Abstract

Aims

Differences in cardiovascular risk factors across Europe provide an opportunity to examine the impact of adiposity on the frequency of diabetes and cardiovascular disease (CVD).

Methods and results

The International Day for Evaluation of Abdominal obesity (IDEA) study evaluated the prevalence of abdominal obesity, elevated body mass index (BMI), and other cardiometabolic risk factors among primary care patients. Abdominal obesity predicted increased diabetes risk, despite socio-economic, demographic, and risk factor differences. Cardiovascular disease was at least two-fold more frequent in Eastern Europe vs. Northwest Europe (P < 0.0001) and 2.5-fold more vs. Southern Europe (P < 0.0001). Waist circumference (WC) predicted increased (P < 0.0001) age- and BMI-adjusted risks of CVD and diabetes. In women, odds ratios (95% confidence intervals) for CVD per 1 SD increase in WC were: Northwest Europe 1.28 (1.18–1.40); Southern Europe 1.26 (1.16–1.37); and Eastern Europe 1.10 (1.03–1.18). Values for diabetes were 1.72 (1.58–1.88), 1.45 (1.35–1.56), and 1.59 (1.46–1.73), with similar findings in men.

Conclusion

Abdominal obesity impacted similarly on the frequency of diabetes across Europe, despite regional differences in cardiovascular risk factors and CVD rates. Increasing abdominal obesity may offset future declines in CVD, even where CVD rates are lower.

Introduction

It is uncertain how socio-demographic and economic changes may influence the development of diabetes and cardiovascular disease (CVD) within Europe, particularly in Eastern Europe.1–5 There are major regional differences in the prevalence of cardiovascular risk factors and CVD incidence in Europe6–12 and the prevalence of CVD in Eastern Europe is higher than elsewhere in Europe.8–11,13 Adiposity has emerged in recent years as a key risk factor for both diabetes and CVD, and elevated waist circumference (WC) predicts risk beyond that of total adiposity as reflected by body mass index (BMI).14–23 The substantial regional differences in ‘classical’ cardiovascular risk factors across Europe provide a unique opportunity to examine the incremental impact of adiposity on diabetes and on CVD.

The International Day for Evaluation of Abdominal obesity (IDEA) study was a large, non-interventional, cross-sectional study that evaluated the prevalence of abdominal obesity and the relationship between anthropometric measures (WC and BMI) and cardio-metabolic risk factors in a primary care setting.24 A graded increase in risk of CVD and diabetes with both increasing BMI and WC for both genders was demonstrated, with a stronger relationship for WC.25

We report the findings in 91 246 patients from three European regions. Across these regions, the prevalence of CVD differs by more than three-fold. In particular, we have examined the consistency of impact of abdominal obesity on CVD and diabetes across European regions, in the context of substantial socio-economic, demographic, lifestyle, and risk factor disparities and variations in the proportion of risk attributable to individual factors.

Methods

Study design

IDEA was an international, non-interventional, cross-sectional study of patients who consulted a primary care physician (PCP). It was not designed to reflect the prevalence of risk factors among communities who remain independent of primary care contact. The rationale and design of the study have been described previously.24 Briefly, an exhaustive list of active PCPs was prepared for each country and each physician was assigned a unique random number. Countries were divided into commonly used administrative regions and physicians selected in the order of their randomly assigned number; selection was weighted according to the percentage of PCPs practising in different regions. Selection continued until the number of PCPs agreeing to participate in the study reached the level needed to recruit the required number of patients. All patients, aged 18–80 years consulting their PCP for any reason, were invited to participate on 2 half-days (between 9 May and 6 July 2005), pre-defined for each country. Women known to be pregnant were excluded.

Age, gender, level of education, professional activity, smoking status, and presence of known CVD (coronary heart disease, stroke, or prior revascularization), diabetes (type 1 or type 2), dyslipidaemia, or hypertension were recorded according to a written, standardized protocol. After appropriate training, a tape measure was used to determine WC midway between the lowest rib and the iliac crest. Weight and height were measured and BMI calculated.

All participating patients provided written, informed consent. Ethics Committee approval was obtained for each participating site.

Sample size estimate

Sample sizes were determined for each country according to the estimated frequency of abdominal obesity in primary care practice. Assuming an expected frequency of abdominal obesity of ∼50%, we calculated that 1100–9600 patients were needed in each country to estimate the frequency with a precision of 1–3%.

Statistics

The SAS statistical package [version 8·2 (SAS Institute Inc., Cary, NC, USA)] was used.

European countries were grouped into three geographical regions: Northwest Europe (Austria, Belgium, Denmark, Finland, France, Germany, Ireland, The Netherlands, Norway, Sweden, and Switzerland), Southern Europe (Greece, Italy, Portugal, Spain, and Turkey), and Eastern Europe (Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Russia, Slovakia, Slovenia, and Ukraine).

Regional frequencies of CVD and diabetes were age-adjusted. For analyses of frequencies of CVD (overall and non-diabetic populations) and diabetes by gender-specific tertiles of WC and BMI categories, data were adjusted by age, region, smoking status, and presence of hypertension (CVD only). Odds ratios (OR) and their 95% confidence intervals (CI) for CVD and diabetes were calculated for an increase of 1 standard deviation (SD) in WC and BMI, and using multiple logistic regression model adjusted for age, linearity was tested by adding a squared terms. To study the independence of either factor, WC was adjusted for BMI and BMI was adjusted for WC.

The population attributable risk (PAR) of CVD and diabetes for abdominal obesity were calculated using the formula: PAR = pd(RR − 1)/RR, where pd is the proportion of cases (CVD or diabetes) exposed to abdominal obesity and RR the age-adjusted relative risk between abdominal obesity and CVD or diabetes.26 Confidence intervals of PAR were estimated using bootstrap estimators. Abdominal obesity was defined as WC > 102 cm for males and >88 cm for females.27

Results

Study population

The study population comprised 37 437 men and 53 809 women from 27 countries (97% of eligible patients agreed to participate). The most marked difference between patients in each region was a lower prevalence of hypertension in Northwest Europe and a substantially higher prevalence in Eastern Europe (Table 1). There were also differences in the prevalence of physician-reported dyslipidaemia and in patient-reported smoking. Patients from Southern Europe had the highest WC on average; BMI measurements were similar in Southern Europe and in Eastern Europe, and in both instances were higher than in Northwest Europe.

Table 1

Demographic data by region

 Northwest Europe (1303 centres)
 
Southern Europe (1771 centres)
 
Eastern Europe (795 centres)
 
 Men (n = 12 796) Women (n = 16 786) Men (n = 13 465) Women (n = 17 824) Men (n = 11 176) Women (n = 19 199) 
Median (25th, 75th percentile) 
 Age (years) 53.0 (40.0, 66.0) 52.0 (38.0, 65.0) 55.0 (42.0, 68.0) 53.0 (40.0, 65.0) 50.0 (37.0, 62.0) 51.0 (38.0, 63.0) 
 Waist circumference (cm)a 97.0 (89.0, 106.0) 87.0 (77.0, 98.0) 99.0 (91.0, 107.0) 91.0 (81.0, 101.0) 96.0 (88.0, 105.0) 89.0 (78.0, 100.0) 
 Body mass index (kg/m226.6 (24.1, 29.7) 25·4 (22·4, 29·4) 27·8 (25·1, 30·7) 27.2 (23.7, 31.2) 27.1 (24.2, 30.3) 27·0 (23·1, 31·3) 

 
Mean (SD) 
 Age (years) 52.3 (16.1) 51.2 (16.5) 53.8 (15.9) 52.4 (15.8) 49.5 (16.0) 50.7 (15.9) 
 Waist circumference (cm)a 97.8 (13.5) 88.3 (14.8) 99.4 (12.9) 91.3 (14.7) 96.9 (13.7) 89.7 (15.7) 
 Body mass index (BMI) (kg/m227.2 (4.6) 26.4 (5.6) 28.2 (4.5) 27.9 (5.6) 27.5 (4.8) 27.6 (6.0) 

 
Frequency (%) 
 Overweight (BMI 25–30 kg/m243.5 30.4 45.9 33.9 40.9 30.3 
 Obese (BMI ≥ 30 kg/m223.0 22.7 30.2 31.7 27.5 32.4 
 Known hypertensionb 36.4 30.3 39.8 36.0 45.6 46.1 
 CVD 19.2 10.8 17.3 9.7 27.9 26.3 
 Diabetes 12.9 8.8 15.4 12.0 11.4 10.3 
 Dyslipidaemia 31.1 24.0 34.6 30.0 31.2 29.9 

 
Smoking status (%) 
 Never 37.1 58.7 34.4 69.7 33.0 68.5 
 Former 35.6 20.6 37.0 13.7 31.9 13.5 
 Current 27.3 20.7 28.6 16.6 35.1 18.0 
 Northwest Europe (1303 centres)
 
Southern Europe (1771 centres)
 
Eastern Europe (795 centres)
 
 Men (n = 12 796) Women (n = 16 786) Men (n = 13 465) Women (n = 17 824) Men (n = 11 176) Women (n = 19 199) 
Median (25th, 75th percentile) 
 Age (years) 53.0 (40.0, 66.0) 52.0 (38.0, 65.0) 55.0 (42.0, 68.0) 53.0 (40.0, 65.0) 50.0 (37.0, 62.0) 51.0 (38.0, 63.0) 
 Waist circumference (cm)a 97.0 (89.0, 106.0) 87.0 (77.0, 98.0) 99.0 (91.0, 107.0) 91.0 (81.0, 101.0) 96.0 (88.0, 105.0) 89.0 (78.0, 100.0) 
 Body mass index (kg/m226.6 (24.1, 29.7) 25·4 (22·4, 29·4) 27·8 (25·1, 30·7) 27.2 (23.7, 31.2) 27.1 (24.2, 30.3) 27·0 (23·1, 31·3) 

 
Mean (SD) 
 Age (years) 52.3 (16.1) 51.2 (16.5) 53.8 (15.9) 52.4 (15.8) 49.5 (16.0) 50.7 (15.9) 
 Waist circumference (cm)a 97.8 (13.5) 88.3 (14.8) 99.4 (12.9) 91.3 (14.7) 96.9 (13.7) 89.7 (15.7) 
 Body mass index (BMI) (kg/m227.2 (4.6) 26.4 (5.6) 28.2 (4.5) 27.9 (5.6) 27.5 (4.8) 27.6 (6.0) 

 
Frequency (%) 
 Overweight (BMI 25–30 kg/m243.5 30.4 45.9 33.9 40.9 30.3 
 Obese (BMI ≥ 30 kg/m223.0 22.7 30.2 31.7 27.5 32.4 
 Known hypertensionb 36.4 30.3 39.8 36.0 45.6 46.1 
 CVD 19.2 10.8 17.3 9.7 27.9 26.3 
 Diabetes 12.9 8.8 15.4 12.0 11.4 10.3 
 Dyslipidaemia 31.1 24.0 34.6 30.0 31.2 29.9 

 
Smoking status (%) 
 Never 37.1 58.7 34.4 69.7 33.0 68.5 
 Former 35.6 20.6 37.0 13.7 31.9 13.5 
 Current 27.3 20.7 28.6 16.6 35.1 18.0 

aDefinition of increased waist circumference according to the National Cholesterol Education Program Adult Treatment Panel III is >102 cm for men and >88 cm for women, and for the International Diabetes Federation is ≥94 cm for men and ≥80 cm for women.

bPhysician recorded and not defined.

Frequencies of cardiovascular disease and diabetes

The frequency of CVD (Table 1) was higher in males than in females in both Northwest Europe (19.2 vs. 10.8%) and Southern Europe (17.3 vs. 9.7%). However, CVD was substantially more frequent in Eastern Europe in both males (27.9%) and females (26.3%) compared with other regions. The risk of CVD in Eastern European women was substantially higher than that in females or males elsewhere in Europe (Figure 1).

Figure 1

Age-adjusted frequency of cardiovascular disease and diabetes in Northwest (NW), Southern (S), and Eastern (E) Europe. Mean values with 95% confidence intervals: males dark columns and grey highlight, females light columns and no highlight.

Figure 1

Age-adjusted frequency of cardiovascular disease and diabetes in Northwest (NW), Southern (S), and Eastern (E) Europe. Mean values with 95% confidence intervals: males dark columns and grey highlight, females light columns and no highlight.

Populations differed by age across the regions. The age-adjusted frequency of CVD (Figure 1) was substantially higher in Eastern Europe compared with Southern Europe and Northwest Europe (P < 0.0001). In contrast, the frequency of diabetes was similar across the three regions, irrespective of adjustment for age (Table 1 and Figure 1).

Diabetes: impact of waist circumference and body mass index

Increasing WC was associated with a greater risk of diabetes, irrespective of gender. In each geographic region, a 1 SD change in WC or BMI was associated with a highly significant increase in the frequency of diabetes (Table 2). The association remained significant even when WC was corrected for BMI (OR ranging from 1.3 to 1.5 in men and from 1.5 to 1.8 in women) or when BMI was corrected for WC (OR ranging from 1.2 to 1.4 in men and 1.3 in women). A logistic regression plot of the frequency of diabetes according to WC demonstrated a strikingly similar association in Northwest Europe, Southern Europe, and Eastern Europe for both men and women (Figure 2).

Figure 2

Regional frequency of diabetes for men and women according to distribution of waist circumference (without adjustment for body mass index). For values with waist circumference adjusted for body mass index and body mass index adjusted for waist circumference, see Table 2.

Figure 2

Regional frequency of diabetes for men and women according to distribution of waist circumference (without adjustment for body mass index). For values with waist circumference adjusted for body mass index and body mass index adjusted for waist circumference, see Table 2.

Table 2

Age-adjusted odds ratios (95% confidence interval) for cardiovascular disease and diabetes according to waist circumference and body mass index, by region

 Age-adjusted OR (95% CI) for CVD for a 1 SD change in WC or BMI
 
 Northwest Europe Southern Europe Eastern Europe 
Men 
 WC adjusted for BMI 1.17 (1.08–1.27), P = 0.0001 1.29 (1.20–1.39), P = 0.0001 1.20 (1.10–1.31), P < 0.0001 
 BMI adjusted for WC 1.12 (1.04–1.22), P = 0.004 1.02 (0.95–1.09) 1.22 (1.12–1.33), P < 0.0001 

 
Women 
 WC adjusted for BMI 1.28 (1.18–1.40), P < 0.0001 1.26 (1.16–1.37), P < 0.0001 1.10 (1.03–1.18), P = 0.0052 
 BMI adjusted for WC 1.08 (1.00–1.18) 1.04 (0.96–1.12) 1.43 (1.34–1.52), P < 0.0001 

 
 Age-adjusted OR (95% CI) for diabetes for a 1 SD change in WC or BMI 
 
 
 Northwest Europe Southern Europe Eastern Europe 

 
Men 
 WC adjusted for BMI 1.35 (1.24–1.48), P < 0.0001 1.29 (1.20–1.39), P< 0.0001 1.46 (1.32–1.63), P < 0.0001 
 BMI adjusted for WC 1.37 (1.26–1.49), P < 0.0001 1.17 (1.09–1.25), P < 0.0001 1.30 (1.17–1.43), P < 0.0001 

 
Women 
 WC adjusted for BMI 1.72 (1.58–1.88), P < 0.0001 1.45 (1.35–1.56), P < 0.0001 1.59 (1.46–1.73), P < 0.0001 
 BMI adjusted for WC 1.32 (1.21–1.43), P < 0.0001 1.30 (1.22–1.39), P < 0.0001 1.31 (1.21–1.41), P < 0.0001 
 Age-adjusted OR (95% CI) for CVD for a 1 SD change in WC or BMI
 
 Northwest Europe Southern Europe Eastern Europe 
Men 
 WC adjusted for BMI 1.17 (1.08–1.27), P = 0.0001 1.29 (1.20–1.39), P = 0.0001 1.20 (1.10–1.31), P < 0.0001 
 BMI adjusted for WC 1.12 (1.04–1.22), P = 0.004 1.02 (0.95–1.09) 1.22 (1.12–1.33), P < 0.0001 

 
Women 
 WC adjusted for BMI 1.28 (1.18–1.40), P < 0.0001 1.26 (1.16–1.37), P < 0.0001 1.10 (1.03–1.18), P = 0.0052 
 BMI adjusted for WC 1.08 (1.00–1.18) 1.04 (0.96–1.12) 1.43 (1.34–1.52), P < 0.0001 

 
 Age-adjusted OR (95% CI) for diabetes for a 1 SD change in WC or BMI 
 
 
 Northwest Europe Southern Europe Eastern Europe 

 
Men 
 WC adjusted for BMI 1.35 (1.24–1.48), P < 0.0001 1.29 (1.20–1.39), P< 0.0001 1.46 (1.32–1.63), P < 0.0001 
 BMI adjusted for WC 1.37 (1.26–1.49), P < 0.0001 1.17 (1.09–1.25), P < 0.0001 1.30 (1.17–1.43), P < 0.0001 

 
Women 
 WC adjusted for BMI 1.72 (1.58–1.88), P < 0.0001 1.45 (1.35–1.56), P < 0.0001 1.59 (1.46–1.73), P < 0.0001 
 BMI adjusted for WC 1.32 (1.21–1.43), P < 0.0001 1.30 (1.22–1.39), P < 0.0001 1.31 (1.21–1.41), P < 0.0001 

Cardiovascular disease: impact of waist circumference and body mass index

Irrespective of geographical region, the adjusted OR for CVD was strongly associated with increased WC and with increased BMI (Table 2). Waist circumference remained independently associated with CVD, even after correction for BMI (OR ranging from 1.2 to 1.3 among men and from 1.1 to 1.3 among women) (Table 2).

Cardiovascular disease was more frequent in Eastern Europe for any given WC, irrespective of gender (Figure 3). Indeed, whereas the relationship between abdominal obesity and CVD was similar for Northwest Europe and Southern Europe, the curves were displaced for Eastern Europe (Figure 3): for any given level of abdominal obesity, the frequency of CVD was higher, in both males and females, compared with Northwest Europe and Southern Europe.

Figure 3

Regional frequency of cardiovascular disease for men and women according to distribution of waist circumference (without adjustment for body mass index). For values with waist circumference adjusted for body mass index and body mass index adjusted for waist circumference, see Table 2.

Figure 3

Regional frequency of cardiovascular disease for men and women according to distribution of waist circumference (without adjustment for body mass index). For values with waist circumference adjusted for body mass index and body mass index adjusted for waist circumference, see Table 2.

Body mass index was also independently associated with CVD after correction for WC, but this association was most evident in Eastern Europe (OR 1.22 in men and 1.43 in women). The interaction term between region and BMI was significant with a P-value <0.001 for both males and females. The frequency of CVD across Europe adjusted for age, region, smoking status, and presence of hypertension is shown in Table 3: irrespective of the presence or absence of diabetes, patients in the highest tertile of BMI had higher rates of CVD for each category of WC. A similar relationship was observed for BMI, WC, and rates of diabetes (Figure 4 and Table 4).

Figure 4

Frequency of diabetes in men and women across Europe adjusted for age, region, and smoking status, by gender-specific waist circumference tertiles and body mass index categories. Table 4 below the figure shows the values by category of waist circumference and gender, and 95% confidence intervals.

Figure 4

Frequency of diabetes in men and women across Europe adjusted for age, region, and smoking status, by gender-specific waist circumference tertiles and body mass index categories. Table 4 below the figure shows the values by category of waist circumference and gender, and 95% confidence intervals.

Table 3

Frequency of cardiovascular disease across Europe adjusted for age, region, smoking status, and presence of hypertension by gender-specific waist circumference tertiles and body mass index categories

 Overall population
 
Patients without diabetes
 
Male WC tertile <92 ≥92 to <103 ≥103 <92 ≥92 to <103 ≥103 
BMI < 25 13.6 (12.7–14.5) (n = 8365) 14.1 (12.8–15.6) (n = 2436) 16.2 (12.2–21.3) (n = 233) 11.4 (10.5–12.3) (n = 7900) 11.8 (10.6–13.2) (n = 2166) 12.9 (9.2–17.9) (n = 191) 
BMI ≥ 25–<30 11.9 (10.6–13.3) (n = 3035) 14.0 (13.2–14.8) (n = 8874) 16.5 (15.5–17.7) (n = 4408) 10.2 (9.0–11.5) (n = 2841) 11.9 (11.1–12.6) (n = 7772) 13.6 (12.5–14.7) (n = 3574) 
BMI ≥ 30 17.3 (12.1–24.1) (n = 186) 15.4 (13.6–17.3) (n = 1455) 17.0 (16.2–17.9) (n = 8445) 15.9 (10.7–23.0) (n = 164) 13.0 (11.2–14.9) (n = 1247) 14.0 (13.2–14.9) (n = 6412) 

 
Female WC tertile <82 ≥82 to <96 ≥96 <82 ≥82 to <96 ≥96 

 
BMI < 25 7.7 (7.1–8.3) (n = 14 823) 8.8 (8.1–9.6) (n = 5682) 8.9 (7.2–10.9) (n = 659) 6.4 (5.9–6.9) (n = 14 414) 7.3 (6.6–8.0) (n = 5289) 7.0 (5.5–8.8) (n = 566) 
BMI ≥ 25–<30 9.4 (8.2–10.7) (n = 2259) 8.2 (7.7–8.8) (n = 9644) 9.6 (8.9–10.3) (n = 5067) 7.5 (6.4–8.7) (n = 2135) 6.9 (6.4–7.5) (n = 8794) 7.9 (7.2–8.7) (n = 4202) 
BMI ≥ 30 11.1 (7.0–17.3) (n = 160) 9.8 (8.9–10.9) (n = 2792) 11.2 (10.6–11.8) (n = 12723) 8.5 (4.8–14.5) (n = 141) 8.2 (7.3–9.2) (n = 2498) 9·0 (8.4–9.6) (n = 9850) 
 Overall population
 
Patients without diabetes
 
Male WC tertile <92 ≥92 to <103 ≥103 <92 ≥92 to <103 ≥103 
BMI < 25 13.6 (12.7–14.5) (n = 8365) 14.1 (12.8–15.6) (n = 2436) 16.2 (12.2–21.3) (n = 233) 11.4 (10.5–12.3) (n = 7900) 11.8 (10.6–13.2) (n = 2166) 12.9 (9.2–17.9) (n = 191) 
BMI ≥ 25–<30 11.9 (10.6–13.3) (n = 3035) 14.0 (13.2–14.8) (n = 8874) 16.5 (15.5–17.7) (n = 4408) 10.2 (9.0–11.5) (n = 2841) 11.9 (11.1–12.6) (n = 7772) 13.6 (12.5–14.7) (n = 3574) 
BMI ≥ 30 17.3 (12.1–24.1) (n = 186) 15.4 (13.6–17.3) (n = 1455) 17.0 (16.2–17.9) (n = 8445) 15.9 (10.7–23.0) (n = 164) 13.0 (11.2–14.9) (n = 1247) 14.0 (13.2–14.9) (n = 6412) 

 
Female WC tertile <82 ≥82 to <96 ≥96 <82 ≥82 to <96 ≥96 

 
BMI < 25 7.7 (7.1–8.3) (n = 14 823) 8.8 (8.1–9.6) (n = 5682) 8.9 (7.2–10.9) (n = 659) 6.4 (5.9–6.9) (n = 14 414) 7.3 (6.6–8.0) (n = 5289) 7.0 (5.5–8.8) (n = 566) 
BMI ≥ 25–<30 9.4 (8.2–10.7) (n = 2259) 8.2 (7.7–8.8) (n = 9644) 9.6 (8.9–10.3) (n = 5067) 7.5 (6.4–8.7) (n = 2135) 6.9 (6.4–7.5) (n = 8794) 7.9 (7.2–8.7) (n = 4202) 
BMI ≥ 30 11.1 (7.0–17.3) (n = 160) 9.8 (8.9–10.9) (n = 2792) 11.2 (10.6–11.8) (n = 12723) 8.5 (4.8–14.5) (n = 141) 8.2 (7.3–9.2) (n = 2498) 9·0 (8.4–9.6) (n = 9850) 

n, number of patients in each category of waist circumference. 95% confidence intervals are given in brackets.

Table 4

Diabetes: age, region, and smoking adjusted across sex-specific tertiles of waist circumference and body mass index

graphic 
graphic 

Proportion of risk attributable to increased waist circumference

The age-adjusted PAR of CVD for abdominal obesity was higher in Eastern Europe than in the other regions [PAR for males was 10.2% (95% CI 8.2–12.0) in Eastern Europe, 6.3% (95% CI 4.7–7.8) in Northwest Europe, and 7.0% (95% CI 5.7–8.4) in Southern Europe]. The corresponding PAR in females was 11.8% (95% CI 10.1–13.2) in Eastern Europe, 4.9% (95% CI 3.9–6.0) in Northwest Europe, and 3.7% (95% CI 2.6–4.7) in Southern Europe. Overall, however, the PARs were low, suggesting that the frequency of CVD would be reduced by <10% if abdominal obesity were eliminated. There was no clear difference between regions in the PAR of diabetes for abdominal obesity in women but a trend for a lower PAR in men in Southern Europe. The PAR for males was 11.8% (95% CI 10.3–13.4) in Eastern Europe, 12.1% (95% CI 10.7–13.5) in Northwest Europe, and 7.3% (95% CI 6.0–8.7) in Southern Europe. The corresponding PAR in females was 10.3% (95% CI 9.3–11.3) in Eastern Europe, 10.8% (95% CI 9.8–11.8) in Northwest Europe, and 10.8% (95% CI 9.8–11.9) in Southern Europe. The data suggest that frequency of diabetes could therefore be reduced by ∼10% by eliminating abdominal obesity, but this may underestimate the longer term impact.

Discussion

We studied an ambulant population in primary care because such patients could have access to appropriate dietary, lifestyle, and therapeutic interventions. Our very large cross-sectional study of ambulant patients attending a PCP demonstrated a striking discordance in the frequency of both CVD and diabetes across major geographic regions. The impact of a 1 SD increase in WC (or BMI) on the frequency of diabetes was similar across geographic regions even after age adjustment. The impact of adiposity on diabetes was strikingly similar across the different regions of Europe, despite socio-economic, demographic, and risk factor differences. This lack of regional differences suggests that abdominal obesity has a major influence on the development of diabetes. In contrast, although there was a tight relationship between adiposity and CVD in individual regions of Europe, there were marked regional differences in disease frequency. In both men and women, the rate of CVD in Eastern Europe was substantially higher for any given level of abdominal adiposity compared with Northwest or Southern Europe [two-fold higher than Northwest Europe (P < 0.0001) and 2.5-fold higher than Southern Europe (P < 0.0001)]. These findings suggest that adiposity adds a consistent incremental adverse influence to the background cardiovascular risk profile. Diabetes itself is a well recognized and potent factor driving the development of CVD and any ‘direct’ effect of adiposity on cardiovascular events is likely to be amplified by the resulting increase in diabetes after a variable time period. Hence, the PAR at the time of the study may underestimate the eventual impact on diabetes and CVD.

The finding that WC is a predictor of CVD irrespective of regional differences in the background CVD prevalence confirms and extends the findings of previous studies showing that abdominal obesity is an independent risk factor for CVD events irrespective of BMI. For instance, the case-controlled INTERHEART study showed that abdominal obesity (high waist-to-hip ratio) significantly increased the risk of myocardial infarction.28 In addition, the EPIC-Norfolk study also showed that increased WC was predictive of an increased risk of coronary heart disease independently of increased BMI in 24 508 subjects followed for 9.1 years.14 Other studies have reached similar conclusions.15–24

The increased CVD risk with an elevated WC is likely to result, at least in part, from excess visceral adiposity, the latter being predictive of insulin resistance and of an atherogenic, pro-thrombotic, and inflammatory profile.17 Thus, despite marked differences in the background prevalence of CVD across European regions, the present analyses suggest that abdominal obesity is a serious additional burden to the cardiovascular health that should become an important target for clinical and public health programmes.

Differences in recognized cardiovascular risk factors may account for some of the marked regional difference in diabetes and CVD, consistent with the findings of INTERHEART28 and previous studies in Eastern Europe.2–5 The most striking difference on risk factor prevalence was the much higher frequency of hypertension in Eastern Europe compared with Northwest Europe and Southern Europe. Differences in regional smoking rates did not appear to be the main driver of the variation in disease prevalence. Other influences not addressed by IDEA may contribute, such as stressful socio-demographic changes.29

The PAR values for CVD associated with abdominal obesity in IDEA were lower than the adjusted value of 33.7% from INTERHEART.28 This may be explained partly by the lower OR for CVD associated with abdominal obesity in IDEA (1.3–1.4) compared with INTERHEART (2.2). Also, the proportion of CVD exposed to abdominal obesity in IDEA was lower (25%) in Northwest and Southern Europe, compared with INTERHEART (46%). Differences between patients (primary care at a relatively early stage of CVD in IDEA vs. patients hospitalized for a first myocardial infarction in INTERHEART) and to the definition of abdominal obesity (upper two tertiles of WC in INTERHEART compared with WC > 102/88 cm in IDEA) probably underlie much of these differences. Nevertheless, our findings on the frequency of adiposity and obesity in Europe were consistent with previous reports, despite such differences in study designs. As a result of the very large sample size and the steps taken to avoid bias in the identification of PCP and recruitment of patients, the IDEA population is likely to be representative of primary care populations in the respective regions.

The impact of adiposity on diabetes was strikingly similar across the regions of Europe, consistent with a major influence on diabetes development. In contrast, there was a tight relationship between adiposity and CVD but marked regional differences in disease frequency. Although a highly consistent relationship was observed between increasing WC and diabetes, irrespective of region and gender, a displaced relationship was observed for CVD. For any given measure of WC, the frequency of CVD was higher, in both men and women, in Eastern Europe compared with other regions. This suggests that adiposity adds a consistent incremental adverse influence on top of the background cardiovascular risk profile, but additional risk factors amplify this relationship in Eastern Europe compared with other regions.

Limitations of our study include the use of self-reported risk factors (e.g. smoking), which may be under-reported, and physician-reported observations (e.g. hypertension and dyslipidaemia), which may underestimate the true prevalence. The threshold for seeking medical attention may differ by geographic region. As a result, our findings may underestimate the strength of the relationship between risk factors and the disease conditions. Despite these cautions, our findings provide the basis for future studies, including those that will aim to determine a mechanism of these observations as well as to examine the link between diabetes development and cardiovascular outcome.

Our findings demonstrated that increased WC predicted an increased frequency of diabetes and CVD irrespective of gender and across the geographic regions of Europe. The findings have important implications. The growth in abdominal and generalized obesity is not limited to specific regions or subgroups of the population. The high frequency of abdominal obesity presents a major challenge irrespective of the different socio-demographic characteristics across Europe and the impact on diabetes may offset future declines in CVD prevalence, even in regions with lower rates of CVD. Population-wide policies are required across Europe at the pre-clinical stage of disease, to target the drivers of cardiovascular risk and to tackle the impact of socio-economic inequality. The high levels of compliance with dietary, exercise, smoking cessation, and pharmacologic therapy achieved in the Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE) trial of patients with stable angina demonstrate that ambulant populations can be amenable to change.30 However, thus far, initiatives in the broader medical community and in populations have been less successful. The impressive impact of smoking cessation legislation on cardiovascular health provides an encouraging example of what can be achieved,31 but much remains to be done if we are to prevent a rise in the future burden of cardiometabolic disease.

Funding

The IDEA study was funded by an educational grant from sanofi-aventis.

Conflict of interest: A.-J.R. is an employee of sanofi-aventis; other authors have provided consultancy services to sanofi-aventis. K.A.A.F. has received grant funding and honoraria from sanofi-aventis, BMS, and GSK. J.-P.D. has received research grants from Glaxo-SmithKline and sanofi aventis, has served as a consultant for MSD and sanofi aventis, and has served on the speakers' bureau for abbott, AstraZeneca, Solvay Pharma, Glaxo-SmithKline, Pfizer and sanofi aventis.

Acknowledgements

The interpretations of the data and the decision to submit the manuscript were made by members of the Steering Committee of the study, independently of the funding source. The authors participated in the conduct of the study and have seen and approved the final version of this manuscript. K.A.A.F. led the interpretation of the data and wrote the manuscript. J.E.D. and J.-P.D. made a substantial contribution to the analysis and interpretation of the data and the drafting of the manuscript. A.-J.R. and S.B. contributed to the analysis of the data.

Appendix

Members of the IDEA Steering Committee (aExecutive Steering Committee Members) were: Beverley Balkaua (France), J.-P.D.a (Canada), Steven Haffnera (USA), Phil Barter (Australia), Jean-Pierre Bassand (France), J.E.D. (UK), K.A.A.F. (UK), Luc Van Gaal (Belgium), Christine Massien (France), A.-J.R. (France), Sydney Smith (USA), Chee-Eng Tan (Singapore), and Hans-Ulrich Wittchen (Germany).

IDEA National Co-ordinators and Investigators were: Austria: E. Rebhandl; Belgium: G. De Backer; Bulgaria: S. Zaharieva; Czech Republic: V. Hainer; Denmark: O.L. Svendsen; Estonia: M. Vigimaa; Finland: M. Savolainen; France: P. Amouyel; Germany: H.U. Wittchen; Greece: S. Raptis and D. Kremastinos; Hungary: L. Halmy; Ireland: V. Maher; Italy: M. Carrruba; Latvia: A. Kalveli and G. Bahs; Lithuania: V. Kasiulevicius; Norway: T. Pedersen; Poland: K. Narkiewicz; Portugal: V. Gil; Russia: R.G. Oganov; Slovakia: A. Dukát; Slovenia: I. Švab; Spain: B. Moreno and F. Casanueva; Sweden: Å. Sjöholm; Switzerland: R. Darioli, A. Gallino and G. Noll; The Netherlands: F.L.J. Visseren; Turkey: V. Sansoy; Ukraine: A. Parkhomenko.

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Supplementary data

Comments

1 Comment
Interpretations of the IDEA CVD-Diabetes study
24 March 2010
Keith AA Fox

We thank Dr Onat for the comments and for the insights from the Turkish Adult Risk Factor study.

We agree that the findings of the IDEA study indicate that additional risk factors must contribute to the displaced curve in figure 3 of our manuscript (for any given level of obesity there was more CVD in Eastern Europe than elsewhere). However we do not have evidence to support Dr Onat's proposal that this is due to due to subclinical inflammation and dysfunction of HDL and apolipoproteins, although this is could be the subject of further study.

Dr Onat also suggests that waist circumference appears to be the primary factor in men while body mass index (BMI) is so in women because tertiles of waist girth do not show a linear relationship in obese women whereas BMI categories do. Dr Onat and colleagues reported that visceral adiposity in men and body fat mass in women seem to be of greater relevance in cardiometabolic risk in Turks. However we caution that this may be an over-interpretation of our data but we agree that gender differences need further exploration.

We do take the point that the characteristics of the Turkish population cited by Dr Onat suggest that the population more closely resemble the Eastern rather than the Southern European group. We must point out that the Geographic groupings were undertaken to explore relationships between CVD risk and obesity rather than to make statements about the characteristics of specific countries.

Conflict of Interest:

None beyond those in the original manuscript

Submitted on 24/03/2010 8:00 PM GMT