Epma Journal最新文献

Background: Although growing evidence suggests close correlations between autoimmunity and amyotrophic lateral sclerosis (ALS), no studies have reported on autoimmune-related genes (ARGs) from the perspective of the prognostic assessment of ALS. The purpose of this study was to investigate whether the circulating ARD signature could be identified as a reliable biomarker for ALS survival for predictive, preventive, and personalized medicine.

Methods: The whole blood transcriptional profiles and clinical characteristics of 454 ALS patients were downloaded from the Gene Expression Omnibus (GEO) database. A total of 4371 ARGs were obtained from GAAD and DisGeNET databases. Wilcoxon test and multivariate Cox regression were applied to identify the differentially expressed and prognostic ARGs. Then, unsupervised clustering was performed to classify patients into two distinct autoimmune-related clusters. PCA method was used to calculate the autoimmune index. LASSO and multivariate Cox regression was performed to establish risk model to predict overall survival for ALS patients. A ceRNA regulatory network was then constructed for regulating the model genes. Finally, we performed single-cell analysis to explore the expression of model genes in mutant SOD1 mice and methylation analysis in ALS patients.

Results: Based on the expressions of 85 prognostic ARGs, two autoimmune-related clusters with various biological features, immune characteristics, and survival outcome were determined. Cluster 1 with a worsen prognosis was more active in immune-related biological pathways and immune infiltration than Cluster 2. A higher autoimmune index was associated with a better prognosis than a lower autoimmune index, and there were significant adverse correlations between the autoimmune index and immune infiltrating cells and immune responses. Nine model genes (KIF17, CD248, ENG, BTNL2, CLEC5A, ADORA3, PRDX5, AIM2, and XKR8) were selected to construct prognostic risk signature, indicating potent potential for survival prediction in ALS. Nomogram integrating risk model and clinical characteristics could predict the prognosis more accurately than other clinicopathological features. We constructed a ceRNA regulatory network for the model genes, including five lncRNAs, four miRNAs, and five mRNAs.

Conclusion: Expression of ARGs is correlated with immune characteristics of ALS, and seven ARG signatures may have practical application as an independent prognostic factor in patients with ALS, which may serve as target for the future prognostic assessment, targeted prevention, patient stratification, and personalization of medical services in ALS.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00299-w.

Background/aims: Predicting the clinical outcomes of primary diffuse large B-cell lymphoma of the central nervous system (PCNS-DLBCL) to methotrexate-based combination immunochemotherapy treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). The red blood cell distribution width (RDW) has been reported to be associated with the clinical outcomes of multiple cancer. However, its prognostic role in PCNS-DLBCL is yet to be evaluated. Therefore, we aimed to effectively stratify PCNS-DLBCL patients with different prognosis in advance and early identify the patients who were appropriate to methotrexate-based combination immunochemotherapy based on the pretreatment level of RDW and a clinical prognostic model.

Methods: A prospective-retrospective, multi-cohort study was conducted from 2010 to 2020. We evaluated RDW in 179 patients (retrospective discovery cohorts of Huashan Center and Renji Center and prospective validation cohort of Cancer Center) with PCNS-DLBCL treated with methotrexate-based combination immunochemotherapy. A generalized additive model with locally estimated scatterplot smoothing was used to identify the relationship between pretreatment RDW levels and clinical outcomes. The high vs low risk of RDW combined with MSKCC score was determined by a minimal P-value approach. The clinical outcomes in different groups were then investigated.

Results: The pretreatment RDW showed a U-shaped relationship with the risk of overall survival (OS, P = 0.047). The low RDW (< 12.6) and high RDW (> 13.4) groups showed significantly worse OS (P < 0.05) and progression-free survival (PFS; P < 0.05) than the median group (13.4 > RDW > 12.6) in the discovery and validation cohort, respectively. RDW could predict the clinical outcomes successfully. In the discovery cohort, RDW achieved the area under the receiver operating characteristic curve (AUC) of 0.9206 in predicting the clinical outcomes, and the predictive value (AUC = 0.7177) of RDW was verified in the validation cohort. In addition, RDW combined with MSKCC predictive model can distinguish clinical outcomes with the AUC of 0.8348 for OS and 0.8125 for PFS. Compared with the RDW and MSKCC prognosis variables, the RDW combined with MSKCC scores better identified a subgroup of patients with favorable long-term survival in the validation cohort (P < 0.001). RDW combined MSKCC score remained to be independently associated with clinical outcomes by multivariable analysis.

Conclusions: Based on the pretreatment RDW and MSKCC scores, a novel predictive tool was established to stratify PCNS-DLBCL patients with different prognosis effectively. The predictive model developed accordingly is promising to judge the response of PCNS-DLBCL to methotrexate-based

Aims: Sarcopenia is characterized by a gradual loss of skeletal muscle mass and strength with increased adverse outcomes. Recently, large-scale epidemiological studies have demonstrated a relationship between several chronic disorders and ocular pathological conditions using an oculomics approach. We hypothesized that sarcopenia can be predicted through eye examinations, without invasive tests or radiologic evaluations in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods: We analyzed data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training set (80%, randomly selected from 2008 to 2010) data were used to construct the machine learning models. Internal (20%, randomly selected from 2008 to 2010) and external (from the KNHANES 2011) validation sets were used to assess the ability to predict sarcopenia. We included 8092 participants in the final dataset. Machine learning models (XGBoost) were trained on ophthalmological examinations and demographic factors to detect sarcopenia.

Results: In the exploratory analysis, decreased levator function (odds ratio [OR], 1.41; P value <0.001), cataracts (OR, 1.31; P value = 0.013), and age-related macular degeneration (OR, 1.38; P value = 0.026) were associated with an increased risk of sarcopenia in men. In women, an increased risk of sarcopenia was associated with blepharoptosis (OR, 1.23; P value = 0.038) and cataracts (OR, 1.29; P value = 0.010). The XGBoost technique showed areas under the receiver operating characteristic curves (AUCs) of 0.746 and 0.762 in men and women, respectively. The external validation achieved AUCs of 0.751 and 0.785 for men and women, respectively. For practical and fast hands-on experience with the predictive model for practitioners who may be willing to test the whole idea of sarcopenia prediction based on oculomics data, we developed a simple web-based calculator application (https://knhanesoculomics.github.io/sarcopenia) to predict the risk of sarcopenia and facilitate screening, based on the model established in this study.

Conclusion: Sarcopenia is treatable before the vicious cycle of sarcopenia-related deterioration begins. Therefore, early identification of individuals at a high risk of sarcopenia is essential in the context of PPPM. Our oculomics-based approach provides an effective strategy for sarcopenia prediction. The proposed method shows promise in significantly increasing the number of patients diagnosed with sarcopenia, potentially facilitating earlier intervention. Through patient oculometric monitoring, various pathological factors related to sarcopenia can be simultaneously analyzed, and doctors can provide personalized medical services according to each cause. Further studies are needed to confirm whether such a prediction algorithm can be used in real-world clinical

Background: Pancreatic cancer presents extremely poor prognosis due to the difficulty of early diagnosis, low resection rate, and high rates of recurrence and metastasis. Immune checkpoint blockades have been widely used in many cancer types but showed limited efficacy in pancreatic cancer. The current study aimed to evaluate the landscape of tumor microenvironment (TME) of pancreatic cancer and identify the potential markers of prognosis and immunotherapy efficacy which might contribute to improve the targeted therapy strategy and efficacy in pancreatic cancer in the context of predictive, preventive, and personalized medicine (PPPM).

Methods: In the current study, a total of 382 pancreatic samples from the datasets of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were selected. LM22 gene signature matrix was applied to quantify the fraction of immune cells based on "CIBERSORT" algorithm. Weighted Gene Co-expression Network Analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm was applied to confirm the hub-network of immune-resistance phenotype. A nomogram model based on COX and Logistic regression was constructed to evaluate the prognostic value and the predictive value of hub-gene in immune-response. The role of transmembrane protein 92 (TMEM92) in regulating cell proliferation was evaluated by MTS assay. Western blot and Real-time PCR were applied to assess the biological effects of PD-L1 inhibition by TMEM92. Moreover, the effect of TMEM92 in immunotherapy was evaluated with PBMC co-culture and by MTS assay.

Results: Two tumor-infiltrating immune cell (TIIC) phenotypes were identified and a weighted gene co-expression network was constructed to confirm the 167 gene signatures correlated with immune-resistance TIIC subtype. TMEM92 was further identified as a core gene of 167 gene signature network based on MCODE algorithm. High TMEM92 expression was significantly correlated with unfavorable prognosis, characterizing by immune resistance. A nomogram model and external validation confirmed that TMEM92 was an independent prognostic factor in pancreatic cancer. An elevated tumor mutation burden (TMB), mostly is consistent with commonly mutations of KRAS and TP53, was found in the high TMEM92 group. The predictive role of TMEM92 in immunotherapeutic response was also confirmed by IMvigor210 datasets. In addition, the specific biological roles of TMEM92 in cancer was explored in vitro. The results showed that abnormal overexpression of TMEM92 was significantly associated with the poor survival rate of pancreatic cancer. Moreover, we demonstrated that TMEM92 inhibit tumour immune responses of the anti-PD-1 antibody with PBMC co-culture.

Conclusion: The current study explored for the first time the immune-resistance phenotype of pancreatic cancer and identified TMEM92 as an innovative marker in predicting clinical outcomes and imm

Background: Risk prediction models can help identify individuals at high risk for type 2 diabetes. However, no such model has been applied to clinical practice in eastern China.

Aims: This study aims to develop a simple model based on physical examination data that can identify high-risk groups for type 2 diabetes in eastern China for predictive, preventive, and personalized medicine.

Methods: A 14-year retrospective cohort study of 15,166 nondiabetic patients (12-94 years; 37% females) undergoing annual physical examinations was conducted. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) models were constructed for univariate analysis, factor selection, and predictive model building. Calibration curves and receiver operating characteristic (ROC) curves were used to assess the calibration and prediction accuracy of the nomogram, and decision curve analysis (DCA) was used to assess its clinical validity.

Results: The 14-year incidence of type 2 diabetes in this study was 4.1%. This study developed a nomogram that predicts the risk of type 2 diabetes. The calibration curve shows that the nomogram has good calibration ability, and in internal validation, the area under ROC curve (AUC) showed statistical accuracy (AUC = 0.865). Finally, DCA supports the clinical predictive value of this nomogram.

Conclusion: This nomogram can serve as a simple, economical, and widely scalable tool to predict individualized risk of type 2 diabetes in eastern China. Successful identification and intervention of high-risk individuals at an early stage can help to provide more effective treatment strategies from the perspectives of predictive, preventive, and personalized medicine.

Depression and suicidal behavior are interrelated, stress-associated mental health conditions, each lacking biological verifiability. Concepts of predictive, preventive, and personalized medicine (3PM) are almost completely missing for both conditions but are of utmost importance. Prior research reported altered levels of the stress hormone cortisol in the scalp hair of depressed individuals, however, data on hair cortisol levels (HCL) for suicide completers (SC) are missing. Here, we aimed to identify differences in HCL between subject with depression (n = 20), SC (n = 45) and mentally stable control subjects (n = 12) to establish the usage of HCL as a new target for 3PM. HCL was measured in extracts of pulverized hair (1-cm and 3-cm hair segments) using ELISA. In 3-cm hair segments, an average increase in HCL for depressed patients (1.66 times higher; p = .011) and SC (5.46 times higher; p = 1.65 × 10^-5) compared to that for controls was observed. Furthermore, the average HCL in SC was significantly increased compared to that in the depressed group (3.28 times higher; p = 1.4 × 10^-5). A significant correlation between HCL in the 1-cm and the 3-cm hair segments, as well as a significant association between the severity of depressive symptoms and HCL (3-cm segment) was found. To conclude, findings of increased HCL in subjects with depression compared to that in controls were replicated and an additional increase in HCL was seen in SC in comparison to patients with depression. The usage of HCL for creating effective patient stratification and predictive approach followed by the targeted prevention and personalization of medical services needs to be validated in follow-up studies.

Abstract: Cardiovascular disease remains the leading cause of disease burden globally with far-reaching consequences including enormous socio-economic burden to healthcare and society at large. Cardiovascular health is decisive for reproductive function, healthy pregnancy and postpartum. During pregnancy, maternal cardiovascular system is exposed to highly increased haemodynamic stress that significantly impacts health status of the mother and offspring. Resulting from sub-optimal maternal health conditions overlooked in pre-pregnancy time, progressive abnormalities can be expected during pregnancy and postpartum. Contextually, there are two main concepts to follow in the framework of predictive, preventive and personalised medicine, namely to develop:1. advanced screening of sub-optimal health conditions in young populations to predict and prevent individual health risks prior to planned pregnancies2. in-depth companion diagnostics during pregnancy to predict and prevent long-lasting postpartum health risks of the mother and offspring.Data collected in the current study demonstrate group-specific complications to health of the mother and offspring and clinical relevance of the related phenotyping in pre-pregnant mothers. Diagnostic approach proposed in this study revealed its great clinical utility demonstrating important synergies between cardiovascular maladaptation and connective tissue dysfunction. Co-diagnosed pre-pregnancy low BMI of the mother, connective tissue dysfunction, increased stiffness of peripheral vessels and decreased blood pressure are considered a highly specific maternal phenotype useful for innovative screening programmes in young populations to predict and prevent severe risks to health of the mother and offspring. This crucial discovery brings together systemic effects characteristic, for example, for individuals with Flammer syndrome predisposed to the phenotype-specific primary vascular dysregulation, pregnancy-associated risks, normal tension glaucoma, ischemic stroke at young age, impaired wound healing and associated disorders. Proposed maternal phenotyping is crucial to predict and effectively protect both the mother and offspring against health-to-disease transition. Pre-pregnancy check-up focused on sub-optimal health and utilising here described phenotypes is pivotal for advanced health policy.

Plain english abstract: Cardiovascular health is decisive for reproductive function and healthy pregnancy. During pregnancy, maternal cardiovascular system may demonstrate health-to-disease transition relevant for the affected mother and offspring. Overlooked in pre-pregnancy time, progressive abnormalities can be expected during pregnancy and lifelong. Here we co-diagnosed maternal pre-pregnancy low bodyweight with systemic effects which may increase risks of pregnancy, eye and heart disorders and ischemic stroke at young age, amongst others. Innovative screening  programmes foc

Thromboembolism is the third leading vascular disease, with a high annual incidence of 1 to 2 cases per 1000 individuals within the general population. The broader term venous thromboembolism generally refers to deep vein thrombosis, pulmonary embolism, and/or a combination of both. Therefore, thromboembolism can affect both - the central and peripheral veins. Arterial thromboembolism causes systemic ischemia by disturbing blood flow and oxygen supply to organs, tissues, and cells causing, therefore, apoptosis and/or necrosis in the affected tissues. Currently applied antithrombotic drugs used, e.g. to protect affected individuals against ischemic stroke, demonstrate significant limitations. For example, platelet inhibitors possess only moderate efficacy. On the other hand, thrombolytics and anticoagulants significantly increase hemorrhage. Contextually, new approaches are extensively under consideration to develop next-generation antithrombotics with improved efficacy and more personalized and targeted application. To this end, phytochemicals show potent antithrombotic efficacy demonstrated in numerous in vitro, ex vivo, and in vivo models as well as in clinical evaluations conducted on healthy individuals and persons at high risk of thrombotic events, such as pregnant women (primary care), cancer, and COVID-19-affected patients (secondary and tertiary care). Here, we hypothesized that specific antithrombotic and antiplatelet effects of plant-derived compounds might be of great clinical utility in primary, secondary, and tertiary care. To increase the efficacy, precise patient stratification based on predictive diagnostics is essential for targeted protection and treatments tailored to the person in the framework of 3P medicine. Contextually, this paper aims at critical review toward the involvement of specific classes of phytochemicals in antiplatelet and anticoagulation adapted to clinical needs. The paper exemplifies selected plant-derived drugs, plant extracts, and whole plant foods/herbs demonstrating their specific antithrombotic, antiplatelet, and fibrinolytic activities relevant for primary, secondary, and tertiary care. One of the examples considered is antithrombotic and antiplatelet protection specifically relevant for COVID-19-affected patient groups.

Background: Surgical replacement of the aortic root is the only intervention that can prevent aortic dissection and cardiovascular death in Marfan syndrome (MFS). However, in some individuals, MFS also causes sleep apnea. If sleep apnea predicts cardiovascular death, a new target for predictive, preventive, and personalized medicine (PPPM) may emerge for those individuals with MFS who have sleep apnea.

Methods: This is an investigator-initiated study with long-term follow-up data of 105 individuals with MFS. All individuals were screened for sleep apnea regardless of symptoms. Cardiovascular death served as a primary endpoint, and aortic events as a secondary outcome.

Results: Sleep apnea with an apnea-hypopnea index (AHI) > 5/h was observed in 21.0% (22/105) with mild sleep apnea in 13% (14/105) and moderate to severe sleep apnea in 7.6% (8/105). After a median follow-up of 7.76 years (interquartile range: 6.84, 8.41), 10% (10/105) had died, with cardiovascular cause of death in 80% (8/10). After adjusting for age and body mass index (BMI), the AHI score emerged as an independent risk factor for cardiovascular death (hazard ratio 1.712, 95% confidence interval [1.061-2.761], p = 0.0276). The secondary outcome of aortic events occurred in 33% (35/105). There was no effect of the AHI score on aortic events after adjusting for age and BMI (hazard ratio 0.965, 95% confidence interval [0.617-1.509]), possibly due to a high number of patients with prior aortic surgery.

Interpretation: Sleep apnea is emerging as an independent predictor of cardiovascular death in MFS. It seems mandatory to screen all individuals with MFS for sleep apnea and to include these individuals, with both MFS and sleep apnea, in further studies to evaluate the impact of preventive measures with regard to cardiovascular death.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00291-4.

Background: Atherosclerosis is a major contributor to morbidity and mortality worldwide. Although several molecular markers associated with atherosclerosis have been developed in recent years, the lack of robust evidence hinders their clinical applications. For these reasons, identification of novel and robust biomarkers will directly contribute to atherosclerosis management in the context of predictive, preventive, and personalized medicine (PPPM). This integrative analysis aimed to identify critical genetic markers of atherosclerosis and further explore the underlying molecular immune mechanism attributing to the altered biomarkers.

Methods: Gene Expression Omnibus (GEO) series datasets were downloaded from GEO. Firstly, differential expression analysis and functional analysis were conducted. Multiple machine-learning strategies were then employed to screen and determine key genetic markers, and receiver operating characteristic (ROC) analysis was used to assess diagnostic value. Subsequently, cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) and a single-cell RNA sequencing (scRNA-seq) data were performed to explore relationships between signatures and immune cells. Lastly, we validated the biomarkers' expression in human and mice experiments.

Results: A total of 611 overlapping differentially expressed genes (DEGs) included 361 upregulated and 250 downregulated genes. Based on the enrichment analysis, DEGs were mapped in terms related to immune cell involvements, immune activating process, and inflaming signals. After using multiple machine-learning strategies, dehydrogenase/reductase 9 (DHRS9) and protein tyrosine phosphatase receptor type J (PTPRJ) were identified as critical biomarkers and presented their high diagnostic accuracy for atherosclerosis. From CIBERSORT analysis, both DHRS9 and PTPRJ were significantly related to diverse immune cells, such as macrophages and mast cells. Further scRNA-seq analysis indicated DHRS9 was specifically upregulated in macrophages of atherosclerotic lesions, which was confirmed in atherosclerotic patients and mice.

Conclusions: Our findings are the first to report the involvement of DHRS9 in the atherogenesis, and the proatherogenic effect of DHRS9 is mediated by immune mechanism. In addition, we confirm that DHRS9 is localized in macrophages within atherosclerotic plaques. Therefore, upregulated DHRS9 could be a novel potential target for the future predictive diagnostics, targeted prevention, patient stratification, and personalization of medical services in atherosclerosis.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00289-y.