Kathleen M McClain1, Christine M Friedenreich2,3, Charles E Matthews1, Joshua N Sampson1, David P Check1, Darren R Brenner3, Kerry S Courneya4, Rachel A Murphy5,6, Steven C Moore1. 1. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA. 2. Department of Cancer Epidemiology and Prevention Research, Cancer Care Alberta, Alberta Health Services, Edmonton, AB, Canada. 3. Departments of Oncology and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. 4. Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Edmonton, AB, Canada. 5. School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada. 6. Cancer Control Research, BC Cancer, Vancouver, BC, Canada.
Abstract
BACKGROUND: Obesity is correlated with many biomarkers, but the extent to which these correlate with underlying body composition is poorly understood. OBJECTIVES: Our objectives were to 1) describe/compare distinct contributions of fat/lean mass with BMI-metabolite correlations and 2) identify novel metabolite biomarkers of fat/lean mass. METHODS: The Alberta Physical Activity and Breast Cancer Prevention Trial was a 2-center randomized trial of healthy, inactive, postmenopausal women (n = 304). BMI (in kg/m2) was calculated using weight and height, whereas DXA estimated fat/lean mass. Ultra-performance liquid chromatography and mass spectrometry measured relative concentrations of serum metabolite concentrations. We estimated partial Pearson correlations between 1052 metabolites and BMI, adjusting for age, smoking, and site. Fat mass index (FMI; kg/m2) and lean mass index (LMI; kg/m2) correlations were estimated similarly, with mutual adjustment to evaluate independent effects. RESULTS: Using a Bonferroni-corrected α level <4.75 × 10-5, we observed 53 BMI-correlated metabolites (|r| = 0.24-0.42). Of those, 21 were robustly correlated with FMI (|r| > 0.20), 25 modestly (0.10 ≤ |r| ≤ 0.20), and 7 virtually null (|r| < 0.10). Ten of 53 were more strongly correlated with LMI than with FMI. Examining non-BMI-correlated metabolites, 6 robustly correlated with FMI (|r| = 0.24-0.31) and 2 with LMI (r = 0.25-0.26). For these, correlations for fat and lean mass were in opposing directions compared with BMI-correlated metabolites, in which correlations were mostly in the same direction. CONCLUSIONS: Our results demonstrate how a thorough evaluation of the components of fat and lean mass, along with BMI, provides a more accurate assessment of the associations between body composition and metabolites than BMI alone. Such an assessment makes evident that some metabolites correlated with BMI predominantly reflect lean mass rather than fat, and some metabolites related to body composition are not correlated with BMI. Correctly characterizing these relations is important for an accurate understanding of how and why obesity is associated with disease. Published by Oxford University Press on behalf of the American Society for Nutrition 2021.
BACKGROUND: Obesity is correlated with many biomarkers, but the extent to which these correlate with underlying body composition is poorly understood. OBJECTIVES: Our objectives were to 1) describe/compare distinct contributions of fat/lean mass with BMI-metabolite correlations and 2) identify novel metabolite biomarkers of fat/lean mass. METHODS: The Alberta Physical Activity and Breast Cancer Prevention Trial was a 2-center randomized trial of healthy, inactive, postmenopausal women (n = 304). BMI (in kg/m2) was calculated using weight and height, whereas DXA estimated fat/lean mass. Ultra-performance liquid chromatography and mass spectrometry measured relative concentrations of serum metabolite concentrations. We estimated partial Pearson correlations between 1052 metabolites and BMI, adjusting for age, smoking, and site. Fat mass index (FMI; kg/m2) and lean mass index (LMI; kg/m2) correlations were estimated similarly, with mutual adjustment to evaluate independent effects. RESULTS: Using a Bonferroni-corrected α level <4.75 × 10-5, we observed 53 BMI-correlated metabolites (|r| = 0.24-0.42). Of those, 21 were robustly correlated with FMI (|r| > 0.20), 25 modestly (0.10 ≤ |r| ≤ 0.20), and 7 virtually null (|r| < 0.10). Ten of 53 were more strongly correlated with LMI than with FMI. Examining non-BMI-correlated metabolites, 6 robustly correlated with FMI (|r| = 0.24-0.31) and 2 with LMI (r = 0.25-0.26). For these, correlations for fat and lean mass were in opposing directions compared with BMI-correlated metabolites, in which correlations were mostly in the same direction. CONCLUSIONS: Our results demonstrate how a thorough evaluation of the components of fat and lean mass, along with BMI, provides a more accurate assessment of the associations between body composition and metabolites than BMI alone. Such an assessment makes evident that some metabolites correlated with BMI predominantly reflect lean mass rather than fat, and some metabolites related to body composition are not correlated with BMI. Correctly characterizing these relations is important for an accurate understanding of how and why obesity is associated with disease. Published by Oxford University Press on behalf of the American Society for Nutrition 2021.
Entities:
Keywords:
adiposity; body composition; fat mass; lean mass; metabolomics; obesity
Authors: R S Lindsay; R L Hanson; J Roumain; E Ravussin; W C Knowler; P A Tataranni Journal: J Clin Endocrinol Metab Date: 2001-09 Impact factor: 5.958
Authors: Susan Cheng; Eugene P Rhee; Martin G Larson; Gregory D Lewis; Elizabeth L McCabe; Dongxiao Shen; Melinda J Palma; Lee D Roberts; Andre Dejam; Amanda L Souza; Amy A Deik; Martin Magnusson; Caroline S Fox; Christopher J O'Donnell; Ramachandran S Vasan; Olle Melander; Clary B Clish; Robert E Gerszten; Thomas J Wang Journal: Circulation Date: 2012-04-11 Impact factor: 29.690
Authors: Tuo Deng; Christopher J Lyon; Stephen Bergin; Michael A Caligiuri; Willa A Hsueh Journal: Annu Rev Pathol Date: 2016-05-23 Impact factor: 23.472
Authors: Scott C Ritchie; Peter Würtz; Artika P Nath; Gad Abraham; Aki S Havulinna; Liam G Fearnley; Antti-Pekka Sarin; Antti J Kangas; Pasi Soininen; Kristiina Aalto; Ilkka Seppälä; Emma Raitoharju; Marko Salmi; Mikael Maksimow; Satu Männistö; Mika Kähönen; Markus Juonala; Samuli Ripatti; Terho Lehtimäki; Sirpa Jalkanen; Markus Perola; Olli Raitakari; Veikko Salomaa; Mika Ala-Korpela; Johannes Kettunen; Michael Inouye Journal: Cell Syst Date: 2015-10-22 Impact factor: 10.304