarding whether glycemic control modifies the association between diabetes and arterial stiffness. For these reasons, identification of traits around diabetes will directly contribute to arterial stiffness and atherosclerosis management in the context of predictive, preventive, and personalized medicine (PPPM). Thus, this study aimed to explore the relationship of diabetes and glycemic control status with arterial stiffness in a real-world setting.

Methods

Data of participants from Beijing Xiaotangshan Examination Center (BXEC) with at least two surveys between 2008 and 2019 were used. Cumulative hazards were presented by inverse probability of treatment weighted (IPTW) Kaplan-Meier curves. Cox models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI). Arterial stiffness was defined as brachial-ankle pulse wave velocity (baPWV) ≥1400 cm/s.

Results

Of 5837 participants, the mean baseline age was 46.5±9.3 years, including 3791 (64.9%) males. During a median follow-up of 4.0 years, 1928 (33.0%) cases of incident arterial stiffness were observed. People with diabetes at baseline had a 48.4% (HR: 1.484, 95% CI: 1.250-1.761) excessive risk of arterial stiffness. Adherence to good glycemic control attenuated the relationship between diabetes and arterial stiffness (HR: 1.264, 95% CI: 0.950-1.681); while uncontrolled diabetes was associated with the highest risk of arterial stiffness (HR: 1.629, 95% CI: 1.323-2.005). Results were consistent using IPTW algorithm and multiple imputed data.

Conclusion

Our study quantified that diabetes status is closely associated with an increased risk of arterial stiffness and supported that adherence to good glycemic control could attenuate the adverse effect of diabetes on arterial stiffness. Therefore, glucose monitoring and control is a cost-effective strategy for the predictive diagnostics, targeted prevention, patient stratification, and personalization of medical services in early vascular damages and arterial stiffness.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00347-z.

Causal relationship between gut Prevotellaceae and risk of sepsis: a two-sample Mendelian randomization and clinical retrospective study in the framework of predictive, preventive, and personalized medicine

Objective

Gut microbiota is closely related to sepsis. Recent studies have suggested that Prevotellaceae could be associated with intestinal inflammation; however, the causal relationship between Prevotellaceae and sepsis remains uncertain. From the perspective of predictive, preventive, and personalized medicine (PPPM), exploring the causal relationship between gut Prevotellaceae and sepsis could provide opportunity for targeted prevention and personalized treatment.

Methods

The genome-wide association study (GWAS) summary-level data of Prevotellaceae (N = 7738) and sepsis were obtained from the Dutch Microbiome Project and the UK Biobank (sepsis, 1380 cases; 429,985 controls). MR analysis was conducted to estimate the associations between Prevotellaceae and sepsis risk. The 16S rRNA sequencing analysis was conducted to calculate the relative abundance of Prevotellaceae in sepsis patients to explore the relationship between Prevotellaceae relative abundance and the 28-day mortality.

Results

Genetic liability to f__Prevotellaceae (OR, 1.91; CI, 1.35-2.71; p = 0.0003) was associated with a high risk of sepsis with inverse-variance weighted (IVW). The median Prevotellaceae relative abundance in non-survivors was significantly higher than in survivors (2.34% vs 0.17%, p < 0.001). Multivariate analysis confirmed that Prevotellaceae relative abundance (OR, 1.10; CI, 1.03-1.22; p = 0.027) was an independent factor of 28-day mortality in sepsis patients. ROC curve analysis indicated that Prevotellaceae relative abundance (AUC: 0.787, 95% CI: 0.671-0.902, p = 0.0003) could predict the prognosis of sepsis patients.

Conclusion

Our results revealed that Prevotellaceae was causally associated with sepsis and affected the prognosis of sepsis patients. These findings may provide insights to clinicians on developing improved sepsis PPPM strategies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00340-6.

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

Background

The human gut microbiota (GM) has been recognized as a significant factor in the development of insomnia, primarily through inflammatory pathways, making it a promising target for therapeutic interventions. Considering the principles of primary prediction, targeted prevention, and personalized treatment medicine (PPPM), identifying specific gut microbiota associated with insomnia and exploring the underlying mechanisms comprehensively are crucial steps towards achieving primary prediction, targeted prevention, and personalized treatment of insomnia.

Working hypothesis and methodology

We hypothesized that alterations in the composition of specific GM could induce insomnia through an inflammatory response, which postulates the existence of a GM-inflammation-insomnia pathway. Mendelian randomization (MR) analyses were employed to examine this pathway and explore the mediative effects of inflammation. We utilized genetic proxies representing GM, insomnia, and inflammatory indicators (including 41 circulating cytokines and C-reactive protein (CRP)), specifically identified from European ancestry. The primary method used to identify insomnia-related GM and examine the medicative effect of inflammation was the inverse variance weighted method, supplemented by the MR-Egger and weighted median methods. Our findings have the potential to identify individuals at risk of insomnia through screening for GM imbalances, leading to the development of targeted prevention and personalized treatment strategies for the condition.

Results

Nine genera and three circulating cytokines were identified to be associated with insomnia; only the associations of Clostridium (innocuum group) and β-NGF on insomnia remained significant after the FDR test, OR = 1.08 (95% CI = 1.04-1.12, P = 1.45 × 10-4, q = 0.02) and OR = 1.06 (95% CI = 1.02-1.10, P = 1.06 × 10-3, q = 0.04), respectively. CRP was associated with an increased risk of insomnia, OR = 1.05 (95% CI = 1.01-1.10, P = 6.42 × 10-3). CRP mediated the association of Coprococcus 1, Holdemania, and Rikenellaceae (RC9gut group) with insomnia. No heterogeneity or pleiotropy were detected.

Conclusions

Our study highlights the role of specific GM alterations in the development of insomnia and provides insights into the mediating effects of inflammation. Targeting these specific GM alterations presents a promising avenue for advancing the transition from reactive medicine to PPPM in managing insomnia, potentially leading to significant clinical benefits.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00345-1.

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.

Development and validation of a short-form suboptimal health status questionnaire

Background

Suboptimal health status (SHS) is a reversible, borderline state between optimal health and disease. Although this condition's definition is widely understood, related questionnaires must be developed to identify individuals with SHS in various populations relative to predictive, preventive, and personalized medicine (PPPM/3PM). This study presents a short-form suboptimal health status questionnaire (SHSQ-SF) that appears to possess sufficient reliability and validity to assess SHS in large-scale populations.

Methods

A total of 6183 participants enrolled from Southern China constituted a training set, while 4113 participants from Northern China constituted an external validation set. The SHSQ-SF includes nine key items from the Suboptimal Health Status Questionnaire-25 (SHSQ-25), an instrument that has been applied to Africans, Asians, and Caucasians. Item analysis and reliability and validity tests were carried out to validate the SHSQ-SF. The receiver operating characteristic (ROC) curve was used to identify an optimal cutoff value for SHS diagnosis, by which the area under the curve (AUC) and 95% confidence interval (CI) were determined.

Results

Cronbach's α coefficient for the training dataset was 0.902; the split-half reliability was 0.863. The Kaiser-Meyer-Olkin (KMO) value was 0.880, and Bartlett's test of sphericity was significant (χ2 = 32,929.680, p < 0.05). Both Kaiser's criteria (eigenvalues > 1) and the scree plot revealed one factor explaining 57.008% of the total variance. Standardized factor loadings for the confirmatory factor analysis (CFA) indices ranged between 0.58 and 0.74, with χ2/dƒ = 4.972, GFI = 0.996, CFI = 0.996, RFI = 0.989, and RMSEA = 0.031. The AUC was equal to 0.985 (95% CI: 0.983-0.988) for the training dataset. A cutoff value (≥ 11) was then identified for SHS diagnosis. The SHSQ-SF showed good discriminatory power for the external validation dataset (AUC = 0.975, 95% CI: 0.971-0.979) with a sensitivity of 96.2% and a specificity of 87.4%.

Conclusions

We developed a short form of the SHS questionnaire that demonstrated sound reliability and validity when assessing SHS in Chinese residents. From a PPPM/3PM perspective, the SHSQ-SF is recommended for the rapid screening of individuals with SHS in large-scale populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00339-z.

Conceptualised psycho-medical footprint for health status outcomes and the potential impacts for early detection and prevention of chronic diseases in the context of 3P medicine

Background

The Suboptimal Health Status Questionnaire-25 (SHSQ-25) is a distinctive medical psychometric diagnostic tool designed for the early detection of chronic diseases. However, the synaptic connections between the 25 symptomatic items and their relevance in supporting the monitoring of suboptimal health outcomes, which are precursors for chronic diseases, have not been thoroughly evaluated within the framework of predictive, preventive, and personalised medicine (PPPM/3PM). This baseline study explores the internal structure of the SHSQ-25 and demonstrates its discriminatory power to predict optimal and suboptimal health status (SHS) and develop photogenic representations of their distinct relationship patterns.

Methods

The cross-sectional study involved healthy Ghanaian participants (n = 217; aged 30-80 years; ~ 61% female), who responded to the SHSQ-25. The median SHS score was used to categorise the population into optimal and SHS. Graphical LASSO model and multi-dimensional scaling configuration methods were employed to describe the network structures for the two populations.

Results

We observed differences in the structural, node placement and node distance of the synaptic networks for the optimal and suboptimal populations. A statistically significant variance in connectivity levels was noted between the optimal (58 non-zero edges) and suboptimal (43 non-zero edges) networks (p = 0.024). Fatigue emerged as a prominently central subclinical condition within the suboptimal population, whilst the cardiovascular system domain had the greatest relevance for the optimal population. The contrast in connectivity levels and the divergent prominence of specific subclinical conditions across domain networks shed light on potential health distinctions.

Conclusions

We have demonstrated the feasibility of creating dynamic visualizers of the evolutionary trends in the relationships between the domains of SHSQ-25 relative to health status outcomes. This will provide in-depth comprehension of the conceptual model to inform personalised strategies to circumvent SHS. Additionally, the findings have implications for both health care and disease prevention because at-risk individuals can be predicted and prioritised for monitoring, and targeted intervention can begin before their symptoms reach an irreversible stage.

Supplementary information

The online version contains supplementary material available at 10.1007/s13167-023-00344-2.

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.