Risk equations for the development of worsened glucose status and type 2 diabetes mellitus in a Swedish intervention program

BMC Public Health

Lars Lindholm³  Stefanie J Klug²  Ingegerd Johansson¹  Fredrik Norström³  Olaf Schoffer²  Margareta Norberg³  Anne Neumann² 

[1] Department of Odontology, Umeå University, Umeå 901 87, SE, Sweden;Cancer Epidemiology, University Cancer Center, University Hospital, Technische Universität Dresden, Fetscherstr. 74, Dresden 01307, Germany;Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå 901 85, SE, Sweden

关键词: Life style;    Early intervention;    Factor analysis, statistical;    Logistic models;    Sweden;    Glucose;    Risk factors;    control;    Prevention &;    Pre-diabetic state;    Diabetes mellitus, type 2;   

Others  :  1161609
DOI  :  10.1186/1471-2458-13-1014

 received in 2013-03-05, accepted in 2013-09-25,  发布年份 2013

【 摘 要 】

Background

Several studies investigated transitions and risk factors from impaired glucose tolerance (IGT) to type 2 diabetes mellitus (T2D). However, there is a lack of information on the probabilities to transit from normal glucose tolerance (NGT) to different pre-diabetic states and from these states to T2D. The objective of our study is to estimate these risk equations and to quantify the influence of single or combined risk factors on these transition probabilities.

Methods

Individuals who participated in the VIP program twice, having the first examination at ages 30, 40 or 50 years of age between 1990 and 1999 and the second examination 10 years later were included in the analysis. Participants were grouped into five groups: NGT, impaired fasting glucose (IFG), IGT, IFG&IGT or T2D. Fourteen potential risk factors for the development of a worse glucose state (pre-diabetes or T2D) were investigated: sex, age, education, perceived health, triglyceride, blood pressure, BMI, smoking, physical activity, snus, alcohol, nutrition and family history. Analysis was conducted in two steps. Firstly, factor analysis was used to find candidate variables; and secondly, logistic regression was employed to quantify the influence of the candidate variables. Bootstrap estimations validated the models.

Results

In total, 29 937 individuals were included in the analysis. Alcohol and perceived health were excluded due to the results of the factor analysis and the logistic regression respectively. Six risk equations indicating different impacts of different risk factors on the transition to a worse glucose state were estimated and validated. The impact of each risk factor depended on the starting or ending pre-diabetes state. High levels of triglyceride, hypertension and high BMI were the strongest risk factors to transit to a worsened glucose state.

Conclusions

The equations could be used to identify individuals with increased risk to develop any of the three pre-diabetic states or T2D and to adapt prevention strategies.

【 授权许可】

   
2013 Neumann et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ, Lin JK, Farzadfar F, Khang YH, Stevens GA, et al.: National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet 2011, 378(9785):31-40.
• [2]Jansson SP, Andersson DK, Svardsudd K: Prevalence and incidence rate of diabetes mellitus in a Swedish community during 30 years of follow-up. Diabetologia 2007, 50(4):703-710.
• [3]Ringborg A, Martinell M, Stalhammar J, Yin DD, Lindgren P: Resource use and costs of type 2 diabetes in Sweden - estimates from population-based register data. Int J Clin Pract 2008, 62(5):708-716.
• [4]Paulweber B, Valensi P, Lindstrom J, Lalic NM, Greaves CJ, McKee M, Kissimova-Skarbek K, Liatis S, Cosson E, Szendroedi J, et al.: A European evidence-based guideline for the prevention of type 2 diabetes. Horm Metab Res 2010, 42(Suppl 1):S3-36.
• [5]Lindstrom J, Neumann A, Sheppard KE, Gilis-Januszewska A, Greaves CJ, Handke U, Pajunen P, Puhl S, Polonen A, Rissanen A, et al.: Take action to prevent diabetes--the IMAGE toolkit for the prevention of type 2 diabetes in Europe. Horm Metab Res 2010, 42(Suppl 1):S37-S55.
• [6]Gillies CL, Abrams KR, Lambert PC, Cooper NJ, Sutton AJ, Hsu RT, 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(7588):299.
• [7]Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P, Keinanen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas M, et al.: Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001, 344(18):1343-1350.
• [8]DeFronzo RA, Abdul-Ghani M: Assessment and treatment of cardiovascular risk in prediabetes: impaired glucose tolerance and impaired fasting glucose. Am J Cardiol 2011, 108(3 Suppl):3B-24B.
• [9]Wang H, Shara NM, Calhoun D, Umans JG, Lee ET, Howard BV: Incidence rates and predictors of diabetes in those with prediabetes: the Strong Heart Study. Diabetes Metab Res Rev 2010, 26(5):378-385.
• [10]Rubin RR, Fujimoto WY, Marrero DG, Brenneman T, Charleston JB, Edelstein SL, Fisher EB, Jordan R, Knowler WC, Lichterman LC, Prince M, Rowe PM: The Diabetes Prevention Program: recruitment methods and results. Control Clin Trials 2002, 23(2):157-171.
• [11]Neumann A, Schwarz P, Lindholm L: Estimating the cost-effectiveness of lifestyle intervention programmes to prevent diabetes based on an example from Germany: Markov modelling. Cost Eff Resour Alloc 2011, 9(1):17. BioMed Central Full Text
• [12]Norberg M, Wall S, Boman K, Weinehall L: The Vasterbotten Intervention Programme: background, design and implications. GlobHealth Action 2010., 3
• [13]Rolandsson O, Norberg M, Nystrom L, Soderberg S, Svensson M, Lindahl B, Weinehall L: How to diagnose and classify diabetes in primary health care: lessons learned from the Diabetes Register in Northern Sweden (DiabNorth). Scand J Prim Health Care 2012, 30(2):81-87.
• [14]WHO: Definition, Diagnosis and Classification of Diabetes Mellitus and Its Complications, Part 1: Diagnosis and Classification of Diabetes Mellitus, Volume 99.2. Geneva: World Health Organization; 1999:1-59.
• [15]McCormack VA, Agudo A, Dahm CC, Overvad K, Olsen A, Tjonneland A, Kaaks R, Boeing H, Manjer J, Almquist M, et al.: Cigar and pipe smoking and cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC). Int J Cancer 2010, 127(10):2402-2411.
• [16]Gorsuch RL: Factor Analysis. Hillsdale, NJ: Lawrence Erlbaum Associated, Inc; 1983.
• [17]Comrey AL, Lee HB: A First Course in Factor Analysis, Volume second. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc; 1992.
• [18]Diaz-Bone R, Künemund H: Einführung in die binäre logistische Regression. Berlin: Freie Universität Berlin; 2003.
• [19]Efron B: Bootstrap methods: another look at the jackknife. Ann Stat 1979, 7(1):1-26.
• [20]Noble D, Mathur R, Dent T, Meads C, Greenhalgh T: Risk models and scores for type 2 diabetes: systematic review. BMJ 2011, 343:d7163.
• [21]de Vegt F, Dekker JM, Jager A, Hienkens E, Kostense PJ, Stehouwer CD, Nijpels G, Bouter LM, Heine RJ: Relation of impaired fasting and postload glucose with incident type 2 diabetes in a Dutch population: The Hoorn Study. JAMA 2001, 285(16):2109-2113.
• [22]Eschwege E, Charles MA, Simon D, Thibult N, Balkau B: Reproducibility of the diagnosis of diabetes over a 30-month follow-up: the Paris Prospective Study. Diabetes Care 2001, 24(11):1941-1944.
• [23]Vaccaro O, Ruffa G, Imperatore G, Iovino V, Rivellese AA, Riccardi G: Risk of diabetes in the new diagnostic category of impaired fasting glucose: a prospective analysis. Diabetes Care 1999, 22(9):1490-1493.
• [24]Carnevale Schianca GP, Fra GP, Colli E, Bigliocca M, Mella R, Scaglia E, Bartoli E: Sex difference in lipid profile in relation to the progression of glucose abnormalities. J Diabetes 2012, 4(1):95-101.
• [25]Regitz-Zagrosek V, Lehmkuhl E, Weickert MO: Gender differences in the metabolic syndrome and their role for cardiovascular disease. Clin Res Cardiol 2006, 95(3):136-147.
• [26]Hanefeld M, Koehler C, Fuecker K, Henkel E, Schaper F, Temelkova-Kurktschiev T: Insulin secretion and insulin sensitivity pattern is different in isolated impaired glucose tolerance and impaired fasting glucose: the risk factor in Impaired Glucose Tolerance for Atherosclerosis and Diabetes study. Diabetes Care 2003, 26(3):868-874.
• [27]Tutuncuoglu P, Sarac F, Saygili F, Ozgen AG, Yilmaz C, Tuzun M: Diabetes and impaired glucose tolerance prevalences in Turkish patients with impaired fasting glucose. Acta Diabetol 2008, 45(3):151-156.
• [28]Meigs JB, Muller DC, Nathan DM, Blake DR, Andres R: The natural history of progression from normal glucose tolerance to type 2 diabetes in the Baltimore Longitudinal Study of Aging. Diabetes 2003, 52(6):1475-1484.
• [29]Klein BE, Klein R, Moss SE: Self-rated health and diabetes of long duration. The Wisconsin Epidemiologic Study of Diabetic Retinopathy. Diabetes Care 1998, 21(2):236-240.
• [30]Wennberg P, Rolandsson O, Jerden L, Boeing H, Sluik D, Kaaks R, Teucher B, Spijkerman A, Bueno de Mesquita B, Dethlefsen C, et al.: Self-rated health and mortality in individuals with diabetes mellitus: prospective cohort study. BMJ Open 2012, 2(1):e000760.
• [31]Jauch-Chara K, Schmoller A, Oltmanns KM: Impaired glucose tolerance in healthy men with low body weight. Nutr J 2011, 10:16. BioMed Central Full Text
• [32]Kuhl J, Hilding A, Ostenson CG, Grill V, Efendic S, Bavenholm P: Characterisation of subjects with early abnormalities of glucose tolerance in the Stockholm Diabetes Prevention Programme: the impact of sex and type 2 diabetes heredity. Diabetologia 2005, 48(1):35-40.
• [33]Kasteridis P, Yen ST: Smoking. Evidence from the Behavioral Risk Factor Surveillance Survey. Health Services Research: Cessation and Body Weight; 2012.
• [34]Norberg M, Eriksson JW, 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(3):263-271.
• [35]Tuomilehto J, Lindstrom J, Hellmich M, Lehmacher W, Westermeier T, Evers T, Bruckner A, Peltonen M, Qiao Q, Chiasson JL: Development and validation of a risk-score model for subjects with impaired glucose tolerance for the assessment of the risk of type 2 diabetes mellitus-The STOP-NIDDM risk-score. Diabetes Res Clin Pract 2010, 87(2):267-274.
• [36]Stern MP, Williams K, Haffner SM: Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med 2002, 136(8):575-581.
• [37]Lindstrom J, Tuomilehto J: The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 2003, 26(3):725-731.
• [38]Briggs A, Claxton K, Mark S: Decision Modelling for Health Economic Evaluation. New York: Oxford University Press; 2006.
• [39]Qiao Q, Lindstrom J, Valle TT, Tuomilehto J: Progression to clinically diagnosed and treated diabetes from impaired glucose tolerance and impaired fasting glycaemia. J Brit Diabetic Assoc 2003, 20(12):1027-1033.
• [40]Rajala U, Qiao Q, Laakso M, Keinanen-Kiukaanniemi S: Antihypertensive drugs as predictors of type 2 diabetes among subjects with impaired glucose tolerance. Diabetes Res Clin Pr 2000, 50(3):231-239.
• [41]Janghorbani M, Amini M: Progression to impaired glucose metabolism in first-degree relatives of patients with type 2 diabetes in Isfahan, Iran. Diabetes Metab Res Rev 2009, 25(8):748-755.
• [42]Kang HW, Kim DJ, Lee MS, Kim KW, Lee MK: Pathophysiologic heterogeneity in the development of type 2 diabetes mellitus in Korean subjects. Diabetes Res Clin Pract 2005, 69(2):180-187.
• [43]Alvarsson M, Hilding A, Ostenson CG: Factors determining normalization of glucose intolerance in middle-aged Swedish men and women: a 8-10-year follow-up. Diabet Med 2009, 26(4):345-353.