The potential for a two-stage diabetes risk algorithm combining non-laboratory-based scores with subsequent routine non-fasting blood tests: Results from prospective studies in older men and women

Diabetic Medicine

Volumn 28, Issue 1, 2011, Pages 23-30

  Wannamethee, S G ^a   Papacosta, O ^a   Whincup, P H ^b   Thomas, M C ^a   Carson, C ^c   Lawlor, D A ^d   Ebrahim, S ^c   Sattar, N ^e  

^a ROYAL FREE AND UNIVERSITY COLLEGE MEDICAL SCHOOL   (United Kingdom)

^b ST GEORGE'S UNIVERSITY OF LONDON   (United Kingdom)

^c LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^d UNIVERSITY OF BRISTOL   (United Kingdom)

^e UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

Prediction scores; Prevention; Type 2 diabetes

Indexed keywords

BIOLOGICAL MARKER; C REACTIVE PROTEIN; GLUCOSE; HIGH DENSITY LIPOPROTEIN CHOLESTEROL; TRIACYLGLYCEROL;

ADULT; AGE; AGED; AREA UNDER THE CURVE; ARTICLE; BLOOD CHEMISTRY; BODY MASS; CHOLESTEROL BLOOD LEVEL; CLINICAL ASSESSMENT; FAMILY HISTORY; FEMALE; FOLLOW UP; GLUCOSE BLOOD LEVEL; HUMAN; HYPERTENSION; ISCHEMIC HEART DISEASE; MAJOR CLINICAL STUDY; MALE; NON INSULIN DEPENDENT DIABETES MELLITUS; PREDICTION; PREDICTOR VARIABLE; PROSPECTIVE STUDY; RECEIVER OPERATING CHARACTERISTIC; RISK ASSESSMENT; SENESCENCE; SENSITIVITY AND SPECIFICITY; SEX; SMOKING; TRIACYLGLYCEROL BLOOD LEVEL; WAIST CIRCUMFERENCE;

AGED; ALGORITHMS; AREA UNDER CURVE; BIOLOGICAL MARKERS; C-REACTIVE PROTEIN; DIABETES MELLITUS, TYPE 2; FEMALE; GREAT BRITAIN; HEMATOLOGIC TESTS; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; PROSPECTIVE STUDIES; RISK ASSESSMENT; RISK FACTORS;

EID: 78650232633     PISSN: 07423071     EISSN: 14645491     Source Type: Journal    
DOI: 10.1111/j.1464-5491.2010.03171.x     Document Type: Article

References (30)

1. Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 2004; 27: 1047-1053.
2. Gillies CL, Abrams KR, Lambert PC, Cooper NJ, Sutton AJ, Hsu RT et al. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. Br Med J 2007; 334: 299-302.
3. Lindström J, Peltonen M, Eriksson JG, Louheranta A, Fogelholm M, Uusitupa M et al. High-fibre, low-fat diet predicts long-term weight loss and decreased type 2 diabetes risk: the Finnish Diabetes Prevention Study. Diabetologia 2006; 49: 912-920.
4. Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 2003; 26: 725-731.
5. Griffin SJ, Little PS, Hales CN, Kinmonth AL, Wareham NJ. Diabetes risk score: towards earlier detection of type 2 diabetes in general practice. Diabetes Metab Res Rev 2000; 16: 164-171.
6. Schienkiewitz A, Schulze MB, Hoffmann K, Kroke A, Boeing H. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 2007; 3: 510-515.
7. Aekplakorn W, Bunnag P, Woodward M, Sritara P, Cheepudomwit S, Yamwong S et al. A risk score for predicting incident diabetes in the Thai population. Diabetes Care 2006; 29: 1872-1877.
8. Hippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. Br Med J 2009; 338: b880.
9. Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D'Agostino RB. Prediction of incident diabetes mellitus in middle-aged adults. Arch Intern Med 2007; 167: 1068-1074.
10. Schmidt MI, Golden SH, Duncan BB, Bang H, Pankow JS, Ballantyne CM et al. Identifying individuals at high risk for diabetes. Diabetes Care 2005; 28: 2013-2018.
11. Chien K, Cai T, Hsu H, Su T, Chang W, Chen M et al. A prediction model for type 2 diabetes risk among Chinese people. Diabetologia 2008; 52: 443-450.
12. Stern P, Williams K, Haffner SM. Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test to identify future cases of type 2 diabetes? Ann Intern Med 2002; 136: 575-581.
13. Balkau B, Lange C, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S et al. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care 2008; 31: 2056-2061.
14. Kolberg JA, Jorgensen T, Gerwien RW, Hamren S, McKenna MP, Moler E et al. Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care 2009; 32: 1207-1212.
15. Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg GW. Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years. Ann Intern Med 2009; 150: 741-751.
16. Shaper AG, Pocock SJ, Walker M, Cohen NM, Wale CJ. British Regional Heart Study: cardiovascular risk factors in middle-aged men in 24 towns. Br Med J 1981; 283: 179-186.
17. Lawlor DA, Bedford C, Taylor M, Ebrahim S. Geographical variation in cardiovascular disease, risk factors, and their control in older women: British Women's Heart and Health Study. J Epidemiol Community Health 2003; 57: 134-140.
18. Thomas M, Walker MK, Emberson JR, Thomson AG, Lawlor DA, Ebrahim S et al. Prevalence of undiagnosed Type 2 diabetes and impaired fasting glucose in older British men and women. Diabet Med 2005; 22: 789-793.
19. Wannamethee SG, Lowe GDO, Whincup PH, Rumley A, Walker M, Lennon L. Physical activity and hemostatic and inflammatory variables in elderly men. Circulation 2002; 105: 1785-1790.
20. Emberson J, Whincup PH, Walker M, Thomas M, Alberti KGMM. Biochemical measures in a population-based study: the effect of fasting duration and time of day. Ann Clin Biochem 2002; 39: 493-501.
21. Lawlor DA, Ebrahim S, Smith GD. Associations of measures of lung function with insulin resistance and Type 2 diabetes: findings from the British Women's Heart and Health Study. Diabetologia 2004; 47: 195-203.
22. Walker M, Shaper AG, Lennon L, Whincup PH. Twenty-year follow-up of a cohort study based in general practices in 24 British towns. J Publ Health Med 2000; 22: 479-485.
23. Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008; 27: 157-172.
24. Sullivan LM, Massaro JM, D'Agostino RB. Tutorial in biostatistics. Presentation of multivariate data for clinical use: The Framingham Study risk score functions. Stat Med 2004; 23: 1631-1660.
25. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361-387.
26. 1c assay in the diagnosis of diabetes. Diabetes Care 2009; 32: 1327-1334.
27. Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A et al. Emerging Risk Factors Collaboration. Major lipids, apolipoproteins, and risk of vascular disease. J Am Med Assoc 2009; 302: 1993-2000.
28. Schulze MB, Weikert C, Pischon T, Bergmann MM, Al-Hasani H, Schleicher E et al. Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: the EPIC-Potsdam Study. Diabetes Care 2009; 32: 2116-2119.
29. Chamnan P, Simmons RK, Khaw KT, Wareham NJ, Griffin SJ. Estimating the population impact of screening strategies for identifying and treating people at high risk of cardiovascular disease: modelling study. Br Med J 2010; 340: c1693.
30. Echouffo-Tcheugui JB, Simmons RK, Williams KM, Barling RS, Prevost AT, Kinmonth AL et al. The Additon-Cambridge trial protocol: a cluster-randomised controlled trial of screening for type 2 diabetes and intensive treatment for screen-detected patients. BMC Public Health 2009; 9: 136.