Body composition and diet of Chinese, Malays and Indians in Sininfluence on cardiovascular risk factorsgapore: and their

This thesis describes the studies on body composition and dietary intakes of the three major ethnic groups residing in Singapore, and how these are related to cardiovascular risk factors in these groups.Body composition : Body fat percentage was measured using a four-compartment model described by Baumgartner. When the relationship between body mass index (BMI) and body fat percentage was studied, it was discovered that Singaporeans have higher percentage of body fat compared to Caucasians with the same BMI and that the BMI cut-off value for obesity in Chinese and Malays is around 27 kg/m 2 , while that for Indians is around 26 kg/m 2 . At levels of BMI and waist-to-hip ratio which are much lower than the WHO recommended cut-off limits for obesity and abdominal fatness respectively, both the absolute and relative risks of developing cardiovascular risk factors are markedly elevated for all three ethnic groups. Both the excessive fat accumulation and increased risks at low levels of BMI signal a need to re-examine cut-off values for obesity among Chinese, Malays and Indians.Diet : Dietary intakes of energy, total fat, saturated fat, polyunsaturated fat, monounsaturated fat and cholesterol were measured using a food frequency questionnaire specially validated for this purpose. In addition, intakes of fruits, vegetables and grain-based foods were also measured using the same questionnaire. Singaporeans generally have a low intake of fruits, vegetables and whole grain products. The intake of total fat is just within the upper recommended limit while that for saturated fat is higher than the recommended level. On a group level, it is found that high intakes of fat, saturated fat and low intakes of polyunsaturated fat and vegetables affect serum cholesterol levels adversely. However, on an individual level, due to the rather homogenous intake patterns among the three groups, this cross sectional study was unable to demonstrate that dietary intakes could explain the differences in serum cholesterol levels among ethnic groups.In summary, the thesis shows that in the light of increased body fat percentage and cardiovascular risks at low BMI, there is a need to re-examine the WHO's cut-off values for the three major ethnic groups in Singapore. Longitudinal studies are also needed for better insight into the effect of dietary intakes and other lifestyle risk factors on cardiovascular risk factors and mortality.</p

Differences in body composition between Singapore Chinese, Beijing Chinese and Dutch children

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Body fat measurement among Singaporean Chinese, Malays and Indians: a comparative study using a four-compartment model and different two-compartment models

This cross-sectional study compared body fat percentage (BF€obtained from a four-compartment (4C) model with BF␏rom hydrometry (using 2H2O), dual-energy X-ray absorptiometry (DXA) and densitometry among the three main ethnic groups (Chinese, Malays and Indians) in Singapore, and determined the suitability of two-compartment (2C) models as surrogate methods for assessing BFmong different ethnic groups. A total of 291 subjects (108 Chinese, seventy-six Malays, 107 Indians) were selected to ensure an adequate representation of age range (18-75 years) and BMI range (16-40 kg/m2) of the general adult population, with almost equal numbers from each gender group. Body weight was measured, together with body height, total body water by 2H2O dilution, densitometry with Bodpod? and bone mineral content with Hologic? QDR-4500. BF␖easurements with a 4C model for the subgroups were: Chinese females 33?5 (SD 7?5), CHINESE MALES 24?4 (sd 6?1), Malay females 37?8 (sd 6?3), Malay males 26?0 (sd 7?6), Indian females 38?2 (sd 7?0), Indian males 28?1 (sd 5?5). Differences between BF␖easured by the 4C and 2C models (hydrometry, DXA and densitometry) were found, with underestimation of BF␒n all the ethnic-gender groups by DXA of 2?1-4?2 BFnd by densitometry of 0?5-3?2 BFŽ On a group level, the differences in BF␋etween the 4C model and 2H2O were the lowest (0?0-1?4 BF␒n the different groups), while differences between the 4C model and DXA were the highest. Differences between the 4C model and 2H2O and between the 4C model and DXA were positively correlated with the 4C model, water fraction (fwater) of fat-free mass (FFM) and the mineral fraction (fmineral) of FFM, and negatively correlated with density of the FFM (DFFM), while the difference between 4C model and densitometry correlated with these variables negatively and positively respectively (i.e. the correlations were opposite). The largest contributors to the observed differences were fwater and DFFM. When validated against the reference 4C model, 2C models were found to be unsuitable for accurate measurements of BFt the individual level, owing to the high errors and violation of assumptions of constant hydration of FFM and DFFM among the ethnic groups. On a group level, the best 2C model for measuring BFmong Singaporeans was found to be 2H2O

BMI and Diabetes Risk in Singaporean Chinese

10.2337/dc08-1674Diabetes Care3261104-1106DICA

Are Ethnic and Gender Specific Equations Needed to Derive Fat Free Mass from Bioelectrical Impedance in Children of South Asian, Black African-Caribbean and White European Origin? Results of the Assessment of Body Composition in Children Study

Background Bioelectrical impedance analysis (BIA) is a potentially valuable method for assessing lean mass and body fat levels in children from different ethnic groups. We examined the need for ethnic- and gender-specific equations for estimating fat free mass (FFM) from BIA in children from different ethnic groups and examined their effects on the assessment of ethnic differences in body fat. Methods Cross-sectional study of children aged 8–10 years in London Primary schools including 325 South Asians, 250 black African-Caribbeans and 289 white Europeans with measurements of height, weight and arm-leg impedance (Z; Bodystat 1500). Total body water was estimated from deuterium dilution and converted to FFM. Multilevel models were used to derive three types of equation {A: FFM = linear combination(height+weight+Z); B: FFM = linear combination(height2/Z); C: FFM = linear combination(height2/Z+weight)}. Results Ethnicity and gender were important predictors of FFM and improved model fit in all equations. The models of best fit were ethnicity and gender specific versions of equation A, followed by equation C; these provided accurate assessments of ethnic differences in FFM and FM. In contrast, the use of generic equations led to underestimation of both the negative South Asian-white European FFM difference and the positive black African-Caribbean-white European FFM difference (by 0.53 kg and by 0.73 kg respectively for equation A). The use of generic equations underestimated the positive South Asian-white European difference in fat mass (FM) and overestimated the positive black African-Caribbean-white European difference in FM (by 4.7% and 10.1% respectively for equation A). Consistent results were observed when the equations were applied to a large external data set. Conclusions Ethnic- and gender-specific equations for predicting FFM from BIA provide better estimates of ethnic differences in FFM and FM in children, while generic equations can misrepresent these ethnic differences

Ethnic Differences in Body Composition and Obesity Related Risk Factors: Study in Chinese and White Males Living in China

The purpose of this cross-sectional observational study was to identify ethnic differences in body composition and obesity-related risk factors between Chinese and white males living in China. 115 Chinese and 114 white male pilots aged 28–63 years were recruited. Fasting body weight, height and blood pressure were measured following standard procedures. Whole-body and segmental body composition were measured using an 8-contact electrode bioimpedance analysis (BIA) system. Fasting serum glucose, fasting plasma total cholesterol (TC), high-density lipoprotein (HDL) cholesterol, and triglycerides (TG) were assessed using automatic biochemistry analyzer. After adjusting for age and body mass index (BMI), Chinese males had significantly higher percentage of body fat (PBF) both with respect to whole body (Chinese: 23.7%±0.2% vs. Whites: 22.4%±0.2%) and the trunk area (Chinese: 25.0%±0.3% vs. Whites: 23.2%±0.3%) compared to their white counterparts. At all BMIs, Chinese males had significantly higher fasting glucose levels (Chinese: 5.7±1.0 mmol/L vs. Whites: 5.2±1.0 mmol/L) but lower high-density lipoprotein levels (Chinese: 0.8±1.0 mmol/L vs. Whites: 1.0±1.0 mmol/L) than white males. In addition, a marginally significantly higher diastolic blood pressure was found among Chinese men than that among white men (Chinese: 80±1.0 mmHg vs. Whites: 77±1.0 mmHg). Chinese males had more body fat and a greater degree of central fat deposition pattern than that seen in white males in the present study. Furthermore, data on blood pressure, fasting glucose and blood lipids suggest that Chinese men may be more prone to obesity-related risk factors than white men

Body composition-derived BMI cut-offs for overweight and obesity in Indians and Creoles of Mauritius: comparison with Caucasians

Global estimates of overweight and obesity prevalence are based on the World Health Organisation (WHO) body mass index (BMI) cut-off values of 25 and 30 kg m⁻², respectively. To validate these BMI cut-offs for adiposity in the island population of Mauritius, we assessed the relationship between BMI and measured body fat mass in this population according to gender and ethnicity.Methods: In 175 young adult Mauritians (age 20-42 years) belonging to the two main ethnic groups—Indians (South Asian descent) and Creoles (African/Malagasy descent), body weight, height and waist circumference (WC) were measured, total body fat assessed by deuterium oxide (D2O) dilution and trunk (abdominal) fat by segmental bioimpedance analysis.Results: Compared to body fat% predicted from BMI using Caucasian-based equations, body fat% assessed by D2O dilution in Mauritians was higher by 3–5 units in Indian men and women as well as in Creole women, but not in Creole men. This gender-specific ethnic difference in body composition between Indians and Creoles is reflected in their BMI–Fat% relationships, as well as in their WC–Trunk Fat% relationships. Overall, WHO BMI cut-offs of 25 and 30 kg m⁻² for overweight and obesity, respectively, seem valid only for Creole men (~24 and 29.5, respectively), but not for Creole women whose BMI cut-offs are 2–4 units lower (21–22 for overweight; 27–28 for obese) nor for Indian men and women whose BMI cut-offs are 3–4 units lower (21–22 for overweight; 26–27 for obese).Conclusions: The use of BMI cut-off points for classifying overweight and obesity need to take into account both ethnicity and gender to avoid gross adiposity status misclassification in this population known to be at high risk for type-2 diabetes and cardiovascular diseases. This is particularly of importance in obesity prevention strategies both in clinical medicine and public health

Is bioelectrical impedance accurate for use in large epidemiological studies?

Percentage of body fat is strongly associated with the risk of several chronic diseases but its accurate measurement is difficult. Bioelectrical impedance analysis (BIA) is a relatively simple, quick and non-invasive technique, to measure body composition. It measures body fat accurately in controlled clinical conditions but its performance in the field is inconsistent. In large epidemiologic studies simpler surrogate techniques such as body mass index (BMI), waist circumference, and waist-hip ratio are frequently used instead of BIA to measure body fatness. We reviewed the rationale, theory, and technique of recently developed systems such as foot (or hand)-to-foot BIA measurement, and the elements that could influence its results in large epidemiologic studies. BIA results are influenced by factors such as the environment, ethnicity, phase of menstrual cycle, and underlying medical conditions. We concluded that BIA measurements validated for specific ethnic groups, populations and conditions can accurately measure body fat in those populations, but not others and suggest that for large epdiemiological studies with diverse populations BIA may not be the appropriate choice for body composition measurement unless specific calibration equations are developed for different groups participating in the study

Ethnic Differences in Survival after Breast Cancer in South East Asia

Background: The burden of breast cancer in Asia is escalating. We evaluated the impact of ethnicity on survival after breast cancer in the multi-ethnic region of South East Asia. Methodology/Principal Findings Using the Singapore-Malaysia hospital-based breast cancer registry, we analyzed the association between ethnicity and mortality following breast cancer in 5,264 patients diagnosed between 1990 and 2007 (Chinese: 71.6%, Malay: 18.4%, Indian: 10.0%). We compared survival rates between ethnic groups and calculated adjusted hazard ratios (HR) to estimate the independent effect of ethnicity on survival. Malays (n = 968) presented at a significantly younger age, with larger tumors, and at later stages than the Chinese and Indians. Malays were also more likely to have axillary lymph node metastasis at similar tumor sizes and to have hormone receptor negative and poorly differentiated tumors. Five year overall survival was highest in the Chinese women (75.8%; 95%CI: 74.4%–77.3%) followed by Indians (68.0%; 95%CI: 63.8%–72.2%), and Malays (58.5%; 95%CI: 55.2%–61.7%). Compared to the Chinese, Malay ethnicity was associated with significantly higher risk of all-cause mortality (HR: 1.34; 95%CI: 1.19–1.51), independent of age, stage, tumor characteristics and treatment. Indian ethnicity was not significantly associated with risk of mortality after breast cancer compared to the Chinese (HR: 1.14; 95%CI: 0.98–1.34). Conclusion: In South East Asia, Malay ethnicity is independently associated with poorer survival after breast cancer. Research into underlying reasons, potentially including variations in tumor biology, psychosocial factors, treatment responsiveness and lifestyle after diagnosis, is warranted