OBESITY IN PATIENTS WITH INTERSTITIAL LUNG DISEASE: A CROSS-SECTIONAL PILOT STUDY

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1 OBESITY IN PATIENTS WITH INTERSTITIAL LUNG DISEASE: A CROSS-SECTIONAL PILOT STUDY Sylvia Rinaldi, MScFN, RD Research Assistant Janet Madill, PhD, RD Primar y Investigator Marco Mura, MD, PhD Co-Investigator

2 FUNDING Supported the Ontario Lung Association/Ontario Respiratory Care Society No conflict of interest

3 BACKGROUND: INTERSTITIAL LUNG DISEASE (ILD) Idiopathic pulmonary fibrosis (IPF) is the most common form Involves inflammation and scarring of the interstitium (1) Estimated to affect ~30,000 Canadians (2) Associated with significant morbidity and mortality (3) 22.8% of bilateral lung and 32.9% of single lung transplants occur in ILD/IPF patients (4,5) 1. Alakhras et al. CHEST. 2007;131(5): Can Pulm Fibros Found Collard et al. Am J Respir Crit Care Med. 2003;168(5): Trillium Gift of Life Network. Transplants, Canadian Institute for Health Information, 2011.

4 BACKGROUND Previous studies, identified 25% of IPF patients as at risk of malnutrition using the Mini Nutrition Assessment (MNA ) (6) mean % predicted resting energy expenditure (pree) using fatfree mass (FFM) and Harris-Benedict Equation (HBE) compared to measured REE using indirect calorimetry was 120.8% and 118.7%, respectively (p<0.001) (7) have found that increased BMI is correlated with increased survival time (1,8) 1. Alakhras. Chest. 2007;131(5): Autore et al. Am J Resp Crit Care. 2013;187:A Fitting et al. Am Rev Resp Dis. 1990;142(3): Mura. Eur Respir J. 2012;40(1):101 9.

5 OBJECTIVE To investigate and ascertain a base line nutritional status and metabolic state of patients with ILD living in southwestern Ontario

6 METHODS Cross-sectional study Sampling procedures Convenience-sampling strategy Inclusion criteria Ambulatory patients over 18 years of age Patients attending the ILD clinic at Victoria Hospital in London, Ontario Sample size n=50

7 METHODS: DATA COLLECTED During clinic visit Bioelectrical impedance analysis 24-hr food recall Subjective global assessment Review of patient charts Height Weight Age ILD classification Blood work Pulmonary function tests 6-min walk test Outside of clinic visit Self administered 3-day food intake record

8 METHODS: STATISTICAL METHODS Statistical Methods Shapiro-Wilks Frequency distributions Means +/- standard deviations Chi-squared test Mann-Whitney U test Kruskal Wallis test p<0.05

9 RESULTS DEMOGRAPHICS Table 1: Participant demographics by gender. Age (years) BMI (kg/m 2 ) 1 Mean (total sample) n=50 Male (44%) Female (56%) 68.3 ± ± ± ± ± ± 7.6

10 RESULTS SUBJECTIVE GLOBAL ASSESSMENT SCORE SGA-A SGA-B SGA-C Figure 1: SGA Classification (n=50). 36% (blue), 58% (red), 6% (yellow) of participants were classified as SGA A, B and C, respectively. No significant differences were observed between gender or age.

11 RESULTS BODY COMPOSITION CLASSIFICATION Sarcopenic Obesity Normal Sarcopenia Obesity Figure 2: Participant classification (n=47): sarcopenic obesity (low FFMI, high FMI), obesity (normal fat-free mass index (FFMI), high fat mass index (FMI)), normal (normal FFMI and FMI) and sarcopenia (low FFMI, normal FMI) (adapted from Gonzalez et al, 2014).

12 RESULTS SARCOPENIC OBESITY 75% 25% SGA-A SGA-B BMI range: kg/m 2 Figure 3: SGA classification and BMI of participants with sarcopenic obesity (n=4).

13 RESULTS: PHASE ANGLE(10) Table 2: Mean phase angle (n=47) Total sample 4.98 ± 1.02 Classification Sarcopenic Obesity 4.13 ± 0.27 Sarcopenia 4.29 ± 0.33 Obesity 5.21 ± 0.18 Normal 5.65 ± 0.41 High FMI (Normal) 5.08 ± 0.98 (4.71 ± 1.13 ) Low FFMI (Normal) 4.24 ± 0.86 (5.27 ± 0.93 )* *p=0.003 Letters indicate significance between classification group, p <0.05

14 DISCUSSION/CONCLUSION Gender differences between BMI The majority of participants were obese with SGA-B Those with sarcopenic obesity had normal BMIs and SGA- A or SGA-B Phase angle lowest among those with sarcopenic obesity Limitations Sample size Future directions: Multi-site study Investigation into additional nutrition indicators

15 RELEVANCE TO PRACTICE Helps to develop nutrition care plans specific to ILD patients More in-depth counselling needs to be developed to address obesity and malnutrition Potential to impact clinical outcomes both pre- and post lung transplantation

16 ACKNOWLEDGEMENTS Dr. Janet Madill Dr. Marco Mura Classmates and Research Volunteers

17 QUESTIONS?

18 REFERENCES 1. Alakhras M, Decker PA, Nadrous HF, Collazo-Clavell M, Ryu JH. Body mass index and mor tality in patients with idiopathic pulmonar y fibrosis. CHEST. 2007;1 31(5): The Canadian Pulmonar y Fibrosis Foundation. About Pulmonar y Fibrosis [Internet]. Can Pulm Fibros Found Available from: yfibrosis.ca/about-pulmonar y-fibrosis. 3. Collard HR, King TE, Bartelson BBI, Vourlekis JS, Schwarz MI. Changes in clinical and physiologic variables predict sur vival in idiopathic pulmonar y fibrosis. Am J Respir Crit Care Med. 2003;168(5): Trillium Gift of Life Network. Transplants [cited 2014 Mar 7]. Available from: toflife.on.ca/en/transplant.htm 5. CIHI (Canadian Institute for Health Information). Canadian Organ Replacement Register Annual Repor t: Treatment of End-Stage Organ Failure in Canada, 2001 to 2010 [Internet]. Ottawa, ON: CIHI; 2011 [cited 2014 March 7]. 142p. Available from:

19 REFERENCES 6. Autore K. Black T. Klesen M. Richards T. Gibson KF. Kaminski N. Lindell KO. Evaluating nutritional satus in patietns with idiopathic pulmonar y fibrosis: A prospective pilot study using the Mini Nutritional Assessment Shor t Form. Am J Resp Crit Care. 2013:1 87;Meeting abstrast. 7. Fitting JM, Frascarolo P, Jéquier E, Leuenberger P. Resting Energy Expenditure in Inter stitial Lung Disease. American Review of Respirator y Disease. 1990;142(3); doi: /ajrccm/ Mura A, Porretta MA, Bargagli E, Sergiacomi G, Zompatori M, Sverzellati N, et al. Predicting sur vival in newly diagnosed idiopathic pulmonar y fibrosis: a 3-year prospective study. Eur Respir J ;40(1): Gonzalez MC, Pastore CA, Orlandi SP, Heymsfield SB. Obesity paradox in cancer: new insights provided by body composition. Am J Clin Nutr. 2014;99: Kyle UG, Genton L, Pichard C. Low phase angle determined by bioelectrical impedence anaylsis is associated with malnutrition and nutrional risk at hospital admission. Clin Nutr. 2013;32: