title: Circulating fatty acids, non-high density lipoprotein cholesterol, and insulin-infused fat oxidation acutely influence whole body insulin sensitivity in nondiabetic men.
abstract: Circulating lipids and tissue lipid depots predict insulin sensitivity. Associations between fat oxidation and insulin sensitivity are variable. We examined whether circulating lipids and fat oxidation independently influence insulin sensitivity. We also examined interrelationships among circulating lipids, fat oxidation, and tissue lipid depots. Fifty-nine nondiabetic males (age, 45.4 +/- 2 yr; body mass index, 29.1 +/- 0.5 kg/m(2)) had fasting circulating nonesterified fatty acids (NEFAs) and lipids measured, euglycemic-hyperinsulinemic clamp for whole body insulin sensitivity [glucose infusion rate (GIR)], substrate oxidation, body composition (determined by dual energy x-ray absorptiometry), and skeletal muscle triglyceride (SMT) measurements. GIR inversely correlated with fasting NEFAs (r = -0.47; P = 0.0002), insulin-infused NEFAs (n = 38; r = -0.62; P < 0.0001), low-density lipoprotein cholesterol (r = -0.50; P < 0.0001), non-high-density lipoprotein cholesterol (r = -0.52; P < 0.0001), basal fat oxidation (r = -0.32; P = 0.03), insulin-infused fat oxidation (r = -0.40; P = 0.02), SMT (r = -0.28; P < 0.05), and central fat (percentage; r = -0.59; P < 0.0001). NEFA levels correlated with central fat, but not with total body fat or SMT. Multiple regression analysis showed non-high-density lipoprotein cholesterol, fasting NEFAs, insulin-infused fat oxidation, and central fat to independently predict GIR, accounting for approximately 60% of the variance. Circulating fatty acids, although closely correlated with central fat, independently predict insulin sensitivity. Insulin-infused fat oxidation independently predicts insulin sensitivity across a wide range of adiposity. Therefore, lipolytic regulation as well as amount of central fat are important in modulating insulin sensitivity.
authors.affiliation: Diabetes and Obesity, Garvan Institute of Medical Research, Sydney 2010, Australia. a.poynten@garvan.unsw.org.au
authors.forename: A M
authors.initials: AM
authors.lastname: Poynten
authors.forename: S K
authors.initials: SK
authors.lastname: Gan
authors.forename: A D
authors.initials: AD
authors.lastname: Kriketos
authors.forename: L V
authors.initials: LV
authors.lastname: Campbell
authors.forename: D J
authors.initials: DJ
authors.lastname: Chisholm
mesh_terms: D000328:Adult; D000368:Aged; D001055:Apolipoproteins B / Q000097:blood; D002784:Cholesterol / Q000097:blood*; D008076:Cholesterol, HDL / Q000097:blood; D008078:Cholesterol, LDL / Q000097:blood; D005230:Fatty Acids, Nonesterified / Q000097:blood* / Q000378:metabolism*; D006801:Humans; D007328:Insulin / Q000494:pharmacology*; D008074:Lipoproteins / Q000097:blood*; D008297:Male; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000166:cytology / Q000502:physiology; D012016:Reference Values; D012044:Regression Analysis; D014280:Triglycerides / Q000097:blood / Q000378:metabolism
chemical_list: D001055:Apolipoproteins B; D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D005230:Fatty Acids, Nonesterified; D007328:Insulin; D008074:Lipoproteins; D014280:Triglycerides; D002784:Cholesterol