EPMA Journal

Radanliev, Petar; De Roure, David; Walton, Rob; Van Kleek, Max; Montalvo, Rafael Mantilla; Santos, Omar; Maddox, La’Treall; Cannady, Stacy

doi: 10.1007/s13167-020-00218-xpmid: 32839666

ObjectivesReview, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis.MethodThis is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources.ResultsMost countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on social machines and connected devices.SolutionsThe proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a social machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the social aspect of human-computer interactions (social machines) and the increased connectivity of humans and devices (Internet of Things).ConclusionInadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

Kinkorová, Judita; Topolčan, Ondřej

doi: 10.1007/s13167-020-00213-2pmid: 32849924

Biobanking is entering the new era—era of big data. New technologies, techniques, and knowledge opened the potential of the whole domain of biobanking. Biobanks collect, analyse, store, and share the samples and associated data. Both samples and especially associated data are growing enormously, and new innovative approaches are required to handle samples and to utilize the potential of biobanking data. The data reached the quantity and quality of big data, and the scientists are facing the questions how to use them more efficiently, both retrospectively and prospectively with the aim to discover new preventive methods, optimize treatment, and follow up and to optimize healthcare processes. Biobanking in the era of big data contribute to the development of predictive, preventive, and personalised medicine, for every patient providing the right treatment at the right time. Biobanking in the era of big data contributes to the paradigm shift towards personalising of healthcare.

Chen, Yiming; Xie, Zhen; Wang, Xue; Xiao, Qingxin; Lu, Xiao; Lu, Sha; Shi, Yezhen; Lv, Shaolei

doi: 10.1007/s13167-020-00212-3pmid: 32849925

BackgroundWe aimed to construct a risk model to assess the diagnostic value of predicting hypertensive disorders of pregnancy (HDPs) by screening a range of prenatal markers, including pregnancy-associated plasma protein A (PAPP-A), free beta-human chorionic gonadotropin (free β-hCG), and fetal nuchal translucency (NT).MethodWe analyzed 902 women, classified into four groups: healthy gravidas (n = 680, controls), gravidas with gestational hypertension (n = 61; GH), gravidas with preeclampsia (n = 90; PE), and gravidas with severe preeclampsia (n = 71, SPE). We then compared the multiple of median (MoM) of PAPP-A, free β-hCG, and NT. A risk model was constructed and receiver operating characteristic curve (ROC) analysis was used to diagnose HDPs.ResultsLevels of PAPP-A and free β-hCG levels in the GH, PE, and SPE groups were significantly lower than those in the control group (χ2= 7.522, P = 0.001; χ2= 17.775, P < 0.001). NT did not differ significantly when compared across all four groups (χ2= 1.592, P > 0.05). When the cut-off values for PAPP-A and free β-hCG were 0.795 MoM and 1.185 MoM, the corresponding sensitivities and specificities were 0.514 and 0.635, and 0.734 and 0.450, respectively. The best risk calculation featured PAPP-A, free β-hCG, and NT; this model exhibited the highest diagnostic value in the SPE group, followed by the GH group and then the PE group.ConclusionThe use of prenatal screening markers during early pregnancy can identify fetal aneuploidy and can also predict HDPs. The development of innovative screening strategies for gravidas and the targeted prevention of HDPs in high-risk gravidas are essential for perinatal care and early intervention, thus creating significant opportunities for predictive and preventive personalized medicine. In our study, we found that the combination of a series of prenatal screening markers in early pregnancy is better than a single marker; our data clearly demonstrate the diagnostic value of combining PAPP-A, free β-hCG, and NT for patients with SPE.

Kitamura, Takuro; Miyazaki, Soichiro; Sulaiman, Harun Bin; Akaike, Ryota; Ito, Yuki; Suzuki, Hideaki

doi: 10.1007/s13167-020-00219-wpmid: 32849926

IntroductionSleep disorders ultimately result in sleep deficiency and poor-quality adversely impacts the immune system, glucose metabolism, body weight control, cardiovascular and cerebrovascular function, cognitive function, psychological stability, work productivity, quality of life, and social safety. Sleep disorders are very common among the elderly and are often comorbid with other diseases such as dementia, and further accelerating the underlying neurodegenerative processes. Initial studies have not clearly revealed the relationship between sleep disorders and dementia. Nonetheless, recent findings have suggested that insomnia and obstructive sleep apnea (OSA) are closely associated with dementia and perhaps they could be good predictors of occurrence of dementia and optimal treatments for sleep deficiencies may prevent or delay the onset dementia.MethodsHere, we conducted a systematic review based on the criteria of predictive, preventive, and personalized medicine on the association of dementia in elderlies with sleep disorder, namely insomnia and OSA. We included 7432 studies and analyzed a total of 14 publications after applying appropriate exclusion criteria.ResultsWe found that OSA patients had a large tendency to develop and/or experience accelerations of both Alzheimer’s disease (AD) and also vascular dementia, whereas insomnia patients only develop and/or experience accelerations of AD. This may be reflected in the fact that AD and vascular dementia have similar and at the same time also different mechanisms of action. Several studies have also revealed that treating sleep disorders in elderly patients prevented or delayed the onset of dementia, mitigating the progression of symptoms in patients who already manifested dementic symptoms and even reversing neurodegeneration in particular brain areas.DiscussionCurrently, the general medical consensus has poorly addressed the role of sleep disorders in exacerbating the risk of dementia. Critically, studies such as the present one emphasizes that the treatment of sleep disorders could be one the preventive measures to evade or to improve dementia symptoms. Additionally, elderly individuals often manifest different sleep deficiency symptoms than younger ones. Given this, an improved age-specific categorization and evaluation methods for sleep deficiency need to be implemented in diagnosing dementia in order to enable personalized assessments and treatments. Collectively, these findings may also assist to improve efforts in predictively detecting and eventually treating dementia.

Birkenbihl, Colin; Emon, Mohammad Asif; Vrooman, Henri; Westwood, Sarah; Lovestone, Simon; , ; Hofmann-Apitius, Martin; Fröhlich, Holger; ,

doi: 10.1007/s13167-020-00216-zpmid: 32843907

Artificial intelligence (AI) approaches pose a great opportunity for individualized, pre-symptomatic disease diagnosis which plays a key role in the context of personalized, predictive, and finally preventive medicine (PPPM). However, to translate PPPM into clinical practice, it is of utmost importance that AI-based models are carefully validated. The validation process comprises several steps, one of which is testing the model on patient-level data from an independent clinical cohort study. However, recruitment criteria can bias statistical analysis of cohort study data and impede model application beyond the training data. To evaluate whether and how data from independent clinical cohort studies differ from each other, this study systematically compares the datasets collected from two major dementia cohorts, namely, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and AddNeuroMed. The presented comparison was conducted on individual feature level and revealed significant differences among both cohorts. Such systematic deviations can potentially hamper the generalizability of results which were based on a single cohort dataset. Despite identified differences, validation of a previously published, ADNI trained model for prediction of personalized dementia risk scores on 244 AddNeuroMed subjects was successful: External validation resulted in a high prediction performance of above 80% area under receiver operator characteristic curve up to 6 years before dementia diagnosis. Propensity score matching identified a subset of patients from AddNeuroMed, which showed significantly smaller demographic differences to ADNI. For these patients, an even higher prediction performance was achieved, which demonstrates the influence systematic differences between cohorts can have on validation results. In conclusion, this study exposes challenges in external validation of AI models on cohort study data and is one of the rare cases in the neurology field in which such external validation was performed. The presented model represents a proof of concept that reliable models for personalized predictive diagnostics are feasible, which, in turn, could lead to adequate disease prevention and hereby enable the PPPM paradigm in the dementia field.

Samec, Marek; Liskova, Alena; Koklesova, Lenka; Samuel, Samson Mathews; Zhai, Kevin; Buhrmann, Constanze; Varghese, Elizabeth; Abotaleb, Mariam; Qaradakhi, Tawar; Zulli, Anthony; Kello, Martin; Mojzis, Jan; Zubor, Pavol; Kwon, Taeg Kyu; Shakibaei, Mehdi; Büsselberg, Dietrich; Sarria, Gustavo R.; Golubnitschaja, Olga; Kubatka, Peter

Kucera, Radek; Pecen, Ladislav; Topolcan, Ondrej; Dahal, Anshu Raj; Costigliola, Vincenzo; Giordano, Frank A.; Golubnitschaja, Olga

doi: 10.1007/s13167-020-00214-1pmid: 32843909

In the early twenty-first century, societies around the world are facing the paradoxal epidemic development of PCa as a non-communicable disease. PCa is the most frequently diagnosed cancer for men in several countries such as the USA. Permanently improving diagnostics and treatments in the PCa management causes an impressive divergence between, on one hand, permanently increasing numbers of diagnosed PCa cases and, on the other hand, stable or even slightly decreasing mortality rates. Still, aspects listed below are waiting for innovate solutions in the context of predictive approaches, targeted prevention and personalisation of medical care (PPPM / 3PM).PCa belongs to the cancer types with the highest incidence worldwide. Corresponding economic burden is enormous. Moreover, the costs of treating PCa are currently increasing more quickly than those of any other cancer. Implementing individualised patient profiles and adapted treatment algorithms would make currently too heterogeneous landscape of PCa treatment costs more transparent providing clear “road map” for the cost saving.PCa is a systemic multi-factorial disease. Consequently, predictive diagnostics by liquid biopsy analysis is instrumental for the disease prediction, targeted prevention and curative treatments at early stages.The incidence of metastasising PCa is rapidly increasing particularly in younger populations. Exemplified by trends observed in the USA, prognosis is that the annual burden will increase by over 40% in 2025. To this end, one of the evident deficits is the reactive character of medical services currently provided to populations. Innovative screening programmes might be useful to identify persons in suboptimal health conditions before the clinical onset of metastasising PCa. Strong predisposition to systemic hypoxic conditions and ischemic lesions (e.g. characteristic for individuals with Flammer syndrome phenotype) and low-grade inflammation might be indicative for specific phenotyping and genotyping in metastasising PCa screening and disease management. Predictive liquid biopsy tests for CTC enumeration and their molecular characterisation are considered to be useful for secondary prevention of metastatic disease in PCa patients.Particular rapidly increasing PCa incidence rates are characteristic for adolescents and young adults aged 15–40 years. Patients with early onset prostate cancer pose unique challenges; multi-factorial risks for these trends are proposed. Consequently, multi-level diagnostics including phenotyping and multi-omics are considered to be the most appropriate tool for the risk assessment, prediction and prognosis. Accumulating evidence suggests that early onset prostate cancer is a distinct phenotype from both aetiological and clinical perspectives deserving particular attention from view point of 3P medical approaches.

Liu, Dan; Li, Jiajia; Li, Na; Lu, Miaolong; Wen, Siqi; Zhan, Xianquan

doi: 10.1007/s13167-020-00215-0pmid: 32849927

BackgroundInvasiveness is a very challenging clinical problem in nonfunctional pituitary adenomas (NFPAs), and currently, there are no effective invasiveness-related molecular biomarkers. The post-neurosurgery treatment is much different as for invasive and noninvasive NFPAs. The aim of this study was to integrate phosphoproteomics and transcriptomics data to reveal phosphorylation-mediated molecular events for invasive characteristics of NFPAs to achieve a potential tool for patient stratification, and prognostic/predictive assessment to discriminate invasive from noninvasive NFPAs for personalized attitude.MethodsThe 6-plex tandem mass tag (TMT) labeling reagents coupled with TiO2 enrichment of phosphopeptides and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were used to identify and quantify each phosphoprotein and phosphosite in NFPAs and controls. Differentially expressed genes (DEGs) between invasive NFPA and control tissues were obtained from the Gene Expression Omnibus (GEO) database. The overlapping analysis was performed between phosphoprotiens and invasive DEGs. Gene Ontology (GO) enrichment, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein–protein interaction (PPI) analyses were used to analyze these overlapped molecules.ResultsIn total, 1035 phosphoproteins with 2982 phosphorylation sites were identified in NFPAs vs. controls, and 2751 DEGs were identified in invasive NFPAs vs. controls. Overlapping analysis of these phosphoproteins and DEGs exposed 130 overlapped molecules (phosphoproteins; invasive DEGs). GO enrichment and KEGG pathway analyses of 130 overlapped molecules revealed multiple biological processes and signaling pathway network alterations, including cell–cell adhesion, platelet activation, GTPase signaling pathway, protein kinase signaling, calcium signaling pathway, estrogen signaling pathway, glucagon signaling pathway, cGMP–PKG signaling pathway, GnRH signaling pathway, inflammatory mediator regulation of TRP channels, vascular smooth muscle contraction, and Fc gamma R-mediated phagocytosis, which were obviously associated with tumor invasive characteristics. For 130 overlapped molecules, PPI network-based molecular complex detection (MCODE) identified 10 hub molecules, namely SLC2A4, TSC2, AKT1, SCG3, ALB, APOL1, ACACA, SPARCL1, CHGB, and IGFBP5. These hub molecules are involved in multiple signaling pathways and represent potential predictive/prognostic markers in NFPA patients as well as they represent potential therapeutic targets.ConclusionsThis study provided the first large-scale phosphoprotein profiling and phosphorylation-related signaling pathway network alterations in human NFPA tissues. Further, overlapping analysis of phosphoproteins and invasive DEGs revealed the phosphorylation-mediated signaling pathway network changes in invasive NFPAs. These findings are the precious resource for in-depth insight into the molecular mechanisms of NFPAs, as well as for the discovery of effective phosphoprotein biomarkers and therapeutic targets for invasive NFPAs.

Im, Ji Hye; Yoo, Byong Chul; Lee, Jun Hwa; Kim, Kyung-Hee; Kim, Tae Hoon; Lee, Kyue-Yim; Kim, Jong Heon; Park, Jong Bae; Kwon, Ji-Woong; Shin, Sang Hoon; Yoo, Heon; Gwak, Ho-Shin

doi:

Li, Na; Zhan, Xianquan

doi: 10.1007/s13167-020-00220-3pmid: 32849929

RelevanceLung cancer is the most common malignant tumor with high morbidity (11.6% of the total diagnosed cancer cases) and mortality (18.4% of the total cancer deaths), and its 5-year survival rate is very low (20%). Clarification of any molecular events and the discovery of effective biomarkers will offer increasing promise for lung canner management. N6-methyladenosine (m6A) modification is one of the important RNA modifications that are closely associated with lung cancer, and are tightly regulated by m6A regulators. Elucidation of pathology-specific m6A regulators will directly contribute to lung cancer medical services in the context of predictive, preventive, and personalized medicine (PPPM).PurposeTo investigate pathology-specific regulators of m6A RNA modifications in lung cancer and further inspect the m6A regulator gene signature as useful tools for PPPM in lung cancers.MethodsThe gene expression data of 19 m6A regulators (m6A-methyltransferases—ZC3H13, KIAA1429, RBM15/15B, WTAP, and METTL3/14; demethylases—FTO and ALKBH5; and m6A-binding proteins—HNRNPC, YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, and HNRNPA2B1) and clinical data of 1013 lung cancer patients [511 lung adenocarcinoma (LUAD) and 502 lung squamous carcinoma (LUSC)] and 109 controls (Con) were obtained from the TCGA database. Quantitative real-time PCR (qRT-PCR) was used to verify m6A regulators in lung cancer cell lines. Protein–protein interaction (PPI), gene co-expression, survival analysis, and heatmap were used to analyze these m6A regulators in this set of lung cancer clinical data. Lasso regression was used to optimize the pathology-specific m6A regulator gene signature. Gene set enrichment analysis (GSEA) was used to reveal the functional characteristics of m6A regulators.ResultsThose 19 m6A regulator profiling was significantly differentially expressed in lung cancer tissues relative to control tissues, which was also verified in lung cancer cell lines. Those m6A regulators interacted mutually, and those regulator-based sample clusters were correlated with clinical traits, including survival status, gender, tobacco smoking history, primary disease, and pathologic stage. Further, lasso regression based on the 19 m6A regulators optimized and identified a three-m6A-regulator signature (KIAA1429, METTL3, and IGF2BP1) as independent prognostic factor, which classified 1013 lung cancer patients into high-risk and low-risk groups according to median value (0.84) of the lasso regression risk scores. This three-m6A-regulator signature profiling was significantly related to lung cancer overall survival, cancer status, and the above-described clinical traits. Further, GSEA revealed that KIAA1429, METTL3, and IGF2BP1 were significantly related to multiple biological behaviors, including proliferation, apoptosis, metastasis, energy metabolism, drug resistance, and recurrence, and that KIAA1429 and IGF2BP1 had potential target genes, including E2F3, WTAP, CCND1, CDK4, EGR2, YBX1, and TLX, which were associated with cancers.ConclusionThis study provided the first view of the pathology-specific regulators of m6A RNA modification in lung cancers and identified the three-m6A-regulator signature (KIAA1429, METTL3, and IGF2BP1) as an independent prognostic model to classify lung cancers into high- and low-risk groups for patient stratification, prognostic assessment, and personalized treatment toward PPPM in lung cancers.