"Prediction is very hard, especially about the future": New biomarkers for type 2 diabetes?

Diabetes

Volumn 62, Issue 5, 2013, Pages 1384-1385

  Lowe, William L ^a   Bain, James R ^b  

^a NORTHWESTERN UNIVERSITY   (United States)

^b DUKE UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

1 LINOLEOYLGLYCEROPHOSPHOCHOLINE; 2 HYDROXYBUTYRIC ACID; BIOLOGICAL MARKER; GLUCOSE; GLYCEROPHOSPHORYLCHOLINE; HYDROXYBUTYRIC ACID; INSULIN; LACTATE DEHYDROGENASE; NICOTINAMIDE ADENINE DINUCLEOTIDE; REDUCED NICOTINAMIDE ADENINE DINUCLEOTIDE; UNCLASSIFIED DRUG;

ALGORITHM; DIABETIC PATIENT; DIET RESTRICTION; FAMILY HISTORY; FOLLOW UP; GENE TARGETING; GENETIC ASSOCIATION; GENETIC RISK; GENETIC VARIABILITY; GLUCOSE TOLERANCE; HEALTH PROGRAM; HISTOPATHOLOGY; HUMAN; IMPAIRED GLUCOSE TOLERANCE; INSULIN BLOOD LEVEL; INSULIN RELEASE; INSULIN RESISTANCE; INSULIN SENSITIVITY; LIFESTYLE MODIFICATION; METABOLOMICS; NON INSULIN DEPENDENT DIABETES MELLITUS; NOTE; OBESITY; PANCREAS ISLET BETA CELL; PERSONALIZED MEDICINE; PREDICTION; PREDICTIVE VALUE; PREVALENCE; PRIORITY JOURNAL; RISK ASSESSMENT;

EID: 84876539834     PISSN: 00121797     EISSN: 1939327X     Source Type: Journal    
DOI: 10.2337/db13-0057     Document Type: Note

References (23)

1. Nguyen NT, Nguyen XM, Lane J, Wang P. Relationship between obesity and diabetes in a US adult population: findings from the National Health and Nutrition Examination Survey, 1999-2006. Obes Surg 2011;21:351-355
2. Knowler WC, Barrett-Connor E, Fowler SE, et al.; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346:393-403 (Pubitemid 34437694)
3. Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D 'Agostino RB Sr. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med 2007;167:1068-1074 (Pubitemid 46835795)
4. Morris AP, Voight BF, Teslovich TM, et al.; Wellcome Trust Case Control Consortium; Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; Asian Genetic Epidemiology Network-Type 2 Diabetes (AGEN-T2D) Consortium; South Asian Type 2 Diabetes (SAT2D) Consortium; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 2012;44:981-990
5. Vassy JL, Durant NH, Kabagambe EK, et al. A genotype risk score predicts type 2 diabetes from young adulthood: the CARDIA study. Diabetologia 2012;55:2604-2612
6. Psychogios N, Hau DD, Peng J, et al. The human serum metabolome. PLoS ONE 2011;6:e16957
7. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB. Metabolomics applied to diabetes research: moving from information to knowledge. Diabetes 2009;58:2429-2443
8. Newgard CB. Interplay between lipids and branched-chain amino acids in development of insulin resistance. Cell Metab 2012;15:606-614
9. Wang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med 2011;17:448-453
10. Würtz P, Soininen P, Kangas AJ, et al. Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 2013;36:648-655
11. Würtz P, Tiainen M, Mäkinen VP, et al. Circulating metabolite predictors of glycemia in middle-aged men and women. Diabetes Care 2012;35:1749-1756
12. Floegel A, Stefan N, Yu Z, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 2013;62:639 -647
13. McCormack SE, Shaham O, McCarthy MA, et al. Circulating branchedchain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents. Pediatr Obes 2013;8:52-61
14. Gall WE, Beebe K, Lawton KA, et al.; RISC Study Group. alpha-Hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE 2010;5:e10883
15. Ferrannini E, Natali A, Camastra S, et al. Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance. Diabetes 2013;62:1730-1737
16. Wang-Sattler R, Yu Z, Herder C, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol 2012;8:615
17. Kumps A, Duez P, Mardens Y. Metabolic, nutritional, iatrogenic, and artifactual sources of urinary organic acids: a comprehensive table. Clin Chem 2002;48:708-717 (Pubitemid 34439102)
18. Rubio-Aliaga I, de Roos B, Duthie SJ, et al. Metabolomics of prolonged fasting in humans reveals new catabolic markers. Metabolomics 2011;7:375-387
19. Fiehn O, Garvey WT, Newman JW, Lok KH, Hoppel CL, Adams SH. Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS ONE 2010;5:e15234
20. Pettersen JE, Landaas S, Eldjarn L. The occurrence of 2-hydroxybutyric acid in urine from patients with lactic acidosis. Clin Chim Acta 1973;48:213-219
21. Cheng S, Rhee EP, Larson MG, et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 2012;125:2222-2231
22. Rhee EP, Cheng S, Larson MG, et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest 2011;121:1402-1411
23. Juraschek SP, Steffes MW, Miller ER 3rd, Selvin E. Alternative markers of hyperglycemia and risk of diabetes. Diabetes Care 2012;35:2265-2270