Estimating the Risk of Developing Type 2 Diabetes: a Comparison of Several Risk Scores: the Cohorte Lausannoise Study

Overview

Journal Diabetes Care

Specialty Endocrinology

Date 2011 Jun 4

PMID 21636799

Citations 6

Authors

Remy Schmid

Peter Vollenweider

Gerard Waeber

Pedro Marques-Vidal

Affiliations

Soon will be listed here.

Abstract

Objective: To compare in the Swiss population the results of several scores estimating the risk of developing type 2 diabetes.

Research Design And Methods: This was a single-center, cross-sectional study conducted between 2003 and 2006 in Lausanne, Switzerland. Overall, 3,251 women and 2,937 men, aged 35-75 years, were assessed, of which 5,760 (93%) were free from diabetes and included in the current study. The risk of developing type 2 diabetes was assessed using seven different risk scores, including clinical data with or without biological data. Participants were considered to be eligible for primary prevention according to the thresholds provided for each score. The results were then extrapolated to the Swiss population of the same sex and age.

Results: The risk of developing type 2 diabetes increased with age in all scores. The prevalence of participants at high risk ranged between 1.6 and 24.9% in men and between 1.1 and 15.7% in women. Extrapolated to the Swiss population of similar age, the overall number of participants at risk, and thus susceptible to intervention, ranged between 46,708 and 636,841. In addition, scores that included the same clinical variables led to a significantly different prevalence of participants at risk (4.2% [95% CI 3.4-5.0] vs. 12.8% [11.5-14.1] in men and 2.9% [2.4-3.6] vs. 6.0% [5.2-6.9] in women). CONCLUSIONS; The prevalence of participants at risk for developing type 2 diabetes varies considerably according to the scoring system used. To adequately prevent type 2 diabetes, risk-scoring systems must be validated for each population considered.

Citing Articles

Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing.

Tong C, Han Y, Zhang S, Li Q, Zhang J, Guo X BMC Public Health. 2022; 22(1):2306.

PMID: 36494707 PMC: 9733342. DOI: 10.1186/s12889-022-14782-6.

Predictive models for diabetes mellitus using machine learning techniques.

Lai H, Huang H, Keshavjee K, Guergachi A, Gao X BMC Endocr Disord. 2019; 19(1):101.

PMID: 31615566 PMC: 6794897. DOI: 10.1186/s12902-019-0436-6.

Association of a Dietary Score with Incident Type 2 Diabetes: The Dietary-Based Diabetes-Risk Score (DDS).

Dominguez L, Bes-Rastrollo M, Basterra-Gortari F, Gea A, Barbagallo M, Martinez-Gonzalez M PLoS One. 2015; 10(11):e0141760.

PMID: 26544985 PMC: 4636153. DOI: 10.1371/journal.pone.0141760.

Comparison of diabetes risk score estimates and cardiometabolic risk profiles in a middle-aged Irish population.

Phillips C, Kearney P, McCarthy V, Harrington J, Fitzgerald A, Perry I PLoS One. 2013; 8(11):e78950.

PMID: 24236074 PMC: 3827294. DOI: 10.1371/journal.pone.0078950.

Screening for impaired fasting glucose and diabetes using available health plan data.

McEwen L, Adams S, Schmittdiel J, Ferrara A, Selby J, Herman W J Diabetes Complications. 2013; 27(6):580-7.

PMID: 23587840 PMC: 3714351. DOI: 10.1016/j.jdiacomp.2013.01.003.

References

Wilson P, Meigs J, Sullivan L, Fox C, Nathan D, DAgostino Sr R . Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med. 2007; 167(10):1068-74. DOI: 10.1001/archinte.167.10.1068. View

Padwal R, Laupacis A . Antihypertensive therapy and incidence of type 2 diabetes: a systematic review. Diabetes Care. 2003; 27(1):247-55. DOI: 10.2337/diacare.27.1.247. View

Rahman M, Simmons R, Harding A, Wareham N, Griffin S . A simple risk score identifies individuals at high risk of developing Type 2 diabetes: a prospective cohort study. Fam Pract. 2008; 25(3):191-6. DOI: 10.1093/fampra/cmn024. View

Mann D, Bertoni A, Shimbo D, Carnethon M, Chen H, Jenny N . Comparative validity of 3 diabetes mellitus risk prediction scoring models in a multiethnic US cohort: the Multi-Ethnic Study of Atherosclerosis. Am J Epidemiol. 2010; 171(9):980-8. PMC: 2877477. DOI: 10.1093/aje/kwq030. View

Schmidt M, Duncan B, Bang H, Pankow J, Ballantyne C, Golden S . Identifying individuals at high risk for diabetes: The Atherosclerosis Risk in Communities study. Diabetes Care. 2005; 28(8):2013-8. DOI: 10.2337/diacare.28.8.2013. View

. Standards of medical care in diabetes--2009. Diabetes Care. 2009; 32 Suppl 1:S13-61. PMC: 2613589. DOI: 10.2337/dc09-S013. View

Clerc O, Nanchen D, Cornuz J, Marques-Vidal P, Gmel G, Daeppen J . Alcohol drinking, the metabolic syndrome and diabetes in a population with high mean alcohol consumption. Diabet Med. 2010; 27(11):1241-9. DOI: 10.1111/j.1464-5491.2010.03094.x. View

Joseph J, Svartberg J, Njolstad I, Schirmer H . Risk factors for type 2 diabetes in groups stratified according to metabolic syndrome: a 10-year follow-up of the Tromsø Study. Eur J Epidemiol. 2010; 26(2):117-24. PMC: 3043259. DOI: 10.1007/s10654-010-9540-7. View

Kahn H, Cheng Y, Thompson T, Imperatore G, Gregg E . Two risk-scoring systems for predicting incident diabetes mellitus in U.S. adults age 45 to 64 years. Ann Intern Med. 2009; 150(11):741-51. DOI: 10.7326/0003-4819-150-11-200906020-00002. View

10.

Lindstrom J, Tuomilehto J . The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003; 26(3):725-31. DOI: 10.2337/diacare.26.3.725. View

11.

Griffin S, Little P, Hales C, Kinmonth A, Wareham N . Diabetes risk score: towards earlier detection of type 2 diabetes in general practice. Diabetes Metab Res Rev. 2000; 16(3):164-71. DOI: 10.1002/1520-7560(200005/06)16:3<164::aid-dmrr103>3.0.co;2-r. View

12.

Knowler W, Barrett-Connor E, Fowler S, Hamman R, Lachin J, Walker E . Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002; 346(6):393-403. PMC: 1370926. DOI: 10.1056/NEJMoa012512. View

13.

Rathmann W, Martin S, Haastert B, Icks A, Holle R, Lowel H . Performance of screening questionnaires and risk scores for undiagnosed diabetes: the KORA Survey 2000. Arch Intern Med. 2005; 165(4):436-41. DOI: 10.1001/archinte.165.4.436. View

14.

Schulze M, Weikert C, Pischon T, Bergmann M, Al-Hasani H, Schleicher E . Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: the EPIC-Potsdam Study. Diabetes Care. 2009; 32(11):2116-9. PMC: 2768223. DOI: 10.2337/dc09-0197. View

15.

Frayling T . Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nat Rev Genet. 2007; 8(9):657-62. DOI: 10.1038/nrg2178. View

16.

Firmann M, Mayor V, Vidal P, Bochud M, Pecoud A, Hayoz D . The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc Disord. 2008; 8:6. PMC: 2311269. DOI: 10.1186/1471-2261-8-6. View

17.

Hense H, Schulte H, Lowel H, Assmann G, Keil U . Framingham risk function overestimates risk of coronary heart disease in men and women from Germany--results from the MONICA Augsburg and the PROCAM cohorts. Eur Heart J. 2003; 24(10):937-45. DOI: 10.1016/s0195-668x(03)00081-2. View

18.

Willi C, Bodenmann P, Ghali W, Faris P, Cornuz J . Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis. JAMA. 2007; 298(22):2654-64. DOI: 10.1001/jama.298.22.2654. View

19.

Balkau B, Lange C, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S . Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care. 2008; 31(10):2056-61. PMC: 2551654. DOI: 10.2337/dc08-0368. View