The poly-omics of ageing through individual-based metabolic modelling

Abstract Background Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype. Results We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor. Conclusions We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells

Book of Abstracts XVIII Congreso de Biometría CEBMADRID

Abstracts of the XVIII Congreso de Biometría CEBMADRID held from 25 to 27 May in MadridInteractive modelling and prediction of patient evolution via multistate models / Leire Garmendia Bergés, Jordi Cortés Martínez and Guadalupe Gómez Melis : This research was funded by the Ministerio de Ciencia e Innovación (Spain) [PID2019104830RBI00]; and the Generalitat de Catalunya (Spain) [2017SGR622 and 2020PANDE00148].Operating characteristics of a model-based approach to incorporate non-concurrent controls in platform trials / Pavla Krotka, Martin Posch, Marta Bofill Roig : EU-PEARL (EU Patient-cEntric clinicAl tRial pLatforms) project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 853966. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development non-profit organisation, Spring works Therapeutics Inc.Modeling COPD hospitalizations using variable domain functional regression / Pavel Hernández Amaro, María Durbán Reguera, María del Carmen Aguilera Morillo, Cristobal Esteban Gonzalez, Inma Arostegui : This work is supported by the grant ID2019-104901RB-I00 from the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033.Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain / Jorge Castillo-Mateo, Alan E. Gelfand, Jesús Asín, Ana C. Cebrián / Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain : This work was partially supported by the Ministerio de Ciencia e Innovación under Grant PID2020-116873GB-I00; Gobierno de Aragón under Research Group E46_20R: Modelos Estocásticos; and JC-M was supported by Gobierno de Aragón under Doctoral Scholarship ORDEN CUS/581/2020.Estimation of the area under the ROC curve with complex survey data / Amaia Iparragirre, Irantzu Barrio, Inmaculada Arostegui : This work was financially supported in part by IT1294-19, PID2020-115882RB-I00, KK-2020/00049. The work of AI was supported by PIF18/213.INLAMSM: Adjusting multivariate lattice models with R and INLA / Francisco Palmí Perales, Virgilio Gómez Rubio and Miguel Ángel Martínez Beneito : This work has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/000491, funded by Consejería de Educación, Cultura y Deportes (Junta de Comunidades de Castilla-La Mancha, Spain) and FEDER, grant MTM2016-77501-P, funded by Ministerio de Economía y Competitividad (Spain), grant PID2019-106341GB-I00 from Ministerio de Ciencia e Innovación (Spain) and a grant to support research groups by the University of Castilla-La Mancha (Spain). F. Palmí-Perales has been supported by a Ph.D. scholarship awarded by the University of Castilla-La Mancha (Spain)

Investigating the Spatial and Statistical Dimensions of Mortuary Choice in the Historical-Period Old City Cemetery in Roslyn, Washington

The historical-period Old City Cemetery in Roslyn, Washington contains individuals from diverse social backgrounds and exhibits considerable variation in mortuary expression. As such, the Old City Cemetery offers a unique opportunity to explore potential differences in social group mortuary practices spatially and statistically. Using burials in Roslyn’s Old City Cemetery, this project developed a methods framework to assess mortuary practice through demographics, burial location, and monument/plot attributes. I tested correlations between demographics and mortuary expression using spatial-statistical cluster analysis (Ripley’s K-Function), spatial density analysis (Kernel Density Estimation), and non-spatial statistical significance assessments (Factor analysis and Pearson’s R), and identified several demographic-based mortuary trends. Similarities in some ages and nationalities were significantly associated with choice in burial location and monument/plot attributes in the Old City Cemetery, suggesting social dynamics in historical-period Roslyn valued these demographic designations. I did not identify any significant trends in choice between similar occupations or causes of death. Cemetery chronology and known decade-based norms appeared partially responsible for burial location siting and choice in monument or plot attributes. This project serves to recommend the viability and importance of incorporating both spatial and statistical dimensions into mortuary analysis of historical-period cemeteries, and I offer that this framework can be applied in such contexts to investigate mortuary expression and social dynamics

Exploring the relationship between age and health conditions using electronic health records: from single diseases to multimorbidities

Background Two enormous challenges facing healthcare systems are ageing and multimorbidity. Clinicians, policymakers, healthcare providers and researchers need to know “who gets which diseases when” in order to effectively prevent, detect and manage multiple conditions. Identification of ageing-related diseases (ARDs) is a starting point for research into common biological pathways in ageing. Examining multimorbidity clusters can facilitate a shift from the single-disease paradigm that pervades medical research and practice to models which reflect the reality of the patient population. Aim To examine how age influences an individual’s likelihood of developing single and multiple health conditions over the lifecourse. Methods and Outputs I used primary care and hospital admission electronic health records (EHRs) of 3,872,451 individuals from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care (HES-APC) dataset in England from 1 April 2010 to 31 March 2015. In collaboration with Professor Aroon Hingorani, Dr Osman Bhatti, Dr Shanaz Husain, Dr Shailen Sutaria, Professor Dorothea Nitsch, Mrs Melanie Hingorani, Dr Constantinos Parisinos, Dr Tom Lumbers and Dr Reecha Sofat, I derived the case definitions for 308 clinically important health conditions, by harmonising Read, ICD-10 and OPCS-4 codes across primary and secondary care records in England. I calculated the age-specific incidence rate, period prevalence and median age at first recorded diagnosis for these conditions and described the 50 most common diseases in each decade of life. I developed a protocol for identifying ARDs using machine-learning and actuarial techniques. Finally, I identified highly correlated multimorbidity clusters and created a tool to visualise comorbidity clusters using a network approach. Conclusions I have developed case definitions (with a panel of clinicians) and calculated disease frequency estimates for 308 clinically important health conditions in the NHS in England. I have described patterns of ageing and multimorbidity using these case definitions, and produced an online app for interrogating comorbidities for an index condition. This work facilitates future research into ageing pathways and multimorbidity

Alcohol-induced temporal transcriptome remodeling in the prefrontal cortex in a mouse model of alcohol dependence

textAlcohol dependence (alcoholism) is a complex disease influenced by both environmental factors and genetic predisposition. Mouse models have been used to study many alcohol dependence-related traits and the genetics that underlie them. Two of the most commonly used mice in alcohol research are the C57BL/6J (B6) and DBA/2J (D2) inbred strains, which diverge on several alcohol-related traits including the development of acute physical dependence. Here we utilized the B6 and D2 mice as a genetic model of acute physical dependence, coupled with mRNA Differential Display (DD) and cDNA microarray analysis, to uncover the transcriptional response of the brain to an acute dose of alcohol as a function of time. About 150 genetically divergent and alcohol-responsive genes were identified between the whole brains of B6 and D2 mice using DD and were added as additional targets to the mouse microarrays. Microarray analysis of the prefrontal cortex of B6 and D2 mice revealed strain-specific, acute alcohol-responsive transcriptome remodeling manifested as temporal patterns of gene expression. Distinct expression patterns were identified for physiologically relevant alcohol-related consequences including intoxication, withdrawal and neuroadaptation. In silico characterization of the differentially expressed genes showed genotype dependent and independent transcriptional regulation and functional classification. In addition, categorization of differentially expressed genes by their cellular profiles revealed that some of the genes were known to be more highly expressed in either excitatory or inhibitory neuronal cell types. Our results indicate that the B6 and D2 prefrontal cortices have very different cellular and molecular responses to acute alcohol exposure. The specific roles that the genes identified in this study may play in mediating the divergent alcohol-related behavior between the strains warrant further study.Cellular and Molecular Biolog

Classification of Distal Growth Plate Ossification States of the Radius Bone Using a Dedicated Ultrasound Device and Machine Learning Techniques for Bone Age Assessments

X-ray imaging, based on ionizing radiation, can be used to determine bone age by examining distal growth plate fusion in the ulna and radius bones. Legal age determination approaches based on ultrasound signals exist but are unsuitable to reliably determine bone age. We present a low-cost, mobile system that uses one-dimensional ultrasound radio frequency signals to obtain a robust binary classifier enabling the determination of bone age from data of girls and women aged 9 to 24 years. These data were acquired as part of a clinical study conducted with 148 subjects. Our system detects the presence or absence of the epiphyseal plate by moving ultrasound array transducers along the forearm, measuring reflection and transmission signals. Even though classical digital signal processing methods did not achieve a robust classifier, we achieved an F1 score of approximately 87% for binary classification of completed bone growth with machine learning approaches, such as the gradient boosting machine method CatBoost. We demonstrate that our ultrasound system can classify the fusion of the distal growth plate of the radius bone and the completion of bone growth with high accuracy. We propose a non-ionizing alternative to established X-ray imaging methods for this purpose

A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data

A prognostic model is a formal combination of multiple predictors from which risk probability of a specific diagnosis can be modelled for patients. Prognostic models have become essential instruments in medicine. The models are used for prediction purposes of guiding doctors to make a smart diagnosis, patient-specific decisions or help in planning the utilization of resources for patient groups who have similar prognostic paths. Dynamic Bayesian networks theoretically provide a very expressive and flexible model to solve temporal problems in medicine. However, this involves various challenges due both to the nature of the clinical domain, and the nature of the DBN modelling and inference process itself. The challenges from the clinical domain include insufficient knowledge of temporal interactions of processes in the medical literature, the sparse nature and variability of medical data collection, and the difficulty in preparing and abstracting clinical data in a suitable format without losing valuable information in the process. Challenges about the DBN methodology and implementation include the lack of tools that allow easy modelling of temporal processes. Overcoming this challenge will help to solve various clinical temporal reasoning problems. In this thesis, we addressed these challenges while building a temporal network with explanations of the effects of predisposing factors, such as age and gender, and the progression information of all diagnoses using claims data from an insurance company in Kenya. We showed that our network could differentiate the possible probability exposure to a diagnosis given the age and gender and possible paths given a patient's history. We also presented evidence that the more patient history is provided, the better the prediction of future diagnosis