Epma Journal最新文献

Over the last two decades, a large number of non-communicable/chronic disorders reached an epidemic level on a global scale such as diabetes mellitus type 2, cardio-vascular disease, several types of malignancies, neurological and eye pathologies-all exerted system's enormous socio-economic burden to primary, secondary, and tertiary healthcare. The paradigm change from reactive to predictive, preventive, and personalized medicine (3PM/PPPM) has been declared as an essential transformation of the overall healthcare approach to benefit the patient and society at large. To this end, specific biomarker panels are instrumental for a cost-effective predictive approach of individualized prevention and treatments tailored to the person. The source of biomarkers is crucial for specificity and reliability of diagnostic tests and treatment targets. Furthermore, any diagnostic approach preferentially should be noninvasive to increase availability of the biomaterial, and to decrease risks of potential complications as well as concomitant costs. These requirements are clearly fulfilled by tear fluid, which represents a precious source of biomarker panels. The well-justified principle of a "sick eye in a sick body" makes comprehensive tear fluid biomarker profiling highly relevant not only for diagnostics of eye pathologies but also for prediction, prognosis, and treatment monitoring of systemic diseases. One prominent example is the Sicca syndrome linked to a cascade of severe complications that include dry eye, neurologic, and oncologic diseases. In this review, protein profiles in tear fluid are highlighted and corresponding biomarkers are exemplified for several relevant pathologies, including dry eye disease, diabetic retinopathy, cancers, and neurological disorders. Corresponding analytical approaches such as sample pre-processing, differential proteomics, electrophoretic techniques, high-performance liquid chromatography (HPLC), enzyme-linked immuno-sorbent assay (ELISA), microarrays, and mass spectrometry (MS) methodology are detailed. Consequently, we proposed the overall strategies based on the tear fluid biomarkers application for 3P medicine practice. In the context of 3P medicine, tear fluid analytical pathways are considered to predict disease development, to target preventive measures, and to create treatment algorithms tailored to individual patient profiles.

Background: Continuous positive airway pressure (CPAP), the reference treatment for obstructive sleep apnoea (OSA), is used by millions of individuals worldwide with remote telemonitoring providing daily information on CPAP usage and efficacy, a currently underused resource. Here, we aimed to implement data science methods to provide tools for personalizing follow-up and preventing treatment failure.

Methods: We analysed telemonitoring data from adults prescribed CPAP treatment. Our primary objective was to use Hidden Markov models (HMMs) to identify the underlying state of treatment efficacy and enable early detection of deterioration. Secondary goals were to identify clusters of rAHI trajectories which need distinct therapeutic strategies.

Results: From telemonitoring records of 2860 CPAP-treated patients (age: 66.31 ± 12.92 years, 69.9% male), HMM estimated three states differing in variability within a given state and probability of shifting from one state to another. The daily inferred state informs on the need for a personalized action, while the sequence of states is a predictive indicator of treatment failure. Six clusters of rAHI trajectories were identified ranging from well-controlled patients (cluster 0: 669 (23%); mean rAHI 0.58 ± 0.59 events/h) to the most unstable (cluster 5: 470 (16%); mean rAHI 9.62 ± 5.62 events/h). CPAP adherence was 30 min higher in cluster 0 compared to clusters 4 and 5 (P value < 0.01).

Conclusion: This new approach based on HMM might constitute the backbone for deployment of patient-centred CPAP management improving the personalized interpretation of telemonitoring data, identifying individuals for targeted therapy and preventing treatment failure or abandonment.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-021-00264-z.

Background: Programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) immune checkpoint inhibitors (ICIs) significantly affect outcomes in non-small cell lung cancer (NSCLC) patients. However, differences in reactions toward PD-1/PD-L1 ICI among patients impose inefficient treatment. Therefore, developing a reliable biomarker to predict PD-1/PD-L1 ICI reaction is highly necessary for predictive, preventive, and personalized (3P) medicine.

Materials and methods: We recruited 63 patients from the National Cancer Center (NCC) and classified them into the training and validation sets. Next, 99 patients were recruited for inclusion into the external validation set at the Samsung Medical Center (SMC). Proteomic analysis enabled us to identify plasma C7 levels, which were significantly different among groups classified by their overall response to the RECIST V 1.1-based assessment. Analytical performance was evaluated to predict the PD-1/PD-L1 ICI response for each type of immunotherapy, and NSCLC histology was evaluated by determining the C7 levels via ELISA.

Results: Plasma C7 levels were significantly different between patients with and without clinical benefits (PFS ≥ 6 months). Among the groups sorted by histology and PD-1/PD-L1 immunotherapy type, only the predicted accuracy for pembrolizumab-treated patients from both NCC and SMC was greater than 73%. In patients treated with pembrolizumab, C7 levels were superior to those of the companion diagnostics 22C3 (70.3%) and SP263 (62.1%). Moreover, for pembrolizumab-treated patients for whom the PD-L1 tumor proportion score (TPS) was < 50%, the predictive accuracy of C7 was nearly 20% higher than that of 22C3 and SP263.

Conclusion: Evaluation of plasma C7 levels shows an accurate prediction of NSCLC patient reactions on pembrolizumab. It demonstrates plasma C7 is an alternative and supportive biomarker to overcome the predictive limitation of previous 22C3 and SP263. Thus, it is clear that clinical use of plasma C7 allows predictive diagnosis on lung cancer patients who have not been successfully treated with current CDx and targeted prevention on metastatic diseases in secondary care caused by a misdiagnosis of current CDx. Reduction of patients' financial burden and increased efficacy of cancer treatment would also enable prediction, prevention, and personalization of medical service on NSCLC patients. In other words, plasma C7 provides efficient medical service and an optimized medical economy followed which finally promotes the prosperity of 3P medicine.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-021-00266-x.

Alzheimer's disease (AD) is associated with abnormal calcium signaling, a pathway regulated by the calcium-dependent protein phosphatase. This study aimed to investigate the molecular function of protein phosphatase 3 regulatory subunit B (PPP3R1) underlying AD, which may provide novel insights for the predictive diagnostics, targeted prevention, and personalization of medical services in AD by targeting PPP3R1. A total of 1860 differentially expressed genes (DEGs) from 13,049 background genes were overlapped in AD/control and PPP3R1-low/high cohorts. Based on these DEGs, six co-expression modules were constructed by weight gene correlation network analysis (WGCNA). The turquoise module had the strongest correlation with AD and low PPP3R1, in which DEGs participated in axon guidance, glutamatergic synapse, long-term potentiation (LTP), mitogen-activated protein kinase (MAPK), Ras, and hypoxia-inducible factor 1 (HIF-1) signaling pathways. Furthermore, the cross-talking pathways of PPP3R1, such as axon guidance, glutamatergic synapse, LTP, and MAPK signaling pathways, were identified in the global regulatory network. The area under the curve (AUC) analysis showed that low PPP3R1 could accurately predict the onset of AD. Therefore, our findings highlight the involvement of PPP3R1 in the pathogenesis of AD via axon guidance, glutamatergic synapse, LTP, and MAPK signaling pathways, and identify downregulation of PPP3R1 as a potential biomarker for AD treatment in the context of 3P medicine-predictive diagnostics, targeted prevention, and personalization of medical services.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-021-00261-2.

Homocysteine (Hcy) metabolism is crucial for regulating methionine availability, protein homeostasis, and DNA-methylation presenting, therefore, key pathways in post-genomic and epigenetic regulation mechanisms. Consequently, impaired Hcy metabolism leading to elevated concentrations of Hcy in the blood plasma (hyperhomocysteinemia) is linked to the overproduction of free radicals, induced oxidative stress, mitochondrial impairments, systemic inflammation and increased risks of eye disorders, coronary artery diseases, atherosclerosis, myocardial infarction, ischemic stroke, thrombotic events, cancer development and progression, osteoporosis, neurodegenerative disorders, pregnancy complications, delayed healing processes, and poor COVID-19 outcomes, among others. This review focuses on the homocysteine metabolism impairments relevant for various pathological conditions. Innovative strategies in the framework of 3P medicine consider Hcy metabolic pathways as the specific target for in vitro diagnostics, predictive medical approaches, cost-effective preventive measures, and optimized treatments tailored to the individualized patient profiles in primary, secondary, and tertiary care.

Relevance: Accumulating evidence suggests a dysfunction of the para-inflammation in the retinal ganglion cell layer and the optic nerve head in patients with glaucoma. Currently, circulating blood platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR) are regarded as novel indicators of systemic inflammation. Biomarkers allow early identification of patients with visual field (VF) loss progression and timely implementation of replacement therapies.

Objective: This study aimed to investigate whether higher inflammatory indices (PLR, NLR, and LMR) were associated with VF loss progression in patients with primary angle-closure glaucoma (PACG) for the predictive diagnostics, targeted prevention, and personalization of medical services.

Methods: This prospective cohort study followed up 277 patients with PACG for at least 24 months, with clinical examination and VF testing every 6 months. Inflammatory cell quantification, including platelets, neutrophils, lymphocytes, and monocytes, was measured using the Sysmex XN-A1 automated inflammatory cells quantification system. Three systemic inflammatory indices, PLR, NLR, and LMR, were determined on the basis of baseline neutrophil, lymphocyte, monocyte, and platelet counts in patients with PACG. The risk factors for PACG were analyzed using logistic regression, Cox proportional hazards regression, and the Kaplan-Meier curve.

Results: Our results revealed that 111 (40.07%) patients showed VF loss progression. The PLR was significantly higher (P = 0.046) in the progression group than in the non-progression group. A higher PLR (OR 1.05, 95% CI 1.01-1.08, P = 0.004) was a risk factor for PACG progression. In multivariate analyses, PLR independently predicted VF loss progression (HR 1.01, 95% CI 1.00-1.01, P = 0.04). Kaplan-Meier curve analysis showed that higher PLR indicated significantly higher rates of VF loss progression (66.91% vs. 52.90%, P = 0.03). Comparable results were observed in the male and female subgroups.

Conclusion: Our findings revealed the significant association between a high PLR and a greater risk of VF loss progression in patients with PACG. PLR may be highly recommended as a novel predictive/diagnostic tool for the assessment of VF loss progression from the perspectives of predictive, preventive, and personalized medicine in vulnerable populations and for individual screening.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-021-00260-3.

Background: Pregnant women, particularly in developing countries are facing a huge burden of preeclampsia (PE) leading to high morbidity and mortality rates. This is due to delayed diagnosis and unrecognised early targeted preventive measures. Adapting innovative solutions via shifting from delayed to early diagnosis of PE in the context of predictive diagnosis, targeted prevention and personalisation of medical care (PPPM/3 PM) is essential. The subjective assessment of suboptimal health status (SHS) and objective biomarkers of oxidative stress (OS) and angiogenic growth mediators (AGMs) could be used as new PPPM approach for PE; however, these factors have only been studied in isolation with no data on their combine assessment. This study profiled early gestational biomarkers of OS and AGMs as 3 PM approach to identify SHS pregnant mothers likely to develop PE specifically, early-onset PE (EO-PE) and late-onset PE (LO-PE).

Methods: A prospective cohort of 593 singleton normotensive pregnant (NTN-P) women were recruited at 10-20th (visit 1) and followed from 21 weeks gestation until the time of PE diagnosis and delivery. At visit 1, SHS was assessed using SHS questionnaire-25 (SHSQ-25) and women were classified as SHS and optimal health status (OHS). Biomarkers of OS (8-hydroxy-2-deoxyguanosine [8-OHdG], 8-epi-prostaglansinF2alpha [8-epi-PGF2α] and total antioxidant capacity [TAC]) and AGMs (vascular endothelial growth factor [VEGF-A], soluble Fms-like tyrosine kinase-1 [sFlt-1], placental growth factor [PlGF] and soluble endoglin [sEng]) were measured at visit 1 and time of PE diagnosis.

Results: Of the 593 mothers, 498 (248 SHS and 250 OHS) returned for delivery and were included in the final analysis. Fifty-six, 97 and 95 of the 248 SHS mothers developed EO-PE, LO-PE and NTN-P respectively, versus 14 EO-PE, 30 LO-PE and 206 NTN-P among the 250 OHS mothers. At the 10-20th week gestation, unbalanced levels of OS and AGMs were observed among SHS women who developed EO-PE than LO-PE compared to NTN-P women (p < 0.0001). The combined ratios of OS and AGMs, mainly the levels of 8-OHdG/PIGF ratio at 10-20th week gestation yielded the best area under the curve (AUC) and highest relative risk (RR) for predicting SHS-pregnant women who developed EO-PE (AUC = 0.93; RR = 6.5; p < 0.0001) and LO-PE (AUC = 0.88, RR = 4.4; p < 0.0001), as well as for OHS-pregnant women who developed EO-PE (AUC = 0.89, RR = 5.6; p < 0.0001) and LO-PE (AUC = 0.85; RR = 5.1; p < 0.0001).

Conclusion: Unlike OHS pregnant women, SHS pregnant women have high incidence of PE coupled with unbalanced levels of OS and AGMs at 10-20 weeks gestation. Combining early gestational profiling of OS and AGMs created an avenue for early differentiation of PE subtypes in the context of 3 PM care for mothers at high risk of PE.

The agenda of pharmacology discovery in the field of personalized oncology was dictated by the search of molecular targets assumed to deterministically drive tumor development. In this perspective, genes play a fundamental "causal" role while cells simply act as causal proxies, i.e., an intermediate between the molecular input and the organismal output. However, the ceaseless genomic change occurring across time within the same primary and metastatic tumor has broken the hope of a personalized treatment based only upon genomic fingerprint. Indeed, current models are unable in capturing the unfathomable complexity behind the outbreak of a disease, as they discard the contribution of non-genetic factors, environment constraints, and the interplay among different tiers of organization. Herein, we posit that a comprehensive personalized model should view at the disease as a "historical" process, in which different spatially and timely distributed factors interact with each other across multiple levels of organization, which collectively interact with a dynamic gene-expression pattern. Given that a disease is a dynamic, non-linear process - and not a static-stable condition - treatments should be tailored according to the "timing-frame" of each condition. This approach can help in detecting those critical transitions through which the system can access different attractors leading ultimately to diverse outcomes - from a pre-disease state to an overt illness or, alternatively, to recovery. Identification of such tipping points can substantiate the predictive and the preventive ambition of the Predictive, Preventive and Personalized Medicine (PPPM/3PM). However, an unusual effort is required to conjugate multi-omics approaches, data collection, and network analysis reconstruction (eventually involving innovative Artificial Intelligent tools) to recognize the critical phases and the relevant targets, which could help in patient stratification and therapy personalization.