How well do anthropometric indices correlate with cardiovascular risk factors? A cross-sectional study in Croatia

Background

AIMS:

To assess the correlation between AI of obesity and CV factor, total CV and stroke risk scores in a nationally representative sample of the Croatian adult population registered with general practitioners (GP). Hypotheses were that we would observe differences in obesity and overweight across regions of Croatia (coastal/inland) and by settlement size (urban/rural) and a higher predictive value of WtHR for CV risk factors, total CV and stroke risk compared to others (BMI, WC, WHR).

Material and Methods

DESIGN:

This is a cross-sectional part of the CRISIC-fm study, conducted from May to July 2008 in 59 GP practices in Croatia. The study population comprised 2467 participants aged ≥40.

This was a 2-stage study: 1. a representative sample of GP practices was selected randomly by 4-stage stratified sampling method [county, region (coastal, inland, urban, rural and number of insured people in the GP’s care in 2007]. The principal investigators of each stratum were initially selected from a random list. If the principal investigator refused to participate, the next GP on the list was invited. The list was created according to the location closest to the principal investigator, in the same stratum. GPs enrolled the first 55 consenting patients aged ≥40 (one per day), who visited the practice during the study period for whatever reason. Exclusion criteria were communication disability (dysphasia, aphasia), severe dementia or mental illness, and disease with estimated life expectancy of less than 6 months. The study was approved by the Research Ethics Committee of the Medical School of Zagreb.

All participants gave written consent.

QUESTIONNAIRES AND MEASUREMENTS:

A 140-item CRISIC-fm standardized questionnaire was developed for the purpose of the project and was validated in a pilot study. Participants were interviewed (face-to-face) by trained researchers. The measures analyzed were: height and weight (the mean of 2 measurements on standardized anthropometric scales), waist and hip circumference (by plastic coated, non-elastic tape), and seated arterial blood pressure (the mean of 2 measurements performed by mercury sphygmomanometer).

Blood samples were taken for biochemical analysis (total cholesterol, HDL and LDL, triglycerides, fasting blood glucose). Diagnostic criteria of arterial hypertension, dyslipidemia and glycemia were based on the current guidelines of professional societies (Box 1). The expected 10-year risk of fatal CV disease in primary prevention was calculated using the SCORE chart for high risk countries and for stroke according to Framingham risk score [13,14].

BIAS:

Standard error of measurement was reduced by using identical standardized measuring instruments at all locations and by repeated measurements (×2). Numerical data verification and logical control of systematic errors were performed.

STATISTICAL ANALYSIS:

Descriptive statistical methods were used to describe participants’ demographic characteristics. The χ² test was used to measure associations between 2 categorical variables (Fisher’s exact test for 2×2 tables). Logistic regression analysis determined the odds ratio (OR) and significance of the independent contribution of each AI in predicting CV risk. Sensitivity and specificity of AI as an area under a curve (AUC) for each CV risk factor were determined by receiver operating characteristic (ROC) analysis. All statistical methods were performed using SPSS for Windows (11.5, SPSS Inc., Chicago, IL, 2002), at a 95% level of significance (P<0.05).

Results

We obtained data from 2467 participants (61.9% women, 38.1% men; 69.3% inland, 30.7% coastal; 26.0% rural, 74.0% urban). The response rate of GPs was 71% and response rate of participants was 78%. Considering AI, 1918 (80%) participants had an increased waist circumference, 225 (30.4%) had increased WHR, 1015 (42.1%) were overweight, 875 (36.3%) were obese, and 1933 (83%) had increased WtHR according to pre-ordained criteria.

In comparing coastal/inland and urban/rural, there was a difference in BMI across regions and by the settlement size (Table 1). Chi-square test showed fewer obese people found in the coastal than in the inland areas (P=0.032). In rural areas there were more obese people (P<0.001), and more individuals with increased WHR (P=0.004) and increased WtHR (P<0.001) than in urban areas.

Systolic blood pressure ≥140mm Hg occurred more often in urban areas (P<0.001), and diastolic blood pressure >90 mm Hg occurred more often in inland (P<0.041) areas. There was no difference in dyslipidemia, while hyperglycemia was found more often in the inland population (P<0.001) (Table 2). Total CV risk according to SCORE did not differ across the regions according to urbanization, whereas the Framingham risk score was higher in inland than in coastal areas (P<0.001), and was higher in urban areas than in rural settlements (P<0.001).

For all logistic regressions we made adjustments for age, sex, physical activity and smoking. Among the AIs, calculated BMI >30 kg/m2 proved to be the best predictor for hypertension, diabetes and dyslipidemia (Table 3) and was a significant predictor for all CV risks in both regions, except for diabetes in the coastal area. WtHR had a significant predictive value for 2 CV risks, hypertension and dyslipidemia (Table 3), and was a significant predictor for all 3 CV risks in the coastal area, but only for hypertension in the inland area. Considering the settlement size, WtHR proved to be a good predictor for all 3 CV risks in urban areas, while in rural areas it was only significant for hypertension. None of the anthropometric indices showed any statistically significant independent contribution to the total CV risk by SCORE, but BMI and WC did for stroke risk according to Framingham (Table 3).

ROC analyses indicated WtHR is a better predictor of hypertension and dyslipidemia than other indices. However, for total CV risk according to SCORE and stroke risk according to Framingham, WHR was the best predictor (Table 4).

Discussion

LIMITATIONS:

There are 2 main limitations to this study. First, the sample of subjects was patients registered with a GP, which does not entirely correspond to the general population sample (those without health insurance or who do not visit a GP for other reasons could not be included). Second, the usual limitation of every cross-sectional study is the existence of only a possible correlation between AI and CV risk factors, since the causal connection cannot be directly determined by such a study design.

Conclusions

The prevalence of general and central obesity is high in the population registered with a GP in Croatia. By combined use of inexpensive and simple anthropometric indices (BMI for general and WtHR for abdominal obesity) GPs could better identify individuals at increased risk for CV events. Well-timed lifestyle and pharmacological intervention when indicated could prevent new and recurrent CV events in the population under the care of GPs.