Medicine in the early twenty-first century: paradigm and anticipation - EPMA position paper 2016

Body mass index-based predictions and personalized clinical strategies for colorectal cancer in the context of PPPM

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.

Comprehensive analysis of autoimmune-related genes in amyotrophic lateral sclerosis from the perspective of 3P medicine

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.

Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans

Aims

Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications.

Methods

The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs. non-fluid optical coherence tomography (OCT) scans. Both networks are validated on external datasets.

Results

Internal testing for our segmentation network produced an IoU score of 83.0% (95% CI = 76.7-89.3%) and a DICE score of 90.4% (86.3-94.4%); for external testing, we obtained an IoU score of 66.7% (63.5-70.0%) and a DICE score of 78.7% (76.0-81.4%). Internal testing of our classification network produced an area under the receiver operating characteristics curve (AUC) of 99.18%, and a Youden index threshold of 0.3806; for external testing, we obtained an AUC of 94.55%, and an accuracy of 94.98% and an F1 score of 85.73% with Youden index.

Conclusion

We have developed an AI-based app with an alternative transformer-based segmentation algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring, and could allow for the generation of retrospective data to research into the varied use of treatments for AMD and DR. The modular system of our app can be scaled to add more iterative features based on user feedback for more efficient monitoring. Further study and scaling up of the algorithm dataset could potentially boost its usability in a real-world clinical setting.

Supplementary information

The online version contains supplementary material available at 10.1007/s13167-022-00301-5.

Identification of FERMT1 and SGCD as key marker in acute aortic dissection from the perspective of predictive, preventive, and personalized medicine

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00302-4.

Ischemic stroke of unclear aetiology: a case-by-case analysis and call for a multi-professional predictive, preventive and personalised approach

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 infarction

Application of the health risk assessment focused on sub-optimal health conditions to effectively prevent health-to-disease transition

Application of AI in medicine, machine learning and treatment algorithms tailored to robust biomarker patterns

Application of innovative screening programmes which adequately consider the needs of young populations

Stroke is a severe brain disease which has reached an epidemic scale worldwide: in 2019, the global stroke prevalence was 101.5 million people. The World Stroke Organisation predicted that globally, 1 in 4 adults over the age of 25 will get a stroke in their lifetime. Not only old people but also teenagers and young adults are affected. Current global costs of stroke are estimated at 721 billion US dollars. Due to undiagnosed so-called “silent” brain infarction, the number of affected individuals is about 14 times greater in the population than clinically recorded. If it remains untreated, silent brain infarction may cause many severe and fatal disorders such as dementia, depression and even suicide. In this position paper, the consortium describes how the rudimental approach to treating severely diseased people could be replaced by an innovative predictive and preventive one to protect people against the health-to-disease transition.

The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas

A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.

Mutual effect of homocysteine and uric acid on arterial stiffness and cardiovascular risk in the context of predictive, preventive, and personalized medicine

Background

Arterial 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.

Methods

This 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.

Results

Individuals 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).

Conclusions

This 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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00298-x.

Deqi Sensation to Predict Acupuncture Effect on Functional Dyspepsia: A Machine Learning Study

Objectives

The aim of the study was to predict the effect of acupuncture for treating functional dyspepsia (FD) using the support vector machine (SVM) techniques based on initial deqi sensations of patients.

Methods

This retrospective study involved 90 FD patients who had received four weeks of acupuncture treatment. The support vector classification model was used to distinguish higher responders (patients with Symptom Index of Dyspepsia improvement score ≥ 2) from lower responders (patients with Symptom Index of Dyspepsia improvement score < 2). A support vector regression model was used to predict the change in the Symptom Index of Dyspepsia at the end of acupuncture treatment. Deqi sensations of patients in the first acupuncture treatment of a 20-session acupuncture intervention were defined as features and used to train models. Models were validated by 10-fold cross-validation and evaluated by accuracy, specificity, sensitivity, the area under the receive-operating curve, the coefficient of determination (R²), and the mean squared error.

Results

The two models could predict the efficacy of acupuncture successfully. These models had an accuracy of 0.84 in predicting acupuncture response, and an R² of 0.16 in the prediction of symptom improvements, respectively. The presence or absence of deqi sensation, the duration of deqi sensation, distention, and pain were finally selected as significant predicting features.

Conclusion

Based on the SVM algorithms and deqi sensation, the current study successfully predicted the acupuncture response as well as clinical symptom improvement in FD patients at the end of treatment. Our prediction models are expected to promote the clinical efficacy of acupuncture treatment for FD, reduce medical expenditures, and optimize the allocation of medical resources.

TMEM92 acts as an immune-resistance and prognostic marker in pancreatic cancer from the perspective of predictive, preventive, and personalized medicine

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 immunotherapeutic efficacy. These findings not only help to recognize high-risk and immune-resistance population which could be supplied targeted prevention, but also provide personalized medical services by intervening TMEM92 function to improve the prognosis of pancreatic cancer. In addition, the biological role of TMEM92 might reveal the potential molecular mechanisms of pancreatic cancer and lead to a novel sight for development of a PPPM approach for pancreatic cancer management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00287-0.

Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies

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.

A nomogram model for the risk prediction of type 2 diabetes in healthy eastern China residents: a 14-year retrospective cohort study from 15,166 participants

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.

Antithrombotic and antiplatelet effects of plant-derived compounds: a great utility potential for primary, secondary, and tertiary care in the framework of 3P medicine

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.

Sleep apnea predicts cardiovascular death in patients with Marfan syndrome: a cohort study

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.

Personalized Medicine for the Critically Ill Patient: A Narrative Review

Personalized Medicine (PM) is rapidly advancing in everyday medical practice. Technological advances allow researchers to reach patients more than ever with their discoveries. The critically ill patient is probably the most complex of all, and personalized medicine must make serious efforts to fulfill the desire to “treat the individual, not the disease”. The complexity of critically ill pathologies arises from the severe state these patients and from the deranged pathways of their diseases. PM constitutes the integration of basic research into clinical practice; however, to make this possible complex and voluminous data require processing through even more complex mathematical models. The result of processing biodata is a digitized individual, from which fragments of information can be extracted for specific purposes. With this review, we aim to describe the current state of PM technologies and methods and explore its application in critically ill patients, as well as some of the challenges associated with PM in intensive care from the perspective of economic, approval, and ethical issues. This review can help in understanding the complexity of, P.M.; the complex processes needed for its application in critically ill patients, the benefits that make the effort of implementation worthwhile, and the current challenges of PM.

Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine

BACKGROUND

Recognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing.

METHODS

This investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used to inform the models. Model performance was mainly evaluated by the area under the receiver operating characteristic (AUC) curve. Additional techniques-permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP)-were applied for explaining the black-box ML models.

RESULTS

Fifteen routine haematological and biochemical features were selected to establish ML-based models for the prediction of IS. The XGBoost-based model achieved the highest predictive performance, reaching AUCs of 0.91 (0.90-0.92) and 0.92 (0.91-0.93) in the internal and external datasets respectively. PFI globally revealed that demographic feature age, routine haematological parameters, haemoglobin and neutrophil count, and biochemical analytes total protein and high-density lipoprotein cholesterol were more influential on the model's prediction. LIME and SHAP showed similar local feature attribution explanations.

CONCLUSION

In the context of PPPM/3PM, we used the selected predictors obtained from the results of common blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction. By incorporating the individualised patient profile, this prediction tool is simple and quick to administer. This is promising to support subjective decision making in resource-limited settings or primary care, thereby shortening the time window for the treatment, and improving outcomes after IS.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13167-022-00283-4.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Methods to evaluate vascular function: a crucial approach towards predictive, preventive, and personalised medicine

Endothelium, the gatekeeper of our blood vessels, is highly heterogeneous and a crucial physical barrier with the ability to produce vasoactive and protective mediators under physiological conditions. It regulates vascular tone, haemostasis, vascular inflammation, remodelling, and angiogenesis. Several cardio-, reno-, and cerebrovascular diseases begin with the dysfunction of endothelial cells, and more recently, COVID-19 was also associated with endothelial disease highlighting the need to monitor its function towards prevention and reduction of vascular dysfunction. Endothelial cells are an important therapeutic target in predictive, preventive, and personalised (3P) medicine with upmost importance in vascular diseases. The development of novel non-invasive techniques to access endothelial dysfunction for use in combination with existing clinical imaging modalities provides a feasible opportunity to reduce the burden of vascular disease.

This review summarises recent advances in the principles of endothelial function measurements. This article presents an overview of invasive and non-invasive techniques to determine vascular function and their major advantages and disadvantages. In addition, the article describes mechanisms underlying the regulation of vascular function and dysfunction and potential new biomarkers of endothelial damage. Recognising these biomarkers is fundamental towards a shift from reactive to 3P medicine in the vascular field. Identifying vascular dysfunction earlier with non-invasive or minimally invasive techniques adds value to predictive diagnostics and targeted prevention (primary, secondary, tertiary care). In addition, vascular dysfunction is a potential target for treatments tailored to the person.

Anti-breast cancer effects of phytochemicals: primary, secondary, and tertiary care

Breast cancer incidence is actually the highest one among all cancers. Overall breast cancer management is associated with challenges considering risk assessment and predictive diagnostics, targeted prevention of metastatic disease, appropriate treatment options, and cost-effectiveness of approaches applied. Accumulated research evidence indicates promising anti-cancer effects of phytochemicals protecting cells against malignant transformation, inhibiting carcinogenesis and metastatic spread, supporting immune system and increasing effectiveness of conventional anti-cancer therapies, among others. Molecular and sub-/cellular mechanisms are highly complex affecting several pathways considered potent targets for advanced diagnostics and cost-effective treatments. Demonstrated anti-cancer affects, therefore, are clinically relevant for improving individual outcomes and might be applicable to the primary (protection against initial cancer development), secondary (protection against potential metastatic disease development), and tertiary (towards cascading complications) care. However, a detailed data analysis is essential to adapt treatment algorithms to individuals’ and patients’ needs. Consequently, advanced concepts of patient stratification, predictive diagnostics, targeted prevention, and treatments tailored to the individualized patient profile are instrumental for the cost-effective application of natural anti-cancer substances to improve overall breast cancer management benefiting affected individuals and the society at large.

Sleep duration and atrial fibrillation risk in the context of predictive, preventive, and personalized medicine: the Suita Study and meta-analysis of prospective cohort studies

Background

Short and long sleep durations are common behaviors that could predict several cardiovascular diseases. However, the association between sleep duration and atrial fibrillation (AF) risk is not well-established. AF is preventable, and risk prevention approaches could reduce its occurrence. Investigating whether sleep duration could predict AF incidence for possible preventive interventions and determining the impact of various lifestyle and clinical characteristics on this association to personalize such interventions are essential. Herein, we investigated the association between sleep duration and AF risk using a prospective cohort study and a meta-analysis of epidemiological evidence.

Methods

Data of 6898 people, aged 30-84 years, from the Suita Study, were analyzed. AF was diagnosed during the follow-up by ECG, medical records, checkups, and death certificates, while a baseline questionnaire was used to assess sleep duration. The Cox regression was used to compute the hazard ratios (HRs) and 95% confidence intervals (CIs) of AF risk for daily sleep ≤ 6 (short sleep), ≥ 8 (long sleep), and irregular sleep, including night-shift work compared with 7 h (moderate sleep). Then, we combined our results with those from other eligible prospective cohort studies in two meta-analyses for the short and long sleep.

Results

In the Suita Study, within a median follow-up period of 14.5 years, short and irregular sleep, but not long sleep, were associated with the increased risk of AF in the age- and sex-adjusted models: HRs (95% CIs) = 1.36 (1.03, 1.80) and 1.62 (1.16, 2.26) and the multivariable-adjusted models: HRs (95% CIs) = 1.34 (1.01, 1.77) and 1.63 (1.16, 2.30), respectively. The significant associations between short and irregular sleep and AF risk remained consistent across different ages, sex, smoking, and drinking groups. However, they were attenuated among overweight and hypertensive participants. In the meta-analyses, short and long sleep durations were associated with AF risk: pooled HRs (95% CIs) = 1.21 (1.02, 1.42) and 1.18 (1.03, 1.35). No signs of significant heterogeneity across studies or publication bias were detected.

Conclusion

Short, long, and irregular sleep could be associated with increased AF risk. In the context of predictive, preventive, and personalized medicine, sleep duration should be considered in future AF risk scores to stratify the general population for potential personalized lifestyle modification interventions. Sleep management services should be considered for AF risk prevention, and these services should be individualized according to clinical characteristics and lifestyle factors.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00275-4.

Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine

Background

Acupuncture is safe and effective for functional dyspepsia (FD), while its efficacy varies among individuals. Predicting the response of different FD patients to acupuncture 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). In the current study, the individual efficacy prediction models were developed based on the support vector machine (SVM) algorithm and routine clinical features, aiming to predict the efficacy of acupuncture in treating FD and identify the FD patients who were appropriate to acupuncture treatment.

Methods

A total of 745 FD patients were collected from two clinical trials. All the patients received a 4-week acupuncture treatment. Based on the demographic and baseline clinical features of 80% of patients in trial 1, the SVM models were established to predict the acupuncture response and improvements of symptoms and quality of life (QoL) at the end of treatment. Then, the left 20% of patients in trial 1 and 193 patients in trial 2 were respectively applied to evaluate the internal and external generalizations of these models.

Results

These models could predict the efficacy of acupuncture successfully. In the internal test set, models achieved an accuracy of 0.773 in predicting acupuncture response and an R ² of 0.446 and 0.413 in the prediction of QoL and symptoms improvements, respectively. Additionally, these models had well generalization in the independent validation set and could also predict, to a certain extent, the long-term efficacy of acupuncture at the 12-week follow-up. The gender, subtype of disease, and education level were finally identified as the critical predicting features.

Conclusion

Based on the SVM algorithm and routine clinical features, this study established the models to predict acupuncture efficacy for FD patients. The prediction models developed accordingly are promising to assist doctors in judging patients' responses to acupuncture in advance, so that they could tailor and adjust acupuncture treatment plans for different patients in a prospective rather than the reactive manner, which could greatly improve the clinical efficacy of acupuncture treatment for FD and save medical expenditures.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00271-8.

Systemic Effects Reflected in Specific Biomarker Patterns Are Instrumental for the Paradigm Change in Prostate Cancer Management: A Strategic Paper

Prostate cancer (PCa) is reported as the most common malignancy and second leading cause of death in America. In Europe, PCa is considered the leading type of tumour in 28 European countries. The costs of treating PCa are currently increasing more rapidly than those of any other cancer. Corresponding economic burden is enormous, due to an overtreatment of slowly developing disease on one hand and underestimation/therapy resistance of particularly aggressive PCa subtypes on the other hand. The incidence of metastatic PCa is rapidly increasing that is particularly characteristic for young adults. PCa is a systemic multi-factorial disease resulting from an imbalanced interplay between risks and protective factors. Sub-optimal behavioural patterns, abnormal stress reactions, imbalanced antioxidant defence, systemic ischemia and inflammation, mitochondriopathies, aberrant metabolic pathways, gene methylation and damage to DNA, amongst others, are synergistically involved in pathomechanisms of PCa development and progression. To this end, PCa-relevant systemic effects are reflected in liquid biopsies such as blood patterns which are instrumental for predictive diagnostics, targeted prevention and personalisation of medical services (PPPM/3P medicine) as a new paradigm in the overall PCa management. This strategic review article highlights systemic effects in prostate cancer development and progression, demonstrates evident challenges in PCa management and provides expert recommendations in the framework of 3P medicine.

Development and validation of a transcriptomic signature-based model as the predictive, preventive, and personalized medical strategy for preterm birth within 7 days in threatened preterm labor women

Preterm birth (PTB) is the leading cause of neonatal death. The essential strategy to prevent PTB is the accurate identification of threatened preterm labor (TPTL) women who will have PTB in a short time (< 7 days). Here, we aim to propose a clinical model to contribute to the effective prediction, precise prevention, and personalized medical treatment for PTB < 7 days in TPTL women through bioinformatics analysis and prospective cohort studies. In this study, the 1090 key genes involved in PTB < 7 days in the peripheral blood of TPTL women were ascertained using WGCNA. Based on this, the biological basis of immune-inflammatory activation (e.g., IFNγ and TNFα signaling) as well as immune cell disorders (e.g., monocytes and Th17 cells) in PTB < 7 days were revealed. Then, four core genes (JOSD1, IDNK, ZMYM3, and IL1B) that best represent their transcriptomic characteristics were screened by SVM and LASSO algorithm. Therefore, a prediction model with an AUC of 0.907 was constructed, which was validated in a larger population (AUC = 0.783). Moreover, the predictive value (AUC = 0.957) and clinical feasibility of this model were verified through the clinical prospective cohort we established. In conclusion, in the context of Predictive, Preventive, and Personalized Medicine (3PM), we have developed and validated a model to predict PTB < 7 days in TPTL women. This is promising to greatly improve the accuracy of clinical prediction, which would facilitate the personalized management of TPTL women to precisely prevent PTB < 7 days and improve maternal-fetal outcomes.

Integrative genomic analysis of PPP3R1 in Alzheimer's disease: a potential biomarker for predictive, preventive, and personalized medical approach

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.

Plasma complement C7 as a target in non-small cell lung cancer patients to implement 3P medicine strategies

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.

Mass spectrometry analysis of human tear fluid biomarkers specific for ocular and systemic diseases in the context of 3P medicine

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.

All around suboptimal health - a joint position paper of the Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine

First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person. This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions:Stress overload associated pathologiesMale and female healthPlanned pregnanciesPeriodontal healthEye disordersInflammatory disorders, wound healing and pain management with associated complicationsMetabolic disorders and suboptimal body weightCardiovascular pathologiesCancersStroke, particularly of unknown aetiology and in young individualsSleep medicineSports medicineImproved individual outcomes under pandemic conditions such as COVID-19.

Discovery of a novel and a rare Kristen rat sarcoma viral oncogene homolog (KRAS) gene mutation in colorectal cancer patients

Colorectal cancer (CRC) is one of the most important causes of morbidity and mortality in the developed world and is gradually more frequent in the developing world including Saudi Arabia. According to the Saudi Cancer Registry report 2015, CRC is the most common cancer in men (14.9%) and the second most prevalent cancer. Oncogenic mutations in the KRAS gene play a central role in tumorigenesis and are mutated in 30-40% of all CRC patients. To explore the prevalence of KRAS gene mutations in the Saudi population, we collected 80 CRC tumor tissues and sequenced the KRAS gene using automated sequencing technologies. The chromatograms presented mutations in 26 patients (32.5%) in four different codons, that is, 12, 13, 17, and 31. Most of the mutations were identified in codon 12 in 16 patients (61.5% of all mutations). We identified a novel mutation c.51 G>A in codon 17, where serine was substituted by arginine (S17R) in four patients. We also identified a very rare mutation, c.91 G>A, in which glutamic acid was replaced by lysine (E31K) in three patients. In conclusion, our findings further the knowledge about KRAS mutations in different ethnic groups is indispensable to fully understand their role in the development and progression of CRC.

Caution, "normal" BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021

An increasing interest in a healthy lifestyle raises questions about optimal body weight. Evidently, it should be clearly discriminated between the standardised "normal" body weight and individually optimal weight. To this end, the basic principle of personalised medicine "one size does not fit all" has to be applied. Contextually, "normal" but e.g. borderline body mass index might be optimal for one person but apparently suboptimal for another one strongly depending on the individual genetic predisposition, geographic origin, cultural and nutritional habits and relevant lifestyle parameters-all included into comprehensive individual patient profile. Even if only slightly deviant, both overweight and underweight are acknowledged risk factors for a shifted metabolism which, if being not optimised, may strongly contribute to the development and progression of severe pathologies. Development of innovative screening programmes is essential to promote population health by application of health risks assessment, individualised patient profiling and multi-parametric analysis, further used for cost-effective targeted prevention and treatments tailored to the person. The following healthcare areas are considered to be potentially strongly benefiting from the above proposed measures: suboptimal health conditions, sports medicine, stress overload and associated complications, planned pregnancies, periodontal health and dentistry, sleep medicine, eye health and disorders, inflammatory disorders, healing and pain management, metabolic disorders, cardiovascular disease, cancers, psychiatric and neurologic disorders, stroke of known and unknown aetiology, improved individual and population outcomes under pandemic conditions such as COVID-19. In a long-term way, a significantly improved healthcare economy is one of benefits of the proposed paradigm shift from reactive to Predictive, Preventive and Personalised Medicine (PPPM/3PM). A tight collaboration between all stakeholders including scientific community, healthcare givers, patient organisations, policy-makers and educators is essential for the smooth implementation of 3PM concepts in daily practice.

Overview of omics biomarkers in pituitary neuroendocrine tumors to design future diagnosis and treatment strategies

Pituitary neuroendocrine tumors (PitNETs) are the second most common type of intracranial neoplasia. Since their manifestation usually causes hormone hypersecretion, effective management of PitNETs is indisputably necessary. Most of the non-functioning PitNETs pose a real challenge in diagnosis as they grow without giving any signs. Despite the good response of prolactinomas to dopamine agonist therapy, some of these tumors persist or recur; also, about 20% are resistant and 10% behave aggressively. The silent corticotropinomas may not cause symptoms until the tumor mass causes a complication. In somatotropinomas, the possibility of recurrence after transsphenoidal resection is more common in pediatric patients than in adult patients. Therefore, detection of tumors at early stages or identification of recurrence and remission after transsphenoidal surgery would allow wiser management of the disease. Extensive studies have been performed to uncover potential signatures that can be used for preventive diagnosis and/or prognosis of PitNETs as well as for targeted therapy. These molecular signatures at multiple biological levels hold promise for the convergence of preventive approaches and patient-centered disease management and offer potential therapeutic strategies. In this review, we provide an overview of the omics-based biomarker research and highlight the multi-omics signatures that have been proposed as pitNET biomarkers. In addition, understanding the multi-omics data integration of current biomarker discovery strategies was discussed in terms of preventive, predictive, and personalized medicine. The topics discussed in this review will help to develop broader visions for pitNET research, diagnosis, and therapy, particularly in the context of personalized medicine.

Wrist actigraphic approach in primary, secondary and tertiary care based on the principles of predictive, preventive and personalised (3P) medicine

Abstract

Sleep quality and duration as well as activity-rest-cycles at individual level are crucial for maintaining physical and mental health. Although several methods do exist to monitor these parameters, optimal approaches are still under consideration and technological development. Wrist actigraphy is a non-invasive electro-physical method validated in the field of chronobiology to record movements and to allow for monitoring human activity-rest-cycles. Based on the continuous recording of motor activity and light exposure, actigraphy provides valuable information about the quality and quantity of the sleep–wake rhythm and about the amount of motor activity at day and night that is highly relevant for predicting a potential disease and its targeted prevention as well as personalisation of medical services provided to individuals in suboptimal health conditions and patients. Being generally used in the field of sleep medicine, actigraphy demonstrates a great potential to be successfully implemented in primary, secondary and tertiary care, psychiatry, oncology, and intensive care, military and sports medicines as well as epidemiological monitoring of behavioural habits as well as well-being medical support, amongst others.

Prediction of disease development and individual outcomes

Activity-rest-cycles have been demonstrated to be an important predictor for many diseases including but not restricted to the development of metabolic, psychiatric and malignant pathologies. Moreover, activity-rest-cycles directly impact individual outcomes in corresponding patient cohorts.

Targeted prevention

Data acquired by actigraphy are instrumental for the evidence-based targeted prevention by analysing individualised patient profiles including light exposure, sleep duration and quality, activity-rest-cycles, intensity and structure of motion pattern.

Personalised therapy

Wrist actigraphic approach is increasingly used in clinical care. Personalised measurements of sedation/agitation rhythms are useful for ICU patients, for evaluation of motor fatigue in oncologic patients, for an individual enhancement of performance in military and sport medicine. In the framework of personalised therapy intervention, patients can be encouraged to optimise their behavioural habits improving recovery and activity patterns. This opens excellent perspectives for the sleep-inducing medication and stimulants replacement as well as for increasing the role of participatory medicine by visualising and encouraging optimal behavioural patterns of the individual.

Complex analysis of the personalized pharmacotherapy in the management of COVID-19 patients and suggestions for applications of predictive, preventive, and personalized medicine attitude

Aims

Coronavirus disease 2019 (COVID-19) is rapidly spreading worldwide. Drug therapy is one of the major treatments, but contradictory results of clinical trials have been reported among different individuals. Furthermore, comprehensive analysis of personalized pharmacotherapy is still lacking. In this study, analyses were performed on 47 well-characterized COVID-19 drugs used in the personalized treatment of COVID-19.

Methods

Clinical trials with published results of drugs use for COVID-19 treatment were collected to evaluate drug efficacy. Drug-to-Drug Interactions (DDIs) were summarized and classified. Functional variations in actionable pharmacogenes were collected and systematically analysed. “Gene Score” and “Drug Score” were defined and calculated to systematically analyse ethnicity-based genetic differences, which are important for the safer use of COVID-19 drugs.

Results

Our results indicated that four antiviral agents (ritonavir, darunavir, daclatasvir and sofosbuvir) and three immune regulators (budesonide, colchicine and prednisone) as well as heparin and enalapril could generate the highest number of DDIs with common concomitantly utilized drugs. Eight drugs (ritonavir, daclatasvir, sofosbuvir, ribavirin, interferon alpha-2b, chloroquine, hydroxychloroquine (HCQ) and ceftriaxone had actionable pharmacogenomics (PGx) biomarkers among all ethnic groups. Fourteen drugs (ritonavir, daclatasvir, prednisone, dexamethasone, ribavirin, HCQ, ceftriaxone, zinc, interferon beta-1a, remdesivir, levofloxacin, lopinavir, human immunoglobulin G and losartan) showed significantly different pharmacogenomic characteristics in relation to the ethnic origin of the patient.

Conclusion

We recommend that particularly for patients with comorbidities to avoid serious DDIs, the predictive, preventive, and personalized medicine (PPPM, 3 PM) strategies have to be applied for COVID-19 treatment, and genetic tests should be performed for drugs with actionable pharmacogenes, especially in some ethnic groups with a higher frequency of functional variations, as our analysis showed. We also suggest that drugs associated with higher ethnic genetic differences should be given priority in future pharmacogenetic studies for COVID-19 management. To facilitate translation of our results into clinical practice, an approach conform with PPPM/3 PM principles was suggested. In summary, the proposed PPPM/3 PM attitude should be obligatory considered for the overall COVID-19 management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-021-00247-0.

Positive influence of gut microbiota on the effects of Korean red ginseng in metabolic syndrome: a randomized, double-blind, placebo-controlled clinical trial

Background

Ginseng, a traditional herbal medicine, has been used for thousands of years to treat various diseases including metabolic syndrome (MS). However, the underlying mechanism(s) of such beneficial actions of ginseng against MS is poorly understood. Emerging evidence indicates a close association of the host gut microbiota with MS. The present study was conducted to examine, whether the beneficial effects of Korean red ginseng (KRG) against MS could be influenced by gut microbial population and whether gut microbial profile could be considered a valuable biomarker for targeted treatment strategy for MS in compliance with the predictive, preventive, and personalized medicine (PPPM / 3PM).

Methods

This clinical study was a randomized, double-blind, placebo-controlled trial evaluating the effects of KRG treatment for 8 weeks on patients with MS. The anthropometric parameters, vital signs, metabolic biomarkers, and gut microbial composition through 16S rRNA gene sequencing were assessed at the baseline and endpoint. The impact of KRG was also evaluated after categorizing the subjects into responders and non-responders, as well as enterotypes 1 and 2 based on their gut microbial profile at the baseline.

Results

Fifty out of 60 subjects who meet the MS criteria completed the trial without showing adverse reactions. The KRG treatment caused a significant decrease in systolic blood pressure (SBP). Microbial analysis revealed a decrease in Firmicutes, Proteobacteria, and an increase in Bacteroidetes in response to KRG. In patient stratification analysis, the responders showing marked improvement in the serum levels of lipid metabolic biomarkers TC and LDL due to the KRG treatment exhibited higher population of both the family Lachnospiraceae and order Clostridiales compared to the non-responders. The homeostasis model assessment-insulin resistance (HOMA-IR) and insulin level were decreased in enterotype 1 (Bacteroides-abundant group) and increased in enterotype 2 (prevotella-abundant group) following the KRG treatment.

Conclusion

In this study, the effects of KRG on the glucose metabolism in MS patients were influenced by the relative abundances of gut microbial population and differed according to the individual enterotype. Therefore, the analysis of enterotype categories is considered to be helpful in predicting the effectiveness of KRG on glucose homeostasis of MS patients individually. This will further help to decide on the appropriate treatment strategy for MS, in compliance with the perspective of PPPM.

The interplay between the vaginal microbiome and innate immunity in the focus of predictive, preventive, and personalized medical approach to combat HPV-induced cervical cancer

HPVs representing the most common sexually transmitted disease are a group of carcinogenic viruses with different oncogenic potential. The immune system and the vaginal microbiome represent the modifiable and important risk factors in HPV-induced carcinogenesis. HPV infection significantly increases vaginal microbiome diversity, leading to gradual increases in the abundance of anaerobic bacteria and consequently the severity of cervical dysplasia. Delineation of the exact composition of the vaginal microbiome and immune environment before HPV acquisition, during persistent/progressive infections and after clearance, provides insights into the complex mechanisms of cervical carcinogenesis. It gives hints regarding the prediction of malignant potential. Relative high HPV prevalence in the general population is a challenge for modern and personalized diagnostics and therapeutic guidelines. Identifying the dominant microbial biomarkers of high-grade and low-grade dysplasia could help us to triage the patients with marked chances of lesion regression or progression. Any unnecessary surgical treatment of cervical dysplasia could negatively affect obstetrical outcomes and sexual life. Therefore, understanding the effect and role of microbiome-based therapies is a breaking point in the conservative management of HPV-associated precanceroses. The detailed evaluation of HPV capabilities to evade immune mechanisms from various biofluids (vaginal swabs, cervicovaginal lavage/secretions, or blood) could promote the identification of new immunological targets for novel individualized diagnostics and therapy. Qualitative and quantitative assessment of local immune and microbial environment and associated risk factors constitutes the critical background for preventive, predictive, and personalized medicine that is essential for improving state-of-the-art medical care in patients with cervical precanceroses and cervical cancer. The review article focuses on the influence and potential diagnostic and therapeutic applications of the local innate immune system and the microbial markers in HPV-related cancers in the context of 3P medicine.

Mining the Biomarker Potential of the Urine Peptidome: From Amino Acids Properties to Proteases

Native biofluid peptides offer important information about diseases, holding promise as biomarkers. Particularly, the non-invasive nature of urine sampling, and its high peptide concentration, make urine peptidomics a useful strategy to study the pathogenesis of renal conditions. Moreover, the high number of detectable peptides as well as their specificity set the ground for the expansion of urine peptidomics to the identification of surrogate biomarkers for extra-renal diseases. Peptidomics further allows the prediction of proteases (degradomics), frequently dysregulated in disease, providing a complimentary source of information on disease pathogenesis and biomarkers. Then, what does urine peptidomics tell us so far? In this paper, we appraise the value of urine peptidomics in biomarker research through a comprehensive analysis of all datasets available to date. We have mined > 50 papers, addressing > 30 different conditions, comprising > 4700 unique peptides. Bioinformatic tools were used to reanalyze peptide profiles aiming at identifying disease fingerprints, to uncover hidden disease-specific peptides physicochemical properties and to predict the most active proteases associated with their generation. The molecular patterns found in this study may be further validated in the future as disease biomarker not only for kidney diseases but also for extra-renal conditions, as a step forward towards the implementation of a paradigm of predictive, preventive and personalized (3P) medicine.

Blood transcriptome profiling as potential biomarkers of suboptimal health status: potential utility of novel biomarkers for predictive, preventive, and personalized medicine strategy

The early identification of Suboptimal Health Status (SHS) creates a window opportunity for the predictive, preventive, and personalized medicine (PPPM) in chronic diseases. Previous studies have observed the alterations in several mRNA levels in SHS individuals. As a promising "omics" technology offering comprehension of genome structure and function at RNA level, transcriptome profiling can provide innovative molecular biomarkers for the predictive identification and targeted prevention of SHS. To explore the potential biomarkers, biological functions, and signalling pathways involved in SHS, an RNA sequencing (RNA-Seq)-based transcriptome analysis was firstly conducted on buffy coat samples collected from 30 participants with SHS and 30 age- and sex-matched healthy controls. Transcriptome analysis identified a total of 46 differentially expressed genes (DEGs), in which 22 transcripts were significantly increased and 24 transcripts were decreased in the SHS group. A total of 23 transcripts were selected as candidate predictive biomarkers for SHS. Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that several biological processes were related to SHS, such as ATP-binding cassette (ABC) transporter and neurodegeneration. Protein-protein interaction (PPI) network analysis identified 10 hub genes related to SHS, including GJA1, TWIST2, KRT1, TUBB3, AMHR2, BMP10, MT3, BMPER, NTM, and TMEM98. A transcriptome predictive model can distinguish SHS individuals from the healthy controls with a sensitivity of 83.3% (95% confidence interval (CI): 73.9-92.7%), a specificity of 90.0% (95% CI: 82.4-97.6%), and an area under the receiver operating characteristic curve of 0.938 (95% CI: 0.882-0.994). In the present study, we demonstrated that blood (buffy coat) samples appear to be a very promising and easily accessible biological material for the transcriptomic analyses focused on the objective identification of SHS by using our transcriptome predictive model. The pattern of particularly determined DEGs can be used as predictive transcriptomic biomarkers for the identification of SHS in an individual who may, subjectively, feel healthy, but at the level of subcellular mechanisms, the changes can provide early information about potential health problems in this person. Our findings also indicate the potential therapeutic targets in dealing with chronic diseases related to SHS, such as T2DM and CVD, and an early onset of neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, as well as the findings suggest the targets for personalized interventions as promoted in PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-021-00238-1.

Making a complex dental care tailored to the person: population health in focus of predictive, preventive and personalised (3P) medical approach

An evident underestimation of the targeted prevention of dental diseases is strongly supported by alarming epidemiologic statistics globally. For example, epidemiologists demonstrated 100% prevalence of dental caries in the Russian population followed by clinical manifestation of periodontal diseases. Inadequately provided oral health services in populations are caused by multi-factorial deficits including but not limited to low socio-economic status of affected individuals, lack of insurance in sub-populations, insufficient density of dedicated medical units. Another important aspect is the “participatory” medicine based on the active participation of population in maintaining oral health: healthcare will remain insufficient as long as the patient is not motivated and does not feel responsible for their oral health. To this end, nearly half of chronically diseased people do not comply with adequate medical services suffering from severely progressing pathologies. Noteworthy, the prominent risk factors and comorbidities linked to the severe disease course and poor outcomes in COVID-19-infected individuals, such as elderly, diabetes mellitus, hypertension and cardiovascular disease, are frequently associated with significantly altered oral microbiome profiles, systemic inflammatory processes and poor oral health. Suggested pathomechanisms consider potential preferences in the interaction between the viral particles and the host microbiota including oral cavity, the respiratory and gastrointestinal tracts. Since an aspiration of periodontopathic bacteria induces the expression of angiotensin-converting enzyme 2, the receptor for SARS-CoV-2, and production of inflammatory cytokines in the lower respiratory tract, poor oral hygiene and periodontal disease have been proposed as leading to COVID-19 aggravation. Consequently, the issue-dedicated expert recommendations are focused on the optimal oral hygiene as being crucial for improved individual outcomes and reduced morbidity under the COVID-19 pandemic condition. Current study demonstrated that age, gender, socio-economic status, quality of environment and life-style, oral hygiene quality, regularity of dental services requested, level of motivation and responsibility for own health status and corresponding behavioural patterns are the key parameters for the patient stratification considering person-tailored approach in a complex dental care in the population. Consequently, innovative screening programmes and adapted treatment schemes are crucial for the complex person-tailored dental care to improve individual outcomes and healthcare provided to the population.

A qualitative exploration of the norms and intentions of nurses toward the clinical use of herbal medicine in Ghana

PURPOSE

The purpose of the study was to determine the norms and intentions of nurses towards the clinical use of herbal medicine.

METHOD

Information on nurses' demographics, norms and intentions was gathered through individual face-to-face interviews. Interviews were analysed using the process of content analysis.

RESULTS

Beliefs of the nurses and motivational factors formed their norms. The nurses believed herbal medicine was God-given and was used by their ancestors with good outcomes. Some were self-motivated to serve herbal medicine while others counted on patient's recovery upon the use of herbal medicine to motivate them. Positive and negative intentions were discovered; thus, preparedness and unpreparedness to serve herbal medicine in the future.

CONCLUSIONS

Though some nurses believe herbal medicine is God-given, used by their ancestors and motivated to serve, others were unwilling because of inadequate training. Training of professional nurses on herbal medicine may improve their negative intentions.

The evolution of personalized healthcare and the pivotal role of European regions in its implementation

Personalized medicine (PM) moves at the same pace of data and technology and calls for important changes in healthcare. New players are participating, providing impulse to PM. We review the conceptual foundations for PM and personalized healthcare and their evolution through scientific publications where a clear definition and the features of the different formulations are identifiable. We then examined PM policy documents of the International Consortium for Personalised Medicine and related initiatives to understand how PM stakeholders have been changing. Regional authorities and stakeholders have joined the race to deliver personalized care and are driving toward what could be termed as the next personalized healthcare. Their role as a key stakeholder in PM is expected to be pivotal.

Negligible therapeutic impact, false-positives, overdiagnosis and lead-time are the reasons why radiographs bring more harm than benefits in the caries diagnosis of preschool children

Background

To evaluate the clinical course and interventions required during two years of follow-up of dental surfaces of deciduous molars diagnosed, and consequently treated, by two different strategies: diagnosis made by clinical examination alone or associated with radiographs.

Methods

This is a secondary analysis of a two-arm randomized clinical trial with parallel groups related to the diagnostic strategy for caries detection in preschool children. 216 children (3–6 years old) were followed-up for two years. All dental surfaces were diagnosed by visual inspection and later, through radiographic assessment. Baseline treatment was made in accordance with the results obtained by visual inspection performed alone or combined with radiographic method, considering the allocated group. Dental surfaces with no restoration needs, or those restored at the beginning of the study were followed-up for two years. The treatment decision was made according to the allocated group. The outcome was the occurrence of failure (a new caries lesion or a restoration replacement) during the follow-up.

Results

4383 proximal and occlusal surfaces of deciduous molars in 216 preschool children were diagnosed and treated according to the abovementioned diagnostic strategies and followed-up for 24 months. The assessment of radiographs made change the initial decision reached by visual inspection in about 30% of the surfaces when all types of interventions were considered. However, most disagreements occurred for initial lesions, where radiographs tended to underestimate them. Discordances between methods occurred in less than 5% of all surfaces when considered lesions requiring operative treatment. For discrepancy cases, the placed interventions guided by following the radiographic results did not present less failures against those made following only visual inspection. As a matter of fact, the use of radiographs in the diagnostic strategy for caries detection in children brought more harms than benefits due to the occurrence of false-positives, overdiagnosis and lead-time bias.

Conclusions

Simultaneous association of visual inspection and radiographic assessment for caries detection in preschool children causes more harms than benefits, and therefore, visual inspection should be conducted alone in the regular clinical practice.

Trial registration Clinicaltrials.gov platform: NCT02078453, registered on 5th March 2014.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-021-01528-w.

Education for future biobankers - The state-of-the-art and outlook

Biobanking as a quickly growing branch of personalised medicine has undergone enormous progress during last two decades. Nowadays it is a well developed and structured multidisciplinary field that reflects developments and advances of biomedical research based on principles of predictive, preventive and personalised medicine (PPPM/3PM). All these trends in PPPM progress have to be translated into practice and education of new generation of scientists and healthcare givers. The importance of biobanks for multitasking research, personalised treatment, and health care systems was emphasised by many scientists and health care experts. As biobanking carries multidisciplinary character currently including more professionals than ten—twenty years ago, new generation of professional biobankers is urgently needed. To create new generation of biobankers who are fully competent to answer more and more scientific and practical questions, new study programmes, novel university curricula, and topic-dedicated courses are essential. The aim of the review is to present basic forms, trends of biobanking education offered by various biobanking related bodies and to highlight future needs. The first step is to cover all activities and duties of biobanks: acquiring, collecting, storageing and sharing biological samples and associated data, using adequate assessment for both - materials and data, taking into consideration ethical, legal, and societal issues (ELSI), responding to all stakeholder needs including pharmaceutical and other related industries, patient organisations and many other interested groups, emerging technologies and innovations as well as current and future requirements of health care systems. To compile educational programmes is a comprehensive task for all actors involved in the field of biobanking who contribute to the harmonised process of creating high educational level for future generation of biobankers. The exchange of experience involving extensive international collaboration is the way how to facilitate the process of creating optimal biobanking education.

Aziz NH, Teik CK, Shafiee MN, Kampan NC. New Predictive Biomarkers for Ovarian Cancer

Ovarian cancer is the eighth-most common cause of death among women worldwide. In the absence of distinctive symptoms in the early stages, the majority of women are diagnosed in advanced stages of the disease. Surgical debulking and systemic adjuvant chemotherapy remain the mainstays of treatment, with the development of chemoresistance in up to 75% of patients with subsequent poor treatment response and reduced survival. Therefore, there is a critical need to revisit existing, and identify potential biomarkers that could lead to the development of novel and more effective predictors for ovarian cancer diagnosis and prognosis. The capacity of these biomarkers to predict the existence, stages, and associated therapeutic efficacy of ovarian cancer would enable improvements in the early diagnosis and survival of ovarian cancer patients. This review not only highlights current evidence-based ovarian-cancer-specific prognostic and diagnostic biomarkers but also provides an update on various technologies and methods currently used to identify novel biomarkers of ovarian cancer.

Cultural bias in motor function patterns: Potential relevance for predictive, preventive, and personalized medicine

Background

Quantification of motor performance has a promising role in personalized medicine by diagnosing and monitoring, e.g. neurodegenerative diseases or health problems related to aging. New motion assessment technologies can evolve into patient-centered eHealth applications on a global scale to support personalized healthcare as well as treatment of disease. However, uncertainty remains on the limits of generalizability of such data, which is relevant specifically for preventive or predictive applications, using normative datasets to screen for incipient disease manifestations or indicators of individual risks.

Objective

This study explored differences between healthy German and Japanese adults in the performance of a short set of six motor tests.

Methods

Six motor tasks related to gait and balance were recorded with a validated 3D camera system. Twenty-five healthy adults from Chiba, Japan, participated in this study and were matched for age, sex, and BMI to a sample of 25 healthy adults from Berlin, Germany. Recordings used the same technical setup and standard instructions and were supervised by the same experienced operator. Differences in motor performance were analyzed using multiple linear regressions models, adjusted for differences in body stature.

Results

From 23 presented parameters, five showed group-related differences after adjustment for height and weight (R ² between .19 and .46, p<.05). Japanese adults transitioned faster between sitting and standing and used a smaller range of hand motion. In stepping-in-place, cadence was similar in both groups, but Japanese adults showed higher knee movement amplitudes. Body height was identified as relevant confounder (standardized beta >.5) for performance of short comfortable and maximum speed walks. For results of posturography, regression models did not reveal effects of group or body stature.

Conclusions

Our results support the existence of a population-specific bias in motor function patterns in young healthy adults. This needs to be considered when motor function is assessed and used for clinical decisions, especially for personalized predictive and preventive medical purposes. The bias affected only the performance of specific items and parameters and is not fully explained by population-specific ethnic differences in body stature. It may be partially explained as cultural bias related to motor habits. Observed effects were small but are expected to be larger in a non-controlled cross-cultural application of motion assessment technologies with relevance for related algorithms that are being developed and used for data processing. In sum, the interpretation of individual data should be related to appropriate population-specific or even better personalized normative values to yield its full potential and avoid misinterpretation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-021-00236-3.

Challenging management of severe chronic disorders in acute pandemic situation: Chronic liver disease under COVID-19 pandemic as the proof-of-principle model to orchestrate the measures in 3PM context

Chronic liver disease management is a comprehensive approach requiring multi-professional expertise and well-orchestrated healthcare measures thoroughly organized by responsible medical units. Contextually, the corresponding multi-faceted chain of healthcare events is likely to be severely disturbed or even temporarily broken under the force majeure conditions such as global pandemics. Consequently, the chronic liver disease is highly representative for the management of any severe chronic disorder under lasting pandemics with unprecedented numbers of acutely diseased persons who, together with the chronically sick patient cohorts, have to be treated using the given capacity of healthcare systems with their limited resources. Current study aimed at exploring potentially negative impacts of the SARS CoV-2 outbreak on the quality of the advanced chronic liver disease (ACLD) management considering two well-classified parameters, namely, (1) the continuity of the patient registrations and (2) the level of mortality rates, comparing pre-COVID-19 statistics with these under the current pandemic in Slovak Republic. Altogether 1091 registrations to cirrhosis registry (with 60.8% versus 39.2% males to females ratio) were included with a median age of 57 years for patients under consideration. Already within the very first 3 months of the pandemic outbreak in Slovakia (lockdown declared from March 16, 2020, until May 20, 2020), the continuity of the patient registrations has been broken followed by significantly increased ACLD-related death rates. During this period of time, the total number of new registrations decreased by about 60% (15 registrations in 2020 versus 38 in 2018 and 38 in 2019). Corresponding mortality increased by about 52% (23 deaths in 2020 versus 10 in 2018 and 12 in 2019). Based on these results and in line with the framework of 3PM guidelines, the pandemic priority pathways (PPP) are strongly recommended for maintaining tertiary care uninterrupted. For the evidence-based implementation of PPP, creation of predictive algorithms and individualized care strategy tailored to the patient is essential. Resulting classification of measures is summarized as follows:

The Green PPP Line is reserved for prioritized (urgent and comprehensive) treatment of patients at highest risk to die from ACLD (tertiary care) as compared to the risk from possible COVID-19 infection.

The Orange PPP Line considers patients at middle risk of adverse outcomes from ACLD with re-addressing them to the secondary care. As further deterioration of ACLD is still probable, pro-active management is ascertained with tertiary center serving as the 24/7 telemedicine consultation hub for a secondary facility (on a physician-physician level).

The Red PPP Line is related to the patients at low risk to die from ACLD, re-addressing them to the primary care. Since patients with stable chronic liver diseases without advanced fibrosis are at trivial inherent risk, they should be kept out of the healthcare setting as far as possible by the telemedical (patient-nurse or patient- physician) measurements.

The assigned priority has to be monitored and re-evaluated individually—in intervals based on the baseline prognostic score such as MELD. The approach is conform with principles of predictive, preventive and personalized medicine (PPPM / 3PM) and demonstrates a potential of great clinical utility for an optimal management of any severe chronic disorder (cardiovascular, neurological and cancer) under lasting pandemics.

Mitochondrial impairments in aetiopathology of multifactorial diseases: common origin but individual outcomes in context of 3P medicine

Mitochondrial injury plays a key role in the aetiopathology of multifactorial diseases exhibiting a "vicious circle" characteristic for pathomechanisms of the mitochondrial and multi-organ damage frequently developed in a reciprocal manner. Although the origin of the damage is common (uncontrolled ROS release, diminished energy production and extensive oxidative stress to life-important biomolecules such as mtDNA and chrDNA), individual outcomes differ significantly representing a spectrum of associated pathologies including but not restricted to neurodegeneration, cardiovascular diseases and cancers. Contextually, the role of predictive, preventive and personalised (PPPM/3P) medicine is to introduce predictive analytical approaches which allow for distinguishing between individual outcomes under circumstance of mitochondrial impairments followed by cost-effective targeted prevention and personalisation of medical services. Current article considers innovative concepts and analytical instruments to advance management of mitochondriopathies and associated pathologies.

Mitochondriopathies as a Clue to Systemic Disorders-Analytical Tools and Mitigating Measures in Context of Predictive, Preventive, and Personalized (3P) Medicine

The mitochondrial respiratory chain is the main site of reactive oxygen species (ROS) production in the cell. Although mitochondria possess a powerful antioxidant system, an excess of ROS cannot be completely neutralized and cumulative oxidative damage may lead to decreasing mitochondrial efficiency in energy production, as well as an increasing ROS excess, which is known to cause a critical imbalance in antioxidant/oxidant mechanisms and a "vicious circle" in mitochondrial injury. Due to insufficient energy production, chronic exposure to ROS overproduction consequently leads to the oxidative damage of life-important biomolecules, including nucleic acids, proteins, lipids, and amino acids, among others. Different forms of mitochondrial dysfunction (mitochondriopathies) may affect the brain, heart, peripheral nervous and endocrine systems, eyes, ears, gut, and kidney, among other organs. Consequently, mitochondriopathies have been proposed as an attractive diagnostic target to be investigated in any patient with unexplained progressive multisystem disorder. This review article highlights the pathomechanisms of mitochondriopathies, details advanced analytical tools, and suggests predictive approaches, targeted prevention and personalization of medical services as instrumental for the overall management of mitochondriopathy-related cascading pathologies.

Is Mental Stress the Primary Cause of Glaucoma?

The prognosis of going blind is very stressful for patients diagnosed with "glaucoma". Worries and fear of losing independence is a constant mental burden, with secondary risks of depression and social isolation. But stress is not only a result of glaucoma but also a possible cause (risk factor). This should not be surprising, given that chronic stress can trigger "psychosomatic" organ dysfunctions anywhere in the body. Why should the organ "eye" be an exception? Indeed, glaucoma patients often suspect that severe emotional stress caused their visual field loss or "foggy vision". The hypothesis that stress is a possible cause of glaucoma is supported by different observations: (i) acute and chronic stress increases intraocular pressure and (ii) long-term stress can lead to vascular dysregulation of the microcirculation in the eye and brain ("Flammer's syndrome"), leading to partial hypoxia and hypoglycaemia (hypo-metabolism). Even if nerve cells do not die, they may then become inactive ("silent" neurons). (iii) Degenerative changes have been reported in the brain of glaucoma patients, affecting not only anterograde or transsynaptic areas of the central visual pathway, but degeneration is also found (iv) in brain areas involved in emotional appraisal and the physiological regulation of stress hormones. There are also psychological hints indicating that stress is a cause of glaucoma: (v) Glaucoma patients with Flammer's syndrome show typical personality traits that are associated with low stress resilience: they often have cold hands or feet, are ambitious (professionally successful), perfectionistic, obsessive, brooding and worrying a lot. (vi) If stress hormone levels and inflammation parameters are reduced in glaucoma patients by relaxation with meditation, this correlates with normalisation of intraocular pressure, and yet another clue is that (vii) visual field improvements after non-invasive current stimulation therapy, that are known to improve circulation and neuronal synchronisation, are much most effective in patients with stress resilient personalities. An appreciation of stress as a "cause" of glaucoma suggests that in addition to standard therapy (i) stress reduction through relaxation techniques should be recommended (e.g. meditation), and (ii) self-medication compliance should not be induced by kindling anxiety and worries with negative communication ("You will go blind!"), but communication should be positive ("The prognosis is optimistic").

Rapid and Extraction-Free Detection of SARS-CoV-2 from Saliva by Colorimetric Reverse-Transcription Loop-Mediated Isothermal Amplification

BACKGROUND

Rapid, reliable, and widespread testing is required to curtail the ongoing COVID-19 pandemic. Current gold-standard nucleic acid tests are hampered by supply shortages in critical reagents including nasal swabs, RNA extraction kits, personal protective equipment, instrumentation, and labor.

METHODS

To overcome these challenges, we developed a rapid colorimetric assay using reverse-transcription loop-mediated isothermal amplification (RT-LAMP) optimized on human saliva samples without an RNA purification step. We describe the optimization of saliva pretreatment protocols to enable analytically sensitive viral detection by RT-LAMP. We optimized the RT-LAMP reaction conditions and implemented high-throughput unbiased methods for assay interpretation. We tested whether saliva pretreatment could also enable viral detection by conventional reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Finally, we validated these assays on clinical samples.

RESULTS

The optimized saliva pretreatment protocol enabled analytically sensitive extraction-free detection of SARS-CoV-2 from saliva by colorimetric RT-LAMP or RT-qPCR. In simulated samples, the optimized RT-LAMP assay had a limit of detection of 59 (95% confidence interval: 44-104) particle copies per reaction. We highlighted the flexibility of LAMP assay implementation using 3 readouts: naked-eye colorimetry, spectrophotometry, and real-time fluorescence. In a set of 30 clinical saliva samples, colorimetric RT-LAMP and RT-qPCR assays performed directly on pretreated saliva samples without RNA extraction had accuracies greater than 90%.

CONCLUSIONS

Rapid and extraction-free detection of SARS-CoV-2 from saliva by colorimetric RT-LAMP is a simple, sensitive, and cost-effective approach with broad potential to expand diagnostic testing for the virus causing COVID-19.

Profiling of Insulin-Like Growth Factor Binding Proteins (IGFBPs) in Obesity and Their Association With Ox-LDL and Hs-CRP in Adolescents

Insulin-like growth factor binding proteins (IGFBPs) are critical modulators of metabolism. In adults, IGFBPs are associated with obesity and insulin resistance. However, the association of IGFBPs with metabolic homeostasis in children and adolescents is not yet fully characterized. In this study we investigated the association of plasma IGFBPs (IGFBP-1, 3 and 7) with weight, central adiposity and cardiovascular disease markers Hs-CRP and Ox-LDL. A total of 420 adolescents (age 11-14 years) were recruited from public middle schools in Kuwait. IGFBPs were measured using bead-based multiplexing while Hs-CRP and Ox-LDL were measured using ELISA. Results showed that levels of IGFBP-1 were significantly lower in obese and overweight children when compared to normal weight children. Correlation analysis showed negative association between the level of IGFBP-1 and waist circumference to height (WC/Ht) ratio. IGFBP-1 level was also negatively associated with Hs-CRP. It was also observed that the levels of IGFBP-3 and IGFBP-7 were negatively correlated with Ox-LDL. Our data demonstrate a strong negative association of IGFBP-1 with overweight/obesity, and the inflammatory marker Hs-CRP. This was not seen with the levels of IGFBP-3 and 7. The association of IGFBP-1 with central adiposity (WC/Ht ratio) was stronger than its association with BMI-for-age z-score. Therefore we suggest that IGFBP-1 could potentially be used as a sensitive biomarker for obesity and its subsequent effects in screening and monitoring of obesity-related metabolic complications in adolescents.

Effect of fluoride, casein phosphopeptide-amorphous calcium phosphate and sodium trimetaphosphate combination treatment on the remineralization of caries lesions: An in vitro study

OBJECTIVE

To evaluate the effects of combination of treatments with fluoridated toothpastes supplemented with sodium trimetaphosphate (TMP) and casein phosphopeptide-amorphous calcium phosphate (MI Paste Plus®), on the remineralization of dental enamel.

DESIGN

Enamel blocks with artificial caries were randomly allocated into six groups (n = 12), according to the toothpastes: 1) without F-TMP-MI Paste Plus® (Placebo); 2) 1100 ppm F (1100 F), 3) MI Paste Plus®, 4) 1100 F + MI Paste Plus® (1100 F-MI Paste Plus®), 5) 1100 F + 3% TMP (1100 F-TMP) and 6) 1100 F-TMP + MI Paste Plus® (1100 F-TMP-MI Paste Plus®). Blocks were treated 2×/day with slurries of toothpastes (1 min). Furthermore, groups 4 and 6 received the application of MI Paste Plus® for 3 min. After pH cycling, the percentage of surface hardness recovery (%SHR); integrated loss of subsurface hardness (ΔKHN); profile analysis and lesion depth subsurface through polarized light microscopy (PLM), confocal laser scanning microscopy (LSCM), scanning electron microscopy (SEM), fluoride (F), calcium (Ca), phosphorus (P) concentrations in the enamel were determined. The data were analyzed by ANOVA (1-criterion) and Student-Newman-Keuls test (p < 0.001).

RESULTS

1100 F-TMP-MI Paste Plus® group showed the best results of %SHR, ΔKHN and PLM (p < 0.001). F concentration was similar between the 1100 F, 1100 F-MI Paste Plus®, and 1100 F-TMP-MI Paste Plus® groups (p > 0.001). 1100 F-TMP-MI Paste Plus® group showed the highest concentration of Ca and P in the enamel (p < 0.001).

CONCLUSION

The association of 1100 F-TMP and MI Paste Plus® led to a significant increase in the remineralization of initial carious lesions.