EPMA Journal

Golubnitschaja, Olga; Potuznik, Pavel; Polivka, Jiri; Pesta, Martin; Kaverina, Olga; Pieper, Claus C.; Kropp, Martina; Thumann, Gabriele; Erb, Carl; Karabatsiakis, Alexander; Stetkarova, Ivana; Polivka, Jiri; Costigliola, Vincenzo

doi: 10.1007/s13167-022-00307-zpmid: 36415625

Due to the reactive medical approach applied to disease management, stroke has reached an epidemic scale worldwide. In 2019, the global stroke prevalence was 101.5 million people, wherefrom 77.2 million (about 76%) suffered from ischemic stroke; 20.7 and 8.4 million suffered from intracerebral and subarachnoid haemorrhage, respectively. Globally in the year 2019 — 3.3, 2.9 and 0.4 million individuals died of ischemic stroke, intracerebral and subarachnoid haemorrhage, respectively. During the last three decades, the absolute number of cases increased substantially. The current prevalence of stroke is 110 million patients worldwide with more than 60% below the age of 70 years. Prognoses by the World Stroke Organisation are pessimistic: globally, it is predicted that 1 in 4 adults over the age of 25 will suffer stroke in their lifetime. Although age is the best known contributing factor, over 16% of all strokes occur in teenagers and young adults aged 15–49 years and the incidence trend in this population is increasing. The corresponding socio-economic burden of stroke, which is the leading cause of disability, is enormous. Global costs of stroke are estimated at 721 billion US dollars, which is 0.66% of the global GDP.Clinically manifested strokes are only the “tip of the iceberg”: it is estimated that the total number of stroke patients is about 14 times greater than the currently applied reactive medical approach is capable to identify and manage. Specifically, lacunar stroke (LS), which is characteristic for silent brain infarction, represents up to 30% of all ischemic strokes. Silent LS, which is diagnosed mainly by routine health check-up and autopsy in individuals without stroke history, has a reported prevalence of silent brain infarction up to 55% in the investigated populations. To this end, silent brain infarction is an independent predictor of ischemic stroke. Further, small vessel disease and silent lacunar brain infarction are considered strong contributors to cognitive impairments, dementia, depression and suicide, amongst others in the general population. In sub-populations such as diabetes mellitus type 2, proliferative diabetic retinopathy is an independent predictor of ischemic stroke.According to various statistical sources, cryptogenic strokes account for 15 to 40% of the entire stroke incidence. The question to consider here is, whether a cryptogenic stroke is fully referable to unidentifiable aetiology or rather to underestimated risks. Considering the latter, translational research might be of great clinical utility to realise innovative predictive and preventive approaches, potentially benefiting high risk individuals and society at large.In this position paper, the consortium has combined multi-professional expertise to provide clear statements towards the paradigm change from reactive to predictive, preventive and personalised medicine in stroke management, the crucial elements of which are:Consolidation of multi-disciplinary expertise including family medicine, predictive and in-depth diagnostics followed by the targeted primary and secondary (e.g. treated cancer) prevention of silent brain infarctionApplication of the health risk assessment focused on sub-optimal health conditions to effectively prevent health-to-disease transitionApplication of AI in medicine, machine learning and treatment algorithms tailored to robust biomarker patternsApplication of innovative screening programmes which adequately consider the needs of young populations

Quek, Ten Cheer; Takahashi, Kengo; Kang, Hyun Goo; Thakur, Sahil; Deshmukh, Mihir; Tseng, Rachel Marjorie Wei Wen; Nguyen, Helen; Tham, Yih-Chung; Rim, Tyler Hyungtaek; Kim, Sung Soo; Yanagi, Yasuo; Liew, Gerald; Cheng, Ching-Yu

Jain, Neha; Nagaich, Upendra; Pandey, Manisha; Chellappan, Dinesh Kumar; Dua, Kamal

doi: 10.1007/s13167-022-00304-2pmid: 36505888

In the current era of medical revolution, genomic testing has guided the healthcare fraternity to develop predictive, preventive, and personalized medicine. Predictive screening involves sequencing a whole genome to comprehensively deliver patient care via enhanced diagnostic sensitivity and specific therapeutic targeting. The best example is the application of whole-exome sequencing when identifying aberrant fetuses with healthy karyotypes and chromosomal microarray analysis in complicated pregnancies. To fit into today’s clinical practice needs, experimental system biology like genomic technologies, and system biology viz., the use of artificial intelligence and machine learning is required to be attuned to the development of preventive and personalized medicine. As diagnostic techniques are advancing, the selection of medical intervention can gradually be influenced by a person’s genetic composition or the cellular profiling of the affected tissue. Clinical genetic practitioners can learn a lot about several conditions from their distinct facial traits. Current research indicates that in terms of diagnosing syndromes, facial analysis techniques are on par with those of qualified therapists. Employing deep learning and computer vision techniques, the face image assessment software DeepGestalt measures resemblances to numerous of disorders. Biomarkers are essential for diagnostic, prognostic, and selection systems for developing personalized medicine viz. DNA from chromosome 21 is counted in prenatal blood as part of the Down’s syndrome biomarker screening. This review is based on a detailed analysis of the scientific literature via a vigilant approach to highlight the applicability of predictive diagnostics for the development of preventive, targeted, personalized medicine for clinical application in the framework of predictive, preventive, and personalized medicine (PPPM/3 PM). Additionally, targeted prevention has also been elaborated in terms of gene-environment interactions and next-generation DNA sequencing. The application of 3 PM has been highlighted by an in-depth analysis of cancer and cardiovascular diseases. The real-time challenges of genome sequencing and personalized medicine have also been discussed.

Wu, Zhiyuan; Zhang, Haiping; Li, Zhiwei; Li, Haibin; Miao, Xinlei; Pan, Huiying; Wang, Jinqi; Liu, Xiangtong; Kang, Xiaoping; Li, Xia; Tao, Lixin; Guo, Xiuhua

doi: 10.1007/s13167-022-00298-xpmid: 36505895

BackgroundArterial stiffness is a major risk factor and effective predictor of cardiovascular diseases and a common pathway of pathological vascular impairments. Homocysteine (Hcy) and uric acid (UA) own the shared metabolic pathways to affect vascular function. Serum uric acid (UA) has a great impact on arterial stiffness and cardiovascular risk, while the mutual effect with Hcy remains unknown yet. This study aimed to evaluate the mutual effect of serum Hcy and UA on arterial stiffness and 10-year cardiovascular risk in the general population. From the perspective of predictive, preventive, and personalized medicine (PPPM/3PM), we assumed that combined assessment of Hcy and UA provides a better tool for targeted prevention and personalized intervention of cardiovascular diseases via suppressing arterial stiffness.MethodsThis study consisted of 17,697 participants from Beijing Health Management Cohort, who underwent health examination between January 2012 and December 2019. Brachial-ankle pulse wave velocity (baPWV) was used as an index of arterial stiffness.ResultsIndividuals with both high Hcy and UA had the highest baPWV, compared with those with low Hcy and low UA (β: 30.76, 95% CI: 18.36–43.16 in males; β: 53.53, 95% CI: 38.46–68.60 in females). In addition, these individuals owned the highest 10-year cardiovascular risk (OR: 1.49, 95% CI: 1.26–1.76 in males; OR: 7.61, 95% CI: 4.63–12.68 in females). Of note, males with high homocysteine and low uric acid were significantly associated with increased cardiovascular risk (OR: 1.30, 95% CI: 1.15–1.47), but not the high uric acid and low homocysteine group (OR: 1.02, 95% CI: 0.90–1.16).ConclusionsThis study found the significantly mutual effect of Hcy and UA on arterial stiffness and cardiovascular risk using a large population and suggested the clinical importance of combined evaluation and control of Hcy and UA for promoting cardiovascular health. The adverse effect of homocysteine on arteriosclerosis should be addressed beyond uric acid, especially for males. Monitoring of the level of both Hcy and UA provides a window opportunity for PPPM/3PM in the progression of arterial stiffness and prevention of CVD. Hcy provides a novel predictor beyond UA of cardiovascular health to identify individuals at high risk of arterial stiffness for the primary prevention and early treatment of CVD. In the progressive stage of arterial stiffness, active control of Hcy and UA levels from the aspects of dietary behavior and medication treatment is conducive to alleviating the level of arterial stiffness and reducing the risk of CVD. Further studies are needed to evaluate the clinical effect of Hcy and UA targeted intervention on arterial stiffness and cardiovascular health.

Ainiwan, Mierxiati; Wang, Qi; Yesitayi, Gulinazi; Ma, Xiang

doi: 10.1007/s13167-022-00302-4pmid: 36505894

  Acute aortic dissection (AAD) is a severe aortic injury disease, which is often life-threatening at the onset. However, its early prevention remains a challenge. Therefore, in the context of predictive, preventive, and personalized medicine (PPPM), it is particularly important to identify novel and powerful biomarkers. This study aimed to identify the key markers that may contribute to the predictive early risk of AAD and analyze their role in immune infiltration. Three datasets, including a total of 23 AAD and 20 healthy control aortic samples, were retrieved from the Gene Expression Omnibus (GEO) database, and a total of 519 differentially expressed genes (DEGs) were screened in the training set. Using the least absolute shrinkage and selection operator (LASSO) regression model and the random forest (RF) algorithm, FERMT1 (AUC = 0.886) and SGCD (AUC = 0.876) were identified as key markers of AAD. A novel AAD risk prediction model was constructed using an artificial neural network (ANN), and in the validation set, the AUC = 0.920. Immune infiltration analysis indicated differential gene expression in regulatory T cells, monocytes, γδ T cells, quiescent NK cells, and mast cells in the patients with AAD and the healthy controls. Correlation and ssGSEA analysis showed that two key markers’ expression in patients with AAD was correlated with many inflammatory mediators and pathways. In addition, the drug-gene interaction network identified motesanib and pyrazoloacridine as potential therapeutic agents for two key markers, which may provide personalized medical services for AAD patients. These findings highlight FERMT1 and SGCD as key biological targets for AAD and reveal the inflammation-related potential molecular mechanism of AAD, which is helpful for early risk prediction and targeted prevention of AAD. In conclusion, our study provides a new perspective for developing a PPPM method for managing AAD patients.

Gu, Yun-Jia; Chen, Li-Ming; Gu, Mu-En; Xu, Hong-Xiao; Li, Jing; Wu, Lu-Yi

doi: 10.1007/s13167-022-00306-0pmid: 36505896

Currently colorectal cancer (CRC) is the third most prevalent cancer worldwide. Body mass index (BMI) is frequently used in CRC screening and risk assessment to quantitatively evaluate weight. However, the impact of BMI on clinical strategies for CRC has received little attention. Within the framework of the predictive, preventive, and personalized medicine (3PM/PPPM), we hypothesized that BMI stratification would affect the primary, secondary, and tertiary care options for CRC and we conducted a critical evidence-based review. BMI dynamically influences CRC outcomes, which helps avoiding adverse treatment effects. The outcome of surgical and radiation treatment is adversely affected by overweight (BMI ≥ 30) or underweight (BMI < 20). A number of interventions, such as enhanced recovery after surgery and robotic surgery, can be applied to CRC at all levels of BMI. BMI-controlling modalities such as exercise, diet control, nutritional therapy, and medications may be potentially beneficial for patients with CRC. Patients with overweight are advised to lose weight through diet, medication, and physical activity while patients suffering of underweight require more focus on nutrition. BMI assists patients with CRC in better managing their weight, which decreases the incidence of adverse prognostic events during treatment. BMI is accessible, noninvasive, and highly predictive of clinical outcomes in CRC. The cost–benefit of the PPPM paradigm in developing countries can be advanced, and the clinical benefit for patients can be improved with the promotion of BMI-based clinical strategy models for CRC.

Liu, Zonglin; Wang, Yueming; Shen, Fu; Zhang, Zhiyuan; Gong, Jing; Fu, Caixia; Shen, Changqing; Li, Rong; Jing, Guodong; Cai, Sanjun; Zhang, Zhen; Sun, Yiqun; Tong, Tong

doi: 10.1007/s13167-022-00303-3pmid: 36505889

BackgroundCurrently, the rate of recurrence or metastasis (ROM) remains high in rectal cancer (RC) patients treated with the standard regimen. The potential of diffusion-weighted imaging (DWI) in predicting ROM risk has been reported, but the efficacy is insufficient.AimsThis study investigated the potential of a new sequence called readout-segmented echo-planar imaging (RS-EPI) DWI in predicting the ROM risk of patients with RC using machine learning methods to achieve the principle of predictive, preventive, and personalized medicine (PPPM) application in RC treatment.MethodsA total of 195 RC patients from two centres who directly received total mesorectal excision were retrospectively enrolled in our study. Machine learning methods, including recursive feature elimination (RFE), the synthetic minority oversampling technique (SMOTE), and the support vector machine (SVM) classifier, were used to construct models based on clinical-pathological factors (clinical model), radiomic features from RS-EPI DWI (radiomics model), and their combination (merged model). The Harrell concordance index (C-index) and the area under the time-dependent receiver operating characteristic curve (AUC) were calculated to evaluate the predictive performance at 1 year, 3 years, and 5 years. Kaplan‒Meier analysis was performed to evaluate the ability to stratify patients according to the risk of ROM.FindingsThe merged model performed well in predicting tumour ROM in patients with RC at 1 year, 3 years, and 5 years in both cohorts (AUC = 0.887/0.813/0.794; 0.819/0.795/0.783) and was significantly superior to the clinical model (AUC = 0.87 [95% CI: 0.80–0.93] vs. 0.71 [95% CI: 0.59–0.81], p = 0.009; C-index = 0.83 [95% CI: 0.76–0.90] vs. 0.68 [95% CI: 0.56–0.79], p = 0.002). It also had a significant ability to differentiate patients with a high and low risk of ROM (HR = 12.189 [95% CI: 4.976–29.853], p < 0.001; HR = 6.427 [95% CI: 2.265–13.036], p = 0.002).ConclusionOur developed merged model based on RS-EPI DWI accurately predicted and effectively stratified patients with RC according to the ROM risk at an early stage with an individualized profile, which may be able to assist physicians in individualizing the treatment protocols and promote a meaningful paradigm shift in RC treatment from traditional reactive medicine to PPPM.

Zhang, Guodong; Wang, Zhengdan; Song, Pingping; Zhan, Xianquan

doi: 10.1007/s13167-022-00300-6pmid: 36505890

Lung cancer has a very high mortality in females and males. Most (~ 85%) of lung cancers are non-small cell lung cancers (NSCLC). When lung cancer is diagnosed, most of them have either local or distant metastasis, with a poor prognosis. In order to achieve better outcomes, it is imperative to identify the molecular signature based on genetic and epigenetic variations for different NSCLC subgroups. We hypothesize that DNA and histone modifications play significant roles in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). Epigenetics has a significant impact on tumorigenicity, tumor heterogeneity, and tumor resistance to chemotherapy, targeted therapy, and immunotherapy. An increasing interest is that epigenomic regulation is recognized as a potential treatment option for NSCLC. Most attention has been paid to the epigenetic alteration patterns of DNA and histones. This article aims to review the roles DNA and histone modifications play in tumorigenesis, early detection and diagnosis, and advancements and therapies of NSCLC, and also explore the connection between DNA and histone modifications and PPPM, which may provide an important contribution to improve the prognosis of NSCLC. We found that the success of targeting DNA and histone modifications is limited in the clinic, and how to combine the therapies to improve patient outcomes is necessary in further studies, especially for predictive diagnostics, targeted prevention, and personalization of medical services in the 3P medicine approach. It is concluded that DNA and histone modifications are potent diagnostic and therapeutic targets to advance non-small cell lung cancer management from the perspective of 3P medicine.

Huang, Xiaoliang; Chen, Zuyuan; Xiang, Xiaoyun; Liu, Yanling; Long, Xingqing; Li, Kezhen; Qin, Mingjian; Long, Chenyan; Mo, Xianwei; Tang, Weizhong; Liu, Jungang

doi: 10.1007/s13167-022-00305-1pmid:

Li, Shifu; Zhang, Qian; Li, Jian; Weng, Ling

doi: 10.1007/s13167-022-00299-wpmid: 36505891

BackgroundAlthough 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.MethodsThe 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.ResultsBased 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.ConclusionExpression 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.