La médecine de précision (MP) est un nouveau paradigme qui révolutionne la pratique médicale actuelle et remodèle complètement la médecine de demain. La MP aspire à placer le patient au centre du parcours de soins en y intégrant les données médicales et biologiques individuelles tout en tenant compte de la grande diversité interindividuelle. La prédiction des états pathologiques chez les patients nécessite une compréhension dynamique et systémique. Les erreurs innées du métabolisme (EIM) sont des troubles génétiques résultant de défauts dans une voie biochimique donnée en raison de la déficience d'une enzyme, de son cofacteur ou d’un transporteur. Les EIM ne sont plus considérées comme des maladies monogéniques mais tendent à être plus complexes et multifactorielles. Le profil métabolomique permet le dépistage d’une pathologie, la recherche de biomarqueurs et l’exploration des voies métaboliques mises en jeu. Dans ce travail de thèse, nous avons utilisé l’approche métabolomique qui est particulièrement pertinente pour les EIM compte tenu de leur physiopathologie de base qui est étroitement liée au métabolisme. Ce travail a permis la mise en place d’une méthodologie métabolomique non ciblée basée sur une stratégie analytique multidimensionnelle comportant la spectrométrie de masse à haute résolution couplée à la chromatographie liquide ultra-haute performance et la mobilité ionique. La mise en place de la méthodologie de prétraitement, d’analyse et d’exploitation des données générées avec des outils de design expérimental et d’analyses multivariées ont été aussi établies. Enfin, cette approche a été appliquée pour l’exploration des EIM avec les mucopolysaccharidoses comme preuve de concept. Les résultats obtenus suggèrent un remodelage majeur du métabolisme des acides aminés dans la mucopolysaccharidose de type I. En résumé, la métabolomique pourrait être un outil complémentaire pertinent en appui à l’approche génomique dans l’exploration des EIM. / The new field of precision medicine is revolutionizing current medical practice and reshaping future medicine. Precision medicine intends to put the patient as the central driver of healthcare by broadening biological knowledge and acknowledging the great diversity of individuals. The prediction of physiological and pathological states in patients requires a dynamic and systemic understanding of these interactions. Inborn errors of metabolism (IEM) are genetic disorders resulting from defects in a given biochemical pathway due to the deficiency of an enzyme, its cofactor or a transporter. IEM are no longer considered to be monogenic diseases, which adds another layer of complexity to their characterization and diagnosis. To meet this need for faster screening, the metabolic profile can be a promising candidate given its ability in disease screening, biomarker discovery and metabolic pathway investigation. In this thesis, we used a metabolomic approach which is particularly relevant for IEM given their basic pathophysiology that is tightly related to metabolism. This thesis allowed the implementation of an untargeted metabolomic methodology based on a multidimensional analytical strategy including high-resolution mass spectrometry coupled with ultra-high-performance liquid chromatography and ion mobility. This work also set a methodology for preprocessing, analysis and interpretation of the generated data using experimental design and multivariate data analysis. Finally, the strategy is applied to the exploration of IEM with mucopolysaccharidoses as a proof of concept. The results suggest a major remodeling of the amino acid metabolisms in mucopolysaccharidosis type I. In summary, metabolomic is a relevant complementary tool to support the genomic approach in the functional investigations and diagnosis of IEM.

Despite major advances in treatment, pediatric cancers in the 5-16 age group remain the most common cause of disease death, and one out of eight children with cancer will not survive. Among children that do survive, some 60% suffer from late effects such as cancer recurrence and increased risk of obesity. This paper will provide a broad overview of pediatric oncology in the context of systems medicine. Systems medicine utilizes an integrative approach that relies on patient information gained from omics technology. A major goal of a systems medicine is to provide personalized medicine that optimizes positive outcomes while minimizing deleterious short and long-term sideeffects. There is an ever increasing development of effective cancer drugs, but a major challenge lies in picking the most effective drug for a particular patient. As detailed below, high-throughput omics technology holds the promise of solving this problem. Omics includes genomics, epigenomics, and proteomics. System medicine integrates omics information and provides detailed insights into disease mechanisms which can then inform the optimal treatment strategy.

No description available.

Background: The blood transcriptome is expected to provide a detailed picture of an organism’s physiological state with potential outcomes for applications in medical diagnostics and molecular and epidemiological research.We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration. Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes. Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications.

Weed resistance to control measures, particularly herbicides, is a growing problem in agriculture. In the case of herbicides, resistance is sometimes connected to genetic changes that directly affect the target site of the herbicide. Other cases are less straightforward where resistance arises without such a clear-cut mechanism. Understanding the genetic and gene regulatory mechanisms that may lead to the rapid evolution of resistance in weedy species is critical to securing our food supply. To study this phenomenon, we exposed young Arabidopsis plants to sublethal levels of one of four weed management stressors, glyphosate herbicide, trifloxysulfuron herbicide, mechanical clipping, and shading. To evaluate responses to these stressors we collected data on gene expression and regulation via epigenetic modification (methylation) and small RNA (sRNA). For all of the treatments except shade, the stress was limited in duration, and the plants were allowed to recover until flowering, to identify changes that persist to reproduction. At flowering, DNA for methylation bisulfite sequencing, RNA, and sRNA were extracted from newly formed rosette leaf tissue. Analyzing the individual datasets revealed many differential responses when compared to the untreated control for gene expression, methylation, and sRNA expression. All three measures showed increases in differential abundance that were unique to each stressor, with very little overlap between stressors. Herbicide treatments tended to exhibit the largest number of significant differential responses, with glyphosate treatment most often associated with the greatest differences and contributing to overlap. To evaluate how large datasets from methylation, gene expression, and sRNA analyses could be connected and mined to link regulatory information with changes in gene expression, the information from each dataset and for each gene was united in a single large matrix and mined with classification algorithms. Although our models were able to differentiate patterns in a set of simulated data, the raw datasets were too noisy for the models to consistently identify differentially expressed genes. However, by focusing on responses at a local level, we identified several genes with differential expression, differential sRNA, and differential methylation. While further studies will be needed to determine whether these epigenetic changes truly influence gene expression at these sites, the changes detected at the treatment level could prime the plants for future incidents of stress, including herbicides. / Doctor of Philosophy / Growing resistance to herbicides, particularly glyphosate, is one of the many problems facing agriculture. The rapid rise of resistance across herbicide classes has caused some to wonder if there is a mechanism of adaptation that does not involve mutations. Epigenetics is the study of changes in the phenotype that cannot be attributed to changes in the genotype. Typically, studies revolve around two features of the chromosomes: cytosine methylation and histone modifications. The former can influence how proteins interact with DNA, and the latter can influence protein access to DNA. Both can affect each other in self-reinforcing loops. They can affect gene expression, and DNA methylation can be directed by small RNA (sRNA), which can also influence gene expression through other pathways. To study these processes and their role in abiotic stress response, we aimed to analyze sRNA, RNA, and DNA from Arabidopsis thaliana plants under stress. The stresses applied were sublethal doses of the herbicides, glyphosate and trifloxysulfuron, as well as mechanical clipping and shade to represent other weed management stressors. The focus of the project was to analyze these responses individually and together to find epigenetic responses to stresses routinely encountered by weeds. We tested RNA for gene expression changes under our stress conditions and identified many, including some pertaining to DNA methylation regulation. The herbicide treatments were associated with upregulated defense genes and downregulated growth genes. Shade treated plants had many downregulated defense and other stress response genes. We also detected differential methylation and sRNA responses when compared to the control plants. Changes to methylation and sRNA only accounted for about 20% of the variation in gene expression. While attempting to link the epigenetic process of methylation to gene expression, we connected all the data sets and developed computer programs to try to make correlations. While these methods worked on a simulated dataset, we did not detect broad patterns of changes to epigenetic pathways that correlated strongly with gene expression in our experiment's data. There are many factors that can influence gene expression that could create noise that would hinder the algorithms' abilities to detect differentially expressed genes. This does not, however, rule out the possibility of epigenetic influence on gene expression in local contexts. Through scoring the traits of individual genes, we found several that interest us for future studies.

Most of microorganisms are ubiquitously able to live in both planktonic and biofilm states, which can be applied to dissolve the energy and environmental issues (e.g., producing biofuels and purifying waste water), but can also lead to serious public health problems. To better harness microorganisms, plenty of studies have been implemented to investigate the metabolism of planktonic and/or biofilm cells via multi-omics approaches (e.g., transcriptomics and proteomics analysis). However, these approaches are limited to provide the direct description of intracellular metabolism (e.g., metabolic fluxes) of microorganisms. Therefore, in this study, I have applied computational modeling approaches (i.e., 13C assisted pathway and flux analysis, flux balance analysis, and machine learning) to both planktonic and biofilm cells for better understanding intracellular metabolisms and providing valuable biological insights. First, I have summarized recent advances in synergizing 13C assisted pathway and flux analysis and metabolic engineering. Second, I have applied 13C assisted pathway and flux analysis to investigate the intracellular metabolisms of planktonic and biofilm cells. Various biological insights have been elucidated, including the metabolic responses under mixed stresses in the planktonic states, the metabolic rewiring in homogenous and heterologous chemical biosynthesis, key pathways of biofilm cells for electricity generation, and mechanisms behind the electricity generation. Third, I have developed a novel platform (i.e., omFBA) to integrate multi-omics data with flux balance analysis for accurate prediction of biological insights (e.g., key flux ratios) of both planktonic and biofilm cells. Fourth, I have designed a computational tool (i.e., CRISTINES) for the advanced genome editing tool (i.e., CRISPR-dCas9 system) to facilitate the sequence designs of guide RNA for programmable control of metabolic fluxes. Lastly, I have also accomplished several outreaches in metabolic engineering. In summary, during my Ph.D. training, I have systematically applied computational modeling approaches to investigate the microbial metabolisms in both planktonic and biofilm states. The biological findings and computational tools can be utilized to guide the scientists and engineers to derive more productive microorganisms via metabolic engineering and synthetic biology. In the future, I will apply 13C assisted pathway analysis to investigate the metabolism of pathogenic biofilm cells for reducing their antibiotic resistance. / Ph. D. / Most of microorganisms are ubiquitously able to live in both planktonic and biofilm states (i.e., floating in a flow and anchoring on a surface, respectively), which can be applied to dissolve the energy and environmental issues (e.g., producing biofuels and purifying waste water), but can also lead to serious public health problems (e.g., chronic infections). Therefore, deciphering the metabolism of both planktonic and biofilm cells are of great importance to better harness microorganism. Plenty of studies have been implemented to investigate the metabolism of planktonic and/or biofilm cells by measuring the abundances of single type of biological components (e.g., gene expression and proteins). However, these approaches are limited to provide the direct description of intracellular metabolism (e.g., enzyme activities) of microorganisms. Therefore, in this study, I have applied computational modeling approaches to both planktonic and biofilm cells for providing valuable biological insights (e.g., enzyme activities). The biological insights include 1) how planktonic cells response to mixed stresses (e.g., acids and organics) 2) how planktonic cells produce various chemicals, and 3) how biofilm cells generate electricity by rewiring the intracellular metabolic pathways. I also developed a novel platform to utilize multiple types of biological data for improving the prediction accuracy of biological insights of both planktonic and biofilm cells. In addition, I designed a computational tool to facilitate the sequence designs of an advanced genome editing tool for precisely controlling the corresponding enzyme activities. Lastly, I have also accomplished several outreaches in metabolic engineering. In summary, during my Ph.D. training, I have systematically applied computational modeling approaches to investigate the microbial metabolisms in both planktonic and biofilm states. The biological findings and computational tools can be utilized to guide the metabolic engineered to derive more productive microorganisms via metabolic engineering and synthetic biology. In the future, I plan to investigate how the pathogenic biofilm cells improve their antibiotic resistance and attempt to reduce such strong resistance.

Tesis por compendio / [ES] En los últimos años, el aprendizaje profundo (DL) se ha convertido en una de las principales áreas de la inteligencia artificial (IA), impulsado principalmente por el avance en la capacidad de procesamiento. Los algoritmos basados en DL han logrado resultados asombrosos en la comprensión y manipulación de diversos tipos de datos, incluyendo imágenes, señales de habla y texto. La revolución digital del sector sanitario ha permitido la generación de nuevas bases de datos, lo que ha facilitado la implementación de modelos de DL bajo el paradigma de aprendizaje supervisado. La incorporación de estos métodos promete mejorar y automatizar la detección y el diagnóstico de enfermedades, permitiendo pronosticar su evolución y facilitar la aplicación de intervenciones clínicas de manera más efectiva. Una de las principales limitaciones de la aplicación de algoritmos de DL supervisados es la necesidad de grandes bases de datos anotadas por expertos, lo que supone una barrera importante en el ámbito médico. Para superar este problema, se está abriendo un nuevo campo de desarrollo de estrategias de aprendizaje no supervisado o débilmente supervisado que utilizan los datos disponibles no anotados o débilmente anotados. Estos enfoques permiten aprovechar al máximo los datos existentes y superar las limitaciones de la dependencia de anotaciones precisas. Para poner de manifiesto que el aprendizaje débilmente supervisado puede ofrecer soluciones óptimas, esta tesis se ha enfocado en el desarrollado de diferentes paradigmas que permiten entrenar modelos con bases de datos débilmente anotadas o anotadas por médicos no expertos. En este sentido, se han utilizado dos modalidades de datos ampliamente empleadas en la literatura para estudiar diversos tipos de cáncer y enfermedades inflamatorias: datos ómicos e imágenes histológicas. En el estudio sobre datos ómicos, se han desarrollado métodos basados en deep clustering que permiten lidiar con las altas dimensiones inherentes a este tipo de datos, desarrollando un modelo predictivo sin la necesidad de anotaciones. Al comparar el método propuesto con otros métodos de clustering presentes en la literatura, se ha observado una mejora en los resultados obtenidos. En cuanto a los estudios con imagen histológica, en esta tesis se ha abordado la detección de diferentes enfermedades, incluyendo cáncer de piel (melanoma spitzoide y neoplasias de células fusocelulares) y colitis ulcerosa. En este contexto, se ha empleado el paradigma de multiple instance learning (MIL) como línea base en todos los marcos desarrollados para hacer frente al gran tamaño de las imágenes histológicas. Además, se han implementado diversas metodologías de aprendizaje, adaptadas a los problemas específicos que se abordan. Para la detección de melanoma spitzoide, se ha utilizado un enfoque de aprendizaje inductivo que requiere un menor volumen de anotaciones. Para abordar el diagnóstico de colitis ulcerosa, que implica la identificación de neutrófilos como biomarcadores, se ha utilizado un enfoque de aprendizaje restrictivo. Con este método, el coste de anotación se ha reducido significativamente al tiempo que se han conseguido mejoras sustanciales en los resultados obtenidos. Finalmente, considerando el limitado número de expertos en el campo de las neoplasias de células fusiformes, se ha diseñado y validado un novedoso protocolo de anotación para anotaciones no expertas. En este contexto, se han desarrollado modelos de aprendizaje profundo que trabajan con la incertidumbre asociada a dichas anotaciones. En conclusión, esta tesis ha desarrollado técnicas de vanguardia para abordar el reto de la necesidad de anotaciones precisas que requiere el sector médico. A partir de datos débilmente anotados o anotados por no expertos, se han propuesto novedosos paradigmas y metodologías basados en deep learning para abordar la detección y diagnóstico de enfermedades utilizando datos ómicos e imágenes histológicas. / [CA] En els últims anys, l'aprenentatge profund (DL) s'ha convertit en una de les principals àrees de la intel·ligència artificial (IA), impulsat principalment per l'avanç en la capacitat de processament. Els algorismes basats en DL han aconseguit resultats sorprenents en la comprensió i manipulació de diversos tipus de dades, incloent-hi imatges, senyals de parla i text. La revolució digital del sector sanitari ha permés la generació de noves bases de dades, la qual cosa ha facilitat la implementació de models de DL sota el paradigma d'aprenentatge supervisat. La incorporació d'aquests mètodes promet millorar i automatitzar la detecció i el diagnòstic de malalties, permetent pronosticar la seua evolució i facilitar l'aplicació d'intervencions clíniques de manera més efectiva. Una de les principals limitacions de l'aplicació d'algorismes de DL supervisats és la necessitat de grans bases de dades anotades per experts, la qual cosa suposa una barrera important en l'àmbit mèdic. Per a superar aquest problema, s'està obrint un nou camp de desenvolupament d'estratègies d'aprenentatge no supervisat o feblement supervisat que utilitzen les dades disponibles no anotades o feblement anotats. Aquests enfocaments permeten aprofitar al màxim les dades existents i superar les limitacions de la dependència d'anotacions precises. Per a posar de manifest que l'aprenentatge feblement supervisat pot oferir solucions òptimes, aquesta tesi s'ha enfocat en el desenvolupat de diferents paradigmes que permeten entrenar models amb bases de dades feblement anotades o anotades per metges no experts. En aquest sentit, s'han utilitzat dues modalitats de dades àmpliament emprades en la literatura per a estudiar diversos tipus de càncer i malalties inflamatòries: dades ómicos i imatges histològiques. En l'estudi sobre dades ómicos, s'han desenvolupat mètodes basats en deep clustering que permeten bregar amb les altes dimensions inherents a aquesta mena de dades, desenvolupant un model predictiu sense la necessitat d'anotacions. En comparar el mètode proposat amb altres mètodes de clustering presents en la literatura, s'ha observat una millora en els resultats obtinguts. Quant als estudis amb imatge histològica, en aquesta tesi s'ha abordat la detecció de diferents malalties, incloent-hi càncer de pell (melanoma spitzoide i neoplàsies de cèl·lules fusocelulares) i colitis ulcerosa. En aquest context, s'ha emprat el paradigma de multiple instance learning (MIL) com a línia base en tots els marcs desenvolupats per a fer front a la gran grandària de les imatges histològiques. A més, s'han implementat diverses metodologies d'aprenentatge, adaptades als problemes específics que s'aborden. Per a la detecció de melanoma spitzoide, s'ha utilitzat un enfocament d'aprenentatge inductiu que requereix un menor volum d'anotacions. Per a abordar el diagnòstic de colitis ulcerosa, que implica la identificació de neutròfils com biomarcadores, s'ha utilitzat un enfocament d'aprenentatge restrictiu. Amb aquest mètode, el cost d'anotació s'ha reduït significativament al mateix temps que s'han aconseguit millores substancials en els resultats obtinguts. Finalment, considerant el limitat nombre d'experts en el camp de les neoplàsies de cèl·lules fusiformes, s'ha dissenyat i validat un nou protocol d'anotació per a anotacions no expertes. En aquest context, s'han desenvolupat models d'aprenentatge profund que treballen amb la incertesa associada a aquestes anotacions. En conclusió, aquesta tesi ha desenvolupat tècniques d'avantguarda per a abordar el repte de la necessitat d'anotacions precises que requereix el sector mèdic. A partir de dades feblement anotades o anotats per no experts, s'han proposat nous paradigmes i metodologies basats en deep learning per a abordar la detecció i diagnòstic de malalties utilitzant dades *ómicos i imatges histològiques. Aquestes innovacions poden millorar l'eficàcia i l'automatització en la detecció precoç i el seguiment de malalties. / [EN] In recent years, deep learning (DL) has become one of the main areas of artificial intelligence (AI), driven mainly by the advancement in processing power. DL-based algorithms have achieved amazing results in understanding and manipulating various types of data, including images, speech signals and text. The digital revolution in the healthcare sector has enabled the generation of new databases, facilitating the implementation of DL models under the supervised learning paradigm. Incorporating these methods promises to improve and automate the detection and diagnosis of diseases, allowing the prediction of their evolution and facilitating the application of clinical interventions with higher efficacy. One of the main limitations in the application of supervised DL algorithms is the need for large databases annotated by experts, which is a major barrier in the medical field. To overcome this problem, a new field of developing unsupervised or weakly supervised learning strategies using the available unannotated or weakly annotated data is opening up. These approaches make the best use of existing data and overcome the limitations of reliance on precise annotations. To demonstrate that weakly supervised learning can offer optimal solutions, this thesis has focused on developing different paradigms that allow training models with weakly annotated or non-expert annotated databases. In this regard, two data modalities widely used in the literature to study various types of cancer and inflammatory diseases have been used: omics data and histological images. In the study on omics data, methods based on deep clustering have been developed to deal with the high dimensions inherent to this type of data, developing a predictive model without requiring annotations. In comparison, the results of the proposed method outperform other existing clustering methods. Regarding histological imaging studies, the detection of different diseases has been addressed in this thesis, including skin cancer (spitzoid melanoma and spindle cell neoplasms) and ulcerative colitis. In this context, the multiple instance learning (MIL) paradigm has been employed as the baseline in all developed frameworks to deal with the large size of histological images. Furthermore, diverse learning methodologies have been implemented, tailored to the specific problems being addressed. For the detection of spitzoid melanoma, an inductive learning approach has been used, which requires a smaller volume of annotations. To address the diagnosis of ulcerative colitis, which involves the identification of neutrophils as biomarkers, a constraint learning approach has been utilized. With this method, the annotation cost has been significantly reduced while achieving substantial improvements in the obtained results. Finally, considering the limited number of experts in the field of spindle cell neoplasms, a novel annotation protocol for non-experts has been designed and validated. In this context, deep learning models that work with the uncertainty associated with such annotations have been developed. In conclusion, this thesis has developed cutting-edge techniques to address the medical sector's challenge of precise data annotation. Using weakly annotated or non-expert annotated data, novel paradigms and methodologies based on deep learning have been proposed to tackle disease detection and diagnosis in omics data and histological images. These innovations can improve effectiveness and automation in early disease detection and monitoring. / The work of Rocío del Amor to carry out this research and to elaborate this dissertation has been supported by the Spanish Ministry of Universities under the FPU grant FPU20/05263. / Amor Del Amor, MRD. (2023). Deep Learning Strategies for Overcoming Diagnosis Challenges with Limited Annotations [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/200227 / Compendio

The development of predictive biomarkers in neuroscience is increasingly enabling bioprediction of adverse behavioural events, from psychosis to impulsive violent reaction. Because many brain-based disorders can be thought of as end-states of a long development, bioprediction carries immense therapeutic potential. In this thesis, I analyse issues raised by the development of bioprediction of brain-based disorder. I argue that ethical analysis of probabilities and risk information bioprediction provides is confounded by philosophical and social structures that have, until recently, functioned nominally well by assuming categorical (binary) concepts of disorder, especially regarding brain-disorder. Through an analysis of the philosophical concept of disorder, I argue that we can and ought to reorient disorder around probability of future harm and stratify disorder based on the magnitude of risk. Rejection of binary concepts in favour of this non-binary (probability-based) one enables synergy with bioprediction and circumnavigation of ethical concerns raised about proposed disorders of risk in psychiatry and neurology; I specifically consider psychosis and dementia risk. I then show how probabilistic thinking enables consideration of the implications of bioprediction for two areas salient in mental health: moral responsibility and justice. Using the example of epilepsy and driving as a model of obligations to protect others against risk of harm, I discuss how the development of bioprediction is poised to enhance moral responsibility. I then engage with legal cases and science surrounding a predictive biomarker of impulsive violent reaction to propose that bioprediction can sometimes rightly diminish responsibility. Finally, I show the relevance of bioprediction to theories of distributive justice that assign priority to the worse off. Because bioprediction enables the identification of those who are worse off in a way of which we have previously been ignorant, a commitment to assign priority to the worse off requires development of and equal access to biopredictive technologies.

Introduction: Type 2 diabetes continues to be one of the most common non-communicable diseases worldwide and complications due to type 2 diabetes, such as cardiovascular disease (CVD) can cause severe disability and even death. Despite advances in the development and validation of cardiovascular risk scores, those used in clinical practice perform inadequately for people with type 2 diabetes. Research has suggested that particular non-traditional biomarkers and novel omics data may provide additional value to risk scores over-and-above traditional predictors. Aims: To determine whether a small panel of non-traditional biomarkers improve prediction models based on a current cardiovascular risk score (QRISK2), either individually or in combination, in people with type 2 diabetes. Furthermore, to investigate a set of 228 metabolites and their associations with CVD, independent of well-established cardiovascular risk factors, in order to identify potential new predictors of CVD for future research. Methods: Analyses used the Edinburgh Type 2 Diabetes Study (ET2DS), a prospective cohort of 1066 men and women with type 2 diabetes aged 60-75 years at baseline. Participants were followed for eight years, during which time 205 had a cardiovascular event. Additionally, for omics analyses, four cohorts from the UCL-LSHTM-Edinburgh-Bristol (UCLEB) consortium were combined with the ET2DS. Across all studies, 1005 (44.73%) participants had CVD at baseline or experienced a cardiovascular event during follow-up. Results: In the ET2DS, higher levels of high sensitivity cardiac troponin (hs-cTnT) and N-terminal pro-brain natriuretic peptide (NT-proBNP) and lower levels of ankle brachial pressure index (ABI) were associated with incident cardiovascular events, independent of QRISK2 and pre-existing cardiovascular disease (odds ratios per one SD increase in biomarker 1.35 (95% CI: 1.13, 1.61), 1.23 (1.02, 1.49) and 0.86 (0.73, 1.00) respectively). The addition of each biomarker to a model including just QRISK2 variables improved the c-statistic, with the biggest increase for hs-cTnT (from 0.722 (0.681, 0.763) to 0.732 (0.690, 0.774)). When multiple biomarkers were considered in combination, the greatest c-statistic was found for a model which included ABI, hs-cTnT and gamma-glutamyl transpeptidase (0.740 (0.699, 0.781)). In the combined cohorts from the UCLEB consortium, a small number of high-density lipoprotein (HDL) particles were found to be significantly associated with CVD: concentration of medium HDL particles, total lipids in medium HDL, phospholipids in medium HDL and phospholipids in small HDL. These associations persisted after adjustment for a range of traditional cardiovascular risk factors including age, sex, blood pressure, smoking and HDL to total cholesterol ratio. Conclusions: In older people with type 2 diabetes, a range of non-traditional biomarkers increased predictive ability for cardiovascular events over-and-above the commonly used QRISK2 score, and a combination of biomarkers may provide the best improvement. Furthermore, a small number of novel omics biomarkers were identified which may further improve risk scores or provide better prediction than traditional lipid measurements such as HDL cholesterol.

No description available.