Chronic pelvic pain syndrome (CPPS) encompasses a heterogeneous group of debilitating conditions including interstitial cystitis/bladder pain syndrome (IC/BPS), chronic abacterial prostatitis, endometriosis, and pelvic congestion syndrome, among others. Despite advances in symptomatic management, CPPS remains challenging due to its multifactorial etiology, complex phenotypes, and poor response to conventional reactive treatments. On the other hand, sympathetic overdrive phenotype (SOP) carriers frequently suffer from increased stress sensitivity, chronic sterile inflammation, pain chronification, and mitochondrial stress – all considered the key CPPS/SOP shared pathomechanisms. Further, the dominant vasoconstriction, altered sense regulation (e.g. the reduced feeling of thirst potentially resulting in systemic dehydration) as well as altered multi-drug resistance protein profiles characteristic for SOP (e.g. exemplified by the Flammer syndrome) may predispose affected individuals to the therapy resistance such as CPPS patients with vulvar-vaginal dryness and abacterial prostatitis. This review article highlights the central role of SOP, systemic mitochondrial stress as well as gut and urinary microbiome alterations – all, per evidence, are considered systemic modifiable risk factors of the CPPS manifestation, disease progression, and therapy resistance. The article introduces 3PM-guided patient-centered solutions aiming to improve life quality and individual outcomes in the CPPS patient cohort. Non-invasive mitochondria-based biosensorics, e.g. applied via the tear fluid analysis, and digital health monitoring, per evidence, enable early health risk assessment and patient stratification at the level of reversible damage to health. Recommended targeted preventive strategies encompass individually adapted lifestyle modifications, modulation of the stress-associated mitochondrial homeostasis, and application of supportive nutraceuticals. Personalized 3PM-guided treatment algorithms transform CPPS management into the patient-centered, cause-oriented and cost-effective proactive care, protecting vulnerable individuals against health-to-disease transition and preventing disease progression in stratified patients.
Cardiovascular disease (CVD) retains a substantial residual burden despite control of conventional risk factors, indicating contributions from additional metabolic, inflammatory and host–microbial pathways. The gut microbiota–bile acid axis links diet and environmental exposures to microbial metabolism, enterohepatic signalling and cardiovascular homeostasis. However, current evidence is commonly organised around isolated diseases or molecular mechanisms, with limited attention to when this axis becomes detectable, how it should be clinically interpreted and whether it can guide individualised prevention. In this review, we propose a spatiotemporal framework for the gut microbiota–bile acid axis across the health–suboptimal health–disease continuum within predictive, preventive and personalised medicine (PPPM/3PM). Mechanistic evidence is most developed for atherosclerosis and atherothrombosis, whereas evidence for atrial fibrillation, heart failure, pulmonary hypertension and cerebrovascular outcomes is more heterogeneous and less mature. Microbial bile salt hydrolase activity and downstream bile acid transformations reshape compartment-specific bile acid pools, with potential effects on cholesterol catabolism, inflammatory and barrier responses, vascular homeostasis, platelet activation and myocardial stress. For 3PM, molecular profiles should be interpreted together with multidomain functional phenotypes, including bowel function and gastrointestinal symptoms, hepatobiliary status, metabolic–inflammatory context, and diet and medication exposure. Microbiota-directed interventions should be function-informed rather than selected by taxonomy alone. Translation requires longitudinal multi-omics, standardised multi-compartment bile acid profiling, causal validation in human-relevant models and prospective studies determining whether phenotype-guided strategies add predictive or preventive value beyond established cardiovascular care.
Aging status, indexed by biological age acceleration (BAacc), is closely associated with cardiovascular disease (CVD). However, transitioning from reactive healthcare to predictive, preventive, and personalised medicine (PPPM/3PM) requires elucidating how longitudinal, dynamic transitions in aging status impact incident CVD risk. We analyzed 4,756 participants from the China Health and Retirement Longitudinal Study. Biological age was calculated using the Klemera-Doubal method (KDM). KDM-BAacc was measured at Wave 1 (baseline) and Wave 3 (follow-up) to derive multidimensional dynamic metrics: change groups, K-means clusters, aging rates, cumulative burden, and extreme phenotypes. Wave 2 was excluded from baseline calculations due to the absence of blood biomarker collection. Incident CVD (stroke and heart disease) was ascertained over a median 9.13-year follow-up. Cox models were adjusted for sociodemographic, lifestyle, and clinical risk factors. Compared to sustained acceleration, reversing aging status (“accelerated to non-accelerated”) significantly lowered risks of composite CVD (HR: 0.72, 95
Periodontitis is a chronic multifactorial inflammatory disease linked to systemic conditions including cardiovascular disease and diabetes, underscoring the need for a holistic diagnostic approach. Current diagnostics rely on clinical probing and radiography, which detect accumulated damage rather than early biological activity or individual progression risk. This reactive paradigm delays intervention and limits personalization. Within predictive, preventive and personalized medicine (PPPM), this review examines emerging diagnostic technologies and evaluates their potential to enable a paradigm shift toward early prediction, targeted prevention, and individualized periodontal management. The convergence of molecular biomarkers, advanced imaging, microbiome profiling, artificial intelligence, and smart oral technologies into a multimodal framework can support the transition from reactive care to PPPM by enabling detection of suboptimal health states before irreversible damage, continuous digital health monitoring beyond episodic visits, and AI-driven patient stratification for individualized protection against health-to-disease transition and disease progression. Salivary and gingival crevicular fluid biomarkers detect inflammatory activity prior to clinical attachment loss, supporting early risk identification. Optical coherence tomography and Raman spectroscopy capture structural and biochemical tissue changes non-invasively. Next-generation sequencing reveals early dysbiotic shifts preceding clinical deterioration. Artificial intelligence integrates these heterogeneous datasets into patient-specific risk signatures, with recent PPPM-oriented studies confirming feasibility of automated oral health assessment. Smart oral devices extend monitoring into daily life, enabling continuous surveillance of behavioral and biochemical risk parameters. For predictive diagnostics, biomarker panels and AI-driven analysis enable identification of preclinical disease activity and individual risk stratification. For targeted prevention, digital monitoring and wearable technologies support continuous risk surveillance and timely individualized interventions. For personalization of medical services, multimodal data integration through AI facilitates patient-specific treatment planning and adaptive care pathways. This integrative framework goes beyond technology-focused reviews by positioning emerging periodontal diagnostics within a unified PPPM paradigm, contributing to the shift from reactive care toward predictive, preventive and personalized disease management.
To examine how distinct cardiovascular health (CVH) trajectories before age 50, estimated by Life’s Essential 8 (LE8), a composite measure comprising four behavioral and four biological components, relate to incident cardiovascular disease (CVD), all-cause mortality, and CVD-free survival in a Chinese population, from a predictive, preventive, and personalized medicine (PPPM) perspective. We included 39,305 CVD-free participants (77.3
Copper overload induces cuproptosis, a unique mitochondria regulated cell death distinct from apoptosis, pyroptosis and ferroptosis. Cuproptosis is involved in cancer progression, drug resistance and immune microenvironment remodeling, making it a promising target for cancer intervention. Key regulators including Copper Transport Protein 1 (CTR1) and Ferredoxin 1 (FDX1) serve as predictive biomarkers: high FDX1 expression indicates enhanced sensitivity to cuproptosis inducers and favorable prognosis in non-small cell lung cancer, supporting precise risk stratification under the Predictive, Preventive and Personalized Medicine(3PM) framework. This review summarizes the molecular mechanisms of cuproptosis and its multifaceted roles in cancer within Predictive, Preventive and Personalized Medicine. Curcumin exhibits potential in modulating cuproptosis homeostasis for high-risk populations with copper overload or precancerous lesions, providing a feasible preventive strategy. We also discuss cuproptosis-targeted therapies, combination regimens and the value of traditional Chinese medicine, while clarifying crosstalk with other cell death pathways. This review addresses the clinical gap of lacking effective predictive and preventive targets for precision oncology, offering novel 3PM-oriented insights for cancer diagnosis, prevention and treatment.
Mental disorders are a major source of disease burden among adolescents and young adults (AYA). A clearer understanding of their long-term temporal trends and variation across sociodemographic settings may help inform more preventive and targeted mental health strategies, consistent with the aims of Predictive, Preventive and Personalized Medicine (PPPM/3PM). We analyzed incidence, prevalence and disability-adjusted life years (DALYs) for mental disorders among individuals aged 15–39 years from 1990 to 2021. Joinpoint regression, age–period–cohort modelling, decomposition analysis and efficiency frontier analysis were applied to identify high-risk populations and key life stages. Future burden (2022–2050) was projected using hybrid regression–ARIMA models under reference and risk-reduction scenarios to explore potential future patterns. Globally, mental disorders accounted for 195.3 million incident cases and 71.0 million DALYs among AYA in 2021. From 1990 to 2021, age-standardized incidence, prevalence, and DALY rates increased significantly, with average annual percentage changes of 0.62, 0.27, and 0.41, respectively. High socio-demographic index (SDI) regions had the highest age-standardized burden and the largest recent increases. Decomposition analysis showed that rising age-specific rates contributed most to increasing burden in high-SDI regions, whereas population growth was the main driver in low-SDI settings. Cohort analysis suggested elevated risks in more recent birth cohorts, with relative risks increasing to approximately 1.20–1.25 among those born after 2000. Under the reference scenario, the future burden among individuals aged 20–29 years was projected to increase substantially by 2050, with DALY rates in the 20–24 age group rising from 2372.9 to 6063.4 per 100,000. Mental disorders remain a substantial and increasing source of disease burden among adolescents and young adults worldwide, with marked variation across sociodemographic settings. These findings highlight the importance of age-specific and context-specific mental health strategies and may help inform more preventive and personalized approaches to AYA mental healthcare.
Chronic sympathoexcitation (sympathetic overdrive syndrome) is defined as the sustained dominance of sympathetic over parasympathetic tone, measurable at the organ, vascular, and neural levels, and differs fundamentally from brief, adaptive “fight-or-flight” responses. Persistent vasoconstrictor drive perturbs cellular metabolism and reshapes host–tumor interfaces. Although this state is well characterized in cardiometabolic medicine, its oncologic implications remain comparatively underexplored, despite converging evidence that adrenergic inputs modulate tumor and stromal behavior. One of the main reasons for presenting this conceptual innovation study is that the sympathetic overdrive phenotype (SOP) carriers, by far, are not rare in the population. Robust statistics towards SOP incidence and prevalence are still missing, despite the urgency of plausible healthcare solutions. Currently, the best described SOP-relevant subpopulation are the Flammer Syndrome phenotype (FSP) carriers who, for instance, are highly prevalent in the academic career-making professional groups. Certain genetic predispositions play a role, however, the key pathways involved into the health-to-disease transition are epigenetically regulated and, to a large extent, represent systemic modifiable phenotype-associated risk factors which are, therefore, a promising target for holistic proactive medical approaches with a high potential to save lives and financial resources.
Despite advanced analytical methods and increasing data availability, most intensive care unit (ICU) prediction models rely on static measurements. However, longitudinal monitoring of biomarkers may better capture disease progression and support timely, individualized interventions within the framework of predictive, preventive, and personalized medicine (PPPM). Since the COVID-19 pandemic, interest in both static and dynamic modelling has expanded. Therefore, this review aimed to summarize current evidence on the use of longitudinal blood biomarker data in ICU prediction models, assess how the pandemic shaped this research, and report validation strategies. This scoping review followed the PRISMA-ScR guidelines. PubMed and Google Scholar were searched for studies on blood biomarker trajectory analysis in the ICU published between 2014 and 2025, covering five years before and after the onset of the COVID-19 pandemic. Forty-seven studies were included, mainly from North America (47
The aeromedical certification of commercial airline pilots relies on periodic, self-disclosure-dependent assessments. However, this reactive model lacks the temporal resolution required to capture the gradual behavioural and physiological perturbations preceding an overt mental health crisis. Concurrently, anonymous epidemiological surveys consistently document subclinical depressive symptoms, insomnia, and burnout among active flight crews. Within the framework of predictive, preventive, and personalised medicine (3PM), these conditions represent suboptimal health states situated in the critical transition zone between health and disease. In this narrative review, we propose a conceptual framework for integrating digital phenotyping and artificial intelligence (AI) into aeromedical mental health monitoring, grounded in the 3PM paradigm. With this aim, we appraise three complementary data streams – including smartphone-derived behavioural markers, wearable-derived physiological signals, and cockpit-derived vocal biomarkers – and examine how aviation-specific confounders modulate their validity. Furthermore, we propose a hybrid federated-plus-on-device AI architecture that reconciles population-level statistical power with individual data sovereignty. Crucially, this framework advances the three constitutive dimensions of 3PM in the aviation context. Regarding prediction, it delivers longitudinally resolved diagnostics capable of detecting health-to-disease trajectories during the prodromal phase, surpassing the temporal limitations of current certification cycles. For prevention, it establishes risk-stratified intervention pathways triggered by deviations from individual baselines, thereby shifting oversight from reactive grounding to pre-emptive peer support. Finally, it affords the personalisation of medical services via on-device AI; by calibrating monitoring to each pilot’s specific behavioural baseline, duty patterns, and circadian exposure, it effectively replaces population-normed evaluations with individually tailored longitudinal surveillance.
Accurately performed thermoregulation is life-important for the human body. Therefore, a relatively narrow temperature range of 36.5–37 °C, which all our biochemical reactions are adapted to, is rigorously kept by the body allowing for the most effective kinetics of all physiological processes. In contrast, feeling inappropriately cold or too hot in the environment with comfortable temperature ranges are symptoms of an altered or even disordered thermoregulation described for a number of syndromes as well as patient cohorts. The rationale of the paper is to contribute to the paradigm shift from reactive to proactive healthcare considering thermoregulation deficits as an important diagnostic and prognostic indicator to be explored and utilized for patient phenotyping and stratification followed by tailored treatment algorithms in primary and secondary care. The conceptual framework of Yin–Yang/Cold–Heat syndromes in Traditional Chinese Medicine (TCM) provides a holistic description of physiological balance, adaptability, and pathological deviation. Recent advances in molecular physiology and biotechnology now permit these ancient classifications to be reframed as quantifiable, systems-level biological states. This review integrates thermosensitive transient receptor potential (TRP) channels with ion-channel networks, inflammatory signaling, and emerging multiomics regulation to reinterpret Cold–Heat syndromes through a modern biotechnological lens. We further incorporate the paracentral dogma concept—highlighting epigenetic, proteomic, and glycomic regulation beyond the classical DNA–RNA–protein axis—to explain how non-template-driven molecular layers dynamically tune TRP channel sensitivity and downstream inflammatory balance. Drawing on advances in genomics, proteomics, glycomedicine, and systems engineering, we propose that Cold–Heat states represent stable yet reversible molecular attractors shaped by environmental exposure, metabolic state, and post-translational modification. Finally, we outline translational opportunities including TRP-based biosensors, epigenetic, protein and glycan biomarkers, and AI-driven Cold–Heat stratification platforms. This integrative framework positions TCM-inspired pattern differentiation as a scalable systems biology paradigm with direct relevance to predictive, preventive and personalized healthcare. A left–right Cold (Yin) to Heat (Yang) continuum integrates layered regulation from genetics, epigenetics, and glycobiology through TRP/ion channels to cytokine-driven clinical phenotypes, visually unifying TCM theory with modern molecular biotechnology.
Gastrointestinal (GID) and cardiovascular diseases (CVD) often coexist, making integrated predictive, preventive, and personalised medicine (PPPM/3PM) management challenging. Although GIDs are established CVD risk factors, their bidirectional link remains unclear, hindering early risk stratification and tailored interventions. This bidirectional study enrolled 460,899/405,701 participants without baseline CVD/GID from the UK Biobank cohort. Associations of GID/CVD with CVD/GID incidence, and associations of comorbidities with all-cause mortality among GID/CVD patients, were estimated via time-dependent Cox regression models. During a median follow-up of 13.5/13.2 years, 70,435 CVD cases and 113,191 incident GID cases occurred. Patients with GID had an elevated risk of CVD (adjusted hazard ratio [aHR] = 1.33, 95
The combined effects of metabolic status and genetic predisposition on healthy ageing remain unclear. We aimed to investigate the association of metabolic status and genetic predisposition with healthy ageing and life expectancy across obesity levels in the context of predictive, preventive, and personalized medicine (PPPM). Cox regression models were used to examine the relationships of metabolic status and polygenic risk score (PRS) with healthy ageing across obesity levels using data from the UK Biobank. Multistate life tables were used to evaluate life expectancy with and without major chronic diseases, cognitive dysfunction, physical impairment, and mental impairment. Compared with individuals with metabolically unhealthy obesity (MUO) and a low PRS for healthy ageing, those with metabolically healthy obesity (MHO) and a high PRS had a significantly increased likelihood of healthy ageing (hazard ratio [HR]: 1.10; 95
Serotonin (5-hydroxytryptamine, 5-HT) is a transdiagnostic, socially calibrated biomarker for precision psychiatry. Convergent neurobiological, genomic, and behavioural data indicate that 5-HT biosynthetic capacity shapes social-cognitive processing. Receptor-level plasticity also contributes to this process. Gut–brain–immune axis signalling plays an additional role. Together, these factors sculpt affective regulation. They also influence life-long mental health trajectories. Within the predictive, preventive, and personalised medicine (predictive preventive personalised medicine (3PM)) paradigm, multiomics risk stratification, early-life dietary or probiotic modification, and individually tailored pharmacological or psychotherapeutic regimens are actionable. Progress in serotonergic (5-HTergic) biomarkers within the 3PM framework is expected to increase medical service accessibility. This enhancement occurs through multiple mechanisms. This paves the way for precise risk stratification to guide timely intervention and personalised psychiatric therapeutics across clinically distinct subpopulations. Most personality disorders and related syndromes have a well-known serotonergic signature. These range from impulsive aggression to abnormal dominance behaviors and the failure of empathy. These features underscore the diagnostic, prognostic, and therapeutic potential of serotonergic biomarkers. However, the realisation of this promise in the clinic is limited by the measurement limitations of the blood–brain barrier (blood–brain barrier (BBB)). It must also address the ethical and privacy implications of genomic screening. The 3PM roadmap is integrated and proposed in the current review. It aims at integrating serotonergic biomarkers into everyday predictive diagnostics. The roadmap further discusses preventive planning as well as tailored interventions in precision psychiatry.
Predictive, preventive, and personalized medicine (3PM) represents the optimal healthcare paradigm, innovative treatment approaches can significantly improve the management of chronic diseases. Hydrogel and hydrogel microneedles have emerged as a transformative platform for transdermal drug delivery. This review presents a comprehensive comparative analysis of hydrogels and hydrogel microneedles, focusing on their structure and morphology, preparation processes, mechanical properties, drug delivery methods, biosafety and degradation behaviors. It further underscores the distinct advantages and emerging applications of hydrogel microneedles including the drugs administration of Ciprofloxacin, Doxorubicin and Insulin, etc., as well as their use in transdermal drug delivery, vaccination, wound healing, tissue regeneration, and biosensing. The innovative merits of hydrogel microneedles are particularly evident in mitochondrial biosensing, digital health monitoring and personalized rehabilitation. These insights will facilitate the rational design and optimization of hydrogel-based microneedles, thereby advancing their application in biomedical therapies.
Metabolic dysfunction-associated steatotic liver disease (MASLD) poses a challenge for Predictive, Preventive, and Personalised Medicine (3PM) due to intrinsic heterogeneity. Current management is reactive. This study’s rationale is that one-size-fits-all labels fail to capture systemic effects and early metabolic shifts. We hypothesized that sex-specific metabolic architectures and transitional hubs can be identified using probabilistic modeling to shift MASLD management toward predictive diagnostics and targeted prevention. We performed unsupervised computational phenotyping using GSEM-based Latent Class Analysis (LCA) across two cohorts: Bogalusa Heart Study (n = 1,392) and NHANES (n = 5,335). LCA disaggregated population heterogeneity into individualised patient profiles with shared cardiometabolic signatures. Partition-based Graph Abstraction (PAGA) was applied to map health-to-disease trajectories. LCA disaggregated MASLD into sex-specific cardiometabolic risk architectures. While aging increased fibrosis risk equally across sexes, evolutionary pathways were profoundly dimorphic. PAGA revealed that females follow interconnected, potentially reversible trajectories—suggesting unique bioenergetic flexibility—whereas males exhibit polarized, abrupt transitions. Clinical relevance varied: in females, a singular high-risk subtype drove mortality (HR = 6.05), whereas risk was broadly distributed in males. Findings were translated into a Python-based 3PM-Risk Calculator for real-time stratification available at https://applacmetsynmasld-i9m9ctxj5icdtyyvdv8tkz.streamlit.app . Our framework enables holistic health risk assessment by identifying high-priority metabolic phenotypes that traditional diagnostics overlook. Pinpointing sex-specific transitional hubs provides a critical window for targeted prevention to intercept disease before irreversible damage occurs. This digital tool facilitates the translation of individualised profiles into clinical practice, fulfilling the 3PM mission to shift from reactive care to proactive, personalised healthcare strategies across the life-course.
Renal clear cell carcinoma (ccRCC) is highly heterogeneous, with significant differences in clinical outcomes such as prognosis and sensitivity to target therapy. The development of predictive, preventive, and personalized medicine (3PM) strategies in the area is essential to execute personalized treatment for ccRCC patients against disease progression effectively. We hypothesized that multi-omics for patients with ccRCC can provide more molecular characteristic information, aiming to develop and validate a personalized, multi-omics prognostic modeling framework for individualized risk stratification of clinical outcomes in ccRCC. We employed a suite of machine learning algorithms for multi-dimensional omics biomarker fusion and for subtype association with single-cell sequencing to classify ccRCC patients into distinct subtypes. Optimal subtypes were determined by comparing silhouette values across various omics combinations. The classifier was validated using two independent external datasets (ICGC-KIRC, 91 patients and GSE167573, 55 patients) and verified with single-cell sequencing data we collected. An interactive web page was developed to facilitate clinical application, enhancing predictive potential. After considering distribution and screening, five omics information from 325 ccRCC patients, including 1000 transcriptome biomarkers, 500 methylation biomarkers, 190 mutation biomarkers, 30 protein biomarkers, and 200 miRNA biomarkers, were included and integrated. Two distinct ccRCC subtypes were identified for personalized treatment: the immune-activated type, characterized by higher immune infiltration and sensitivity to TKIs like sunitinib and sorafenib; and the pathology-characterized type, which has a better prognosis and is more likely to respond to immune checkpoint inhibitor immunotherapy. Single-cell analysis revealed that immune-activated subtypes are significantly associated with myeloid cells and B cells, while pathology-characterized subtypes are significantly associated with endothelial cells and fibroblasts. The interactive web page ( https://zclab-cnp.shinyapps.io/biomarker/ ) provides a convenient tool for clinical precision medicine research. Patients or their treating physicians can upload their sequencing data, and Nearest Template Prediction based on the differentially expressed genes identified in this study can be conducted to ultimately obtain the corresponding patient subgroups. Our study leverages multi-dimensional omics biomarker fusion and machine learning to support accurate risk stratification, personalized ccRCC management and individualized protection against ccRCC progression. Successful clinical application requires transfer learning in local patients, regular patients recalibration, and labortary validation, leading to a valuable reference for ccRCC 3PM strategies.
Glaucoma-related vision loss is associated with stress, anxiety, and reduced quality of life. Because availability of psychological support and evidence-based life style information for patients are limited, we developed “VisionWise” (ViWi), a personalised digital lifestyle education course for glaucoma patients. We hypothesized that ViWi participation could improve subjective well-being, increase knowledge about the disease, increase positive lifestyle habits and possibly improve subjective vision. In this exploratory AB/BA study (N = 27), patients of the AB group participated in a nine-week ViWi course (A) followed by a waiting period (B) while the BA group waited before taking the course. All were asked to complete questionnaires on stress (PSQ-20), depression (PHQ-8), anxiety (GAD-7), subjective vision (NEI-VFQ-25), personality (NEO-FFI), and glaucoma-related knowledge at three time points. Intervention effects were analysed as pre-post comparison: AB:T1–T2 and BA:T1–T3. Subjective general vision improved significantly in group AB (p < .001) but also - unexpectedly - in group BA (p < .001) already during the waiting period, suggesting a placebo or expectancy effect. Changes were observed regarding stress-related measures, depressive symptoms, and anxiety. Some personality dispositions (higher agreeableness) were associated with greater reductions in anxiety and depressive symptoms. Usability ratings of ViWi were high, and no adverse events were observed. ViWi is a feasible and personalised digital lifestyle coaching tool to partially restore subjective vision in glaucoma patients. It can be used at home and empowers patients to cope with vision loss through behavioural eye exercises, management of stress-related risk factors, and novel strategies to adapt to low vision. Although the observed vision improvements are in part explainable by a placebo effect, this finding indirectly supports the relevance of positive attitudes in helping patients better control anxiety and stress, which are known co-factors in glaucoma progression. ViWi also supports patients in becoming better informed and more proactive in their glaucoma management. Our pilot study may guide future study designs to verify and optimise training effects through individualization, user engagement, and online accessibility as low-cost means “what else” they can do to complement standard glaucoma care. Within the framework of predictive, preventive and personalized medicine (PPPM), the present findings highlight the potential of digital lifestyle interventions to support patients´ awareness of psychosocial and stress-related risk factors, while exploratory analyses provide predictive insights into risk constellations associated with intervention outcomes. By addressing stress-related and behavioural factors, ViWi may contribute to a paradigm shift from reactive disease management towards a more personalised, positive, and patient-centred glaucoma care.
Although antihypertensive medication is central to hypertension management, substantial residual risks persist. This study evaluated the associations of healthy lifestyle behaviors with all-cause mortality and cardiovascular disease (CVD) among individuals with hypertension, and assessed whether favorable lifestyles provided additional benefits beyond antihypertensive medication. From the perspective of predictive, preventive, and personalized medicine (PPPM/3PM), we assumed that comprehensive lifestyle assessment could refine risk stratification and help identify priority targets. This study included 16,314 participants with hypertension from the Prospective Urban Rural Epidemiology (PURE)-China study. A healthy lifestyle score (0–6, higher scores indicating healthier behaviors) was constructed based on six lifestyle behaviors. Antihypertensive medication use was defined as regular intake at least once per week. Cox frailty models were used to estimate hazard ratios (HRs) and 95
Thyroid eye disease (TED) is a prevalent autoimmune orbital disorder that significantly impairs visual function and appearance, adversely affecting patients’ quality of life. The development of effective preventive strategies is paramount to optimizing clinical outcomes and disease management. Within the paradigm of predictive, preventive, and personalised medicine (3PM), the identification of modifiable risk factors for TED and the formulation of individualized prevention protocols are critical for targeted risk minimization. However, despite advances in understanding TED pathogenesis, the susceptible populations of TED remain inadequately defined, resulting in generalized and often ineffective preventive measures. Furthermore, while several risk factors for TED development have been identified, particularly tobacco exposure, radioiodine treatment and thyroid dysfunction, their differential contributions across distinct disease courses (onset, progression, and recurrence) remain poorly elucidated. This review comprehensively summarizes current clinical evidence on susceptible populations for TED and classifies modifiable risk factors into five categories: physical and chemical factors, endocrine and autoimmune factors, metabolic and nutritional factors, social and psychological factors, and other factors, with updated clinical evidence of their impacts on disease course. Building upon contemporary clinical evidence, we present a stratified strategy for TED prevention spanning onset, progression, and recurrence that tailored to disease phase-specific vulnerabilities. Early identification and mitigation of modifiable risk factors are pivotal to transitioning TED management from reactive medical services to a 3PM paradigm, ultimately reducing disease incidence, improving clinical outcomes and patients’ quality of life.
