Beyond BMI Webinar Slides
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The document details a webinar discussing body composition phenotyping and the UK's biobank, focusing on how advanced imaging methods can enhance understanding of metabolic diseases. Key speakers provided insights into the UK Biobank's extensive data collection efforts and the advancements in imaging biomarkers, which are essential for precision medicine. This research aims to redefine obesity metrics beyond BMI to better understand individual health risks and improve patient stratification in clinical trials.
Beyond BMI Webinar Slides
- 1. Beyond BMI: Body Composition Phenotyping in the UK Biobank A Pistoia Alliance Debates Webinar Moderated by Carmen Nitsche October 25, 2017
- 2. This webinar is being recorded
- 3. ©PistoiaAlliance The Panel 3 Olof Dahlqvist Leinhard, PhD, Co-Founder & Chief Scientific Officer, AMRA Olof Dahlqvist Leinhard is AMRA's Chief Scientific Officer and Co-Founder, with background as an MR physicist. He is responsible for AMRA’s technical vision, for leading the execution of technology platforms, for overseeing technology research and product development, and for aiding in the clinical translation of AMRA's research findings. He also teaches and runs a research group at the Centre for Medical Image Science and Visualisation (CMIV) at Linköping University, Sweden. Dr. Naomi Allen, BSc MSc Dphil Senior epidemiologist, UK Biobank Naomi Allen is an Associate Professor in Epidemiology and Senior Epidemiologist for UK Biobank. She isresponsible for processing the linkage of routine electronic medical records into the study for long-term follow-up (including deaths, cancers, primary and secondary care data as well as other health-related datasets). She helps to co-ordinate the introduction of new enhancements into the resource (such as the development of web- based questionnaires and proposals for cohort-wide biomarker assays) and provides scientific advice to researchers worldwide wishing to access UK Biobank. october 25, 2017 Beyond BMI - Body Composition Phenotyping in the UK Biobank Theresa Tuthill, PhD, Head of Imaging Methodologies, Biomarkers and Development Group, Early Clinical Development, Pfizer Theresa Tuthill, PhD, is Head of the Imaging Methodologies, Biomarkers and Development group within Early Clinical Development at Pfizer. Though trained as an Electrical Engineer, she oversees a small group dedicated to the development of imaging biomarkers for metabolic, cardiovascular, and safety applications in clinical trials.
- 4. Poll Question 1: Are you currently using UK biobank data? A. Yes, I personally do B. No, but my organization does C. No, but I/we plan to in the future D. No
- 5. Improving the health of future generations www.ukbiobank.ac.uk Overview of UK Biobank Naomi Allen naomi.allen@ndph.ox.ac.uk
- 6. UK Biobank is a major national health resource designed to improve the prevention, diagnosis and treatment of a wide range of illnesses that affect middle and older age Aim of UK Biobank
- 7. UK Biobank in a nutshell • A large prospective cohort study • 500,000 UK adults age 40-69 at recruitment, 2006-2010 • Baseline data on a wide range of lifestyle factors, environment, medical history, physical measures & biological samples • Consent for follow-up through health records for all types of health research • Open-access to researchers worldwide (academia & industry)
- 8. Recruitment into UK Biobank • Using individual GP practices for recruitment purposes impractical • Direct mailing of invitations using contact details held by the NHS • Invited 9.2 million; 5.5% response rate
- 9. Rented office space as an assessment centre
- 10. • Socio-demographic information • Lifestyle factors (diet, physical activity, smoking, sleep) • Environmental exposures • Reproductive history & screening • Sexual history • Family history of common diseases • General health & medical history Large subsets • Noise exposure • Psychological status • Cognitive function tests • Hearing test • Blood pressure • Hand grip strength • Body composition • Lung function test • Heel ultrasound Large subsets • Vascular reactivity • Exercise test/ECG • Eye measures (visual acuity, refractive error, OCT scan) Touchscreen questions Physical measures Baseline assessment
- 11. • Blood • Whole blood • Serum • Plasma • Red blood cells • Buffy coat • Urine • Saliva Total: 15 million 0.85ml aliquots Biological samples collected
- 12. Repeat assessment n=20,000 Web-based questionnaires N~200,000 Physical activity monitor n=100,000 Baseline biochemistry n=500,000 Available Q1 2018 Genotyping n=500,000 Imaging n=100,000 Available 2015-2023 2010 onwards: enhancements
- 13. • Genotyping: Bespoke Affymetrix array of 850,000 genome-wide genetic markers • Imputation: ~90 million genetic variants • Data for all 500,000 participants made available July 2017 • Largest study in the world with genotyping, lifestyle and imaging data • Exome-wide sequencing: Initiative between UK Biobank and Regeneron/GSK for all 500,000 participants Genetic analysis of samples
- 14. • Aim: to perform multi-modal imaging scans on 100,000 participants, 2014-2023 • Brain, cardiac and whole body MRI, carotid ultrasound and whole-body DXA scans • Can define phenotypes closely related to disease and investigate how genetics and lifestyle factors influence intermediate precursors of disease Imaging: heart, brain, bones and body
- 15. • Over 16,000 people have already been scanned • Imaging centres in Stockport, Newcastle (Reading to be opened March-April 2018) • Opportunities for repeat imaging in 10,000 • Biggest study of its kind ever undertaken • Collaboration with academic and commercial partners to generate imaging derived phenotypes UK Biobank Imaging Study
- 16. Death notifications: 14,000 participants Cancer registrations: 79,000 participants Hospital admissions: 400,000 participants Primary care records: 230,000 so far • to be made available 2018 Linkages to electronic health records
- 17. Access to UK Biobank • Opened for access March 2012 • Available to all bona fide researchers – Academic and commercial – UK and international • 5,700 approved registrations • 1,000 applications submitted – 700 projects approved and underway • 250 publications • Apply online at www.ukbiobank.ac.uk
- 18. Poll Question 2: Are you using imaging biomarkers? A. Yes, I personally do B. No, but my organization does C. No, but I/we plan to in the future D. No
- 19. The Body Composition Profile Enhancing the Understanding of Metabolic Syndrome using UK Biobank Imaging Data Olof Dahlqvist Leinhard, MSc, PhD Advanced MR Analytics AB, AMRA, Linköping, Sweden Center for Medical Image Science and Visualization, CMIV Linköping University, Linköping, Sweden CENTER FOR MEDICAL IMAGE SCIENCE AND VISUALIZATION, CMIV olof.dahlqvist.leinhard@liu.se Chief Scientific Officer, Founder
- 20. From Population Medicine to Precision Medicine 6.8 L5.2 L0.7 L 1.6 L 2.2 L 3.2 L Different Body Compositions. Different Metabolic Risk. Visceral Adipose Tissue Six Men with BMI 21
- 21. AMRA® Profiler Research A New Standard in Body Composition Rapid 6-Minute MRI 4 Individualized 3 Platform Agnostic Modern 1.5 and 3T GE, Siemens and Philips 2 Accurate & Precise 1 3D Volumetric
- 22. Cloud-Based Process No Installation 6-Minute Scan Rapid Turnover Time Secure Data Transfer Quality Assured Results
- 23. Cancer Yesterday and Today’s Approach to Cancer Today Cancer Research UK; http://www.cancerresearchuk.org/about-cancer/what-is-cancer. Yesterday 200 types of cancers & treatments
- 24. Shaping Tomorrow’s Approach to Obesity Obesity Today Tomorrow
- 31. Comparison to Dallas Heart Study (DHS) Results • VAT was quantified in 973 obese subjects and followed for 9.1 years • Doubled risk for CVD events in high VAT subjects
- 33. Health Care Burden • Based on Health Episode Statistics (HES) Data • From United Kingdom’s secondary care hospital services • Collected to allow hospitals to be paid for delivered care • Includes, e.g., information of diagnosis and operations, and administration • Definition: Number of hospital nights truncated at 30 nights • Standardized way of reporting • Requires referral by physician • Robust to outliers • Insensitive to type and amount of ICD-10 codes Frequency Nbr of nights hospitalization
- 34. Statistical modelling BCP Effect on Health Care Burden VATi ASATi Liver Fat IMAT Univariate p-value *** *** *** *** 𝛽-value 0.34 ± 0.04 0.21 ± 0.03 0.23 ± 0.07 0.15 ± 0.02 Multivariate p-value *** n.s. ** *** 𝛽-value 0.30 ± 0.07 - −0.29 ± 0.09 0.09 ± 0.02 * p < 0.05, ** p < 0.01, *** p < 0.001, n.s. non-significant BCP Effect on Health Care Burden Statistical results adjusted for sex and age 1. West J. ECO Annual Congress 2017: Oral presentation OS7:OC65. 2. Romu T. ECO Annual Congress 2017: Poster T1P59.
- 36. Linköping University • Anette Karlsson • Thord Andersson • Per Widholm • Thobias Romu AMRA • Jennifer Linge • Janne West • Patrik Tunon • Brandon Whitcher • Magnus Borga Pfizer • Theresa Tuthill • Melissa Miller • Alexandra Dumitriu Acknowledgement University of Westminster • Jimmy Bell • Louise Thomas Imperial College • Alexandra Blakemore • Andrianos Yiorkas This research has been conducted using the UK Biobank Resource. (Access application 6569) CENTER FOR MEDICAL IMAGE SCIENCE AND VISUALIZATION, CMIV
- 37. www.amra.se © Advanced MR Analytics AB Redefining Obesity, From BMI to BCP
- 38. Theresa Tuthill, PhD Imaging, Pfizer Radiomics for Metabolic Disease: Mining Large Data Sets Pistoia Alliance October 25, 2017
- 39. Radiomics • Radiomics – defined as the conversion of images to higher dimensional data and the subsequent mining of these data for improved decision support. • Also known as … Imiomics • The mining of radiomic data to detect correlations with genomic patterns is known as radiogenomics. • Most commonly used in Oncology to characterize tumors. Gillies RJ, et al. Radiology 2015;278:563–77. Aerts, HJWL, et al. Nature communications 5 (2014). Coroller, TP, et al. Radiotherapy and Oncology 119.3 (2016): 480-486
- 40. Oncology Example Used to discriminate between cancers that progress quickly and those that are stable. • Patterns of change can be predictive of response to treatment. • Early studies showed a relationship between quantitative image features and gene expression patterns in patients with cancer Gillies RJ, et al. Radiology 2015;278:563–77. Include tumor texture, blood flow, cell density, necrosis, etc
- 41. Challenges with Imaging Biomarkers • Distinction between imaging biomarkers and bio-specimen derived biomarkers. – Scanners are designed to produce images which are interpreted by diagnostic radiologists – Innovation is largely driven by competition to improve image quality – Quantified measurements are often vendor-specific • Key Issues for Imaging Biomarkers – Validation of technology • Repeatability/reproducibility – Need for standardization of acquisition – Data reduction • Whole body scan can contain millions of measurements – Clinical Use : Diagnostic and/or treatment? vs
- 42. Radiomic Analysis for Understanding Disease • Creating predictive models involves receiving input from clinical data, radiology data, pathology data, protein data and gene testing data – Larger data sets provide more power • Look at imaging data and the various ‘-omic’ data (radiomics, pathomics, proteomics, genomics) to discover their relationship with each other • A multidisciplinary data-mining effort involving radiologists, medical physicists, statisticians, bio- informatists, geneticists, and other researchers − Imaging parameters need standardized acquisition and analysis (segmentation, regions of interest, etc.) Clinical Data Pathology Data Radiology Data Gillies RJ, et al. Radiology 2015;278:563–77.dat
- 43. Characterizing Body Types with Disease Risk Current standard is to use BMI and Waist Hip Ratio Visceral obesity: Increased risk of macrovascular disease Peripheral obesity: Decreased risk of metabolic disease Fu, J et al. Cell metabolism 21.4 (2015): 507-508. Lebovitz, HE, International journal of clinical practice. Supplement 134 (2003): 18-27.
- 44. Alternative Body Composition: Need standardization • VAT and SAT can be estimated from CT and MR images – Single slice imaging poorly predicts VAT and SAT changes in longitudinal studies1 • Whole body MRI allows more complete estimation • AMRA has standardized quantification2 – Automated segmentation of fat and muscle – VAT defined as the adipose tissue within the abdominal cavity – ASAT defined as subcutaneous adipose tissue in the abdomen from the top of the femoral head to the top of the thoracic vertebrae T9 1Shen, W., et al. Obesity 20.12 (2012): 2458-2463. 2West, J., et al. PloS one 11.9 (2016): e0163332.
- 45. Large Imaging Databanks to Mine? • UK Biobank – Started in 2006 – 500,000 subjects in age range 40 - 69 years – Collected measures included blood, urine and saliva samples (genome- wide genetic data and biomarker panel available on all subjects) – Access to electronic medical records – Imaging subcohort – 7,000 in Pilot Project, May 2014 - October 2015 • Single imaging site in Stockport, NW England • 3 adjacent imaging suites: – MRI (Brain, full body & heart), – DXA (Bone density) – Carotid ultrasound • AMRA body composition analysis of full body MRI http://www.ukbiobank.ac.uk/
- 46. Defining Disease Groups • Use hospital in-patient records – Filter based on ICD-10 codes • Activity based on questionnaire • For healthy cohort, remove subjects with … – Cardiovascular disease – Metabolic disease – Cancer, strong infectious diseases, etc.
- 47. Healthy women have lower liver fat and VAT than healthy men. 0 5 10 15 20 0 10 20 30 40 50 60 VAT Frequency Male Female 0 5 10 15 20 0 10 20 30 40 50 60 Liver Fat Fraction (%) Frequency Male Female 95th Percentile Liver Fat VAT Female 3.8 2.9 Male 6.0 4.6
- 48. Can we group people based on BCP?
- 49. Clustering by Characteristics to Find Natural Groupings Need algorithms for higher dimensional data What features should be used? Should the data be normalized? Does the data contain any outliers? Jain, AK. Pattern recognition letters 31.8 (2010): 651-666
- 50. Unsupervised Clustering of Body Composition Profile High Low Color Key and Histogram Male Female Together
- 51. Phenomapping through Cluster Analysis • Clustering based on body composition parameters • Identify subgroups that may underlie metabolically un-healthy subjects • Define and characterize mutually exclusive groups – Blinded to disease outcomes • Within a cluster, determine the number of subjects with a specific self-reported disease • Compare this ratio with that of the combined remaining clusters • Ultimate goal is to define therapeutically homogeneous patient subclasses
- 55. What are the practical usages?
- 56. Target ATarget B Target C Target ATarget B Target C Radiomics to Inform Clinical Trials • What is cutoff for “Healthy liver fat”? – For patient identification • What is the distribution of liver fat in selected cohorts? – For inclusion/exclusion criteria • What genetic loci are associated with liver fat? – For target identification and target validation • What are phenotypic clusterings? – For patient stratification Match pathway intervention to patient’s pathogenic trajectory
- 57. Using Data to Aid in Patient Stratification for Clinical Trials 0 5 10 15 20 25 30 35 0 5 10 15 20 25 30 General Population Liver Fat Fraction (%) Frequency Controls Diabetics 0 5 10 15 20 25 30 35 0 5 10 15 20 25 30 Population w/ BMI>28 Liver Fat Fraction (%) Frequency Controls Diabetics Cutoff %Subjects Over Cutoff Mean Liver Fat of Cohort over Cutoff %Subjects Over Cutoff in BMI>28 cohort (and Mean Liver Fat) %Subjects Over Cutoff in BMI>30 cohort (and Mean Liver Fat) 8% 11.5 13.9% 25.8 (14.2%) 30.6 (14.4%) 8% (in T2D) 45.2 14.9% 57.1 (15.5%) 60.2 (16.2%) BMI, body mass index; T2D, type 2 diabetes mellitus. Can we use BMI to screen for patients with high liver fat?
- 58. Understanding Medication and Liver Fat : Ex. Type II Diabetes Controls 18% Metformin Only 15% Metformin + Pioglitazone 5% Metformin + Gliclazide 8% Metformin + Statins 46% Gliclazide + Statins 8% T2DM Patients with <5% Liver Fat (Normal): Visit 3 Controls 7% Metformin Only 13% Metformin + Pioglitazone 4% Metformin + Gliclazide 18% Metformin + Statins 45% Gliclazide + Statins 13% T2DM Patients with >5% (High) Liver Fat: Visit 3 Subjects with Liver Fat > 5%Subjects with Liver Fat < 5% Sulfonylureas previously thought to have a neutral effect on liver fat.
- 59. Next Steps… • Analysis using full imaging cohort • Include additional parameters available later this year – Serum and urine biomarkers – Health records with ICD10 codes – Additional imaging biomarkers • Liver MRI cT1 – indicator of fibrosis • Carotid Ultrasound – atherosclerosis indicator • Increase focus to include … – Cardiovascular disease – Muscle diseases Blood data/samples Urine data/samples Genetic data Questionnaire Existing diseases Health outcome Death register
- 60. Take Home Points … • Radiomics provides insightful phenotyping. • Imaging data, combined with other patient data, can be mined with sophisticated bioinformatics tools to develop models that may potentially improve – diagnostic, – prognostic, and – predictive accuracy. • Radiomics could benefit numerous therapeutic areas
- 61. Acknowledgements Multidisciplinary data-mining efforts involve statisticians, bio-informatists, geneticists, and other researchers. Many Thanks to … • Melissa Miller - Genetics • Joan Sopczynski – Predictive Informatics • Yili Chen - Predictive Informatics • Alexandra Dumitriu – Computational Biomedicine • Craig Hyde – BioStatistics • Jillian Yong – Imaging (Boston University) And our Collaborators … • Jennifer Linge – AMRA Biostatistical Analyst • Jimmy Bell – University of Westminster
- 62. Audience Q&A Please use the Question function in GoToWebinar
- 63. Participants by socio-demographic factors Characteristic Category Numbers (%) Age 40-49 119,000 (24%) 50-59 168,000 (34%) 60-69 213,000 (42%) Sex Male 228,000 (46%) Female 270,000 (54%) Ethnicity White 473,000 (95%) Other 27,000 (5%) Deprivation More 92,000 (18%) Average 166,000 (33%) Less 241,000 (46%) Total 500,000 Generalisability (not representativeness): Heterogeneity of study population allows associations with disease to be studied reliably
- 64. Build better software for life sciences using user experience The next Pistoia Alliance Discussion Webinar: Moderator: Paula deMatos Panel: Ewan Birney - Director of the EBI Joel Miller - UX lead Amgen Reed Fehr - Program Director, Customer Experience at idean Date: December 5th, 2017 8am PT/11am ET/4pm GMT