The 10+1 Santorini Conference

Dr. Behrooz Z. Alizadeh MD PhD
Unit of Personalized Medicine, Dept. Epidemiology, University Medical Centre, Groningen, Groningen, The Netherlands.
b.z.alizadeh@umcg.nl

This presentation discusses the complexities in the traditional classification of mental disorders specifically focusing on challenges related to the absence of objective diagnostic criteria and the intricate nature of symptoms, particularly regarding the understanding of disease course subtypes. It provides insights by focusing on deep clinical phenotyping and genetic profiling of the patients, employing data-driven approaches. This consists of a mixture distribution of positive and negative symptoms, cognitive impairments, and psychosocial functioning, aligned with a vector of patients and cohort parameters.
The identified latent subtypes exhibited diverse profiles and exhibited stable, deteriorating, relapsing, and ameliorating longitudinal naturalistic courses over time. The study comprehensively depicts the lifelong journey of patients with schizophrenia spectrum disorder, covering genetic makeup at conception, early-life premorbid adjustment and social functioning, clinical profiles and treatment modalities during the disease, social functioning and living arrangements, and quality of life post-diagnosis. While baseline symptoms, premorbid adjustment, psychotic-like experiences, and health-related quality of life are highlighted as key and strong predictors, genetic susceptibility does not play a significant role in disease outcomes. The research emphasizes the importance of Premorbid adjustment emerges as a potential source of heterogeneity and an early indicator of non-favourable long-term outcomes in individuals diagnosed outcomes in mental disorders. The presentation suggests that (pharmaco)genetic-based models lose relevance in predicting disease outcomes when robust environmental factors buffer genetic effects, altering the trajectory of affected individuals.
The findings are comprehensive, novel, and of clinical interest for precisely identifying high-risk population groups and patients with varying disease prognoses, and the selection of optimal intervention. The primary challenge in the traditional classification of mental disorders remains the understanding of disease courses and the implementation of personalised pharmacogenetic-based treatments at the individual level.

VEGF-A a potential biomarker for personalised medicine in chronic diseases

Helena Murray
Antrim, United Kingdom

Vascular endothelial growth factor–A (VEGF–A) is implicated in angiogenesis, lymphangiogenesis, vascular permeability, and haematopoiesis. It is associated with numerous pathologies including cardio-vascular diseases and cancer.
We specifically developed an integrative systems biology strategy for clinical improvement of this biomarker.
A high heritability of this trait, 60%, was estimated in the STANISLAS cohort giving us the arguments to continue for a deep characterization of its genetic component.
Therefore, we searched, by a Genome Wide Association Study (GWAS), the VEGF–A genetic variants and the inter-connexions of these biomarkers with other disease-associated molecules in healthy populations.
The GWAS was performed in 3,527 healthy participants (Framingham Heart Study) and the most significant results (P <5×10-8) were replicated in 1,727 individuals (STANISLAS Family Study, PIVUS study).
Four polymorphisms (rs6921438, rs4416670, rs6993770, rs10738760) explaining ~50% of VEGF–A heritability were identified. A further meta-GWAS identified 6 additional rs explaining VEGF–A levels variability.
Functional transcriptomic analyses were performed in peripheral blood mononuclear cells (PBMCs). SNP rs6993770 was shown to increase VEGF121 mRNA levels.
VEGF-A related variants, directly or via gene x gene x environment interactions had significant effects on HDL, LDL, TNF-a, IL-6, E selectin and ICAM-1 plasma levels. Furthermore, associations between VEGF–A and blood lipids were assessed in a discovery (n=1,006) and in a replication population (n=1,145) of healthy individuals.
Recently, we found that VEGF-A related polymorphisms (interaction rs7043199*rs6993770) are involved in predicting the risk of Alzheimer’s disease (AD) with a protective role against AD.
Ongoing investigations focus on clinical implementation of the ‘–omics’ determinants of this biomarker.
Our integrative strategy resulted in significant results indicating molecular links between VEGF–A cardio-vascular and cancer diseases biology and AD and the importance of epistatic and gene x environment interactions. This example illustrates an improved strategy to be applied for every biomarker with high heritability levels, consequently with potential interest in Personalised Medicine, using familial design.

Early inflammatory and fibrotic biomarkers of NAFLD and NASH 

Mark Ruddock, Mary Jo Kurth, Joanne Watt, John Lamont, Peter Fitzgerald 

Randox Laboratories Ltd, 55 Diamond Road, Crumlin, Co. Antrim BT29 4QY, Northern Ireland, UK 

Non-alcoholic fatty liver disease (NAFLD) is characterized by the accumulation of excess fat in the liver (hepatic steatosis), without a history of alcohol abuse or other secondary causes of chronic liver disease. Fatty liver is a prevalent condition, affecting 25% of the global population, and has become the most common chronic liver disorder in Western countries. While liver biopsy is the established gold standard for diagnosing and staging NAFLD, its invasiveness, cost, and associated risks underscore the need for alternative approaches.  In this study involving n=135 participants (n=34 controls, n=26 with simple steatosis, n=61 with NAFLD/NASH, and n=14 with alcoholic liver disease), patient serum samples were obtained, together with a detailed clinical history. Employing high-sensitivity cytokine array I, immunoassays, and ELISAs, duplicate analyses were conducted on a total of n=20 individual biomarkers.  Results revealed that 13 out of the 20 biomarkers (65%) exhibited significant differences between the groups. These biomarkers included IFNγ, EGF, IL-1β, IL-6, IL-8, IL-10, TNFα, FABP-1, PIIINP, ST2/IL-33R, albumin, AST, and ALT. Further investigation identified 5 promising biomarker candidates for additional investigation: 3 related to inflammation (IL-6, IL-8, and TNFα) and 2 were associated with fibrosis (PIIINP and ST2/IL-33R).  It is acknowledged that single biomarkers may not serve as a definitive diagnostic or predictive tool given the complex heterogeneity of NAFLD. However, the combination of biomarkers holds promise for stratifying risk and staging liver disease, particularly for patients averse to undergoing biopsy. Future studies will compare the 5 identified biomarkers with existing diagnostic tests and assess their correlation with fibrotic deposition in liver tissue to further validate their clinical utility. 

Use of an in vivo like 3D liver spheroid model for determining mechanisms and regulation of drug metabolism, drug toxicity and liver fibrosis under conditions of steatosis and NASH 

Magnus Ingelman-Sundberg 

Section of pharmacogenetics, Karolinska Institutet, Stockholm, Sweden

Significant species variations underscore the importance of conducting in vitro research using human models. We have developed a 3D liver spheroid system that closely mirrors the transcriptome, proteome, and metabolome of the corresponding donor liver. This model has proven highly effective in: i) predicting and understanding mechanisms behind drug-induced hepatotoxicity, ii) assessing the hepatic disposition and metabolite formation of low-clearance drugs, iii) elucidating the mechanisms of viral-induced hepatitis and the specific actions of viruses within the liver, iv) evaluating mechanisms of hepatocyte proliferation and v) understanding the processes by which a high-fat diet induces liver fibrosis and the molecular drivers of liver fibrosis degradation. Notably, this system facilitates the identification of novel drug targets and molecules crucial for preventing non-alcoholic steatohepatitis (NASH).

In the lecture, I will present this system, with a particular focus on recent advancements achieved through a triple culture system comprising hepatocytes, stellate cells, and liver epithelial cells (LSECs). This approach provides additional insights into the mechanisms underlying the formation and degradation of liver fibrosis, as well as the mechanisms by which steatosis and fibrosis cause clinically significant alterations in the expression of, e.g drug-metabolizing enzymes and drug transporters.

The quest for missing heritability: role of rare variant and gene-environment interactions

Guillaume Pare, MD
McMaster University, Ontario, Canada

Biobank-scale genotyping has enabled the routine use of statistical genetics applications, such as Mendelian randomization, polygenic scores and heritability estimations. However, many questions remain less well explored, such as the contribution of gene-by-environment interactions (GxE) and the role of rare coding variants (RV) in complex traits. Leveraging the speed, versatility and robustness of large linear models, we sought to address these two questions. First, we introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. Identification of GxE is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325K UK Biobank participants. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes and find significant GxE for 8 environment-outcome pairs. The majority of GxE variance involves SNPs without strong marginal or interaction associations. Second, we developed a novel framework, the Rare variant heritability (RARity) estimator, to assess RV heritability (h2RV) without assuming a particular genetic architecture. We applied RARity to 31 complex traits in the UK Biobank (N=167K) and showed that gene-level RV aggregation suffers from 79% (95% CI: 68-93%) loss of h2RV. Using unaggregated variants, 27 traits had h2RV>5%, with height having the highest h2RV at 21.9% (95% CI: 19.0-24.8%). The total heritability, including common and rare variants, recovered pedigree-based estimates for 11 traits. We also used RARity to reveal 11 novel gene-phenotype relationships. Finally, we demonstrated that in silico pathogenicity prediction (variant-level) and gene-level annotations do not generally enrich for RVs that over-contribute to complex trait variance, and thus, novel methods are needed to predict RV functionality.

Genetic diagnosis and multi-omics clustering for diabetes precision medicine

Philippe Froguel, MD, PhD
Imperial College London and INSERM/CNRS Lille

Type 2 Diabetes (T2D) is a multifactorial disease with a strong but heterogeneous genetic background: it includes a monogenic component causing early onset diabetes, and highly polygenic forms with >500 loci increasing T2D risk. Recently, there were increasing evidence for a genetic continuum between rare and most common forms of diabetes: indeed, up to 10% of patients with regular T2D carry a “rare” pathogenic mutation in one of the genes causing monogenic diabetes, and this proportion increases to >20% in more “atypical” forms of T2D (not obese, diagnosis <40y). Dissecting both patients’ cases “oligogenic” and polygenic background is increasingly helpful for personalized diabetes care. Besides, multi-omics approaches including metabolomics, metagenome evaluation, proteomics and epigenetic analyses are assessed to better cluster patients in homogeneous etiological categories in order to foster precision medicine in a context were 1/estimated 1.3 billion people will be diabetic by 2050 and 2/ new medications are available that should dramatically decrease incidence of T2D complications (SGLT2-Inhibitors, incretin receptors agonists…) but at higher costs, which justifies precision diabetes care.

Best Practices for Earning and Maintaining Public Trust in Nutrition 

Dante Preciado, CAE 

Vice President, Engagement and International Affairs, American Society for Nutrition / nutrition.org 

Executive Director, National Board of Physician Nutrition Specialists / nbpns.org 

9211 Corporate Blvd., Suite 300, Rockville, MD 20850, USA 

Public trust in nutrition science is the foundation on which progress in nutrition and health relies, including good public health. There is perhaps no more important issue facing nutrition science today than ensuring that the research that is conducted and disseminated is trusted by all stakeholders. These stakeholders include those who use research results to define the direction of their own research, make policy decisions such as nutritional recommendations and practice guidelines, and make funding decisions and priorities. The American Society for Nutrition (ASN) strives to work collaboratively with various stakeholders across sectors and disciplines while maintaining transparency and scientific rigor in nutrition science. In 2017, ASN commissioned an independent Advisory Committee to conduct a comprehensive review of the available literature on public trust in nutrition science and the factors that influence it and conducted outreach to stakeholders regarding to publicly available information. Seven overlapping domains were identified that can significantly influence public trust. The Committee delivered its findings and proposed best practices to support public trust, appropriate for ASN and other food and nutrition organizations motivated by the belief that public trust remains key to realizing the benefits of past, present, and future scientific advances. Its adoption by food and nutrition organizations strengthens and helps ensure that the public’s continued trust in nutrition science is earned and maintained. 

Personalised nutrition. The paradigm of obesity

Georges Dedoussis
Harokopio University of Athens, Athens, Greece

According to estimates of the World Health Organization (WHO) for 2016, a respective 40% and 13% of the global population presented overweight and obesity. The dramatic rise in obesity rates during the past decades has led WHO to reshape our perception of the disorder in a global epidemic, currently even using the term “globesity” to describe it.

Obesity is a multifactorial disorder, in its core determined by genetic makeup, cardiometabolic determinants, lifestyle habits and environmental parameters. The majority of cases of overweight or obesity are attributed to the cumulative action of a multitude of genes, usually exerting an aggravated impact in the presence of favorable contextual factors such as unbalanced diet or lack of physical activity.

Literature has focused on the combined effect of gene-environment interactions on increased weight, with special focus on the modifying or mediating effect of the obesogenic environment on the penetrance and expressivity of genes associated with obesity susceptibility. More specifically, studies have focused on the effect of dietary and physical activity habits or gene-diet interactions in obesity-associated phenotypes. The field of nutrigenetics investigates the impact of the genetic factor on the metabolic and other responses to food intake. In similar context but on the opposite side, the term nutrigenomics is used to describe the field specializing in unraveling the modifying role of nutritional components in gene expression.

The genetic architecture of increased body weight has been the subject of many genome-wide association studies (GWAS) during the past years, with significant findings allowing for the development of approaches using individualization techniques to effectively prevent and combat weight gain. The identification of multiple genetic loci associated to obesity allowed for the creation of aggravated variables named Polygenic Risk Scores (PRSs), aiming at quantifying individualized genetic risk. Such findings have allowed for the inclusion of the genetic component as contributor to prevention and treatment strategies, where the behavioral approaches adopted are not the sole determinant of successful weight management.

Current literature highlights the need for multidisciplinary attempts to further investigate causes of this purpose and propose personalized approaches as a practical means to combat excess weight. In this context, the newly-started, EU-funded BETTER4U project, consisting of 28 partners, aims at the extensive identification of all predictors related to weight gain and the creation of an artificial intelligence (AI)-based causal model and intervention methodology for personalized lifestyle recommendations.

Liquid biopsy: From discovery to clinical implementation 

Klaus Pantel  

Institute of Tumor Biology, University Cancer Center Hamburg, University Medical Center Hamburg Eppendorf, Hamburg, Germany, pantel@uke.de 

Liquid Biopsy has been defined as the analysis of tumor cells or products released from primary or metastatic tumor tissues into the blood or other body fluids. Over the past ten years, CTCs, ctDNA and extracellular vesicles have received enormous attention as new biomarkers and subject of translational research. In particular, CTC research has opened new avenues for a better understanding of tumor biology in cancer patients, including intra-patient heterogeneity and evolution towards resistance to therapy. Although both biomarkers are already used in numerous clinical trials, their clinical utility is still under investigation with first promising results. Clinical applications include early cancer detection, improved cancer staging, early detection of relapse, real-time monitoring of therapeutic efficacy and detection of therapeutic targets and resistance mechanisms. In particular, interventional clinical studies are required to demonstrate clinical utility of liquid biopsy as an important prerequisite for the introduction of this new diagnostic approach into clinical practice. Moreover, assay harmonization and standardization as conducted by international consortia like the European Liquid Biopsy Society (ELBS; www.elbs.eu) is essential. Here, I will discuss a conceptual framework of CTC and ctDNA assays and point out current challenges of CTC and ctDNA research with emphasis on solid tumors, which might structure this dynamic field of translational cancer research. 

The utility of Liquid Biopsy in Immuno-Oncology: an emphasis on circulating tumor cells 

Catherine Alix-Panabières ^1,2,3

1. Laboratory of Rare Human Circulating Cells (LCCRH), University Medical Centre of Montpellier, Montpellier, France. 
2. CREEC/CANECEV, MIVEGEC (CREES), University of Montpellier, CNRS, IRD, Montpellier, France. 
3. European Liquid Biopsy Society (ELBS) 

Cancer-related deaths are mainly caused by metastatic spread of tumor cells from the primary lesion to distant sites via the blood circulation. Understanding the mechanisms of blood-borne tumor cell dissemination by the detection and molecular characterization of circulating tumor cells (CTCs) in the blood of patients with cancer has opened a new era in cancer research. 

However, blood is known to be a hostile environment for CTCs. Although the primary tumor presumably sheds thousands of cells into the bloodstream every day, only a very small percentage of these cells survive in the bloodstream and become detectable as CTCs in a blood sample. Within the immunological synapse, a multitude of inhibitory receptors have been identified. Programmed cell death protein-1 (PD-1) and its ligand, PD-L1, have been one of the most prominent examples to antagonize immune escape mechanisms employed by tumor cells. 

In my talk, I will discuss about (i) CTCPD-L1(+) plus extracellular vesicles expressing PD-L1 as important biomarkers in liquid biopsy in breast and non-small cell lung cancers as well as (ii) metastasis-competent CTCs from colon and breast cancers to discover new targetable immune checkpoint inhibitors. Indeed, these more aggressive and selected clones of CTCs have the capacity to initiate secondary tumors in distant organs; interestingly, they are not expressing PD-L1 but survived the constant immune attacks. 

Functional Analysis of Cardiovascular Disease Risk Variants 

Panos Deloukas 

William Harvey Research Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, UK 

We conducted a large genome-wide association studies (GWAS) meta-analysis for coronary artery disease (CAD) in 210,842 CAD cases among 1,378,170 participants of predominantly European ancestry and detected 899 conditionally independent associations of which 279 at genome-wide level of significance and the remaining 620 at 1% false discovery rate (FDR). 

Similarity-based clustering suggested roles for early developmental processes, cell cycle signalling and vascular cell migration and proliferation in the pathogenesis of CAD. Focusing on the 279 CAD risk loci, we prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. 

Following the construction of ATAC-seq maps of coronary endothelial and smooth muscle cells cultured under unstimulated and VEGF stimulated conditions, we intersected the 899 CAD associated variants and all their proxies (r2 ≥0.8; ~22,000 SNPs) with ATAC-seq peaks and prioritised 653 variants for functional analysis via STARR-seq. We found 74 variants showing significant changes in regulatory activity between alleles, in human umbilical vein endothelial cells (HUVECs). Amongst the most significant of these variants, the T allele of rs76681511 was found to regulate the activity of ADAMTS7, a metalloproteinase discovered to decreased plaque stability within vasculature. Furthermore, we have generated STARR-seq data in liver cells as elevated lipid levels is an established risk factor for CAD. 

Our analyses have identified many enriched loci associated with vascular CAD processes. The generation of individual isogenic cell lines will provide further understanding of the underlying mechanisms for these prioritised variant(s) and may lead to the identification of novel pathways for therapeutic intervention. 

Can we predict the future? The utility of genetic risk scores for classification and prediction of autoimmune disease

Richard Oram
Exeter, United Kingdom

Autoimmune diseases are polygenic and commonly have high heritability, with a significant component of this heritable risk being conferred by a small number of HLA risk alleles present on Chromosome 6. Strong HLA associations are known for numerous autoimmune diseases such as type 1 diabetes, coeliac disease and ankylosing spondylitis, these are not widely used in clinical classification or prediction. Polygenic or genetic risk scores sum individual risk genetic risk for a disease from all associated variants and can be expressed as a single number. There is increasing interest in utilising polygenic scores in clinical care.

We recently showed that relatively small numbers of type 1 diabetes and coeliac disease associated variants can be combined to make highly discriminative polygenic or genetic risk scores for type 1 diabetes and coeliac disease. These scores can be applied in clinical classification, for example to distinguish type 1 diabetes from type 2 diabetes or monogenic diabetes. We have now integrated these into clinical care in the UK national monogenic diabetes testing service.

Type 1 diabetes has a long latent phase of months to years before clinical presentation with high blood glucose. This latent phase can be recognised by strong biomarkers called autoantibodies, which define the pre-clinical stages of type 1 diabetes. Commonly these autoantibodies first appear in very early life. We are testing the utility of type 1 diabetes genetic risk scores for prediction of future type 1 diabetes. With the recent FDA approval of the first pre-clinical immunotherapy for type 1 diabetes, there is an urgent requirement for public health screening for type 1 diabetes, and this is likely to involve combined population screening using autoantibodies and genetics.

Artificial intelligence in External Quality Assessment – Proficiency Testing schemes (EQA-PT) in Laboratory Medicine.   

Dr Alexander Haliassos, MD, PhD, EurSpLM 

ESEAP, Athens, Greece 

External Quality Assessment (EQA) or proficiency testing (PT) schemes used to address only the analytical procedures of the medical laboratories. 

Their operation is based on the use of Inter-Laboratory comparisons for the determination of the Laboratory Performance, as well the evaluation the analytical performance of the laboratories in accordance to predetermined conditions. It is important to consider that every EQA scheme introduces some limitations and that it is not appropriate to use them as the sole means of evaluating laboratory quality. 

They provide scientific evidence of laboratory competence for Patients, Accrediting Bodies, and Regulatory Agencies, and serve as a unique source of information that is not obtainable in other ways. Recently schemes have been introduced to evaluate also the pre-analytical activities and post-analytical processes. 

Computer science defines Artificial intelligence (AI) research as the study of “intelligent agents“. The Artificial intelligence, or machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. Intelligent agent is any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Alternatively, the term artificial intelligence is often used to describe machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem solving”. Machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems, autonomously operating cars, intelligent routing in content delivery networks, and military simulations. 

The Artificial intelligence can be used in EQA-PT schemes mainly for the: 

• Intelligent calculation of the consensus mean. 
• Optimized exclusion of outliers. 
• Intelligent graphs scaling and groups (methods-analyzers) classification. 
• Expert results stratification and rating of the performance of participants. 
• Detection and elimination of most non-analytical errors. 

But also can be used for the automated and enhanced detection of Errors in EQA testing as the: 

Non-analytical errors i.e. errors from samples manufacturing, conservation and transport, errors on the reconstitution of samples i.e. wrong diluent (saline vs distilled water) or wrong dilution (pipetting error-factor), reporting errors i.e. sample inversion if more than one sample is used (A to B and vice-versa), use of wrong reporting units, inappropriate method or instrument selection, clerical transcription errors (inappropriate decimal point, mistyping etc.). 

Analytical errors as the EQA materials can exhibit a matrix effect in the examination system used by a participating laboratory (Non commutable materials) or any other sources of analytical problems from reagents, instruments, test methods, calibrations and calculations. Moreover, the analytical problems should be investigated to determine whether error is random or systemic. 

The competency of the staff can be evaluated automatically and if human errors can be addressed and corrected as in the evaluation of limitations of analytical instruments (samples outside their linear instrument range) the non-dilution of samples, the erroneous reporting under the LOD area, the non-detection of interferences (Hemolysis-Icterus-Lipemia Index Analysis, Biotin interference). 

Internet of things is the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data and can be involved in EQA schemes for the efficient Temperature tracking of control materials, the Sample Identification and the detection of Filling levels (reconstitution accuracy of samples). 

In conclusion the use of Artificial intelligence along with Internet of Things technologies in the field of External Quality Assessment – Proficiency Testing schemes in Laboratory Medicine can: 

• Improve the functioning of these schemes and makes them easier to use, 
• Trace and eliminate analytical and non-analytical errors, and 
• Enhance the information provided to their users, 

leading to the improvement of the overall quality of participating laboratories by empowering their staff and giving them a measurable way of testing their individual proficiency. 

Cardiac inflammation: the relative contribution of autoimmunity and genes

Federica M Marelli-Berg1,
Silvia Fanti1*, Edward Stephenson1,2*, Etel Rocha-Vieira1,3, Alexandros Protonotarios2,4, Stavroula Kanoni1, Perry Elliott2,4, Saidi A Mohiddin1,2*,

1William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.
2Barts Heart Centre, Barts Health NHS Trust, St Bartholomew’s Hospital, West Smithfield, London EC1A 7BE
3Federal University of Vales do Jequitinhonha e Mucuri, Diamantina, Minas Gerais, Brazil
4Institute of Cardiovascular Science, University College London, 72 Huntley Street, London WC1E 6AG

Autoimmunity is increasingly recognized as a key contributing factor in heart muscle diseases. The functional features of cardiac autoimmunity in humans remain undefined, due to the challenge of studying immune responses in-situ. We have previously described a subset of cMet-expressing memory T-lymphocytes, which preferentially migrate to cardiac tissue in mice and humans.
In-depth phenotyping of peripheral blood T-cells, including cMet+ T-cells, was undertaken in groups of patients with inflammatory and non-inflammatory cardiomyopathies, patients with non-cardiac autoimmunity and healthy controls. Validation studies were carried out using human cardiac tissue and in an experimental model of cardiac inflammation.
We show that cMet+ T-cells are selectively increased in the circulation and in the heart of patients with inflammatory cardiomyopathies. cMet+ T-cells display distinct phenotype and function compared to cMet-negative T-cells, including preferential proliferation to cardiac myosin and co-production of multiple cytokines (IL-4, IL-17 and IL-22). Further, circulating cMet+ T-cell subpopulations in different heart muscle diseases identify distinct and overlapping mechanisms of heart inflammation including in genetic cardiomyopathies.
In experimental autoimmune myocarditis, rise of autoantigen-specific cMet+ T-cells in peripheral blood marks the loss of immune tolerance to the heart. Importantly, disease development can be halted by pharmacological cMet inhibition, indicating a causative role for cMet+ T-cells.

Myocarditis: The challenge of diagnosis and the opportunities for treatment

Professor Perry Elliott
Institute of Cardiovascular Science, University College London, UK

Myocardial injury of any kind can prompt an inflammatory response. The term myocarditis refers to inflammatory disease of the myocardium diagnosed by established consensus histological, immunological and immunohistochemical criteria. Somewhat confusingly, the term inflammatory cardiomyopathy is also used when myocarditis occurs in association with cardiac dysfunction.
Reported causes of myocarditis are numerous and include infection (viruses, bacteria, parasites, protozoa), toxic agents and systemic autoimmune disorders. In many cases, acute myocarditis resolves within a few weeks but in some individuals, it leads to persistent cardiac dysfunction or progression to end-stage heart failure. It may also cause cardiac arrhythmia or sudden cardiac death. However, the prevalence of myocarditis and specific aetiologies remains elusive due to (1) the heterogeneity of clinical presentations, (2) a reluctance to perform invasive and potentially hazardous endomyocardial biopsy to establish a tissue diagnosis, and (3) a poor understanding of immunotypes associated with acute and chronic presentations of myocarditis. This diagnostic uncertainty contributes to inconsistent and often ineffective therapy for proven or suspected myocardial inflammation.
The concept of personalised, precision medicine recognises that complex diseases should no longer be considered a single entity and that different subtypes of patients within a given condition can be identified and treatment can be tailored to the underlying cause. That myocarditis plays a role in myocardial disease seems irrefutable. The corollary is that identification of patients with different sub-types of myocarditis offers an opportunity to significantly alter the course of disease.
In this presentation, I will review advances in non-invasive approaches to the diagnosis of myocardial inflammation and will consider the relevance of emerging data on the role of T-cell mediated immunity in different clinical scenarios to the development of prospective randomised studies.

Direct and indirect markers of response to anti-tumor vaccines or oncolytic virotherapy 

Eric Quéméneur 

TRANSGENE S.A., Parc d’innovation, 67405 Illkirch-Graffenstaden, France 

Thanks to their intrinsic adjuvant properties, and amenability to genetic engineering, viral vectors represent attractive platforms for the development of immunotherapies against tumors. The demonstrated safety, and unique capacity of Vaccinia genomes to accept large genetic inserts (up to 20-25 kb) has opened the way to the design of advanced immunotherapies expressing complex recombinant payloads. Our company has designed several viral vector-based products, either cancer vaccines or oncolytic viruses. Currently five products are clinically assessed in a large diversity of solid tumor indications (www.transgene.fr/en). To support our development plans, several methods have been implemented in these trials to monitor the immune, and molecular responses to these two classes of immunotherapies, in addition to regular endpoints of clinical efficacy. 

For vaccines, the main challenge was to track the kinetics of cytokine response, and the induction of adaptive immune response. Our results with TG4001 (MVA-based expressing full length membrane-anchored HPV E6-E7 protein antigens), and TG4050 (MVA expressing 30 patient-specific neoantigens) clearly demonstrated the strong activity of TG4001, as well as of TG4050 in inducing a diverse repertoire of effector CD8+ T cells within a few weeks after repeated administration of the vaccine. The potential value of circulating tumor DNA (ctDNA) as a surrogate marker for tumor lysis has also been assessed.  

Oncolytic virotherapy engages replicative vectors that can be used in multiple routes of administration (i.e. intratumoral, locoregional, intravenous). Thus, the analytical challenge was more extensive, than for vaccines, combining the monitoring of viral vector biodistribution, and amplication (quantitative virometry based on qPCR), in addition to immune monitoring. Two studies involving intravenously administered OVs (neoadjuvant Pexa-Vec, and TG6002) confirmed our hypotheses on ability for the vector to reach the tumor, express its recombinant payloads in the tumor microenvironment, and induce immune infiltration. 

Representative examples derived from our analytical experience with both vaccines and OVs will be discussed in terms of support to the mechanisms of action, as well as potential predictive markers of response.  

The importance of blood biomarkers in oncology 

Mark Ruddock, Mary Jo Kurth, Joanne Watt, John Lamont, Peter Fitzgerald 

Randox Laboratories Ltd, 55 Diamond Road, Crumlin, Co. Antrim BT29 4QY, Northern Ireland, UK 

Blood biomarkers play a crucial role in oncology, offering valuable information for the detection, diagnosis, prognosis, and treatment monitoring of various cancers. Early detection of biomarkers can significantly improve treatment outcomes, for example prostate-specific antigen (PSA) for prostate cancer, CA-125 for ovarian cancer, and carcinoembryonic antigen (CEA) for colorectal cancer.  Blood biomarkers provide insights into the aggressiveness of a cancer and help predict the likely course of the disease, for example, elevated levels of certain biomarkers may indicate a higher risk of disease progression or recurrence.  Blood biomarkers are instrumental in monitoring the effectiveness of treatment response to chemotherapy, targeted therapies, and/or immunotherapy. Moreover, changes in biomarker levels over time can indicate whether the treatment is working or if adjustments are needed.  Furthermore, blood biomarkers contribute to the era of personalised medicine by helping identify specific molecular characteristics of a patient’s cancer.  This additional information enables oncologists to tailor treatments to the unique genetic makeup of each patient’s tumour, optimising therapeutic outcomes.  Biomarker analysis is minimally invasive for patients. Therefore, serial blood testing allows for ongoing patient monitoring throughout the course of treatment and follow-up.  In addition, blood biomarkers can help assess an individual’s risk of developing specific types of cancer, influencing decisions regarding screening and preventive measures.  In summary, blood biomarkers in oncology play a pivotal role across the entire cancer care continuum, from early detection and diagnosis to treatment monitoring and personalised therapeutic approaches.  Blood biomarker measurements provide real-time information enabling evidence-based decision making for clinicians and improving patient outcomes. 

Integrated Risk Scores (IRS) for cardiovascular disease risk prediction – Advances towards precision medicine 

Stavroula Kanoni 

Clinical Pharmacology and Precision Medicine, William Harvey Research Institute, Barts & the London Medical School, Queen Mary University of London, UK 

Cardiovascular disease (CVD) affects 14% of adults above the age of 16 and is leading cause of death worldwide. Coronary Heart Disease (CHD) is the leading cause of CVD death in both sexes (14% men, 8% women). Controlling modifiable risk factors, like diet, exercise or smoking coupled with early prediction of genetic disease susceptibility (40-50% heritability for CHD) can play a pivotal role in the prevention of CVD. Early detection of individuals at high risk for CVD can also help prevent premature deaths. This is very important for specific populations at higher risk for CVD. South Asians have >2-fold higher prevalence for CHD compared to Europeans and tend to develop more severe CHD at a younger age including early MI.  

Traditional risk stratification methods for CVD (QRISK3, pooled cohort equation, Framingham score) rely on conventional factors, yet they may not comprehensively encompass the entire atherogenic landscape. Recent studies have highlighted the importance of the genetic predisposition and have assessed the cumulative effect of genetic variants on CHD risk, in the form of Polygenic Risk Scores (PRS). The predictive power of a CHD-PRS for individuals at the top 10% of the distribution resembles that of a monogenic disease but the predictive ability drops rapidly outside this bracket, with PRSs only modestly enhancing CVD prediction. There is an urgent need for cutting-edge, artificial intelligence (AI)-driven integrated predictive tools that combine multi-modal CHD risk factors, demonstrating robust performance across diverse populations.  

We have been developing an innovative integrated transcriptomics, polygenic, lifestyle and clinical risk score (IRS) for British South-Asians, with transferability to other ancestral populations. Employing state-of-the-art Machine Learning (ML) models and AI algorithms, we have integrated the PRS with transcriptome signatures, clinical factors (consistent with QRISK3 components), and lifestyle information (diet, smoking, alcohol consumption, physical activity). This comprehensive approach aims to develop a high-precision prediction tool for CVD that outperforms existing models in accuracy and reliability.  

Digital Twins in Precision Medicine and Personalized Nutrition 

Aristides G. Eliopoulos & Kalliopi G. Gkouskou 

Department of Biology, School of Medicine of the National and Kapodistrian University of Athens (NKUA) & GENOSOPHY P.C.  

Nutritional and lifestyle changes remain at the core of healthy ageing and disease prevention. Accumulating evidence underscores the impact of genetic, metabolic and host gut microbial factors on individual responses to nutrients, paving the way for the stratification of nutritional guidelines. However, technological advances that incorporate bioclinical health data at an unprecedented scale and depth conceptualize a future where preventative dietary interventions will exceed stratification and will be highly individualized. We will discuss the transformative capacity of AI to couple multi-modal bioclinical, genetic, metabolomic, lifestyle, immune and gut microbial data, enabling the implementation of a digital replica of oneself, a “virtual digital twin”, which could serve to guide nutrition in a truly personalized manner. Focusing on macronutrient tailoring, we will discuss the impact of genetic variation on responses to carbohydrate, lipid, protein, and fiber consumption. Our bioinformatic analysis of genomic variants guiding macronutrient intake reveals enriched pathways, such as circadian rhythm, melatonin metabolism, cholesterol and lipoprotein remodelling and PPAR signalling as potential targets of macronutrients for the personalized management of obesity. Notably, our in silico predictions hint at the potential repurposing of the SYK kinase inhibitor fostamatinib for obesity treatment in relevant genetic profiles. We propose that the application of the digital twin concept may revolutionize the management of non-communicable diseases by realizing the core principles of predictive, preventive, personalized and participatory (4P) medicine towards tangible and long-lasting effects on health. 

Diagnostic utility of cerebrospinal fluid biomarkers in patients with Alzheimer’s disease 

Sanja Stankovic ^1,2, Elka Stefanova ³

1. Department of Biochemistry, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, SERBIA
2. Center for Medical Biochemistry, University Clinical Centre of Serbia, Belgrade, SERBIA
3. Clinic of neurology, University Clinical Centre of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, SERBIA 

Background. Alzheimer’s disease (AD) is a chronic, neurodegenerative disease that affects millions of people worldwide. It is multifactorial in nature and involves complex molecular mechanism that causes neuronal degeneration. The first neuropathological changes of the AD can be detected many years prior to the onset of clinical symptoms. Accurately diagnosing neurodegenerative dementia is often challenging due to patient heterogeneity, overlapping clinical features, etc. Numerous studies explored potential biomarkers of AD. Three cerebrospinal fluid (CSF) biomarkers (beta-amyloid 1–42 (Aβ42), total tau (t-tau) and phosphorylated-tau (p-tau)) seems to have the highest diagnostic accuracy for early AD diagnosis. CSF biomarkers could be useful for diagnosing of suspected AD patients, independently of the clinical stage, by reflecting the presence of brain amyloidosis (A+) and tauopathy (T+).  

Objective. The objective of this study was to examine the utility of CSF biomarkers for the early discrimination of patients in AD continuum from those with non-AD pathologic changes.
Design. The study included 72 patients presented to Neurology Clinic University Clinical Center of Serbia, Belgrade, Serbia (67% with AD). The CSF AD biomarkers levels including Aβ42, t-tau and p-tau181 were determined using fully automated electrochemiluminescence immunoassays on the automated e601 analyzer (Roche Diagnostics, Germany). Two ratios pTau/Aβ42 and tTau/Aβ42 were calculated. Patients were divided into ATN classes based on CSF biomarkers. The clinical diagnoses were made according to the NIA-AA recommendations.  

Results. AD patients had significantly lower median value of Aβ42 and higher tTau/Aβ42 (p<0.001) compared with non-AD patients. Although with borderline significance (p=0.06), the ratio pTau/Aβ42 was higher in AD compared to non-AD group. In AD patients, significant correlation (r<0.001) was found between Aβ42 and two ratios, as well as between pTau and tTau. 70 patients in A+ profiles showed dementia (AD, n=48; non-AD, n=22), compared to 2 patients in A–T–N– (non-AD).  

Conclusion. Our study showed CSF Aβ42 and CSF t-tau/Aβ42 ratio are very robust indicators of AD. The Elecsys assays have high analytical performance that may improve CSF biomarker-based AD diagnosis.
Keywords: Alzheimer’s disease, biomarker, cerebrospinal fluid.

The spectrum of founder and other novel pathogenic variants in autosomal dominant Alport syndrome in Cyprus 

Constantinos Deltas ^1,2,, Gregory Papagregoriou ¹Christiana Polydorou¹, Stavroulla Louka¹, Apostolos Malatras¹, Kyriacos Ioannou³, Kyproula Demetriou⁴, Alexia Panagides⁴, Chrysoula Pipili⁵, Aristos Michael⁶, Lakis Yioukas⁷, Panayiota Loukaidou⁸, Andreas Soloukides⁹, Isavella Savva⁹, Alkis Pierides¹⁰

1. Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia, Cyprus
2. University of Cyprus Medical School, University of Cyprus, Nicosia, Cyprus
3. Nefrontida Kidney Center, Nicosia, Cyprus
4. Department of Nephrology, Nicosia General Hospital, Nicosia, Cyprus
5. Department of Nephrology, Ammochostos General Hospital, Nicosia, Cyprus
6. Department of Nephrology, Limassol General Hospital, Limassol, Cyprus
7. Department of Nephrology, Pafos General Hospital, Pafos, Cyprus
8. Department of Nephrology, Larnaca General Hospital, Larnaca, Cyprus
9. Transplantation Center, Nicosia General Hospital, Nicosia, Cyprus
10. Department of Nephrology, Hippokrateion Hospital, Nicosia, Cyprus 

Background: Autosomal dominant Alport syndrome (ADAS), previously termed thin basement membrane nephropathy (TBMN), results from heterozygous variants in the COL4A3/COL4A4 genes. ADAS is distinct from the more severe autosomal recessive form leading to early kidney failure, before 30-yo, if untreated. 

Methods: We assessed families with familial microscopic hematuria (MH) and/or proteinuria using sequencing techniques. 

Results: Nearly every Cypriot family affected with ADAS has patients who remain with MH or low-grade proteinuria for life, while some progress to kidney failure. In 2007 & 2009, we reported on 127 patients of 11 Cypriot families with a dual diagnosis of focal segmental glomerulosclerosis and TBMN, segregating heterozygous variants in the COL4A3/A4 genes (Voskarides K et al, JASN 2007). On long follow-up, up to 30% of the patients developed kidney failure by age 70yrs (mean 55yrs). Later, similar findings were published by several groups around the world. The distribution of the variants in question (COL4A3/A4) are below: a total of 49 families and 318 patients have the COL4A3 variant, while 38 families and 165 patients have the COL4A4 variant, both dispersed all over Cyprus. A notable concentration of 22 families with 205 patients is identified with the COL4A3:p.Gly1334Glu variant in Mesaoria, east of Nicosia. Additionally, the COL4A3:p.Gly871Cys variant affects 6 families and 36 patients, originating from villages at the northern coast occupied by Turkey since 1974. Lastly, the COL4A4:p.Gly1248Arg variant is found in 4 families with 7 patients from south-east Cyprus. In total, 30 mutations were identified, 11 in COL4A3, 19 in COL4A4, including Gly substitutions, 8 and 13 respectively. Others include small indels and whole exon deletions. de novo variants have not been identified.  In a cohort of 155 patients carrying founder COL4A3:p.Gly1334Glu, 47 (30%) reached ESKD at ages from 40-90yrs. Rarely do they reach ESKD before 40yrs.  

Conclusion: Heterozygous COL4A3/A4 variant carriers risk kidney failure at variable age, which current methods cannot predict. Family history and genetic modifiers are influential. The CYPROME project identified Pathogenic and Likely Pathogenic variants in asymptomatic individuals predisposed to chronic kidney disease (CKD). 

SiMS score and siMS risk score increased in type 2 diabetes mellitus patients and development of coronary artery disease

Vesna Dimitrijevic Sreckovic ¹,, Hristina Petrovic ², Drasko Gostiljac ¹, Sasa Ilic ¹, Ljubica Dimitrijevic ¹, Ivan Soldatovic ³

1. Clinic for endocrinology, diabetes and metabolic diseases, Belgrade, SERBIA
2. University children’s hospital, Belgrade, SERBIA
3. Institute for medical statistics and informatics, Belgrade, SERBIA 

Background: Abdominal obesity, hyperinsulinemia, hyperglycemia, hyperlipoproteinemia and hypertension are the most evident signs of metabolic syndrome (MS). Microalbuminuria, seen in patients with DM2, CAD and MS is marker of endothelial dysfunction. 

Objectives: Analyzing siMS score, as a method for quantification of MS, and siMS risk score for atherosclerosis in patients with myocardial infarction (MI) without DM2 and DM2 patients with and without CAD. Corelation of siMS score and siMS risk score with co-founding factors of MS is evaluated. 

Methods: Overweight/obese individuals with Pre–MS and MS (age >45) were classified according to the presence of DM2 and CAD: I-control group- no DM2, no CAD(22), II-MI, no DM2(24), III-DM2, no CAD(25) and IV-DM2 with CAD(70). ATP-III classification was applied for diagnosing MS. Patients with less than three criteria were considered pre-MS.  

Results: WC(I-76.7±8.3,II-98.2±12.3,III-96.0±15.2,IV-100.4±9.4cm), BMI(I-22.9±3.2,II-27.9±3.9,III-29.4±6.0,IV-30.6±4.4kg/m2), triglycerides(I-1.0±0.4,II-2.2±1.1,III-2.5±2.0,IV-2.7±2.1mmol/l), HOMA-IR(I-2.6±1.0,II-5.1±3.7,III-8.1±4.6,IV-13.2±4.5mmol/µU/ml), microalbuminuria(I-19.8±5.4,II-45.8±67.0,III-54.4±65.5,IV-74.4±106.2mg/24h), PAI-1(I-2.1±0.5,II-6.3±1.2,III-4.6±2.9,IV-5.3±2.4U/ml), HbA1C(I-5.2±0.2,II-5.2±0.7,III-7.6±1.6,IV-7.9±1.6%). Pre-MS(I-18.2,II-37,5,III–24,IV-36.2%) and MS(I-0,II-41.7,III–28,IV-43.5%). siMS score(I-2.18±0.49,II-3.19±1.04,III-4.5±2.04,IV-4.48±1.42). siMS score correlated with hyperinsulinemia, HOMA IR(p<0.001), PAI-1(p=0.045), microalbuminuria(p=0.05), HbA1C(p<0.01). siMS risk score(I-2.12±1.01,II-4.26±1.58,III-6.4±2.4,IV-5.88±2.11). siMS risk score correlated with hyperinsulinemia, HOMA IR(p<0.001), HbA1C(p=0.005). 

Conclusion: Patients with DM2 with or without CAD were characterized by visceral obesity, hypertriglyceridemia, hyperinsulinism, PAI-1, microalbuminuria, unsatisfactory glycoregulation, higher values of siMS score, and siMS risk score compared to the control group and persons with MI and without DM2. Correlation of siMS score and siMS risk score with hyperinsulinemia, IR, PAI-1, microalbuminuria, hypertriglyceridemia and HbA1C confirms the role of MS factors on the progression coronary artery disease. 

Keywords: metabolic syndrome, siMS score, siMS risk score, coronary artery disease, diabetes mellitus

Integrative exploration of metabolic syndrome in older men

Estelle Pujos-Guillot ¹, Blandine Comte ¹, Marion Brandolini-Bunlon ¹, Pierrette Gaudreau ²

1. Human nutrition, Inrae, Clermont-Ferrand, FRANCE
2. Medicine, Montreal university, Montreal, CANADA

Background: Metabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis. 

Objective: The objective of the present work was to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men. 
Design: A case/control study was designed within the Quebec NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted approach. A full feature selection strategy was developed to build a comprehensive molecular MetS signature, stable over time. 

Results: We characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo– and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…). These findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS. 

Conclusions: The refinement of the comprehensive signature, performed both in terms of measurement reliability, but also by showing the consistent association between the modulated metabolites/lipids and the underlying biological mechanisms, is increasing the value of the proposed biomarker combination within the reduced signature for further investigation and possible clinical application. 
Acknowledgements: The NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIHR; MOP-62842). All metabolomics and lipidomics analyses were funded and performed within the MetaboHUB French infrastructure (ANR-INBS11-0010). 

Keywords: metabolic syndrome, deep phenotyping, metabolomics, data integration

Preventing Obesity Through Biologically And Behaviorally Tailored Interventions; Description Of The BETTER4U Project

Maria Kafyra on behalf of the BETTER4U Consortium Members
Department of nutrition and dietetics, Harokopio University of Athens, Athens, GREECE

Background: Obesity is an indispensable underlying cause for many non-communicable diseases. Its growing prevalence and association with adverse health outcomes renders the optimization of prevention measures a public health priority.

Objective: BETTER4U seeks to address the prevalence of obesity in Europe by leveraging modern artificial intelligence (AI) technologies and collaborating with an international, multidisciplinary group of experts.

Design: BETTER4U aims to identify and manage obesity determinants through personalized lifestyle recommendations in 4 phases: Phase 1 will identify all obesity-related determinants through meta-review and meta-analyses of data from current literature and previous projects conducted by the consortium (i.e. genetic, omics layers, microbiota, socio-economic, geographical, cultural and lifestyle features); phase 2 will focus on the creation of a robust mechanism for individualized interventions for obesity prevention, using an AI-enhanced causal model along with federated learning; phase 3 will assess effectiveness and cost-effectiveness through a pilot study and a randomized clinical trial in adults from 7 European sites, where participants will receive personalized recommendations from healthcare professionals via an AI platform and technologically-assisted, real-time monitoring tools, aspiring to result in the BETTER4U integrated methodology, and; phase 4 will focus on the communication and dissemination of said methodology.

Results: BETTER4U aims to provide three-fold results: i) the creation of an extensive dataset for identifying all obesity determinants; ii) the development and use of AI and monitoring tools to derive and implement personalized recommendations; iii) the real-world application of said recommendations through an intervention which will result in personalized methodology guidelines.

Conclusions: Overall, BETTER4U aims to unravel the complex interplay of obesity determinants using AI and monitoring tools to develop and evaluate the efficacy of the novel BETTER4ALL intervention methodology. The project aims to widely distribute the BETTER4U intervention methodology guidelines by making the latter publicly available through public health systems and via using people-centered, sustainable care approaches.

Keywords: Obesity Determinants, Causal Models, Lifestyle Intervention, Integrated Methodology, Personalized Guidelines

Favorable effects of semaglutides on weight reduction and reducing the risk of chronic diabetes complications

Vesna Dimitrijevic Sreckovic ¹, Ljubica Dimitrijevic ¹, Sasa Ilic ¹, Ivan Soldatovic ², Gordana Milic ¹, Masa Ristic ¹, Milica Milosevic ¹, Drasko Gostiljac ¹

1. Clinic for endocrinology, diabetes and metabolic diseases, Belgrade, SERBIA
2. Institute for medical statistics and informatics, Belgrade, SERBIA

Background: Semaglutide, GLP-1 receptor agonist, currently occupies an important place in the treatment of diabetes mellitus type 2 (DM2). High dose semaglutide treatment of 2.4 mg/week has showed beneficial effects on body weight reduction and improvement of glycoregulation. 

Objectives: The aim of this study was to examine treatment with low doses of semaglutide on anthropometric parameters and glycoregulation in obese patients with DM2. 

Methods: The study included 63 obese patients (BMI>30kg/m2) with DM2, who had semaglutide s.c. in a maximum dose of 1mg/week, monitored at 3- and 6-months period. Patients were divided into two groups: I-patients with oral hypoglycemic agents (OHAs) and II- patients with OHAs+insulin in three distinct periods (0- start, 1- 3rd month, 2- 6th month). Anthropometric parameters were measured, and biochemical analysis was performed. siMS score, as a method for quantification of metabolic syndrome (MS) was used. 

Results: body weight: (I-0: 119.3±9.1, I-1: 114.4±22.4, I-2: 114.0±23.9kg, II-0: 107.3±15.9, II-1: 101.1±18, II-2: 98.4±14,7kg), WC:(I-0: 122.7±14.6, I-1: 118.7±12.3, I-2: 119.3±14.0cm, II-0: 115.6±9.4, II-1: 113.5±7.2, II-2: 111.1±8.6cm), BMI:(I-0: 39.4±6.2, I-1: 37.7±5.5, I-2: 37.3±5.kg/m2, II-0: 36.7±4,1, II-1: 34.3±3.5, II-2: 33.2±3.6kg/m2), glycaemia:(I-0: 8.2±2.0, I-1: 6.2±1.3, I-2: 6.7±1.8mmol/l, II-0: 11.5±3.5, II-1: 7.5±1.5, II-2: 7.8±2.3mmol/l), HbA1C:(I-0:7.6±0.9, I-1:6.3±0.9, I-2:6.0±0.7%, II-0:8.8±1.4, II-1:7.3±1.4, II-2:7.3±1.7%), siMS score:(I-0:4.39±0.79, I-1:3.72±0.86, I-2:4.05±1.07, II-0:5.10±1.25, II-1:3.64±1.05, II-2:3.96±81). After 3 months weight was decreased by 5.1±4.4kg in I group and 5.8±4.1kg in II group, after 6 months by 7.7±5.3kg in I group and 7.4±5.1kg in II group. Statistical significance between groups was obtained for body weight, WC, BMI, glycaemia, HbA1c with statistical significance (p<0.05). 

Conclusion: Short-term treatment with low dose semaglutide showed a significant reduction in visceral obesity, BMI, glycemia, HbA1c after 3- and 6-months period, confirmed by a reduction in the siMS score in both groups, though more pronounced in second group. HbA1c was reduced by 1.52% in both groups equally, which is an indicator of a significant reduction in the risk of vascular complications. 

Keywords: semaglutide, GL1- receptor agonist, obesity, diabetes mellitus.

Development of target-guided approaches for exosome-associated miRNAs analysis standardization in the context of chronic degenerative diseases

Andrea Quattrone ¹, Selena Mimmi ², Anna Maria Zimbo ², Valentina Crapella ², Salvatore Biagio Valia ², Anna Maria Tolomeo ³, Giovanni Cuda ², Aldo Quattrone ¹ and Enrico Iaccino ².

1. Neuroscience Research Center, Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy 
2. Department of Experimental and Clinical Medicine, University Magna Graecia of Catanzaro, Catanzaro, Italy 
3. Department of Cardiac, Thoracic and Vascular Science and Public Health, University of Padova, Padua, Italy 

Background and Aim: Liquid biopsy tests have gained attention as a valuable alternative to traditional diagnostics. Exosomes are small extracellular vesicles (EVs) of 30-150 nm in diameter released by both normal and malignant cells, delivering a set of components, such as proteins or miRNAs, from parental cells to target cells, influencing the phenotype and behavior. Here, we describe a proof-of-principle application of cell-specific exosome targeting allowing the identification, isolation, and molecular characterization and miRNAs analysis of specific exosome populations based on their different antigenic reactivity. 

Methods: We successfully demonstrated that tumor B cell-derived exosomes express the IgBCR of their parental B-cells, thus constituting a personal “barcode” of tumor clones that can be subsequently targeted by so-called “Id-peptides.” Furthermore, we demonstrated that miRNAs-exosomes’ content changes according to therapy response in the context of neurodegenerative dyscrasia. Last, we successfully validated the identification of ACE-2-expressing exosomes in patients affected by SARS-CoV2 as prognostic tools and immunomodulators. 

Results: In murine myeloma multiple (MM) model 5T33MM we have specifically targeted and sorted tumor-derived exosomes characterized by the expression of the same Ig BcR of MM cells. Interesting, the analysis of serum MM-released exosomes allowed an earlier detection of MM growth compared to the conventional measure of paraprotein. In COVID-19 disease we demonstrated an increase of ACE2-expressing exosomes directly proportional to a favorable prognosis. Of interest, these ACE2-expressing exosomes are characterized by specific miRNAs profile involved into immune modulation.  

Conclusion: These new shreds of evidence show a promising and novel use of exosomes as biomarker sources for i) the early detection of minimal residual disease in hematological disease compared to conventional protocols, ii) the prediction of treatment response in neurodegenerative disease, and iii) innovative first line immune modulators in infection diseases.  

Reem Hamad ¹, Muntaser Ibrahim ^1
1Diversity and diseases department, Institute of endemic diseases, Khartoum, SUDAN

Introduction and objectives: One major tenet of modern molecular medicine is the concept of gene/variant actionability. This concept though central to translational genomics is shrouded with limitations of outdated universal approaches.
Frequency maps between human populations and individuals, with far reaching implications to actionability particularly in genetic models of complex inheritance, and non-major genetic effects, like cancer where gene environment interactions have the upper hand in deciding the phenotype.

Methods: Here we present examples of discrepancies in both regional and private contexts impacting actionability in disease like colorectal and breast cancer and furthermore propose AI algorithms such as symbolic regression to circumvent such hurdles and render the variant(s) translation in terms of actionability.

Results and conclusions: This approach generates high-performing models that can both predict disease outcomes and reveal putative disease mechanisms. Due to their high performance, simplicity and explicit functional form, these biomarker signatures can be readily explained, thereby making them attractive tools for high-stakes applications in primary care, clinical decision making and patient stratification.
It explained the DNA methylation and predicted 3 biomarkers to be involved as biomarkers in Hepatocellular Carcinoma with an accuracy of 91.3%, a sensitivity of 100%, and a specificity of 87.5% on the test data.
In addition, symbolic regression identified 31 lncRNAs involved in cellular proliferation, tumor metabolism, and invasion-metastasis cascade with SNHG18 and HELLPAR as the highlights of the results.

Keywords: Actionability, symbolic regression, cancer.

Quality matters 
Georges Dagher  

Inserm, France, Milano-Bicocca University, Italy, Graz, Medical University, Austria, State Key Lab Stem Cells, Chinese Academy of Sciences, China 

Human biospecimens provide the basis for research leading to better understanding of human disease biology and discovery of new treatments that are tailored to individual patients with cancer or other common complex diseases. The collection, processing, preservation, storage and providing access to these resources are key activities of biobanks. Biobanks must ensure proper quality of samples and data, ethical and legal compliance as well as transparent and efficient access procedures. The standards for biobanking outlined herein are intended to be implemented in biobanks and to supply researchers with high quality samples fitted for an intended use. 

Trends in precision medicine, pharmacogenetics as adjuvant in establishing a correct immunosuppressive therapy of kidney transplant

Sergio Bernardini,
Dept. of Experimental Medicine, University of Tor Vergata, Rome, IT

Allogenic transplant of solid organ is now considered the election clinical procedure in patients with organ failure. Indeed, in the last decades important milestones have been reached especially in kidney transplant. Although the transplanted organ can compensate the function of original one, the main problem remains the immune response by the host organism that can lead to a possible rejection. The management of the immune response is at the basis for a long-life successful organ transplantation. To achieve this objective, drugs targeting the key immunological players of the immunological pathways, fundamental in graft rejection mechanisms have been developed. These can be now used in clinical management of transplanted patients. It is important to note that the wide inter and intra-patient variability of both the pharmacokinetics (PK) and pharmacodynamics (PD) represent the major clinical problem. Although the plasma concentrations of drugs are constantly monitored, variation of these parameters mainly due to the polymorphic status (genetic heterogeneity) of the genes involved in xenobiotic-metabolizing enzymes, transport proteins, and in some cases drug targets, can affects metabolite concentrations in each specific patient. This can induce an important toxicity or loss of efficacy. Numerous studies have been recently conducted to examine the relationship among genetic factors, drug pharmacokinetics, and therapy outcomes. Now several polymorphic genes, influencing drug’s efficacy have been catalogued, and they are now easily detected using Next generation sequencing (NGS) methods. Further research, and new investment in creating validated NGS commercial panel for the analysis of pharmacogenetics in renal transplant recipients will represent the future of precision medicine in the management of immunosuppressive therapy.

From Pharmacogenomics to Polygenic Risk Score (PRS) for Drug Response Prediction – Are We There Yet? 

Candan HIZEL PERRY 

OPTITHRERA, Inc., Montreal, QC, Canada 

CRCHUM – Université de Montréal, Montreal, QC, Canada 

Clinical response to pharmacotherapy often varies among individual patients, ranging from beneficial effect to even fatal adverse drug reactions (ADRs.)  Pharmacogenomics (PGs) approach, where variation in drug effects is studied as a function of genetic variation at genomic level holds the promise of precision medicine through elucidating genetic determinants responsible for drug therapy outcomes. One of the ultimate goals of genetic research is to inform genomic medicine to enable more effective strategies of disease prevention and treatment. Against this backdrop, as genotyping technology has progressed, being an agnostic approach, GWAS have matured into efficient and effective tools for interrogating the entire genome for genetic variants (most commonly single nucleotide polymorphisms, SNPs) that are associated with a specific disease or a drug response phenotype. Over the last decade personalized genotype-based approach through pharmacogenetic testing is gaining prominence and evidence-based guidelines have been developed accordingly to advise prescribers in daily clinical practice on an optimal drug regimen according to genotypes or predicted phenotypes.  However, most of the time the conventional approach of drug response prediction can not address the polygenic nature of a drug response. Recently PGx study has harnessed the power of GWAS that has enabled the construction of polygenic risk score (PRS) facilitating the field to move beyond the study of candidate genes to scanning hundreds of thousands of genetic markers for each subject. PRS which based on the cumulative effects of numerous SNPs across the genome, mostly developed in the context of predicting the risk of a specific disease and allowing for patient stratification (eg, for cancer, cardiovascular disease, and type 2 diabetes mellitus). However, due to the polygenic nature of most observable drug response outcomes arising from the interplay of multiple genetic and environmental factors, PRS can also be leveraged to assess the genetic susceptibility of an individual to drug-related adverse events, treatment outcomes, and dosage requirements. In sum, the integration of PRS into clinical practice is still evolving, but it holds great potential for enhancing precision medicine by enabling targeted interventions and risk reduction strategies. 

Simone Vanoni, PharmGenetix GmbH, Anif/Niederalm, Austria

Currently, there are 163 distinct CYP2D6 star alleles officially catalogued in PharmVar, of which 7% are normal function, 10% are decrease or severely decrease of function, 25% are no function, and a whopping 58% are unknown function. With variant testing coverage ever expanding, the latter statistic represents a serious challenge in predicting phenotype from genotype. In an attempt to bridge this knowledge gap, we have developed an in vitro functional assay using a synthetic CYP2D6 substrate and validated it with atomoxetine, a well-known non-synthetic CYP2D6 substrate

Interindividual variability and pharmacogenomics: possible ways forward

Munir Pirmohamed,
Department of Pharmacology and Therapeutics, The Wolfson Centre for Personalised Medicine, University of Liverpool, L69 3GL, UK

Interindividual variability in drug response (efficacy and/or safety) is the rule rather than the exception in clinical practice. This can be due to a number of clinical factors and genomic factors. While a genomic variant may play a major role in determining drug response, it is important not to forget about the clinical factors which also contribute. Our approach at present is unimodal (i.e. we look at one factor at a time), and we need to move to more multimodal approaches (combining genetic and clinical factors). This becomes even more important as our population grows older (with decreasing renal and hepatic function) and there is an increasing prevalence of multimorbidity and consequently polypharmacy. This puts them at higher risk of adverse drug reactions, drug interactions, and worse clinical outcomes. Tackling this will be a challenge, but genomics will add value to the usual approach of undertaking medicines reviews. In such populations, panel genotyping is likely to be the most pragmatic, and will enable us to move from reactive to pre-emptive genotyping approaches. This will be more convenient for the clinician and for the patient, as it fits in seamlessly with the current clinical pathways. The questions which need to be evaluated is when people should have panel genotyping undertaken, and which will be the most clinically-effective and cost-effective approach.

Pharmacogenetics in clinical practice: experiences and challenges  

Ron H.N. van Schaik  

Erasmus MC, IFCC Expert laboratory for Pharmacogenetics, Dept. Clinical Chemistry, Rotterdam, The Netherlands  

The discovery that the metabolism of drugs is highly variable between patients, but can be predicted by DNA analysis of genes encoding drug metabolizing enzymes or drug transporters, encouraged the field for taking pharmacogenetics into clinical care. Focus was initially on cytochrome P450 enzymes: genotyping for CYP2D6 (involved in the metabolism of 20% of all drugs, but being deficient in 5-10% of the population) and CYP2C19 (involved in metabolism of 20% of drugs, while 2-11% of the population is deficient) could benefit psychiatry, cardiology and oncology therapies. Currently, 15-30 genes for drug metabolizing enzymes and drug transporters that can be (and are) used clinically for optimizing personalization of drug therapy. The field is growing mature as a clinical diagnostic tool, in which we see a shift from reactive single-gene testing to pre-emptive panel testing. In this presentations, successes and challenges for implementing pharmacogenetics into routine health care will be highlighted.  

Patient-centered drug therapy integrating Pharmacogenetics and e-health (www.proyectomedea.es )  

Adrián Llerena 

Pharmacogenetics and Personalized Medicine Unit, INUBE Extremadura Biosanitary Research Institute, Spain. 

MedeA is a strategy of Clinical implementation of Pharmacogenetics Personalized/Precision Medicine in Extremadura Public Health Care (1.1. M inhabitants). MedeA will integrate genetic analysis with other relevant information to individualize drug prescription in regular clinical practice within the context of e-health. The main characteristics are the integration of pharmacogenetics analyses (1) plus relevant (2) clinical information for drug prescription based on electronic medical record tools (drug prescription record, physiopathology, biochemistry, hematological analyses, etc) and (3) drug-drug interactions in order to develop a Decision-Supporting Tool (Personalized Prescription System -PoPS-) that will be connected to the Electronical Medical Record System. The Implementation programe will be evaluated in a large sample of (5.000 patients). As a consecuence the PPS will be updated to refine the algorithm and later extended for the whole population. To the best of our knowledge this is a very unique Project covering, not only genetics but all factors influencing drug treatment in an algorithm that allow the choice of the better drug for a given patient in the context of drug polytherapy, using AI for evaluation and integrated in the e-health system. Both health professionals and patients will be able to check what is the better drug choice using all information available in the electronical medical record. 

Pharmacogenetics and its appliance in clinical daily life – experiences from the Central Laboratory of the University Hospital of Innsbruck 

Andrea Griesmacher, Christian Irsara, Lorin Loacker 

Central Institute of Clinical and Chemical Laboratory Diagnostics, University Hospital of Innsbruck, Austria 

Introduction. The pharmacogenetic genotype plays a major role in interindividual variability in drug response, absorption, metabolism and pharmacodynamics. Thus, pharmacogenetic testing enables optimizing drug efficacy and minimizing drug toxicity. Although there is increasing awareness for this topic, a regular appliance in clinical daily life is not the standard yet. 

Methods. In the central laboratory of the University Hospital of Innsbruck, we have performed several pharmacogenetic parameters since 2012 by PCR-based methods. Among those especially genotyping of Thiopurine Methyltransferase (TPMT) for Azathioprine and 6-Mercaptopurine therapy as well as Dihydropyrimidine Dehydrogenase (DPD) for 5-Fluorouracil or Capecitabine therapy are increasingly requested by haematologists, oncologists and dermatologists. On the other hand, cytochrome P450 genotyping such as CYP3A4/A5, CYP2C9 or CYP2C19 is only rarely in demand for selected cases.

Results. Over the last years the testing requests in the central laboratory of the University Hospital of Innsbruck rose markedly for DPD (2018: 3, 2019: 8, 2020: 84, 2021: 268, 2022: 301) and to a lesser extent for the CYP P450 enzymes (6, 7, 35, 17, 14) while those for TPMT (235, 280, 214, 265, 301) essentially remained stable. 

Conclusion. In conclusion, we see an increasing awareness for pharmacogenetic testing. Especially the need for DPD testing has been rising, since several clinical guidelines have recommended a general screening before 5-FU therapy. However, there still is room for improvement and education of medical staff should be continued. Regarding CYP P450 2C9 and CYP2C19 we expect a further increase in the requests due to the newly introduced drugs Siponimod and Mevacamten respectively.