Body Composition in NASH Clinical Trials

Transcription

1 Body Composition in NASH Clinical Trials Jonathan Riek, PhD VP, Musculoskeletal and Metabolic Imaging, BioTel Research Olof Dahlqvist Leinhard, PhD Chief Scientific Officer & Co-Founder, AMRA 2/26/2018 1

2 Overview Body Composition DXA Ultrasound CT MRI Risks and Mitigations for Imaging in a NASH Clinical Trial Advanced MRI 2/26/2018 2

3 Body Composition 2/26/2018 3

4 Is Body Composition Weight? 2/26/2018 4

5 Is Body Composition BMI? BMI = Weight/Height < BMI 25 = Normal BMI > 25 = Overweight BMI > 30 = Obese BMI = /26/2018 5

6 Measurable Body Composition 2/26/2018 6

7 Why is Body Composition Relevant to NASH CDC - Behavioral Risk Factor Surveillance Survey BRFSS ( ) (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 20% 25% 29% 25% 30% 2/26/2018 7

8 DXA Dual-Energy X-ray Absorptiometry 2/26/2018 8

9 Bone Mineral Content from DXA 2/26/2018 9

10 Body Composition from DXA 2/26/

11 Ultrasound 2/26/

12 Liver Fat from Ultrasound Sonographic hepatic-renal ratio as indicator of hepatic steatosis: comparison with 1 H magnetic resonance spectroscopy Mancini, Marcello et al. Metabolism - Clinical and Experimental, Volume 58, Issue 12, (2009) 2/26/

13 Liver Stiffness from Ultrasound Shear Wave Elastography 2/26/

14 CT X-Ray Computed Tomography CT Radiodensity Air: HU Water: 0 HU Fat: -100 HU to -50 HU Muscle: 10 HU to 40 HU 2/26/

15 Body Composition from CT Montano Loza AJ, Angulo P, Meza Junco J, et al. Sarcopenic obesity and myosteatosis are associated with higher mortality in patients with cirrhosis. Journal of Cachexia, Sarcopenia and Muscle. 2016;7(2): /26/

16 Hepatic Fat Fraction from CT Typical abdominal CT scan Fat fraction map Kramer H, Pickhardt PJ, Kliewer MA, et al. Accuracy of Liver Fat Quantification With Advanced CT, MRI, and Ultrasound Techniques: Prospective Comparison With MR Spectroscopy. AJR American journal of roentgenology. 2017;208(1): /26/

17 MRI Magnetic Resonance Imaging 2/26/

18 Why is MRI a Good Tool for Body Composition? H 2 O 2/26/

19 Body Composition with MRI Abdominal Fat PD Image Water Image Fat Image Thigh Muscle & Fat PD Image Segmentation Water Image Fat Image Liver Fat - PDFF Liver Stiffness - MRE 2/26/

20 Challenges with PDFF Manufacturers (GE, Philips, Siemens) have implemented on their scanners Purchasable option Not all scanners can run the required sequence Default parameters may not be consistent with upcoming QIBA profile Toshiba (Canon Medical Systems) and Hitachi do not have PDFF implemented on their scanners Most scanners can scan a 6-echo sequence so we can use UCSD s magnitude algorithm to calculate PDFF. Biggest challenge is getting 6-echo sequence correct at qualification. 2/26/

21 Use of phantoms with varying fat fractions water, 10%, 20% and 30% Mitigation of PDFF Risks Volunteer scan with the phantoms during qualification Combination of built-in PDFF and in-house calculated PDFF 2/26/

22 MRE may not available near the clinical site MRE Risks Current site staff may not be properly trained May not be part of daily scanning Analysis has the potential to be biased 2/26/

23 If it is an exploratory endpoint, use it when available, and not as an eligibility criterion Use MRE:Connect from Resoundant MRE Risk Mitigations Proper site training Analysis that follows the QIBA draft profile recommendations 2/26/

24 Body Composition Profiling A Comprehensive Concept to Understand Metabolic Disease Olof Dahlqvist Leinhard, PhD Chief Scientific Officer & Co-Founder, AMRA Jonathan Riek, PhD VP, Musculoskeletal and Metabolic Imaging, BioTel Research 2/26/

25 From Population Medicine to Precision Medicine Six Men with BMI 21 VAT (Visceral Adipose Tissue) 0.7 L 1.6 L 2.2 L 3.2 L 5.2 L 6.8 L Different Body Compositions. Different Metabolic Risk. 2/26/

26 AMRA Profiler Research A New Standard in Body Composition Rapid 6-Minute MRI Fat Measurements Visceral Adipose Tissue (VAT) Abdominal Subcutaneous Adipose Tissue (ASAT) Proton-Density Liver Fat Fraction Total Adipose Tissue Volume (TAT) Intramuscular Adipose Tissue (IMAT) c c c High Accuracy & Precision Validated precision 1-3 percent Standardized & Reproducible Platform agnostic: 1.5 and 3T GE, Siemens, Philips Cost-Effective 6-min scan, automated analysis, multiple measurements, One-Stop Shop Muscle Measurements Muscle Group Volumes Individual Muscle Volumes Thigh Muscle Volume Total Lean Tissue Volume (TLT) 2/26/

27 Direct vs. Indirect Measurements: Identical Twins Twin A Twin B Indirect Measurements BMI (kg/m 2 ) Weight (kg) Twin A Twin B Waist circ. (cm) AMRA Measurements Liver Fat (PDFF) (%) 5.2 VAT (L) ASAT (L) VAT/(VAT+ASAT) (%) Acknowledgement: Claude Sirlin, Rohit Loomba, USCD NAFLD Research Center Total Thigh Muscle (L) 10.5 Thigh Muscle/ Weight (%) /26/

28 Direct vs. Indirect Measurements: Bariatric Surgery Day 0 Day 112 Day 203 Indirect Measurements Day BMI (kg/m 2 ) Weight (kg) AMRA Measurements VAT (L) ASAT (L) 18.26* VAT/(VAT+ASAT) (%) Total Thigh Muscle (L) Acknowledgement: Claude Sirlin, UCSD and Scott Reeder, University of Wisconsin Thigh Muscle/ Weight (%) /26/

29 High Accuracy & Precision Biomarker Precision Accuracy Visceral Fat 1-5 (VAT) CV % 95% CI ±0.1 L Abdominal Subc. Fat 1-5 (ASAT) CV % 95% CI ±0.6 L Thigh Muscle 4-7 CV % 95% CI ±0.15 L (lower leg) Intra-muscular Fat 4,8 (IMAT) 95% CI ±2.1% 95% CI ±2.9% Liver PDFF 5 95% CI ±1.6% CI, confidence interval; CV, coefficient of variance Bias 95% CI 0.37% [ ] 1. OD Leinhard, et al., ICPR, Tampa, FL, D Newman, et al., J Magn Reson Imaging, M Borga, et al., NMR in Biomed, 28(12): , J West, et al 2018, PLoS ONE 13(2): e M Middleton et al., Radiology 2017, in press. 6. A Karlsson, et al., J Magn Reson Imaging, 41(6): , MS Thomas, et al., Eur Rad, 24(9): , T Romu et al, in preparation. PDFF by T1-correction and T2*-correction using a constant T2* of 25 ms. Root of average square difference from the x = y identity line, compared to ground truth 1 H MRS 2/26/

30 Difference [kg] Difference [kg] DXA Fat mass predicted from BCP [kg] DXA Lean tissue predicted from BCP [kg] Comparing AMRA Profiler Research with DXA AMRA Profiler Research Accurate prediction of Whole Body DXA lean and fat mass DXA can be replaced by MRI examination Quantification of individual muscles Distinct anatomical definitions in 3D, not a projection of body regions Accurate and precise measurement of ectopic fat and organ fat infiltration Visceral adipose tissue Liver PDFF Muscle fat infiltration in individual muscles Cannot assess bone density No ionizing radiation 4,753 UK Biobank datasets comparing GE Lunar idxa versus AMRA Profiler Research Total fat mass DXA fat [kg] Total lean tissue mass r 2 =0.98 r 2 =0.95 DXA lean tissue [kg] 95% LoA: kg 95% LoA: kg Mean fat [kg] Mean lean tissue [kg] Borga M et al submitted 2/26/

31 BCP: Body Composition Profile Subphenotyping NAFLD 2/26/

32 BCP: Body Composition Profile 6 minutes MRI-scan 2/26/

33 UK Biobank The World s Largest Biobank Including MR 2/26/

34 Metabolic Disease Better Described by BCP Obese According to BMI 2/26/

35 Normalized CHD propensity Linking BCP Phenotypes to Disease Propensity According to BMI Overweight ( kg/m 2 ) Diagnostic Performance of BMI & BCP CHD T2D Normalized T2D propensity Metabolic Disease Free Increased CHD Association Increased T2D Association Increased CHD & T2D Association 1 Linge J, et al. Obesity Week /26/18 According to BCP

36 BCP Effect on Health Care Burden, Hospital Nights Last 10 years Statistical results adjusted for sex and age * p < 0.05, ** p < 0.01, *** p < 0.001, n.s. non-significant 1. West J. ECO Annual Congress 2017: Oral presentation OS7:OC Romu T. ECO Annual Congress 2017: Poster T1P59. 2/26/

37 BCP Effect on Health Care Burden, Hospital Nights Last 10 years Statistical results adjusted for sex and age * p < 0.05, ** p < 0.01, *** p < 0.001, n.s. non-significant 1. West J. ECO Annual Congress 2017: Oral presentation OS7:OC Romu T. ECO Annual Congress 2017: Poster T1P59. 2/26/

38 NAFLD Beyond the Liver NASH therapies goes beyond the liver Standardized, reproducible, and precise BCP biomarkers assess key whole-body metabolic changes BCP phenotyping can be used to assess risk of T2DM, CHD, and cachexia is a data-driven analysis can be used to assess progression, regression, and non-responders in clinical trails 2/26/

39 A NAFLD Metabolic Journey Key metabolic body composition changes Key AMRA BCP biomarkers and phenotypes for clinical trails 2/26/

40 Benefits AMRA Profiler Research High Accuracy & Precision Validated precision 1-3 percent Standardized & Reproducible Platform agnostic: 1.5 and 3T GE, Siemens, Philips Cost-Effective 6-min scan, automated analysis, multiple measurements, One-Stop Shop Reduced Patient Burden: Rapid scan, fewer coils, no radiation, no contrast Improved Patient Selection in research studies by enabling inclusion criterial using multiple variables Direct biomarkers with high precision enable early detection of interventional effects Improved Understanding of Disease Development 2/26/2018 Our biomarkers are intended for clinical and academic research. 40

41 Acknowledgement 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 University of Westminster Jimmy Bell Louise Thomas Imperial College Alexandra Blakemore Andrianos Yiorkas UCSD Claude Sirlin Michael Middleton Rohit Loomba This research has been conducted using the UK Biobank Resource. (Access application 6569) CENTER FOR MEDICAL IMAGE SCIENCE AND VISUALIZATION, CMIV 2/26/

42 Thank You 2/26/