Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases

Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies—such as genomics, transcriptomics, proteomics and metabolomics—are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.

Different Pathophysiology and Outcomes of Heart Failure With Preserved Ejection Fraction Stratified by K-Means Clustering

Background: Stratified medicine may enable the development of effective treatments for particular groups of patients with heart failure with preserved ejection fraction (HFpEF); however, the heterogeneity of this syndrome makes it difficult to group patients together by common disease features. The aim of the present study was to find new subgroups of HFpEF using machine learning.Methods: K-means clustering was used to stratify patients with HFpEF. We retrospectively enrolled 350 outpatients with HFpEF. Their clinical characteristics, blood sample test results and hemodynamic parameters assessed by echocardiography, electrocardiography and jugular venous pulse, and clinical outcomes were applied to k-means clustering. The optimal k was detected using Hartigan's rule.Results: HFpEF was stratified into four groups. The characteristic feature in group 1 was left ventricular relaxation abnormality. Compared with group 1, patients in groups 2, 3, and 4 had a high mean mitral E/e′ ratio. The estimated glomerular filtration rate was lower in group 2 than in group 3 (median 51 ml/min/1.73 m2 vs. 63 ml/min/1.73 m2p < 0.05). The prevalence of less-distensible right ventricle and atrial fibrillation was higher, and the deceleration time of mitral inflow was shorter in group 3 than in group 2 (93 vs. 22% p < 0.05, 95 vs. 1% p < 0.05, and median 167 vs. 223 ms p < 0.05, respectively). Group 4 was characterized by older age (median 85 years) and had a high systolic pulmonary arterial pressure (median 37 mmHg), less-distensible right ventricle (89%) and renal dysfunction (median 54 ml/min/1.73 m2). Compared with group 1, group 4 exhibited the highest risk of the cardiac events (hazard ratio [HR]: 19; 95% confidence interval [CI] 8.9–41); group 2 and 3 demonstrated similar rates of cardiac events (group 2 HR: 5.1; 95% CI 2.2–12; group 3 HR: 3.7; 95%CI, 1.3–10). The event-free rates were the lowest in group 4 (p for trend < 0.001).Conclusions: K-means clustering divided HFpEF into 4 groups. Older patients with HFpEF may suffer from complication of RV afterload mismatch and renal dysfunction. Our study may be useful for stratified medicine for HFpEF.

SAT0506 MULTIPLE PATTERNS OF ‘RESPONSE’ TO METHOTHREXATE IDENTIFIED IN A NATIONAL JUVENILE IDIOPATHIC ARTHRITIS COHORT

Background:Disease activity following treatment for JIA is currently understood in terms of ‘response’ or ‘non-response’. This state is usually defined using composite measures such as the ACR Pedi scores or cut-offs on the Juvenile Arthritis Disease Activity Scores (JADAS). However, response is a complex state and it is likely that separate, identifiable clusters of children and young people (CYP) have different, varying levels of response across the individual measures of JIA disease activity. Identifying these clusters may facilitate stratified medicine in JIA.Objectives:To identify clusters of CYP with distinct patterns of change across the individual JADAS components following MTX initiation for JIA.Methods:MTX-naïve CYP enrolled into the MTX cohorts of the British Society for Paediatric and Adolescent Rheumatology Etanercept Cohort Study or the UK Biologics for Children with Rheumatic Diseases registers before January 2018 were selected. JADAS components (active joint count to 71, physician global assessment (0-100mm), parent global evaluation (0-100mm) and ESR (mm/hr)) were collected at MTX start and at (approximately) 6- and 12-month follow-ups. Outcomes were Log1p transformed for analysis and all outcome data were censored following start of a biologic. CYP were excluded if they had clinically inactive disease at MTX initiation, initiated a biologic within a month of MTX or had no available JADAS data at any time point.Multivariate group-based trajectory models explored MTX response clusters over the first year following MTX initiation using censored-normal models. Linear, quadratic and cubic polynomials were tested, with one to ten trajectories tested within each polynomial group. Optimal models within each polynomial group were selected using Bayesian Information Criteria, after excluding those with groups representing <1% of the cohort, average posterior probability for assigned group <70% or relative entropy <0.5.Results:Of 657 CYP, the majority were female (69%) and of white ethnicity (85%), with RF-negative polyarticular JIA the most common disease category (33%).The optimal model identified multiple patterns of disease activity following MTX initiation, with greater complexity than the traditional ‘response’ or ‘non-response’ paradigm. Although there were no substantial differences in ESR trajectories between the groups, there were differences in initial disease severity and speeds of improvement across active joint counts, physician and parental global assessments over time. In addition, individual JADAS components did not always change in parallel over time, even within the same cluster of CYP.Conclusion:There are multiple patterns of disease activity following MTX initiation for CYP with JIA. This suggests that a simple response/non-response analysis at a single time point may be inadequate. Understanding clinical or biological factors associated with these clusters could facilitate stratified medicine in JIA.Acknowledgments:The CLUSTER consortium is supported by contributions of its industry partners, currently Pfizer, AbbVie UCB, GSK, and SobiDisclosure of Interests:Stephanie Shoop-Worrall: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Lucy Wedderburn Speakers bureau: Pfizer, Wendy Thomson: None declared, Nophar Geifman: None declared

The application of stratified medicine to dementia care

SUMMARYStratified medicine has been successfully used in many areas of medicine, perhaps most notably oncology. There is now both a growing evidence base and mounting enthusiasm, supported at a governmental level and across industry, academia and clinical medicine, to apply this approach to neurodegenerative illnesses, including dementia, as these provide the greatest clinical and social challenge of our times. In this article we consider definitions of stratified medicine, look at its application in other medical specialties, review the national context in the UK and consider the current state, future potential and specific considerations of applying stratified medicine to dementia.

O29 Methotrexate response clusters in JIA

Background Treatment response in juvenile idiopathic arthritis (JIA) is currently viewed as a binary outcome of response versus non-response. In a heterogenous disease such as JIA, it is likely that different, identifiable clusters of children and young people (CYP) may display varying patterns of the different features of the disease. Identifying these response clusters is an important step toward stratified medicine in JIA. The aim of the study was to explore trajectories of juvenile arthritis disease activity score (JADAS) components following methotrexate (MTX) initiation for JIA. Methods MTX-naïve CYP with JIA were selected if enrolled prior to January 2018 in either the UK BSPAR Etanercept Register or the Biologics for Children with Rheumatic Diseases Study at point of starting MTX. JADAS components (active joint count, physician’s global assessment (0-10cm), parental global evaluation (0-10cm) and standardised ESR (0-10) were calculated based on data collected in the year following MTX initiation. Multivariate group-based trajectory models were used to explore MTX response clusters across the different JADAS components, using censored-normal (global scores, ESR) and zero-inflated Poisson (active joint count) models. Optimal models were selected based on a combination of model fit (BIC), parsimony and clinical plausibility. Results Of 611 CYP selected, the majority were female (69%) and of white ethnicity (85%), with RF-negative JIA the most common disease category (33%). The optimal model identified multiple patterns of disease activity following initiation of MTX, which have greater complexity than simple ‘response’ versus ‘non-response’ clusters. Differences between clusters included initial intensity of disease features and speeds of improvement over time. In addition, the components of the JADAS did not always follow similar patterns over time, even within the same outcome cluster. Conclusion This study has identified that within CYP initiating MTX, different patterns of disease activity are apparent, suggesting that a simple responder/non-responder analysis at a set point may be inadequate. Understanding both clinical factors associated with, and biological mechanisms underpinning, these clusters would aid stratified medicine in JIA. Disclosures S.J.W. Shoop-Worrall None. K.L. Hyrich None. L.R. Wedderburn None. W. Thomson None. N. Geifman None.