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In Singapore. there exists differences in risk factors for coronary heart disease among the three main ethnic groups: Chinese, Malays and Indians. This study aimed to investigate if differences in dietary intakes of fat, types of fat, cholesterol, fruits, vegetables and grain foods could explain the differences in serum cholesterol levels between the ethnic groups. A total of 2408 adult subjects (61.0% Chinese, 21.4% Malays and 17.6% Indians) were selected systematically from the subjects who took part in the National Health Survey in 1998. The design of the study was based on a cross-sectional study. A food frequency questionnaire was used to assess intakes of energy, total fat, saturated fat, polyunsaturated fat, monounsaturated fat, cholesterol, fruits, vegetables and cereal-based foods. The Hegsted score was calculated. Serum total cholesterol, low density lipoprotein cholesterol, high density lipoprotein cholesterol were analysed and the ratio of total cholesterol to high density lipoprotein cholesterol was computed. The results showed that on a group level (six sex-ethnic groups), Hegsted score, dietary intakes of fat, satutrated fat, cholesterol, vegetables and grain foods were found to be correlated to serum cholesterol levels. However, selected dietary factors did not explain the differences in serum cholesterol levels between ethnic groups when multivariate regression analysis was performed, with adjustment for age, body mass index, waist-hip ratio, cigarette smoking, occupation, education level and physical activity level. This cross-sectional study shows that while selected dietary factors are correlated to serum cholesterol at a group level, they do not explain the differences in serum cholesterol levels between ethnic groups independently of age, obesity, occupation, educational level and other lifestyle risk factors. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the relative validity of a 159-item semiquantitative food frequency questionnaire (FFQ) for use among adult Singaporeans. This FFQ should be able to classify intakes of energy, total fat, saturated fat, polyunsaturated fat, monounsaturated fat and cholesterol into quintiles of intakes for purposes of epidemiological studies. A total of 126 subjects (84 women and 42 men) took part in the study which included an interview using the newly developed FFQ (assess past month's intake) and three 24-h (24 h) recalls (reference method, collected over a period of 1 month). Subjects also collected two 24 h urinary samples for urea from which total nitrogen excretion was assessed to validate protein intake. When compared to the reference method, the FFQ slighty overestimated the intakes of energy, total fat and types of fat as reflected by the difference in means and the ratio of FFQ to 24 h intakes. The overestimation ranged from 1 to 11% of the reference method. Dietary cholesterol was underestimated by 17% by the FFQ. These differences were however, not statistically significant. Pearson's correlation coefficients (95% confidence intervals (CI)) between intakes assessed by FFQ and reference method varied from 0.58 (0.45, 0.69) for total fat to 0.39 (0.23, 0.53) for polyunsaturated fat. Cross-classification into quintiles resulted in correct classification into the same or adjacent quintiles in 70% of subjects, with only one or two subjects being grossly misclassified. Nitrogen (N) intake from the 24 h recalls did not differ significantly from that estimated from the urinary nitrogen excretion. The mean (± SD) difference was 0.0 ± 0.4 g and the Pearson correlation coefficient (95% CI) was 0.55 (0.31, 0.72). It is concluded that the newly developed FFQ is an adequate tool for classifying individuals' intakes into quintiles for epidemiological studies among Singaporean adults. [ABSTRACT FROM AUTHOR]

The global prevalence of obesity, characterised by a body mass index (BMI) >= 30 kg/m2, is high and is increasing. Obesity is associated with a higher risk of developing non-communicable diseases such as cardiovascular disease (CVD) and cancer. In Singapore the prevalence of obesity differs among the three main ethnic groups (Chinese, Malays and Indians) but is relatively low compared to Western societies. Despite the low prevalence of obesity (BMI >=30 kg/m2), the morbidity and mortality for CVD are high in Singapore. In this paper, the odds ratio for presence of risk factors for CVD was studied in relation to BMI quintiles and in relation to body fat distribution as measured by waist-to-hip ratio (WHR) quintiles in a representative sample of adult Singaporean Chinese. The lowest quintile was used as the reference category. The boundaries for the BMI quintiles were 18.9, 20.7, 22.6 and 25.0 kg/m2 for females and 20.0, 21.7, 23.5 and 25.6 kg/m2 for males. The boundaries for WHR quintiles were 0.68, 0.71, 0.74 and 0.79 for females and 0.77, 0.82, 0.85 and 0.89 for males. As observed in other studies, the odds ratios for high serum total cholesterol, low HDL cholesterol, high total cholesterol/HDL cholesterol ratio, high serum triglyceride level, high blood pressure and high fasting blood glucose were higher in upper BMI and WHR quintiles. The effects were more pronounced in males compared with females. The odds ratios for having at least one of the mentioned risk factors in the different BMI quintiles for females were 1.3 (not significant (ns)), 1.6, 2.1 and 2.7, while in males they were 2.7, 4.1, 6.2 and 7.3. For the WHR quintiles the odds ratios were 0.9 (ns), 1.3 (ns), 1.9 and 2.1 for females, while for males they were 2.1, 4.7, 6.7 and 12.6. As the elevated risks are already apparent at low levels of BMI and low levels of WHR, it can be queried whether the cut-off points for obesity based on BMI and for abdominal fat distribution based on WHR as suggested by the WHO are applicable to the Singaporean Chinese population. There are indications in the literature that Asian populations have higher body fat percentages at lower BMI. This may explain the high odds ratios for CVD risk factors at low BMI and WHR and the high morbidity and mortality from CVD in Singapore, despite relatively low population mean BMI and obesity rates. [ABSTRACT FROM AUTHOR]