Isha Agarwal1, Nicole L Glazer2, Eddy Barasch3, Luc Djousse4, John S Gottdiener5, Joachim H Ix6, Jorge R Kizer7, Eric B Rimm8, David S Siscovick9, George L King10, Ken J Mukamal11. 1. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Department of Nutrition, Harvard School of Public Health, Boston, MA, USA. Electronic address: Isha_Agarwal@hms.harvard.edu. 2. Department of Medicine, Boston University, Boston, MA, USA. 3. Department of Research and Education, St. Francis Hospital, The Heart Center, Roslyn, NY, USA; SUNY at Stony Brook, Stony Brook, NY, USA. 4. Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. 5. Department of Medicine, University of Maryland Medical School, Baltimore, MD, USA. 6. Department of Medicine, University of California San Diego, San Diego, CA, USA; Veterans Affairs San Diego Healthcare System, San Diego, CA, USA. 7. Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Epidemiology, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Population Health, Albert Einstein College of Medicine, Bronx, NY, USA. 8. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Department of Nutrition, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA. 9. The New York Academy of Medicine, New York, NY, USA; Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA; Department of Epidemiology, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA. 10. Research Division, Joslin Diabetes Center and Department of Medicine, Harvard Medical School, Boston, MA, USA. 11. Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Abstract
AIM: Fibrosis is one postulated pathway by which diabetes produces cardiac and other systemic complications. Our aim was to determine which metabolic parameters are associated with circulating fibrosis-related biomarkers transforming growth factor-β (TGF-β) and procollagen type III N-terminal propeptide (PIIINP). METHODS: We used linear regression to determine the cross-sectional associations of diverse metabolic parameters, including fasting glucose, fasting insulin, body mass index, fatty acid binding protein 4, and non-esterified fatty acids, with circulating levels of TGF-β (n = 1559) and PIIINP (n = 3024) among community-living older adults in the Cardiovascular Health Study. RESULTS: Among the main metabolic parameters we examined, only fasting glucose was associated with TGF-β (P = 0.03). In contrast, multiple metabolic parameters were associated with PIIINP, including fasting insulin, body mass index, and non-esterified fatty acids (P<0.001, P<0.001, P=0.001, respectively). These associations remained statistically significant after mutual adjustment, except the association between BMI and PIIINP. CONCLUSIONS: Isolated hyperglycemia is associated with higher serum concentrations of TGF-β, while a broader phenotype of insulin resistance is associated with higher serum PIIINP. Whether simultaneous pharmacologic targeting of these two metabolic phenotypes can synergistically reduce the risk of cardiac and other manifestations of fibrosis remains to be determined.
AIM: Fibrosis is one postulated pathway by which diabetes produces cardiac and other systemic complications. Our aim was to determine which metabolic parameters are associated with circulating fibrosis-related biomarkers transforming growth factor-β (TGF-β) and procollagen type III N-terminal propeptide (PIIINP). METHODS: We used linear regression to determine the cross-sectional associations of diverse metabolic parameters, including fasting glucose, fasting insulin, body mass index, fatty acid binding protein 4, and non-esterified fatty acids, with circulating levels of TGF-β (n = 1559) and PIIINP (n = 3024) among community-living older adults in the Cardiovascular Health Study. RESULTS: Among the main metabolic parameters we examined, only fasting glucose was associated with TGF-β (P = 0.03). In contrast, multiple metabolic parameters were associated with PIIINP, including fasting insulin, body mass index, and non-esterified fatty acids (P<0.001, P<0.001, P=0.001, respectively). These associations remained statistically significant after mutual adjustment, except the association between BMI and PIIINP. CONCLUSIONS:Isolated hyperglycemia is associated with higher serum concentrations of TGF-β, while a broader phenotype of insulin resistance is associated with higher serum PIIINP. Whether simultaneous pharmacologic targeting of these two metabolic phenotypes can synergistically reduce the risk of cardiac and other manifestations of fibrosis remains to be determined.
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