Cardiometabolic profile of people screened for high risk of type 2 diabetes in a national diabetes prevention programme (FIN-D2D)

p r i m a r y c a r e d i a b e t e s 4 ( 2 0 1 0 ) 231–239

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Primary Care Diabetes journal homepage: http://www.elsevier.com/locate/pcd

Original research

Cardiometabolic profile of people screened for high risk of type 2 diabetes in a national diabetes prevention programme (FIN-D2D) Timo Saaristo a,b,∗ , Leena Moilanen c , Jari Jokelainen d , Eeva Korpi-Hyövälti e , Mauno Vanhala f , Juha Saltevo g , Leo Niskanen c , Markku Peltonen h , Heikki Oksa a , Henna Cederberg d , Jaakko Tuomilehto i , Matti Uusitupa j , Sirkka Keinänen-Kiukaanniemi d,k a

Pirkanmaa Hospital District, Tampere, Finland Finnish Diabetes Association, Tampere, Finland c Department of Medicine, Kuopio University Hospital, Northern Savo Hospital District, Kuopio, Finland d Institute of Health Sciences, University of Oulu, Oulu, Finland e Department of Internal Medicine, Seinäjoki Central Hospital, Seinäjoki, Finland f Unit of Family Practice, Central Finland Central Hospital, Jyväskylä, and Kuopio University Hospital, and University of Eastern Finland, Kuopio, Finland g Department of Internal Medicine, Central Finland Hospital District, Jyväskylä, Finland h National Institute for Health and Welfare, Helsinki, Finland i University of Helsinki, Helsinki, Finland j Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland k North Ostrobothnia Hospital District and Health Centre of Oulu, Oulu, Finland b

a r t i c l e

i n f o

a b s t r a c t

Article history:

Aims: To study screening of high-risk individuals as part of a national diabetes prevention

Received 10 December 2009

programme in primary health care settings in Finland between 2003 and 2007, and evaluate

Received in revised form

the cardiometabolic risk profile of persons identified for intervention.

10 May 2010

Methods: High-risk individuals were identified by the Finnish Diabetes Risk Score (FINDRISC),

Accepted 21 May 2010

history of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), cardiovascular

Available online 18 June 2010

disease (CVD), or gestational diabetes. Participants subsequently underwent an oral glucose tolerance test. CVD morbidity risk was estimated by the Framingham Study Risk Equation

Keywords:

and CVD mortality risk by the Systematic Coronary Risk Evaluation Formula (SCORE).

FINDRISC

Results: A high-risk cohort of 10,149 (of whom 30.3% men) was identified (mean age 54.7 for

High-risk strategy

men, 53.0 for women). Altogether 18.8% of men and 11.5% of women had screen-detected

Screening

diabetes. In total 68.1% of men and 49.4% of women had abnormal glucose tolerance (IFG,

Metabolic syndrome

IGT or screen-detected diabetes). Furthermore, 43.2% and 41.5% of men, and 13.3% and 11.3%

Type 2 diabetes

of women, respectively, had a high predicted risk of CVD morbidity or mortality.

Impaired fasting glucose

∗ Corresponding author at: Finnish Diabetes Association, Kirjoniementie 15, 33680 Tampere, Finland. Tel.: +358 3 2860 413; fax: +358 3 2860 422. E-mail addresses: timo.saaristo@pshp.fi, timo.saaristo@diabetes.fi (T. Saaristo). 1751-9918/$ – see front matter © 2010 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.pcd.2010.05.005

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Impaired glucose tolerance

Conclusion: Prevalence of dysglycemia including undiagnosed diabetes and the predicted risk

Cardiovascular disease

for CVD was alarmly high in the identified high-risk cohort, particularly in men.

Framingham risk engine

© 2010 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

SCORE Primary health care

1.

Introduction

Type 2 diabetes can be delayed or prevented by treating people with dysglycemia by means of lifestyle intervention or medication, as shown by major clinical trials of diabetes prevention [1–5]. Translating this message from the clinical trials to clinical practice is an urgent challenge but, surprisingly, has not yet been achieved at a population level extensively anywhere [6–13]. Special screening models are called for in primary health care in order to identify those at an increased risk of diabetes, and those who actually already have undetected diabetes, and to be able to refer them for effective interventions. Otherwise, the true primary prevention of diabetes and late complications related to hyperglycemia, elevated blood pressure and elevated cholesterol levels will not be possible. To meet this challenge the first large scale national Programme for the Prevention of Type 2 Diabetes was launched in Finland [14]. It was based on evidence derived mainly from the Finnish Diabetes Prevention Study and was implemented through the FIN-D2D project between 2003 and 2007 in the Finnish primary health care [15,16]. FIN-D2D included population-, high-risk- and early treatment strategies. The high-risk strategy was directed to implement screening and lifestyle interventions of people at high risk of type 2 diabetes as part of routine primary health care, aiming for diabetes prevention and cardiovascular (CVD) risk factor reduction. In the current study we assessed (1) how the screening for high-risk individuals in the primary health care setting was carried out in FIN-D2D and (2) what the cardiometabolic risk profile of persons identified for intervention was.

2.

Research design and methods

Prevention of diabetes was implemented as part of the daily practice in five Finnish hospital districts, covering 400 primary health care centers and occupational health clinics, and a population of 1.5 million. In this area 69% of the population was 18 years and older [17]. The main screening tool was the modified Finnish Diabetes Risk Score (FINDRISC), which included a question on family history of diabetes in addition to the original seven questions [18]. The FINDRISC questionnaire was distributed in the FIN-D2D area population in pharmacies, in public events such as health fairs and ice hockey matches, and also as part of a nationwide diabetes-awareness campaign (Fig. 1). It was also available on the internet where 200,000 visitors from the whole country (with 5.3 million people) filled in the questionnaire in 2003–2007. FINDRISC was also used in opportunistic screening in primary health care as part of the normal health care attendance. FINDRISC scores of 15 or above were considered high risk. Alternative referral criteria to FIN-

D2D included a history of impaired glucose tolerance (IGT), impaired fasting glucose (IFG), myocardial infarction or other CVD event or gestational diabetes (Fig. 1) [15,16]. The screening positive people were informed about the importance of lifestyle modification in diabetes prevention and eligible individuals were recruited for the FIN-D2D project. Health care providers were asked to register information on high-risk persons for evaluation purposes into the local electronic patient record system, or in data collecting forms (Fig. 1). Subsequent evaluation of risk factors and glucose tolerance, intervention and follow-up was done as part of standard primary health care. Information on sociodemographic variables, health status, lifestyle, past medical history and regular medication were collected by local nurses according to the working instructions in the Project Plan [15]. Height was measured to the nearest cm. Weight (kg) was measured in light clothing. Waist circumference was measured to the nearest cm. Body mass index (BMI) was calculated as weight (kg) divided by height2 (m). Blood pressure (mm Hg) was measured twice from the right arm in sitting position to the nearest 1 mm Hg with at least 1-min interval and the mean reading was recorded. The definition of body mass index (BMI) 25–29 kg/m2 for overweight and BMI ≥30 kg/m2 for obesity was used. An oral glucose tolerance test (OGTT) was performed with a 75 g glucose load and fasting and 2-h plasma samples were collected [19]. Either capillary (20%) or venous plasma samples (80%) were used. Plasma lipids and lipoproteins were determined locally from fasting venous blood samples using enzymatic methods. All measurements met the national primary health care standards. Glucose tolerance was classified according to the WHO 1999 criteria [19]. Fasting venous or capillary plasma glucose level ≥7.0 mmol/l or 2 h venous plasma glucose >11.1 mmol/l or 2 h capillary plasma glucose >12.2 mmol/l were classified as screen-detected type 2 diabetes (ST2DM); 2 h venous plasma glucose >7.8 mmol/l and <11.1 mmol/l or 2 h capillary plasma glucose >8.9 mmol/l and <12.2 mmol/l, and fasting plasma glucose <7.0 mmol/l as impaired glucose tolerance (IGT); fasting plasma glucose ≥6.1 but <7.0 mmol/l, and 2 h venous plasma glucose <7.8 mmol/l or 2 h capillary plasma glucose <8.9 mmol/l as impaired fasting glucose (IFG). Abnormal glucose tolerance included ST2DM, IGT and IFG. Metabolic syndrome was defined by the American National Cholesterol Education Program (NCEP) modified criteria [20], and the International Diabetes Federation (IDF) 2005 criteria [21]. Predicted 10-year risk of CVD morbidity was evaluated by the Framingham Study Risk Equation with a cut-off point ≥20% for high risk [22]. The Systematic Coronary Risk Evaluation Formula (SCORE) with a cut-off point ≥5% for high risk was used to predict the 10-year risk of CVD mortality [23]. Mean values and standard deviations (SD) were calculated for the baseline characteristics.

p r i m a r y c a r e d i a b e t e s 4 ( 2 0 1 0 ) 231–239

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Fig. 1 – Flowchart of the formation of the high-risk cohort in FIN-D2D. The high-risk cohort (10,149) is the yield of the screening process (that is, individuals with data for evaluation purposes). FINDRISC = Finnish Diabetes Risk Score, OGTT = Oral glucose tolerance test, CVD = History of cardiovascular disease, IFG = Impaired fasting glucose, IGT = Impaired glucose tolerance, NGT = Normal glucose tolerance.

One-way ANOVA and Chi-square test were used for comparison of grouped data. The Cochran–Armitage test for trend was used to examine the predicted CVD morbidity and mortality against glucose tolerance categories with age. SAS (v. 9.2) for Windows was used for all statistical analysis. FIN-D2D was conducted within the Finnish health care system according to locally accepted action plans as part of normal clinical practice. The participation in the project was voluntary and took place during routine health care visits. As the participants visited primary health care for health check ups and lifestyle interventions as part of normal clinical routine, the informed consent was not used, but the participants were given written information on diabetes prevention and FIN-D2D. A permission to collect data on high-risk individuals from the participating health care centers for evaluation was granted to the National Public Health Institute by the Ministry of Social Affairs and Health in Finland.

3.

Results

In total, 10,149 individuals who were identified being at high risk of developing type 2 diabetes were recruited for evaluation; of these 3379 (33.3%) were men and 6770 (66.7%) women. Due to the nature of the screening procedure, it was not possible to register the total number of people screened. Majority of participants (51% of men and 57% of women) entered FIND2D through the FINDRISC screening with a score ≥15 (mean score 17). When past medical history triggered referral to FIND2D, 8% of men and 2% of women had a history of myocardial infarction or other ischemic CVD event, 13% of women had a history of gestational diabetes and altogether 34% of men and 21% of women a history of IFG or IGT.

Baseline characteristics of the high-risk cohort by reason for referral are demonstrated in Table 1. Mean age was 54.7 and 53.0 years, for men and women, respectively. Altogether, 36.8% of men and 30.6% of women were overweight and 57.1% of men and 60.9% of women obese. Up to 66.3% of men and 82.4% of women were centrally obese by the criteria of waist circumference >102 cm and >88 cm, respectively. Participants identified by FINDRISC were younger, more obese in general and abdominally and interestingly, their diastolic blood pressure, serum total cholesterol and LDL-cholesterol levels were higher than those referred on the basis of a history of CVD. Women with a history of gestational diabetes (n = 900, data not shown in Tables 1–3) were markedly younger compared to the other groups (median age 40.1 years), with lower risk factors (median BMI 30.1 kg/m2 , waist 95.5 cm, serum total cholesterol 4.9 mmol/l, 11.3% smokers, elevated blood pressure in 26.3%), and 71.9% had normoglycemia. Clinical description of past medical history, prevalence of CVD risk factors and regular medication by reason for referral are shown in Table 2. Altogether 40% reported elevated serum cholesterol or other lipid disorder, and 14.7% of men and 6.3% of women had a history of coronary artery disease. Over half (54.8% of men and 58.4% of women) had hypercholesterolemia (≥5 mmol/l) and more than two-thirds (78.1% of men and 68.6% of women) were hypertensive (BP > 135/85 mm Hg). The proportion of regular smokers was 17.3% in men and 10.1% in women. Altogether 17.4% of men and 11.7% of women had cholesterol-lowering medication, and 4.7% of men and 2.5% of women medication for coronary artery disease. One in four reported having antihypertensive medication. In total, 8353 participants underwent an OGTT at baseline (Fig. 1). Glucose tolerance status and prevalence of metabolic

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Table 1 – Baseline characteristics of the high-risk individuals by reason for referral to FIN-D2D. Variable

FINDRISC

CVD Mean (std)

IFG/IGT N

ANOVA p-value

N

Mean (std)

N

Mean (std)

Men Age (years) Height (cm) Weight (kg) BMI (kg/m2 ) Waist (cm) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Serum total cholesterol (mmol/l) HDL cholesterol (mmol/l) LDL cholesterol (mmol/l) Serum triglycerides (mmol/l)a Fasting glucose (mmol/l) FINDRISC score

1709 1706 1709 1706 1607 1680 1680 1583 1569 1503 1567 1401 1583

54.30 (9.74) 176.4 (6.64) 99.20 (16.27) 31.84 (4.71) 109.6 (11.24) 143.2 (17.43) 89.17 (10.13) 5.24 (1.04) 1.25 (0.37) 3.16 (0.89) 1.89 (1.21) 6.12 (0.89) 17.48 (2.61)

259 259 259 259 237 252 252 239 237 233 236 212 166

60.39 (8.62)* 174.4 (6.43)* 89.60 (14.18)* 29.42 (4.03)* 103.6 (10.78)* 138.5 (17.84)* 83.09 (10.82)* 4.37 (0.90)* 1.18 (0.30)* 2.47 (0.76)* 1.65 (0.93)* 6.10 (0.68) 16.07 (4.18)*

1163 1160 1163 1160 1103 1147 1147 1119 1099 1060 1100 979 786

54.19 (10.33) 176.0 (6.28) 95.38 (16.75)* 30.74 (4.83)* 107.1 (12.21)* 143.5 (26.39) 88.47 (10.13) 5.19 (1.02) 1.26 (0.34) 3.08 (0.90)* 1.90 (1.17) 6.35 (0.85)* 16.38 (3.95)*

<0.001 <0.001 <0.001 <0.001 <0.001 0.002 <0.001 <0.001 0.003 <0.001 0.009 <0.001 <0.001

Women Age (years) Height (cm) Weight (kg) BMI(kg/m2 ) Waist (cm) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Serum total cholesterol (mmol/l) HDL cholesterol (mmol/l) LDL cholesterol (mmol/l) Serum triglycerides (mmol/l)a Fasting glucose (mmol/l) FINDRISC score

3860 3851 3860 3851 3651 3796 3796 3571 3551 3510 3533 3236 3602

54.64 (9.86) 162.6 (5.74) 85.81 (14.74) 32.44 (5.15) 101.3 (11.89) 140.5 (18.03) 86.33 (9.60) 5.27 (0.94) 1.49 (0.41) 3.09 (0.87) 1.50 (0.76) 5.75 (0.80) 17.67 (2.56)

129 128 129 128 118 127 127 123 123 122 123 107 85

62.49 (8.12)* 160.7 (5.58)* 77.53 (14.61)* 29.99 (5.10)* 95.07 (13.45)* 140.2 (20.79) 81.30 (9.90)* 4.68 (0.98)* 1.43 (0.38) 2.60 (0.89)* 1.43 (0.67) 5.84 (0.81) 16.32 (3.76)*

1419 1412 1419 1412 1337 1396 1396 1351 1333 1314 1333 1189 970

55.79 (10.43)* 162.3 (5.73) 84.65 (17.19)* 32.08 (6.03)* 100.9 (13.67) 140.8 (18.37) 86.24 (9.34) 5.24 (0.99) 1.47 (0.43) 3.05 (0.90) 1.62 (0.88)* 6.22 (0.93)* 17.40 (3.80)*

<0.001 <0.001 <0.001 <0.001 <0.001 0.8600 <0.001 <0.001 0.063 <0.001 <0.001 <0.001 <0.001

Data on women with a history of gestational diabetes not shown. FINDRISC = Finnish Diabetes Risk Score. CVD = Previous myocardial infarction or other artery disease. IFG/IGT = Diagnosed earlier with impaired fasting glucose (IFG) or impaired glucose tolerance (IGT). a Log-transformed values are used in analysis. ∗ p < 0.005 compared to FINDRISC.

syndrome by reason for referral are demonstrated in Table 3. Total prevalence of abnormal glucose tolerance (including IFG, IGT and ST2DM) for men and women was 68.1% and 49.4%, and prevalence of screen-detected diabetes 18.8% and 11.5%, respectively. Prevalence of the metabolic syndrome was high independent of the definition used. By the IDF 2005 criteria, the prevalence of the metabolic syndrome was 75.3% in men and 66.8% in women, respectively, and somewhat lower with the NCEP criteria. Predicted high 10-year risk of CVD morbidity (Framingham score ≥20%) and mortality (SCORE ≥5%) for age and glucose tolerance categories are illustrated in Fig. 2. Significantly more men had a high risk of CVD morbidity (43.2%) and mortality (41.5%) compared to women (13.3% and 11.3%, respectively). As expected, age increased the predicted risk of CVD morbidity and mortality in all glucose tolerance categories (p < 0.001). Deterioration of glucose homeostasis from normal glucose tolerance to IGT increased the predicted risk of CVD morbidity in all age groups and both sexes (p < 0.001). The presence of diabetes increased the predicted risk of CVD morbidity markedly (p < 0.001). Predicted risk of CVD mortality increased by deterioration of glucose tolerance from normal glucose tolerance to diabetes in women, but not in men.

4.

Discussion

This study describes how the screening was carried out and what could be achieved in the first large implementation project of a national diabetes prevention programme carried out in Finland. It also characterizes the current CVD risk profile in people screened for intervention. A cohort of over ten thousand people at high risk for type 2 diabetes was recruited for lifestyle counselling in primary health care. High rates of abnormal glucose tolerance and markedly elevated CVD risk factors were prevalent within the study population. FIN-D2D successfully demonstrates that by means of opportunistic screening in primary health care and distribution of the FINDRISC risk score forms to the general public, a high-risk cohort of 10,000 was successfully identified for further evaluation. Opportunistic screening, the method of choice in the project, yielded a significant number of individuals at high risk for diabetes. The methods used in the FIN-D2D project differ markedly from those used in the other recent diabetes screening programmes, where mostly targeted screening and invitation for testing has been used [24,25]. Previous experiences from community-based

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Table 2 – Past self-reported medical history, prevalence of cardiovascular risk factors and regular medication in the high-risk cohort by reason for referral to FIN-D2D. (Data on women with a history of gestational diabetes not shown.). Variable

FINDRISC N

n (%)

1480 1480 1480 1480 1480 1480

952 (64.3%) 41 (2.8%) 71 (4.8%) 34 (2.3%) 13 (0.9%) 562 (38.0%)

1480 1480 1480

Prevalence of cardiovascular risk factor Current smoking Serum total cholesterol ≥ 5mmol/l LDL cholesterol ≥ 2.5mmol/l Serum triglycerides ≥ 1.7 mmol/l Blood pressure ≥ 135/85 mm Hg Regular medication Acetylsalicylic Acid Cholesterol-lowering medication Antihypertensive medication Coronary artery disease medication

CVD

Chi-square test

n (%)

N

n (%)

218 218 218 218 218 218

132 (60.6%) 22 (10.1%) 121 (55.5%) 30 (13.8%) 8 (3.7%) 130 (59.6%)

1004 1004 1004 1004 1004 1004

589 (58.7%) 39 (3.9%) 56 (5.6%) 30 (3.0%) 12 (1.2%) 429 (42.7%)

0.016 <0.001 <0.001 <0.001 0.002 <0.001

151 (10.2%) 158 (10.7%) 121 (8.2%)

218 218 218

16 (7.3%) 25 (11.5%) 20 (9.2%)

1004 1004 1004

105 (10.5%) 105 (10.5%) 98 (9.8%)

0.370 0.908 0.388

1474 1583 1503 1567 1680

259 (17.6%) 927 (58.6%) 1144 (76.1%) 679 (43.3%) 1334 (79.4%)

217 239 233 236 252

29 (13.4%) 59 (24.7%) 104 (44.6%) 89 (37.7%) 159 (63.1%)

995 1119 1058 1100 1147

174 (17.5%) 629 (56.2%) 789 (74.6%) 500 (45.5%) 908 (79.2%)

0.295 <0.001 <0.001 0.086 <0.001

1709 1709 1709 1709

158 (9.2%) 265 (15.5%) 497 (29.1%) 60 (3.5%)

259 259 259 259

78 (30.1%) 120 (46.3%) 68 (26.3%) 64 (24.7%)

1163 1163 1163 1163

122 (10.5%) 177 (15.2%) 264 (22.7%) 28 (2.4%)

<0.001 <0.001 <0.001 <0.001

3316 3316 3316 3316 3316 3316

2021 (60.9%) 44 (1.3%) 96 (2.9%) 93 (2.8%) 13 (0.4%) 1256 (37.9%)

112 112 112 112 112 112

75 (67.0%) 12 (10.7%) 50 (44.6%) 23 (20.5%) 2 (1.8%) 64 (57.1%)

1240 1240 1240 1240 1240 1240

774 (62.4%) 29 (2.3%) 54 (4.4%) 35 (2.8%) 8 (0.6%) 543 (43.8%)

0.320 <0.001 <0.001 <0.001 0.078 <0.001

3316 3316 3316

501 (15.1%) 435 (13.1%) 413 (12.5%)

112 112 112

13 (11.6%) 9 (8.0%) 15 (13.4%)

1240 1240 1240

218 (17.6%) 162 (13.1%) 120 (9.7%)

0.060 0.288 0.030

Prevalence of cardiovascular risk factor Current smoking Serum total cholesterol ≥ 5mmol/l LDL cholesterol ≥ 2.5mmol/l Serum triglycerides ≥ 1.7 mmol/l Blood pressure ≥ 135/85 mm Hg

3301 3571 3509 3533 3796

314 (9.5%) 2221 (62.2%) 2634 (75.1%) 1027 (29.1%) 2749 (72.4%)

112 123 122 123 127

2 (1.8%) 41 (33.3%) 54 (44.3%) 30 (24.4%) 81 (63.8%)

1234 1351 1314 1333 1396

150 (12.2%) 780 (57.7%) 929 (70.7%) 477 (35.8%) 1009 (72.3%)

<0.001 <0.001 <0.001 <0.001 0.101

Regular medication Acetylsalicylic Acid Cholesterol-lowering medication Antihypertensive medication Coronary artery disease medication Antidepressive medication

3860 3860 3860 3860 3860

280 (7.3%) 463 (12.0%) 993 (25.7%) 85 (2.2%) 244 (6.3%)

129 129 129 129 129

29 (22.5%) 53 (41.1%) 29 (22.5%) 28 (21.7%) 7 (5.4%)

1419 1419 1419 1419 1419

110 (7.8%) 201 (14.2%) 332 (23.4%) 40 (2.8%) 88 (6.2%)

<0.001 <0.001 0.178 <0.001 0.912

Men Past medical history Elevated blood pressure, hypertension Heart failure/Cardiac insufficiency Coronary artery disease Stroke or TIA Intermittent claudication High or elevated cholesterol or other dyslipidaemia Depression or other psychiatric illness Reduced mobility Other chronic disease

Women Past medical history Elevated blood pressure, hypertension Heart failure/Cardiac insufficiency Coronary artery disease Stroke or TIA Intermittent claudication High or elevated cholesterol or other dyslipidaemia Depression or other psychiatric illness Reduced mobility Other chronic disease

N

IFG/IGT

p-Value

FINDRISC = Finnish Diabetes Risk Score. CVD = Previous myocardial infarction or other artery disease. IFG/IGT = Diagnosed earlier with impaired fasting glucose (IFG) or impaired glucose tolerance (IGT).

diabetes prevention programmes in the DE-PLAN project have demonstrated low response rates, such as 2–52% in Saxon Diabetes Prevention Program [24] and 10% in Austria [25]. The main screening tool was the FINDRISC score. The test was simple and feasible. The high cut-off point of 15 or greater was used in FINDRISC because it was estimated that primary

health care services would probably not be able to cope with the number of people needing lifestyle intervention had a lower and more optimal cut-off point been used [26]. On the other hand, without the risk score as a first phase screening tool the number of OGTTs in the second phase would have risen markedly. Although time-consuming OGTT was considered the most appropriate method for clinical assessment of

236

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Table 3 – Glucose status and metabolic syndrome in the high-risk cohort by reason for referral to FIN-D2D. (Data on women with a history of gestational diabetes not shown.). Variable

FINDRISC

CVD

N

n (%)

1326 1326 1326 1326

523 (39.4%) 295 (22.2%) 289 (21.8%) 219 (16.5%)

206 206 206 206

72 (35.0%) 52 (25.2%) 49 (23.8%) 33 (16.0%)

942 942 942 942

199 (21.1%) 247 (26.2%) 295 (31.3%) 201 (21.3%)

Metabolic syndrome, by NCEP criteria IDF criteria

1652 1556

1071 (64.8%) 1197 (76.9%)

251 237

138 (55.0%) 152 (64.1%)

1143 1082

715 (62.6%) 824 (76.2%)

Women OGTT-status Normoglycaemia Impaired fasting glucose Impaired glucose tolerance Screen-detected type 2 diabetes

3071 3071 3071 3071

1685 (54.9%) 442 (14.4%) 652 (21.2%) 292 (9.5%)

106 106 106 106

56 (52.8%) 9 (8.5%) 26 (24.5%) 15 (14.2%)

1156 1156 1156 1156

302 (26.1%) 256 (22.1%) 353 (30.5%) 245 (21.2%)

Metabolic syndrome, by NCEP criteria IDF criteria

3745 3456

2252 (60.1%) 2362 (68.3%)

128 116

68 (53.1%) 65 (56.0%)

1387 1294

974 (70.2%) 1003 (77.5%)

Men OGTT-status Normoglycaemia Impaired fasting glucose Impaired glucose tolerance Screen-detected type 2 diabetes

N

IFG/IGT n (%)

N

n (%)

Chi-square test p-Value <0.001

0.009 <0.001

<0.001

<0.001 <0.001

FINDRISC = Finnish Diabetes Risk Score. CVD = Previous myocardial infarction or other artery disease. IFG/IGT = Diagnosed earlier with impaired fasting glucose (IFG) or impaired glucose tolerance (IGT).

glucose tolerance status and detecting undiagnosed diabetes [19,27–30]. A remarkable proportion of the screening was based on a history of the IFG or IGT showing that OGTTs had been carried out also before the start of the project in the area. Recruitment on the basis of a history of CVD was lower than expected. Prevalence of CVD was presumed to be high among middleaged and elderly patients in primary health care. Yet, only 9% of male and 2% of female participants were recruited on the basis of a history of a CVD event. One explanation for that can be that quite many of those having previously diagnosed CVD were, however, recruited or at least recorded to the high-risk cohort on the basis of FINDRISC score 15 or more, and another explanation can be that the risk of diabetes is not recognized or is underestimated in patients having diagnosed CVD. It is known that the guidelines on prevention of CVD are not being followed in everyday clinical practice [31]. Prevalence of glucose abnormalities, including screendetected diabetes among individuals tested with OGTT, was high. Merely one-third of men and half of women identified in FIN-D2D had normal glucose status. Comparison of the FIN-D2D cohort with data from the same region (subjects aged 45–74 years), reveals a lower overall prevalence of abnormal glucose regulation in the region (42% in men and 33% in women), IGT prevalence of 16% and 17% (men and women, respectively), and IFG prevalence of 10% and 5% (men and women, respectively) [32]. Thus, in accordance with the original aim of the screening, FIN-D2D successfully identified individuals with a particularly high risk for diabetes. The high selected cut-off point of the FINDRISC score detected individuals with relatively advanced deterioration of glucose metabolism, nearing diabetes and they also had many risk factors for CVD. Prediction of CVD as well as diabetes, by means

of the FINDRISC score has been observed previously [33]. Most people were screened opportunistically from ordinary primary health care patients, who were expected to have a poorer health than the general population, and the final registration of screening data might also have been focused on individuals at higher risk. Prevalence of untreated CVD risk factors in the high-risk cohort was high. Prevalence of coronary artery disease and elevated blood pressure was higher in the cohort ascertained for FIN-D2D than in the population of the project area in the national FINRISK surveys 2002 and 2007 [34,35]. An adverse CVD risk profile seen here should be potentially modifiable [36]. Yet, only few of the people in the high-risk cohort reported having medication for dyslipidemia or hypertension. Smoking rates in FIN-D2D were higher than in the DPS study (7%) [37], but lower than in the general Finnish population (21%) [35]; people willing to participate in interventions are known to smoke less than in the general population. A remarkable number of the people at high risk for diabetes also had a high predicted risk for CVD morbidity and mortality. In men in particular the predicted CVD risk was extraordinarily high. Deteriorating glucose homeostasis increased the predicted risk of CVD morbidity in both sexes but CVD mortality only in women, which may be caused by sex differences in participation in screening. Age, per se, was a strong predictor of CVD mortality and morbidity. Yet in the youngest age groups, and particularly in women, type 2 diabetes was associated with morbidity rates comparable to those in, or even above, the oldest age groups. Even though a recent meta-analysis did not support the hypothesis that diabetes is a “coronary heart equivalent” [38], large Finnish data showed that in diabetic women, the risk of CVD and total mortality is at least as high as in non-diabetic

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Fig. 2 – Proportion of men and women with high predicted 10-year risk of CVD event (Framingham score 20% or more) and fatal CVD (SCORE 5% or more) by age category and glucose tolerance. NGT = normal glucose tolerance, IFG = impaired fasting glucose, IGT = impaired glucose tolerance, ST2DM = screen-detected diabetes. In parenthesis number of individuals at high CVD risk versus total number of individuals in each age and glucose tolerance category.

women with a history of myocardial infarction, but not quite as high in men [39,40]. Furthermore, even a prediabetic state is a strong risk factor for CVD [41]. However, lifestyle intervention for prevention of diabetes alone does not necessarily translate to reduction of CVD risk. Interestingly, in the DPS study cohort, the risk of death was markedly lower in both the intervention and control groups than in the comparable population-basedstudy cohort with IGT, but no significant differences were found in CVD morbidity between the intervention and con-

trol groups, or between the combined DPS study cohort and population-based control group, when adjusted for risk factors [37]. Results of the China Da Qing Diabetes Prevention Study were also in line with the DPS study in this aspect [42]. Consequently, early screening for diabetes should be combined with screening for CVD risks. Undiagnosed diabetes is accompanied with a high CVD risk and needs to be detected early and targeted to intensive CVD risk factor management in middle-aged persons.

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This analysis is limited by the missing documentation on the exact number of people screened by the FINDRISC or otherwise. For practical reasons, in FIN-D2D we decided to analyze only a high-risk cohort for evaluation purposes. Yet, it is not known whether these individuals represent the healthier or unhealthier part of the whole high-risk population. The screening system implemented did not reach all the individuals, particularly men, at risk of diabetes and still it was particularly the men who would benefit from the prevention of diabetes and CVD. Given the high prevalence of abnormal glucose tolerance in the population [32] it is clear that the high-risk approach alone is not enough to stop the diabetes epidemic. Other concurrent strategies, as outlined earlier in the Finnish Diabetes Programme [14], above all an intensive population-level approach, are urgently needed. Nevertheless, convincing evidence is available from randomised controlled trials, demonstrating that high-risk individuals benefit from lifestyle interventions. Thus, the high-risk approach is scientifically proven and healthy lifestyle advice to such individuals is justified. In conclusion, this report of a first implementation of a national programme for diabetes prevention demonstrates that by means of opportunistic screening a high-risk cohort of over 10,000 was easily identified for intervention within primary care. The high-risk cohort was characterized by a high prevalence of screen-detected type 2 diabetes, abnormal glucose tolerance, multiple CVD risk factors, and high predicted CVD morbidity and mortality. Their management in primary health care is urgent and challenging.

Conflict of interest The authors state that they have no conflicts on interest.

Acknowledgements FIN-D2D was supported by financing from hospital districts of Pirkanmaa, Southern Ostrobothnia, North Ostrobothnia, Central Finland and Northern Savo, the Finnish National Public Health Institute, the Finnish Diabetes Association, the Ministry of Social Affairs and Health in Finland, Finland’s Slottery Machine Association, the Academy of Finland (grant number 129293), and Commission of the European Communities, Directorate C-Public Health (grant agreement number 2004310) in cooperation with the FIN-D2D Study Group, and the Steering Committee: Huttunen J, Kesäniemi A, Kiuru S, Niskanen L, Oksa H, Pihlajamäki J, Puolakka J, Puska P, Saaristo T, Vanhala M, and Uusitupa M.

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