J. Clin. Endocrinol. Metab. 2009 94:920-926 originally published online Dec 23, 2008; , doi: 10.1210/jc.2007-2427  

Bornstein, Jan Schulze, Jaakko Tuomilehto and Jaana Lindström Peter E. H. Schwarz, Jiang Li, Manja Reimann, Alta E. Schutte, Antje Bergmann, Markolf Hanefeld, Stefan R.

  Progression towards Type 2 Diabetes

The Finnish Diabetes Risk Score Is Associated with Insulin Resistance and

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The Finnish Diabetes Risk Score Is Associated withInsulin Resistance and Progression towards Type 2Diabetes

Peter E. H. Schwarz,* Jiang Li,* Manja Reimann, Alta E. Schutte, Antje Bergmann,Markolf Hanefeld, Stefan R. Bornstein, Jan Schulze, Jaakko Tuomilehto,and Jaana Lindstrom

Department of Medicine III (P.E.H.S., J.L., M.R., A.B., S.R.B., J.S.), Medical Faculty Carl Gustav Carus of the TechnicalUniversity Dresden, 01307 Dresden, Germany; School for Physiology, Nutrition and Consumer Sciences (A.E.S.) ofNorth-West University (Potchefstroom Campus), 2520 Potchefstroom, South Africa; Centre for Clinical Studies (M.H.),GWT-TUD GmbH, 01187 Dresden, Germany; Department of Public Health (J.T., J.L.), University of Helsinki, 00300Helsinki, Finland; Diabetes Unit (J.L.), Department of Health Promotion and Chronic Disease Prevention, National PublicHealth Institute, 00300 Helsinki, Finland; and South Ostrobothnia Central Hospital (J.T.), 60200 Seinäjoki, Finland

Objective: The Finnish Diabetes Risk Score (FINDRISC) questionnaire is a practical screening tool toestimate the diabetes risk and the probability of asymptomatic type 2 diabetes. In this study weevaluated the usefulness of the FINDRISC to predict insulin resistance in a population at increaseddiabetes risk.

Design: Data of 771 and 526 participants in a cross-sectional survey (1996) and a cohort study(1997–2000), respectively, were used for the analysis. Data on the FINDRISC and oral glucose tol-erance test parameters were available from each participant. The predictive value of the FINDRISCwas cross-sectionally evaluated using the area under the curve-receiver operating characteristicsmethod and by correlation analyses. A validation of the cross-sectional results was performed onthe prospective data from the cohort study.

Results: The FINDRISC was significantly correlated with markers of insulin resistance. The re-ceiver operating characteristics-area under the curve for the prediction of a homeostasis modelassessment insulin resistance index of more than five was 0.78 in the cross-sectional survey and0.74 at baseline of the cohort study. Moreover, the FINDRISC at baseline was significantlyassociated with disease evolution (P � 0.01), which was defined as the change of glucosetolerance during the 3 yr follow-up.

Conclusions: The results indicate that the FINDRISC can be applied to detect insulin resistance in apopulation at high risk for type 2 diabetes and predict future impairment of glucose tolerance.(J Clin Endocrinol Metab 94: 920–926, 2009)

The dramatic increase in newly diagnosed cases of type 2 di-abetes has developed into a major public health concern in

this century (1). Having diabetes means having a significantlyreduced quality of life and reduced life expectancy (2). Further-more, diabetes and impairment of glucose tolerance are verycommon among the elderly (3), and recently also in younger

people, with a most sudden increase in prevalence in the agegroup younger than 30 yr (4). An increasing number of people intheir working age are affected by diabetes, increasing the eco-nomic burden of the health care system due to an earlier onset ofcomplications, and subsequently, a longer and more intensivemedical treatment period.

ISSN Print 0021-972X ISSN Online 1945-7197Printed in U.S.A.Copyright © 2009 by The Endocrine Societydoi: 10.1210/jc.2007-2427 Received November 1, 2007. Accepted December 12, 2008.First Published Online December 23, 2008* P.E.H.S. and J.L. contributed equally to this work.

Abbreviations: AUC, Area under the curve; BMI, body mass index; CV, coefficient ofvariation; FFA, free fatty acid; FINDRISC, Finnish Diabetes Risk Score; HbA1c, glycosylatedhemoglobin; HDL-c, high-density lipoprotein cholesterol; HOMA-IR, homeostasis modelassessment insulin resistance index; IFG, impaired fasting glucose; IGT, impaired glucosetolerance; LDL-c, low-density lipoprotein cholesterol; NGT, normal glucose tolerance; NPV,negative predictive value; OGTT, oral glucose tolerance test; PPV, positive predictive value;ROC, receiver operating characteristics; TC, total cholesterol.

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

E n d o c r i n e C a r e

920 jcem.endojournals.org J Clin Endocrinol Metab. March 2009, 94(3):920–926

Type 2 diabetes is a progressive disease. Before its clinicalonset, there is a long latent asymptomatic period that may lastdecades. The development of type 2 diabetes is a multistage pro-cess originating from genetic disposition (5, 6). Unhealthy life-style may trigger the development of insulin resistance in a sus-ceptible genotype (6) that is usually followed by impairment ofglucose tolerance (7). In this prediabetic period, insulin resis-tance remains often unrecognized because enhanced insulin se-cretion maintains glucose levels within normal ranges (8, 9).Owing to the fact that diagnostic criteria for diabetes are basedon the presence of hyperglycemia, this disease is commonly di-agnosed too late (10, 11). As yet there exist neither diagnosticcriteria for insulin resistance nor suitable screening tools pre-cluding an early detection of metabolic disturbances. The onlyreliable way of assessing insulin resistance is by an euglycemicclamp, which, unfortunately, is a very costly and time-consum-ing endeavor to date. The surrogate marker homeostasis modelassessment insulin resistance index (HOMA-IR) established byMatthews et al. (12) that integrates measures of fasting plasmaglucose and fasting plasma insulin is currently widely used. How-ever, standardized reference values are lacking. Therefore, thereare attempts to develop simple, fast, and noninvasive scoringsystems for identification of high-risk subjects (13–21). The val-idated Finnish Diabetes Risk Score (FINDRISC) has been suc-cessfully implemented as a practical screening tool to assess thediabetes risk and to detect undiagnosed type 2 diabetes (22, 23).Beyond this line, it also proved suitable in prediction of coronaryheart disease, stroke, and total mortality in the Caucasian pop-ulation (24–27). The present study aimed at evaluating the abil-ity of the FINDRISC to predict insulin resistance in subjects athigh risk for diabetes mellitus.

Subjects and Methods

SubjectsTo address this objective, data of two different samples were ana-

lyzed. The first sample drawn in 1996 consisted of 921 subjects with afamily history of metabolic syndrome. The second sample drawn in 1997was used for validation purposes and consisted of 735 subjects fromGerman families with a family history of type 2 diabetes or related insulinresistance disorders such as obesity or dyslipidemia. The individuals ofboth surveys were from the city of Dresden and adjoining areas. Exclu-sion criteria were previously diagnosed diabetes, severe renal disease,disease with a strong impact on life expectancy, and therapy with drugsknown to influence glucose tolerance (thiazide diuretics, �-blockers, andsteroids). Each subject underwent a physical examination that was fol-lowed by a 75-g oral glucose tolerance test (OGTT). Blood samples weretaken at fasting, and at 30, 60, 90, and 120 min after the glucose chal-lenge for measurement of glucose, insulin, proinsulin C peptide, and freefatty acids (FFAs). In addition, parameters of lipoprotein metabolism [totalcholesterol (TC), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c)] were determined from fastingblood samples. Data on sociodemographical variables, medical history, life-style, and family history of diabetes were obtained by questionnaires, in-cluding the FINDRISC questionnaire. A total of 771 and 526 individualsfrom the 1996 survey and the 1997 baseline survey, respectively, completedboth the OGTT and the FINDRISC questionnaire.

A follow-up examination was performed 3 yr after the initial surveyin the 1997 cohort. Subjects at an increased diabetes risk based on theFINDRISC at baseline were either enrolled into a lifestyle intervention or

underwent pharmacological therapy. The intervention program has beenpreviously described in detail (28). Of 526 subjects, 515 completed thefollow-up examination. The detailed procedure of the recruitment ofparticipants and the methods used have been described previously (29,30). Informed consent was obtained from all participants, and the studywas approved by the local ethics committee.

AnalysesBased on the baseline OGTT data, the subjects were categorized as

having either normal glucose tolerance (NGT), impaired glucose toler-ance (IGT), including those with impaired fasting glucose (IFG) and type2 diabetes mellitus according to the World Health Organization/Amer-ican Diabetes Association criteria of 1997/1999 (31). Subjects with fol-low-up examination were also defined according to the evolution of theirdiabetic status as unchanged, progression, or regression. Estimates forglucose tolerance were calculated from OGTT parameters. TheHOMA-IR was calculated using the formula as described by Matthewset al. (12). The area under the curve (AUC) for AUC(insulin), AUC(pro-insulin), and AUC(FFA) values was estimated from the following equa-tion (insulin as example):

AUC (insulin) � 15 � (“insulin at 0 min” � 2

� “insulin at 30 min” � 2 � “insulin at 60 min” � 2

� “insulin at 90 min” � “insulin at 120 min”)

Laboratory proceduresPlasma glucose was measured using the hexokinase method [inter-

assay coefficient of variation (CV) 1.5%]. Serum TC and triglycerideswere determined using enzymatic techniques (Roche Molecular Bio-chemicals, Mannheim, Germany). HDL-c was determined after precip-itation with dextran sulfate (Roche Molecular Biochemicals), and serumLDL-c was calculated using Friedewald’s formula (32). HbA1c valueswere analyzed by HPLC. The analyses of insulin and proinsulin levelswere performed by commercially available enzyme immunoassays (Bio-Source EUROPE S.A Belgium; interassay CV 7.5%, no cross-reactivitywith human proinsulin; DRG Diagnostics, Marburg Germany; interas-say CV 7.5%, no cross-reactivity with human insulin and C peptide).

FINDRISCThe FINDRISC comprises eight items (22, 25) regarding age, body

mass index (BMI), waist circumference, physical activity, diet, use ofantihypertensive medication, history of high blood glucose, and familyhistory of diabetes. In the current study, a modified and validated Ger-man version of the questionnaire was applied (33). In this shortenedversion, the variables diet and physical activity were omitted becauseboth items did not add much power for the prediction of diabetes risk inprevious studies (25). Thus, the maximal achievable score of the modi-fied questionnaire is 23.

StatisticsStatistical analysis was performed using the Statistical Package for the

Social Sciences (SPSS) software for Windows (version 12.0; SPSS, Inc.,Chicago, IL). Clinical data are expressed as median and interquartilerange 25%–75%, unless otherwise stated. Individuals with IGT and/orIFG were analyzed as a combined glucose intolerance group. Associa-tions and correlation coefficients between the FINDRISC and clinicalparameters were evaluated by the Spearman correlation test. The meansof the FINDRISC total score were compared between the different evo-lution categories using the Kruskal-Wallis test. A value of P � 0.05 wasassumed to indicate significance. The predictive value of the modifiedFINDRISC (34, 35) for insulin resistance as defined by HOMA-IR valuemore than five was evaluated using the AUC in a receiver operatingcharacteristics (ROC) curve. The sensitivities were plotted against they-axis, and the false-positive rates (one-specificity) were plotted againstthe x-axis, then the ROC curve was plotted. The optimal cutpoints were

J Clin Endocrinol Metab, March 2009, 94(3):920–926 jcem.endojournals.org 921

located at the peak of the curve where the sum of sensitivity and speci-ficity is maximal (36). The method of Hanley and McNeil (37) was usedto compare the AUCs.

Results

Of the 326 men and 445 women initially included in the 1996cross-sectional survey, 417 (54.1%) were diagnosed with NGT,287 (37.2%) exhibited IGT/IFG, and 67 (8.7%) were newly di-agnosed with type 2 diabetes. Men had a higher prevalence ofabnormal glucose tolerance than women (51vs. 42%; P�0.012,�2 test). Similar proportions were determined in the 1997 cohortat baseline and after 3 yr follow-up. At baseline, 61 subjects(11.6%) were newly diagnosed with type 2 diabetes, and 306(58.2%) individuals had IGT/IFG. The prevalence of impairedglucose tolerance was 77.3 and 62.6% in men and women, re-spectively (P � 0.01). The corresponding values in the follow-upstudy were 66.4 and 52.1% (P � 0.01). In addition, 40 individ-

uals progressed from NGT to IGT/IFG, and 36 previouslyhealthy persons were diagnosed with manifest diabetes after 3 yrfollow-up. The clinical characteristics of the two study samplesare shown in Table 1.

Association of the FINDRISC and insulin resistanceThe mean FINDRISC total score of the 1996 survey was

9.33 � 5.92 (mean � SD). The total score ranged from zero to 23in this group. The individual FINDRISC was evenly distributedwith the majority of subjects ranging between zero and 20. In the1997 baseline survey, the mean FINDRISC was 7.27 � 4.45. Thetotal score ranged from one to 17. The correlation coefficients forthe FINDRISCs and markers of insulin resistance are shown inTable 2. The FINDRISC was significantly positively associatedwith AUC (insulin), AUC (proinsulin), AUC (FFA), HOMA-IR,and HbA1c in the two baseline surveys. These associations werestill present after 3 yr follow-up. LDL-c was not correlated withthe FINDRISC in the cohort, but a positive association was

TABLE 1. Selected clinical characteristics of the study participants

Participants at the1996 survey

1997 survey cohort

Participants at thebaseline survey

Participants at the3-yr follow-up

No. of men/women 326/445 256/270 250/265No. of NGT/IFG-IGT/T2D 417/287/67 159/306/61 211/234/70Age (yr) 43 (30–57) 59 (51–63) 62 (54–66)BMI (kg/m2) 25 (22–28) 26 (24–28) 26 (24–29)WHR 0.85 (0.78–0.91) 0.89 (0.82–0.96) 0.88 (0.81–0.94)HbA1c (%) 5.2 (4.9–5.5) 5.6 (5.3–6.0) 5.4 (5.1–5.8)FPG (mmol/liter) 5.38 (4.99–5.85) 5.84 (5.48–6.39) 5.61 (5.17–6.10)2-h PG (mmol/liter) 6.43 (5.22–8.02) 6.55 (5.51–8.20) 6.68 (5.38–8.59)TC (mmol/liter) 5.5 (4.7–6.3) 5.7 (5.1–6.4) 5.5 (4.9–6.2)HDL-c (mmol/liter) 1.47 (1.21–1.79) 1.39 (1.14–1.67) 1.43 (1.18–1.74)LDL-c (mmol/liter) 3.32 (2.09–4.05) 3.52 (2.91–4.09) 3.32 (2.76–3.92)Fasting insulin (pmol/liter) 62 (43–95) 69 (48–102) 62 (44–89)2-h insulin (pmol/liter) 279 (178–466) 295 (186–501) 253 (137–407)Fasting proinsulin (pmol/liter) 1.81 (1.14–3.02) 1.85 (1.09–3.16) 3.51 (2.38–5.70)2-h proinsulin (pmol/liter) 11.02 (6.42–17.90) 9.25 (5.89–16.02) 17.90 (11.26–27.32)Fasting FFA (mmol/liter) 0.46 (0.31–0.62) 0.43 (0.29–0.60) 0.54 (0.40–0.79)2-h FFA (mmol/liter) 0.04 (0.03–0.07) 0.05 (0.03–0.08) 0.06 (0.04–0.10)HOMA-IR 2.16 (1.42–3.46) 2.62 (1.76–4.03) 2.21 (1.54–3.34)

Data are shown as median (interquartile range). FPG, Fasting plasma glucose; 2-h PG, plasma glucose 2 hr after glucose load; IGT, in which IFG was also included; T2D,type 2 diabetes; WHR, waist to hip ratio.

TABLE 2. The relationship of the FINDRISC with clinical parameters

1996 survey cohort

1997 survey cohort

Baseline 3-yr follow-upa

Coefficients Significance (P value)b Coefficients Significance (P value)b Coefficients Significance (P value)b

AUC (insulin) 0.24 �0.01 0.35 �0.01 0.31 �0.01AUC (proinsulin) 0.28 �0.01 0.25 �0.01 0.24 �0.01AUC (FFA) 0.34 �0.01 0.20 �0.01 0.23 �0.01HOMA-IR 0.42 �0.01 0.45 �0.01 0.46 �0.01LDL-c 0.34 �0.01 �0.017 0.73 �0.02 0.69HDL-c �0.07 0.06 �0.17 0.01 �0.21 �0.01HbA1c 0.39 �0.01 0.29 �0.01 0.21 �0.01

a The associations are between the FINDRISC of the baseline survey and clinical parameters of the follow-up survey.b Spearman correlation test.

922 Schwarz et al. FINDRISC and Insulin Resistance J Clin Endocrinol Metab, March 2009, 94(3):920–926

found in the 1996 survey. In the same survey, there was a ten-dency of an association between HDL-c and FINDRISC (P �

0.06, Spearman test). In contrast, the HDL-c level was signifi-cantly inversely correlated with the FINDRISC value in the co-hort. The relationship between the FINDRISC value and mark-ers of insulin resistance is depicted in Fig. 1 (R2 is the coefficientof determination for the linear regressions). Accordingly, morethan 6% of variability in most indicators can be explained by theFINDRISC.

The predictive performance of the FINDRISC for highHOMA-IR values

The ROC curves are shown in Fig. 2. The AUC values of 0.78and 0.74 for the 1996 and 1997 baseline studies, respectively, didnot differ significantly between the two study samples (P�0.05).The optimal cutpoints were 12 and nine, respectively. In the 1996survey, the sensitivity, specificity, positive predictive value(PPV), and negative predictive value (NPV) for a FINDRISCvalue of 12 or more were 77.5, 67.9, 19.7, and 96.8%, respec-tively. The corresponding values in the 1997 baseline study fora FINDRISC value of nine or more were 72.7, 68.2, 29.4, and88.1%, respectively (Table 3). The relative risk for a FINDRISCvalue of 12 or more vs. FINDRISC value less than 12 was 6.04(95% confidence interval 3.53–10.33) in the 1996 cohort and2.48 (95% confidence interval 1.58–3.88) in the 1997 baselineinvestigation. Using a HOMA-IR value of two instead of five asthe cutpoint, the ROC-AUC was 0.69 for the 1996 survey and0.68 for the 1997 baseline study.

If we excluded all individuals whose HOMA-IR was morethan five and all diabetic patients of 1997 baseline study and usedthe FINDRISC to predict HOMA-IR (� 5), the relevant ROC-AUC was 0.72 (Fig. 2). At the optimal cutpoint of nine, thesensitivity and specificity were 70.0 and 74.4%, respectively. Of

the 256 individuals participating in the follow-up examinations,only 10 had an HOMA-IR more than five.

Association of the FINDRISC and the evolution ofhyperglycemia

The FINDRISC was significantly directly associated withdisease evolution (P � 0.01, Kruskal Wallis test). A meanFINDRISC value found in subjects remaining NGT was 5.32 �

3.68 (n � 116), subjects with disease regression 6.74 � 3.55 (n �

114), in subjects remaining IGT/IFG 7.54 � 4.08 (n � 175),

1996 study

Score

2322

2120

1918

1716

1514

1312

1110

98

76

54

32

10

HOMA

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5

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2322

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54

32

10

AUC(Insulin)

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100000

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0

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1716

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100

80

60

40

20

0

1997 baseline study

Score

1715131197531

HOMA

20

16

12

8

4

0

Score

1715131197531

AUC(Insulin)

200000

150000

100000

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AUC(FFS)

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0

R2=0.02 R2=0.06 R2=0.10 R2=0.14

R2=0.11 R2=0.07 R2=0.06 R2=0.10

HOMA-IR AUC (Insulin) AUC(Proinsulin) AUC(FFA)

FIG. 1. Relationship between insulin resistance parameters and FINDRISC (scatter diagrams). The scatter plots illustrate the relationship between FINDRISC and clinicalparameters reflecting insulin resistance. The FINDRISC is depicted on the x-axis of each figure; the clinical parameters are depicted on the y-axis. R2 is the coefficient ofdetermination for the linear regressions.

FIG. 2. ROC curve for the prediction of subjects with HOMA-IR greater than fiveby the FINDRISC in the 1996 and 1997 baseline studies, as well as in the follow-up investigation.

J Clin Endocrinol Metab, March 2009, 94(3):920–926 jcem.endojournals.org 923

subjects with disease progression 8.49 � 5.24 (n � 76), andsubjects remaining diabetic 10.68 � 4.10 (n � 34). Subjects withthe highest FINDRISC value had the highest proportion of in-dividuals with diabetes at baseline, and the largest proportion ofthem remained diabetic during the follow-up, whereas thosewith a low FINDRISC value comprised the highest proportion ofindividuals remaining NGT.

Discussion

The data presented in this investigation provide evidence that theFINDRISC is significantly associated with markers of insulinresistance and with disease evolution. Because insulin resistancealways precedes IGT (7), the FINDRISC may be a useful instru-ment to identify people at the earliest stage of disease develop-ment. Compared with the elaborate and expensive standard pro-cedure using biochemistry, the FINDRISC questionnairerepresents a simple and cost-efficient tool with a good predictivevalue to detect undiagnosed diabetes, which can be used in large-scale studies and even on a care level (25). In addition, we nowshow that the FINDRISC is also able to reliably predict insulinresistance.

The FINDRISC was significantly associated with an unfavor-able progression of glycemic parameters. These associationswere validated against an independent data set. The results ob-tained were consistent with those of the original analysis, pro-viding a strong argument for the robustness of our findings. Theability of the FINDRISC to detect insulin resistance was similarto that to predict the development of type 2 diabetes, and better

than to detect previously undiagnosed diabetes (27). This is inkeeping with the fact that insulin resistance precedes the devel-opment of diabetes (38, 39). Therefore, the FINDRISC mayrather be applied to predict future diabetes than being used fordiabetes diagnosis (25–27). Our cutoff value for HOMA-IR wasbased on the general assumption that a value between 2.5 andfive indicates a moderate risk, and a value greater than five isindicative for a high risk for insulin resistance (34, 35). Becausethe AUC values of ROC curves for a cutoff of “5” were greaterthan 0.70, it seems that the FINDRISC can be actually used as apredictive tool for insulin resistance. The most relevant applica-tion field of FINDRISC is on the primary care level, where pop-ulation-based screening strategies are needed and widely imple-mented. The use by primary care physicians or other health careprofessionals would facilitate the detection of high-risk subjectsand the institution of early preventive measures. The associationof the FINDRISC with measures of insulin resistance makes theapplication of the FINDRISC more relevant and the clinical rel-evance stronger.

Some limitations of our study warrant consideration. Onecould argue that the analysis of only six risk items in theFINDRISC questionnaire is not reliable because the two ex-cluded variables, diet and physical activity, have an evidencedimpact on diabetes development (40 – 42). It could be shownin two independent studies using the FINDRISC that these twoitems did not add much power to the prediction of diabetesrisk (25, 43). Other studies also reported similar observations(44, 45). The developers of the FINDRISC justified the inclu-sion of these items, owing to its relevance for the developmentof diabetes, particularly because the FINDRISC (and other

TABLE 3. The sensitivity, specificity, PPV, and NPV by each score in the two surveys

Total score

1996 survey 1997 baseline survey

Sensitivity Specificity PPV NPV Sensitivity Specificity PPV NPV

0 1 0 0.0921 0.972 0.066 0.095 0.959 1 0 0.1572 0.972 0.097 0.098 0.972 1 0.052 0.164 1.0003 0.972 0.109 0.100 0.975 0.984 0.141 0.176 0.9794 0.972 0.221 0.112 0.987 0.967 0.281 0.200 0.9795 0.958 0.256 0.115 0.984 0.951 0.346 0.213 0.9746 0.930 0.354 0.127 0.980 0.885 0.388 0.212 0.9487 0.901 0.420 0.136 0.977 0.852 0.483 0.235 0.9468 0.873 0.454 0.139 0.972 0.803 0.578 0.262 0.9409 0.845 0.516 0.150 0.970 0.721 0.682 0.297 0.929

10 0.831 0.573 0.165 0.971 0.557 0.737 0.283 0.89911 0.817 0.633 0.184 0.972 0.443 0.804 0.296 0.88612 0.775 0.679 0.197 0.968 0.41 0.817 0.294 0.88113 0.704 0.741 0.216 0.961 0.377 0.838 0.302 0.87814 0.662 0.787 0.239 0.958 0.361 0.869 0.339 0.88015 0.535 0.825 0.236 0.946 0.262 0.92 0.379 0.87016 0.451 0.860 0.246 0.939 0.164 0.96 0.433 0.86017 0.423 0.893 0.286 0.939 0.098 0.976 0.432 0.85318 0.380 0.911 0.302 0.93519 0.310 0.936 0.329 0.93020 0.268 0.951 0.357 0.92821 0.183 0.977 0.446 0.92222 0.127 0.990 0.563 0.91823 0.028 0.996 0.415 0.910

Values in bold represent the best cut-points.

924 Schwarz et al. FINDRISC and Insulin Resistance J Clin Endocrinol Metab, March 2009, 94(3):920–926

similar tools) is primarily targeted to laymen or to be used inthe context of diabetes prevention. Although the FINDRISChas not been tested in all ethnic groups, it may be widelyapplicable because it focuses on general risk factors for type2 diabetes, which are globally prevalent. An adjustment ofcutpoints and relative weight of some items may be needed incertain population groups.

The second limitation of our study is that all subjects of thetwo cohorts had high risks for type 2 diabetes mellitus, thus, theselection bias may lead to an underestimation of associations.The mean BMI showed that the 1996 and 1997-baseline sampleswere “overweight,” corresponding to 25 (22–28) and 26 kg/m2

(24–28), respectively. We found that to discriminate highHOMA-IR values (more than two or more than five) in the 1996survey and 1997 baseline survey, the performance (ROC-AUC)of the FINDRISC was similar to that of continuous BMI (datanot shown). However, the benefits of completing a questionnairecompared with a single BMI measure is a possible increase inawareness regarding individual risk factors. Therefore, it is nec-essary to validate the association of FINDRISC and insulin re-sistance in a randomized study and also in other populations.Moreover, there were large differences of HOMA in 1996 and1997 baseline survey (mean � SD were 0.34 � 0.28 and 0.43 �

0.28, respectively, data were transformed in log), which couldinfluence the accuracy of studies too.

Another important aspect is that all individuals at increaseddiabetes risk or hyperglycemia at the 1997 baseline survey hadreceived relevant intervention or treatment in the followingyears. This might explain the higher hyperglycemia prevalence atbaseline than after the 3 yr follow-up in this study sample.Among all these intervened individuals, more than 60% pre-sented reduced HOMA-IR values at outcome, which could resultin an underestimated incidence of insulin resistance and an un-derestimated predictive performance of FINDRISC in the pro-spective analysis. Furthermore, the FINDRISC was correlatedwith HOMA-IR at the follow-up, both in those who gainedweight and those with unchanged/reduced body weight as well asafter adjustment for weight change.

In the present analysis, we did not pool the data of the twosamples due to different recruitment procedures. Despite thesmall sample sizes, the overall results of both study groups sup-ported each other. Therefore, the strength of our study is that thecross-sectional results were validated in an independent cohort.

In conclusion, our analysis shows that the FINDRISC may bea suitable tool to identify people with insulin resistance, and alsothose who are likely to progress toward hyperglycemia and type2 diabetes.

When implemented in primary health care, the FINDRISCwould assist health care professionals in decision making regard-ing a further medical investigation and the institution of preven-tive measures. Furthermore, the application of the FINDRISC ina population-based program aims also at a learning effect. Peoplecompleting the FINDRISC become aware of their own prevalentrisk factors.

Importantly, several authorities such as the European Asso-ciation for the Study of Diabetes, the European Society of Car-diology, and the International Diabetes Federation Consensus

Group have recommended the FINDRISC to be used for riskstratification purposes in the European population (46, 47).

Acknowledgments

We thank all the patients who cooperated in this study and their referringphysicians and diabetologists in Saxony.

Address all correspondence and requests for reprints to: Peter E. H.Schwarz, Medical Faculty Carl-Gustav-Carus of the Technical UniversityDresden, Medical Clinic III, Building 10, Room 108, Fetscherstrasse 74,01309Dresden,Germany.E-mail:peter.schwarz@uniklinikum-dresden.de.

This study was supported by the Commission of the European Com-munities, Directorate C-Public Health and Risk Assessment, Health &Consumer Protection, Grant Agreement no. 2004310 with the Project“DE-PLAN” and by the Dresden University of Technology FundingGrant, Med Drive.

Disclosure Information: The authors have nothing to declare.

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