New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. - Dupuis, Josée; Langenberg, Claudia; Prokopenko, Inga; Saxena, Richa; Soranzo, Nicole; Jackson, Anne U; Wheeler, Eleanor; Glazer, Nicole L; Bouatia-Naji, Nabila; Gloyn, Anna L; Lindgren, Cecilia M; Mägi, Reedik; et al. (Nat Genet, 2010)
Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.
Keywords: Adolescent; Adult; Alleles; Blood Glucose; Child; Databases, Genetic; Diabetes Mellitus, Type 2; DNA Copy Number Variations; Fasting; Gene Expression Regulation; Genetic Loci; Genetic Predisposition to Disease; Genome-Wide Association Study; Homeostasis; Humans; Meta-Analysis as Topic; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Quantitative Trait, Heritable; Reproducibility of Results

Associations between metabolic dysregulation and circulating biomarkers of fibrosis: the Cardiovascular Health Study. - Agarwal, Isha; Glazer, Nicole L; Barasch, Eddy; Djoussé, Luc; Gottdiener, John S; Ix, Joachim H; Kizer, Jorge R; Rimm, Eric B; Siscovick, David S; King, George L; Mukamal, Ken J (Metabolism, 2015)
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.
Keywords: Aged; Aged, 80 and over; Biomarkers; Blood Glucose; Cardiovascular System; Cross-Sectional Studies; Diabetes Complications; Diabetes Mellitus; Fatty Acids, Nonesterified; Female; Fibrosis; Health; Humans; Insulin; Insulin Resistance; Male; Metabolic Diseases; Peptide Fragments; Procollagen; Transforming Growth Factor beta