Primary angle-closure glaucoma (PACG) is a common cause of blindness. Early screening is critical to prevent vision loss, yet current methods rely on specialized ophthalmic imaging, which are resource-intensive and reactive, detecting structural damage only after symptom onset. Therefore, we propose a novel clinlabomics-based machine learning prediction model as a screening tool to stratify individuals at high risk for glaucoma, enabling targeted ophthalmic evaluations, preventing progression of optic nerve damage, and facilitating personalized, long-term monitoring in alignment with the principles of predictive, preventive, and personalized medicine (PPPM/3PM). This is a multicenter, retrospective study. We retrieved clinical laboratory data from digital medical records between April 2016 and April 2021 in the Eye and ENT Hospital of Fudan University as a discovery set, consisting of 949 normal subjects and 1152 PACG patients. The internal validation was conducted on the dataset of 646 normal subjects and 657 PACG patients from June 2021 to October 2024, also from the Eye and ENT Hospital of Fudan University; the external validation was performed on a dataset of 246 normal subjects and 136 PACG patients from March 2023 to June 2024, from Shanghai Xuhui Central Hospital and Wanbei Coal Electric Group General Hospital. Based on whether there was optic nerve damage, patients were categorized into early PACG patients, namely primary angle closure(PAC) patients, and non-early PACG. Specifically, in the internal validation cohort of 657 PACG patients, 160 were PAC. In the external validation cohort of 136 PACG patients, 41 were PAC. With the inclusion of 50 features, 12 machine learning models were selected and compared to develop the screening model. The feature reduction was performed by SHAP model and Delong test, and the final model was explained by SHAP method. The evaluation parameters of the models include AUC, AUCPR, sensitivity, specificity, and accuracy. A total of 1841 normal subjects and 1945 PACG patients were included in the study. Among the 12 machine learning models, 4 models, LGBM (AUC = 0.92), XGB (AUC = 0.92), Ada (AUC = 0.91), and GB (AUC = 0.91), performed better than others (P > 0.05). After feature reduction based on feature importance ranking, a final LGBM model of accurate screening PACG ability with six features including TT, PDW, MCV, APTT, TC, and PT was developed, achieving AUC of 0.91, AUCPR of 0.94, sensitivity of 0.89, specificity of 0.79, PPV of 0.84, NPV of 0.85, accuracy of 0.84, and F1 score of 0.86. This final model maintained strong performance in internal validation (AUC = 0.87, accuracy = 0.83, F1 score = 0.85) and external validation (AUC = 0.85, accuracy = 0.89, F1 score = 0.84). The screening efficacy of the final model for PAC was also assessed, where the ROC was 0.85 in the internal validation and 0.84 in the external validation. To enhance its practical application and dissemination, the final model was transformed into an accessible web application. This study establishes a clinically applicable clinlabomics-based model that implements PPPM principles for glaucoma management through routine blood parameters. Our predictive model enables early identification of high-risk PACG patients, while also facilitating cost-effective population screening and personalized risk assessment through explainable artificial intelligence. The current study demonstrates that routine blood parameters serve as critical indicators for glaucoma risk stratification, predictive diagnosis, and targeted intervention. Consequently, this innovative screening approach provides an essential tool for optimizing clinical outcomes in high-risk populations and improving glaucoma care accessibility, particularly in underserved communities with limited ophthalmic resources. The online version contains supplementary material available at 10.1007/s13167-025-00419-2.
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.
High-altitude de-acclimatization (HADA) is accompanied by a complex spectrum of long-term physiological and functional remodeling processes, potentially affecting long-term health outcomes. Existing studies on HADA have predominantly focused on cardiovascular and nervous system changes. However, the immune system-an essential regulator of disease susceptibility, inter-individual variability, and long-term health risks-remains insufficiently investigated in the context of HADA. Given the central role of immune regulation in maintaining systemic homeostasis and determining individual health trajectories, elucidating immune alterations associated with HADA is essential. The present study aims to characterize immune system remodeling during HADA with particular emphasis on its functional outcomes and mechanisms. By addressing these scientific questions, this study seeks to provide an immune system perspective for the health maintenance of HADA individuals, promoting paradigm shift from reactive medical services toward predictive, preventive, and personalized medicine (3PM). Peripheral blood was collected from both human cohorts and mice models while other immune organs including spleen, thymus and bone marrow were obtained from mice models. Proportions of immune cell populations in peripheral blood and other immune organs were analyzed using flow cytometry. The immune-suppressive functions of Tregs were determined by in vitro co-culture with CD8+ T cells. Transcriptomic and chromatin-accessibility feature induced by HADA were obtained through RNA-seq and ATAC-seq. The functional validation of HADA target gene was performed using specific agonist during in vitro co-culture system. HADA perturbed the proportions of immune populations in multiple immune site. Both data from human and mice showed increased regulatory T cells (Tregs) and enhanced immune-suppressive function in the peripheral blood. As a consequence, these Tregs mediated long-term immune suppression and compromised the immunity against tumor cells. Multi-omic analyses predicted Nrf2 as the key mediator of molecular alterations in Tregs caused by HADA, which was further confirmed by the functional assay. This study advances high-altitude medicine by demonstrating that HADA induces long-lasting immunosuppressive effects through Nrf2-mediated Treg remodeling, with important implications for immune homeostasis and long-term health risks. These findings highlight the role of the immune system, particularly Tregs, in HADA-induced health impairments and identify Nrf2 as a potential therapeutic target. Moreover, immune biomarkers-especially Treg phenotypes and Nrf2 activity-may serve as promising candidates for risk stratification and predictive diagnostics in populations transitioning between high- and low-altitude environments. Preventive strategies should prioritize immune-informed recovery protocols, oxidative stress modulation, and lifestyle or nutritional interventions tailored to individual immune profiles. The online version contains supplementary material available at 10.1007/s13167-026-00442-x.
Metabolomics measurements of eccrine sweat may provide novel and relevant biomedical information to support predictive, preventive and personalised medicine (3PM). However, only limited data is available regarding metabolic alterations accompanying chemotherapy of breast cancer patients related to residual cancer burden (RCB) or therapy response. Here, we have applied Metabo-Tip, a non-invasive metabolomics assay based on the analysis of eccrine sweat from the fingertips, to investigate the feasibility of such an approach, especially with respect to drug monitoring, assessing lifestyle parameters and stratification of breast cancer patients. Eccrine sweat samples were collected from breast cancer patients (n = 9) during the first cycle of neoadjuvant chemotherapy at four time points in this proof-of-concept study at a Tertiary University Hospital. Metabolites in eccrine sweat were analysed using mass spectrometry. Blood plasma samples from the same timepoints were also collected and analysed using a validated targeted metabolomics kit, in addition to proteomics and fatty acids/oxylipin analysis. A total of 247 exogenous small molecules and endogenous metabolites were identified in eccrine sweat of the breast cancer patients. Cyclophosphamide and ondansetron were successfully detected and monitored in eccrine sweat of individual patients and accurately reflected the administration schedule. The non-essential amino acids asparagine, serine and proline, as well as ornithine were significantly regulated in eccrine sweat and blood plasma over the therapy cycle. However, their distinct time-dependent profiles indicated compartment-specific distributions. Indeed, the metabolite composition of eccrine sweat seems to largely resemble the composition of the interstitial fluid. Plasma proteins and fatty acids/oxylipins were not affected by the first treatment cycle. Individual smoking habit was revealed by the simultaneous detection of nicotine and its primary metabolite cotinine in eccrine sweat. Stratification according to RCB revealed pronounced differences in the metabolic composition of eccrine sweat in these patients at baseline, e.g., essential amino acids, possibly due to the systemic contribution of breast cancer and its impact on metabolic turnover. Mass spectrometry-based analysis of metabolites from eccrine sweat of breast cancer patients successfully qualified lifestyle parameters for risk assessment and allowed us to monitor drug treatment and systemic response to therapy. Moreover, eccrine sweat revealed a potentially predictive metabolic pattern stratifying patients by the extent of the metabolic activity of breast cancer tissue at baseline. Eccrine sweat is derived from the otherwise hardly accessible interstitial fluid and, thus, opens up a new dimension for biomonitoring of breast cancer in secondary and tertiary care. The simple sample collection without the need for trained personnel could also enable decentralised long-term biomonitoring to assess stable disease or disease progression. Eccrine sweat analysis may indeed significantly advance 3PM for the benefit of breast cancer patients. The online version contains supplementary material available at 10.1007/s13167-025-00396-6.
Drug abuse poses an enormous threat to global public health. Long-term drug abuse can reduce the quality of life of patients and increase the healthcare burden on society. There is growing interest in developing new methods to mitigate the effects of drug abuse. The gut microbiota plays a key role in maintaining homeostasis within the brain-gut-lung axis, which is critical in drug-abusing patients. The microbiota-brain-gut-lung axis refers to the interactions of microbes with the brain, gut, and lung. The effects of drug abuse on the gut microbiota are increasingly recognized, especially the pathogenesis by which the microbiota-brain-gut-lung axis is involved in regulating organ-organ communication, to explore new therapeutic approaches for clinical drug abuse. Currently, in addition to antibiotics, antiviral drugs, anti-tumor drugs, corticosteroids, drugs for the treatment of neurodegenerative diseases, and anesthetics also cause gut microbiota imbalance. This review summarizes the effects of drug abuse on gut microbiota and the important role of the microbiota-brain-gut-lung axis in drug abuse. Identifying changes in the gut microbiota associated with drug abuse and their underlying mechanisms under the principles of predictive, preventive, and personalized medicine (PPPM) is a critical step toward achieving PPPM. These strategies include FMT, probiotic supplements, and engineered bacteria that can benefit sub-healthy individuals with gut dysbiosis caused by drug abuse.
Chronic kidney disease (CKD) is associated with premature aging, which reflects in the difference between biological age and chronological age. Immunoglobulin G (IgG) N-glycosylation profiles have emerged as promising biomarkers for biological aging. From the perspective of predictive, preventive, and personalized medicine (PPPM/3PM), we assumed that the evaluation of kidney-specific biological age based on IgG N-glycosylation profiles provides a better tool for targeted prevention and personalized intervention of CKD by monitoring kidney aging. This study analyzed data from the Beijing Health Management Cohort. Plasma IgG N-glycosylation profiles were quantified into 24 glycan peaks (GPs), and feature selection was conducted using adaptive elastic net followed by logistic regression. IgG N-glycosylation kidney biological age (GlyKage) was calculated using linear regression, and the difference between GlyKage and chronological age (GlyKageDiff) was calculated. The associations of GlyKage and GlyKageDiff with CKD were evaluated using the adjusted multivariable logistic regression. Odds ratio (OR) and 95% confidence interval (CI) were calculated. Diagnostic models were developed using an 8:2 train-test split of the dataset, by incorporating different predictor variables and using support vector machine, XGBoost, and LightGBM. From 3123 participants with blood samples collected during 2014-2015, we selected 2382 participants for evaluating GlyKage and developing the CKD diagnostic models. We selected four GPs associated with CKD, included GP3, GP11, GP13, GP24, to calculate GlyKage. In adjusted models, each one-unit increase of GlyKage was associated with higher CKD risk (OR = 1.136, 95% CI: 1.114-1.159), and individuals with high GlyKageDiff (the top 25% values) had a higher CKD risk (OR = 12.179, 95% CI: 6.530-25.364). In the test set of the diagnostic model, compared to chronological age, GlyKage and GlyKageDiff increased the area under curve (AUC) value by 10.8% and 15.2% respectively. The AUC value of the final model was 0.945 (95% CI: 0.916-0.971). GlyKage and GlyKageDiff are associated with a higher risk of CKD, and show a substantial value in the diagnosis of CKD. In the context of PPPM/3PM, evaluating GlyKage helps identify individuals at high risk for CKD, facilitating early intervention and management. Furthermore, evaluating an individual's kidney aging status meets the need for personalized healthcare. The online version contains supplementary material available at 10.1007/s13167-026-00440-z.
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.
Guided by the paradigm of predictive, preventive, and personalized medicine (PPPM/3PM), this study systematically evaluated the epidemiological burden of Polycystic ovarian syndrome (PCOS) and its future trends in countries along the Belt and Road Initiative (BRI). Utilizing data from the Global Burden of Disease Study, we applied Joinpoint regression, Net Drift analysis, age-period-cohort (APC) modeling, and Bayesian APC forecasting. Findings indicated a persistent upward trend in PCOS burden across most BRI countries from 1990 to 2021, particularly in Equatorial Guinea and the Maldives. By 2021, China reported the highest numbers of both new cases (258,930) and prevalent cases (10,077,520). Italy exhibited the highest age-standardized rates of incidence, prevalence, and disability-adjusted life years; nevertheless, it was the only country to demonstrate a declining burden over the three-decade period, suggesting a potential moderating role of population aging and structural changes. Age-specific analysis pinpointed 15-19 years as the critical window for disease onset, while women aged 20-49 represented the core population affected by PCOS and its associated health impacts. Forecasting suggests that the number of prevalent cases in China will exceed 14,410,378 by 2046, underscoring the urgency of proactive management. The PCOS burden demonstrates marked geographical heterogeneity, closely linked to levels of socioeconomic development. Embracing the PPPM framework, burden data are translated into management strategies that target key screening windows, high-risk populations, and specific phenotypes, which will facilitate cross-border health data sharing and promote the coordinated and equitable allocation of medical resources, thereby addressing regional disparities and providing critical support for achieving precision medicine goals in global women's health. The online version contains supplementary material available at 10.1007/s13167-026-00443-w.
Due to their phenotype-associated attitude predominantly oriented towards high performance, Flammer syndrome (FS) phenotype carriers are blessed to a successful career in corresponding professional branches. This advantage is however associated with significant health risks. FSP carriers are extremely stress-sensitive. Corresponding pathways are epigenetically regulated, and modifiable risk factors are associated with the phenotype-specific psycho-somatic patterns such as a drive for meticulousness, perfectionism and exercised rigour applying strictness, discipline, or thoroughness to their own behaviour and actions. The FS phenotype is typically characterised by chronication of the transient sympathoexcitation and its dominance over parasympathetic relaxation. Chronification of the parasympathetic-sympathetic imbalance in form of sympathetic overdrive leads to chronic ischemic events in peripheral vessels and progressing tissue damage associated with the cyclic ischemia-reperfusion. Ischemic damage can be roughly estimated by levels of the vasoconstrictor endotelin-1 (ET-1) measured in blood. However, other risk factors on the one hand and compensatory mechanisms on the other hand are decisive for the damage extent at individual level. For example, chronically increased ET-1 and even mild hyperhomocysteinaemia synergistically may cause a progressing disease of small vessels, systemic inflammation and chronification of mitochondrial stress potentially resulting in chronic fatigue and mitochondrial burnout with a spectrum of associated pathologies in affected individuals. That is why predictive diagnostics utilising comprehensive individualised patient profiles are crucial for the cost-effective targeted prevention and creation of personalised treatment algorithms. Due to the high level of algorithms' complexity, an application of AI is essential. FS is usually established early in life during pubertal maturation of otherwise healthy individuals. Therefore, FS phenotyping is instrumental for 3PM-guided cost-effective primary healthcare. To meet the needs of this patient cohort, an application of the digital health monitoring including records of mitochondrial homeostasis is strongly recommended to protect the FS phenotype carriers against health-to-disease transition. To this end, patient friendly non-invasive approach is already established utilising tear fluid multi-omics, mitochondria as vital biosensors and AI-based multi-professional data interpretation; the approach is offered to the FS phenotype carriers.
Cancer drug resistance poses a significant challenge in oncology, primarily driven by cancer cell plasticity, which promotes tumor initiation, progression, metastasis, and therapeutic evasion in many different cancers. Breast cancers (BCs) are a prominent example of that, with an estimated 2.3 million new cases and 670,000 BC-related deaths registered worldwide annually. Triple-negative BC is especially challenging for treatments demonstrating particularly aggressive disease course, an early manifestation of metastatic disease, frequent drug-resistant cancer types, and poor individual outcomes. Although chemosensitizing agents have been developed, their clinical utility in oncology remains unproven. The mitogen-activated protein kinase (MAPK) pathway is considered a critical regulator of intracellular and extracellular signaling highly relevant for both - genetic and epigenetic modifications. Dysregulation of the MAPK signaling pathways plays a significant role in conferring chemoresistance in BC. Contextually, targeting the MAPK pathway represents a promising strategy for overcoming drug resistance and enhancing the therapeutic efficacy of anticancer agents in BC treatment. On the other hand, flavonoids, a prominent class of phytochemicals, are key modulators of MAPK signaling. Flavonoids interact with the ERK, JNK, p38, and ERK5 pathways of the MAPK signaling cascade and present a promising avenue for developing novel anti-cancer therapies and re-sensitizing agents for the treatment of BC. Compounds such as quercetin, kaempferol, genistein, luteolin, myricetin, EGCG, baicalein, baicalin, nobiletin, morin, delphinidin, acacetin, isorhamnetin, apigenin, silymarin, among others, have been identified as specific modulators of MAPK signaling, exerting complex downstream effects in BC cells increasing therewith drug efficacy and suppressing tumor growth and aggressivity. These properties reflect mechanisms of great clinical relevance to overcome therapeutic resistance in overall BC management. This article highlights corresponding mechanisms and provides clinically relevant illustrations in the framework of 3P medicine for primary (protection of individuals at high risk against health-to-disease transition) and secondary care (protection against metastatic BC progression). 3PM novelty makes good use of patient phenotyping and stratification, predictive multi-level diagnostics, and application of Artificial Intelligence (AI) tools to the individualized interpretation of big data - all proposed for cost-effective treatments tailored to individualized patient profiles with clear benefits to patients and advanced BC management.
Atherosclerosis and chronic kidney disease are major contributors to cardiovascular disease (CVD) and premature mortality worldwide. However, how kidney function decline and carotid plaque (CP) progression influence each other over time remains unclear. In the context of predictive, preventive, and personalised medicine (PPPM/3PM), we investigated the bidirectional associations between kidney function decline and CP progression by leveraging both baseline and repeated measurements of estimated glomerular filtration rate (eGFR) and total plaque area (TPA). Understanding these relationships may facilitate early risk stratification at the subclinical stage and guide targeted preventive and personalised interventions for high-risk individuals, ultimately improving long-term cardiorenal outcomes. We derived three sub-cohorts from the Beijing Health Management Cohort. Sub-cohort 1 included 11,657 participants who underwent at least two examinations between 2010 and 2018; cross-lagged panel analyses were conducted to evaluate the bidirectional associations between eGFR and TPA. Sub-cohort 2 comprised 4173 participants free of CP at baseline; Cox proportional hazards models were used to assess associations of eGFR slope and cumulative eGFR with incident CP. Sub-cohort 3 consisted of 7601 participants with baseline eGFR ≥ 60 mL/min/1.73 m2; Cox models were applied to examine associations between TPA slope, cumulative TPA, and kidney function decline. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. C-statistics, integrated discrimination improvement, and the net reclassification index were used to estimate the incremental predictive value. In sub-cohort 1, cross-lagged panel analyses demonstrated a significant bidirectional association between eGFR and TPA after adjusting for confounders. Higher baseline eGFR predicted lower subsequent TPA (β = -0.029, P < 0.001), whereas higher baseline TPA predicted lower subsequent eGFR (β = -0.070, P < 0.001). In sub-cohort 2, during a median follow-up of 3.98 years, 922 participants developed incident CP. The eGFR slope (HR: 0.804, 95%CI: 0.750-0.862) and cumulative eGFR (HR: 0.805, 95%CI: 0.751-0.863) were negatively associated with incident CP. In sub-cohort 3, over a median follow-up of 4.86 years, kidney function decline occurred in 239 participants. The TPA slope (HR: 1.222, 95%CI: 1.133-1.317) and cumulative TPA (HR: 1.244, 95%CI: 1.136-1.362) were positively associated with kidney function decline. Finally, incorporating eGFR and TPA measures, particularly their slopes and cumulative levels, yielded greater incremental improvements in predicting incident CP and kidney function decline, respectively. These findings demonstrate a bidirectional association between kidney function decline and CP progression, supported by baseline levels, slopes, and cumulative exposure metrics. This reciprocal relationship underscores the potential of integrated, dynamic monitoring of eGFR and TPA as predictive diagnostic tools for identifying subclinical cardiorenal risk, thereby guiding personalised preventive strategies within the PPPM/3PM paradigm. The online version contains supplementary material available at 10.1007/s13167-025-00425-4.
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). 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. 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. 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. The online version contains supplementary material available at 10.1007/s13167-024-00394-0.
Shift workers, such as medical personnel, and pilots, are facing an increased risk of depressive symptoms. Depressive symptoms significantly impact an individual's quality of life and affect work performance, decision-making abilities, and overall public safety. This study aims to establish a multidimensional depressive symptom prediction model based on a large sample of commercial airline pilots to facilitate early identification, prevention, and personalized intervention strategies. This population-based study included 11,111 participants, with 7918 pilots in the training set and 3193 pilots in the external validation set. Depressive symptom severity was assessed using the Patient Health Questionnaire-9 (PHQ-9). Physiological, psychological, and lifestyle factors potentially associated with depressive symptom risk were collected. The optimal predictors for model development were selected using the Boruta algorithm combined with the LASSO method, and a nomogram was developed using multivariate logistic regression to predict depressive symptoms in pilots. The model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and accuracy measures, such as the Brier score and Spiegelhalter z-test. Additionally, decision curve analysis (DCA) was performed to assess the model's clinical utility. A total of 7918 pilots were included in the training set and 3193 were included in the external validation set. Five characteristic indicators were selected based on their significance in the prediction of depressive symptom risk: living status, alcohol drinking, family history of mental health disorder, subjective health, and subjective sleep quality. The model showed acceptable overall discrimination (AUCtrain = 0.836, 95%CI 0.818 to 0.854; AUCvalidation = 0.840, 95%CI 0.811 to 0.868) and calibration (Brier scoretrain = 0.048; Brier scorevalidation = 0.051). The decision curve analysis showed that the net benefit was superior to intervening on all participants or not intervening on all participants. This study provides a reliable tool for early prediction and customized management of depressive symptoms among commercial airline pilots. This approach promotes the development of the field by transitioning from passive mental health care to active mental health prevention, emphasizing personalized prevention strategies.
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.
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. The online version contains supplementary material available at 10.1007/s13167-026-00445-8.
Acute mountain sickness (AMS) is a self-limiting illness, involving a complex series of physiological responses to rapid ascent to high altitudes, where the body is exposed to lower oxygen levels (hypoxia) and changes in atmospheric pressure. AMS is the mildest and most common form of altitude sickness; however, without adequate preparation and adherence to ascent guidelines, it can progress to life-threatening conditions. Due to the multi-factorial predisposition of AMS among individuals, identifying AMS biomarkers before high altitude exposure from multiple dimensions (e.g., clinical, metabolic, and proteomic markers) and integrating them to build an AMS predictive model enables early diagnosis and personalized interventions, which allows targeted allocation of medical resources, such as prophylactic medications (e.g., acetazolamide) and supplemental oxygen, to those who need them most and prevention of unnecessary complications. Consequently, predicting AMS utilizing biomarkers from multidimensional phenotypic data before high-altitude exposure is essential for the paradigm change in high-altitude medical research from currently applied reactive 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, this study recruited 83 Han Chinese male volunteers and obtained clinical, proteomic, and metabolomic profiles for analysis before they ascended to high altitudes. The Mann-Whitney U test was used to identify clinical features distinguishing AMS from non-AMS. The proteomic and metabolomic features were concatenated and clustered to find co-expression modules associated with AMS. A machine learning model, Mutual Information-radial kernel-based Support Vector Machine-Recursive Feature Elimination (MI-radialSVM-RFE) was employed for biomarkers selection and AMS prediction. A molecular docking technique was used to select molecular biomarkers that can bind with Traditional Chinese Medicine (TCM) ingredients. Among 83 participants, 66 were selected for detailed analysis after quality control steps. Six protein-metabolite co-expression modules were identified as significantly associated with AMS. The MI-radialSVM-RFE model selected 12 biomarkers (two clinical features: systolic blood pressure (SBP) and peak expiratory flow (PEF); six proteins: Acyl-CoA synthetase long-chain family member 4 (ACSL4), immunoglobulin kappa variable 1D-16 (IGKV1D-16), coagulation factor XIII B subunit (F13B), prosaposin (PSAP), poliovirus receptor (PVR), and multimerin-2 (MMRN2); and four metabolites: 2-Methyl-1,3-cyclohexadiene, calcitriol, 4-Acetamido-2-amino-6-nitrotoluene, and 20-Hydroxy-PGE2) for the AMS prediction model. The model exhibited excellent predictive performance in both training (n = 66) and validating cohorts (n = 24) with AUCs of 0.97 and 0.94, respectively. Additionally, molecular docking analysis suggested PSAP and ACSL4 proteins as potential molecular targets for AMS prevention. This study advances high-altitude medicine by developing a predictive model for AMS using clinical, proteomic, and metabolomic data. The identified biomarkers linked to energy metabolism, immune response, and vascular regulation offer insights into AMS mechanisms. High-altitude predictive approaches should focus on implementing biomarker-driven risk screening using clinical, proteomic, and metabolomic data to identify high-risk individuals before high-altitude exposure. Preventive measures should prioritize pre-acclimatization protocols, tailored nutritional strategies and interventions guided by biomarker profiles, and lifestyle adjustments, such as maintaining mitochondrial health through proper nutritional strategies. The online version contains supplementary material available at 10.1007/s13167-025-00404-9.
Immune checkpoint inhibitors (ICIs), such as anti-PD-1, anti-PD-L1, and anti-CTLA-4 therapies, have revolutionized cancer treatment by harnessing the body's immune system to eliminate cancer cells. Despite their considerable promise, the efficacy of ICIs significantly differs based on tumor types and specific patient conditions, highlighting the necessity for personalized approaches in the framework of predictive preventive personalized medicine (PPPM; 3PM). This review proposes a stratification instrument within the 3PM framework to enhance the therapeutic efficacy of ICIs across Pan-cancer. Predictive approaches need to be utilized to enhance the effectiveness of ICIs. For example, biomarkers such as particular genetic alterations and metabolic pathways provide key information on patient treatment responses. To predict treatment outcomes, uncover resistance mechanisms, and tailor medications, we examine biomarkers including PDL-1 and CTLA4. Focusing on cancers like melanoma, bladder, and renal cell carcinoma, we highlight advances in combination therapies and cellular approaches to overcome resistance. We conducted an analysis of clinical trials and public datasets (TCGA, GEO) to evaluate ICI responses across number of cancer types. Survival analysis employed Kaplan-Meier curves and Cox regression. Pan-cancer analysis shows response rates ranging from 19.8% in bladder cancer to > 39% in melanoma when combination therapy is used, emphasizing the potential of 3PM to improve outcomes. By exploring resistance mechanisms and emerging therapeutic innovations, we propose a cost-effective model for better patient stratification and care. Validation of this model requires standardized biomarkers and prospective trials, promising a shift toward precision oncology. Within the 3PM framework, this review addresses the urgent need for cost-effective stratification tools and adaptive combinatorial strategies to optimize outcomes.
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.
In the era of shifting healthcare, a "reactive" approach to colorectal cancer (CRC) management-that is, initiating treatment only after the onset of symptoms-remains a major global health challenge. This study uses the Global Burden of Disease (GBD) 2021 data to quantify CRC burden and provide a foundation for developing targeted Predictive, Preventive, and Personalized Medicine (PPPM/3PM) strategies worldwide, especially in China. We conducted a comprehensive analysis using data from the Global Burden of Disease GBD 2021 study, obtained through VizHub and GBD Foresight Visualization tools. Statistical analyses were performed using R (4.4.2; available from: https://cloud.r-project.org/) and Biowinford (Available from: http://biowinford.site:3838/trial/), incorporating key parameters including age, sex, disease-specific factors, disability-adjusted life years (DALYs), and socio-demographic index (SDI) and modifiable risk factors, such as behavioral and dietary factors. This methodological framework is designed to identify high-risk populations and regions, thereby enabling predictive diagnostics and targeted prevention strategies as core tenets of the PPPM model. From 1990 to 2021, the global age-standardized rate of CRC prevalence increased by 24.6%, with the number of prevalent cases rising from 4.26 million to 11.7 million. During the same period, China experienced an increase of 141.2% in its ASR, as its prevalent cases increased from 0.6 million to 3.6 million. While age-standardized death rates declined globally (-20.7%), regional disparities persisted, with men bearing a higher burden and DALYs rising in parts of Africa and Asia. For instance, the number of deaths in East Asia, North Africa and the Middle East was 287,900 and 37,400, respectively; the corresponding DALYs were 7,149,000 and 1,012,700. Major modifiable risks were high BMI, diet high in red meat, and low physical activity. Projections to 2050 indicate a continued rise in cases in China and Africa. Our study provides evidence to support the shift towards PPPM in CRC care. With rising urbanization, dietary shifts, and aging populations, predictive diagnostics using Artificial Intelligence and big data, targeted prevention of modifiable risks, and personalized treatments based on genetic and tumor data could be essential. Detailed burden mapping forms the foundation for this proactive approach, especially in high-burden regions like China. The online version contains supplementary material available at 10.1007/s13167-025-00431-6.
Chronic atrophic gastritis with accelerated progression represents a major risk factor for gastric cancer development, primarily driven by pathological processes including microbial imbalance, oxidative stress, chronic inflammation, immune dysregulation, mucin dynamics, and glycobiological disorders. Its global incidence is on the rise. Current diagnostic and therapeutic approaches under the reactive medicine paradigm demonstrate limited efficacy in controlling disease progression. Against this backdrop, supramolecular self-assembling delivery systems derived from herbal medicine offer breakthrough solutions to the current diagnostic and therapeutic challenges of chronic atrophic gastritis. Their advantages include natural origin, simple assembly, multi-target synergistic effects, high biocompatibility, and low potential toxicity. By summarizing the molecular mechanisms underlying chronic atrophic gastritis and the strengths and limitations of traditional Chinese and Western medical approaches, this study proposes the health benefits of herb-derived supramolecular self-assembling delivery systems for chronic atrophic gastritis under predictive, preventive, and personalized medicine (PPPM/3PM). This review further proposes an innovative strategy within the PPPM/3PM framework, positioning the herb-derived supramolecular self-assembling delivery system as a predictive medical approach, a cost-effective preventive measure, and a tailored optimal treatment plan for patients with chronic atrophic gastritis across primary care (protecting susceptible populations from health deterioration to disease), secondary care (protecting affected populations from disease progression), and tertiary care (enhancing patient quality of life and reducing complications). Supramolecular self-assembling delivery systems derived from natural herbal medicines enable personalized prevention and treatment of chronic atrophic gastritis with superior cost-effectiveness compared to conventional reactive medicine.