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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2007-2427
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Right arrow Diabetes and Insulin
The Journal of Clinical Endocrinology & Metabolism Vol. 94, No. 3 920-926
Copyright © 2009 by The Endocrine Society

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

Peter E. H. Schwarz1, Jiang Li1, Manja Reimann, Alta E. Schutte, Antje Bergmann, Markolf Hanefeld, Stefan R. Bornstein, Jan Schulze, Jaakko Tuomilehto and Jaana Lindström

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 Technical University Dresden, 01307 Dresden, Germany; School for Physiology, Nutrition and Consumer Sciences (A.E.S.) of North-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, 00300 Helsinki, Finland; Diabetes Unit (J.L.), Department of Health Promotion and Chronic Disease Prevention, National Public Health Institute, 00300 Helsinki, Finland; and South Ostrobothnia Central Hospital (J.T.), 60200 Seinäjoki, Finland

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


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Objective: The Finnish Diabetes Risk Score (FINDRISC) questionnaire is a practical screening tool to estimate the diabetes risk and the probability of asymptomatic type 2 diabetes. In this study we evaluated the usefulness of the FINDRISC to predict insulin resistance in a population at increased diabetes 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 tolerance test parameters were available from each participant. The predictive value of the FINDRISC was cross-sectionally evaluated using the area under the curve-receiver operating characteristics method and by correlation analyses. A validation of the cross-sectional results was performed on the prospective data from the cohort study.

Results: The FINDRISC was significantly correlated with markers of insulin resistance. The receiver operating characteristics-area under the curve for the prediction of a homeostasis model assessment insulin resistance index of more than five was 0.78 in the cross-sectional survey and 0.74 at baseline of the cohort study. Moreover, the FINDRISC at baseline was significantly associated with disease evolution (P < 0.01), which was defined as the change of glucose tolerance during the 3 yr follow-up.

Conclusions: The results indicate that the FINDRISC can be applied to detect insulin resistance in a population at high risk for type 2 diabetes and predict future impairment of glucose tolerance.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
The dramatic increase in newly diagnosed cases of type 2 diabetes has developed into a major public health concern in this century (1). Having diabetes means having a significantly reduced quality of life and reduced life expectancy (2). Furthermore, diabetes and impairment of glucose tolerance are very common among the elderly (3), and recently also in younger people, with a most sudden increase in prevalence in the age group younger than 30 yr (4). An increasing number of people in their working age are affected by diabetes, increasing the economic burden of the health care system due to an earlier onset of complications, and subsequently, a longer and more intensive medical treatment period.

Type 2 diabetes is a progressive disease. Before its clinical onset, there is a long latent asymptomatic period that may last decades. The development of type 2 diabetes is a multistage process originating from genetic disposition (5, 6). Unhealthy lifestyle may trigger the development of insulin resistance in a susceptible genotype (6) that is usually followed by impairment of glucose tolerance (7). In this prediabetic period, insulin resistance remains often unrecognized because enhanced insulin secretion maintains glucose levels within normal ranges (8, 9). Owing to the fact that diagnostic criteria for diabetes are based on the presence of hyperglycemia, this disease is commonly diagnosed too late (10, 11). As yet there exist neither diagnostic criteria for insulin resistance nor suitable screening tools precluding an early detection of metabolic disturbances. The only reliable way of assessing insulin resistance is by an euglycemic clamp, which, unfortunately, is a very costly and time-consuming endeavor to date. The surrogate marker homeostasis model assessment insulin resistance index (HOMA-IR) established by Matthews et al. (12) that integrates measures of fasting plasma glucose and fasting plasma insulin is currently widely used. However, standardized reference values are lacking. Therefore, there are attempts to develop simple, fast, and noninvasive scoring systems for identification of high-risk subjects (13, 14, 15, 16, 17, 18, 19, 20, 21). The validated Finnish Diabetes Risk Score (FINDRISC) has been successfully implemented as a practical screening tool to assess the diabetes risk and to detect undiagnosed type 2 diabetes (22, 23). Beyond this line, it also proved suitable in prediction of coronary heart disease, stroke, and total mortality in the Caucasian population (24, 25, 26, 27). The present study aimed at evaluating the ability of the FINDRISC to predict insulin resistance in subjects at high risk for diabetes mellitus.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Subjects

To address this objective, data of two different samples were analyzed. The first sample drawn in 1996 consisted of 921 subjects with a family history of metabolic syndrome. The second sample drawn in 1997 was used for validation purposes and consisted of 735 subjects from German families with a family history of type 2 diabetes or related insulin resistance disorders such as obesity or dyslipidemia. The individuals of both surveys were from the city of Dresden and adjoining areas. Exclusion criteria were previously diagnosed diabetes, severe renal disease, disease with a strong impact on life expectancy, and therapy with drugs known to influence glucose tolerance (thiazide diuretics, β-blockers, and steroids). Each subject underwent a physical examination that was followed by a 75-g oral glucose tolerance test (OGTT). Blood samples were taken at fasting, and at 30, 60, 90, and 120 min after the glucose challenge for measurement of glucose, insulin, proinsulin C peptide, and free fatty acids (FFAs). In addition, parameters of lipoprotein metabolism [total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c)] were determined from fasting blood samples. Data on sociodemographical variables, medical history, lifestyle, and family history of diabetes were obtained by questionnaires, including the FINDRISC questionnaire. A total of 771 and 526 individuals from the 1996 survey and the 1997 baseline survey, respectively, completed both the OGTT and the FINDRISC questionnaire.

A follow-up examination was performed 3 yr after the initial survey in the 1997 cohort. Subjects at an increased diabetes risk based on the FINDRISC at baseline were either enrolled into a lifestyle intervention or underwent pharmacological therapy. The intervention program has been previously described in detail (28). Of 526 subjects, 515 completed the follow-up examination. The detailed procedure of the recruitment of participants and the methods used have been described previously (29, 30). Informed consent was obtained from all participants, and the study was approved by the local ethics committee.

Analyses

Based on the baseline OGTT data, the subjects were categorized as having either normal glucose tolerance (NGT), impaired glucose tolerance (IGT), including those with impaired fasting glucose (IFG) and type 2 diabetes mellitus according to the World Health Organization/American Diabetes Association criteria of 1997/1999 (31). Subjects with follow-up examination were also defined according to the evolution of their diabetic status as unchanged, progression, or regression. Estimates for glucose tolerance were calculated from OGTT parameters. The HOMA-IR was calculated using the formula as described by Matthews et al. (12). The area under the curve (AUC) for AUC(insulin), AUC(proinsulin), and AUC(FFA) values was estimated from the following equation (insulin as example):

Formula

Laboratory procedures

Plasma glucose was measured using the hexokinase method [interassay coefficient of variation (CV) 1.5%]. Serum TC and triglycerides were determined using enzymatic techniques (Roche Molecular Biochemicals, Mannheim, Germany). HDL-c was determined after precipitation with dextran sulfate (Roche Molecular Biochemicals), and serum LDL-c was calculated using Friedewald’s formula (32). HbA1c values were analyzed by HPLC. The analyses of insulin and proinsulin levels were performed by commercially available enzyme immunoassays (BioSource EUROPE S.A Belgium; interassay CV 7.5%, no cross-reactivity with human proinsulin; DRG Diagnostics, Marburg Germany; interassay CV 7.5%, no cross-reactivity with human insulin and C peptide).

FINDRISC

The FINDRISC comprises eight items (22, 25) regarding age, body mass index (BMI), waist circumference, physical activity, diet, use of antihypertensive medication, history of high blood glucose, and family history of diabetes. In the current study, a modified and validated German version of the questionnaire was applied (33). In this shortened version, the variables diet and physical activity were omitted because both items did not add much power for the prediction of diabetes risk in previous studies (25). Thus, the maximal achievable score of the modified questionnaire is 23.

Statistics

Statistical 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 interquartile range 25%–75%, unless otherwise stated. Individuals with IGT and/or IFG were analyzed as a combined glucose intolerance group. Associations and correlation coefficients between the FINDRISC and clinical parameters were evaluated by the Spearman correlation test. The means of the FINDRISC total score were compared between the different evolution categories using the Kruskal-Wallis test. A value of P < 0.05 was assumed to indicate significance. The predictive value of the modified FINDRISC (34, 35) for insulin resistance as defined by HOMA-IR value more than five was evaluated using the AUC in a receiver operating characteristics (ROC) curve. The sensitivities were plotted against the y-axis, and the false-positive rates (one-specificity) were plotted against the x-axis, then the ROC curve was plotted. The optimal cutpoints were located at the peak of the curve where the sum of sensitivity and specificity is maximal (36). The method of Hanley and McNeil (37) was used to compare the AUCs.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Of the 326 men and 445 women initially included in the 1996 cross-sectional survey, 417 (54.1%) were diagnosed with NGT, 287 (37.2%) exhibited IGT/IFG, and 67 (8.7%) were newly diagnosed with type 2 diabetes. Men had a higher prevalence of abnormal glucose tolerance than women (51 vs. 42%; P = 0.012, {chi}2 test). Similar proportions were determined in the 1997 cohort at 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 impaired glucose tolerance was 77.3 and 62.6% in men and women, respectively (P < 0.01). The corresponding values in the follow-up study were 66.4 and 52.1% (P < 0.01). In addition, 40 individuals progressed from NGT to IGT/IFG, and 36 previously healthy persons were diagnosed with manifest diabetes after 3 yr follow-up. The clinical characteristics of the two study samples are shown in Table 1Go.


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  		TABLE 1. Selected clinical characteristics of the study participants

 
Association of the FINDRISC and insulin resistance

The mean FINDRISC total score of the 1996 survey was 9.33 ± 5.92 (mean ± SD). The total score ranged from zero to 23 in this group. The individual FINDRISC was evenly distributed with the majority of subjects ranging between zero and 20. In the 1997 baseline survey, the mean FINDRISC was 7.27 ± 4.45. The total score ranged from one to 17. The correlation coefficients for the FINDRISCs and markers of insulin resistance are shown in Table 2Go. The FINDRISC was significantly positively associated with AUC (insulin), AUC (proinsulin), AUC (FFA), HOMA-IR, and HbA1c in the two baseline surveys. These associations were still present after 3 yr follow-up. LDL-c was not correlated with the FINDRISC in the cohort, but a positive association was found in the 1996 survey. In the same survey, there was a tendency of an association between HDL-c and FINDRISC (P = 0.06, Spearman test). In contrast, the HDL-c level was significantly inversely correlated with the FINDRISC value in the cohort. The relationship between the FINDRISC value and markers of insulin resistance is depicted in Fig. 1Go (R2 is the coefficient of determination for the linear regressions). Accordingly, more than 6% of variability in most indicators can be explained by the FINDRISC.


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  		TABLE 2. The relationship of the FINDRISC with clinical parameters

 

Figure 1
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  		FIG. 1. Relationship between insulin resistance parameters and FINDRISC (scatter diagrams). The scatter plots illustrate the relationship between FINDRISC and clinical parameters 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 of determination for the linear regressions.

 
The predictive performance of the FINDRISC for high HOMA-IR values

The ROC curves are shown in Fig. 2Go. The AUC values of 0.78 and 0.74 for the 1996 and 1997 baseline studies, respectively, did not differ significantly between the two study samples (P > 0.05). The optimal cutpoints were 12 and nine, respectively. In the 1996 survey, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for a FINDRISC value of 12 or more were 77.5, 67.9, 19.7, and 96.8%, respectively. The corresponding values in the 1997 baseline study for a FINDRISC value of nine or more were 72.7, 68.2, 29.4, and 88.1%, respectively (Table 3Go). The relative risk for a FINDRISC value of 12 or more vs. FINDRISC value less than 12 was 6.04 (95% confidence interval 3.53–10.33) in the 1996 cohort and 2.48 (95% confidence interval 1.58–3.88) in the 1997 baseline investigation. Using a HOMA-IR value of two instead of five as the cutpoint, the ROC-AUC was 0.69 for the 1996 survey and 0.68 for the 1997 baseline study.


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

 

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

 
If we excluded all individuals whose HOMA-IR was more than five and all diabetic patients of 1997 baseline study and used the FINDRISC to predict HOMA-IR (> 5), the relevant ROC-AUC was 0.72 (Fig. 2Go). At the optimal cutpoint of nine, the sensitivity 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 of hyperglycemia

The FINDRISC was significantly directly associated with disease evolution (P < 0.01, Kruskal Wallis test). A mean FINDRISC 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), subjects with disease progression 8.49 ± 5.24 (n = 76), and subjects remaining diabetic 10.68 ± 4.10 (n = 34). Subjects with the highest FINDRISC value had the highest proportion of individuals with diabetes at baseline, and the largest proportion of them remained diabetic during the follow-up, whereas those with a low FINDRISC value comprised the highest proportion of individuals remaining NGT.


   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
The data presented in this investigation provide evidence that the FINDRISC is significantly associated with markers of insulin resistance and with disease evolution. Because insulin resistance always precedes IGT (7), the FINDRISC may be a useful instrument to identify people at the earliest stage of disease development. Compared with the elaborate and expensive standard procedure using biochemistry, the FINDRISC questionnaire represents a simple and cost-efficient tool with a good predictive value to detect undiagnosed diabetes, which can be used in large-scale studies and even on a care level (25). In addition, we now show that the FINDRISC is also able to reliably predict insulin resistance.

The FINDRISC was significantly associated with an unfavorable progression of glycemic parameters. These associations were validated against an independent data set. The results obtained were consistent with those of the original analysis, providing a strong argument for the robustness of our findings. The ability of the FINDRISC to detect insulin resistance was similar to that to predict the development of type 2 diabetes, and better than to detect previously undiagnosed diabetes (27). This is in keeping with the fact that insulin resistance precedes the development of diabetes (38, 39). Therefore, the FINDRISC may rather be applied to predict future diabetes than being used for diabetes diagnosis (25, 26, 27). Our cutoff value for HOMA-IR was based on the general assumption that a value between 2.5 and five indicates a moderate risk, and a value greater than five is indicative for a high risk for insulin resistance (34, 35). Because the AUC values of ROC curves for a cutoff of "5" were greater than 0.70, it seems that the FINDRISC can be actually used as a predictive tool for insulin resistance. The most relevant application field of FINDRISC is on the primary care level, where population-based screening strategies are needed and widely implemented. The use by primary care physicians or other health care professionals would facilitate the detection of high-risk subjects and the institution of early preventive measures. The association of the FINDRISC with measures of insulin resistance makes the application of the FINDRISC more relevant and the clinical relevance stronger.

Some limitations of our study warrant consideration. One could argue that the analysis of only six risk items in the FINDRISC questionnaire is not reliable because the two excluded variables, diet and physical activity, have an evidenced impact on diabetes development (40, 41, 42). It could be shown in two independent studies using the FINDRISC that these two items did not add much power to the prediction of diabetes risk (25, 43). Other studies also reported similar observations (44, 45). The developers of the FINDRISC justified the inclusion of these items, owing to its relevance for the development of diabetes, particularly because the FINDRISC (and other similar tools) is primarily targeted to laymen or to be used in the context of diabetes prevention. Although the FINDRISC has not been tested in all ethnic groups, it may be widely applicable because it focuses on general risk factors for type 2 diabetes, which are globally prevalent. An adjustment of cutpoints and relative weight of some items may be needed in certain population groups.

The second limitation of our study is that all subjects of the two cohorts had high risks for type 2 diabetes mellitus, thus, the selection bias may lead to an underestimation of associations. The mean BMI showed that the 1996 and 1997-baseline samples were "overweight," corresponding to 25 (22, 23, 24, 25, 26, 27, 28) and 26 kg/m2 (24, 25, 26, 27, 28), respectively. We found that to discriminate high HOMA-IR values (more than two or more than five) in the 1996 survey and 1997 baseline survey, the performance (ROC-AUC) of the FINDRISC was similar to that of continuous BMI (data not shown). However, the benefits of completing a questionnaire compared with a single BMI measure is a possible increase in awareness regarding individual risk factors. Therefore, it is necessary to validate the association of FINDRISC and insulin resistance in a randomized study and also in other populations. Moreover, there were large differences of HOMA in 1996 and 1997 baseline survey (mean ± SD were 0.34 ± 0.28 and 0.43 ± 0.28, respectively, data were transformed in log), which could influence the accuracy of studies too.

Another important aspect is that all individuals at increased diabetes risk or hyperglycemia at the 1997 baseline survey had received relevant intervention or treatment in the following years. This might explain the higher hyperglycemia prevalence at baseline than after the 3 yr follow-up in this study sample. Among all these intervened individuals, more than 60% presented reduced HOMA-IR values at outcome, which could result in an underestimated incidence of insulin resistance and an underestimated predictive performance of FINDRISC in the prospective analysis. Furthermore, the FINDRISC was correlated with HOMA-IR at the follow-up, both in those who gained weight and those with unchanged/reduced body weight as well as after adjustment for weight change.

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

In conclusion, our analysis shows that the FINDRISC may be a suitable tool to identify people with insulin resistance, and also those who are likely to progress toward hyperglycemia and type 2 diabetes.

When implemented in primary health care, the FINDRISC would assist health care professionals in decision making regarding a further medical investigation and the institution of preventive measures. Furthermore, the application of the FINDRISC in a population-based program aims also at a learning effect. People completing the FINDRISC become aware of their own prevalent risk factors.

Importantly, several authorities such as the European Association for the Study of Diabetes, the European Society of Cardiology, and the International Diabetes Federation Consensus Group have recommended the FINDRISC to be used for risk stratification purposes in the European population (46, 47).


   Acknowledgments
 
We thank all the patients who cooperated in this study and their referring physicians and diabetologists in Saxony.


   Footnotes
 
This study was supported by the Commission of the European Communities, 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 Funding Grant, Med Drive.

Disclosure Information: The authors have nothing to declare.

First Published Online December 23, 2008

1 P.E.H.S. and J.L. contributed equally to this work. Back

Abbreviations: AUC, Area under the curve; BMI, body mass index; CV, coefficient of variation; FFA, free fatty acid; FINDRISC, Finnish Diabetes Risk Score; HbA1c, glycosylated hemoglobin; HDL-c, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment insulin resistance index; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; 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.

Received November 1, 2007.

Accepted December 12, 2008.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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