The "Virtual Digital Twins" Concept in Precision Nutrition

Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning

Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, including microorganisms. However, the hypothesis posits that implementing micro- and nanoscale DTs, especially for biological entities like bacteria, presents substantial challenges. These challenges stem from the complexities of data extraction, transmission, and computation, along with the necessity for a specialized Internet of Things (IoT) infrastructure. To address these challenges, this article proposes a novel framework that leverages bio-network technologies, including the Internet of Bio-Nano Things (IoBNT), and decentralized deep learning algorithms such as federated learning (FL) and convolutional neural networks (CNN). The methodology involves using CNNs for robust pattern recognition and FL to reduce bandwidth consumption while enhancing security. IoBNT devices are utilized for precise microscopic data acquisition and transmission, which ensures minimal error rates. The results demonstrate a multi-class classification accuracy of 98.7% across 33 bacteria categories, achieving over 99% bandwidth savings. Additionally, IoBNT integration reduces biological data transfer errors by up to 98%, even under worst-case conditions. This framework is further supported by an adaptable, user-friendly dashboard, expanding its applicability across pharmaceutical and biotechnology industries.

A perspective on intermittent fasting and cardiovascular risk in the era of obesity pharmacotherapy

Intermittent fasting has been linked to metabolic health by improving lipid profiles, reducing body weight, and increasing insulin sensitivity. However, several randomized clinical trials have shown that intermittent fasting is not more effective than standard daily caloric restriction for short-term weight loss or cardiometabolic improvements in patients with obesity. Observational studies also suggest cardiovascular benefits from extended rather than reduced eating windows, and indicate that long-term intermittent fasting regimens may increase the risk of cardiovascular disease mortality. In this perspective, we discuss evidence that may support potential adverse effects of intermittent fasting on cardiovascular health through the loss of lean mass, circadian misalignment and poor dietary choices associated with reward-based eating. Given the ongoing revolution in obesity pharmacotherapy, we argue that future research should integrate anti-obesity medications with dietary strategies that confer robust benefits to cardiometabolic health, combine exercise regimens, and consider genetic factors to personalize obesity treatment. Comprehensive approaches combining diet, pharmacotherapy, and lifestyle modifications will become crucial for managing obesity and minimizing long-term cardiovascular risk.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

A genomics perspective of personalized prevention and management of obesity

This review discusses the landscape of personalized prevention and management of obesity from a nutrigenetics perspective. Focusing on macronutrient tailoring, we discuss the impact of genetic variation on responses to carbohydrate, lipid, protein, and fiber consumption. Our bioinformatic analysis of genomic variants guiding macronutrient intake revealed enrichment of pathways associated with circadian rhythm, melatonin metabolism, cholesterol and lipoprotein remodeling and PPAR signaling as potential targets of macronutrients for the management of obesity in relevant genetic backgrounds. Notably, our data-based in silico predictions suggest the potential of repurposing the SYK inhibitor fostamatinib for obesity treatment in relevant genetic profiles. In addition to dietary considerations, we address genetic variations guiding lifestyle changes in weight management, including exercise and chrononutrition. Finally, we emphasize the need for a refined understanding and expanded research into the complex genetic landscape underlying obesity and its management.

A national data sharing solution for the prevention and treatment of obesity-a qualitative study of stakeholders' needs

Study objectives

Obesity-related health data is needed for studies and precision medicine, but existing registers, medical chart systems, and digital platforms are seldom compatible. Before creating improved ways of sharing health data, this study aimed to gather opinions, experiences, and wishes from stakeholders that may use obesity-related health data: healthcare, researchers, people with overweight or obesity, the pharmaceutical industry, and IT-specialists.

Methods

We performed semistructured interviews with 28 stakeholders and analyzed qualitative text data with inductive content analysis. We grouped the suggested parameters in categories.

Results

Time efficient data entering was perceived crucial. Access to health data was important to all participants. Some parameters, such as age, BMI, and sex were requested by all stakeholders. Other data were stakeholder specific, such as population-specific laboratory references, suggested by healthcare professionals only. For people with overweight or obesity, ability to share data with healthcare staff about fitness level or previous weight loss attempts, was important.

Conclusion

The results from this study can be used in the design and implementation of a national health data sharing solution that may be used for precision healthcare use and to evaluate and guide obesity treatment and preventive measures. Data parameters requested by all populations, such as BMI, sex, and age, should be prioritized when designing a data solution. Ability for individuals with overweight or obesity to share health data, may improve healthcare appointments and reduce weight stigma.

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

Changes in body weight and body composition during the menstrual cycle

OBJECTIVES

The general perception is that menstrual cycle is a factor related to body weight and body composition fluctuations in women. The lack of a standardized methodology of the so far conducted studies has led to controversial results. The aim of the current study is to identify if there are any changes in body weight and body composition during the menstrual cycle.

METHODS

In the current study measurements of body weight, circumferences, skinfolds and body composition with bioelectrical impedance analysis were conducted twice per week in 42 women during their menstrual cycle.

RESULTS

Body weight was found to be statistically significantly higher during menstruation compared to the first week of the menstrual cycle by 0.450 kg, which could be attributed to a statistically significant increase of 0.474 kg observed in extracellular water. No other statistically significant changes were observed regarding body composition.

CONCLUSIONS

An increase of approximately 0.5 kg was observed during women's menstrual cycle, mostly due to extracellular fluid retention at menstruation days. These findings could be taken into account to interpret body weight and composition periodic fluctuations in women of reproductive age.

Modeling and Remodeling the Cell: How Digital Twins and HCMV Can Elucidate the Complex Interactions of Viral Latency, Epigenetic Regulation, and Immune Responses

Purpose of Review

Human cytomegalovirus (HCMV), while asymptomatic in most, causes significant complications during fetal development, following transplant or in immunosuppressed individuals. The host-virus interactions regulating viral latency and reactivation and viral control of the cellular environment (immune regulation, differentiation, epigenetics) are highly complex. Understanding these processes is essential to controlling infection and can be leveraged as a novel approach for understanding basic cell biology.

Recent Findings

Immune digital twins (IDTs) are digital simulations integrating knowledge of human immunology, physiology, and patient-specific clinical data to predict individualized immune responses and targeted treatments. Recent studies used IDTs to elucidate mechanisms of T cells, dendritic cells, and epigenetic control-all key to HCMV biology.

Summary

Here, we discuss how leveraging the unique biology of HCMV and IDTs will clarify immune response dynamics, host-virus interactions, and viral latency and reactivation and serve as a powerful IDT-validation platform for individualized and holistic health management.

Intelligent Digital Twins for Personalized Migraine Care

Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed.

Ageotypes revisited: The brain and central nervous system dysfunction as a major nutritional and lifestyle target for healthy aging

Undeniably, biological age can significantly differ between individuals of similar chronological age. Longitudinal, deep multi-omic profiling has recently enabled the identification of individuals with distinct aging phenotypes, termed 'ageotypes'. This effort has provided a plethora of data and new insights into the diverse molecular mechanisms presumed to drive aging. Translational opportunities stemming from this knowledge continue to evolve, providing an opportunity for the provision of nutritional interventions aiming to decelerate the aging process. In this framework, the contemporary ageotypes classification was revisited via in silico analyses, with the brain and nervous system being identified as the primary targets of age-related biomolecules, acting through inflammatory and metabolic pathways. Nutritional and lifestyle factors affecting these pathways in the brain and central nervous system that could help guide personalized recommendations for the attainment of healthy aging are discussed.

Digital Twin: A Future Health Challenge in Prevention, Early Diagnosis and Personalisation of Medical Care in Paediatrics

Modern medicine must move from a wait-and-see and remedial system to a preventive and interdisciplinary science that aims to provide patients with personalised and precise treatment planning [...].

Phospholamban R14del disease: The past, the present and the future

Arrhythmogenic cardiomyopathy affects significant number of patients worldwide and is characterized by life-threatening ventricular arrhythmias and sudden cardiac death. Mutations in multiple genes with diverse functions have been reported to date including phospholamban (PLN), a key regulator of sarcoplasmic reticulum (SR) Ca²⁺ homeostasis and cardiac contractility. The PLN-R14del variant in specific is recognized as the cause in an increasing number of patients worldwide, and extensive investigations have enabled rapid advances towards the delineation of PLN-R14del disease pathogenesis and discovery of an effective treatment. We provide a critical overview of current knowledge on PLN-R14del disease pathophysiology, including clinical, animal model, cellular and biochemical studies, as well as diverse therapeutic approaches that are being pursued. The milestones achieved in <20 years, since the discovery of the PLN R14del mutation (2006), serve as a paradigm of international scientific collaboration and patient involvement towards finding a cure.

Early metabolomic, lipid and lipoprotein changes in response to medical and surgical therapeutic approaches to obesity

BACKGROUND

Glucagon-like peptide-1 receptor agonists (GLP-1RA) and bariatric surgery have proven to be effective treatments for obesity and cardiometabolic conditions. We aimed to explore the early metabolomic changes in response to GLP-1RA (liraglutide) therapy vs. placebo and in comparison to bariatric surgery.

METHODS

Three clinical studies were conducted: a bariatric surgery cohort study of participants with morbid obesity who underwent either Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) studied over four and twelve weeks, and two randomized placebo-controlled, crossover double blind studies of liraglutide vs. placebo administration in participants with type 2 diabetes (T2D) and participants with obesity studied for three and five weeks, respectively. Nuclear magnetic resonance spectroscopy-derived metabolomic data were assessed in all eligible participants who completed all the scheduled in-clinic visits. The primary outcome of the study was to explore the changes of the metabolome among participants with obesity with and without T2D receiving the GLP-1RA liraglutide vs. placebo and participants with obesity undergoing bariatric surgery during the three to five-week study period. In addition, we assessed the bariatric surgery effects longitudinally over the twelve weeks of the study and the differences between the bariatric surgery subgroups on the metabolome. The trials are registered with ClinicalTrials.gov, numbers NCT03851874, NCT01562678 and NCT02944500.

RESULTS

Bariatric surgery had a more pronounced effect on weight and body mass index reduction (-14.19 ± 5.27 kg and - 5.19 ± 5.27, respectively, p < 0.001 for both) and resulted in more pronounced metabolomic and lipidomic changes compared to liraglutide therapy at four weeks postoperatively. Significant changes were observed in lipoprotein parameters, inflammatory markers, ketone bodies, citrate, and branched-chain amino acids after the first three to five weeks of intervention. After adjusting for the amount of weight loss, a significant difference among the study groups remained only for acetoacetate, β-hydroxybutyrate, and citrate (p < 0.05 after FDR correction). Glucose levels were significantly reduced in all intervention groups but mainly in the T2D group receiving GLP-1RA treatment. After adjusting for weight loss, only glucose levels remained significant (p = 0.001 after FDR correction), mainly due to the glucose change in the T2D group receiving GLP-1RA. Similar results with those observed at four weeks were observed in the surgical group when delta changes at twelve weeks were assessed. Comparing the two types of bariatric surgery, an intervention effect was more pronounced in the RYGB subgroup regarding total triglycerides, triglyceride-rich lipoprotein size, and trimethylamine-N-oxide (p for intervention: 0.031, 0.028, 0.036, respectively). However, after applying FDR correction, these changes deemed to be only suggestive; only time effects remained significant with no significant changes persisting in relation to the types of bariatric surgery.

CONCLUSIONS

The results of this study suggest that the early metabolomic, lipid and lipoprotein changes observed between liraglutide treatment and bariatric surgery are similar and result largely from the changes in patients' body weight. Specific changes observed in the short-term post-surgical period between bariatric vs. nonsurgical treated participants, i.e., acetoacetate, β-hydroxybutyrate, and citrate changes, may reflect changes in patient diets and calorie intake indicating potential calorie and diet-driven metabolomics/lipidomic effects in the short-term postoperatively. Significant differences observed between SG and RYGB need to be confirmed and extended by future studies.

Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review

Objective

Digital twins (DTs) have received widespread attention recently, providing new ideas and possibilities for future healthcare. This review aims to provide a quantitative review to analyze specific study contents, research focus, and trends of DT in healthcare. Simultaneously, this review intends to expand the connotation of "healthcare" into two directions, namely "Disease treatment" and "Health enhancement" to analyze the content within the "DT + healthcare" field thoroughly.

Methods

A data mining method named Structure Topic Modeling (STM) was used as the analytical tool due to its topic analysis ability and versatility. Google Scholar, Web of Science, and China National Knowledge Infrastructure supplied the material papers in this review.

Results

A total of 94 high-quality papers published between 2018 and 2022 were gathered and categorized into eight topics, collectively covering the transformative impact across a broader spectrum in healthcare. Three main findings have emerged: (1) papers published in healthcare predominantly concentrate on technology development (artificial intelligence, Internet of Things, etc.) and application scenarios(personalized, precise, and real-time health service); (2) the popularity of research topics is influenced by various factors, including policies, COVID-19, and emerging technologies; and (3) the preference for topics is diverse, with a general inclination toward the attribute of "Health enhancement."

Conclusions

This review underscores the significance of real-time capability and accuracy in shaping the future of DT, where algorithms and data transmission methods assume central importance in achieving these goals. Moreover, technological advancements, such as omics and Metaverse, have opened up new possibilities for DT in healthcare. These findings contribute to the existing literature by offering quantitative insights and valuable guidance to keep researchers ahead of the curve.

Digital twin in healthcare: Recent updates and challenges

As simulation is playing an increasingly important role in medicine, providing the individual patient with a customised diagnosis and treatment is envisaged as part of future precision medicine. Such customisation will become possible through the emergence of digital twin (DT) technology. The objective of this article is to review the progress of prominent research on DT technology in medicine and discuss the potential applications and future opportunities as well as several challenges remaining in digital healthcare. A review of the literature was conducted using PubMed, Web of Science, Google Scholar, Scopus and related bibliographic resources, in which the following terms and their derivatives were considered during the search: DT, medicine and digital health virtual healthcare. Finally, analyses of the literature yielded 465 pertinent articles, of which we selected 22 for detailed review. We summarised the application examples of DT in medicine and analysed the applications in many fields of medicine. It revealed encouraging results that DT is being increasing applied in medicine. Results from this literature review indicated that DT healthcare, as a key fusion approach of future medicine, will bring the advantages of precision diagnose and personalised treatment into reality.

Dynamic Digital Twin: Diagnosis, Treatment, Prediction, and Prevention of Disease During the Life Course

A digital twin (DT), originally defined as a virtual representation of a physical asset, system, or process, is a new concept in health care. A DT in health care is not a single technology but a domain-adapted multimodal modeling approach incorporating the acquisition, management, analysis, prediction, and interpretation of data, aiming to improve medical decision-making. However, there are many challenges and barriers that must be overcome before a DT can be used in health care. In this viewpoint paper, we build on the current literature, address these challenges, and describe a dynamic DT in health care for optimizing individual patient health care journeys, specifically for women at risk for cardiovascular complications in the preconception and pregnancy periods and across the life course. We describe how we can commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim to define future goals, trade-offs, and methods that will guide the development of the dynamic DT and implementation strategies in health care.

Precision nutrition: Maintaining scientific integrity while realizing market potential

Precision Nutrition (PN) is an approach to developing comprehensive and dynamic nutritional recommendations based on individual variables, including genetics, microbiome, metabolic profile, health status, physical activity, dietary pattern, food environment as well as socioeconomic and psychosocial characteristics. PN can help answer the question “What should I eat to be healthy?”, recognizing that what is healthful for one individual may not be the same for another, and understanding that health and responses to diet change over time. The growth of the PN market has been driven by increasing consumer interest in individualized products and services coupled with advances in technology, analytics, and omic sciences. However, important concerns are evident regarding the adequacy of scientific substantiation supporting claims for current products and services. An additional limitation to accessing PN is the current cost of diagnostic tests and wearable devices. Despite these challenges, PN holds great promise as a tool to improve healthspan and reduce healthcare costs. Accelerating advancement in PN will require: (a) investment in multidisciplinary collaborations to enable the development of user-friendly tools applying technological advances in omics, sensors, artificial intelligence, big data management, and analytics; (b) engagement of healthcare professionals and payers to support equitable and broader adoption of PN as medicine shifts toward preventive and personalized approaches; and (c) system-wide collaboration between stakeholders to advocate for continued support for evidence-based PN, develop a regulatory framework to maintain consumer trust and engagement, and allow PN to reach its full potential.

Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry

Digital twins (DTs) play a vital role in revolutionising the healthcare industry, leading to more personalised, intelligent, and proactive healthcare. With the evolution of personalised healthcare, there is a significant need to represent a virtual replica for individuals to provide the right type of care in the right way and at the right time. Therefore, in this paper, we surveyed the concept of a personal digital twin (PDT) as an enhanced version of the DT with actionable insight capabilities. In particular, PDT can bring value to patients by enabling more accurate decision making and proper treatment selection and optimisation. Then, we explored the progression of PDT as a revolutionary technology in healthcare research and industry. However, although several research works have been performed for smart healthcare using DT, PDT is still at an early stage. Consequently, we believe that this work can be a step towards smart personalised healthcare industry by guiding the design of industrial personalised healthcare systems. Accordingly, we introduced a reference framework that empowers smart personalised healthcare using PDTs by bringing together existing advanced technologies (i.e., DT, blockchain, and AI). Then, we described some selected use cases, including the mitigation of COVID-19 contagion, COVID-19 survivor follow-up care, personalised COVID-19 medicine, personalised osteoporosis prevention, personalised cancer survivor follow-up care, and personalised nutrition. Finally, we identified further challenges to pave the PDT paradigm toward the smart personalised healthcare industry.

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.

A Scoping Review of Digital Twins in the Context of the Covid-19 Pandemic

Background

Digital Twins (DTs), virtual copies of physical entities, are a promising tool to help manage and predict outbreaks of Covid-19. By providing a detailed model of each patient, DTs can be used to determine what method of care will be most effective for that individual. The improvement in patient experience and care delivery will help to reduce demand on healthcare services and to improve hospital management.

Objectives

The aim of this study is to address 2 research questions: (1) How effective are DTs in predicting and managing infectious diseases such as Covid-19? and (2) What are the prospects and challenges associated with the use of DTs in healthcare?

Methods

The review was structured according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) framework. Titles and abstracts of references in PubMed, IEEE Xplore, Scopus, ScienceDirect and Google Scholar were searched using selected keywords (relating to digital twins, healthcare and Covid-19). The papers were screened in accordance with the inclusion and exclusion criteria so that all papers published in English relating to the use of digital twins in healthcare were included. A narrative synthesis was used to analyse the included papers.

Results

Eighteen papers met the inclusion criteria and were included in the review. None of the included papers examined the use of DTs in the context of Covid-19, or infectious disease outbreaks in general. Academic research about the applications, opportunities and challenges of DT technology in healthcare in general was found to be in early stages.

Conclusions

The review identifies a need for further research into the use of DTs in healthcare, particularly in the context of infectious disease outbreaks. Based on frameworks identified during the review, this paper presents a preliminary conceptual framework for the use of DTs for hospital management during the Covid-19 outbreak to address this research gap.

Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining

Finding, exploring and filtering frequent sentence-based associations between a disease and a biomedical entity, co-mentioned in disease-related PubMed literature, is a challenge, as the volume of publications increases. Darling is a web application, which utilizes Name Entity Recognition to identify human-related biomedical terms in PubMed articles, mentioned in OMIM, DisGeNET and Human Phenotype Ontology (HPO) disease records, and generates an interactive biomedical entity association network. Nodes in this network represent genes, proteins, chemicals, functions, tissues, diseases, environments and phenotypes. Users can search by identifiers, terms/entities or free text and explore the relevant abstracts in an annotated format.

The Challenge and Importance of Integrating Drug-Nutrient-Genome Interactions in Personalized Cardiovascular Healthcare

Despite the rich armamentarium of available drugs against different forms of cardiovascular disease (CVD), major challenges persist in their safe and effective use. These include high rates of adverse drug reactions, increased heterogeneity in patient responses, suboptimal drug efficacy, and in some cases limited compliance. Dietary elements (including food, beverages, and supplements) can modulate drug absorption, distribution, metabolism, excretion, and action, with significant implications for drug efficacy and safety. Genetic variation can further modulate the response to diet, to a drug, and to the interaction of the two. These interactions represent a largely unexplored territory that holds considerable promise in the field of personalized medicine in CVD. Herein, we highlight examples of clinically relevant drug–nutrient–genome interactions, map the challenges faced to date, and discuss their future perspectives in personalized cardiovascular healthcare in light of the rapid technological advances.

Genetically-Guided Medical Nutrition Therapy in Type 2 Diabetes Mellitus and Pre-diabetes: A Series of n-of-1 Superiority Trials

Type 2 diabetes mellitus (T2DM) is a heterogeneous metabolic disorder of multifactorial etiology that includes genetic and dietary influences. By addressing the latter, medical nutrition therapy (MNT) contributes to the management of T2DM or pre-diabetes toward achieving glycaemic control and improved insulin sensitivity. However, the clinical outcomes of MNT vary and may further benefit from personalized nutritional plans that take into consideration genetic variations associated with individual responses to macronutrients. The aim of the present series of n-of-1 trials was to assess the effects of genetically-guided vs. conventional MNT on patients with pre-diabetes or T2DM. A quasi-experimental, cross-over design was adopted in three Caucasian adult men with either diagnosis. Complete diet, bioclinical and anthropometric assessment was performed and a conventional MNT, based on the clinical practice guidelines was applied for 8 weeks. After a week of “wash-out,” a precision MNT was prescribed for an additional 8-week period, based on the genetic characteristics of each patient. Outcomes of interest included changes in body weight (BW), fasting plasma glucose (FPG), and blood pressure (BP). Collectively, the trials indicated improvements in BW, FPG, BP, and glycosylated hemoglobin (HbA1c) following the genetically-guided precision MNT intervention. Moreover, both patients with pre-diabetes experienced remission of the condition. We conclude that improved BW loss and glycemic control can be achieved in patients with pre-diabetes/T2DM, by coupling MNT to their genetic makeup, guiding optimal diet, macronutrient composition, exercise and oral nutrient supplementation in a personalized manner.

CYP1A2 polymorphisms modify the association of habitual coffee consumption with appetite, macronutrient intake, and body mass index: results from an observational cohort and a cross-over randomized study

BACKGROUND/OBJECTIVES

Evidence regarding the influence of coffee on appetite and weight control is equivocal and the influence of covariates, such as genetic variation in caffeine metabolism, remains unknown. Herein, we addressed the novel hypothesis that genetic variation in CYP1A2, a gene responsible for more than 95% of caffeine metabolism, differentially impacts the association of coffee consumption with appetite and BMI among individuals with different genetic predispositions to obesity.

SUBJECTS/METHODS

A cross-over randomized intervention study involving 18 volunteers assessed the effects of coffee consumption on dietary intake, appetite, and levels of the appetite-controlling hormones asprosin and leptin. Data on habitual coffee intake, BMI, and perceived appetite were obtained from an observational cohort of 284 volunteers using validated questionnaires. Participants were stratified according to a validated genetic risk score (GRS) for obesity and to the -163C > A (rs762551) polymorphism of CYP1A2 as rapid (AA), intermediate (AC), or slow (CC) caffeine metabolizers.

RESULTS

Coffee consumption led to lower energy and dietary fat intake and circulating asprosin levels (P for interaction of rs762551 genotype*coffee consumption=0.056, 0.039, and 0.043, respectively) as compared to slow/intermediate metabolizers. High coffee consumption was more prevalent in rapid compared to slow metabolizers (P = 0.008 after adjustment for age, sex, and BMI) and was associated with lower appetite perception and lower BMI only in rapid metabolizers (P for interaction of rs762551 genotype*coffee consumption = 0.002 and 0.048, respectively). This differential association of rs762551 genotype and coffee consumption with BMI was more evident in individuals at higher genetic risk of obesity (mean adjusted difference in BMI = -5.82 kg/m² for rapid versus slow/intermediate metabolizers who consumed more than 14 cups of coffee per week).

CONCLUSIONS

CYP1A2 rs762551 polymorphism modifies the association of habitual coffee consumption with BMI, in part by influencing appetite, energy intake and circulating levels of the orexigenic hormone asprosin. This association is more evident in subjects with high genetic predisposition to obesity. ClinicalTrials.gov: registered Clinical Trial NCT04514588.

Retrospective study of glycemic variability, BMI, and blood pressure in diabetes patients in the Digital Twin Precision Treatment Program

The objective of this retrospective observational cohort study was to measure glycemic variability and reductions in body mass index (BMI), blood pressure (BP), and use of antihypertensive medications in type 2 diabetes (T2D) patients participating in the digital twin-enabled Twin Precision Treatment (TPT) Program. Study participants included 19 females and 45 males with T2D who chose to participate in the TPT Program and adhered to program protocols. Nine additional enrollees were excluded due to major program non-adherence. Enrollees were required to have adequate hepatic and renal function, no myocardial infarction, stroke, or angina ≤ 90 days before enrollment, and no history of ketoacidosis or major psychiatric disorders. The TPT program uses Digital Twin technology, machine learning algorithms, and precision nutrition to aid treatment of patients with T2D. Each study participant had ≥ 3 months of follow-up. Outcome measures included glucose percentage coefficient of variation (%CV), low blood glucose index (LBGI), high blood glucose index (HBGI), systolic and diastolic BP, number of antihypertensive medications, and BMI. Sixty-four patients participated in the program. Mean (± standard deviation) %CV, LBGI, and HBGI values were low (17.34 ± 4.35, 1.37 ± 1.37, and 2.13 ± 2.79, respectively) throughout the 90-day program. BMI decreased from 29.23 ± 5.83 at baseline to 27.43 ± 5.25 kg/m2. Systolic BP fell from 134.72 ± 17.73 to 124.58 ± 11.62 mm Hg. Diastolic BP decreased from 83.95 ± 10.20 to 80.33 ± 7.04 mm Hg. The percent of patients taking antihypertensive medications decreased from 35.9% at baseline to 4.7% at 90 days. During 90 days of the TPT Program, patients achieved low glycemic variability and significant reductions in BMI and BP. Antihypertensive medication use was eliminated in nearly all patients. Future research will focus on randomized case-control comparisons.

A New Paradigm for Clinical Nutrition Services in the Era of the Fourth Industrial Revolution

The role of clinical nutrition services is emphasized in the care of chronic diseases; the prevalence of chronic diseases continues to increase due to the living environment change, westernized dietary life and the aging population in Korea. The effectiveness of clinical nutrition services in the treatment of diseases in inpatients has been demonstrated in several studies. However, in recent days, innovative changes are pursued in clinical nutrition services through a convergence with information and communication technology (ICT), a core technology of the fourth industrial revolution such as big data, deep learning, and artificial intelligence (AI). The health care environment is changing from a medical treatment-oriented service to a preventive and personalized paradigm. Furthermore, we live in an era of personalization where we can personalize dietary aspects including food choice, cooking recipes, and nutrition in daily life. In addition, ICT technology can build a personalized nutrition platform in consideration of individual patient's diseases, genetic trait, and environment, all of which can be technical means in personalized nutrition management services. Personalized nutrition based on ICT technology is able to provide more standardized and high-quality clinical nutrition services to the patients. The purpose of this review is to examine the core technologies of the fourth industrial revolution affecting clinical nutrition services, and ultimately discuss how clinical nutrition professional should respond to ICT technology-related fields in the era of the new technological innovations.

In silico trials predict that combination strategies for enhancing vesicular stomatitis oncolytic virus are determined by tumor aggressivity

Background

Immunotherapies, driven by immune-mediated antitumorigenicity, offer the potential for significant improvements to the treatment of multiple cancer types. Identifying therapeutic strategies that bolster antitumor immunity while limiting immune suppression is critical to selecting treatment combinations and schedules that offer durable therapeutic benefits. Combination oncolytic virus (OV) therapy, wherein complementary OVs are administered in succession, offer such promise, yet their translation from preclinical studies to clinical implementation is a major challenge. Overcoming this obstacle requires answering fundamental questions about how to effectively design and tailor schedules to provide the most benefit to patients.

Methods

We developed a computational biology model of combined oncolytic vaccinia (an enhancer virus) and vesicular stomatitis virus (VSV) calibrated to and validated against multiple data sources. We then optimized protocols in a cohort of heterogeneous virtual individuals by leveraging this model and our previously established in silico clinical trial platform.

Results

Enhancer multiplicity was shown to have little to no impact on the average response to therapy. However, the duration of the VSV injection lag was found to be determinant for survival outcomes. Importantly, through treatment individualization, we found that optimal combination schedules are closely linked to tumor aggressivity. We predicted that patients with aggressively growing tumors required a single enhancer followed by a VSV injection 1 day later, whereas a small subset of patients with the slowest growing tumors needed multiple enhancers followed by a longer VSV delay of 15 days, suggesting that intrinsic tumor growth rates could inform the segregation of patients into clinical trials and ultimately determine patient survival. These results were validated in entirely new cohorts of virtual individuals with aggressive or non-aggressive subtypes.

Conclusions

Based on our results, improved therapeutic schedules for combinations with enhancer OVs can be studied and implemented. Our results further underline the impact of interdisciplinary approaches to preclinical planning and the importance of computational approaches to drug discovery and development.

Genetically Guided Mediterranean Diet for the Personalized Nutritional Management of Type 2 Diabetes Mellitus

The current consensus for the prevention and management of type 2 diabetes mellitus (T2DM) is that high-quality diets and adherence to a healthy lifestyle provide significant health benefits. Remarkably, however, there is little agreement on the proportions of macronutrients in the diet that should be recommended to people suffering from pre-diabetes or T2DM. We herein discuss emerging evidence that underscores the importance of gene-diet interactions in the improvement of glycemic biomarkers in T2DM. We propose that we can achieve better glycemic control in T2DM patients by coupling Mediterranean diets to genetic information as a predictor for optimal diet macronutrient composition in a personalized manner. We provide evidence to support this concept by presenting a case study of a T2DM patient who achieved rapid glycemic control when adhered to a personalized, genetically-guided Mediterranean Diet.

Genotype-guided dietary supplementation in precision nutrition

Achieving adequate micronutrient status, while avoiding deficiencies, represents a challenge for people globally. Consequently, many individuals resort to oral nutrient supplementation (ONS) in order to correct suboptimal dietary intakes. Advances in the fields of nutrigenetics and nutritional genomics have identified differences in response to micronutrient supplementation according to genetic makeup, adding dietary supplement use to the clinician's toolkit in the precision nutrition era. This review focuses on published evidence linking genetic variants to the responses associated with some of the most popular dietary supplements. With an increasing number of health professionals becoming involved in the prescription of ONS, identifying and matching individuals to the appropriate dietary supplement according to their genotype is important for achieving optimal health benefits and micronutrient equilibrium, while reducing the adverse events and financial costs often associated with excessive ONS.

Dietary Management of Obesity: A Review of the Evidence

Obesity is a multi-factorial disease and its prevention and management require knowledge of the complex interactions underlying it and adopting a whole system approach that addresses obesogenic environments within country specific contexts. The pathophysiology behind obesity involves a myriad of genetic, epigenetic, physiological, and macroenvironmental factors that drive food intake and appetite and increase the obesity risk for susceptible individuals. Metabolically, food intake and appetite are regulated via intricate processes and feedback systems between the brain, gastrointestinal system, adipose and endocrine tissues that aim to maintain body weight and energy homeostasis but are also responsive to environmental cues that may trigger overconsumption of food beyond homeostatic needs. Under restricted caloric intake conditions such as dieting, these processes elicit compensatory metabolic mechanisms that promote energy intake and weight regain, posing great challenges to diet adherence and weight loss attempts. To mitigate these responses and enhance diet adherence and weight loss, different dietary strategies have been suggested in the literature based on their differential effects on satiety and metabolism. In this review article, we offer an overview of the literature on obesity and its underlying pathological mechanisms, and we present an evidence based comparative analysis of the effects of different popular dietary strategies on weight loss, metabolic responses and diet adherence in obesity.

Dietary Factors and Supplements Influencing Prostate Specific-Antigen (PSA) Concentrations in Men with Prostate Cancer and Increased Cancer Risk: An Evidence Analysis Review Based on Randomized Controlled Trials

The quest for dietary patterns and supplements efficient in down-regulating prostate-specific antigen (PSA) concentrations among men with prostate cancer (PCa) or increased PCa risk has been long. Several antioxidants, including lycopene, selenium, curcumin, coenzyme Q10, phytoestrogens (including isoflavones and flavonoids), green tea catechins, cernitin, vitamins (C, E, D) and multivitamins, medicinal mushrooms (Ganoderma lucidum), fruit extracts (saw palmetto, cranberries, pomegranate), walnuts and fatty acids, as well as combined supplementations of all, have been examined in randomized controlled trials (RCTs) in humans, on the primary, secondary, and tertiary PCa prevention level. Despite the plethora of trials and the variety of examined interventions, the evidence supporting the efficacy of most dietary factors appears inadequate to recommend their use.