The Diabetes Risk Score

8/17/2019 The Diabetes Risk Score

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The Diabetes Risk Score

A practical tool to predict type 2 diabetes risk

JAANA LINDSTRÖM, MSC1

JAAKKO TUOMILEHTO, MD, PHD1,2

OBJECTIVE — Interventions to prevent type 2 diabetes should be directed toward individ-uals at increased risk for the disease. To identify such individuals without laboratory tests, wedeveloped the Diabetes Risk Score.

RESEARCH DESIGN AND METHODS — A random population sample of 35- to64-year-old men and women with no antidiabetic drug treatment at baseline were followed for10 years. New cases of drug-treated type 2 diabetes were ascertained from the National DrugRegistry. Multivariate logistic regression model coefficients were used to assign each variablecategory a score. The Diabetes Risk Score was composed as the sum of these individual scores.

The validity of the score was tested in an independent population survey performed in 1992 withprospective follow-up for 5 years.

RESULTS — Age, BMI, waist circumference, history of antihypertensive drug treatment andhigh blood glucose, physical activity, and daily consumption of fruits,berries, or vegetables wereselected as categorical variables. Complete baseline risk data were found in 4,435 subjects with182 incident cases of diabetes. The Diabetes Risk Score value varied from 0 to 20. To predictdrug-treated diabetes, the score value9 had sensitivity of 0.78 and 0.81,specificity of 0.77 and0.76, and positive predictive value of 0.13 and 0.05 in the 1987 and 1992 cohorts, respectively.

CONCLUSIONS — The Diabetes Risk Score is a simple, fast, inexpensive, noninvasive, andreliable tool to identify individuals at high risk for type 2 diabetes.

Diabetes Care 26:725–731, 2003

The prevalence of type 2 diabetes isincreasing in all populations world-wide. It is a major risk factor for

death and numerous nonfatal complica-tions that will form a large burden to thepatients, their families, and the healthcare system. Several recent interventionstudies have undisputedly proved thattype 2 diabetes can be efficiently pre-vented by lifestyle modification in high-risk individuals (1–3). Now, the majortask for public healthadministrations is to

identify individuals who would benefitfrom intensive lifestyle counseling.

Screening for blood glucose has been

used or proposed as the possible tool toidentify individuals with high diabetesrisk or asymptomatic diabetes. There isdebate regarding whether screening forfasting glucose is sufficient or whether anoral glucose tolerance test is needed fordetection of asymptomatic diabetes (4).Measuring either fasting or postchallenge(postprandial) blood glucose is an inva-sive procedure and is costly and timecon-suming. Blood glucose has a large randomvariation and only gives information on a

subject’s current glycemic status. How-ever, the true primary prevention wouldbe to identify high-risk subjects whenthey

are still in a normoglycemic state and totreat them by interventions that preventtheir transition from normoglycemia toimpaired glucose tolerance and to overtdiabetes.

The aim of thisstudy was to develop asimple, practical, and informative scoringsystem to characterizeindividuals accord-ing to their future risk of type 2 diabetes.Furthermore, we have evaluated the use-fulness of the scoring system in detectingasymptomatic diabetes in a cross-sectional setting.

RESEARCH DESIGN AND

METHODS — A random sample wasdrawn from the National Population Reg-ister in 1987 and another independentsample was drawn in 1992 (the FINRISKStudies). The samples included 6.6% of the population aged 25–64 years andwere stratified so that at least 250 subjectsof each sex and 10-year age group werechosen from North Karelia, Kuopio, andSouth-Western Finland, as well as fromthe Helsinki-Vantaa region in 1992. Par-ticipation rates were 82%in the 1987 sur-vey (n 4,746) and 76% in the 1992survey (n 4,615). Baseline surveys wereperformed from January to April 1987(model development data) and from Feb-ruary to May 1992 (model validationdata). The sampling schemes and surveyprocedures have been described in detailelsewhere (5–9).

The subjects received by mail a ques-tionnaire on medical history and healthbehavior and an invitation to a clinicalexamination, which included measure-ments of weight (in light indoor clothes,

to the nearest 100 g), height (withoutshoes, to the nearest 1 mm), and waistcircumference (at a level midway betweenthe lowest rib and the iliac crest, to thenearest 5 mm). BMI was calculated divid-ing the weight (kg) by the height squared(m2).

The endpoint of follow-up wasdevel-opment of drug-treated diabetes. Datawere collected through computer-baseddata linkage with the nationwide SocialInsurance Institution drug register untilthe end of 1997. This drug register com-

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From the 1Diabetes and Genetic Epidemiology Unit, Department of Epidemiology and Health Promotion,National Public Health Institute, Helsinki, Finland; and the 2Department of Public Health, University of Helsinki, Helsinki, Finland.

Address correspondence and reprint requests to Jaana Lindström, National Public Health Institute,Mannerheimintie 166, 00300 Helsinki, Finland. E-mail: [email protected].

Received for publication 15 July 2002 and accepted in revised form 18 November 2002.Abbreviations: AUC, area under the curve; PPV, positive predictive value; ROC, receiver-operating

characteristic; WHO, World Health Organization. A table elsewhere in this issue shows conventional and Système International (SI) units and conversion

factors for many substances.

E p i d e m i o l o g y / H e a l t h S e r v i c e s / P s y c h o s o c i a l R e s e a r c h

O R I G I N A L A R T I C L E

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prises information about all Finnish peo-ple who have been approved to receivefree-of-charge drug treatment for certainchronic diseases, including diabetes. Thesubjects aged34 years and those on an-tidiabetic drug treatment at the time of thebaseline survey were excluded from theanalyses.

Logistic regression was used to com-pute -coef ficients for known risk factorsfor diabetes. Because the aim was to pro-duce a simple risk calculator that could beconveniently used in primary care andalso by individuals themselves, only pa-rameters that are easy to assess withoutany laboratory tests or other clinical mea-surements requiring special skills wereentered into the model (Table 1). The lo-gistic regression analyses with drug-

treated diabetes diagnosed duringfollow-up as the dependent variable werep e r fo r m ed u s i ng t h e L O G IS T I C-procedure of SAS software (version 8.2;SAS Institute, Cary, NC). Interactionterms between the independent variableswere not considered, because we wantedto keep the Diabetes Risk Score simpleand easy to use. Coef ficients () of themodel were used to assign a score valuefor each variable, and the composite Dia-betes Risk Score was calculated as thesumof those scores. The sensitivity (probabil-

ity that the test is positive for subjects whowill get drug-treated diabetes in the fu-ture) and the specificity (the probabilitythat the test is negative for subjects with-out drug-treated diabetes) with 95% CIs(10) were calculated for each DiabetesRisk Score level in differentiating the sub-

jects who developed drug-treated diabe-tes from those who did not. Then,receiver-operating characteristic (ROC)curves were plotted for the Diabetes RiskScore; the sensitivity was plotted on the y-axis, and the false-positive rate (1-specificity) was plotted on the x-axis. Themore accurately discriminatory the test,the steeper the upward portion of theROC curve and the higher the area underthe curve (AUC), the optimal cut pointbeing the peak of the curve (11). The

FREQ procedures trend-option was usedto calculate trend test for rates (Table 2).

To identify prevalent diabetes in the1987 survey, the subjects were asked tofast for at least 4 h before the scheduledexamination. Blood samples for determi-nation of fasting blood glucose levels werecollected from participants aged 45– 64years. Then, the 2-h oral glucose toler-ance test with a standard 75 g of glucosewas administered, and the second bloodsample was collected after 2 h. Venousfull blood was collected into tubes con-

taining oxalate-fluoride and mailed to thecentral laboratory. Blood glucose level wasdetermined with hexokinase-glucose-6-phosphate dehydrogenase method assoon as the samples were received (1–2days after the blood was collected).

In the 1992 survey, individuals aged45– 64 years were invited to a repeat visita few weeks after the first survey visit toundergo standard oral glucose tolerancetest and for collection of blood samplesfor determination of fasting and 2-hplasma glucose levels. The test was com-pleted after an overnight fast. Samples forplasma glucose determination were col-lected in heparinized and fluoridatedtubes and centrifuged immediately.Plasma samples were mailed the same dayto a central laboratory, where glucose

concentration was determined with thehexokinase method.

If fasting time was inadequate, glu-cose values were not accepted. In addi-t i o n , i f g l u c o s e s o l u t i o n w a s n o tconsumed or if the postload blood samplewas collected either 5 min too early or 5min too late, the postload glucose valuewas not accepted. In such cases, subjectscould only be classified as having diabe-tes, based on high fasting value.

Subjects not under antidiabetic drugtreatment were diagnosed as having dia-

Table 1—Logistic regression models with drug-treated diabetes during follow-up as the dependent variable

Concise model: n 4,595

(194 of whom developed diabetes)

Full model: n 4,435

(182 of whom developed diabetes)

ScoreOR (95% CI) Coef ficient OR (95% CI) Coef ficient

Intercept — 5.514 — 5.658 Age (years)45–54 1.87 (1.12–3.13) 0.628 1.92 (1.13–3.25) 0.650 255–64 2.44 (1.48–4.01) 0.892 2.56 (1.53–4.28) 0.940 3

BMI (kg/m2)25 to 30 1.18 (0.57–2.45) 0.165 1.02 (0.48–2.15) 0.015 130 2.99 (1.31–6.81) 1.096 2.55 (1.10–5.92) 0.938 3

Waist circumference (cm)

Men, 94 to 102; women, 80 to 88 2.36 (1.25–4.45) 0.857 2.78 (1.43–5.40) 1.021 3Men, 102; women, 88 3.86 (1.93–7.71) 1.350 4.16 (2.00–8.63) 1.424 4

Use of blood pressure medication* 2.04 (1.46–2.83) 0.711 2.04 (1.45–2.88) 0.714 2History of high blood glucose† 8.49 (5.66–12.73) 2.139 9.61 (6.31–14.63) 2.263 5Physical activity 4 h/week‡ — — 1.31 (0.88–1.95) 0.268 2Daily consumption of vegetables, fruits, or berries — — 1.18 (0.85–1.64) 0.165 1

Area under the ROC curve 0.857 0.860 0.852Only subjects with no missing baseline risk factor data were included into the speci fic model. The score values were estimated based on the coef ficients of thelogistic regression model and are presented for the full model. The concise model includes only these statistically signi ficant variables. The full model includes alsophysical activity and fruitand vegetable consumption. *Question “Have youever used drugs forhigh blood pressure?: No/Yes” in the questionnaire; †question “Haveyou ever been told by a health-care professional that you have diabetes or latent diabetes?: No/Latent diabetes/Diabetes ” in the questionnaire; ‡individuals who, intheir spare time, “read, watch TV, and work in the household with tasks that don’t strain physically” and whose “work is mainly done sitting and does not requiremuch walking.” The next category was “physical activity at least 4 hours per week.”


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betes, according to World Health Organi-zation (WHO) 1999 criteria (12), if theyhad either fasting plasma glucose 7.0mmol/l (fasting whole blood glucose

6.1 mmol/l) and/or 2-h plasma glucose11.1 mmol/l (2-h whole blood glucose10.0 mmol/l).

RESULTS — Of the 4,746 subjects inthe 1987 survey who were not on antidi-abetic drug therapy at baseline, drug-treated diabetes developed in 196 duringthe follow-up of 10 years.

Model developmentThe 10-year incidence of drug-treated di-abetes during the follow-up was 4.1%.The incidence increased by increasingage, BMI, and waist circumference, di-vided according to “waist action levels”suggested by Lean et al. (13).

The baseline survey questionnaire in-cluded several questions about bloodpressure. Overall, high blood pressurewas associated with higher incidence of drug-treated diabetes.

The question about history of bloodpressure medication was selected into theDiabetes Risk Score because it is an un-equivocal marker of clinically evident hy-pertension and can be determined

without blood pressure measurement.The question about history of latent dia-betes or diabetes covered transient or bor-derline elevated blood glucose andgestational diabetes, as well as diabetestreated with diet alone at baseline. A totalof 35 subjects reported at baseline thatthey had been told they had diabetes butnever had any antidiabetic drug treat-ment. Of these individuals, 32 had at leastfasting glucose levels measured at base-line; 16 had glucose values considered di-abetic. During follow-up, 21 of these

individuals started using antidiabeticdrugs according to the drug register data(15 of these subjects had glucose levelsconsidered diabetic at baseline).

The multivariable logistic regressionmodels based on the follow-up of the1987 survey are shown in Table 1. Statis-tically significant independent predictorsof future drug-treated diabetes were age,BMI, waist circumference, antihyperten-sive drug therapy, and history of highblood glucose levels. The concise modelincludes only these statistically significantvariables. The full model includes alsophysical activity and fruit and vegetableconsumption. Even though these twovariables did not add much to the predic-tive power of the statistical model, theywere included in the Diabetes Risk Scoreto emphasize the importance of physicalactivity and diet in the prevention of dia-betes. BMI between 25 and 30 kg/m2 wasnot a statistically significant predictor inthe multivariate models. Nevertheless, itwas included in the final Diabetes RiskScore because it is obviously the interme-diate stage between normal weight andobesity, with a reasonably high impact ondiabetes risk (odds ratio 2.53) even whenother risk factors are in the model.

In the multivariate model, male sex

was a statistically significant predictor of drug-treated diabetes risk; the odds ratiowas 1.58 (95% CI 1.15–2.18) in the con-cise model and 1.67 (1.19 –2.34) in thefull model. On the other hand, inclusionor exclusion of sex into the modelschanged the coef ficients of the other in-dependent variables only slightly. There-fore, we did not include sex in the finalmultivariate model and the final DiabetesRisk Score.

A total of 4,595 subjects had com-plete baseline data for the concise model,

and of these individuals, drug-treated di-abetes developed in 194 during follow-up. For the full model, 4,435 subjects hadcomplete baseline data and drug-treated

diabetes developed in 182 subjects.The Diabetes Risk Score value (lastcolumn in Table 1) was defined using thefull model, from the -coef ficient as fol-lows: for 0.01– 0.2, the score is 1; for 0.21– 0.8, the score is 2; for 0.81–1.2,thescoreis3;for 1.21–2.2,the score is 4; and for 2.2, the score is5. The lowest category of each variablewas given a score of 0, except for the useof fruits and vegetables, where daily usewas scored as 0, and physical activity,where “more than 4 h/week” was scoredas 0. The total Diabetes Risk Score wascalculated as the sum of the individualscores and varied from 0 to 20.

Model validationOf the 4,615 subjects in the 1992 survey,drug-treated diabetes developed in 67during 5-year follow-up. The DiabetesRisk Score could be calculated for eachsubject who had complete baseline infor-mation on the selected risk factors (n 4,586). The 1987 and 1992 surveys hadsimilar data, except for the intake of veg-etables, fruits, or berries: in the 1992 sur-

ve y, t he re we re se ve ra l fre que nc yquestions about use of raw and cookedvegetables, fruits, and berries. If the totalfrequency was 33/month, the individ-ual was placed into the lower intake cate-gory. Only 15% of subjects were in thislow group, compared with 52% in the1987 survey, which may reflect a true in-crease in vegetable and fruit consumptionduring the 5 years between the surveys ormay have arisen from the differences inthe questions.

The ROC curves (Fig. 1) demonstrate

Table 2—Diabetes incidence by Diabetes Risk Score in 1987 and 1992 cohorts during follow-up through the year 1997

Score

1987 Cohort 1992 Cohort

Men Women Men Women

n

Diabetes

incidence

n

Diabetes

incidence

n

Diabetes

incidence

n

Diabetes

incidence

n % n % n % n %

0–3 669 2 0.3 851 5 0.6 731 2 0.3 981 1 0.14–8 936 22 2.4 878 11 1.3 863 7 0.8 862 3 0.49–12 421 44 10.5 455 30 6.6 492 13 2.6 494 11 2.213–20 101 33 32.7 124 35 28.2 78 18 23.1 85 12 14.1P for trend 0.001 0.001 0.001 0.001

Lindström and Tuomilehto



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that the Diabetes Risk Score based on the1987 cohort predicted drug-treated dia-betes very well (AUC 0.85). The pre-diction was similarly good in the 1992cohort (AUC 0.87). The Diabetes RiskScore value 9 was selected as the cut pointfor increased risk of drug-treated diabe-tes, along with sensitivity of 0.78 andspecificity 0.77 in the 1987 cohort andsensitivity of 0.81 and specificity 0.76 inthe 1992 cohort. The positive predictive

value (PPV), the probability of drug-treated diabetes developing during fol-low-up if the Diabetes Risk Score was 9 orhigher, was 0.13 for 1987 cohort (10-yearfollow-up) and 0.05 for 1992 cohort (5-year follow-up). The overall incidencewas lower in the 1992 cohort due to theshorter follow-up period.

In Table 2, the men and women of both cohorts are classified into four Dia-betes Risk Score categories. The incidenceof drug-treated diabetes was markedly el-evated in the two highest categories. In

the 1987 cohort, 25% of both men andwomen fell into the two highest catego-ries; in the 1992 cohort, 26% of men and24% of women were classified in the twohighest categories. Therefore, the Diabe-tes Risk Score cut point of 9 identified thehigh-risk quartile of the population, iden-tifying 70% of the incident cases of drug-treated diabetes.

We also analyzed the performance of the Diabetes Risk Score cross-sectionally

in identifying subjects who had eitherfasting or 2-h glucose levels exceeding thethreshold of diabetes. A total of 2,525subjects in the 1987 cohort and 1,976subjects in the 1992 cohort could be clas-sified according to results of oral glucosetolerance test and had complete DiabetesRisk Score data. The crude prevalence of undiagnosed diabetes was 3.5% (n 87)in the 1987 survey and 5.7% (n 112) inthe 1992 survey (known diabetic patientstreated with antidiabetic drugs and sub-

jects with incomplete baseline data ex-

cluded from the analyses). The ROCcurves (not shown) indicated good per-formance of the Diabetes Risk Score alsoin the cross-sectional setting (AUC 0.80 for both surveys). For cut point Di-abetes Risk Score of 9, sensitivity was0.77 (95% CI 0.66 – 0.85) and 0.76(0.67– 0.83), specificity was 0.66 (0.64 –0.68) and 0.68 (0.66 – 0.70), PPV was0.07 (0.06 – 0.09) and 0.12 (0.10 – 0.15),and negative predictive value (the proba-bility of not having diabetic glucose levelsif Diabetes Risk Score was 9) was 0.99(0.98– 0.99) and 0.98 (0.97– 0.99) in the1987 and 1992 oral glucose tolerancetests, respectively.

CONCLUSIONS — Recent studieshave shown that type 2 diabetes can beprevented in high-risk subjects with im-

paired glucose tolerance by lifestyle inter-vention (1–3). Therefore, a strongargument exists in favor of screening forsubjects who are at increased risk for di-abetes (14).

Our study is unique in that it focuseson predicting future drug-treated diabe-tes with several factors that are easy tomeasure with noninvasive methods, areknown to be associated with risk of type 2diabetes, are easily comprehensible, anddirect attention to modifiable risk factorsof diabetes. The interpretation of the in-dividual’s diabetes risk is easy and can beexpressed as a probability relatively accu-rately. Drug-treated diabetes is very un-likely to develop in individuals with a lowDiabetes Risk Score. Therefore, these in-dividuals can be excluded from furtherprocedures such as glucose testing with-out causing a problem of false-negativeresults. Defining a suitable cut point is atrade-off between sensitivityandspecificity.

We included in the analyses all subjects who were not on antidiabetic drugtherapy at baseline. Therefore, patientswith diabetes who were treated with diet

alone were included in the prospectivefollow-up, where the outcome was initia-tion of antidiabetic drug treatment. Initi-a t ion of drug t he ra py indic a t e s adeterioration of glucose homeostasis alsoin patients who, at baseline, may havebeen treated with diet alone. This ap-proach decreased the possibility of biasbecause, during follow-up, it would nothave been possible to ascertain diet-treated cases. It is obvious that the recentincident cases, typically treated with diet,were missed in follow-up. Therefore, in-

Figure 1—ROCcurves showing the performance of the Diabetes Risk Score in predicting diabetesin the 1987 and 1992 cohorts; follow-up of both cohorts continued until the end of 1997. The areaunder the 1987 curve was 0.85 and the area under the 1992 curve was 0.87. For cut point DiabetesRisk Score 9 (black marker), sensitivity was 0.78 (95% CI 0.71– 0.84) and 0.81 (0.69– 0.89),specificity was 0.77 (0.76–0.79) and 0.76 (0.74–0.77), PPV was 0.13 (0.11–0.15) and 0.05(0.04– 0.06),and negative predictive value was0.99 (0.98– 0.99) and0.996 (0.993– 0.998)in the1987 and 1992 cohorts, respectively.




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cidence of diabetes is an underestimate of the true value. We are also aware of thepossibility of circular argument of identi-fying subjects based on the same risk fac-tors that would evoke their physician toprescribe blood glucose testing, missingthe diagnosis of less typical cases. How-ever, the finding that the Diabetes RiskScore performed equally well in the cross-sectionalanalysis attenuated this concern.

We did not exclude people with highglucose levels at baseline because wetested the Diabetes Risk Score under theassumption that no biochemical tests areperformed at that stage. As shown by ouranalyses in the subset in which glucosevalues were available at baseline, use of ahigh Diabetes Risk Score value as a pri-mary screening tool would ef ficientlyidentify unrecognized diabetes. Most

cases of diabetes would then be diagnosedat the subsequent oral glucose tolerancetest in individuals with a high DiabetesRisk Score value.

The Diabetes Risk Score values werederived from the coef ficients of the logis-tic model by classifying them into five cat-egories. A more precise method would beto sum the original coef ficients or theirexpansions. The sum of the coef ficientswould have a wide distribution andwould therefore be impractical in clinicaluse. If the Diabetes Risk Score is used as acomputerized version, the estimatedprobability ( p) of drug-treated diabetes(during the following 10 years) for anycombination of risk factors can be calcu-lated from the coef ficients as follows:

pdiabetes e0 1 x1 2 x2 ...

1 e0 1 x1 2 x2 ...

where 0

is the intercept and 1

, 2, etc.

represent the regression coef ficients of thevarious categories of therisk factors x

1, x

2,

etc. In Table 1, we have shown the coef-ficients for the full model that was used to

formulate the Diabetes Risk Score as wellas the concise model with fewer variables.

We also calculated the model excludingthose subjects who, at baseline, reportedthat they had diet-treated diabetes. Thecoef ficient for history of high blood glu-cose was reduced from 2.263 to 1.860(odds ratio 9.61– 6.45).

A few reports (15–20) have suggestedmethods of screening for undiagnosed di-abetes. In these assessments, the outcomewas prevalent diabetes in a cross-sectionalsetting. In a follow-up study (21) with a

median follow-up of 8 years, BMI at base-line predicteddiabetes as well as fasting or2-h plasma glucose; in that study, noother risk factors for diabetes were ana-lyzed. In a recent follow-up study, Sternet al. (22) developed two models to pre-dict diabetes incidence: a clinical modelincluding age, sex, ethnicity, fasting glu-cose, systolic blood pressure, HDL cho-lesterol, BMI, and family history of diabetes; and a full model that also in-cluded 2-h glucose, diastolic blood pres-sure, total and LDL cholesterol, andtriglyceride. Therefore, they included intheir models most of the parameters of themetabolic syndrome as defined by the

WHO Consultation (12). Their finding isnot surprising, because it is well knownthat people with signs of the metabolicsyndrome have increased risk of type 2

diabetes.The PPVs of the reported predictivemodels in identifying prevalent, undiag-nosed diabetes have ranged from 5.6 and10%. The performance of our DiabetesRisk Score, with PPVs of 7 and 12% incross-sectional settings in the 1987 and1992 cohorts, respectively, is compara-ble. Therefore, our method, even thoughit was developed using incident drug-treated diabetes as the outcome, mightalso be accurate in predicting earlierstages of type 2 diabetes. This will be seenwhen our Diabetes Risk Score is appliedin such situations in the future.

There are a few other risk factorsabout which we did not have informationand therefore could not include in the Di-abetes Risk Score. Family history of dia-betes, which reflects the genetic pre-disposition for the disease, is known to bean important marker for increased risk of diabetes (23,24). The genetic predisposi-tion may be necessary but not suf ficientfor development of type 2 diabetes. Withhealthy lifestyle, even individuals with ge-netic susceptibility to diabetes can avoid

the symptomatic phase of the disease. Wepropose that family history should be in-cluded in this kind of model; score values5 and 3 would probably be appropriatefor positive history in first- and second-degree relatives, respectively.

Previous gestational diabetes isknown to be a strong risk factor for futurediabetes (25–27). Our question abouthistory of glucose intolerance also coversgestational diabetes. Physical activity(28,29), the quality and quantity of di-etary fat, and the intake of fiber (30 –32)

have been demonstrated to modify risk of diabetes. We included into our predictionmodel the questions on physical activityat work and/or on leisure time and con-sumption of fruit, berries, and vegetables,of which we had information at baseline,to increase awareness of theimportance of the modifiable risk factors for diabetes.

The Diabetes Risk Score has been de-signed to be a screening tool for identify-ing high-risk subjects in the populationand for increasing awareness of the mod-ifiable risk factors and healthy lifestyle.Filling in the Diabetes Risk Score may en-courage a person who gets a high value tohave his/her blood glucose measured. Inprinciple, however, no glucose testing isnecessary to decide what should be doneif the Diabetes Risk Score value is deter-mined to be high, because such individu-

als will benefit from improvements intheir lifestyle regardless their glucose lev-els. On the other hand, many individualswith a high Diabetes Risk Score may haveunrecognized, asymptomatic diabetesand, therefore, may require blood glucosetesting for diagnosis, other clinical assess-ments, and therapy. It is known that 30 –60% of individuals with diabetes in thecommunity are undiagnosed (33,34) andthat undiagnosed diabetes is associatedwith increased mortality and risk of car-diovascular disease (35,36); therefore, di-abetes is an important public healthproblem. This simple, safe, and inexpen-sive screening test will drastically reducethe number of invasive glucose tests re-quired at the screening phase. We believethat the public health implications of thisDiabetes Risk Score are considerable. It isa cost-ef ficient and practical way to iden-tify individuals at high risk for drug-treated diabetes in the general popu-lation. This strategy has been recentlyadopted in Finland, where a nationwideprogram for prevention of type 2 diabetes(37) is being launched, and one of the

tools in this prevention program is theDiabetes Risk Score.

Acknowledgments— The development of the Diabetes Risk Score was partly funded bythe Finnish Diabetes Association, the Acad-emy of Finland (grants 38387 and 46558),and the Yrjö Jahnsson Foundation.

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Documents

The Diabetes Risk Score