P4 medicine: how systems medicine will transform the healthcare sector and society

The Need for Green and Responsible Medical Informatics and Digital Health: Looking Forward with One Digital Health

One Health is an important initiative to view the world in a more integrative sense of our health and environment. Digital Health provides essential support to all of us as healthcare professionals and customers. One Digital Health (ODH) combines both One Health and Digital Health to provide a technologically integrative view. ODH gives an essential place to the environment and ecosystems. Thus, health technologies and digital health must be "green" and eco-friendly as much as possible. We suggest in this position paper examples of developing and implementing ODH-related concepts, systems, and products with a respectful consideration of the environment. For humans and animals, developing cutting-edge technologies to improve wellness and healthcare is critical. Nevertheless, we can learn from One Health that digitalization and so One Digital Health must be built to implement green, eco-friendly, and responsible thinking.

Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011-2014

Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of wearable devices in quantifying circadian rhythm to elucidate its connection to longevity, through continuously collected data remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-h accelerometer activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.S. adults from the 2011-2014 National Health and Nutrition Examination Survey. Using hierarchical clustering, we identified five clusters and described them as follows: "High activity", "Low activity", "Mild circadian rhythm (CR) disruption", "Severe CR disruption", and "Very low activity". Young adults with extreme CR disturbance are seemingly healthy with few comorbid conditions, but in fact associated with higher white blood cell, neutrophils, and lymphocyte counts (0.05-0.07 log-unit, all p < 0.05) and accelerated biological aging (1.42 years, p < 0.001). Older adults with CR disruption are significantly associated with increased systemic inflammation indexes (0.09-0.12 log-unit, all p < 0.05), biological aging advance (1.28 years, p = 0.021), and all-cause mortality risk (HR = 1.58, p = 0.042). Our findings highlight the importance of circadian alignment on longevity across all ages and suggest that data from wearable accelerometers can help in identifying at-risk populations and personalize treatments for healthier aging.

Commentary: Patient Perspectives on Artificial Intelligence; What have We Learned and How Should We Move Forward?

Artificial intelligence (AI) in healthcare has now begun to make its contributions to real-world patient care with varying degrees of both public and clinical acceptability around it. The heavy investment from governments, industry and academia needed to reach this point has helped to surface different perspectives on AI. As clinical AI applications become a reality, however, there is an increasing need to harness and integrate patient perspectives, which address the distinct needs of different populations, healthcare systems and clinical problems more closely. Despite this need, patient perspectives on AI implementation have little presence in academic literature and within implementation science and are not sufficiently considered throughout the MedTech and eHealthtech product development cycle, which brings its own challenges and opportunities. This joint patient expert/clinician commentary aims to briefly summarise views on AI. It reflects upon recommendations on how stakeholders such as clinicians and Health & MedTech small and medium-sized enterprises (SMEs) can make practical usage of these views. The recommendations of the authors centre around how to work better with patients to enable both product centric and patient centric innovation and person-centred care.

Metabolomics of infectious diseases in the era of personalized medicine

Infectious diseases continue to be a major cause of morbidity and mortality worldwide. Diseases cause perturbation of the host's immune system provoking a response that involves genes, proteins and metabolites. While genes are regulated by epigenetic or other host factors, proteins can undergo post-translational modification to enable/modify function. As a result, it is difficult to correlate the disease phenotype based solely on genetic and proteomic information only. Metabolites, however, can provide direct information on the biochemical activity during diseased state. Therefore, metabolites may, potentially, represent a phenotypic signature of a diseased state. Measuring and assessing metabolites in large scale falls under the omics technology known as "metabolomics". Comprehensive and/or specific metabolic profiling in biological fluids can be used as biomarkers of disease diagnosis. In addition, metabolomics together with genomics can be used to differentiate patients with differential treatment response and development of host targeted therapy instead of pathogen targeted therapy where pathogens are more prone to mutation and lead to antimicrobial resistance. Thus, metabolomics can be used for patient stratification, personalized drug formulation and disease control and management. Currently, several therapeutics and in vitro diagnostics kits have been approved by US Food and Drug Administration (FDA) for personalized treatment and diagnosis of infectious diseases. However, the actual number of therapeutics or diagnostics kits required for tailored treatment is limited as metabolomics and personalized medicine require the involvement of personnel from multidisciplinary fields ranging from technological development, bioscience, bioinformatics, biostatistics, clinicians, and biotechnology companies. Given the significance of metabolomics, in this review, we discussed different aspects of metabolomics particularly potentials of metabolomics as diagnostic biomarkers and use of small molecules for host targeted treatment for infectious diseases, and their scopes and challenges in personalized medicine.

Sensor-Controlled Digital Game for Heart Failure Self-management: Protocol for a Randomized Controlled Trial

BACKGROUND

Heart failure (HF) is the leading cause of hospitalization among older adults in the United States. There are substantial racial and geographic disparities in HF outcomes, with patients living in southern US states having a mortality rate 69% higher than the national average. Self-management behaviors, particularly daily weight monitoring and physical activity, are extremely important in improving HF outcomes; however, patients typically have particularly low adherence to these behaviors. With the rise of digital technologies to improve health outcomes and motivate health behaviors, sensor-controlled digital games (SCDGs) have become a promising approach. SCDGs, which leverage sensor-connected technologies, offer the benefits of being portable and scalable and allowing for continuous observation and motivation of health behaviors in their real-world contexts. They are also becoming increasingly popular among older adults and offer an immersive and accessible way to measure self-management behaviors and improve adherence. No SCDGs have been designed for older adults or evaluated to test their outcomes.

OBJECTIVE

This randomized clinical trial aims to assess the efficacy of a SCDG in integrating the behavioral data of participants with HF from weight scale and activity tracker sensors to activate game progress, rewards, and feedback and, ultimately, to improve adherence to important self-management behaviors.

METHODS

A total of 200 participants with HF, aged ≥45 years, will be recruited and randomized into 2 groups: the SCDG playing group (intervention group) and sensor-only group (control group). Both groups will receive a weight scale, physical activity tracker, and accompanying app, whereas only the intervention group will play the SCDG. This design, thereby, assesses the contributions of the game. All participants will complete a baseline survey as well as posttests at 6 and 12 weeks to assess the immediate effect of the intervention. They will also complete a third posttest at 24 weeks to assess the maintenance of behavioral changes. Efficacy and benefits will be assessed by measuring improvements in HF-related proximal outcomes (self-management behaviors of daily weight monitoring and physical activity) and distal outcomes (HF hospitalization, quality of life, and functional status) between baseline and weeks 6, 12, and 24. The primary outcome measured will be days with weight monitoring, for which this design provides at least 80% power to detect differences between the 2 groups.

RESULTS

Recruitment began in the fall of 2022, and the first patient was enrolled in the study on November 7, 2022. Recruitment of the last participant is expected in quarter 1 of 2025. Publication of complete results and data from this study is expected in 2026.

CONCLUSIONS

This project will generate insight and guidance for scalable and easy-to-use digital gaming solutions to motivate persistent adherence to HF self-management behaviors and improve health outcomes among individuals with HF.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05056129; https://clinicaltrials.gov/ct2/show/NCT05056129.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45801.

The role of clinical glyco(proteo)mics in precision medicine

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.

Personalize, participate, predict, and prevent: 4Ps in inflammatory bowel disease

Inflammatory bowel disease (IBD), which includes Crohn's disease (CD) and ulcerative colitis (UC), is a complex, immune-mediated, disorder which leads to several gastrointestinal and systemic manifestations determining a poor quality of life, disability, and other negative health outcomes. Our knowledge of this condition has greatly improved over the last few decades, and a comprehensive management should take into account both biological (i.e., disease-related, patient-related) and non-biological (i.e., socioeconomic, cultural, environmental, behavioral) factors which contribute to the disease phenotype. From this point of view, the so called 4P medicine framework, including personalization, prediction, prevention, and participation could be useful for tailoring ad hoc interventions in IBD patients. In this review, we discuss the cutting-edge issues regarding personalization in special settings (i.e., pregnancy, oncology, infectious diseases), patient participation (i.e., how to communicate, disability, tackling stigma and resilience, quality of care), disease prediction (i.e., faecal markers, response to treatments), and prevention (i.e., dysplasia through endoscopy, infections through vaccinations, and post-surgical recurrence). Finally, we provide an outlook discussing the unmet needs for implementing this conceptual framework in clinical practice.

Real-world usage of digital health applications (DiGA) in rheumatology: results from a German patient survey

Mobile health applications and digital therapeutics (DTx) aim to improve current patient care. Real-world data on DTx are, however, scarce. The aim of this study was to evaluate the adherence, acceptance, and efficacy of DTx in a clinical routine rheumatology setting. We conducted a prospective observational cohort study assessing the use, adherence, acceptance, and efficacy of the DTx DiGA (Digitale Gesundheitsanwendungen) by survey over 12 weeks. Patients included had to have a rheumatic disease and had been prescribed a DiGA. Acceptance was assessed using the Net promoter score (NPS). 48 patients were prescribed DiGA. Of these, 39/48 (81%) completed the follow-up survey. 21/39 (54%) patients downloaded the DTx and 20/39 (51%) used the DTx at least once. 9/39 (23%) of patients stopped quickly afterward and 5/39 (13%) reported having completed the whole DTx program. Lack of time and commitment were reported as the main reasons for non-use. Overall acceptance of DiGA was high (Net promoter score (NPS) mean (SD) 7.8/10 (2.3)). While the majority of patients (60%) reported no improvement, one subgroup of patients (7/20, 35%) who regularly used an exercise-based DTx for back pain reported symptom improvement. Acceptance of DTx in patients with rheumatic diseases is high, however onboarding to DTx use and adherence to DTx is still challenging in patients with rheumatic diseases. In a subgroup of patients with back pain, however, the use of an exercise-based DTx led to symptom improvement.

Perspectives of (/memorandum for) systems thinking on COVID-19 pandemic and pathology

Is data-driven analysis sufficient for understanding the COVID-19 pandemic and for justifying public health regulations? In this paper, we argue that such analysis is insufficient. Rather what is needed is the identification and implementation of over-arching hypothesis-related and/or theory-based rationales to conduct effective SARS-CoV2/COVID-19 (Corona) research. To that end, we analyse and compare several published recommendations for conceptual and methodological frameworks in medical research (e.g., public health, preventive medicine and health promotion) to current research approaches in medical Corona research. Although there were several efforts published in the literature to develop integrative conceptual frameworks before the COVID-19 pandemic, such as social ecology for public health issues and systems thinking in health care, only a few attempts to utilize these concepts can be found in medical Corona research. For this reason, we propose nested and integrative systemic modelling approaches to understand Corona pandemic and Corona pathology. We conclude that institutional efforts for knowledge integration and systemic thinking, but also for integrated science, are urgently needed to avoid or mitigate future pandemics and to resolve infection pathology.

'Medical Corona Science': Philosophical and systemic issues: Re-thinking medicine? On the epistemology of Corona medicine

RATIONALE, AIMS, AND OBJECTIVES

The disciplinary profile and the quality of production of knowledge on Corona pandemic is studied. This scientific field is called 'Medical Corona Science'.

METHODS

Criteria of analytical philosophy of science and science studies are systematically applied.

RESULTS

It is shown that mainly auxiliary medical disciplines such as virology and epidemiology but not clinical disciplines provide Corona knowledge. We see a laboratory-centered, technology- and data-driven science, largely ignoring clinical issues. Therefore we call these approaches "Medical Corona Science" (MCS). We see the need to adapt to features of a 'post-normal science', a 'mode 2 science' and of 'Integration and Implementation Science', especially as clinical knowledge must be integrated. There is also a severe lack of theoretical considerations that could help to frame the pandemic as a complex dynamic system.

CONCLUSIONS

We suggest a deeper meta-scientific discussion of the epistemic value of MCS and propose the application of tools from systems science.

Pathology-supported genetic testing presents opportunities for improved disability outcomes in multiple sclerosis

Background: Lipid metabolism may impact disability in people with multiple sclerosis (pwMS). Methods: Fifty-one pwMS entered an ultrasound and MRI study, of whom 19 had followed a pathology-supported genetic testing program for more than 10 years (pwMS-ON). Genetic variation, blood biochemistry, vascular blood flow velocities, diet and exercise were investigated. Results: pwMS-ON had significantly lower (p < 0.01) disability (Expanded Disability Status Scale) than pwMS not on the program (1.91 ± 0.75 vs 3.87 ± 2.32). A genetic variant in the lipid transporter FABP2 gene (rs1799883; 2445G>A, A54T) was significantly associated (p < 0.01) with disability in pwMS not on the program, but not in pwMS-ON (p = 0.88). Vascular blood flow velocities were lower in the presence of the A-allele. Conclusion: Pathology-supported genetic testing may provide guidance for lifestyle interventions with a significant impact on improved disability in pwMS.

Patient-centered pharmacovigilance: priority actions from the inherited bleeding disorders community

Pharmacovigilance, the science and practice of monitoring the effects of medicinals and their safety, is the responsibility of all stakeholders involved in the development, manufacture, regulation, distribution, prescription, and use of drugs and devices. The patient is the stakeholder most impacted by and the greatest source of information on safety issues. It is rare, however, for the patient to take a central role and exert leadership in the design and execution of pharmacovigilance. Patient organizations in the inherited bleeding disorders community are among the most established and empowered, particularly in the rare disorders. In this review, two of the largest bleeding disorders patient organizations, Hemophilia Federation of America (HFA) and National Hemophilia Foundation (NHF), offer insights into the priority actions required of all stakeholders to improve pharmacovigilance. The recent and ongoing increase in incidents raising safety concerns and a therapeutic landscape on the cusp of unprecedented expansion heighten the urgency of a recommitment to the primacy of patient safety and well-being in drug development and distribution.

Plain Language Summary

Patients at the center of product safety Every medical device and therapeutic product has potential benefits and harms. The pharmaceutical and biomedical companies that develop them must demonstrate that they are effective, and the safety risks are limited or manageable, for regulators to approve them for use and sale. After the product has been approved and people are using it in their daily lives, it is important to continue to collect information about any negative side effects or adverse events; this is called pharmacovigilance. Regulators, like the United States (US) Food and Drug Administration, the companies that sell and distribute the products, and healthcare professionals who prescribe them are all required to participate in collecting, reporting, analyzing, and communicating this information. The people with the most firsthand knowledge of the benefits and harms of the drug or device are the patients who use them. They have an important responsibility to learn how to recognize adverse events, how to report them, and to stay informed of any news about the product from the other partners in the pharmacovigilance network. Those partners have a crucial responsibility to provide clear, easy-to-understand information to patients about any new safety concerns that come to light. The community of people with inherited bleeding disorders has recently encountered problems with poor communication of product safety issues, prompting two large US patient organizations, National Hemophilia Foundation and Hemophilia Federation of America, to hold a Safety Summit with all the pharmacovigilance network partners. Together they developed recommendations to improve the collection and communication of information about product safety so that patients can make well-informed, timely decisions about their use of drugs and devices. This article presents these recommendations in the context of how pharmacovigilance is supposed to work and some of the challenges encountered by the community.

The Need to Prioritize Model-Updating Processes in Clinical Artificial Intelligence (AI) Models: Protocol for a Scoping Review

BACKGROUND

With an increase in the number of artificial intelligence (AI) and machine learning (ML) algorithms available for clinical settings, appropriate model updating and implementation of updates are imperative to ensure applicability, reproducibility, and patient safety.

OBJECTIVE

The objective of this scoping review was to evaluate and assess the model-updating practices of AI and ML clinical models that are used in direct patient-provider clinical decision-making.

METHODS

We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist and the PRISMA-P protocol guidance in addition to a modified CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist to conduct this scoping review. A comprehensive medical literature search of databases, including Embase, MEDLINE, PsycINFO, Cochrane, Scopus, and Web of Science, was conducted to identify AI and ML algorithms that would impact clinical decision-making at the level of direct patient care. Our primary end point is the rate at which model updating is recommended by published algorithms; we will also conduct an assessment of study quality and risk of bias in all publications reviewed. In addition, we will evaluate the rate at which published algorithms include ethnic and gender demographic distribution information in their training data as a secondary end point.

RESULTS

Our initial literature search yielded approximately 13,693 articles, with approximately 7810 articles to consider for full reviews among our team of 7 reviewers. We plan to complete the review process and disseminate the results by spring of 2023.

CONCLUSIONS

Although AI and ML applications in health care have the potential to improve patient care by reducing errors between measurement and model output, currently there exists more hype than hope because of the lack of proper external validation of these models. We expect to find that the AI and ML model-updating methods are proxies for model applicability and generalizability on implementation. Our findings will add to the field by determining the degree to which published models meet the criteria for clinical validity, real-life implementation, and best practices to optimize model development, and in so doing, reduce the overpromise and underachievement of the contemporary model development process.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/37685.

Análisis bioético del uso de la biotecnología genómica en la nutrición traslacional

Los avances de la biotecnología y las fascinantes perspectivas de la genómica nutricional en el escenario de la práctica clínica conducen a la consideración de distintos aspectos que impactan en el beneficio integral del ser humano. En ese sentido, la integración de la nutrición personalizada en la atención clínica requiere de un análisis bioético centrado en la unidad de la persona que, con base en su perfil nutrigenético único, contribuya al cuidado de la salud por medio del tratamiento nutricional individualizado. El análisis bioético contempla los principios de totalidad terapéutica, libertad e integridad. Además, el uso de las pruebas nutrigenéticas destaca no solo la confidencialidad de datos, que se presupone, sino que lleva a considerar el derecho a la intimidad.

Integrating oculomics with genomics reveals imaging biomarkers for preventive and personalized prediction of arterial aneurysms

Objective

Arterial aneurysms are life-threatening but usually asymptomatic before requiring hospitalization. Oculomics of retinal vascular features (RVFs) extracted from retinal fundus images can reflect systemic vascular properties and therefore were hypothesized to provide valuable information on detecting the risk of aneurysms. By integrating oculomics with genomics, this study aimed to (i) identify predictive RVFs as imaging biomarkers for aneurysms and (ii) evaluate the value of these RVFs in supporting early detection of aneurysms in the context of predictive, preventive and personalized medicine (PPPM).

Methods

This study involved 51,597 UK Biobank participants who had retinal images available to extract oculomics of RVFs. Phenome-wide association analyses (PheWASs) were conducted to identify RVFs associated with the genetic risks of the main types of aneurysms, including abdominal aortic aneurysm (AAA), thoracic aneurysm (TAA), intracranial aneurysm (ICA) and Marfan syndrome (MFS). An aneurysm-RVF model was then developed to predict future aneurysms. The performance of the model was assessed in both derivation and validation cohorts and was compared with other models employing clinical risk factors. An RVF risk score was derived from our aneurysm-RVF model to identify patients with an increased risk of aneurysms.

Results

PheWAS identified a total of 32 RVFs that were significantly associated with the genetic risks of aneurysms. Of these, the number of vessels in the optic disc ('ntreeA') was associated with both AAA (β = -0.36, P = 6.75e-10) and ICA (β = -0.11, P = 5.51e-06). In addition, the mean angles between each artery branch ('curveangle_mean_a') were commonly associated with 4 MFS genes (FBN1: β = -0.10, P = 1.63e-12; COL16A1: β = -0.07, P = 3.14e-09; LOC105373592: β = -0.06, P = 1.89e-05; C8orf81/LOC441376: β = 0.07, P = 1.02e-05). The developed aneurysm-RVF model showed good discrimination ability in predicting the risks of aneurysms. In the derivation cohort, the C-index of the aneurysm-RVF model was 0.809 [95% CI: 0.780-0.838], which was similar to the clinical risk model (0.806 [0.778-0.834]) but higher than the baseline model (0.739 [0.733-0.746]). Similar performance was observed in the validation cohort, with a C-index of 0.798 (0.727-0.869) for the aneurysm-RVF model, 0.795 (0.718-0.871) for the clinical risk model and 0.719 (0.620-0.816) for the baseline model. An aneurysm risk score was derived from the aneurysm-RVF model for each study participant. The individuals in the upper tertile of the aneurysm risk score had a significantly higher risk of aneurysm compared to those in the lower tertile (hazard ratio = 17.8 [6.5-48.8], P = 1.02e-05).

Conclusion

We identified a significant association between certain RVFs and the risk of aneurysms and revealed the impressive capability of using RVFs to predict the future risk of aneurysms by a PPPM approach. Our finds have great potential to support not only the predictive diagnosis of aneurysms but also a preventive and more personalized screening plan which may benefit both patients and the healthcare system.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00315-7.

Precision diagnostics in children

Medical practice is transforming from a reactive to a pro-active and preventive discipline that is underpinned by precision medicine. The advances in technologies in such fields as genomics, proteomics, metabolomics, transcriptomics and artificial intelligence have resulted in a paradigm shift in our understanding of specific diseases in childhood, greatly enhanced by our ability to combine data from changes within cells to the impact of environmental and population changes. Diseases in children have been reclassified as we understand more about their genomic origin and their evolution. Genomic discoveries, additional 'omics' data and advances such as optical genome mapping have driven rapid improvements in the precision and speed of diagnoses of diseases in children and are now being incorporated into newborn screening, have improved targeted therapies in childhood and have supported the development of predictive biomarkers to assess therapeutic impact and determine prognosis in congenital and acquired diseases of childhood. New medical device technologies are facilitating data capture at a population level to support higher diagnostic accuracy and tailored therapies in children according to predicted population outcome, and digital ecosystems now tailor therapies and provide support for their specific needs. By capturing biological and environmental data as early as possible in childhood, we can understand factors that predict disease or maintain health and track changes across a more extensive longitudinal path. Data from multiple health and external sources over long-time periods starting from birth or even in the in utero environment will provide further clarity about how to sustain health and prevent or predict disease. In this respect, we will not only use data to diagnose disease, but precision diagnostics will aid the 'diagnosis of good health'. The principle of 'start early and change more' will thus underpin the value of applying a personalised medicine approach early in life.

Big data in corneal diseases and cataract: Current applications and future directions

The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of "5 Vs"-variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.

Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023

In 2023, childhood hypoglycaemia remains a major public health problem and significant risk factor for consequent adverse neurodevelopment. Irrespective of the underlying cause, key elements of clinical management include the detection, prediction and prevention of episodes of hypoglycaemia. These tasks are increasingly served by Continuous Glucose Monitoring (CGM) devices that measure subcutaneous glucose at near-continuous frequency. While the use of CGM in type 1 diabetes is well established, the evidence for widespread use in rare hypoglycaemia disorders is less than convincing. However, in the few years since our last review there have been multiple developments and increased user feedback, requiring a review of clinical application. Despite advances in device technology, point accuracy of CGM remains low for children with non-diabetes hypoglycaemia. Simple provision of CGM devices has not replicated the efficacy seen in those with diabetes and is yet to show benefit. Machine learning techniques for hypoglycaemia prevention have so far failed to demonstrate sufficient prediction accuracy for real world use even in those with diabetes. Furthermore, access to CGM globally is restricted by costs kept high by the commercially-driven speed of technical innovation. Nonetheless, the ability of CGM to digitally phenotype disease groups has led to a better understanding of natural history of disease, facilitated diagnoses and informed changes in clinical management. Large CGM datasets have prompted re-evaluation of hypoglycaemia incidence and facilitated improved trial design. Importantly, an individualised approach and focus on the behavioural determinants of hypoglycaemia has led to real world reduction in hypoglycaemia. In this state of the art review, we critically analyse the updated evidence for use of CGM in non-diabetic childhood hypoglycaemia disorders since 2020 and provide suggestions for qualified use.

Analysis of Single Biomacromolecules and Viruses: Is It a Myth or Reality?

The beginning of the twenty-first century witnessed novel breakthrough research directions in the life sciences, such as genomics, transcriptomics, translatomics, proteomics, metabolomics, and bioinformatics. A newly developed single-molecule approach addresses the physical and chemical properties and the functional activity of single (individual) biomacromolecules and viral particles. Within the alternative approach, the combination of "single-molecule approaches" is opposed to "omics approaches". This new approach is fundamentally unique in terms of its research object (a single biomacromolecule). Most studies are currently performed using postgenomic technologies that allow the properties of several hundreds of millions or even billions of biomacromolecules to be analyzed. This paper discusses the relevance and theoretical, methodological, and practical issues related to the development potential of a single-molecule approach using methods based on molecular detectors.

A Translational Tool to Facilitate Use of Apolipoprotein B for Clinical Decision-Making

BACKGROUND

Despite recent large-scale discordance studies showing definitively that atherosclerotic cardiovascular disease (ASCVD) risk correlates better with apolipoprotein B (apoB) than with low-density lipoprotein cholesterol (LDL-C), the latter remains the recommended metric for guiding lipid-lowering treatment decisions in the United States. A major barrier to change, in this regard, is the lack of guideline-recommended apoB treatment targets. We developed a simple method to "translate" apoB values into population-equivalent LDL-C units, allowing apoB-based treatment decisions to be made using LDL-C targets.

METHODS

Sequentially collected, population-based samples underwent standard lipid panel analysis and apoB testing by immunoassay. Those with triglycerides greater than 1000 mg/dl were excluded, leaving a study cohort of 15 153 individuals.

RESULTS

Linear regression of calculated LDL-C values against percentile-equivalent apoB values yielded an equation to convert apoB into percentile-equivalent LDL-C units: [LDL-C equivalents = 1.38(apoB) - 29] (R2 = 0.999). The extent of discordance between LDL-C and apoB was examined in subgroups with similar LDL-C, ranging from very low (55-70 mg/dL) to very high (175-190 mg/dL). Among individuals with very low LDL-C, 40% had discordantly higher apoB, indicating higher ASCVD risk. Of those with very high LDL-C, 49% had discordantly lower apoB. Across the range, a minority of patients (25%-40%) had concordant levels of apoB, confirming that discordance between these biomarkers is highly prevalent. Similar results were found in discordance analysis between apoB and non-high-density lipoprotein cholesterol (HDL-C).

CONCLUSIONS

Providing visibility to discrepancies among LDL-C, non-HDL-C, and apoB should help to facilitate more rapid and widespread adoption of apoB for managing ASCVD risk.

Vitamin D and Systems Biology

The biological actions of the vitamin D receptor (VDR) have been investigated intensively for over 100 years and has led to the identification of significant insights into the repertoire of its biological actions. These were initially established to be centered on the regulation of calcium transport in the colon and deposition in bone. Beyond these well-known calcemic roles, other roles have emerged in the regulation of cell differentiation processes and have an impact on metabolism. The purpose of the current review is to consider where applying systems biology (SB) approaches may begin to generate a more precise understanding of where the VDR is, and is not, biologically impactful. Two SB approaches have been developed and begun to reveal insight into VDR biological functions. In a top-down SB approach genome-wide scale data are statistically analyzed, and from which a role for the VDR emerges in terms of being a hub in a biological network. Such approaches have confirmed significant roles, for example, in myeloid differentiation and the control of inflammation and innate immunity. In a bottom-up SB approach, current biological understanding is built into a kinetic model which is then applied to existing biological data to explain the function and identify unknown behavior. To date, this has not been applied to the VDR, but has to the related ERα and identified previously unknown mechanisms of control. One arena where applying top-down and bottom-up SB approaches may be informative is in the setting of prostate cancer health disparities.

Artificial intelligence for sustainable oral healthcare

OBJECTIVES

Oral health is grounded in the United National (UN) 2030 Agenda for Sustainable Developement and its 17 Goals (SDGs), in particular SDG 3 (Ensure healthy lives and promote well-being for all at all ages). The World Health Organization (WHO) Global Strategy on Oral Health calls for prioritizing environmentally sustainable and less invasive oral health care, and planetary health. Artificial Intelligence (AI) has the potential to power the next generation of oral health services and care, however its relationship with the broader UN and WHO concepts of sustainability remains poorly defined and articulated. We review the double-edged relationships between AI and oral health, to suggest actions that promote a sustainable deployment of AI for oral health.

DATA

Concepts regarding AI, sustainability and sustainable development were identified and defined. A review of several double-edged relationship between AI and SDGs were exposed for the field of Oral Health.

SOURCES

Medline and international declarations of the WHO, the UN and the World Dental Federation (FDI) were screened.

STUDY SELECTION

One the one hand, AI may reduce transportation, optimize care delivery (SDG 3 "Good Health and Well-Being", SDG 13 "Climate Action"), and increase accessibility of services and reduce inequality (SDG 10 "Reduced Inequalities", SDG 4 "Quality Education"). On the other hand, the deployment, implementation and maintenance of AI require significant resources (SDG 12 "Responsible Consumption and Production"), and costs for AI may aggravate inequalities. Also, AI may be biased, reinforcing inequalities (SDG 10) and discrimination (SDG 5), and may violate principles of security, privacy and confidentiality of personal information (SDG 16).

CONCLUSIONS

Systematic assessment of the positive impact and adverse effects of AI on sustainable oral health may help to foster the former and curb the latter based on evidence.

CLINICAL SIGNIFICANCE

If sustainability imperatives are actively taken into consideration, the community of oral health professionals should then employ AI for improving effectiveness, efficiency, and safety of oral healthcare; strengthen oral health surveillance; foster education and accessibility of care; ensure fairness, transparency and governance of AI for oral health; develop legislation and infrastructure to expand the use of digital health technologies including AI.

Renal function decline and heart failure hospitalisation in patients with type 2 diabetes: Dynamic predictions from the prospective SURDIAGENE cohort

AIMS

For type 2 diabetes persons, we assessed the association between renal function decline and heart failure hospitalisation (HFH) and validated dynamic HFH predictions (DynHFH) based on repeated estimated Glomerular Filtration Rate (eGFR) values.

METHODS

We studied 1413 patients from the SURDIAGENE cohort. From a joint model for longitudinal CKD-EPI measures and HFH risk, we calculated the probability of being HFH-free in the next five years.

RESULTS

The mean eGFR decline was estimated at 1.48 ml/min/1.73 m² per year (95 % CI from 1.23 to 1.74). We observed that eGFR decline was significantly associated with the HFH risk (_adjHR = 1.15 for an increase in yearly decline of 1 ml/min/1.73 m², 95 % CI from 1.03 to 1.26) independently of 7 baseline variables (from clinical, biological and ECG domains). Discrimination was good along the prediction times: AUC at 0.87 (95 %CI from 0.84 to 0.91) at patient inclusion and 0.77 (95 %CI from 0.67 to 0.87) at seven years' follow-up.

CONCLUSIONS

Renal function decline was significantly associated with the HFH risk. In the era of computer-assisted medical decisions, the DynHFH, a tool that dynamically predicts HFH in type 2 diabetes persons (https://shiny.idbc.fr/DynHFH), might be helpful for precision medicine and the implementation of stratified medical decision-making.

Representations of personalised medicine in family medicine: a qualitative analysis

Background

The promise of personalised medicine (PM) to transform healthcare has sparked great enthusiasm in the last years. Yet, its lack of consensus around the nature and scope of the concept has ended in terminological confusion amongst the users in primary care. We aimed to investigate the perceptions of doctors and their patients in response to this evolving concept. This present article focuses on the general understanding of personalised medicine, underlining the confusion over the concept.

Methods

Semi-structured comprehensive interviews were conducted with 10 general practitioners (GPs) and 10 of their patients. The purposive sampling took into account the doctor’s age, sex, and place of practice (rural/urban); each doctor recruited one patient of the same age and sex. Each interview began with the same open-ended question about the participant’s knowledge of the topic, after which a working definition was provided to continue the discussion. Using the grounded theory method, the analysis consisted of open coding, axial coding and selective coding.

Results

From our present analysis focusing on the general understanding of PM, three main themes representing the concept emerged. The first two representations being “centred on the person as a whole” and “focused on alternative and complementary methods”, in which the therapeutic relationship was stated as key. The third theme “medicine open to innovation” involved the few participants who had a good understanding of the concept and could associate personalised medicine with genomics. For those who value therapeutic relationship, the risks of accepting innovation could result in “fast-food” medicine and interpersonal barriers.

Discussion

PM is predominantly unfamiliar in family medicine. It is misinterpreted as a holistic or integrative type of medicine. This semantic confusion probably lies in the choice of the label “personalised” or from the lack of a uniform definition for the term.

Real-world data mining meets clinical practice: Research challenges and perspective

As Big Data Analysis meets healthcare applications, domain-specific challenges and opportunities materialize in all aspects of data science. Advanced statistical methods and Artificial Intelligence (AI) on Electronic Health Records (EHRs) are used both for knowledge discovery purposes and clinical decision support. Such techniques enable the emerging Predictive, Preventative, Personalized, and Participatory Medicine (P4M) paradigm. Working with the Infectious Disease Clinic of the University Hospital of Modena, Italy, we have developed a range of Data-Driven (DD) approaches to solve critical clinical applications using statistics, Machine Learning (ML) and Big Data Analytics on real-world EHR. Here, we describe our perspective on the challenges we encountered. Some are connected to medical data and their sparse, scarce, and unbalanced nature. Others are bound to the application environment, as medical AI tools can affect people's health and life. For each of these problems, we report some available techniques to tackle them, present examples drawn from our experience, and propose which approaches, in our opinion, could lead to successful real-world, end-to-end implementations.

DESY report number

DESY-22-153.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Public Attitude towards Biobanking: An Italian University Survey

Biobanks have established a critical role in biomedical research by collecting, preserving, organizing, and disseminating biospecimens and related health data, contributing to precision medicine development. Participation in biobanks is influenced by several factors, such as trust in institutions and scientists, knowledge about biobanking, and the consideration of benefit sharing. Understanding public attitudes, fears, and concerns toward biobanking is fundamental to designing targeted interventions to increase trust towards biobanks. The aim of our study was to investigate the level of knowledge and perception of biobanks in students and personnel of the University of Piemonte Orientale. An online questionnaire was designed and administered via e-mail. A total of 17,758 UPO personnel and students were invited to participate in the survey, and 1521 (9.3%) subjects completed the survey. The results showed that 65.0% of the participants were aware of the term "biobank" and knew what the activity of a biobank was, and 76.3% of subjects were willing to provide biospecimens to a research biobank, whereas 67.3% of the respondents were willing to contribute, in addition to biospecimens, their health and lifestyle data. Concerns were raised about the confidentiality of the information (25.6%) and the commercial use of the samples (25.0%). In conclusion, participants were aware of the role that biobanks play in research and were eager to participate for the sake of furthering scientific research. Still, several concerns need to be addressed regarding the confidentiality of the data along with the commercial use of the samples and associated data.

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.

Efficacy and safety of a mobile app intervention in patients with inflammatory arthritis: a prospective pilot study

EULAR highlighted the essential role of digital health in increasing self-management and improving clinical outcomes in patients with arthritis. The objective of this study was to evaluate the efficacy and safety of the digital health application (DHA) in patients with inflammatory arthritis. We assessed demographic parameters, treatment regimen, disease activity, and other patient-reported outcomes at baseline and after 4 weeks of DHA use added to standard care treatment. Of 17 patients, who completed the study, 7 (41.2%) patients were male, ranging from 19 to 63 (40.5 ± 12.2) years. No significant change in antirheumatic treatment was observed during the study. Statistically significant improvements (p < 0.05) were noted for health-related quality of life (increase in Physical Component Summary of Short Form-36 (SF-36) by 23.6%) and disease activity (decrease of Clinical Disease Activity Index and Simple Disease Activity Index by 38.4% and 39.9%, respectively). Clinically significant improvement was demonstrated for SF-36 Total Score (+ 14.4%), disease activity (Rheumatoid Arthritis Disease Activity Index− 5 to 15.9%), and depression (Patient Health Questionnaire− 9 to 13.5%). None of the efficacy parameters showed negative trends. No adverse events were reported throughout the study. The usability level was high i.e., the mean mHealth Application Usability Questionnaire Score of 5.96 (max.: 7.0) demonstrated a high level of application usability. This suggests that using a personalized disease management program based on DHA significantly improves several measures of patient-reported outcomes and disease activity in patients with inflammatory arthritis in a timely manner. These findings highlight the potential of complementary digital therapy in patients with inflammatory arthritis.

The economic imperatives for technology enabled wellness centered healthcare

A 2020 World Health Organization report underscored the impact of rising healthcare spending globally and questioned the long-term economic sustainability of current funding models. Increases in costs associated with care of late-stage irreversible diseases and the increasing prevalence of debilitating neurodegenerative disorders, coupled with increases in life expectancy are likely to overload the healthcare systems in many nations within the next decade if not addressed. One option for sustainability of the healthcare system is a change in emphasis from illness to wellness centered care. An attractive model is the P4 (Predictive, Preventative, Personalized and Participatory) medicine approach. Recent advances in connected health technology can help accelerate this transition; they offer prediction, diagnosis, and monitoring of health-related parameters. We explain how to integrate such technologies with conventional approaches and guide public health policy toward wellness-based care models and strategies to relieve the escalating economic burdens of managed care.

Public preferences towards data management and governance in Swiss biobanks: results from a nationwide survey

OBJECTIVES

This article aims to measure the willingness of the Swiss public to participate in personalised health research, and their preferences regarding data management and governance.

SETTING

Results are presented from a nationwide survey of members of the Swiss public.

PARTICIPANTS

15 106 randomly selected Swiss residents received the survey in September 2019. The response rate was 34.1% (n=5156). Respondent age ranged from 18 to 79 years, with fairly uniform spread across sex and age categories between 25 and 64 years.

PRIMARY AND SECONDARY OUTCOME MEASURES

Willingness to participate in personalised health research and opinions regarding data management and governance.

RESULTS

Most respondents preferred to be contacted and reconsented for each new project using their data (39%, 95% CI: 37.4% to 40.7%), or stated that their preference depends on the project type (29.4%, 95% CI: 27.9% to 31%). Additionally, a majority (52%, 95% CI: 50.3% to 53.8%) preferred their data or samples be stored anonymously or in coded form (43.4%, 95% CI: 41.7% to 45.1%). Of those who preferred that their data be anonymised, most also indicated a wish to be recontacted for each new project (36.8%, 95% CI: 34.5% to 39.2%); however, these preferences are in conflict. Most respondents desired to personally own their data. Finally, most Swiss respondents trust their doctors, along with researchers at universities, to protect their data.

CONCLUSION

Insight into public preference can enable Swiss biobanks and research institutions to create management and governance strategies that match the expectations and preferences of potential participants. Models allowing participants to choose how to interact with the process, while more complex, may increase individual willingness to provide data to biobanks.

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them

Current technological and medical advances lend substantial momentum to efforts to attain new medical certainties. Artificial Intelligence can enable unprecedented precision and capabilities in forecasting the health conditions of individuals. But, as we lay out, this novel access to medical information threatens to exacerbate adverse selection in the health insurance market. We conduct an interdisciplinary conceptual analysis to study how this risk might be averted, considering legal, ethical, and economic angles. We ask whether it is viable and effective to ban or limit AI and its medical use as well as to limit medical certainties and find that neither of these limitation-based approaches provides an entirely sufficient resolution. Hence, we argue that this challenge must not be neglected in future discussions regarding medical applications of AI forecasting, that it should be addressed on a structural level and we encourage further research on the topic.

Advanced Microsamples: Current Applications and Considerations for Mass Spectrometry-Based Metabolic Phenotyping Pipelines

Microsamples are collections usually less than 50 µL, although all devices that we have captured as part of this review do not fit within this definition (as some can perform collections of up to 600 µL); however, they are considered microsamples that can be self-administered. These microsamples have been introduced in pre-clinical, clinical, and research settings to overcome obstacles in sampling via traditional venepuncture. However, venepuncture remains the sampling gold standard for the metabolic phenotyping of blood. This presents several challenges in metabolic phenotyping workflows: accessibility for individuals in rural and remote areas (due to the need for trained personnel), the unamenable nature to frequent sampling protocols in longitudinal research (for its invasive nature), and sample collection difficulty in the young and elderly. Furthermore, venous sample stability may be compromised when the temperate conditions necessary for cold-chain transport are beyond control. Alternatively, research utilising microsamples extends phenotyping possibilities to inborn errors of metabolism, therapeutic drug monitoring, nutrition, as well as sport and anti-doping. Although the application of microsamples in metabolic phenotyping exists, it is still in its infancy, with whole blood being overwhelmingly the primary biofluid collected through the collection method of dried blood spots. Research into the metabolic phenotyping of microsamples is limited; however, with advances in commercially available microsampling devices, common barriers such as volumetric inaccuracies and the ‘haematocrit effect’ in dried blood spot microsampling can be overcome. In this review, we provide an overview of the common uses and workflows for microsampling in metabolic phenotyping research. We discuss the advancements in technologies, highlighting key considerations and remaining knowledge gaps for the employment of microsamples in metabolic phenotyping research. This review supports the translation of research from the ‘bench to the community’.

Oxidative Stress in Type 2 Diabetes: The Case for Future Pediatric Redoxomics Studies

Considerable evidence supports the role of oxidative stress in adult type 2 diabetes (T2D). Due to increasing rates of pediatric obesity, lack of physical activity, and consumption of excess food calories, it is projected that the number of children living with insulin resistance, prediabetes, and T2D will markedly increase with enormous worldwide economic costs. Understanding the factors contributing to oxidative stress and T2D risk may help develop optimal early intervention strategies. Evidence suggests that oxidative stress, triggered by excess dietary fat consumption, causes excess mitochondrial hydrogen peroxide emission in skeletal muscle, alters redox status, and promotes insulin resistance leading to T2D. The pathophysiological events arising from excess calorie-induced mitochondrial reactive oxygen species production are complex and not yet investigated in children. Systems medicine is an integrative approach leveraging conventional medical information and environmental factors with data obtained from “omics” technologies such as genomics, proteomics, and metabolomics. In adults with T2D, systems medicine shows promise in risk assessment and predicting drug response. Redoxomics is a branch of systems medicine focusing on “omics” data related to redox status. Systems medicine with a complementary emphasis on redoxomics can potentially optimize future healthcare strategies for adults and children with T2D.

Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.

Active Circulating Blood Volume During Hemodialysis: A Bench Model

Intradialytic hypotension due to excessive fluid removal is a common complication of hemodialysis. A bench model was constructed to evaluate quantification of active circulating blood volume (ACBV). The model included a central pump representing the heart and compartments to represent the central and peripheral circulation. A blood oxygenator was used to simulate lung volume and two containers represented fast and slow circulation compartments. A separate dialysis circuit with a blood pump and two ultrasound flow-dilution probes was incorporated. Vascular access was simulated with both a shunt (fistula or graft) and a central venous catheter. Hypertonic saline (5%) was circulated in the system. A bolus of isotonic saline was introduced in the dialysis circuit, which dispersed through the physiologic model. ACBV was measured by comparing the baseline dilution curve to the curve as it returned to the probes. To evaluate the sensitivity of this technique, we investigated changing cardiac output, central venous volume, shunt flow, vascular access type, and HD pump flow. Overall percentage error (mean ± SD) across all tests (n = 15 conditions, each in triplicate) was 2.6% ± 7.4%. This study demonstrates the ability to accurately measure ACBV on the bench.

The Sleep Revolution project: the concept and objectives

Obstructive sleep apnea is linked to severe health consequences such as hypertension, daytime sleepiness, and cardiovascular disease. Nearly a billion people are estimated to have obstructive sleep apnea with a substantial economic burden. However, the current diagnostic parameter of obstructive sleep apnea, the apnea-hypopnea index, correlates poorly with related comorbidities and symptoms. Obstructive sleep apnea severity is measured by counting respiratory events, while other physiologically relevant consequences are ignored. Furthermore, as the clinical methods for analysing polysomnographic signals are outdated, laborious, and expensive, most patients with obstructive sleep apnea remain undiagnosed. Therefore, more personalised diagnostic approaches are urgently needed. The Sleep Revolution, funded by the European Union's Horizon 2020 Research and Innovation Programme, aims to tackle these shortcomings by developing machine learning tools to better estimate obstructive sleep apnea severity and phenotypes. This allows for improved personalised treatment options, including increased patient participation. Also, implementing these tools will alleviate the costs and increase the availability of sleep studies by decreasing manual scoring labour. Finally, the project aims to design a digital platform that functions as a bridge between researchers, patients, and clinicians, with an electronic sleep diary, objective cognitive tests, and questionnaires in a mobile application. These ambitious goals will be achieved through extensive collaboration between 39 centres, including expertise from sleep medicine, computer science, and industry and by utilising tens of thousands of retrospectively and prospectively collected sleep recordings. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies, the Sleep Revolution has the unique possibility to create new standardised guidelines for sleep medicine.

Public Views About Involvement in Decision-Making on Health Data Sharing, Access, Use and Reuse: The Importance of Trust in Science and Other Institutions

Background

Data-intensive and needs-driven research can deliver substantial health benefits. However, concerns with privacy loss, undisclosed surveillance, and discrimination are on the rise due to mounting data breaches. This can undermine the trustworthiness of data processing institutions and reduce people's willingness to share their data. Involving the public in health data governance can help to address this problem by imbuing data processing frameworks with societal values. This study assesses public views about involvement in individual-level decisions concerned with health data and their association with trust in science and other institutions.

Methods

Cross-sectional study with 162 patients and 489 informal carers followed at two reference centers for rare diseases in an academic hospital in Portugal (June 2019–March 2020). Participants rated the importance of involvement in decision-making concerning health data sharing, access, use, and reuse from “not important” to “very important”. Its association with sociodemographic characteristics, interpersonal trust, trust in national and international institutions, and the importance of trust in research teams and host institutions was tested.

Results

Most participants perceived involvement in decision-making about data sharing (85.1%), access (87.1%), use (85%) and reuse (79.9%) to be important or very important. Participants who ascribed a high degree of importance to trust in research host institutions were significantly more likely to value involvement in such decisions. A similar position was expressed by participants who valued trust in research teams for data sharing, access, and use. Participants with low levels of trust in national and international institutions and with lower levels of education attributed less importance to being involved in decisions about data use.

Conclusion

The high value attributed by participants to involvement in individual-level data governance stresses the need to broaden opportunities for public participation in health data decision-making, namely by introducing a meta consent approach. The important role played by trust in science and in other institutions in shaping participants' views about involvement highlights the relevance of pairing such a meta consent approach with the provision of transparent information about the implications of data sharing, the resources needed to make informed choices and the development of harm mitigation tools and redress.

Improving the Effectiveness of Healthcare: Diagnosis-Centered Care Vs. Person-Centered Health Promotion, a Long Forgotten New Model

Performance of healthcare can be measured as its ability to restore and preserve health with acceptable costs for the society. Under the current prevalence of chronic disease, medical care (the major content of healthcare) underperforms in all key indicators: clinical effectiveness, benefit/risk ratio of interventions, cost/benefit ratio, and general population health. In Russia key performance indicators (KPI) of healthcare do not allow effective decision-making; a similar situation is seen worldwide: most KPIs are either focused on the process (not results) of medical care, or depend on efforts out of control of healthcare decision-makers.

The key root factors limiting clinical effectiveness and cost-effectiveness of healthcare are reactive diagnosis-centered organizational model of care and the underlying biomedical paradigm, generally inadequate in chronic diseases. They make healthcare intervene too late, use less effective prevention and treatment instruments, and be in a state of resource scarcity. In Russia there is also a lack of interdisciplinary and interagency cooperation essential for health preservation and promotion.

Performance of healthcare system in overcoming the chronic disease epidemic can be improved through supplementing the current ‘reactive’ organizational model with preventive person-centered model based on the biopsychosocial paradigm. Enabling patients for early lifestyle-based interventions, the core P4 medicine approach, should prevail in managing chronic disease. Communication and information technologies should allow fast scaling up of the best person-centered practices.

Additive Manufacturing Strategies for Personalized Drug Delivery Systems and Medical Devices: Fused Filament Fabrication and Semi Solid Extrusion

Novel additive manufacturing (AM) techniques and particularly 3D printing (3DP) have achieved a decade of success in pharmaceutical and biomedical fields. Highly innovative personalized therapeutical solutions may be designed and manufactured through a layer-by-layer approach starting from a digital model realized according to the needs of a specific patient or a patient group. The combination of patient-tailored drug dose, dosage, or diagnostic form (shape and size) and drug release adjustment has the potential to ensure the optimal patient therapy. Among the different 3D printing techniques, extrusion-based technologies, such as fused filament fabrication (FFF) and semi solid extrusion (SSE), are the most investigated for their high versatility, precision, feasibility, and cheapness. This review provides an overview on different 3DP techniques to produce personalized drug delivery systems and medical devices, highlighting, for each method, the critical printing process parameters, the main starting materials, as well as advantages and limitations. Furthermore, the recent developments of fused filament fabrication and semi solid extrusion 3DP are discussed. In this regard, the current state of the art, based on a detailed literature survey of the different 3D products printed via extrusion-based techniques, envisioning future directions in the clinical applications and diffusion of such systems, is summarized.

Individualized Medicine in Africa: Bringing the Practice Into the Realms of Population Heterogeneity

The declared aim of "personalized", "stratified" or "precision" approaches is to place individual variation, as ascertained through genomic and various other biomarkers, at the heart of Scientific Medicine using it to predict risk of disease or response to therapy and to tailor interventions and target therapies so as to maximize benefit and minimize risk for individual patients and efficiency for the health care system overall. It is often contrasted to current practices for which the scientific base is rooted in concepts of a "universal biology" and a "typical" or "average patient" and in which variation is ignored. Yet both approaches equally overlook the hierarchical nature of human variation and the critical importance of differences between populations. Impact of genetic heterogeneity has to be seen within that context to be meaningful and subsequently useful. In Africa such complexity is compounded by the high effective size of its populations, their diverse histories and the diversity of the environmental terrains they occupy, rendering analysis of gene environment interactions including the establishment of phenotype genotype correlations even more cumbersome. Henceforth "Individualized" methods and approaches can only magnify the shortcomings of universal approaches if adopted without due regard to these complexities. In the current perspective we review examples of potential hurdles that may confront biomedical scientists and analysts in genomic medicine in clinical and public health genomics in Africa citing specific examples from the current SARS-COV2 pandemic and the challenges of establishing reference biobanks and pharmacogenomics reference values.

Clinical phenotypes for risk stratification in small-for-gestational-age fetuses

OBJECTIVE

To evaluate whether clinical phenotypes of small-for-gestational-age (SGA) fetuses can be identified and used for adverse perinatal outcome risk stratification to facilitate clinical decision-making.

METHODS

This was a multicenter observational cohort study conducted in two tertiary care university hospitals. SGA fetuses were classified according to maternal, fetal and placental conditions using a two-step cluster algorithm, in which fetuses with more than one condition were assigned to the cluster associated with the highest mortality risk. Delivery and perinatal outcomes were compared using chi-square test among SGA clusters, and the associations between outcomes and each cluster were evaluated by calculating odds ratios (OR), adjusted for gestational age.

RESULTS

The study included 17 631 consecutive singleton pregnancies, of which 1274 (7.2%) were defined as SGA at birth according to INTERGROWTH-21^st standards. Nine SGA clinical phenotypes were identified using a predefined conceptual framework. All delivery and perinatal outcomes analyzed were significantly different among the nine phenotypes. The whole SGA cohort had a three-times higher risk of perinatal mortality compared with non-SGA fetuses (1.4% vs 0.4%; P < 0.001). SGA clinical phenotypes exhibited three patterns of perinatal mortality risk: the highest risk was associated with congenital anomaly (8.3%; OR, 17.17 (95% CI, 2.17-136.12)) and second- or third-trimester hemorrhage (8.3%; OR, 9.94 (95% CI, 1.23-80.02)) clusters; medium risk was associated with gestational diabetes (3.8%; OR, 9.59 (95% CI, 1.27-72.57)), preterm birth (3.2%; OR, 4.65 (95% CI, 0.62-35.01)) and intrauterine growth restriction (3.1%; OR, 5.93 (95% CI, 3.21-10.95)) clusters; and the lowest risk was associated with the remaining clusters. Perinatal mortality rate did not differ between SGA fetuses without other clinical conditions (54.1% of SGA fetuses) and appropriate-for-gestational-age fetuses (0.1% vs 0.4%; OR, 0.41 (95% CI, 0.06-2.94); P = 0.27). SGA combined with other obstetric pathologies increased significantly the risk of perinatal mortality, as demonstrated by the increased odds of perinatal death in SGA cases with gestational diabetes compared to non-SGA cases with the same condition (OR, 24.40 (95% CI, 1.31-453.91)).

CONCLUSIONS

We identified nine SGA clinical phenotypes associated with different patterns of risk for adverse perinatal outcome. Our findings suggest that considering clinical characteristics in addition to ultrasound findings could improve risk stratification and decision-making for management of SGA fetuses. Future clinical trials investigating management of fetuses with SGA should take into account clinical information in addition to Doppler parameters and estimated fetal weight. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

S, Patel AB, Chen ZS. An Ayurgenomics Approach: Prakriti-Based Drug Discovery and Development for Personalized Care

Originating in ancient India, Ayurveda is an alternative medicinal approach that provides substantial evidence for a theoretical-level analysis of all aspects of life. Unlike modern medicine, Ayurveda is based upon tridoshas (Vata, pitta, and Kapha) and Prakriti. On the other hand, the research of all the genes involved at the proteomics, metabolomics, and transcriptome levels are referred to as genomics. Geoclimatic regions (deshanupatini), familial characteristics (kulanupatini), and ethnicity (jatiprasakta) have all been shown to affect phenotypic variability. The combination of genomics with Ayurveda known as ayurgenomics provided new insights into tridosha that may pave the way for precision medicine (personalized medicine). Through successful coordination of “omics,” Prakriti-based treatments can help change the existing situation in health care. Prakriti refers to an individual’s behavioral trait, which is established at the moment of birth and cannot be fully altered during one’s existence. Ayurvedic methodologies are based on three Prakriti aspects: aushadhi (medication), vihara (lifestyle), and ahara (diet). A foundation of Prakriti-based medicine, preventative medicine, and improvement of life quality with longevity can be accomplished through these ayurvedic characteristics. In this perspective, we try to understand prakriti’s use in personalized medicine, and how to integrate it with programs for drug development and discovery.

P4 Medicine as a model for precision periodontal care

Objectives

P4 Medicine is based on a proactive approach for clinical patient care incorporating the four “pillars” of prediction, prevention, personalization, and participation for patient management. The purpose of this review is to demonstrate how the concepts of P4 medicine can be incorporated into the management of periodontal diseases (particularly periodontitis) termed P4 periodontics.

Methods

This is a narrative review that used current literature to explore how P4 periodontics can be aligned with the 2018 Classification of Periodontal Diseases, current periodontal treatment paradigms, and periodontal regenerative technologies.

Results

The proposed model of P4 periodontics is highly aligned with the 2018 Classification of Periodontal Diseases and represents a logical extension of this classification into treatment paradigms. Each stage of periodontitis can be related to a holistic approach to clinical management. The role of “big data” in future P4 periodontics is discussed and the concepts of a treat-to-target focus for treatment outcomes are proposed as part of personalized periodontics. Personalized regenerative and rejuvenative periodontal therapies will refocus our thinking from risk management to regenerative solutions to manage the effects of disease and aging.

Conclusions

P4 Periodontics allows us to focus not only on early prevention and intervention but also allow for personalized late-stage reversal of the disease trajectory and the use of personalized regenerative procedures to reconstruct damaged tissues and restore them to health.

Clinical Significance

P4 Periodontics is a novel means of viewing a holistic, integrative, and proactive approach to periodontal treatment.

Laying the groundwork for the Biobank of Rare Malignant Neoplasms at the service of the Hellenic Network of Precision Medicine on Cancer

Biobanks constitute an integral part of precision medicine. They provide a repository of biospecimens that may be used to elucidate the pathophysiology, support diagnoses, and guide the treatment of diseases. The pilot biobank of rare malignant neoplasms has been established in the context of the Hellenic Network of Precision Medicine on Cancer and aims to enhance future clinical and/or research studies in Greece by collecting, processing, and storing rare malignant neoplasm samples with associated data. The biobank currently comprises 553 samples; 384 samples of hematopoietic and lymphoid tissue malignancies, 72 samples of pediatric brain tumors and 97 samples of malignant skin neoplasms. In this article, sample collections and their individual significance in clinical research are described in detail along with computational methods developed specifically for this project. A concise review of the Greek biobanking landscape is also delineated, in addition to recommended technologies, methodologies and protocols that were integrated during the creation of the biobank. This project is expected to re‑enforce current clinical and research studies, introduce advances in clinical and genetic research and potentially aid in future targeted drug discovery. It is our belief that the future of medical research is entwined with accessible, effective, and ethical biobanking and that our project will facilitate research planning in the '‑omic' era by contributing high‑quality samples along with their associated data.

Democratizing Global Health Care Through Scalable Emergent (Beyond the Mobile) Wireless Technologies

Advances in mobile phone technologies coupled with the availability of modern wireless networks are beginning to have a marked impact on digital health through the growing array of apps and connected devices. That said, limited deployment outside of developed nations will require additional approaches to collectively reach the 8 billion people on earth. Another consideration for development of digital health centered around mobile devices lies in the need for pairing steps, firmware updates, and a variety of user inputs, which can increase friction for the patient. An alternate, so-called Beyond the Mobile approach where medicaments, devices, and health services communicate directly to the cloud offers an attractive means to expand and fully realize our connected health utopia. In addition to offering highly personalized experiences, such approaches could address cost, security, and convenience concerns associated with smartphone-based systems, translating to improved engagement and adherence rates among patients. Furthermore, connecting these Internet of Medical Things instruments through next-generation networks offers the potential to reach patients with acute needs in nonurban regions of developing nations. Herein, we outline how deployment of Beyond the Mobile technologies through low-power wide-area networks could offer a scalable means to democratize digital health and contribute to improved patient outcomes globally.