Epma Journal最新文献

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

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

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

Results and data interpretation in the framework of 3p medicine: Based on the anatomical and topographic features of the patients with PAC, mathematical models have been developed that provide a personalized choice of LE or LPI in the treatment. Multi-level diagnostics is the key tool in the overall advanced approach. To this end, for the future application of AI in the area, it is strongly recommended to consider the following:Clinically relevant phenotyping applicable to advanced population screeningSystemic effects causing suboptimal health conditions considered in order to cost-effectively protect affected individuals against health-to-disease transitionClinically relevant health risk assessment utilizing health/disease-specific molecular patterns detectable in body fluids with high predictive power such as a comprehensive tear fluid analysis.

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

Background: Glaucoma is the leading cause of irreversible blindness worldwide. Emerged evidence has shown that glaucoma is considered an immune system related disorder. The gut is the largest immune organ in the human body and the gut microbiota (GM) plays an irreversible role in maintaining immune homeostasis. But, how the GM influences glaucoma remains unrevealed. This study aimed at investigating the key molecules/pathways mediating the GM and the glaucoma to provide new biomarkers for future predictive, preventive, and personalized medicine.

Methods: Datasets from the primary open-angle glaucoma (POAG) patients (GSE138125) and datasets for target genes of GM/GM metabolites were downloaded from a public database. For GSE138125, the differentially expressed genes (DEGs) between healthy and POAG samples were identified. And the online Venn diagram tool was used to obtain the DEGs from POAG related to GM. After which GM-related DEGs were analyzed by correlation analysis, pathway enrichment analysis, and protein-protein interaction (PPI) network analysis. Human trabecular meshwork cells were used for validation, and the mRNA level of hub genes was verified by quantitative real-time polymerase chain reaction (RT-qPCR) in the in vitro glaucoma model.

Results: A total of 16 GM-related DEGs in POAG were identified from the above 2 datasets (9 upregulated genes and 7 downregulated genes). Pathway enrichment analysis indicated that these genes are mostly enriched in immune regulation especially macrophages-related pathways. Then 6 hub genes were identified by PPI network analysis and construction of key modules. Finally, RT-qPCR confirmed that the expression of the hub genes in the in vitro glaucoma model was consistent with the results of bioinformatics analysis of the mRNA chip.

Conclusion: This bioinformatic study elucidates NFKB1, IL18, KITLG, TLR9, FKBP2, and HDAC4 as hub genes for POAG and GM regulation. Immune response modulated by macrophages plays an important role in POAG and may be potential targets for future predictive, preventive, and personalized diagnosis and treatment.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-023-00336-2.

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

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

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

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

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

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

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

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

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

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

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

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

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

Human growth hormone (GH) is the indispensable hormone for the maintenance of normal physiological functions of the human body, including the growth, development, metabolism, and even immunoregulation. The GH is synthesized, secreted, and stored by somatotroph cells in adenohypophysis. Abnormal GH is associated with various GH-related diseases, such as acromegaly, dwarfism, diabetes, and cancer. Currently, some studies found there are dozens or even hundreds of GH proteoforms in tissue and serum as well as a series of GH-binding protein (GHBP) proteoforms and GH receptor (GHR) proteoforms were also identified. The structure-function relationship of protein hormone proteoforms is significantly important to reveal their overall physiological and pathophysiological mechanisms. We propose the use of proteoformics to study the relationship between every GH proteoform and different physiological/pathophysiological states to clarify the pathogenic mechanism of GH-related disease such as pituitary neuroendocrine tumor and conduct precise molecular classification to promote predictive preventive personalized medicine (PPPM / 3P medicine). This article reviews GH proteoformics in GH-related disease such as pituitary neuroendocrine tumor, which has the potential role to provide novel insight into pathogenic mechanism, discover novel therapeutic targets, identify effective GH proteoform biomarker for patient stratification, predictive diagnosis, and prognostic assessment, improve therapy method, and further accelerate the development of 3P medicine.