Sankar D Navaneethan1, John P Kirwan2, Erick M Remer3, Erika Schneider3, Bryan Addeman4, Susana Arrigain5, Ed Horwitz6, Jeffrey C Fink7, James P Lash8, Charles A McKenzie9, Mahboob Rahman10, Panduranga S Rao11, Jesse D Schold12, Tariq Shafi13, Jonathan J Taliercio14, Raymond R Townsend15, Harold I Feldman16. 1. Section of Nephrology, Department of Medicine, Baylor University College of Medicine, Houston, TX; Section of Nephrology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX; Institute of Clinical and Translational Research, Baylor College of Medicine, Houston, TX. Electronic address: sankar.navaneethan@bcm.edu. 2. Integrated Physiology and Molecular Medicine, Pennington Biomedical Research Center, Baton Rouge, LA. 3. Imaging Institute, Cleveland Clinic Foundation, Cleveland, OH. 4. Canatomical Services, Toronto, Ontario, Canada. 5. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH. 6. Department of Nephrology, MetroHealth Medical Center, Cleveland, OH. 7. Department of Medicine, University of Maryland School of Medicine, Baltimore, MD. 8. Division of Nephrology, Department of Medicine, University of Illinois at Chicago College of Medicine, Chicago, IL. 9. Canatomical Services, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada. 10. Division of Nephrology and Hypertension, Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH. 11. Division of Nephrology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI. 12. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH; Center for Populations Health Research, Lerner Research Institute, Cleveland Clinic, Cleveland, OH. 13. Department of Medicine, University of Mississippi Medical Center, Jackson, MS. 14. Department of Nephrology and Hypertension, Cleveland Clinic, Cleveland, OH. 15. Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA. 16. Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD.
Abstract
RATIONALE & OBJECTIVES: Adiposity and physical fitness levels are major drivers of cardiometabolic risk, but these relationships have not been well-characterized in chronic kidney disease (CKD). We examined the associations of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intrahepatic fat, and physical function with inflammation, insulin resistance, and adipokine levels in patients with CKD. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Participants with stages 3-5 CKD not receiving maintenance dialysis, followed up at one of 8 clinical sites in the Chronic Renal Insufficiency Cohort (CRIC) Study, and who underwent magnetic resonance imaging of the abdomen at an annual CRIC Study visit (n = 419). PREDICTORS: VAT volume, SAT volume, intrahepatic fat, body mass index, waist circumference, and time taken to complete the 400-m walk test (physical function). OUTCOMES: Markers of inflammation (interleukin 1β [IL-1β], IL-6, tumor necrosis factor receptor 1 [TNFR1], and TNFR2), insulin resistance (homeostasis model assessment of insulin resistance), and adipokine levels (adiponectin, total and high molecular weight, resistin, and leptin). ANALYTICAL APPROACH: Multivariable linear regression of VAT and SAT volume, intrahepatic fat, and physical function with individual markers (log-transformed values), adjusting for relevant covariates. RESULTS: Mean age of the study population was 64.3 years; 41% were women, and mean estimated glomerular filtration rate was 53.2±14.6 (SD) mL/min/1.73m2. More than 85% were overweight or obese, and 40% had diabetes. Higher VAT volume, SAT volume, and liver proton density fat fraction were associated with lower levels of total and high-molecular-weight adiponectin, higher levels of leptin and insulin resistance, and lower high-density lipoprotein cholesterol and higher serum triglyceride levels. A slower 400-m walk time was associated only with higher levels of leptin, total adiponectin, plasma IL-6, and TNFR1 and did not modify the associations between fat measures and cardiometabolic risk factors. LIMITATIONS: Lack of longitudinal data and dietary details. CONCLUSIONS: Various measures of adiposity are associated with cardiometabolic risk factors. Physical function was also associated with the cardiometabolic risk factors studied and does not modify associations between fat measures and cardiometabolic risk factors. Longitudinal studies of the relationship between body fat and aerobic fitness with cardiovascular and kidney disease progression are warranted.
RATIONALE & OBJECTIVES: Adiposity and physical fitness levels are major drivers of cardiometabolic risk, but these relationships have not been well-characterized in chronic kidney disease (CKD). We examined the associations of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intrahepatic fat, and physical function with inflammation, insulin resistance, and adipokine levels in patients with CKD. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Participants with stages 3-5 CKD not receiving maintenance dialysis, followed up at one of 8 clinical sites in the Chronic Renal Insufficiency Cohort (CRIC) Study, and who underwent magnetic resonance imaging of the abdomen at an annual CRIC Study visit (n = 419). PREDICTORS: VAT volume, SAT volume, intrahepatic fat, body mass index, waist circumference, and time taken to complete the 400-m walk test (physical function). OUTCOMES: Markers of inflammation (interleukin 1β [IL-1β], IL-6, tumor necrosis factor receptor 1 [TNFR1], and TNFR2), insulin resistance (homeostasis model assessment of insulin resistance), and adipokine levels (adiponectin, total and high molecular weight, resistin, and leptin). ANALYTICAL APPROACH: Multivariable linear regression of VAT and SAT volume, intrahepatic fat, and physical function with individual markers (log-transformed values), adjusting for relevant covariates. RESULTS: Mean age of the study population was 64.3 years; 41% were women, and mean estimated glomerular filtration rate was 53.2±14.6 (SD) mL/min/1.73m2. More than 85% were overweight or obese, and 40% had diabetes. Higher VAT volume, SAT volume, and liver proton density fat fraction were associated with lower levels of total and high-molecular-weight adiponectin, higher levels of leptin and insulin resistance, and lower high-density lipoprotein cholesterol and higher serum triglyceride levels. A slower 400-m walk time was associated only with higher levels of leptin, total adiponectin, plasma IL-6, and TNFR1 and did not modify the associations between fat measures and cardiometabolic risk factors. LIMITATIONS: Lack of longitudinal data and dietary details. CONCLUSIONS: Various measures of adiposity are associated with cardiometabolic risk factors. Physical function was also associated with the cardiometabolic risk factors studied and does not modify associations between fat measures and cardiometabolic risk factors. Longitudinal studies of the relationship between body fat and aerobic fitness with cardiovascular and kidney disease progression are warranted.
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