Body Composition Course Syllabus and Associated Reading Materials

Transcription

1 The International Society for Clinical Densitometry Body Composition Course Syllabus and Associated Reading Materials April 2006 December % fat, 231 lbs 17.6% fat, 125 lbs

2 International Society for Clinical Densitometry 2017 DXA Body Composition Analysis Course Syllabus Dual-energy X-ray absorptiometry (DXA) is the gold-standard technology and technique in the analysis of body composition. DXA provides assessment and quantification of fat mass, lean mass and bone mineral content, both in a single body region and at the whole-body level. This live course focuses on the increase of knowledge and competence of clinicians and other healthcare professionals in order to successfully use DXA body composition analysis in the management of obesity, geriatric sarcopenia and other low muscle mass states, general health and pediatric skeletal disease. The inclusion of case studies will reinforce the knowledge gained and allow for application in the diagnosis, treatment and therapies of patients presenting with low bone mineral density. Target Audience The content is designed for healthcare providers responsible for reading and interpreting DXA scans, including specialists and generalists working in family medicine, general and internal medicine, rheumatology, endocrinology, obstetrics/gynecology, radiology, physical and occupational therapy, and research, as well as technologists. Overall Course Objectives As a result of this course, learners will be better prepared to: 1. Identify proper DXA technology for body composition analysis of fat, lean muscle and bone mass. 2. List other methods of body composition analysis used in the clinical setting. 3. Differentiate reference values and indices. 4. Identify limitations of reference values and indices. 5. Summarize scan acquisition for adult and pediatric patients.

3 Accreditation Statement The International Society for Clinical Densitometry is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to offer continuing medical education for physicians. For information about the accreditation of this program, please contact ISCD at Credit Designation Clinicians: The International Society for Clinical Densitometry designates this educational activity for a maximum of 3.75 AMA PRA Category 1 Credit(s). Physicians should only claim credits commensurate with the extent of their participation in the activity. Technologists: The course qualifies for 4.5 category A credits through the ASRT. Technologists must sign in and out each day to verify attendance and to receive credit. Partial credit will not be given. Instructions to Receive Credit Physicians will be required to complete an evaluation of the activity online after conclusion of the activity. As a part of that evaluation they will claim the amount of credit commensurate with the extent of their participation in the activity. After completing the evaluation, a certificate will be generated that the physician may print to indicate credits earned. Policy on Commercial Support and Conflict of Interest The ISCD maintains a policy on the use of commercial support, which ensures that all educational activities sponsored by the ISCD provide in-depth presentations that are fair, balanced, independent, and scientifically rigorous. ISCD requires faculty, planners, managers and other individuals and their spouse/life partner who are in a position to control the content of this activity to disclose any real or apparent conflict of interest they may have as related to the content of this activity. All identified conflicts of interest are thoroughly vetted by ISCD for fair balance, scientific objectivity of studies mentioned in the materials or used as the basis for content, and appropriateness of patient care recommendations. Individual disclosures are included in course material.

4 Course Disclosures Course Planning Committee Neil Binkley, MD, CCD David Kendler, MD, CCD Diane Krueger, BD, CBDT Mary K. Oates, MD, CCD John Shepherd, PhD, CCD Consulting: Amgen, Eli Lilly, Merck, Radius; Research: Amgen, GE Healthcare, Eli Lilly, Merck, Novartis, Viking Consulting: Amgen, Eli Lilly, AstraZeneca, Astellas; Speakers Bureaus: Amgen, Eli Lilly, GlaxoSmithKline; Research: Amgen, Eli Lilly, Astellas, AstraZeneca Consulting: Amgen, Eli Lilly, Merck, Radius; Research: Amgen, GE Healthcare, Eli Lilly, Merck, Novartis, Viking Consulting: Amgen; Speakers Bureaus: Amgen; Research: Amgen Consulting: Hologic, Bodyspec; Research: General Electric Course Content Reviewers Joao Lindolfo Borges, MD Peter Brown, MS Bjoern Buehring, MD, CCD Monique Carroll, RT, CBDT Larry Jankowski, CBDT Kyla Kent, CBDT Marina Magrey, MD Mary Saier John Shepherd, PhD, CCD LaTarsha Whittaker, CBDT No Financial Relationships to Disclose No Financial Relationships to Disclose Consulting: GE, Lunar No Financial Relationships to Disclose Honoraria: Hologic No Financial Relationships to Disclose No Financial Relationships to Disclose No Financial Relationships to Disclose Consulting: Hologic, Bodyspec; Research: General Electric No Financial Relationships to Disclose

5 Course Content Reviewers Continued Merle Wigeson, MB.BCh., DTM&H., DPH., M.Med.Rad(D)., FFRad., FRANZCR No Financial Relationships to Disclose Phenu Zachariah, DO, CCD No Financial Relationships to Disclose 2017 Course Faculty Mary K. Oates, MD, CCD John Shepherd, PhD, CCD Consulting: Amgen; Speakers Bureaus: Amgen; Research: Amgen Consulting: Hologic, Bodyspec; Research: General Electric

6 International Society for Clinical Densitometry 2017 DXA Body Composition Analysis Course Schedule Saturday, April 22, :00-2:15pm Introduction and Review of Prerequisite Online Lectures 1 & 2 2:15-3:00pm Lecture 3: Whole Body DXA Scan Acquisition and Analysis 3:00-3:45pm Lecture 4: DXA Body Composition Reporting and Reference Data 3:45-4:00pm Break 4:00-5:00pm Lecture 5: Current Uses of DXA Body Composition in Clinical Practice and Research 5:00-6:00pm Lecture 6: DXA Whole Body Composition in Obesity, Sports, Commercial Applications and Future Uses

7 Disclaimer The material presented in the ISCD Quality Bone Densitometry Course is educational and does not constitute a medical or professional service. Great effort has been made to assure that the course material is timely and accurate. However, due to the rapidly changing nature of the field, some information provided may be outdated or invalidated by subsequent developments. The lecturers and authors shall not be held liable or responsible to any person or entity with respect to any loss or damage alleged to be caused directly or indirectly by the information presented at this program. Disclosure of Unlabeled Use This educational activity may contain discussion of published and/or investigational uses of agents that are not indicated by the FDA. Accredited provider does not recommend the use of any agent outside of labeled indications. The opinions expressed in this educational activity are those of the faculty and do not necessarily represent views of any organization associated with this activity. Please refer to the official prescribing information for each product for discussion of approved indications, contra indications, and warnings. Special Acknowledgement ISCD would like to express gratitude to the many people who over the last 10 years have contributed their vision, expertise, time and insight into the development of the ISCD course curriculum.

8 2017 DXA Body Composition Analysis Faculty Bios Mary Oates, MD, CCD Dr. Mary K. Oates is a board certified Physical Medicine and Rehabilitation specialist. She has been specializing in the diagnosis and treatment of osteoporosis since 1994 and is a Certified Clinical Densitometrist. She has offices in the Pacific Central Coast Health Centers in Santa Maria and San Luis Obispo, California. She founded the Marian and French Hospital Osteoporosis Centers and is a volunteer faculty member at Cal Poly State University in San Luis Obispo, California in the department of Nutrition. Her clinic practice in Santa Maria, California also focuses on the evaluation of nerve and muscle disorders with EMG and nerve conduction studies. She is actively involved with community education and outreach projects aimed at the prevention and treatment of osteoporosis. She has participated in several osteoporosis pharmaceutical research studies, and observational Body Composition studies using DXA for the evaluation of bone, muscle and fat. She is currently the Co- Chairman of the National Osteoporosis Foundation s Annual Meeting Committee, Fracture Liaison Group and the ISCD Body Composition program. Dr. Oates is a graduate of Ohio State University College of Medicine and The University of California, Irvine residency program in Physical Medicine and Rehabilitation. John Shepherd, PhD, CCD Dr. Shepherd is a Professor in the Department of Radiology at the University of California at San Francisco (UCSF). He is also the Director of the UCSF s Center for Translational Science Institute s Body Composition, Energy Metabolism and Exercise Physiology Laboratory. Dr. Shepherd received his PhD in Engineering Physics from the University of Virginia and postdoctoral training in Biophysics from Princeton University. He leads a group of researchers studying advanced body composition and bone densitometry techniques. His group provides DXA and body composition expertise to many publicly funded studies including NHANES, Health Aging and Body Composition Study, and Bone Mineral Density in Children Study. He is a frequent expert consultant to the UN's International Atomic Energy Agency for body composition assessment and has been sent on over 15 missions to developing countries around the world. He has served the ISCD in multiple ways including the BDC (faculty), BCC (faculty), five PDC committees, Southwest Regional Representative, and is, as of this meeting, the new President. He is a previous recipient of the ISCD Oscar Gluck Humanitarian and Global Leadership Awards and is a Fulbright Scholar to Sweden. He has published over 150 peer-reviewed publications.

9 Lecture 3 Scan Acquisition and Analysis ISCD Body Composition Course Learning Objectives Design and implement DXA facility total body scanning protocols Determine appropriate patient preparation for scan acquisition Describe acquisition and analysis techniques for whole body DXA Recognize scan artifacts, understand how they impact results and appropriate management April Learning Objectives Design and implement DXA facility total body scanning protocols Determine appropriate patient preparation for scan acquisition Describe acquisition and analysis techniques for whole body DXA Recognize scan artifacts, understand how they impact results and appropriate management DXA Scanners with Body Composition Capability Hologic GE Lunar Norland DMS Horizon A, W idxa XR 800 Stratos Discovery A, W, Wi Prodigy Elite Delphi A, W QDR 4500 A, W QDR 2000 QDR 1000W DPX Some may require software upgrade, especially to gain access to specialty features 3 4 DXA Whole Body Scan Scan time: 3-10 minutes Radiation Dose: <10 microsieverts Weight range: 1 kg to 227 kg DXA for very large patients - Norland Elite Double wide table (137 cm) Extra long (228 cm) 283 kg weight limit 4 m x 4 m room needed Hologic Horizon GE idxa 5 1

10 Implement Standard Operating Procedures Establish and implement standard protocols for acquisition and analysis Use manufacturer recommendations and published standards Consider unusual situations be consistent Tall or obese patients Unavoidable or internal artifacts Document protocol deviations/unusual situations in comment sections of scan files General Recommendations for Standard Procedures ISCD Official Position Consistent positioning and preparation (e.g. fasting state, clothing, time of day, physical activity, empty bladder) of the patient is important for accurate and precise measures. Consistently use manufacturer s recommendations for ROI placement. Consistently use manufacturer s recommendations for artifact removal. 7 8 ISCD Official Position Incorporate Contraindications and Limitations into Standard Procedures Pregnancy is a contraindication to DXA body composition. Limitations in the use of clinical DXA for total body composition or bone mineral density are: Weight over the table limit Recent administration of contrast material and/or artifact - Radiopharmaceutical agents may interfere with accuracy of results using systems from some DXA manufacturers Exam Preparation - Scheduling Scheduling exam Validate ability to lay still on their back for at least 10 minutes Verify weight is below the maximum table limit Ensure no contrast or radioactive material has been administered in last days Confirm females are not pregnant Pregnancy testing may be appropriate Consider 10 day rule 9 10 Contrast Media Alters Measurements Contrast will appear bright white on screen image during acquisition Oral or IV contrast Barium Other agents may not be apparent, i.e., technetium 11 Delaying DXA after radionuclide imaging or with contrast is advised Radionucleotides can impact bone results Technetium dose dependent reduction in fat-free mass and increase in fat mass* CT Iodine contrast can produce large accuracy errors on bone and soft tissue results, over 124% BMC, 75% Fat, 110% lean, and <2 % total mass** Try to always perform DXA before other imaging If after other imaging, delay DXA days is advised. Differences can be manufacturer specific *Fosbøl et. al. J Clin Densitom, 16: **Sala, A., et al., J Clin Densitom 9(1) (2006)

11 Clothing Exam Preparation Patient Instruction - Avoid heavy cloth (denim, canvas, sweats) & layered or baggy items (cargo pants, cuffs, collars or hoodies) No metal or dense plastic No clothing fasteners: buttons, snaps, zippers, rivets, etc. (especially sports bras) Consider changing into gown or scrubs Hair should be let down during scan Consider fasting period prior to scan Confounders: Clothing and Hair Thick heavy clothing can be included in results as lean or fat mass (a) cargo pants Bound hair may tilt head forward and impact chin line placement (b), hair unbound (c) a. b. c Hydration May Alter Tissue Results (1) DXA measures water as lean mass Hydration related lean measurement error is proportional to change in hydration status DXA fat measures are robust to variations in hydration DXA fat error < 1% with hydration changes of up to +/- 5% Regional estimates could be affected by large local fluid changes (ie. dialysis, ascites, edema) Hydration May Alter Tissue Results (2) Dialysis with removal of kg of fluid decreased DXA measured lean mass 1 Trunk 61% Legs 30% Arms 5.5% Heavy exercise elite cyclists and weight trainers 2 Exercise with food & water intake increased measurement error up to 10% Kelly et al. Appl Radiat Isot 1998;49: Going et al Am J Clin Nutr 1993;57: Pietrobelli et al. Am J Physiol 1998;274: E Horber et al. Brit J Radiol 1992; 65: Nana et al. Med Sci Sports Exerc 2013; 45: Example of Trunk Lean Mass Changes Immediately After Meal Trunk Lean Mass (lbs) Trunk Fat Mass (lbs) % Trunk Body Fat VAT Mass (lbs) Before Meal After Meal Change lbs +0.1 lbs -0.5 % lbs A single meal can affect whole body DXA body composition results. In this example, change in lean was mostly due to giant drink. Considerations to Minimize Hydration Effects Void bladder No large drinks/meals before scans No heavy exercise before scans Consider fasting in research environment or special circumstances The Meal

12 Learning Objectives Design and implement DXA facility total body scanning protocols Determine appropriate patient preparation for scan acquisition Describe acquisition and analysis techniques for whole body DXA Recognize scan artifacts, understand how they impact results and appropriate management 19 Considerations Prior to Scan Acquisition Patient cooperation Were preparation instructions followed? Are physical impediments to lying still present? Do you anticipate positioning challenges? Too tall or wide for scan field Obese Pediatrics Conditions or diseases Edema, ascites Artifacts Remove if possible, document if unable 20 Exam Preparation Immediately Prior to Scan Acquisition Clear all items from scanner bed (positioners, phantom, pillows etc.) Confirm no pregnancy or recent radiologic contrast exams Instruct to void bladder Remove all external sources of artifact and verify appropriate clothing Let long hair down to ensure head is flat on table Obtain prior scan for consistent positioning Provide Instruction Prior to Scan Lay still and quiet Breathe normally avoid deep breaths Avoid speaking as cough, etc.) requires scan restart However encourage communication of difficulties or discomfort Inform of scan duration Positioning Procedures ISCD Official Position Positioning of the arms, hands, legs and feet whenever possible should be according to the NHANES method (palms down isolated from the body, feet neutral, ankles strapped, arms straight or slightly angled, face up with neutral chin). Offset-scanning should be used in patients who are too wide to fit within the scan boundaries, using a validated procedure for a specific scanner model. Ideal Patient Positioning Center in scan field Straight, aligned with scanner long axis Entire body in scan field Chin up with head at scan field top Arms at sides slightly separated from trunk Place hands palms down separated from body; strap ankles together

13 Scan Acquisition Procedure Confounders: Motion Monitor acquisition for errors Movement Metal or other artifacts Anatomy outside scan field; watch for drifting anatomy outside scan area (especially hands) Abort, correct, and restart as needed Limit the number of restarts Expert opinion limit Small movements will appear on scans Appearance differs between manufactures due to single motion (Hologic a.) vs rectilinear (GE b.) fan beam direction b. a. s Hands palms down, not under hips/legs Ideally Acquired Scan 27 Alternate Positioning Methods Exclude either head or feet or use two scan sum technique Consider purpose of scan to determine positioning method Exclude head for tall patients if monitoring leg mass Assure inclusion of at least ½ body for off-set scan if monitoring weight loss intervention Methods to force whole body into field if evaluating symmetry or injury/rehab Reproduce positioning on follow-up exams Document unusual anatomy or variation from acquisition protocol 28 Tall Patient Exclusion Positioning Exclude either head or feet, be consistent Consider primary purpose of results Omit feet - least amount of tissue excluded Omit part of head highly cortical site, includes all tissue for appendicular assessment - When excluding > ~2/3 rd of head, disable SmartScan (GE Lunar only) Maximize scan field Position high on table Dorsiflex feet use velcro strap to stabilize 29 Tall Patient Protocol Sum of Two Scans If body length > scan field length Exclude head to include feet Reposition to include head for second 80-cm length partial scan Need to manually substitute head results to generate total body results Require disabling SmartScan when excluding > ~2/3 rd of head (GE Lunar only) Silva et al. Eur J Clin Nutr 2013;67:

14 Obesity Scan Mode Selection GE Lunar System selects Standard or Thick scan mode based on height and weight entered in biography Hologic Use Whole Body mode, except with infants High Performance Whole Body scan mode (available only on A models for research) Norland Use standard scan speed and resolution Off-set Positioning Method (GE, Hologic) Option for patients too wide for scan field Half of body is fully within scan field Partially scanned side is estimated from fully scanned side Incorrect Correct 32 both arms out One arm out 33 Manufacturer Specific Off-set Differences GE Lunar: Mirror Imaging Partially scanned anatomy is estimated by fully scanned side. Results indicated by (e) Depending on what was missing, may include one arm/leg/half body Hologic: Reflection Reflects one arm and/or one leg only, not trunk Lunar idxa Difference between mirror imaging scan mode and standard whole body scans Parameter Right Side Left Side %Fat Fat Mass [kg] Lean Mass [kg] Rothney, Obesity, Hologic QDR 4500A Difference to whole body scan with arm and leg reflected Parameter Difference [abs] Difference [%] %Fat Fat Mass [g] Lean Mass [g] Left/right differences: right arms and legs usually had larger BMD and tissue mass Consistent analysis in longitudinal studies Sherman, ISCD Ann. Mtg., 2011 Accuracy of Off-set Scanning Total body, right side and left side measured on idxa (N = 52, BMI > 30) * Measured total body fat and lean were highly correlated with right or left side estimates r Estimated BMC slightly above or below total measurement Half-body scanning can accurately predict whole body composition (r ) ** * Rothney, et al. Obesity 2009;Jun 17: ** Tataranni PA et al. Am J Clin Nutr 1995;62:

15 Centered Obese/Tall Positioning (Norland Elite) Useful for wide and tall patients where left/right symmetry measures are required. Large table eliminates need for lateral and vertical offset scanning Elite only Acquisition Criteria Similar in Children Hands palms down, not under hips/legs 1 year old 5 year old Wide Tall Acquisition Challenges in Children Limited attention span & ability to follow instruction Infants and small children may not stay still Consider swaddling or determine how much movement is acceptable Coughing, seizures, spasticity movement artifact Medical conditions requiring - PICC lines, G-tubes, tracheostomy, spine rods, etc. Contractures affect positioning Extremely low bone mass may lead to poor tissue differentiation Can Perform Manual Reflection to Replace Regions with Movement Limb motion can be excluded by reflecting the valid limb Can force estimate whole side in GE software cannot estimate just a region (i.e. limb) Hologic reflected results need to be manually generated (i.e. subtract value of limb with motion and replace with limb Good agreement between routine and manually estimated value from other side) 40 results Rodrigopulle 2014 EJCN 68_ Infant Acquisition, Manual and Standard Analysis Techniques Available Scan modes Hologic Discovery A whole body infant software Apex v12 GE Lunar encore v16 Infant preparation Clean study diaper, no other clothing Breast- or formula-fed, rocked to sleep Infant Swaddling in receiving blanket - Snug swaddle to reduce movments - Arms by side, legs straight - Masking tape to flatten out legs Analysis 6-region analysis using sub-roi Consistent ROI numbering; use template Substitute contralateral region if movement Hologic manual method* GE-Lunar standard method *Shepherd 2013 Presented at ICCBH, Rotterdam 43 Infant Acquisition Challenges No Movement/ No swaddling Movement/ Thumb sucking Lots of Movement Shepherd 2013 Presented at ICCBH, Rotterdam 44 7

16 Total Body Scan Analysis implants Ensure ROI placement correctly segregates anatomy Follow manufacturer recommendations Analysis Head ROI Head cut is placed immediately below the chin Variation in placement will impact android and gynoid regions Challenges: chin overlaps shoulders kyphosis or pediatric scans) Both Analysis Arm ROI arms from body Challenges: and trunk Analysis Spine ROI Cuts placed as close to spine as possible excluding all ribs Challenges: spine curvature Allows non-linear spine cut to contour with spine - GE v only GE Hologic Before/After Analysis Lumbar Spine ROI Thoraco-lumbar line must separate T12 and L1 on Hologic to distingish rib region from lumbar region to obtain accurate results Analysis Pelvis ROI Pelvis Pelvis cut is immediately above the iliac crests Challenges: Asymmetrical pelvis, position line above highest crest

17 Analysis Leg ROI (1) Both cuts bisect femoral necks and do not touch the pelvis Challenges: Tilted legs, make bisecting neck & separating legs difficult Analysis Leg ROI (2) Left and right forearm/leg Challenges: Poor positioning; hands under legs, feet apart Analysis Leg ROI (3) Center leg cut separates the right and left leg Challenges: Overlapping or angled feet difficult to separate heels and toes Analysis - Android/Gynoid ROIs Android 20% of chin to pelvis distance a 1.5 * a 2.0 * a Gynoid 53 Identical definition between manufacturers, regions are contingent on chin line placement 54 Analysis Visceral Adipose Fat ROI Exterior line should be at skin edge Android/Gynoid ROI Analysis Challenges Abdominal Wall Lines GE: No additional analysis required in addition to android ROI Hologic: VAT ROI is inside android region, adjust lateral lines as needed. (8% of scans need adjustment*) Left markers for abdominal wall (arrow) are in the subcutaneous tissue Powers et al ISCD Annual Meeting Poster Presentation

18 Incorrect Analysis Results in Measurement Error Measurement Difference (Auto Manual Results) Maximum Vat = g Minimum SAT = g Auto-analysis Analysis Infant (Hologic*) Place global ROI around following regions R1 Cut at chin, surrounds head R2/3 Cut at shoulder joint, surrounds arms R4/5 Cut at iliac crest, separate and surround legs R6 Trace around chin, arm and iliac crest, surrounds trunk Allows for manual reflection reporting Manual Correction 57 Shepherd 2017 J Peds 181: Analysis Challenges ROI Placement Difficult Due to Movement Reflection Reporting for Infants Unlike adults, reflection reporting is manually based on the adequately imaged side. Precision of arms and legs with reflection Rt leg movement, reflect R5 Acceptable scan Rt leg movement, reflect R5 6-ROI analysis allows for manual exclusion and reflection of regions with artifacts or motion Infant WB Measurements Reflection (Intrascan) N=103 Mean RMSE %CV BMD (g/cm 2 ) BMC (g) Fat Mass (g) Mass (g) Lean Mass (g) Percent Fat (%) Shepherd 2017 J Peds 181: Shepherd 2017 J Peds 181: Osteogenesis Imperfecta Can Make Distinguishing Bone from Soft Tissue Challenging Osteogenesis Imperfect is an uncommon low bone mass disease of often diagnosed in childhood Bone density can be so low that x-ray attenuation is similar to soft tissue thereby challenging the software to accurately identify bone Analysis Serial Scans Always copy or compare from baseline scan Use to identify differences and ensure consistency Adjust lines to custom fit new scan

19 Analysis - Custom ROIs Customized regions can be created Various shapes Size adjustable Can copy or duplicate Can save to apply to other patients and/or future studies Learning Objectives Design and implement DXA facility total body scanning protocols Determine appropriate patient preparation for scan acquisition Describe acquisition and analysis techniques for whole body DXA Recognize scan artifacts, understand how they impact results and appropriate management Artifact Sources Removable Artifacts Removable/Avoidable Jewelry (ring, necklace, watch, piercings, etc.) Hearing aides Clothing fasteners (buttons, zippers, rivets, etc.) Other (Wallet, keys, coins, cell phone, hair accessories) Motion Non-removable Implants/surgical hardware (hip, breast, fusion, etc.) Thick hair Casts Dental procedures: fillings, caps, braces, bridges Pillow & earrings Car Keys Bracelet, cast & ring Corset ribs & snaps Internal Artifacts - Impact and Examples Hologic Hardware is included elevates measured BMC and BMD GE Lunar Excludes hardware pixels from results; does not impact final measurement Pacemaker Penile Implant Minimize Artifact Impact on Results Remove all avoidable artifacts and reacquire Assess impact on non-removable results/interpretation Compare to contra-lateral or previous scan Avoid artifact with sub-region analysis or GE artifact tool Document known source Request consistency at follow-up (jewelry, hearing aids, etc.) so minimal impact on change over time Arthroplasty

20 Method to Estimate Impact of Internal Artifact (1) BMC: R = 352g; L = 603g Place an ROI box around the artifact Place a mirrored or copied custom ROI over area without artifact BMC: R = 46g; L = 39g 69 Methods to Estimate Impact of Internal Artifacts (2) Use point mode and/or custom analysis Point-typing confirms artifacts are dense & measured as bone Wrist BMC: R = 7g; L = 17g Artifact feature allows exclusion (noted in blue) TBBMC with implant as bone = 1713 TBBMC with implant excluded = Scan Print-outs are Configurable Select specific regions of interest Trending options Specify regions Select mass (lean, fat, bone) Select reference population Display comparisons in percentile Color coded images can be generated in software, but not printed Hologic Body Composition Colors Colors signify fat, lean, bone Yellow = fat Red = muscle Blue = bone Consistent colors for serial evaluation. Graphic bar below image illustrates tissue determination GE Lunar Body Composition Colors Color %fat mapping Adjustable thresholds Color palette to visualize %fat Learning Objectives Design and implement DXA facility total body scanning protocols Determine appropriate patient preparation for scan acquisition Describe acquisition and analysis techniques for whole body DXA Recognize scan artifacts, understand how they impact results and appropriate management

21 Lecture 4 Body Composition Reporting and Reference Data ISCD Body Composition Course OBJECTIVES Apply the ISCD Official Positions for reporting body composition data Review of current sources of reference data on DXA machines and various ethnicities and special sub populations in the literature Recognize the controversy surrounding reference BMI and body composition data Identify additional reporting parameters including derived indices and how may be used April What Measures Should Appear on All Reports ISCD Official Position BMI Should be Included in Report Adult total body (with head) values of: BMI (despite its limitations) BMD and BMC total mass total lean mass total fat mass percent fat mass Petak, et. al. J Clin Densitom, 2013;16: Petak, et. al. J Clin Densitom, 2013;16: High BMI May Not Equate to Overweight in Athletes %FAT is more accurate representation of obesity than BMI 48% of women misclassified as Non Obese using BMI 22% of men misclassified as Non Obese using BMI Center for collegiate hockey team with BMI = 29: < 10% fat BMI 29.4 Not All Patients That Are Overweight or Obese by BMI Have Metabolic Disease 80 Shah 2012 PLoS ONE 7(4): e Women above red line are obese (American Society of Bariatric Physicians, DXA %FAT: 30%). Men above blue horizontal line are obese (DXA %FAT: 25%). Upper left quadrant shows 48% of women are misclassified as non-obese by BMI yet obese by percent body fat. 13

22 There is no consensus for percent body fat guidelines for defining obesity AACE (1998) ¹ Obese males >25% females >35% Gallagher attempted to define Obesity by stratifying %BF in the same distribution as BMI² age males >19% >21% >24% females >32% >33% >35% Those with Normal Weight Obesity are at Increased Risk of Cardiometabolic Disease Biomarkers-BP, elevate TG, decreased HDL-C, elevated glucose, Insulin resistance, hscrp 977 Canadian men and women Cardiometabolic Risk LOW MEDIUM HIGH n=324 n=324 n= 329 AGE 34.5 (13.1) 40.2 (13.0) 44.2 (13.6) BMI kg/m² 21.5 (1.5) 22.6 (1.4) 23.5 (1.2) % Body Fat associated with level of CV risk 22.8 (6.8) 29.6 (6.1) 35.8 (6.0) ¹ AACE Obesity Task Force, ²Gallagher D, et al. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr Sep;72(3): Shea JL, et al. Body fat percentage is associated with cardiometabolic dysregulation in BMIdefined normal weight subjects. Nutr Metab Cardiovasc Dis Sep;22(9): PubMed PMID: Epub 2011/01/ The Concept of Normal Weight Obesity (Review) NWO associated with a higher risk of developing metabolic syndrome, cardiometabolic dysfunction and higher mortality The definition of obesity has become a dynamic concept that has evolved for the past 5 decades, as the understanding of adipose tissue continues to evolve The diagnosis of obesity at the individual level will at some point include measurement of BF content and fat distribution The decision about the diagnostic threshold for fat mass or BF % will depend on optimal cutoffs based on epidemiologic studies with long-term follow-up and information on obesity related comorbidities and mortality Report Total and Regional Fat Mass, Lean Mass, BMC Oliveros, et. al., Prog Cardiovasc Dis, 2014; 56, Petak, et. al. J Clin Densitom, 2013;16: Report Total Body Bone Mineral Density Percent Fat = Fat Mass/Total Mass Hologic reports: % Fat = Fat/(Fat + Lean + Bone Mass) Petak, et. al. J Clin Densitom, 2013;16: GE Lunar reports two outputs: Region % Fat = Fat/(Fat + Lean + Bone Mass) Preferred Tissue % Fat = Fat/(Fat + Lean Mass ) Default value in GE Composer reports. Can be changed to preferred Region % Fat when designing a Composer report 88 14

23 Hologic Body Composition Results Lean = Lean Soft Tissue Mass In the DXA 3-compartment model, Total Mass = Fat + Lean + Bone Mineral Content = Fat + Lean + BMC Manufacturer report this consistently on current software versions. *** *** Hologic included BMC in Lean prior to APEX What Reference Data Base should be Used to Represent the Generally Healthy Population? When comparing to the US population, the NHANES body composition data are most appropriate for different races, both sexes, and for ages from 8 to 85 years. [Note: Reference to a population does not imply health status.] ISCD Official Position Use Z- Scores or Percentiles to Report DXA Body Composition Values Both Z-scores and percentiles are appropriate to report if derived using methods to adjust for non-normality. LMS method generates appropriate Z-scores and is used by Hologic and GE Lunar DXA software ISCD Official Position Petak, et. al. J Clin Densitom, 2013;16: Sample DXA Report Output: Hologic NHANES Reference on Hologic Report Light and dark blue zones depict +/- 2 SD Z- score of % fat

24 NHANES Female Median %Fat for quantifying Z-scores NHANES Male Median %Fat for quantifying Z-scores Females WHITE BLACK MEXICAN AMERICAN Males White Black Mexican American Age M σ L M σ L M σ L Age M σ L M σ L M σ L Kelly 2009 PLoS One 4(9): e Kelly 2009 PLoS One 4(9): e Cross Calibration Equation was Developed to Convert Whole Body NHANES Database Obtained on Hologic for use on GE Machines Total % Fat Prodigy= * Hologic Hologic= * Prodigy 40% fat on Hologic = % on Prodigy 30% fat on Hologic = % on Prodigy 20% fat on Hologic = % on Prodigy The average absolute difference for % Fat was 2.5% The National BMI Average Increased From So, should we comparing an individual s result to a population based reference that is becoming increasingly more obese? Fan 2014 J Clin Densitom 17(3): Controversies Regarding DXA Body Composition National Reference Ranges GE encore v < 14.0 Uses Proprietary GE Reference Data Population means are not indicative of good health nor absence of disease Population means have changed over time Recognize that DXA % body-fat measurements will differ from other methodologies (DXA, BIA, skinfold) Despite these controversies, DXA outputs do provide comparisons to reference databases Data were derived from several sources: 270 subjects (The Monarch Foundation) 169 subjects (Ohio State University) 1468 other subjects obtained from literature Total of 1905 subjects Subjects were healthy Qualified to exclude subjects with chronic disease or medications known to affect bone. Data were pooled by decade from age Available for the Prodigy and idxa

25 Additionally, You May Want to use Ethnic Specific Databases Because of Differences in the Relationship Between BMI And Body Fat Compared to European men of matched height, age and weight, Asian Indians have less skeletal muscle in limbs and more central abdominal fat Male Pacific Islanders have more skeletal muscle in limbs In both men and women, % abdominal fat was significantly higher in Maori, Pacific and Asian Indians, which may explain the higher prevalence of DM2and HTN in these ethnic groups compared to Europeans. Rush EC, et al. Body size, body composition and fat distribution: comparative analysis of European, Maori, Pacific Island and Asian Indian adults. Br J Nutr Aug;102(4): GE Lunar International Body Composition Reference Databases Available in User Options Asia Australia (2) Brazil China England Germany Japan Korea USA (6) KOREAN HANES Median % Body Fat Possible Reference % Body Fat Data for Indian Population 15,036 Koreans (6692 men, 8344 women) Age MALE FEMALE % +/-10.1% 35.1% +/-7.1% MEN WOMEN AGE < >50 < >50 %BF / / / / / /- 0.2 In conclusion, Indians have a higher percentage body fat than Western populations, at all ages and BMI. Ahn SH, et al. Different relationships between body compositions and bone mineral density according to gender and age in Korean populations (KNHANES ). J Clin Endocrinol Metab Oct;99(10): Marwaha RK, et al. Normative data of body fat mass and its distribution as assessed by DXA in Indian adult population. J Clin Densitom Jan-Mar;17(1): PubMed PMID: Possible Reference Database for % Body Fat for Brazilian Women Age % Body Fat / Observation of % Body Fat and % Trunk Fat in Native Chinese, White and Black Americans n= 1147 Chinese subjects tested , Americans in NY MEN WOMEN Black Chinese White Black Chinese White N Age 40.6 (11.8) 48.1(14.2) 44.1(14.9) 48.0(15.7) 46.5(13.1) 45.7(16.5) BMI kg/cm² (3.88) (3.08) (3.44) (5.57) (4.51) (6.04) Sousa M, et al. Body composition parameters in healthy Brazilian women differ from white, black, and Hispanic American women reference range. J Clin Densitom Jul-Sep;16(3): % Body Fat (7.54) (7.50) (8.70) (10.32) (5.93) (10.67) Lu H, et al. Relationships of percent body fat and percent trunk fat with bone mineral density among Chinese, black, and white subjects. Osteoporos Int Dec;22(12):

26 You May want to Use Sport Specific Databases NCAA I X- run 13% Jr. College Baseball 22% Swim 16% Mary K. Oates, M.D. ASBMR poster There Are Published Reference Values for Body Composition in Athletes Reference values for male and female athletes measured in- season 898 athletes Archery, shooting, basketball, fencing, gymnastics, handball, hockey, pentathalon, motosport, rowing, rugby, sailing, soccer, swimming Santos DA, et al. Reference values for body composition and anthropometric measurements in athletes. PLoS One. 2014;9(5):e PubMed PMID: US Professional Football Players Body Composition Differs by Position Wide receiver Lineman 9.4% fat LMI= % fat LMI= 3.1 position Range and Mean % Body Fat for National Football League Players Differs by Position Offensive Lineman Defensive Lineman Tight End Linebacker Running Back Punter Kicker Quarter Back Defensive Back Wide Receiver n Mean % BF Range (+/-) Dengel DR, et al. Body composition and bone mineral density of national football league players. J Strength Cond Res Jan;28(1):1-6. PubMed PMID: LMI = lean mass index = (lean mass)/height Additional DXA Measures of Adiposity and Lean Mass ISCD Official Position Should We Consider Not Only Fat Mass but Also Fat Distribution? Visceral Adipose Tissue (VAT) Appendicular Lean Mass = ALM Appendicular lean mass index (ALMI: appendicular lean mass/ht²), Android/Gynoid percent fat mass ratio, Trunk to leg fat mass ratio, Lean mass index (LMI: total lean mass/ht²), Fat mass index (FMI: fat mass/ht²) The clinical utility of these measures is currently uncertain. Petak, et. al. J Clin Densitom, 2013;16: Android Apple shape means more android fat: Higher risk of health problems Pear shape means less android fat: Lower risk of health problems Gynoid 18

27 Fat Distribution Varies ANDROID- Upper-body obesity Fat stored in upper body and abdominal area (apple shaped) Occurs more frequently in men Carries greater risk for CAD, hypertension, stroke, and diabetes GYNOID -Lower-body obesity Occurs more frequently in women Fat stored in the lower body around the hips, buttocks, and thighs (pear shaped) 118 Android/Gynoid Regions Provide Composition Analogs to Waist and Hip Circumferences Android ROI a 1.5* a 2.0* a Gynoid ROI Same placement for both GE and Hologic Automatically placed from standard cut line placement Android ROI height is proportional to the chin to iliac crest length. Gynoid ROI is twice as tall as the android region 119 Android and Gynoid Region Examples Hologic GE Android Gynoid Region definitions are identical for GE and Hologic 120 Abdominal and Gynoid Fat Mass are Associated with Cardiovascular Risk Factors in Men and Women Lunar DPX-L and Lunar IQ N=417 women and 175 men Abdominal fat mass and the Abdominal/Gynoid fat mass strongly associated with HTN, IGT and elevated triglycerides 1 kg abdominal fat or 10 kg total body fat corresponded to moving from 0 risk factors for CV disease to 3 risk factors Wiklund et al. J Clin Endocrino Metab. Nov 2008, 93(11): All Fat is Not the Same ~80% of body fat is subcutaneous Main function (perhaps) energy store Visceral fat is within the envelope of the abdominal musculature and subcutaneous fat Omental, mesenteric & retroperitoneal Visceral fat; ~10 to 20% of total fat in men and 5-8% in women More metabolically active; impacts a variety of clinical risks, e.g., fasting glucose, cholesterol and triglycerides Drains directly to liver via portal vein Provides direct hepatic access to FFA and adipokines Ibrahim, Obesity Rev. 2009;11: CT Can Directly Measure VAT

28 Visceral Adipose Tissue Anatomy VAT Can be Estimated by DXA Identifies the Subcutaneous Fat Ring SubQ fat inner wall SubQ fat outer wall SAT inner wall SAT outer wall VAT = Total Fat SubQ Fat VAT = Total Fat SubQ Fat The subq fat amount in the abdominal region is modeled 125 Exterior line should be at skin edge Analysis - VAT VAT by DXA Compares Well with Abdominal CT DXA VAT Validation Quantitation Abdominal Wall Lines GE VAT Hologic VAT GE VAT region uses android region, VAT interior region not visible Hologic VAT ROI is inside android region, adjust lateral lines as needed (infrequent) VAT = Visceral Adipose = Android fat subcutaneous fat 127 Kaul, et. al, Obesity. 2012; 20: Micklesfield, et. al, Obesity. 2012; 20: VAT May Be Reported as Volume, Mass or Area but Area is preferred GE and Hologic VAT are Highly Comparable but Not Interchangeable Historical VAT studies all reported Area from CT Area GE Area can be derived using the following: Female: VAT L 4/5 (cm 2 ) = 0.096* VAT volume (cm 3 ) (cm 2 ) Male: VAT L 4/5 (cm 2 ) = 0.069* VAT volume (cm 3 ) (cm 2 ) Hologic report GE report Xia 2014 J Clin Densitom 17(1): N = 124 (102 F) Ages 18 to 81 GE Encore 14.3 Hologic APEX 4.0 Hologic volume is in a 5cm slice GE Volume is in the entire android region Average crosssectional AREAs are most similar but are still not interchangeable Fan B 2013 Presented at ISCD Annual Meeting

29 Visceral Fat is an Independent Predictor of All Cause Mortality Visceral fat by CT 291 men; 97 decedents 194 controls Logistic regression used to determine association between fat and all-cause mortality Visceral fat, subcutaneous fat, liver fat and waist circumference were significant individual predictors of mortality. VAT and WC Showed Greater Clinical Utility than Other Obesity Measures Metabolic Syndrome Score AUC VAT BMI WC Fat Mass % Fat white women AA women White Men AA Men Kuk, et. al, Obesity. 2006; 14: Katzmarzyk PT AJCN 97: VAT Risk Thresholds a Women > 163 cm 2 associated with marked increase CHD risk b > cm 2 associated with poor metabolic profile d > 70 cm 2 had a 84% sensitivity, 80% specificity, 81% accuracy of metabolic syndrome c Men > 125 cm 2 associated with high risk of metabolic syndrome c > 106 cm 2 associated with elevated risk of CHD b DXA Adiposity Measures ISCD Official Position The use of DXA adiposity measures (percent fat mass or fat mass index) may be useful in risk-stratifying patients for cardio-metabolic outcomes. Specific thresholds to define obesity have not been established. a Kelly 2013 VAT White Paper b Nicklas 2003 Diabetes Care 26: c Pickhardt 2012 AJR Am J Roent 198(5): d Brochu 2003 Metab 52(5): Body Composition Indices are a better way of monitoring adiposity and muscularity Total Mass = Fat Mass + Lean Mass %FAT = Fat Mass / (Fat Mass + Lean Mass) * 100 Weight Indices BMI Body Composition Indices Fat Mass Index = FMI = Fat Mass / Height 2 Lean Mass Index = LMI = FFMI = Lean Mass / Height 2 Appendicular Lean Mass Index = ALMI = (Arms Lean + Legs Lean) / Height 2 Both %FAT and BMI change based on lean mass, but fat mass index (FMI) will not change Green = ALMI Example: Indices monitor change in body composition better than %fat Woman, 45 years, 175 cm tall Baseline Weight = 100 kg BMI = 32.7 kg/m 2 (Obese) Fat mass = 27 kg Lean mass = 73 kg FMI = 8.8 (excess fat) LMI = 23.8 %FAT = 27% (Not Obese) Loses 10 kg lean and no fat (Diet without exercise) BMI goes DOWN FMI remains UNCHANGED %FAT goes UP Final Weight = 90 kg BMI = 29.3 kg/m 2 (Not Obese) Fat mass = 27 kg Lean mass = 63 kg FMI = 8.8 (excess fat) LMI = 20.6 %FAT = 30% (Obese) With 10 kg loss of lean mass, BMI went down, %FAT went up, FMI didn t change

30 FMI 4/3/2017 Hatorri Charts provide a graphical way to see how Hattori four Chart indices (Pediatric) are related 5 BMI=18 BMI=16 BMI=17 25% Fat 4.5 GE Lunar reports Appendicular Lean Mass/ht² as RSMI FMI = 3.25 LMI = 12 BMI = Fat = 23% 1.5 BMI=13 BMI=14 BMI= LMI 20% Fat 15% Fat Hattori 1997 Am J Hum Biol 9: 573 ± 578 RSMI = Relative Skeletal Muscle Index= (bilateral arm + bilateral leg lean mass)/ht 2 RMR is calculated RMR (Resting Metabolic Rate) based on Harris- Benedict equation or Mifflin-St Jeor RMR(male) = (6.775 x age[yrs]) + ( x weight[kg]) + ( x height[cm]) NOTE THIS RESULT DOES NOT USE DXA DERIVED LEAN MASS Metabolic Syndrome Bariatric Surgery Optional Reporting for Individual Cases That May be Helpful VAT ALMI A/G trunk to leg fat mass ratio LMI ALM/BMI FMI x x x x x x x x Athletes x x x Sarcopenia x x X Lypodystrophy X OBJECTIVES Apply the ISCD Official Positions for reporting body composition data Review of current sources of reference data on DXA machines and various ethnicities and special sub populations in the literature Recognize the controversy surrounding reference BMI and body composition data Identify additional reporting parameters including derived indices and how may be used 145 Lecture 5 Current Uses of DXA Body Composition in Clinical Practice and Research ISCD Body Composition Course Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: HIV lipodystrophy Renal function assessment Cancer associated cachexia Anorexia Sarcopenia, Sarcopenic Obesity, Sarcoporosis, Dysmobility Recent Literature review of DXA Body Composition Topics April

31 Many Potential Clinical Roles Exist for Total Body DXA Internal Medicine Obesity, cardiovascular risk, cystic fibrosis, HIV Sports Medicine Physical training/exercise, injury rehabilitation, nutrition Endocrinology GH deficiency, hyperparathyroidism, PCOS, hypogonadism, androgen/estrogen Rx, glucocortioid Rx Geriatrics Sarcopenia, neuromuscular changes, nutrition Gastroenterology Malabsorption syndromes, eating disorders Pediatrics Growth disorders, nutrition, chronic disease Pharmacology Lean mass for drug dosing Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: HIV lipodystrophy Renal function assessment Cancer associated cachexia Anorexia Sarcopenia, Sarcopenic Obesity, Sarcoporosis, Dysmobility Recent Literature review of DXA Body Composition Topics ISCD Official Position DXA total body composition with regional analysis can be used in patients living with HIV to assess fat distribution in those using anti-retroviral agents associated with a risk of lipoatrophy (currently stavudine [d4t] and zidovudine [ZDV, AZT]). HIV-associated Lipodystrophy A syndrome in HIV-infected people being treated with antiretroviral meds Abnormal central fat accumulation and localized fat loss; may be disfiguring (face, neck, abdomen Can present as lipoatrophy or lipohypertrophy only Is associated with metabolic abnormalities, e.g., hyperglycemia and hyperlipidemia Thus increased atherosclerosis and diabetes risk Treatment with nucleoside reverse transcriptase inhibitors (NRTIs) is a major risk factor These agents are no longer commonly used Clinical Presentation is Variable includes lipohypertrophy and lipoatrophy Lipohypertrophy Dorsocervical fat pad enlarges Neck circumference increases Breast hypertrophy Abdominal visceral fat accumulates Lipomatoses may occur Pubic lipomas develop Lipoatrophy Loss of subcutaneous cheek fat Depletion of subcutaneous tissue from arms shoulders, thighs and buttocks One DXA Approach for Lipodystrophy Diagnosis Percent central to percent lower extremity fat mass ratio (FMR) Ratio 1.5 and 1.96 has been proposed Freitas 2010 J Clin Densitom 13: Degris 2013 J Clin Densitom 13:

32 Potential Clinical Utility of Lipodystrophy Diagnosis Lipodystrophy has cosmetic consequences; may lead to non-adherence with HIV medications Associated with hypercholesterolemia, insulin resistance/dm A standardized lipodystrophy definition allows more reliable estimates of prevalence and incidence Tools (e.g., DXA) to facilitate early identification might lead to alteration of HIV therapy or monitor future treatments Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: HIV lipodystrophy Renal function assessment Cancer associated cachexia Anorexia Sarcopenia, Sarcopenic Obesity, Sarcoporosis, Dysmobility Recent Literature review of DXA Body Composition Topics Carr 2003 Lancet 361: Body Composition Measurement May Have an Important Role in Pharmacology to Optimize Dosing Lean mass decreases or total body fat increase lead to changes in phamacokinetics and pharmacodynamics of drugs Use Lean Body Weight from DXA in drug dosing equations Relevant in pediatrics, obesity and sarcopenia Lean Mass Measurement May Improve Creatinine Clearance Equations In Obese People Assume muscle is the main source of intracellular creatinine Creatinine clearance is known to increase in a linear manner with lean body weight LBW may provide a more accurate estimate of CrCl Could use TBW, IBW,FFW or LBW to improve the Cockcroft-Gault equation in obese or sarcopenic patients 156 Janmahasatian 2008 Br J Clin Pharmacol. 65(6): Body Composition Plays an Important Role in the Interpretation of Renal Function The relative effects of fat versus muscle mass on cystatin C and estimates of renal function in healthy young men * Lean mass modulates glomerular filtration rate in males of normal and extreme body composition ** GFR should be indexed to lean mass Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: HIV lipodystrophy Renal function assessment Cancer associated cachexia Anorexia Sarcopenia, Sarcopenic Obesity, Sarcoporosis, Dysmobility Recent Literature review of DXA Body Composition Topics * Chew-Harris 2013 Ann Clin Biochem 50: ** Chew-Harris 2014 Intern Med J 44(8):

33 DXA can Identify and Monitor Cancer Associated Cachexia and Obesity Risk of Dose Limiting Toxicity (DLT) Increased Significantly with Sarcopenic Obesity Sarcopenia and sarcopenic obesity in cancer are associated with higher incidence of chemotherapy toxicity, shorter time to tumor progression, poorer outcomes of surgery, physical impairment and shorter survival. DXA was used to evaluate esophageal cancer patient s body composition Of 72 patients 43% had sarcopenia and 14% had sarcopenic obesity prior to chemotherapy Sarcopenic patients with normal BMI trended to higher DLT In sarcopenic obese patients, risk of DLT increased significantly Prado 2016 Proc Nutr Soc. 75(2): Anandavadivelan 2016 Clin Nutr 35(3): Example of Low Lean Mass as a Determinant of Chemotherapy Toxicity CT measured mass in 55 women with metastatic breast cancer Low lean mass was a significant predictor of toxicity and time to progression in metastatic breast cancer patients treated with capecitabine Suggest body composition test to predict toxicity and individualize chemotherapy dosing DXA Can Detect Effects of Androgen Deprivation For Prostate Cancer 48 men with prostate cancer and androgen deprivation therapy compared to 70 controls Androgen deprivation therapy patients: Significantly reduced muscle strength upper and lower body and impaired functional performance Significantly lower whole body and hip BMD Higher percent body fat Trend to lower lean body mass Appendicular skeletal muscle positively associated with body strength and functional performance Prado 2009 Clin Cancer Res 15(8): Reis 2009 Clinics (Sao Paulo) 64(8): Conclusions: DXA use in Pharmacology & Monitoring of Chronic Disease Body composition assessment may provide more accurate approach to assessment of renal function Knowledge of body composition may allow alteration of chemotherapeutic agent dosing to reduce toxicity May provide useful information to monitor treatment of chronic diseases such as hormone deficiencies Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: HIV lipodystrophy Renal function assessment Cancer associated cachexia Anorexia (AN) Sarcopenia, Sarcopenic Obesity, Sarcoporosis, Dysmobility Recent Literature review of DXA Body Composition Topics

34 DXA Validated by CT in Patients Ranging from Anorexic to Obese DXA is a useful method for assessing body composition in premenopausal women within the phenotypic spectrum ranging from obesity to AN. DXA trunk fat correlated with CT visceral fat (r = , P < ). Anorexia The basic DSM-IV diagnostic criteria for anorexia are: Refusal to maintain a healthy weight range for age and height, or failure to make expected weight gains at times of growth and physical development Fear of gaining weight or becoming fat Distorted body image Loss of periods (menstrual cycle) in women who are not using any external source of estrogen, e.g. oral contraceptives. Bredella 2010 Obesity 18 (11): DXA Can be Used to Monitor Body Composition in Studies of Anorexia Monitoring Nutritional and Behavioral Treatment for Anorexia Nervosa Successful Treatment Followed by Stability Short term weight gain in 26 patients (age 27.6 ± 6.6) from therapy results in mainly gain of fat with no preferential fat distribution. Gynoid body shape maintained.* With therapy, weight gain was greater in the group of patients with fat mass < 4 kg or BMI < 14 kg/m 2 than in the group with fat mass 4 kg or BMI 14 kg/m 2 (p = 0.037).** 3/2007 3/ **Yamashita 2010 Int J Eat Disord. 43: *Orphanidou 1997 Am J Clin Nutr 65(4): % BF 16.8% BF 15.7% BF 169 Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: HIV lipodystrophy Renal function assessment Cancer associated cachexia Anorexia Sarcopenia, Sarcopenic Obesity, Sarcoporosis, Dysmobility Recent Literature review of DXA Body Composition Topics Sarcopenia: the Age-related Gradual Loss of Muscle mass, Strength and Function Sarc for flesh (muscle), penia for deficiency Historically, the term was coined in 1989 following the observation that lean mass loss was very closely linked to aging and decline in ambulation, mobility and energy intake 170 Rosenberg 1989 Am J Clin Nutr 50:

35 Sarcopenia: the Age-related Gradual Loss of Muscle mass, Strength and Function Sarc for flesh (muscle), penia for deficiency More recently defined as: The age-associated loss of skeletal muscle mass and function. a complex syndrome associated with muscle mass loss alone or in conjunction with increased fat mass. Consequences of Sarcopenia Include Impaired ability to perform activities of daily living/functional impairment Falls Fractures Reduced quality of life Healthcare costs Death Impaired muscle strength is highly predictive of incident disability and all-cause mortality in the elderly. Fielding 2011 J Am Med Dir Assoc 12: Cesari and Pahor 2008 J Nutr Health Aging 12: Clinical Definition of Sarcopenia Has Evolved and Continues to be Refined Original definition = age related loss of muscle mass Cannot simply use muscle mass as the decline in strength exceeds the decline in mass Muscle quality decreases with age ISCD Official Position DXA total body composition with regional analysis can be used in patients with muscle weakness or poor physical functioning to assess fat and lean mass. The impact on clinical outcomes is uncertain Muscle Mass-Based Approaches to Sarcopenia Diagnosis Many approaches measure appendicular (arm + leg) muscle mass, adjust for height (ALM/ht 2 ) and compare to the mean of a young, healthy, gender-matched group Commonly uses ALM/ht²= muscle mass index 2 SD below healthy young individuals measured by DXA 1 1 Baumgartner 1998 J Epidemiol. 147: An Example of Why ALM/ht 2 Should Not Be The Sole Diagnostic Criterion for Sarcopenia Appendicular Lean Mass/Height 2 cutpoint < 5.45 kg/m 2 ALM/ht kg/m 2 ALM/ht kg/m 2 51 year-old healthy competitive cyclist 86 year-old frail nursing home resident

36 2010 European and International Consensus Definitions of Sarcopenia Are Similar Includes Muscle Mass and Function Skeletal Mass Index men (ASM/ht 2 ) Skeletal Mass Index women (ASM/ht 2 ) European 1 International 2 <7.26*; <7.25** <7.23 <5.5*; <5.67** <5.67 Gait speed <0.8 m/sec < 1m/sec Grip strength *Rosetta ** Health ABC < 25 kg (F) < 30 kg (M) Low ASM and either low gait speed or low grip strength N/A Low ASM and low gait speed 1 Cruz-Jentoft 2010 Age Ageing 39: Fielding 2011 J Am Med Dir Assoc 12: The Foundation for the NIH Sarcopenia Project Consortium of NIA, NIAMS, Center for Drug Evaluation, academia, 6 pharmaceutical companies Goal: To use multiple existing data sources to identify criteria for clinically relevant weakness and low lean mass Utilized data from 9 studies; 26,625 participants Studies included MrOS, InChianti, BPRHS, RBS, FHS, AGES, HABC 11,427 men/15,198 women Studenski 2014 J Gerontol A Biol Sci Med Sci 69: The Foundation for the NIH Sarcopenia Project Low grip strength, low grip strength to BMI ratio, and low ALM to BMI ratio were associated with increased likelihood for incident mobility impairment Weakness increased likelihood of mobility impairment The Foundation for the NIH Sarcopenia Project Proposed operational definition of sarcopenia Weakness and low lean mass Grip strength: M < 26 kg; F < 16 kg ALM to BMI ratio: M <0.789; F < Slowness with weakness and low lean mass Gait speed 0.8 m/sec Grip strength: M < 26 kg; F < 16 kg ALM to BMI ratio: M <0.789; F < McLean 2014 J Gerontol A Biol Sci Med Sci 69: Dam 2014 J Gerontol A Biol Sci Med Sci 69: Sarcopenia Prevalence Varies Based on Definition Used Definition Foundation of NIH sarcopenia project Prevalence (%) Male N = 7113 Prevalence (%) Female N = 2950 Weakness + low lean mass Slowness + weakness + low lean mass International working group European working group on sarcopenia Sarcopenia Severe sarcopenia Agreement for diagnosing sarcopenia was low. Consensus on the criteria for the diagnosis of sarcopenia is much needed to characterize populations for study and identify adults for treatment. Dam 2014 J Gerontol A Biol Sci Med Sci 69: European Consensus Group Recommends CT, MRI or DXA CT and MRI are gold standards for estimating muscle mass in research DXA is an attractive alternative method for research and clinical use SMI (Skeletal Mass Index) ASM/ht 2 cut points Men (Rosetta) and 7.25 kg/m 2 (Health ABC) Women (Rosetta) and 5.67 kg/m 2 (Health ABC) Cruz-Jentoft 2010 Age Ageing 39:

37 ISCD Official Position Current Definitions All Include Appendicular Lean Mass by DXA Low lean mass could be defined using appendicular lean mass divided by height squared (ALM/height 2 ) with Z- scores derived from a young adult race and sex-matched population. Thresholds for low lean mass from consensus guidelines for sarcopenia await confirmation /3/2017 Competing Definitions of Sarcopenia* All contain DXA measures of lean and/or fat mass Definition DXA Lean Indices Cutoff Value Functional Cutoff H or (kg/m 2 ) Indices Values GE Baumgartner [9] ALM/Ht M 5.45 W None GE Delmonico 1 ALM/Ht M None H [10] 5.67 W Delmonico 2 ALM = f (height, Fat), Z- -2 None H [11] score Cruz-Jentoft ALM/Ht M Gait Speed < 0.8 m/s GE [12] 5.54 W and/or and/or grip strength < 30 kg M < 20 kg W Fielding [13] ALM/Ht M Gait speed < 1 m/s H 5.67 W Morley [13] ALM/Ht M 5.18 Gait speed < 1 m/s H Muscaritoli [14] SMI (ALM/TBM) 37% M Gait speed < 0.8 m/s GE 28% W Studenski 1 [15] ALM/BMI M W H Studenski 2 [15] ALM/BMI M W Grip speed < 26 kg M H < 16 kg W * Bischoff-Ferrari 2015 Osteo Int 26 (12): Prospective rate of falls in sarcopenic versus non-sarcopenic seniors Sarcopenic Obesity Inadequate Muscle Mass/Strength in the Presence of Elevated Body Fat Studenski, ASBMR Annual Meeting, 2011 Persons with Reduced Body Mass Out of Proportion With Their Adipose Mass Fielding 2011 J Am Med Dir Assoc 12: /3/2017 Best performance was documented for the Baumgartner (body composition only) and the Cruz-Jentoft (body composition + function) definition of sarcopenia. Bischoff-Ferrari 2015 Osteo Int 26 (12): Fat Infiltration Adversely Affects Muscle Function 3,011 men and women Age 70-80; Health ABC Mean 4.7 years f/u Muscle density by CT as a fat infiltration proxy Fat infiltration of muscle associated with greater risk of hospitalization Adapted from Cawthon 2009 JAGS 198: Some Proposed Criteria for Sarcopenic Obesity Baumgartner: ASM/Ht 2 less than 2 SD below mean young reference and body fat greater than 28% in men and 40% in women Davison (from NHANES): Upper 2 quintiles of body fat and lower three quintiles of muscle mass FNIH Sarcopenia Project suggests ALM/BMI as a diagnostic criteria for sarcopenia Essentially is muscle mass/weight adjusted for height Low ALM/BMI = < (male); < (female) At a given amount of muscle mass, a higher BMI makes this ratio look worse 29

38 BMD T-score Increased Fractures 4/3/2017 Example of How ALM/BMI Changes as Fat Mass Increases Assume ALM = 16 kg (mean of 80 year old women) Low < per FNIH BMI ALM/BMI Sarcopenic Obesity Predicts Instrumental Activities Of Daily Living (IADL) Disability In Older Adults N= 451 elderly men and women followed for 8 years in the New Mexico Aging Process Study Sarcopenia defined as ASM < 2SD below mean from 229 healthy subjects, age < 7.26 kg/m 2 for men and 5.45 kg/m 2 for women Obese defined as > 60 th percentile 28% fat for men and 40% women Subjects with sarcopenic obesity were 2-3 times more likely to report onset of IADL disability during follow up than lean sarcopenic or non sarcopenic obese subjects and those with normal composition Baumgartner 2004 Obes Res 12(12): Sarcoporosis The combination of low bone density and low muscle mass increases risk of injurious falls Integrates concept of Sarcopenia and Osteoporosis 192 The combination of low bone density and low muscle mass increases risk of injurious falls Sarcopenic Men Women Osteosarcopenic Sarcopenia Ostepenia (T<-1.0) Osteosarcoporotic Sarcopenia Osteoporosis (T -2.5) In study of 68 women (65-90 years), women with osteosarcopenia had the lowest grip strength, low chair rise, low STS power compared to osteopenic, sarcopenic, and control women. Sarcopenic -1 Osteosarcopenic -2.5 Osteosarcoporotic Increased Falls Control Osteopenic Osteoporotic Appendicular Lean Mass Index Drey 2016 Aging Clin Exp Res 28: Muscle Attenuation DXA Attenuation Segmented SAT DXA Muscle Attenuation DXA-derived subcutaneous fat thickness is a strong marker for hip fracture risk in both men and women, especially in men with high ALM/ht 2 Dysmobility Syndrome Defined by Binkley et al. as An excess of adipose tissue in combination with low muscle mass= sarcopenic obesity, which in turn is associated with impaired function and increased disability risk Low skeletal mass index Low grip strength Low gait speed Low leg lean mass:fat mass ratio T- score in osteoporotic range Fall in the last year Malkov 2015 JBMR 30(8):

39 EWGSOP Algorithm for Defining Sarcopenia in Older Population Definitions of sarcopenia: associations with previous falls and fracture in a population sample Applied sarcopenia definitions by EWGSOP, IWGS and fnih Prevalence of sarcopenia was 3.3% EWGSOP, 8.3% IWGS and 2.0% fnih. Those with sarcopenia defined by IWGS reported higher falls and fractures Grip Strength < 30 kg men, < 20 Kg women Skeletal Mass Index 7.26 kg/m² and 5.45 kg/m² 196 Clynes 2015 Calcif Tissue Int 97(5): Dysmobility Syndrome and Mortality Risk in US Men and Women age 50 years and Older Dysmobility was associated with increased mortality risk in adults age 50 years and older, with risk being higher in those age than those age 70+ age Mass and Function Assessment Could be to Incorporated into Routine DXA Practice Looker OI 26(1): Hong Kong Chinese study also showed high prevalence of sarcopenia in hip fracture patients Ho 2016 Hong Kong Med J 22: Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: HIV lipodystrophy Renal function assessment Cancer associated cachexia Anorexia Sarcopenia, Sarcopenic Obesity, Sarcoporosis, Dysmobility Recent Literature review of DXA Body Composition Topics DXA Body Composition Literature Review-See Attached Bibliography (1) Body Composition after Bariatric Surgery DXA Body Composition in Athletes DXA Body Composition documents exercise interventions Pediatric DXA Body Composition DXA Body Composition in Special Populations

40 DXA Body Composition Literature Review-See Attached Bibliography(2) Using DXA Lean Mass to improve estimates of renal function Using DXA Body Composition in Cancer treatment to better estimate DLT ( Dose Limiting Toxicity), improve nutritional status and limit cachexia Sarcopenia Sarcoporosis Sarcopenic Obesity Dysmobility DXA Body Composition Literature Review-See Attached Bibliography(3) Using DXA Body Composition to monitor nutritional interventions and assess metabolic risks Publications from Korean National Healthand Nutrition Examination Survey (KNHANES) RED-S Relative Energy Deficiency in Sports Guidelines to Use of DXA - IAEA Body composition and bone density measurements theory and practice (Left) and in the first two years of life (right) Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: HIV lipodystrophy Renal function assessment Cancer associated cachexia Anorexia Sarcopenia, Sarcopenic Obesity, Sarcoporosis, Dysmobility Recent Literature review of DXA Body Composition Topics Free Downloads Lecture 6 Use of DXA in Obesity and Sports, Commercial Applications and Future Uses ISCD Body Composition Course Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: Weight Management and Obesity Sports performance assessment, symmetry, reference ranges and Relative Energy Deficiency Current Consumer applications Future Body Composition topics April

41 Monitoring Total and Regional Results is the Unique Strength of DXA Body Composition Analysis All reports should include total body (with head) values of BMI, BMD, BMC, total mass, total lean mass, total fat mass, and percent fat mass Other parameters reported should be individualized based on specific goals Rate of Change Report 6 exams can be shown graphically and in data tables Compartmental trending graph Output can be customized Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: Weight Management and Obesity Sports performance assessment, symmetry, reference ranges and Relative Energy Deficiency Current Consumer applications Future Body Composition topics ISCD Official Position DXA total body composition with regional analysis can be used in obese patients undergoing bariatric surgery (or medical, diet, or weight loss regimens with anticipated large weight loss) to assess fat and lean mass changes when weight loss exceeds approximately 10%. The impact on clinical outcomes is uncertain DXA May be Used to Assess Fat/Lean Change Over Time with Interventions for Obesity Intense Medical Management (Supervised very low calorie diet) Bariatric Surgery Gradual diet and exercise An Example of DXA to Monitor Physician Supervised Ketogenic Diet 47 yo Black female 72.5 inches tall, 242 lbs BMI 28.6 kg/m 2 Total body fat 41.3% Lean mass lbs A/G ratio 1.12 VAT 1.74 lbs Estimated values from mirror image This patient demonstrates typical positioning issues with tall & wide patients

42 DXA 12 weeks on Ketogenic Diet Fat loss Lean loss 2.6% fat loss, 14.8 lb fat mass loss, 7.1 lean mass loss, 0.47 lb VAT loss 214 Decrease in Resting Metabolic Rate (RMR) due to Decrease in Lean Mass Mifflin St-Jour equation 19.7 X FFM(kg) = RMR (cal) EXAMPLE: Black female lost 10 kg on ketogenic protein diet (6.7 kg and 3.3 kg lean) Baseline RMR = (19.7 X 58 kg ) = 1556 cal Final RMR= (19.7 X 54.7 kg ) = 1491 cal Results in 65 calories less burned daily at new body weight = 23,728 calories/year = will gain 3.1 kg of fat /year if doesn t eat less or exercise more or gain muscle 215 DXA Can Document Regional Changes After Bariatric Surgery 1 Loss of fat mass Loss of lean muscle mass Loss of bone mineral density Procedure Specific Body Composition Changes Following Bariatric Surgery Greatest lean mass loss seen with very low caloric diet, roux en Y bypass and biliopancreatic diversion Numerous studies document this capability 2 1 Mechanick 2008 Endocr Pract.14(Suppl 1): Chaston 2006 Intl J of Obesity 31(5): Chaston, et. al, International Journal of Obesity, 2006, 31(5), Body composition after RYGBP is metabolically healthier An Example of Weight Loss Following Lap-Band Surgery 48 y.o. woman Before and After Surgery DXA 0.2should be considered as a valid supplementary tool for the clinical assessment and follow-up in patients undergoing bariatric surgery. 0 baseline 3 months 12 months Total Fat Total Lean VAT 41 females Roux-en-Y Gastric Bypass (RYGBP), age=40.6 +/- 10 years, BMI = /- 6.6 kg/m², VAT = / kg 130 kg 102 kg (Lost 28 kg; 9.3 kg of trunk fat) Bazzocchi 2015 Obesity surgery 25(3): /1/2009: % fat = /26/2010: % fat 45.4%

43 Pre-Operative DXA Prior to Sleeve Gastrectomy 29 yo Hispanic female cm tall, kg BMI >35 kg/m 2 Total body fat 50.2% Lean mass Fat mass 56 kg 59.4 kg A/G ratio 1.33 VAT Not calculated Typical positioning issues with tall and wide patients noted Note: VAT not calculated as left trunk outside of the ROI 220 DXA 5 Months after Sleeve Gastrectomy Monitors Fat, Lean and VAT 29 yo Hispanic female cm tall, 87.3 kg BMI >34.6 kg/m 2 Total body fat 43.6% (-6.6%) Lean mass 46.3 kg (-21.2) Fat mass 37.9 kg (-47.4) A/G ratio 1.24 VAT 1.25 kg Estimation no longer required 221 Calculate Decrease in RMR due to Decrease in Lean Mass Mifflin St-Jour equation 19.7 X FFM(kg) = RMR Baseline DXA Preceeding Strength Training, Running and 250 Daily Calorie Reduction EXAMPLE: White male lost 57.3 kg after Gastric sleeve (49.5 kg fat, 7.9 kg lean Baseline RMR = (19.7 X 75 kg ) = 1890 cal FU RMR= (19.7 X 67 kg ) = 1735 cal Results in 155 calories less burned daily at new body weight = 56,804 calories/year = will gain 7.4 kg fat/year if doesn t eat less or exercise more or gain more muscle 222 Total % Fat = 26.7% Total Fat Mass= 34.2 lbs Total Lean Mass= lbs Total Mass= lbs 223 Calorie Intake Reduction or Energy Expenditure for Weight Loss Rule That She Followed Six Month Follow up Total Body DXA Fat Goal rate of Weight Loss/week Calorie Reduction per day 0.5 lbs -250 Kcals 1.0 lbs -500 Kcals 1.5 lbs -750 Kcals 2.0 lbs -1,000 Kcal Lean lb fat loss, 6.3% decrease in total % fat; lean unchanged

44 Calculate Stable RMR due to Maintaining Lean Mass Mifflin St-Jour equation 19.7 X FFM(kg) = RMR EXAMPLE: White female lost 4.68 kg after diet and exercise, gained 0.05 kg lean and lost 4.73 kg fat Baseline RMR = (19.7 X 40.3 kg ) = 1207 cal FU RMR= (19.7 X 40.4 kg ) = 1208 cal Results in 1 calorie more burned daily at new body weight= Stable RMR Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: Weight Management and Obesity Sports performance assessment, symmetry, reference ranges and Relative Energy Deficiency Current Consumer applications Future Body Composition topics DXA May be Useful in Sports Performance to: Monitor training and nutrition Monitor effect of season Evaluate rehabilitation following injury and return to sport Another Example of Undernutrition is the Female Athlete Triad Consists of: 1. Low energy availability/ disordered eating 2. Low bone mass 3. Menstrual disturbances of amenorrhea IOC position paper 1 - recommends DXA for evaluation of BMD and Body Composition Similar position papers from NCAA, ACSM, NATA, ADA Health Consequences of Relative Energy Deficiency in Sport (RED-S) RED-S is an expanded concept of the Female Athlete Triad to acknowledge a wider range of outcomes and the application to male athletes Potential Performance Effects RED-S (*Psychological consequences can either precede RED-S or be the result of RED-S) Mountjoy 2014 Br J Sports Med 48: Mountjoy 2014 Br J Sports Med 48:

45 DXA Can be used to Track Body Composition Changes Among Female NCAA D1 Athletes Across the Competitive Season and over a 3 Year time Frame Another Example of DXA to Monitor Training Lean mass increase at same BMI (31.2) kg/m² while preparing for bodybuilding competition October 2007 June 2008 Increase in Lean Mass in Swimming and Volleyball players over 3 years Increase in total mass, fat mass and % BF in basketball players over 3 years Swimming and track increased lean mass and track decreased fat mass from pre to post season Stanforth 2014 J Strength Cond Res. 28 (2): % fat, kg, 16.9 kg fat, 81.8 kg lean 12.4% fat, kg, 12.8 kg fat, 86.6 kg lean 233 Monitoring Effect of Season: Women Collegiate Athletes Off-season, preseason, postseason DXA in softball, basketball, volleyball, swimming, track jumpers and sprinters Significant pre and postseason changes in each sport in softball, basketball and volleyball Monitoring Rehabilitation 19 Year-old Female Athlete Collegiate basketball player DXA performed December of freshman and sophomore years ACL tear June between freshman/soph years; surgery performed promptly post injury Dedicated rehabilitation program postoperatively Carbuhn 2010 J Stength Cond Res 24 (7): Baseline 1 Year Follow-up Despite One Year of Rehabilitation, DXA Documents That Right/Left Leg Lean Mass Discrepancy Persists Baseline Lean (g) 12 month Lean (g) Difference in Lean (g) LSC grams Left Leg 7,022 6, Right Leg 7,204 7, Legs 14,226 14, Weight # Weight # BMI 22.9 kg/m 2 BMI 24.3 kg/m Total Body 42,741 44,251 +1, This reinforces the need to know your overall and regional LSC 37

46 Trend Changes in Male Swimmer Female Body Builder Monitoring Change BMI 21 Fat gain and lean loss with total mass loss 19.7% total fat on 2/9/2012, lean 90.1 lbs, fat 23.4 lbs 10.9 lb fat lossblack line, now 12.5 lbs fat mass 8.3 lb lean gainmagenta line, now 98.4 lbs lean mass 10.8% fat 8 months later Female Body Builder 8 Month Dramatic Changes USCF Body Composition Monitoring Protocol Every intervention has a start, middle, and end. Start whole body DXA Scan 1 Evaluate risk categories Set goals Middle Scan 2 Are the regional/total measures going in the right direction? Decrease fat mass Maintaining or increasing lean mass End Scan 3 Re-evaluate risk categories Strategize for long term monitoring 241 Why Monitoring with DXA Can Guide Weight Management 47 yo female, 175 cm tall initial Weight (kg) After 10 kg lean loss from diet, no exercise BMI (kg/m²) down Fat Mass (kg) Lean Mass (kg) FMI unchanged LMI %Fat Now obese 41 Year old male with 4 scans over a 3 month period. Training for first marathon. His body fat decreased from 31.8% to 21.2% Fat shown in yellow

47 Body Composition and RMR Can Guide Diet and Exercise Resting Metabolic Rate (RMR) is the amount of energy expended while at rest in a neutrally temperate environment RMR can be used to determine caloric intake for different activity levels and ages Fat mass, lean mass and bone all have different RMRs RMR goes up with increases in muscle mass (FFM) even if your weight doesn t change Summary of Models of RMR and associated technologies Reporting RMR from Body Composition Wang 2001 Obesity research 9(5): GE: Uses Baumgartner and Mifflin St-Jour. Scale and questionnaire Whole Body 3D Optical, MRI, DXA MRI, DXA BM = body mass H = height SSA = Skin Surface Area SM = skeletal muscle AT = adipose tissue Hologic: Doesn t report RMR but it can be calculated from one of the following equations on the next slide d DXA, BIA, Bod Pod There are a number of RMR equations using FFM and FM which can be obtained from DXA Dore equation compared best to Indirect Calorimetry Most derived in healthy may not be valid for children or the not healthy Otterstetter 2015 JACN ahead-of-print: men, 41 women, Bod Pod for FFM Using Nelson or Dore for DXA are reasonable approaches Otterstetter 2015 JACN ahead-of-print:

48 The RMR of 1 Subject at Different Weights Varies According to Fat and Lean Mass RMR (kcal)= (FFM) + 6.4(FM) -2.1(age) Exercise to Add Muscle Without Dieting Will Increase Lean Mass and Decrease %Fat, but Weight Will be Same or Higher, and Will Increase RMR Exercise RMR will be higher with more lean or mass or less % fat Diet With Exercise Tends to Keep RMR More Neutral to Higher Because You Gain Muscle, Lose Fat Weight thus % Fat is Lower Which Keeps Burning Calories Dieting without Exercise Typically Results in Muscle Loss and Then Lower RMR, Higher % Fat Diett An Extreme Example 0 weeks Weight: 208 lbs BMI: 25.3 FMI: Unknown LMI: Unknown %FAT: Unknown 12 weeks later Fothergill 2016 Obesity 00, doi: /oby

49 RMR (kcal/day) 4/3/ weeks later (2009) Weight: 208 lbs BMI: 25.3 FMI: Unknown LMI: Unknown %FAT: Unknown Week 0 Weight: 430 lb BMI: 60 Danny C Height: 5 11 Week 12 Weight: 191 lb BMI: years later (2014): Gained all weight back 6 years later (2016): 265 lb (bypass) Rudy's RMR Chart RMR at 400 lb (2009) 1500 RMR at 208 lb (2009) % 15% 25% 35% 45% 55% 65% 75% Percent Fat years later: 295 lbs Estimated to burn 800 cal Less than would be expected for his size 257 Initial and 6 yr fat loss was linear with weight change but shows lean change as well Lean Loss GE idxa using the mirror image analysis Fothergill 2016 Obesity 00, doi: /oby After 6 Years Their RMR has not Recovered and Their % Fat is Back to Baseline After 6 Years, Their Leptin Levels are Still Suppressed, Hungry all the Time

50 Summary of Biggest Loser Study Keeping weight off is hard! Losing lean mass lowers your metabolism (RMR) There are long term consequences to large weight losses that suppress your RMR more than expected for body mass You are hungry all the time Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: Weight Management and Obesity Sports performance assessment, symmetry, reference ranges and Relative Energy Deficiency Current Consumer applications Future Body Composition topics Physician for The Biggest Loser Recommends DXA for Body Composition Author Recommends DXA for Body Composition By LAURA JOHANNES Aug. 17, :28 p.m. ET

51 4/3/2017 The FitTrace App High accuracy body composition for consumers to: Track Share Compare Understand Interface with other apps Personal Fitness Record Know Your Fitness Know Your Fitness Basic Analysis - Single Scan Trending Analysis Know Your Fitness Know Your Fitness Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: Weight Management and Obesity Sports performance assessment, symmetry, reference ranges and Relative Energy Deficiency Current Consumer applications Future Body Composition topics Injury and Asymmetry Virtually all sports have a high risk of injury, especially to the limbs. Knee injuries account for nearly one third of the injuries sustained by elite ski racers and half of these injuries result in a significant time loss from sport (> 28 days). [Jordan 2015 Scand J Med Sci Sports 25: e301 e309.] ACL Injury even after neuromuscular function has return, known risk factors for ACL injury, (thigh muscle strength, bilateral limb asymmetry) often persist in non-athlete populations following ACL injury and ACL- Reconstruction despite rehabilitation and return to normal activities. DXA leg lean mass and asymmetry is being explored as a modifiable risk factor for injury and sports performance

52 Presentation Title and/or Sub Brand Name Here 4/3/ Presentation Title and/or Sub Brand Name Here 4/3/ /3/2017 Lower limb functional asymmetry associated with leg muscle asymmetry Hologic Discovery A r = 0.57; P < r = 0.66; P < 0.01 Leg lean asymmetry is associated with poor kicking performance Low accuracy Australian football kickers have asymmetrical lean mass in their legs ACL-R skiers uninjured skiers Left - kinetic impulse asymmetry index for the concentric phase of the countermovement jump (CMJ) Right kinetic impulse asymmetry index for phase 2 of the squat jump (SJ) (A) Normal versus (B) special ROI analysis for lower leg. symmetry index (SI) for lean mass components Lean mass imbalance of accurate (dark) and inaccurate (light) kickers, showing a deficiency (lower mass) in the kicking leg (left) and support leg (right) Jordan 2015 Scand J Med Sci Sports 25: e301 e309. Hart 2014 J Sports Sci Med 13_157 Calculating Body Volumes using DXA Regional and total volumes can be calculated using physical densities Volumes are intuitive and visual Applications Lymphedema Trunk to Leg Volume Ratio Total Body Volume Sizing Joseph Wilson UCSF/UC Berkeley PhD /3/ Example: Deriving Volumes from DXA Images and Reports 1. Pick equation appropriate for make of DXA system (Hologic or GE) 2. Pick regional values from DXA report (arms, legs, trunk, head, total) 3. Plug in the region s fat, lean, and BMC values to the equation to get the region s volume. How Can DXA be useful in studying limb asymmetry? DXA provides lean mass symmetry Arms Legs Ideal Sports Situation Functional symmetry and lean mass symmetry Lean mass asymmetry seems to lead to functional asymmetry, poor sport performance, injury DXA symmetry assessments may be useful for sports Symmetry Index (SI) Radar Charts with NHANES reference data Trunk to leg volume ratios for health assessment What should be the Future Default DXA Assessment? Current practice is to scan the spine and one hip to evaluate osteoporosis and fracture risk. This follows directly from ISCD positions Measure BMD at both the PA spine and hip in all patients What about fall risk? Lean mass is the best predictor or fall risk

53 Is a Paradigm Shift Possible? Three scans provides substantially more information than two. Spine monitoring change, TBS Hip defines Osteoporosis, used in FRAX Whole Body defines sarcopenia, ideal for probability of falls obesity diabetes risk Mortality Metabolic markers With modern computing capabilities, there is no reason diagnostically accurate total body, spine, and hip measures could not be integrated into one scan. 280 Should We Pursue "One Scan" Time/cost-effective, more accurate/precise use of DXA Scan time: < 5 minutes One positioning, continuous scan Radiation Dose: <10 microsieverts No head No feet Dual Hip BMD Legs down spine BMD ALM/Ht 2 Primed to take advantage of advances in imaging analysis 281 Learning Objectives Describe use of body composition assessment to aid in clinical decision making in: Weight Management and Obesity Sports performance assessment, symmetry, reference ranges and Relative Energy Deficiency Current Consumer applications Future Body Composition topics