Download PDF
Horm Metab Res 2010; 42: S3-S36
DOI: 10.1055/s-0029-1240928
Guidelines

© Georg Thieme Verlag KG Stuttgart · New York

A European Evidence-Based Guideline for the Prevention of Type 2 Diabetes

B. Paulweber1 , P. Valensi2 , J. Lindström3 , N. M. Lalic4 , C. J. Greaves5 , M. McKee6 , K. Kissimova-Skarbek7 , S. Liatis8 , E. Cosson2 , J. Szendroedi9 , K. E. Sheppard5 , K. Charlesworth6 , A.-M. Felton10 , M. Hall11 , A. Rissanen12 , J. Tuomilehto13 , P. E. Schwarz14 , M. Roden9 Writing Group: M. Paulweber, A. Stadlmayr, L. Kedenko, N. Katsilambros, K. Makrilakis, Z. Kamenov, P. Evans, A. Gilis-Januszewska, K. Lalic, A. Jotic, P. Djordevic, V. Dimitrijevic-Sreckovic, U. Hühmer, B. Kulzer, S. Puhl, Y. H. Lee-Barkey, A. AlKerwi, C. Abraham, W. Hardeman IMAGE Study Group: T. Acosta, M. Adler, A. AlKerwi, N. Barengo, R. Barengo, J. M. Boavida, K. Charlesworth, V. Christov, B. Claussen, X. Cos, E. Cosson, S. Deceukelier, V. Dimitrijevic-Sreckovic, P. Djordjevic, P. Evans, A.-M. Felton, M. Fischer, R. Gabriel-Sanchez, A. Gilis-Januszewska, M. Goldfracht, J. L. Gomez, C. J. Greaves, M. Hall, U. Handke, H. Hauner, J. Herbst, N. Hermanns, L. Herrebrugh, C. Huber, U. Hühmer, J. Huttunen, A. Jotic, Z. Kamenov, S. Karadeniz, N. Katsilambros, M. Khalangot, K. Kissimova-Skarbek, D. Köhler, V. Kopp, P. Kronsbein, B. Kulzer, D. Kyne-Grzebalski, K. Lalic, N. Lalic, R. Landgraf, Y. H. Lee-Barkey, S. Liatis, J. Lindström, K. Makrilakis, C. McIntosh, M. McKee, A. C. Mesquita, D. Misina, F. Muylle, A. Neumann, A. C. Paiva, P. Pajunen, B. Paulweber, M. Peltonen, L. Perrenoud, A. Pfeiffer, A. Pölönen, S. Puhl, F. Raposo, T. Reinehr, A. Rissanen, C. Robinson, M. Roden, U. Rothe, T. Saaristo, J. Scholl, P. E. Schwarz, K. E. Sheppard, S. Spiers, T. Stemper, B. Stratmann, J. Szendroedi, Z. Szybinski, T. Tankova, V. Telle-Hjellset, G. Terry, D. Tolks, F. Toti, J. Tuomilehto, A. Undeutsch, C. Valadas, P. Valensi, D. Velickiene, P. Vermunt, R. Weiss, J. Wens, T. Yilmaz
  • 1Paracelsus Medical University, Salzburg, Austria
  • 2Department of Endocrinology Diabetology Nutrition, Jean Verdier Hospital, AP‐HP, Paris-Nord University, CRNH‐IdF, Bondy, France
  • 3Department of Chronic Disease Prevention Diabetes Prevention Unit, National Institute for Health and Welfare (THL), Helsinki, Finland
  • 4Diabetes Center, Institute for Endocrinology, Diabetes and Metabolic Diseases School of Medicine, Clinical Center of Serbia, University of Belgrade, Belgrade, Serbia
  • 5Peninsula Medical School, University of Exeter, Exeter, United Kingdom
  • 6European Centre on Health of Societies in Transition, London School of Hygiene and Tropical Medicine, London, United Kingdom
  • 7Executive Office, International Diabetes Federation (IDF), Brussels, Belgium
  • 8Diabetes Center, Laiko Hospital, Athens University Medical School, Athens, Greece
  • 9Karl-Landsteiner Institute for Endocrinology and Metabolism, Vienna, Austria, Institute for Clinical Diabetology, German Diabetes Center, and Department of Metabolic Diseases, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
  • 10President Federation of European Nurses in Diabetes (FEND), London, United Kingdom
  • 11Board member, International Diabetes Foundation-Europe (IDF-Europe), Brussels, Belgium
  • 12Vice-President, European Association for the Study of Obesity (EASO) and Obesity Research Unit, Helsinki University Central Hospital, Helsinki, Finland
  • 13Hjelt Institute, Department of Public Health, University of Helsinki, Helsinki, Finland, and South Ostrobothnia Central Hospital, Seinäjoki, Finland, and Spanish Diabetes Foundation, Madrid, Spain
  • 14Carl Gustav Carus Medical Faculty, Technical University of Dresden, Dresden, Germany
Further Information

Publication History

Publication Date:
13 April 2010 (online)

Also available at
   Permissions and Reprints

Abstract

Background: The prevalence and socioeconomic burden of type 2 diabetes (T2DM) and associated co-morbidities are rising worldwide. Aims: This guideline provides evidence-based recommendations for preventing T2DM. Methods: A European multidisciplinary consortium systematically reviewed the evidence on the effectiveness of screening and interventions for T2DM prevention using SIGN criteria. Results: Obesity and sedentary lifestyle are the main modifiable risk factors. Age and ethnicity are non-modifiable risk factors. Case-finding should follow a step-wise procedure using risk questionnaires and oral glucose tolerance testing. Persons with impaired glucose tolerance and/or fasting glucose are at high-risk and should be prioritized for intensive intervention. Interventions supporting lifestyle changes delay the onset of T2DM in high-risk adults (number-needed-to-treat: 6.4 over 1.8–4.6 years). These should be supported by inter-sectoral strategies that create health promoting environments. Sustained body weight reduction by ≥ 5 % lowers risk. Currently metformin, acarbose and orlistat can be considered as second-line prevention options. The population approach should use organized measures to raise awareness and change lifestyle with specific approaches for adolescents, minorities and disadvantaged people. Interventions promoting lifestyle changes are more effective if they target both diet and physical activity, mobilize social support, involve the planned use of established behaviour change techniques, and provide frequent contacts. Cost-effectiveness analysis should take a societal perspective. Conclusions: Prevention using lifestyle modifications in high-risk individuals is cost-effective and should be embedded in evaluated models of care. Effective prevention plans are predicated upon sustained government initiatives comprising advocacy, community support, fiscal and legislative changes, private sector engagement and continuous media communication.

References

  • 1 Cautinho M, Gerstein H C, Wang Y, Yusuf S. The relationship between glucose and incident cardiovascular events. A metaregression analysis of published data from 20 studies of 95,783 individuals followed for 12.4 years.  Diabetes Care. 1999;  22 (Suppl. 2) 233-240
    Google Scholar
  • 2 Tominaga M, Eguchi H, Manaka H, Igarashi K, Kato T, Sekikawa A. Impaired glucose tolerance is a risk factor for cardiovascular disease, but not impaired fasting glucose. The Funagata Diabetes Study.  Diabetes Care. 1999;  22 920-924
    Google Scholar
  • 3 Galassi A, Reynolds K, He J. Metabolic syndrome and risk of cardiovascular disease: a meta-analysis.  Am J Med. 2006;  119 812-819
    Google Scholar
  • 4 Narayan K M, Boyle J P, Thompson T J, Sorensen S W, Williamson D F. Lifetime risk for diabetes mellitus in the United States.  JAMA. 2003;  290 1884-1890
    Google Scholar
  • 5 The DECODE Study Group on behalf of the European Diabetes Epidemiology Group . Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria.  Arch Intern Med. 2001;  161 397-405
    Google Scholar
  • 6 Shaw J. Epidemiology of childhood type 2 diabetes and obesity.  Pediatr Diabetes. 2007;  8 (Suppl. 9) 7-15
    Google Scholar
  • 7 Rocchini A P. Childhood obesity and a diabetes epidemic.  N Engl J Med. 2002;  346 854-855
    Google Scholar
  • 8 Arslanian S A. Type 2 diabetes mellitus in children: pathophysiology and risk factors.  J Pediatr Endocrinol Metab. 2000;  13 (Suppl. 6) 1385-1394
    Google Scholar
  • 9 Cali A M, Caprio S. Prediabetes and type 2 diabetes in youth: an emerging epidemic disease?.  Curr Opin Endocrinol Diabetes Obes. 2008;  15 123-127
    Google Scholar
  • 10 Zimmet P, Alberti K G, Kaufman F, Tajima N, Silink M, Arslanian S, Wong G, Bennett P, Shaw J, Caprio S. The metabolic syndrome in children and adolescents – an IDF consensus report.  Pediatr Diabetes. 2007;  8 299-306
    Google Scholar
  • 11 Fagot-Campagna A. Emergence of type 2 diabetes mellitus in children: epidemiological evidence.  J Pediatr Endocrinol Metab. 2000;  13 (Suppl. 6) 1395-1402
    Google Scholar
  • 12 Dimitrijevic-Sreckovic V, Colak E, Djordjevic P, Gostiljac D, Sreckovic B, Popovic S, Canovic F, Ilic M, Obrenovic R, Vukcevic V, Nikolic D, Nisic T, Milic G, Pejcic G. Prothrombogenic factors and reduced antioxidative defense in children and adolescents with pre-metabolic and metabolic syndrome.  Clin Chem Lab Med. 2007;  45 1140-1144
    Google Scholar
  • 13 Dietz W H. Health consequences of obesity in youth: childhood predictors of adult disease.  Pediatrics. 1998;  101 518-525
    Google Scholar
  • 14 Barnett A H, Eff C, Leslie R D, Pyke D A. Diabetes in identical twins. A study of 200 pairs.  Diabetologia. 1981;  20 87-93
    Google Scholar
  • 15 Newman B, Selby J V, King M C, Slemenda C, Fabsitz R, Friedman G D. Concordance for type 2 (non-insulin-dependent) diabetes mellitus in male twins.  Diabetologia. 1987;  30 763-768
    Google Scholar
  • 16 Poulsen P, Kyvik K O, Vaag A, Beck-Nielsen H. Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance–a population-based twin study.  Diabetologia. 1999;  42 139-145
    Google Scholar
  • 17 Tattersall R B, Pyke D A. Discordant identical twins. IV. Diabetes mellitus.  Practitioner. 1972;  209 569-573
    Google Scholar
  • 18 Thomas F, Balkau B, Vauzelle-Kervroedan F, Papoz L. Maternal effect and familial aggregation in NIDDM. The CODIAB Study. CODIAB-INSERM-ZENECA Study Group.  Diabetes. 1994;  43 63-67
    Google Scholar
  • 19 Lyssenko V, Jonsson A, Almgren P, Pulizzi N, Isomaa B, Tuomi T, Berglund G, Altshuler D, Nilsson P, Groop L. Clinical risk factors, DNA variants, and the development of type 2 diabetes.  N Engl J Med. 2008;  359 2220-2232
    Google Scholar
  • 20 Carulli L, Rondinella S, Lombardini S, Canedi I, Loria P, Carulli N. Review article: diabetes, genetics and ethnicity.  Aliment Pharmacol Ther. 2005;  22 (Suppl. 2) 16-19
    Google Scholar
  • 21 Wellcome Trust Case Control Consortium . Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.  Nature. 2007;  447 661-678
    Google Scholar
  • 22 Frayling T M. Genome-wide association studies provide new insights into type 2 diabetes aetiology.  Nat Rev Genet. 2007;  8 657-662
    Google Scholar
  • 23 Saxena R, Voight B F, Lyssenko V, Burtt N P, de Bakker P I, Chen H, Roix J J, Kathiresan S, Hirschhorn J N, Daly M J, Hughes T E, Groop L, Altshuler D, Almgren P, Florez J C, Meyer J, Ardlie K, Bengtsson Bostrom K, Isomaa B, Lettre G, Lindblad U, Lyon H N, Melander O, Newton-Cheh C, Nilsson P, Orho-Melander M, Rastam L, Speliotes E K, Taskinen M R, Tuomi T, Guiducci C, Berglund A, Carlson J, Gianniny L, Hackett R, Hall L, Holmkvist J, Laurila E, Sjogren M, Sterner M, Surti A, Svensson M, Tewhey R, Blumenstiel B, Parkin M, Defelice M, Barry R, Brodeur W, Camarata J, Chia N, Fava M, Gibbons J, Handsaker B, Healy C, Nguyen K, Gates C, Sougnez C, Gage D, Nizzari M, Gabriel S B, Chirn G W, Ma Q, Parikh H, Richardson D, Ricke D, Purcell S. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.  Science. 2007;  316 1331-1336
    Google Scholar
  • 24 Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson T J, Montpetit A, Pshezhetsky A V, Prentki M, Posner B I, Balding D J, Meyre D, Polychronakos C, Froguel P. A genome-wide association study identifies novel risk loci for type 2 diabetes.  Nature. 2007;  445 881-885
    Google Scholar
  • 25 Zeggini E, Scott L J, Saxena R, Voight B F, Marchini J L, Hu T, de Bakker P I, Abecasis G R, Almgren P, Andersen G, Ardlie K, Bostrom K B, Bergman R N, Bonnycastle L L, Borch-Johnsen K, Burtt N P, Chen H, Chines P S, Daly M J, Deodhar P, Ding C J, Doney A S, Duren W L, Elliott K S, Erdos M R, Frayling T M, Freathy R M, Gianniny L, Grallert H, Grarup N, Groves C J, Guiducci C, Hansen T, Herder C, Hitman G A, Hughes T E, Isomaa B, Jackson A U, Jorgensen T, Kong A, Kubalanza K, Kuruvilla F G, Kuusisto J, Langenberg C, Lango H, Lauritzen T, Li Y, Lindgren C M, Lyssenko V, Marvelle A F, Meisinger C, Midthjell K, Mohlke K L, Morken M A, Morris A D, Narisu N, Nilsson P, Owen K R, Palmer C N, Payne F, Perry J R, Pettersen E, Platou C, Prokopenko I, Qi L, Qin L, Rayner N W, Rees M, Roix J J, Sandbaek A, Shields B, Sjogren M, Steinthorsdottir V, Stringham H M, Swift A J, Thorleifsson G, Thorsteinsdottir U, Timpson N J, Tuomi T, Tuomilehto J, Walker M, Watanabe R M, Weedon M N, Willer C J, Illig T, Hveem K, Hu F B, Laakso M, Stefansson K, Pedersen O, Wareham N J, Barroso I, Hattersley A T, Collins F S, Groop L, McCarthy M I, Boehnke M, Altshuler D. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes.  Nat Genet. 2008;  40 638-645
    Google Scholar
  • 26 Zeggini E, Weedon M N, Lindgren C M, Frayling T M, Elliott K S, Lango H, Timpson N J, Perry J R, Rayner N W, Freathy R M, Barrett J C, Shields B, Morris A P, Ellard S, Groves C J, Harries L W, Marchini J L, Owen K R, Knight B, Cardon L R, Walker M, Hitman G A, Morris A D, Doney A S, McCarthy M I, Hattersley A T. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.  Science. 2007;  316 1336-1341
    Google Scholar
  • 27 Stolerman E S, Florez J C. Genomics of type 2 diabetes mellitus: implications for the clinician.  Nat Rev Endocrinol. 2009;  5 429-436
    Google Scholar
  • 28 Fujimoto W Y. Overview of non-insulin-dependent diabetes mellitus (NIDDM) in different population groups.  Diabet Med. 1996;  13 S7-10
    Google Scholar
  • 29 Umpierrez G E, Gonzalez A, Umpierrez D, Pimentel D. Diabetes mellitus in the Hispanic/Latino population: an increasing health care challenge in the United States.  Am J Med Sci. 2007;  334 274-282
    Google Scholar
  • 30 Davis T M. Ethnic diversity in type 2 diabetes.  Diabet Med. 2008;  25 (Suppl. 2) 52-56
    Google Scholar
  • 31 Abate N, Chandalia M. Ethnicity and type 2 diabetes: focus on Asian Indians.  J Diabetes Complications. 2001;  15 320-327
    Google Scholar
  • 32 American Diabetes Association . Gestational diabetes mellitus.  Diabetes Care. 2004;  27 (Suppl. 1) S88-90
    Google Scholar
  • 33 American Diabetes Association . Executive summary: standards of medical care in diabetes – 2009.  Diabetes Care. 2009;  32 (Suppl. 1) S6-12
    Google Scholar
  • 34 Ben-Haroush A, Yogev Y, Hod M. Epidemiology of gestational diabetes mellitus and its association with Type 2 diabetes.  Diabet Med. 2004;  21 103-113
    Google Scholar
  • 35 Bellamy L, Casas J P, Hingorani A D, Williams D. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis.  Lancet. 2009;  373 1773-1779
    Google Scholar
  • 36 Dooley S L, Metzger B E, Cho N H. Gestational diabetes mellitus. Influence of race on disease prevalence and perinatal outcome in a U.S. population.  Diabetes. 1991;  40 (Suppl. 2) 25-29
    Google Scholar
  • 37 Ratner R E, Christophi C A, Metzger B E, Dabelea D, Bennett P H, Pi-Sunyer X, Fowler S, Kahn S E. Prevention of diabetes in women with a history of gestational diabetes: effects of Metformin and lifestyle interventions.  J Clin Endocrinol Metab. 2008;  93 4774-4779
    Google Scholar
  • 38 Metzger B E, Buchanan T A, Coustan D R, de Leiva A, Dunger D B, Hadden D R, Hod M, Kitzmiller J L, Kjos S L, Oats J N, Pettitt D J, Sacks D A, Zoupas C. Summary and recommendations of the Fifth International Workshop-Conference on Gestational Diabetes Mellitus.  Diabetes Care. 2007;  30 (Suppl. 2) S251-260
    Google Scholar
  • 39 Norman R J, Dewailly D, Legro R S, Hickey T E. Polycystic ovary syndrome.  Lancet. 2007;  370 685-697
    Google Scholar
  • 40 Dunaif A, Givens J R, Haseltine F P eds.. Diagnostic criteria for polycystic ovary syndrome: towards a rational approach. Chap.: Polycystic ovary syndrome. Boston; Blackwell Scientific Publications 1992
    Google Scholar
  • 41 Rotterdam ESHRE/ASRM-sponsored PCOS Consensus Workshop Group . Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome.  Fertil Steril. 2004;  81 19-25
    Google Scholar
  • 42 Azziz R, Carmina E, Dewailly D, Diamanti-Kandarakis E, Escobar-Morreale H F, Futterweit W, Janssen O E, Legro R S, Norman R J, Taylor A E, Witchel S F. Positions statement: criteria for defining polycystic ovary syndrome as a predominantly hyperandrogenic syndrome: an Androgen Excess Society guideline.  J Clin Endocrinol Metab. 2006;  91 4237-4245
    Google Scholar
  • 43 Goodarzi M O, Azziz R. Diagnosis, epidemiology, and genetics of the polycystic ovary syndrome.  Best Pract Res Clin Endocrinol Metab. 2006;  20 193-205
    Google Scholar
  • 44 Broekmans F J, Knauff E A, Valkenburg O, Laven J S, Eijkemans M J, Fauser B C. PCOS according to the Rotterdam consensus criteria: Change in prevalence among WHO-II anovulation and association with metabolic factors.  BJOG. 2006;  113 1210-1217
    Google Scholar
  • 45 Dabadghao P, Roberts B J, Wang J, Davies M J, Norman R J. Glucose tolerance abnormalities in Australian women with polycystic ovary syndrome.  Med J Aust. 2007;  187 328-331
    Google Scholar
  • 46 Ehrmann D A, Barnes R B, Rosenfield R L, Cavaghan M K, Imperial J. Prevalence of impaired glucose tolerance and diabetes in women with polycystic ovary syndrome.  Diabetes Care. 1999;  22 141-146
    Google Scholar
  • 47 Dunaif A. Hyperandrogenic anovulation (PCOS): a unique disorder of insulin action associated with an increased risk of non-insulin-dependent diabetes mellitus.  Am J Med. 1995;  98 33S-39S
    Google Scholar
  • 48 Laven J S, Imani B, Eijkemans M J, Fauser B C. New approach to polycystic ovary syndrome and other forms of anovulatory infertility.  Obstet Gynecol Surv. 2002;  57 755-767
    Google Scholar
  • 49 Boomsma C M, Fauser B C, Macklon N S. Pregnancy complications in women with polycystic ovary syndrome.  Semin Reprod Med. 2008;  26 72-84
    Google Scholar
  • 50 Boomsma C M, Eijkemans M J, Hughes E G, Visser G H, Fauser B C, Macklon N S. A meta-analysis of pregnancy outcomes in women with polycystic ovary syndrome.  Hum Reprod Update. 2006;  12 673-683
    Google Scholar
  • 51 Harris M I. Impaired glucose tolerance in the U.S. population.  Diabetes Care. 1989;  12 464-474
    Google Scholar
  • 52 DeFronzo R A, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease.  Diabetes Care. 1991;  14 173-194
    Google Scholar
  • 53 Norris S L, Zhang X, Avenell A, Gregg E, Schmid C H, Lau J. Long-term non-pharmacological weight loss interventions for adults with prediabetes.  Cochrane Database Syst Rev. 2005;  CD005270
    Google Scholar
  • 54 Cummings D E, Flum D R. Gastrointestinal surgery as a treatment for diabetes.  JAMA. 2008;  299 341-343
    Google Scholar
  • 55 Colditz G A, Willett W C, Rotnitzky A, Manson J E. Weight gain as a risk factor for clinical diabetes mellitus in women.  Ann Intern Med. 1995;  122 481-486
    Google Scholar
  • 56 Chan J M, Rimm E B, Colditz G A, Stampfer M J, Willett W C. Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men.  Diabetes Care. 1994;  17 961-969
    Google Scholar
  • 57 Meisinger C, Doring A, Thorand B, Heier M, Lowel H. Body fat distribution and risk of type 2 diabetes in the general population: are there differences between men and women? The MONICA/KORA Augsburg cohort study.  Am J Clin Nutr. 2006;  84 483-489
    Google Scholar
  • 58 Vazquez G, Duval S, Jacobs Jr D R, Silventoinen K. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: a meta-analysis.  Epidemiol Rev. 2007;  29 115-128
    Google Scholar
  • 59 Rana J S, Li T Y, Manson J E, Hu F B. Adiposity compared with physical inactivity and risk of type 2 diabetes in women.  Diabetes Care. 2007;  30 53-58
    Google Scholar
  • 60 Hamman R F, Wing R R, Edelstein S L, Lachin J M, Bray G A, Delahanty L, Hoskin M, Kriska A M, Mayer-Davis E J, Pi-Sunyer X, Regensteiner J, Venditti B, Wylie-Rosett J. Effect of weight loss with lifestyle intervention on risk of diabetes.  Diabetes Care. 2006;  29 2102-2107
    Google Scholar
  • 61 Tuomilehto J, Lindstrom J, Eriksson J G, Valle T T, Hamalainen H, Ilanne-Parikka P, Keinanen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas M, Salminen V, Uusitupa M. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.  N Engl J Med. 2001;  344 1343-1350
    Google Scholar
  • 62 Helmrich S P, Ragland D R, Leung R W, Paffenbarger Jr R S. Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus.  N Engl J Med. 1991;  325 147-152
    Google Scholar
  • 63 Hu F B, Sigal R J, Rich-Edwards J W, Colditz G A, Solomon C G, Willett W C, Speizer F E, Manson J E. Walking compared with vigorous physical activity and risk of type 2 diabetes in women: a prospective study.  JAMA. 1999;  282 1433-1439
    Google Scholar
  • 64 Hu G, Lindstrom J, Valle T T, Eriksson J G, Jousilahti P, Silventoinen K, Qiao Q, Tuomilehto J. Physical activity, body mass index, and risk of type 2 diabetes in patients with normal or impaired glucose regulation.  Arch Intern Med. 2004;  164 892-896
    Google Scholar
  • 65 Knowler W C, Barrett-Connor E, Fowler S E, Hamman R F, Lachin J M, Walker E A, Nathan D M. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.  N Engl J Med. 2002;  346 393-403
    Google Scholar
  • 66 Lynch J, Helmrich S P, Lakka T A, Kaplan G A, Cohen R D, Salonen R, Salonen J T. Moderately intense physical activities and high levels of cardiorespiratory fitness reduce the risk of non-insulin-dependent diabetes mellitus in middle-aged men.  Arch Intern Med. 1996;  156 1307-1314
    Google Scholar
  • 67 Manson J E, Nathan D M, Krolewski A S, Stampfer M J, Willett W C, Hennekens C H. A prospective study of exercise and incidence of diabetes among US male physicians.  JAMA. 1992;  268 63-67
    Google Scholar
  • 68 Pan X R, Li G W, Hu Y H, Wang J X, Yang W Y, An Z X, Hu Z X, Lin J, Xiao J Z, Cao H B, Liu P A, Jiang X G, Jiang Y Y, Wang J P, Zheng H, Zhang H, Bennett P H, Howard B V. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study.  Diabetes Care. 1997;  20 537-544
    Google Scholar
  • 69 Yamaoka K, Tango T. Efficacy of lifestyle education to prevent type 2 diabetes: a meta-analysis of randomized controlled trials.  Diabetes Care. 2005;  28 2780-2786
    Google Scholar
  • 70 Hales C N, Barker D J. Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis.  Diabetologia. 1992;  35 595-601
    Google Scholar
  • 71 Valdez R, Athens M A, Thompson G H, Bradshaw B S, Stern M P. Birthweight and adult health outcomes in a biethnic population in the USA.  Diabetologia. 1994;  37 624-631
    Google Scholar
  • 72 Forsen T, Eriksson J, Tuomilehto J, Reunanen A, Osmond C, Barker D. The fetal and childhood growth of persons who develop type 2 diabetes.  Ann Intern Med. 2000;  133 176-182
    Google Scholar
  • 73 Phillips D I, Barker D J, Hales C N, Hirst S, Osmond C. Thinness at birth and insulin resistance in adult life.  Diabetologia. 1994;  37 150-154
    Google Scholar
  • 74 Dyck R F, Klomp H, Tan L. From “thrifty genotype” to “hefty fetal phenotype”: the relationship between high birthweight and diabetes in Saskatchewan Registered Indians.  Can J Public Health. 2001;  92 340-344
    Google Scholar
  • 75 Harder T, Rodekamp E, Schellong K, Dudenhausen J W, Plagemann A. Birth weight and subsequent risk of type 2 diabetes: a meta-analysis.  Am J Epidemiol. 2007;  165 849-857
    Google Scholar
  • 76 Hofman P L, Regan F, Jackson W E, Jefferies C, Knight D B, Robinson E M, Cutfield W S. Premature birth and later insulin resistance.  N Engl J Med. 2004;  351 2179-2186
    Google Scholar
  • 77 Hovi P, Andersson S, Eriksson J G, Jarvenpaa A L, Strang-Karlsson S, Makitie O, Kajantie E. Glucose regulation in young adults with very low birth weight.  N Engl J Med. 2007;  356 2053-2063
    Google Scholar
  • 78 Genuth S, Alberti K G, Bennett P, Buse J, Defronzo R, Kahn R, Kitzmiller J, Knowler W C, Lebovitz H, Lernmark A, Nathan D, Palmer J, Rizza R, Saudek C, Shaw J, Steffes M, Stern M, Tuomilehto J, Zimmet P. Follow-up report on the diagnosis of diabetes mellitus.  Diabetes Care. 2003;  26 3160-3167
    Google Scholar
  • 79 Kim S H, Chunawala L, Linde R, Reaven G M. Comparison of the 1997 and 2003 American Diabetes Association classification of impaired fasting glucose: impact on prevalence of impaired fasting glucose, coronary heart disease risk factors, and coronary heart disease in a community-based medical practice.  J Am Coll Cardiol. 2006;  48 293-297
    Google Scholar
  • 80 Alberti K G, Zimmet P Z. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.  Diabet Med. 1998;  15 539-553
    Google Scholar
  • 81 Gerstein H C, Santaguida P, Raina P, Morrison K M, Balion C, Hunt D, Yazdi H, Booker L. Annual incidence and relative risk of diabetes in people with various categories of dysglycemia: a systematic overview and meta-analysis of prospective studies.  Diabetes Res Clin Pract. 2007;  78 305-312
    Google Scholar
  • 82 Santaguida P L, Bailon C, Hunt D, Morrison K, Gerstein H, Raina P, Booker L, Yazdi H. Diagnosis, prognosis, and treatmentof impaired glicose tolerance and impaired fasting glucose. AHRQ Evidence Reports and Summaries (2006); available at:. http://www.ahrq.gov
    Google Scholar
  • 83 de Vegt F, Dekker J M, Jager A, Hienkens E, Kostense P J, Stehouwer C D, Nijpels G, Bouter L M, Heine R J. Relation of impaired fasting and postload glucose with incident type 2 diabetes in a Dutch population: The Hoorn Study.  JAMA. 2001;  285 2109-2113
    Google Scholar
  • 84 Shaw J E, Zimmet P Z, de Courten M, Dowse G K, Chitson P, Gareeboo H, Hemraj F, Fareed D, Tuomilehto J, Alberti K G. Impaired fasting glucose or impaired glucose tolerance. What best predicts future diabetes in Mauritius?.  Diabetes Care. 1999;  22 399-402
    Google Scholar
  • 85 Stern M P, Williams K, Haffner S M. (2002) Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test?.  Ann Intern Med. 2002;  136 575-581
    Google Scholar
  • 86 National Cholesterol Education Program (NCEP) . Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report.  Circulation. 2002;  106 3143-3421
    Google Scholar
  • 87 Reaven G M. Banting lecture 1988. Role of insulin resistance in human disease.  Diabetes. 1988;  37 1595-1607
    Google Scholar
  • 88 Grundy S M. Does the metabolic syndrome exist?.  Diabetes Care. 2006;  29 1689-1692 discussion 1693
    Google Scholar
  • 89 Kahn R, Buse J, Ferrannini E, Stern M. The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes.  Diabetes Care. 2005;  28 2289-2304
    Google Scholar
  • 90 Oda E. The metabolic syndrome (emperor) wears no clothes: response to Kahn.  Diabetes Care. 2006;  29 2566
    Google Scholar
  • 91 Alberti K G, Eckel R H, Grundy S M, Zimmet P Z, Cleeman J I, Donato K A, Fruchart J C, James W P, Loria C M, Smith Jr S C. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity.  Circulation. 2009;  120 1640-1645
    Google Scholar
  • 92 Ford E S, Li C, Sattar N. Metabolic syndrome and incident diabetes: current state of the evidence.  Diabetes Care. 2008;  31 1898-1904
    Google Scholar
  • 93 Ford E S. Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence.  Diabetes Care. 2005;  28 1769-1778
    Google Scholar
  • 94 Stern M P, Williams K, Gonzalez-Villalpando C, Hunt K J, Haffner S M. Does the metabolic syndrome improve identification of individuals at risk of type 2 diabetes and/or cardiovascular disease?.  Diabetes Care. 2004;  27 2676-2681
    Google Scholar
  • 95 Wilson P W, D'Agostino R B, Parise H, Sullivan L, Meigs J B. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus.  Circulation. 2005;  112 3066-3072
    Google Scholar
  • 96 Lorenzo C, Williams K, Hunt K J, Haffner S M. The National Cholesterol Education Program – Adult Treatment Panel III, International Diabetes Federation, and World Health Organization definitions of the metabolic syndrome as predictors of incident cardiovascular disease and diabetes.  Diabetes Care. 2007;  30 8-13
    Google Scholar
  • 97 Lindstrom J, Peltonen M, Eriksson J G, Louheranta A, Fogelholm M, Uusitupa M, Tuomilehto J. 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
    Google Scholar
  • 98 Salmeron J, Ascherio A, Rimm E B, Colditz G A, Spiegelman D, Jenkins D J, Stampfer M J, Wing A L, Willett W C. Dietary fiber, glycemic load, and risk of NIDDM in men.  Diabetes Care. 1997;  20 545-550
    Google Scholar
  • 99 Salmeron J, Manson J E, Stampfer M J, Colditz G A, Wing A L, Willett W C. Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women.  JAMA. 1997;  277 472-477
    Google Scholar
  • 100 Stevens J, Ahn K, Juhaeri, Houston D, Steffan L, Couper D. Dietary fiber intake and glycemic index and incidence of diabetes in African-American and white adults: the ARIC study.  Diabetes Care. 2002;  25 1715-1721
    Google Scholar
  • 101 Anderson J W, Baird P, Davis Jr R H, Ferreri S, Knudtson M, Koraym A, Waters V, Williams C L. Health benefits of dietary fiber.  Nutr Rev. 2009;  67 188-205
    Google Scholar
  • 102 Weickert M O, Pfeiffer A F. Metabolic effects of dietary fiber consumption and prevention of diabetes.  J Nutr. 2008;  138 439-442
    Google Scholar
  • 103 Meyer K A, Kushi L H, Jacobs Jr D R, Slavin J, Sellers T A, Folsom A R. Carbohydrates, dietary fiber, and incident type 2 diabetes in older women.  Am J Clin Nutr. 2000;  71 921-930
    Google Scholar
  • 104 Barclay A W, Petocz P, McMillan-Price J, Flood V M, Prvan T, Mitchell P, Brand-Miller J C. Glycemic index, glycemic load, and chronic disease risk – a meta-analysis of observational studies.  Am J Clin Nutr. 2008;  87 627-637
    Google Scholar
  • 105 Thanopoulou A C, Karamanos B G, Angelico F V, Assaad-Khalil S H, Barbato A F, Del Ben M P, Djordjevic P B, Dimitrijevic-Sreckovic V S, Gallotti C A, Katsilambros N L, Migdalis I N, Mrabet M M, Petkova M K, Roussi D P, Tenconi M T. Dietary fat intake as risk factor for the development of diabetes: multinational, multicenter study of the Mediterranean Group for the Study of Diabetes (MGSD).  Diabetes Care. 2003;  26 302-307
    Google Scholar
  • 106 Vessby B, Aro A, Skarfors E, Berglund L, Salminen I, Lithell H. The risk to develop NIDDM is related to the fatty acid composition of the serum cholesterol esters.  Diabetes. 1994;  43 1353-1357
    Google Scholar
  • 107 Wang L, Folsom A R, Zheng Z J, Pankow J S, Eckfeldt J H. Plasma fatty acid composition and incidence of diabetes in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) Study.  Am J Clin Nutr. 2003;  78 91-98
    Google Scholar
  • 108 Due A, Larsen T M, Hermansen K, Stender S, Holst J J, Toubro S, Martinussen T, Astrup A. Comparison of the effects on insulin resistance and glucose tolerance of 6-mo high-monounsaturated-fat, low-fat, and control diets.  Am J Clin Nutr. 2008;  87 855-862
    Google Scholar
  • 109 van Woudenbergh G J, van Ballegooijen A J, Kuijsten A, Sijbrands E J, van Rooij F J, Geleijnse J M, Hofman A, Witteman J C, Feskens E J. Eating fish and risk of type 2 diabetes: A population-based, prospective follow-up study.  Diabetes Care. 2009;  32 2021-2026
    Google Scholar
  • 110 Kaushik M, Mozaffarian D, Spiegelman D, Manson J E, Willett W C, Hu F B. Long-chain omega-3 fatty acids, fish intake, and the risk of type 2 diabetes mellitus.  Am J Clin Nutr. 2009;  90 613-620
    Google Scholar
  • 111 Paniagua J A, de la Sacristana A G, Sanchez E, Romero I, Vidal-Puig A, Berral F J, Escribano A, Moyano M J, Perez-Martinez P, Lopez-Miranda J, Perez-Jimenez F. A MUFA-rich diet improves posprandial glucose, lipid and GLP-1 responses in insulin-resistant subjects.  J Am Coll Nutr. 2007;  26 434-444
    Google Scholar
  • 112 Salmeron J, Hu F B, Manson J E, Stampfer M J, Colditz G A, Rimm E B, Willett W C. Dietary fat intake and risk of type 2 diabetes in women.  Am J Clin Nutr. 2001;  73 1019-1026
    Google Scholar
  • 113 Mente A, de Koning L, Shannon H S, Anand S S. A systematic review of the evidence supporting a causal link between dietary factors and coronary heart disease.  Arch Intern Med. 2009;  169 659-669
    Google Scholar
  • 114 Conigrave K M, Hu B F, Camargo Jr C A, Stampfer M J, Willett W C, Rimm E B. A prospective study of drinking patterns in relation to risk of type 2 diabetes among men.  Diabetes. 2001;  50 2390-2395
    Google Scholar
  • 115 Stampfer M J, Colditz G A, Willett W C, Manson J E, Arky R A, Hennekens C H, Speizer F E. A prospective study of moderate alcohol drinking and risk of diabetes in women.  Am J Epidemiol. 1988;  128 549-558
    Google Scholar
  • 116 Ford E S, Mokdad A H. Fruit and vegetable consumption and diabetes mellitus incidence among U.S. adults.  Prev Med. 2001;  32 33-39
    Google Scholar
  • 117 Jiang R, Manson J E, Stampfer M J, Liu S, Willett W C, Hu F B. Nut and peanut butter consumption and risk of type 2 diabetes in women.  JAMA. 2002;  288 2554-2560
    Google Scholar
  • 118 Smith B, Wingard D L, Smith T C, Kritz-Silverstein D, Barrett-Connor E. Does coffee consumption reduce the risk of type 2 diabetes in individuals with impaired glucose?.  Diabetes Care. 2006;  29 2385-2390
    Google Scholar
  • 119 Mann J I, De Leeuw I, Hermansen K, Karamanos B, Karlstrom B, Katsilambros N, Riccardi G, Rivellese A A, Rizkalla S, Slama G, Toeller M, Uusitupa M, Vessby B. Evidence-based nutritional approaches to the treatment and prevention of diabetes mellitus.  Nutr Metab Cardiovasc Dis. 2004;  14 373-394
    Google Scholar
  • 120 Jinlin F, Binyou W, Terry C. A new approach to the study of diet and risk of type 2 diabetes.  J Postgrad Med. 2007;  53 139-143
    Google Scholar
  • 121 Luna B, Feinglos M N. Drug-induced hyperglycemia.  JAMA. 2001;  286 1945-1948
    Google Scholar
  • 122 Franciosi M, De Berardis G, Rossi M C, Sacco M, Belfiglio M, Pellegrini F, Tognoni G, Valentini M, Nicolucci A. Use of the diabetes risk score for opportunistic screening of undiagnosed diabetes and impaired glucose tolerance: the IGLOO (Impaired Glucose Tolerance and Long-Term Outcomes Observational) study.  Diabetes Care. 2005;  28 1187-1194
    Google Scholar
  • 123 Jin H, Meyer J M, Jeste D V. Atypical antipsychotics and glucose dysregulation: a systematic review.  Schizophr Res. 2004;  71 195-212
    Google Scholar
  • 124 Tebas P. Insulin resistance and diabetes mellitus associated with antiretroviral use in HIV-infected patients: pathogenesis, prevention, and treatment options.  J Acquir Immune Defic Syndr. 2008;  49 (Suppl. 2) S86-92
    Google Scholar
  • 125 Elliott R B. Diabetes – a man made disease.  Med Hypotheses. 2006;  67 388-391
    Google Scholar
  • 126 Elinder L S, Jansson M. Obesogenic environments – aspects on measurement and indicators.  Public Health Nutr. 2009;  12 307-315
    Google Scholar
  • 127 Wylie-Rosett J. The diabetes epidemic: what can we do?.  J Am Diet Assoc. 2009;  109 1160-1162
    Google Scholar
  • 128 James W P. The epidemiology of obesity: the size of the problem.  J Intern Med. 2008;  263 336-352
    Google Scholar
  • 129 Chiolero A, Faeh D, Paccaud F, Cornuz J. Consequences of smoking for body weight, body fat distribution, and insulin resistance.  Am J Clin Nutr. 2008;  87 801-809
    Google Scholar
  • 130 Fagard R H, Nilsson P M. Smoking and diabetes – The double health hazard!.  Prim Care Diabetes. 2009;  3 (4) 205-209
    Google Scholar
  • 131 Jones O A, Maguire M L, Griffin J L. Environmental pollution and diabetes: a neglected association.  Lancet. 2008;  371 287-288
    Google Scholar
  • 132 Cranmer M, Louie S, Kennedy R H, Kern P A, Fonseca V A. Exposure to 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin (TCDD) is associated with hyperinsulinemia and insulin resistance.  Toxicol Sci. 2000;  56 431-436
    Google Scholar
  • 133 Lee D H, Lee I K, Jin S H, Steffes M, Jacobs Jr D R. Association between serum concentrations of persistent organic pollutants and insulin resistance among nondiabetic adults: results from the National Health and Nutrition Examination Survey 1999–2002.  Diabetes Care. 2007;  30 622-628
    Google Scholar
  • 134 Lee D H, Lee I K, Song K, Steffes M, Toscano W, Baker B A, Jacobs Jr D R. A strong dose-response relation between serum concentrations of persistent organic pollutants and diabetes: results from the National Health and Examination Survey 1999–2002.  Diabetes Care. 2006;  29 1638-1644
    Google Scholar
  • 135 Porta M. Persistent organic pollutants and the burden of diabetes.  Lancet. 2006;  368 558-559
    Google Scholar
  • 136 Rylander L, Rignell-Hydbom A, Hagmar L. A cross-sectional study of the association between persistent organochlorine pollutants and diabetes.  Environ Health. 2005;  4 28
    Google Scholar
  • 137 Raikkonen K, Matthews K A, Kuller L H. Depressive symptoms and stressful life events predict metabolic syndrome among middle-aged women: a comparison of World Health Organization, Adult Treatment Panel III, and International Diabetes Foundation definitions.  Diabetes Care. 2007;  30 872-877
    Google Scholar
  • 138 Lustman P J, Clouse R E. Depression: a potentially modifiable risk factor for diabetes and its complications.  Diabetes Spectrum. 2004;  17 147-148
    Google Scholar
  • 139 Williams M M, Clouse R E, Lustman P J. Treating depression to prevent diabetes and its complications: understanding depression as a medical risk factor.  Clinical Diabetes. 2006;  24 79-86
    Google Scholar
  • 140 Knol M J, Twisk J W, Beekman A T, Heine R J, Snoek F J, Pouwer F. Depression as a risk factor for the onset of type 2 diabetes mellitus. A meta-analysis.  Diabetologia. 2006;  49 837-845
    Google Scholar
  • 141 Rubin R R, Ma Y, Marrero D G, Peyrot M, Barrett-Connor E L, Kahn S E, Haffner S M, Price D W, Knowler W C. Elevated depression symptoms, antidepressant medicine use, and risk of developing diabetes during the diabetes prevention program.  Diabetes Care. 2008;  31 420-426
    Google Scholar
  • 142 Blane D, Smith G D, Bartley M. Social class differences in years of potential life lost: size, trends, and principal causes.  BMJ. 1990;  301 429-432
    Google Scholar
  • 143 Blaxter M. Evidence on inequality in health from a national survey.  Lancet. 1987;  2 30-33
    Google Scholar
  • 144 Brown A F, Ettner S L, Piette J, Weinberger M, Gregg E, Shapiro M F, Karter A J, Safford M, Waitzfelder B, Prata P A, Beckles G L. Socioeconomic position and health among persons with diabetes mellitus: a conceptual framework and review of the literature.  Epidemiol Rev. 2004;  26 63-77
    Google Scholar
  • 145 Kunst A E, Mackenbach J P. The size of mortality differences associated with educational level in nine industrialized countries.  Am J Public Health. 1994;  84 932-937
    Google Scholar
  • 146 Marmot M G, McDowall M E. Mortality decline and widening social inequalities.  Lancet. 1986;  2 274-276
    Google Scholar
  • 147 Rose G, Marmot M G. Social class and coronary heart disease.  Br Heart J. 1981;  45 13-19
    Google Scholar
  • 148 Woodward M, Shewry M C, Smith W C, Tunstall-Pedoe H. Social status and coronary heart disease: results from the Scottish Heart Health Study.  Prev Med. 1992;  21 136-148
    Google Scholar
  • 149 Brancati F L, Whelton P K, Kuller L H, Klag M J. Diabetes mellitus, race, and socioeconomic status. A population-based study.  Ann Epidemiol. 1996;  6 67-73
    Google Scholar
  • 150 Choi B C, Shi F. Risk factors for diabetes mellitus by age and sex: results of the National Population Health Survey.  Diabetologia. 2001;  44 1221-1231
    Google Scholar
  • 151 Connolly V, Unwin N, Sherriff P, Bilous R, Kelly W. Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas.  J Epidemiol Community Health. 2000;  54 173-177
    Google Scholar
  • 152 Hjelm K, Isacsson A, Apelqvist J, Sundquist J, Nyberg P. Foreign- and Swedish-born diabetic patients – a population-based study of prevalence, glycaemic control and social position.  Scand J Soc Med. 1996;  24 243-252
    Google Scholar
  • 153 Ismail A A, Beeching N J, Gill G V, Bellis M A. Capture-recapture-adjusted prevalence rates of type 2 diabetes are related to social deprivation.  QJM. 1999;  92 707-710
    Google Scholar
  • 154 Marmot M G, Smith G D, Stansfeld S, Patel C, North F, Head J, White I, Brunner E, Feeney A. Health inequalities among British civil servants: the Whitehall II study.  Lancet. 1991;  337 1387-1393
    Google Scholar
  • 155 Robbins J M, Vaccarino V, Zhang H, Kasl S V. Socioeconomic status and type 2 diabetes in African American and non-Hispanic white women and men: evidence from the Third National Health and Nutrition Examination Survey.  Am J Public Health. 2001;  91 76-83
    Google Scholar
  • 156 Stern M P, Rosenthal M, Haffner S M, Hazuda H P, Franco L J. Sex difference in the effects of sociocultural status on diabetes and cardiovascular risk factors in Mexican Americans. The San Antonio Heart Study.  Am J Epidemiol. 1984;  120 834-851
    Google Scholar
  • 157 Whitford D L, Griffin S J, Prevost A T. Influences on the variation in prevalence of type 2 diabetes between general practices: practice, patient or socioeconomic factors?.  Br J Gen Pract. 2003;  53 9-14
    Google Scholar
  • 158 Haffner S M. Epidemiology of type 2 diabetes: risk factors.  Diabetes Care. 1998;  21 (Suppl. 3) C3-6
    Google Scholar
  • 159 Harris M I. Diabetes in America: epidemiology and scope of the problem.  Diabetes Care. 1998;  21 (Suppl. 3) C11-14
    Google Scholar
  • 160 Maty S C, Everson-Rose S A, Haan M N, Raghunathan T E, Kaplan G A. Education, income, occupation, and the 34-year incidence (1965–99) of Type 2 diabetes in the Alameda County Study.  Int J Epidemiol. 2005;  34 1274-1281
    Google Scholar
  • 161 Mokdad A H, Bowman B A, Ford E S, Vinicor F, Marks J S, Koplan J P. The continuing epidemics of obesity and diabetes in the United States.  JAMA. 2001;  286 1195-1200
    Google Scholar
  • 162 Larosa C, Sgueglia G A, Sestito A, Infusino F, Niccoli G, Lamendola P, Mariani L, Santangeli P, Lombardo A, Crea F, Lanza G A. Predictors of impaired heart rate variability and clinical outcome in patients with acute myocardial infarction treated by primary angioplasty.  J Cardiovasc Med (Hagerstown). 2008;  9 76-80
    Google Scholar
  • 163 Mokdad A H, Ford E S, Bowman B A, Nelson D E, Engelgau M M, Vinicor F, Marks J S. Diabetes trends in the U.S.: 1990–1998.  Diabetes Care. 2000;  23 1278-1283
    Google Scholar
  • 164 Agardh E E, Ahlbom A, Andersson T, Efendic S, Grill V, Hallqvist J, Ostenson C G. Socio-economic position at three points in life in association with type 2 diabetes and impaired glucose tolerance in middle-aged Swedish men and women.  Int J Epidemiol. 2007;  36 84-92
    Google Scholar
  • 165 Lawlor D A, Patel R, Fraser A, Smith G D, Ebrahim S. The association of life course socio-economic position with diagnosis, treatment, control and survival of women with diabetes: findings from the British Women's Heart and Health Study.  Diabet Med. 2007;  24 892-900
    Google Scholar
  • 166 Lynch J W, Kaplan G A, Cohen R D, Kauhanen J, Wilson T W, Smith N L, Salonen J T. Childhood and adult socioeconomic status as predictors of mortality in Finland.  Lancet. 1994;  343 524-527
    Google Scholar
  • 167 Marmot M, Shipley M, Brunner E, Hemingway H. Relative contribution of early life and adult socioeconomic factors to adult morbidity in the Whitehall II study.  J Epidemiol Community Health. 2001;  55 301-307
    Google Scholar
  • 168 Vagero D, Leon D. Effect of social class in childhood and adulthood on adult mortality.  Lancet. 1994;  343 1224-1225
    Google Scholar
  • 169 Brunner E, Shipley M J, Blane D, Smith G D, Marmot M G. When does cardiovascular risk start? Past and present socioeconomic circumstances and risk factors in adulthood.  J Epidemiol Community Health. 1999;  53 757-764
    Google Scholar
  • 170 Giskes K, Turrell G, van Lenthe F J, Brug J, Mackenbach J P. A multilevel study of socio-economic inequalities in food choice behaviour and dietary intake among the Dutch population: the GLOBE study.  Public Health Nutr. 2006;  9 75-83
    Google Scholar
  • 171 Wannamethee S G, Whincup P H, Shaper G, Walker M. Influence of fathers' social class on cardiovascular disease in middle-aged men.  Lancet. 1996;  348 1259-1263
    Google Scholar
  • 172 Alberti K G, Zimmet P, Shaw J. International Diabetes Federation: a consensus on Type 2 diabetes prevention.  Diabet Med. 2007;  24 451-463
    Google Scholar
  • 173 Simmons R K, Harding A H, Jakes R W, Welch A, Wareham N J, Griffin S J. How much might achievement of diabetes prevention behaviour goals reduce the incidence of diabetes if implemented at the population level?.  Diabetologia. 2006;  49 905-911
    Google Scholar
  • 174 Tabak A G, Jokela M, Akbaraly T N, Brunner E J, Kivimaki M, Witte D R. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study.  Lancet. 2009;  373 2215-2221
    Google Scholar
  • 175 Lorenzo C, Okoloise M, Williams K, Stern M P, Haffner S M. The metabolic syndrome as predictor of type 2 diabetes: the San Antonio heart study.  Diabetes Care. 2003;  26 3153-3159
    Google Scholar
  • 176 Garber A J, Handelsman Y, Einhorn D, Bergman D A, Bloomgarden Z T, Fonseca V, Garvey W T, Gavin 3rd J R, Grunberger G, Horton E S, Jellinger P S, Jones K L, Lebovitz H, Levy P, McGuire D K, Moghissi E S, Nesto R W. Diagnosis and management of prediabetes in the continuum of hyperglycemia: when do the risks of diabetes begin? A consensus statement from the American College of Endocrinology and the American Association of Clinical Endocrinologists.  Endocr Pract. 2008;  14 933-946
    Google Scholar
  • 177 Mensink M, Feskens E J, Saris W H, De Bruin T W, Blaak E E. Study on Lifestyle Intervention and Impaired Glucose Tolerance Maastricht (SLIM): preliminary results after one year.  Int J Obes Relat Metab Disord. 2003;  27 377-384
    Google Scholar
  • 178 Roumen C, Corpeleijn E, Feskens E J M, Mensink M, Saris W H M, Blaak E E. Impact of 3-year lifestyle intervention on postprandial glucose metabolism: the SLIM study.  Diabetic Medicine. 2008;  25 597-605
    Google Scholar
  • 179 Gillies C L, Abrams K R, Lambert P C, Cooper N J, Sutton A J, Hsu R T, Khunti K. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis.  BMJ. 2007;  334 299
    Google Scholar
  • 180 DECODE Study Group . Age- and sex-specific prevalences of diabetes and impaired glucose regulation in 13 European cohorts.  Diabetes Care. 2003;  26 61-69
    Google Scholar
  • 181 Harris M I, Flegal K M, Cowie C C, Eberhardt M S, Goldstein D E, Little R R, Wiedmeyer H M, Byrd-Holt D D. Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in U.S. adults. The Third National Health and Nutrition Examination Survey, 1988–1994.  Diabetes Care. 1998;  21 518-524
    Google Scholar
  • 182 Qiao Q, Hu G, Tuomilehto J, Nakagami T, Balkau B, Borch-Johnsen K, Ramachandran A, Mohan V, Iyer S R, Tominaga M, Kiyohara Y, Kato I, Okubo K, Nagai M, Shibazaki S, Yang Z, Tong Z, Fan Q, Wang B, Chew S K, Tan B Y, Heng D, Emmanuel S, Tajima N, Iwamoto Y, Snehalatha C, Vijay V, Kapur A, Dong Y, Nan H, Gao W, Shi H, Fu F. Age- and sex-specific prevalence of diabetes and impaired glucose regulation in 11 Asian cohorts.  Diabetes Care. 2003;  26 1770-1780
    Google Scholar
  • 183 Rasmussen S S, Glumer C, Sandbaek A, Lauritzen T, Borch-Johnsen K. Determinants of progression from impaired fasting glucose and impaired glucose tolerance to diabetes in a high-risk screened population: 3 year follow-up in the ADDITION study, Denmark.  Diabetologia. 2008;  51 249-257
    Google Scholar
  • 184 Li G, Zhang P, Wang J, Gregg E W, Yang W, Gong Q, Li H, Li H, Jiang Y, An Y, Shuai Y, Zhang B, Zhang J, Thompson T J, Gerzoff R B, Roglic G, Hu Y, Bennett P H. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study.  Lancet. 2008;  371 1783-1789
    Google Scholar
  • 185 Lindstrom J, Ilanne-Parikka P, Peltonen M, Aunola S, Eriksson J G, Hemio K, Hamalainen H, Harkonen P, Keinanen-Kiukaanniemi S, Laakso M, Louheranta A, Mannelin M, Paturi M, Sundvall J, Valle T T, Uusitupa M, Tuomilehto J. Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study.  Lancet. 2006;  368 1673-1679
    Google Scholar
  • 186 Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar A D, Vijay V. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1).  Diabetologia. 2006;  49 289-297
    Google Scholar
  • 187 Kosaka K, Noda M, Kuzuya T. Prevention of type 2 diabetes by lifestyle intervention: a Japanese trial in IGT males.  Diabetes Res Clin Pract. 2005;  67 152-162
    Google Scholar
  • 188 Gerstein H C, Yusuf S, Bosch J, Pogue J, Sheridan P, Dinccag N, Hanefeld M, Hoogwerf B, Laakso M, Mohan V, Shaw J, Zinman B, Holman R R. Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose: a randomised controlled trial.  Lancet. 2006;  368 1096-1105
    Google Scholar
  • 189 O'Dea K, Patel M, Kubisch D, Hopper J, Traianedes K. Obesity, diabetes, and hyperlipidemia in a central Australian aboriginal community with a long history of acculturation.  Diabetes Care. 1993;  16 1004-1010
    Google Scholar
  • 190 Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk.  Diabetes Care. 2003;  26 725-731
    Google Scholar
  • 191 McNeely M J, Boyko E J, Leonetti D L, Kahn S E, Fujimoto W Y. Comparison of a clinical model, the oral glucose tolerance test, and fasting glucose for prediction of type 2 diabetes risk in Japanese Americans.  Diabetes Care. 2003;  26 758-763
    Google Scholar
  • 192 Schmidt M I, Duncan B B, Bang H, Pankow J S, Ballantyne C M, Golden S H, Folsom A R, Chambless L E. Identifying individuals at high risk for diabetes: The Atherosclerosis Risk in Communities study.  Diabetes Care. 2005;  28 2013-2018
    Google Scholar
  • 193 Kanaya A M, Wassel Fyr C L, de Rekeneire N, Shorr R I, Schwartz A V, Goodpaster B H, Newman A B, Harris T, Barrett-Connor E. Predicting the development of diabetes in older adults: the derivation and validation of a prediction rule.  Diabetes Care. 2005;  28 404-408
    Google Scholar
  • 194 Norberg M, Eriksson J W, Lindahl B, Andersson C, Rolandsson O, Stenlund H, Weinehall L. A combination of HbA1c, fasting glucose and BMI is effective in screening for individuals at risk of future type 2 diabetes: OGTT is not needed.  J Intern Med. 2006;  260 263-271
    Google Scholar
  • 195 Balkau B, Hillier T, Vierron E, D'Hour A, Lepinay P, Royer B, Born C. Comment to: Borch-Johnsen K, Colagiuri S, Balkau B et al. Creating a pandemic of prediabetes: the proposed new diagnostic criteria for impaired fasting glycaemia (Diabetologia 2004; 47: 1396–1402).  Diabetologia. 2005;  48 801-802
    Google Scholar
  • 196 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.  BMJ. 2009;  338 b880
    Google Scholar
  • 197 Tabaei B P, Herman W H. A multivariate logistic regression equation to screen for diabetes: development and validation.  Diabetes Care. 2002;  25 1999-2003
    Google Scholar
  • 198 Saaristo T, Peltonen M, Lindstrom J, Saarikoski L, Sundvall J, Eriksson J G, Tuomilehto J. Cross-sectional evaluation of the Finnish Diabetes Risk Score: a tool to identify undetected type 2 diabetes, abnormal glucose tolerance and metabolic syndrome.  Diab Vasc Dis Res. 2005;  2 67-72
    Google Scholar
  • 199 Bergmann A, Li J, Wang L, Schulze J, Bornstein S R, Schwarz P E. A simplified Finnish diabetes risk score to predict type 2 diabetes risk and disease evolution in a German population.  Horm Metab Res. 2007;  39 677-682
    Google Scholar
  • 200 Glumer C, Carstensen B, Sandbaek A, Lauritzen T, Jorgensen T, Borch-Johnsen K. A Danish diabetes risk score for targeted screening: the Inter99 study.  Diabetes Care. 2004;  27 727-733
    Google Scholar
  • 201 Griffin S J, Little P S, Hales C N, Kinmonth A L, Wareham N J. Diabetes risk score: towards earlier detection of type 2 diabetes in general practice.  Diabetes Metab Res Rev. 2000;  16 164-171
    Google Scholar
  • 202 Spijkerman A, Griffin S, Dekker J, Nijpels G, Wareham N J. What is the risk of mortality for people who are screen positive in a diabetes screening programme but who do not have diabetes on biochemical testing? Diabetes screening programmes from a public health perspective.  J Med Screen. 2002;  9 187-190
    Google Scholar
  • 203 Park P J, Griffin S J, Sargeant L, Wareham N J. The performance of a risk score in predicting undiagnosed hyperglycemia.  Diabetes Care. 2002;  25 984-988
    Google Scholar
  • 204 Spijkerman A M, Yuyun M F, Griffin S J, Dekker J M, Nijpels G, Wareham N J. The performance of a risk score as a screening test for undiagnosed hyperglycemia in ethnic minority groups: data from the 1999 health survey for England.  Diabetes Care. 2004;  27 116-122
    Google Scholar
  • 205 Heldgaard P E, Griffin S J. Routinely collected general practice data aids identification of people with hyperglycaemia and metabolic syndrome.  Diabet Med. 2006;  23 996-1002
    Google Scholar
  • 206 Baan C A, Ruige J B, Stolk R P, Witteman J C, Dekker J M, Heine R J, Feskens E J. Performance of a predictive model to identify undiagnosed diabetes in a health care setting.  Diabetes Care. 1999;  22 213-219
    Google Scholar
  • 207 Schulze M B, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Mohlig M, Pfeiffer A F, Spranger J, Thamer C, Haring H U, Fritsche A, Joost H G. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes.  Diabetes Care. 2007;  30 510-515
    Google Scholar
  • 208 Balkau B, Lange C, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S, Fumeron F, Froguel P, Vaxillaire M, Cauchi S, Ducimetiere P, Eschwege E. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR).  Diabetes Care. 2008;  31 2056-2061
    Google Scholar
  • 209 Heikes K E, Eddy D M, Arondekar B, Schlessinger L. Diabetes Risk Calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes.  Diabetes Care. 2008;  31 1040-1045
    Google Scholar
  • 210 Mohan V, Deepa R, Deepa M, Somannavar S, Datta M. A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects.  J Assoc Physicians India. 2005;  53 759-763
    Google Scholar
  • 211 Keesukphan P, Chanprasertyothin S, Ongphiphadhanakul B, Puavilai G. The development and validation of a diabetes risk score for high-risk Thai adults.  J Med Assoc Thai. 2007;  90 149-154
    Google Scholar
  • 212 Rathmann W, Martin S, Haastert B, Icks A, Holle R, Lowel H, Giani G. Performance of screening questionnaires and risk scores for undiagnosed diabetes: the KORA Survey 2000.  Arch Intern Med. 2005;  165 436-441
    Google Scholar
  • 213 Al-Lawati J A, Tuomilehto J. Diabetes risk score in Oman: a tool to identify prevalent type 2 diabetes among Arabs of the Middle East.  Diabetes Res Clin Pract. 2007;  77 438-444
    Google Scholar
  • 214 Glumer C, Vistisen D, Borch-Johnsen K, Colagiuri S. Risk scores for type 2 diabetes can be applied in some populations but not all.  Diabetes Care. 2006;  29 410-414
    Google Scholar
  • 215 Ryan J, Velez F, Chung-Bridges K, Lewis J B, Schwartz R, Jennings T. Prevalence of undetected high risk for type 2 diabetes mellitus in primary care : a South Florida primary care practice-based research network study.  Insulin. 2007;  2 109-117
    Google Scholar
  • 216 Adriaanse M, Snoek F. The psychological impact of screening for type 2 diabetes.  Diabetes Metab Res Rev. 2006;  22 20-25
    Google Scholar
  • 217 Engelgau M M, Thompson T J, Smith P J, Herman W H, Aubert R E, Gunter E W, Wetterhall S F, Sous E S, Ali M A. Screening for diabetes mellitus in adults. The utility of random capillary blood glucose measurements.  Diabetes Care. 1995;  18 463-466
    Google Scholar
  • 218 Rohlfing C L, Little R R, Wiedmeyer H M, England J D, Madsen R, Harris M I, Flegal K M, Eberhardt M S, Goldstein D E. Use of GHb (HbA1c) in screening for undiagnosed diabetes in the U.S. population.  Diabetes Care. 2000;  23 187-191
    Google Scholar
  • 219 Mannucci E, Ognibene A, Sposato I, Brogi M, Gallori G, Bardini G, Cremasco F, Messeri G, Rotella C M. Fasting plasma glucose and glycated haemoglobin in the screening of diabetes and impaired glucose tolerance.  Acta Diabetol. 2003;  40 181-186
    Google Scholar
  • 220 British Diabetes Association .Diabetes UK. Position statement: early identification of peolple with type 2 diabetes. Report. London, UK; Diabetes UK, British Diabetes Association 2006
    Google Scholar
  • 221 Haute Autorité de Santé .Agence nationale d'accréditation ete d'évaluation en santé A (2003). Principes de dépistage du diabète de type 2. Report. Paris; Haute Autorité de Santé 2003
    Google Scholar
  • 222 ADA . Clinical practice recommendations 2008.  Diabetes Care. 2008;  31 S1-S110
    Google Scholar
  • 223 Ealovega M W, Tabaei B P, Brandle M, Burke R, Herman W H. Opportunistic screening for diabetes in routine clinical practice.  Diabetes Care. 2004;  27 (1) 9-12
    Google Scholar
  • 224 Cosson E, Nguyen M T, Ba H, Hamo-Tchatchouang E, Valensi P. Screening overweighed or obese subjects for prediabetes and diabetes: performing directly oral glucose tolerance test in selected patients may be beneficial and cost-effective. 5th World Congress on Prevention of Diabetes and its Complications. Helsinki; WCPD 2008
    Google Scholar
  • 225 Zimmet P, Thomas C. Genotype, obesity and cardiovascular disease – has technical and social advancement outstripped evolution?.  J Intern Med. 2003;  254 114-125
    Google Scholar
  • 226 Ryden L, Standl E, Bartnik M, Van den Berghe G, Betteridge J, de Boer M J, Cosentino F, Jonsson B, Laakso M, Malmberg K, Priori S, Ostergren J, Tuomilehto J, Thrainsdottir I, Vanhorebeek I, Stramba-Badiale M, Lindgren P, Qiao Q, Priori S G, Blanc J J, Budaj A, Camm J, Dean V, Deckers J, Dickstein K, Lekakis J, McGregor K, Metra M, Morais J, Osterspey A, Tamargo J, Zamorano J L, Deckers J W, Bertrand M, Charbonnel B, Erdmann E, Ferrannini E, Flyvbjerg A, Gohlke H, Juanatey J R, Graham I, Monteiro P F, Parhofer K, Pyorala K, Raz I, Schernthaner G, Volpe M, Wood D. Guidelines on diabetes, pre-diabetes, and cardiovascular diseases: executive summary. The Task Force on Diabetes and Cardiovascular Diseases of the European Society of Cardiology (ESC) and of the European Association for the Study of Diabetes (EASD).  Eur Heart J. 2007;  28 88-136
    Google Scholar
  • 227 Pan X R, Li G W, Hu Y H, Wang J X, Yang W Y, An Z X, Hu Z X, Lin J, Xiao J Z, Cao H B, Liu P A, Jiang X G, Jiang Y Y, Wang J P, Zheng H, Zhang H, Bennett P H, Howard B V. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study.  Diabetes Care. 1997;  20 537-544
    Google Scholar
  • 228 Li G, Zhang P, Wang J, Gregg E W, Yang W, Gong Q, Li H, Jiang Y, An Y, Shuai Y, Zhang B, Zhang J, Thompson T J, Gerzoff R B, Roglic G, Hu Y, Bennett P H. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study.  Lancet. 2008;  371 1783-1789
    Google Scholar
  • 229 Orchard T J, Temprosa M, Goldberg R, Haffner S, Ratner R, Marcovina S, Fowler S. The effect of metformin and intensive lifestyle intervention on the metabolic syndrome: the Diabetes Prevention Program randomized trial.  Ann Intern Med. 2005;  142 611-619
    Google Scholar
  • 230 Ratner R, Goldberg R, Haffner S, Marcovina S, Orchard T, Fowler S, Temprosa M. Impact of intensive lifestyle and metformin therapy on cardiovascular disease risk factors in the diabetes prevention program.  Diabetes Care. 2005;  28 888-894
    Google Scholar
  • 231 Tuomilehto J, Lindström J, Eriksson J G, Valle T T, Hämäläinen H, Ilanne-Parikka P, Keinänen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas M, Salminen V, Uusitupa M. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.  N Engl J Med. 2001;  344 1343-1350
    Google Scholar
  • 232 Ilanne-Parikka P, Eriksson J G, Lindström J, Peltonen M, Aunola S, Hämäläinen H, Keinänen-Kiukaanniemi S, Laakso M, Valle T T, Lahtela J, Uusitupa M, Tuomilehto J. Effect of lifestyle intervention on the occurrence of metabolic syndrome and its components in the Finnish Diabetes Prevention Study.  Diabetes Care. 2008;  31 805-807
    Google Scholar
  • 233 Yach D, McKee M, Lopez A D, Novotny T. Improving diet and physical activity: 12 lessons from controlling tobacco smoking.  BMJ. 2005;  330 898-900
    Google Scholar
  • 234 The 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
    Google Scholar
  • 235 Ramachandran A, Snehalatha C, Satyavani K, Sivasankari S, Vijay V. Metabolic syndrome does not increase the risk of conversion of impaired glucose tolerance to diabetes in Asian Indians – Result of Indian diabetes prevention programme.  Diabetes Res Clin Pract. 2007;  76 215-218
    Google Scholar
  • 236 Sartor G, Schersten B, Carlstrom S, Melander A, Norden A, Persson G. Ten-year follow-up of subjects with impaired glucose tolerance. Prevention of diabetes by tolbutamide and diet regulation.  Diabetes. 1980;  29 41-49
    Google Scholar
  • 237 Jarrett R J, Keen H, Fuller J H, McCartney M. Worsening to diabetes in men with impaired glucose tolerance (“borderline diabetes”).  Diabetologia. 1979;  16 25-30
    Google Scholar
  • 238 Eriksson K F, Lindgärde F. Prevention of type 2 (non-insulin-dependent) diabetes mellitus by diet and physical exercise. The 6-year Malmö feasibility study.  Diabetologia. 1991;  34 891-898
    Google Scholar
  • 239 Schwarz P E, Lindstrom J, Kissimova-Scarbeck K, Szybinski Z, Barengo N C, Peltonen M, Tuomilehto J. The European perspective of type 2 diabetes prevention: diabetes in Europe – prevention using lifestyle, physical activity and nutritional intervention (DE-PLAN) project.  Exp Clin Endocrinol Diabetes. 2008;  116 167-172
    Google Scholar
  • 240 Wein P, Beischer N, Harris C, Permezel M. A trial of simple versus intensified dietary modification for prevention of progression to diabetes mellitus in women with impaired glucose tolerance.  Aust N Z J Obstet Gynaecol. 1999;  39 162-166
    Google Scholar
  • 241 Swinburn B A, Metcalf P A, Ley S J. Long-term (5-year) effects of a reduced-fat diet intervention in individuals with glucose intolerance.  Diabetes Care. 2001;  24 619-624
    Google Scholar
  • 242 Davey Smith G, Bracha Y, Svendsen K H, Neaton J D, Haffner S M, Kuller L H. Incidence of type 2 diabetes in the randomized multiple risk factor intervention trial.  Ann Intern Med. 2005;  142 313-322
    Google Scholar
  • 243 Bo S, Ciccone G, Baldi C, Benini L, Dusio F, Forastiere G, Lucia C, Nuti C, Durazzo M, Cassader M, Gentile L, Pagano G. Effectiveness of a lifestyle intervention on metabolic syndrome. A randomized controlled trial.  J General Intern Med. 2007;  22 1695-1703
    Google Scholar
  • 244 Barlow S E, Dietz W H. Obesity evaluation and treatment: Expert Committee recommendations. The Maternal and Child Health Bureau, Health Resources and Services Administration and the Department of Health and Human Services.  Pediatrics. 1998;  102 E29
    Google Scholar
  • 245 Krebs N F, Jacobson M S. Prevention of pediatric overweight and obesity.  Pediatrics. 2003;  112 424-430
    Google Scholar
  • 246 Dietz W H, Gortmaker S L. Preventing obesity in children and adolescents.  Annu Rev Public Health. 2001;  22 337-353
    Google Scholar
  • 247 Gillies C L, Abrams K R, Lambert P C, Cooper N J, Sutton A J, Hsu R T, Khunti K. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis.  BMJ. 2007;  334 299-307
    Google Scholar
  • 248 Bantle J P, Wylie-Rosett J, Albright A L, Apovian C M, Clark N G, Franz M J, Hoogwerf B J, Lichtenstein A H, Mayer-Davis E, Mooradian A D, Wheeler M L. Nutrition recommendations and interventions for diabetes: a position statement of the American Diabetes Association.  Diabetes Care. 2008;  31 (Suppl. 1) S61-78
    Google Scholar
  • 249 Richelsen B, Tonstad S, Rossner S, Toubro S, Niskanen L, Madsbad S, Mustajoki P, Rissanen A. Effect of orlistat on weight regain and cardiovascular risk factors following a very-low-energy diet in abdominally obese patients: a 3-year randomized, placebo-controlled study.  Diabetes Care. 2007;  30 27-32
    Google Scholar
  • 250 Torgerson J S, Hauptman J, Boldrin M N, Sjostrom L. XENical in the prevention of diabetes in obese subjects (XENDOS) study: a randomized study of orlistat as an adjunct to lifestyle changes for the prevention of type 2 diabetes in obese patients.  Diabetes Care. 2004;  27 155-161
    Google Scholar
  • 251 Van Gaal L F, Scheen A J, Rissanen A M, Rossner S, Hanotin C, Ziegler O. Long-term effect of CB1 blockade with rimonabant on cardiometabolic risk factors: two year results from the RIO-Europe Study.  Eur Heart J. 2008;  29 1761-1771
    Google Scholar
  • 252 Nilsson P M. Is weight loss beneficial for reduction of morbidity and mortality? What is the controversy about?.  Diabetes Care. 2008;  31 (Suppl. 2) S278-283
    Google Scholar
  • 253 Buchwald H, Estok R, Fahrbach K, Banel D, Jensen M D, Pories W J, Bantle J P, Sledge I. Weight and type 2 diabetes after bariatric surgery: systematic review and meta-analysis.  Am J Med. 2009;  122 (3) 248-256 e5
    Google Scholar
  • 254 Colquitt J L, Picot J, Loveman E, Clegg A J. Surgery for obesity.  Cochrane Database Syst Rev. 2009;  CD003641
    Google Scholar
  • 255 Chiasson J L, Josse R G, Gomis R, Hanefeld M, Karasik A, Laakso M. Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial.  Lancet. 2002;  359 2072-2077
    Google Scholar
  • 256 Chiasson J L, Josse R G, Gomis R, Hanefeld M, Karasik A, Laakso M. Acarbose treatment and the risk of cardiovascular disease and hypertension in patients with impaired glucose tolerance: the STOP-NIDDM trial.  JAMA. 2003;  290 486-494
    Google Scholar
  • 257 Sawicki P T, Kaiser T. Response to Chiasson et al.: Acarbose for the prevention of Type 2 diabetes, hypertension and cardiovascular disease in subjects with impaired glucose tolerance: facts and interpretations concerning the critical analysis of the STOP-NIDDM Trial data.  Diabetologia. 2004;  47 976-977
    Google Scholar
  • 258 Eriksson J G, Lehtovirta M, Ehrnstrom B, Salmela S, Groop L. Long-term beneficial effects of glipizide treatment on glucose tolerance in subjects with impaired glucose tolerance.  J Intern Med. 2006;  259 553-560
    Google Scholar
  • 259 Jarrett R J, Keen H, McCartney P. The Whitehall Study: ten year follow-up report on men with impaired glucose tolerance with reference to worsening to diabetes and predictors of death.  Diabet Med. 1984;  1 279-283
    Google Scholar
  • 260 The Diabetes Prevention Program Research Group . The Diabetes Prevention Program: baseline characteristics of the randomized cohort. The Diabetes Prevention Program Research Group.  Diabetes Care. 2000;  23 1619-1629
    Google Scholar
  • 261 Knowler W C, Hamman R F, Edelstein S L, Barrett-Connor E, Ehrmann D A, Walker E A, Fowler S E, Nathan D M, Kahn S E. Prevention of type 2 diabetes with troglitazone in the Diabetes Prevention Program.  Diabetes. 2005;  54 1150-1156
    Google Scholar
  • 262 Buchanan T A, Xiang A H, Peters R K, Kjos S L, Marroquin A, Goico J, Ochoa C, Tan S, Berkowitz K, Hodis H N, Azen S P. Preservation of pancreatic beta-cell function and prevention of type 2 diabetes by pharmacological treatment of insulin resistance in high-risk hispanic women.  Diabetes. 2002;  51 2796-2803
    Google Scholar
  • 263 Xiang A H, Peters R K, Kjos S L, Marroquin A, Goico J, Ochoa C, Kawakubo M, Buchanan T A. Effect of pioglitazone on pancreatic beta-cell function and diabetes risk in Hispanic women with prior gestational diabetes.  Diabetes. 2006;  55 517-522
    Google Scholar
  • 264 Bosch J, Yusuf S, Gerstein H C, Pogue J, Sheridan P, Dagenais G, Diaz R, Avezum A, Lanas F, Probstfield J, Fodor G, Holman R R. Effect of ramipril on the incidence of diabetes.  N Engl J Med. 2006;  355 1551-1562
    Google Scholar
  • 265 Andraws R, Brown D L. Effect of inhibition of the renin-angiotensin system on development of type 2 diabetes mellitus (meta-analysis of randomized trials).  Am J Cardiol. 2007;  99 1006-1012
    Google Scholar
  • 266 Jandeleit-Dahm K A, Tikellis C, Reid C M, Johnston C I, Cooper M E. Why blockade of the renin-angiotensin system reduces the incidence of new-onset diabetes.  J Hypertens. 2005;  23 463-473
    Google Scholar
  • 267 Freeman D J, Norrie J, Sattar N, Neely R D, Cobbe S M, Ford I, Isles C, Lorimer A R, Macfarlane P W, McKillop J H, Packard C J, Shepherd J, Gaw A. Pravastatin and the development of diabetes mellitus: evidence for a protective treatment effect in the West of Scotland Coronary Prevention Study.  Circulation. 2001;  103 357-362
    Google Scholar
  • 268 Collins R, Armitage J, Parish S, Sleigh P, Peto R. MRC/BHF Heart Protection Study of cholesterol-lowering with simvastatin in 5963 people with diabetes: a randomised placebo-controlled trial.  Lancet. 2003;  361 2005-2016
    Google Scholar
  • 269 Sever P S, Dahlof B, Poulter N R, Wedel H, Beevers G, Caulfield M, Collins R, Kjeldsen S E, Kristinsson A, McInnes G T, Mehlsen J, Nieminen M, O'Brien E, Ostergren J. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial – Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial.  Lancet. 2003;  361 1149-1158
    Google Scholar
  • 270 The LIPID Study Group . Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range of initial cholesterol levels. The Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) Study Group.  N Engl J Med. 1998;  339 1349-1357
    Google Scholar
  • 271 Sjostrom L, Narbro K, Sjostrom C D, Karason K, Larsson B, Wedel H, Lystig T, Sullivan M, Bouchard C, Carlsson B, Bengtsson C, Dahlgren S, Gummesson A, Jacobson P, Karlsson J, Lindroos A K, Lonroth H, Naslund I, Olbers T, Stenlof K, Torgerson J, Agren G, Carlsson L M. Effects of bariatric surgery on mortality in Swedish obese subjects.  N Engl J Med. 2007;  357 741-752
    Google Scholar
  • 272 WHO .WHO Study Group, Primary Prevention of Diabetes Mellitus. Geneva: Technical Report Series 1994: No. 844. 
    Google Scholar
  • 273 Schwarz P E, Schuppenies A, Gruhl U, Hoffmann R, Bornstein S R, Schulze J, Landgraf R. [Prevention of type 2 diabetes in Germany. Ideas, evidence, implementation].  Med Klin (Munich). 2006;  101 730-736
    Google Scholar
  • 274 Saaristo T, Peltonen M, Keinanen-Kiukaanniemi S, Vanhala M, Saltevo J, Niskanen L, Oksa H, Korpi-Hyovalti E, Tuomilehto J. National type 2 diabetes prevention programme in Finland: FIN-D2D.  Int J Circumpolar Health. 2007;  66 101-112
    Google Scholar
  • 275 Wang J, Luben R, Khaw K T, Bingham S, Wareham N J, Forouhi N G. Dietary energy density predicts the risk of incident type 2 diabetes: the European Prospective Investigation of Cancer (EPIC)-Norfolk Study.  Diabetes Care. 2008;  31 2120-2125
    Google Scholar
  • 276 Rosenbloom A L, Silverstein J H, Amemiya S, Zeitler P, Klingensmith G J. Type 2 diabetes in children and adolescents.  Pediatr Diabetes. 2009;  10 (Suppl. 12) 17-32
    Google Scholar
  • 277 Carnethon M R. Diabetes prevention in US ethnic minorities: role of the social environment.  J Am Diet Assoc. 2008;  108 942-944
    Google Scholar
  • 278 Cramer J S, Sibley R F, Bartlett D P, Kahn L S, Loffredo L. An adaptation of the diabetes prevention program for use with high-risk, minority patients with type 2 diabetes.  Diabetes Educ. 2007;  33 503-508
    Google Scholar
  • 279 Azjen I. The theory of planned behavior.  OBHDP. 1991;  50 (2) 179-211
    Google Scholar
  • 280 Miller W R, Rollnick S. Motivational interviewing: Preparing people for change, 2nd ed. New York; Guildford Press 2002
    Google Scholar
  • 281 Bandura A. Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ; Prentice-Hall 1986
    Google Scholar
  • 282 Leventhal H, Nerenz D R, Steele D J. Illness representations and coping with health threats. Baum A, Taylor SE, Singer JE Handbook of psychology and health, Vol. 4. Hillsdale, NJ; Lawrence Erlbaum Associates 1984: 219-252
    Google Scholar
  • 283 Oxman A D, Guyatt G H. Validation of an index of the quality of review articles.  J Clin Epidemiol. 1991;  44 1271-1278
    Google Scholar
  • 284 Kreuter G. Health program planning: an educational and ecological approach. New York; McGraw-Hill 2005
    Google Scholar
  • 285 Bartholomew L K, Parcel G S, Kok G, Gottlieb N H. Intervention mapping: a process for designing theory- and evidence-based health education programs. Mountain View; Mayfield 2001
    Google Scholar
  • 286 Anonymous. Management of diabetes: A national clinical guideline. Edinburgh; Scottish Intercollegiate Guidelines Network Publication 2001: No.55
    Google Scholar
  • 287 Chow C K, Lock K, Teo K, Subramanian S V, McKee M, Yusuf S. Environmental and societal influences acting on cardiovascular risk factors and disease at a population level: a review.  Int J Epidemiol. 2009;  DOI: 10.1093/ije/dyn258
    Google Scholar
  • 288 Nolte E, Knai C, McKee M eds.. Managing chronic conditions: An introduction to the experience in eight countries. Copenhagen; European Observatory on Health Systems and Policies 2008
    Google Scholar
  • 289 Nolte E, McKee M eds.. Caring for people with chronic conditions. A health system perspective. Maidenhead; Open University Press 2008
    Google Scholar
  • 290 Dubois C-A, McKee M, Nolte E eds.. Human resources for health in Europe. Buckingham; Open University Press 2005
    Google Scholar
  • 291 Rechel B, Dubois C-A, McKee M eds.. The health care workforce in Europe: learning from experience. Copenhagen; European Observatory on Health Systems and Policies 2006
    Google Scholar
  • 292 Saltman R B, Busse R, Figueras J. Social health insurance systems in western Europe. Maidenhead; Open University Press 2004
    Google Scholar
  • 293 Saltman R B, Rico A, Boerma W G W. Primary care in the driver's seat?. Maidenhead; Open University Press 2005
    Google Scholar
  • 294 Bodenheimer T, Wagner E H, Grumbach K. Improving primary care for patients with chronic illness.  JAMA. 2002;  288 1775-1779
    Google Scholar
  • 295 Swinburn B, Egger G, Raza F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity.  Prev Med. 1999;  29 563-570
    Google Scholar
  • 296 Swinburn B, Egger G. Preventive strategies against weight gain and obesity.  Obes Rev. 2002;  3 289-301
    Google Scholar
  • 297 Saelens B E, Sallis J F, Black J B, Chen D. Neighborhood-based differences in physical activity: an environment scale evaluation.  Am J Public Health. 2003;  93 1552-1558
    Google Scholar
  • 298 Ewing R, Brownson R C, Berrigan D. Relationship between urban sprawl and weight of United States youth.  Am J Prev Med. 2006;  31 464-474
    Google Scholar
  • 299 Morland K, Diez Roux A V, Wing S. Supermarkets, other food stores, and obesity: the atherosclerosis risk in communities study.  Am J Prev Med. 2006;  30 333-339
    Google Scholar
  • 300 Boehmer T K, Lovegreen S L, Haire-Joshu D, Brownson R C. What constitutes an obesogenic environment in rural communities?.  Am J Health Promot. 2006;  20 411-421
    Google Scholar
  • 301 Kumanyika S K, Obarzanek E, Stettler N, Bell R, Field A E, Fortmann S P, Franklin B A, Gillman M W, Lewis C E, Poston 2nd W C, Stevens J, Hong Y. Population-based prevention of obesity: the need for comprehensive promotion of healthful eating, physical activity, and energy balance: a scientific statement from American Heart Association Council on Epidemiology and Prevention, Interdisciplinary Committee for Prevention (formerly the expert panel on population and prevention science).  Circulation. 2008;  118 428-464
    Google Scholar
  • 302 Ståhl T, Wismar M, Ollila E, Lahtinen E, Leppo K eds.. Health in all policies: prospects and potentials. Helsinki; STAKES 2007
    Google Scholar
  • 303 Morsanutto A, Berto P, Lopatriello S, Gelisio R, Voinovich D, Cippo P P, Mantovani L G. Major complications have an impact on total annual medical cost of diabetes: results of a database analysis.  J Diabetes Complications. 2006;  20 163-169
    Google Scholar
  • 304 Norlund A. Cost of illness of adult diabetes mellitus underestimated if comorbidity is not considered.  J Intern Med. 2001;  250 57-65
    Google Scholar
  • 305 Unwin N, Whiting D, Gan D eds.. International Diabetes Federation, IDF Diabetes Atlas, 4th edition. 2009; available online:. http://www.eatlas.idf.org
    Google Scholar
  • 306 Zhang P, Zhang X, Betz Brown J et al. International Diabetes Federation. Economic impact of diabetes. 2009; available online:. http://www.eatlas.idf.org (Background paper of the IDF Diabetes Atlas fourth edition)
    Google Scholar
  • 307 Eddy D M, Schlessinger L, Kahn R. Clinical outcomes and cost-effectiveness of strategies for managing people at high risk for diabetes.  Ann Intern Med. 2005;  143 251-264
    Google Scholar
  • 308 NICE .Obesity guidance on the prevention, identification, assessment and management of overweight and obesity in adults and children. London, UK; National Institute for Health and Clinical Excellence 2006
    Google Scholar
  • 309 Gillies C L, Lambert P C, Abrams K R, Sutton A J, Cooper N J, Hsu R T, Davies M J, Khunti K. Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis.  BMJ. 2008;  336 1180-1185
    Google Scholar
  • 310 Lauritzen T, Borch-Johnsen K, Sandbaek A. Is prevention of Type-2 diabetes feasible and efficient in primary care? A systematic PubMed review.  Prim Care Diabetes. 2007;  1 5-11
    Google Scholar
  • 311 Hernan W H, Brandle M, Zhang P, Williamson D F, Matulik M J, Ratner R E, Lachin J M, Engelgau M M. Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program.  Diabetes Care. 2003;  26 36-47
    Google Scholar
  • 312 Ramachandran A, Snehalatha C, Yamuna A, Mary S, Ping Z. Cost-effectiveness of the interventions in the primary prevention of diabetes among Asian Indians: within-trial results of the Indian Diabetes Prevention Programme (IDPP).  Diabetes Care. 2007;  30 2548-2552
    Google Scholar
  • 313 Lindgren P, Lindstrom J, Tuomilehto J, Uusitupa M, Peltonen M, Jonsson B, de Faire U, Hellenius M L. Lifestyle intervention to prevent diabetes in men and women with impaired glucose tolerance is cost-effective.  Int J Technol Assess Health Care. 2007;  23 177-183
    Google Scholar
  • 314 Tan-Torres Edejer T, Baltussen R, Adam T, Hutubessy R, Acharya A, Evans D B, Murray C J Ls. Making choices in health. WHO guide to cost-effectiveness analysis. Geneva; WHO 2003
    Google Scholar
  • 315 Gold MR S J, Russell L B, Weinstein M C. Cost-effectiveness in health and medicine. New York; Oxford University Press 1996
    Google Scholar
  • 316 Garber A M, Phelps C E. Economic foundations of cost-effectiveness analysis.  J Health Econ. 1997;  16 1-31
    Google Scholar
  • 317 Herman W H, Hoerger T J, Brandle M, Hicks K, Sorensen S, Zhang P, Hamman R F, Ackermann R T, Engelgau M M, Ratner R E. The cost-effectiveness of lifestyle modification or metformin in preventing type 2 diabetes in adults with impaired glucose tolerance.  Ann Intern Med. 2005;  142 323-332
    Google Scholar
  • 318 Jacobs-van der Bruggen M A, Bos G, Bemelmans W J, Hoogenveen R T, Vijgen S M, Baan C A. Lifestyle interventions are cost-effective in people with different levels of diabetes risk: results from a modeling study.  Diabetes Care. 2007;  30 128-134
    Google Scholar
  • 319 Herman W H, Smith P J, Thompson T J, Engelgau M M, Aubert R E. A new and simple questionnaire to identify people at increased risk for undiagnosed diabetes.  Diabetes Care. 1995;  18 382-387
    Google Scholar
  • 320 Lindström J, Louheranta A, Mannelin M, Rastas M, Salminen V, Eriksson J, Uusitupa M, Tuomilehto J. The Finnish Diabetes Prevention Study (DPS): Lifestyle intervention and 3-year results on diet and physical activity.  Diabetes Care. 2003;  26 3230-3236
    Google Scholar
  • 321 Lindström J, Ilanne-Parikka P, Peltonen M, Aunola S, Eriksson J G, Hemiö K, Hämäläinen H, Härkönen P, Keinänen-Kiukaanniemi S, Laakso M, Louheranta A, Mannelin M, Rastas M, Sundvall J, Valle T T, Uusitupa M, Tuomilehto J. Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: The follow-up results of the Finnish Diabetes Prevention Study.  Lancet. 2006;  368 1673-1679
    Google Scholar
  • 322 Orchard T J, Temprosa M, Goldberg R, Haffner S, Ratner R, Marcovina S, Fowler S. for the Diabetes Prevention Program Research G . The effect of Metformin and intensive lifestyle intervention on the metabolic syndrome: The Diabetes Prevention Program Randomized Trial.  Ann Intern Med. 2005;  142 611-619
    Google Scholar
  • 323 Avenell A, Broom J, Brown T J, Poobalan A, Aucott L, Stearns S C, Smith W C S, Jung R T, Campbell M K, Grant A M. Systematic review of the long-term effects and economic consequences of treatments for obesity and implications for health improvement.  Health Technology Assessment. 2004;  8 (21) iii-iv 1-182
    Google Scholar
  • 324 McTigue K M, Harris R, Hemphill B, Lux L, Sutton S, Bunton A J, Lohr K. Screening and interventions for obesity in adults: summary of the evidence for the U.S. Preventive Services Task Force.  Ann Intern Med. 2003;  139 933-949
    Google Scholar
  • 325 Shaw K A, O'Rourke P, Del Mar C, Kenardy J. Psychological interventions for overweight or obesity.  Cochrane Database of Systematic Reviews. 2005;  CD003818
    Google Scholar

Appendix 1

Oral Glucose Tolerance Test (OGTT)

The oral glucose tolerance test (OGTT) is recommended by the WHO for diagnosis of T2DM.

Preparation and cautions

The OGTT should be performed in the morning, after at least three days of unrestricted carbohydrate intake (more than 150 g of carbohydrate daily). The test should not be done during an acute illness, as the results may not reflect the patient's glucose metabolism when healthy. A full test dose of glucose for adults should not be given to a person weighing less than 43 kg, due to the fact excessive amount of glucose may produce a false positive result.

The OGTT procedure

The test should be implemented after an overnight fast of 10 to 16 hours (water is allowed). Smoking or physical activity is not permitted during the test. Usually the OGTT is scheduled to begin in the morning (7–9 am) as glucose tolerance exhibits a diurnal rhythm with a significant decrease in the afternoon. At baseline, the blood sample for glucose determination is taken. The patient is then given a glucose solution to drink. The standard dose is 75 g of glucose in 250–300 ml of water. It should be ingested within 5 minutes. For children, the test load should be 1.75 g per kg of body weight, up to a maximum of 75 g of glucose, The next blood sample is collected at 120 min after the glucose load.

Plasma glucose measurement in blood samples

The processing of the samples after collection is important to ensure accurate measurement of plasma glucose. This requires rapid separation of the plasma after collection. Laboratory measurements rely upon the use of separated plasma and only immediate separation can prevent the lowering of the glucose in the sample. Only if the plasma separation is completely impossible to be done immediately upon collection, glycolysis inhibitors, e.g. sodium fluoride (6 mg per ml of the whole blood) can be used. Rapid cooling of the sample may also be helpful in reducing the loss of glucose if the plasma cannot be immediately separated. In this case, the sample should be placed immediately after collection into ice-water but the plasma separation should occur within 30 minutes. The plasma should be frozen until the glucose concentration can be measured.

International Federation of Clinical Chemistry (IFCC) recommended that all glucose measuring devices report the results in plasma values. The reason for this recommendation is the fact that plasma glucose values are approximately 11 % higher than the values of whole blood glucose measured in the same sample. Moreover, WHO recommendation is that venous plasma glucose should be the standard method for measuring and reporting. However, it should be noted if one converts from venous to capillary plasma glucose the conversion is different in the case of fasting or post-load glucose values. Fasting values for venous and capillary plasma glucose are identical, while the conversion is necessary only for post-load glucose.

Appendix 2

Methods and Procedures

Methods

The IMAGE project is described in detail on its website (http://www.image-project.eu/). Briefly, the development of this guideline followed a pre-defined step-wise procedure addressing:

(i) Stakeholder involvement: the IMAGE guideline development group included diabetes specialists, public health and primary care health professionals, behavioural and social scientists, epidemiologists, patients' organisations, health professional organisations, multidisciplinary, health economists, and health promotion, health policy and health services researchers (for details see Acknowledgements and website). All stakeholders were consulted at numerous stages including the design of the project, definition of the scope and purpose, identification of relevant evidence and developing and refining drafts and final versions of the guideline.

(ii) Scope and purpose: the overall objectives of the guideline were developed through consultation with all stakeholders by email, teleconference and a 2-day symposium. By this process, the clinical questions and target population covered by the guideline were defined and separate working groups established to synthesise the evidence under the following headings: definitions of risk and target population; screening tools, diagnosis and detection; prevention of T2DM and its comorbidities; supporting change in lifestyle behavior for adults at risk of T2DM; models of care and economic aspects of T2DM prevention; and recommendations for economic evaluation of T2DM prevention strategies.

(iii) Evidence identification and review: systematic methods were used to identify relevant evidence using defined search strategies appropriate to the specific topic (see Methodology sections), use of multiple databases, follow up of cited references, and consultation with experts in the field. Criteria for selecting and evaluating the quality of the evidence were based only on publications in peer-reviewed scientific journals and are described in detail (see Methodology sections). Throughout the guideline, SIGN guidance was used to define the criteria for levels (quality) of evidence and grades of the resulting recommendation, which are provided at the end of each chapter. Health benefits, side effects, and risks were considered in formulating these recommendations which were linked to the supporting evidence. Prior to publication, experts externally reviewed the guideline. A procedure for updating the guideline is to be defined.

(iv) Clarity and presentation: the recommendations were reviewed to ensure they are specific and unambiguous. Contextually specific issues arising in each participating European country were discussed to minimise any misunderstanding or misinterpretation. Different options for management are clearly presented and the key recommendations are easily identifiable. The guideline is supported by tools and materials for its application (see website).

(iv) Implementation and dissemination: Potential organizational barriers to applying the recommendations were discussed and addressed where possible. The potential cost implications of applying the recommendations were considered (recognising that precise values will depend on national circumstances such as mix of inputs and unit costs) and the guideline presents key review criteria for monitoring and/or audit purposes. A plan for disseminating the guideline to relevant professional groups and persons with increased diabetes risk is in development.

(v) Editorial independence: The guideline is editorially independent from the funding body. Conflicts of interest of guideline development members have been recorded in the Acknowledgements.

Procedures

At the initial meeting of the guideline development group (Munich, November 2007), the partners discussed the overall project strategies and, based on their specific expertise, assigned themselves to the different working groups. Working group leaders were decided by consensus within each group. Communication occurred within and across the working groups by email, intranet and face-to-face meetings. During a 2-day meeting (Vienna, March 2008), the available information was pre-screened, exclusion and inclusion criteria defined, methodology for evidence identification, grading and recommendation development was further discussed and additional partners allocated to the working groups. Drafts on specific topics were circulated by email and discussed at a further 1-day meeting (Helsinki, June 2008) and across the WPs at a further 2-day meeting (Mallorca, November 2008). In January 2009, the completed drafts were disseminated as a first version of the completed guideline to all stakeholders via email and intranet. After consensus was reached on the contents, the guideline was shortened and edited. Consensus on the final version of the guideline, authors list and publication strategies was achieved during the final 2-day IMAGE meeting (Lisbon, October 2009).

Strengths and limitations

The evidence-based guideline focuses primarily on the European environment. It does not address specific requirements for ethnic minority groups and people with different social and cultural backgrounds. Although the working groups took note of the specific need for prevention of obesity and diabetes in children with metabolic risk factors, it was determined that this laid outside the scope of this guideline. Although many of the interventions identified can be expected to have similar effects in children, the metabolic, psychosocial, behavioural and medical requirements may be different. Despite these limitations, the IMAGE guideline applies to more than 80 % of people with increased metabolic risk in Europe. Further work is necessary to extend the scope of the guideline and to address the needs of children and specific ethnic groups.

Univ. Prof. Dr. Michael Roden

Karl-Landsteiner Institute for Endocrinology and Metabolism
Hanusch Hospital

1140 Vienna

Austria


Institute for Clinical Diabetology, German Diabetes Center, and Department of Metabolic Diseases
University Clinics
Heinrich Heine University Düsseldorf

Auf'm Hennekamp 65

40225 Düsseldorf

Germany

Email: michael.roden@ddz.uni-duesseldorf.de