METHODS: Data from the University Sains Malaysia (USM) Pregnancy Cohort which consists of 153 mother-offspring pairs were used. Data were collected using interview-administered questionnaires and anthropometric measurements were also obtained. Multiple linear regression and generalised equation estimation (GEE) were used to examine the direction and impact of the association between parental BMI and child growth and body composition (weight for age, height for age, body mass index for age, weight for height and fat mass at age 2m, 6m, and 12m). Potential confounders, including validated measures of maternal diets and physical activity during pregnancy, were considered.
RESULTS: Of 153 parents, one-quarter of the mothers and 42.2% of the fathers, respectively, were overweight or obese before pregnancy. A significant association was found between maternal BMI and child's weight for height z-score (WHZ) and body mass index for age z-score (BAZ).
CONCLUSIONS: Having high pre-pregnancy BMI may increase BMI and WAZ of offspring in early life. Findings from this study emphasise the importance of monitoring maternal weight status, particularly before and during pregnancy and early life of offspring among Malaysians.
METHODS: Data from World Health Survey conducted in 2002-2004 in low-middle- and high-income countries were used. Participants aged 18 years and over were selected using multistage, stratified cluster sampling. BMI was used as an outcome variable. Culture of the countries was measured using Hofstede's cultural dimensions: Uncertainty avoidance, individualism, Power Distance and masculinity. The potential determinants of individual-level BMI were participants' sex, age, marital status, education, occupation as well as household-wealth and location (rural/urban) at the individual-level. The country-level factors used were average national income (GNI-PPP), income inequality (Gini-index) and Hofstede's cultural dimensions. A two-level random-intercepts and fixed-slopes model structure with individuals nested within countries were fitted, treating BMI as a continuous outcome variable.
RESULTS: A sample of 156,192 people from 53 countries was included in this analysis. The design-based (weighted) mean BMI (SE) in these 53 countries was 23.95(0.08). Uncertainty avoidance (UAI) and individualism (IDV) were significantly associated with BMI, showing that people in more individualistic or high uncertainty avoidance countries had higher BMI than collectivist or low uncertainty avoidance ones. This model explained that one unit increase in UAI or IDV was associated with 0.03 unit increase in BMI. Power distance and masculinity were not associated with BMI of the people. National level Income was also significantly associated with individual-level BMI.
CONCLUSION: National culture has a substantial association with BMI of the individuals in the country. This association is important for understanding the pattern of obesity or overweight across different cultures and countries. It is also important to recognise the importance of the association of culture and BMI in developing public health interventions to reduce obesity or overweight.
METHODS: A case-control study comprising 134 breast cancer patients and 265 cancer-free controls were conducted. Dietary intakes were assessed using a validated food frequency questionnaire (FFQ), from which the HEI-2015 score was calculated. Logistic regression was used to derive the odds ratios (ORs) for measuring the association between HEI-2015 scores and breast cancer risk.
RESULTS: Subjects in the top quartile of HEI-2015 had a 46% lower chance of breast cancer compared with subjects in the bottom quartile (OR 0.54; 95% CI 0.30, 0.98). After adjustment for potential confounders such as age, age at menarche, oral contraceptive drug use, menopausal status, marital status, body mass index, smoking and education level, the association between HEI-2015 score and a lower risk of breast cancer was enhanced (OR 0.32; 95% CI 0.16, 0.65).
CONCLUSION: We successfully demonstrated that a higher HEI-2015 score was associated with a reduced breast cancer risk.
METHODS: Standardised anthropometric measurements were compared against the self-reported values from 5,132 adult residents in a cross-sectional, epidemiological survey. Discrepancies in self-reports from measurements were examined by comparing overall mean differences. Intraclass correlations, Cohen's kappa and Bland-Altman plots with limits of agreement, and sub-analysis by sex and ethnicity were also explored.
RESULTS: Data were obtained from 5,132 respondents. The mean age of respondents was 43.9 years. Overall, the height was overestimated (0.2cm), while there was an underestimation of weight (0.8kg) and derived BMI (0.4kg/m2). Women had a larger discrepancy in height (0.35cm, 95% confidence interval [CI] 0.22 to 0.49), weight (-0.95kg, 95% CI -1.11 to -0.79) and BMI (-0.49kg/m2, 95% CI -0.57 to -0.41) compared with men. Height reporting bias was highest among Indians (0.28cm, 95% CI 0.12 to 0.44) compared with Chinese and Malays, while weight (-1.32kg, 95% CI -1.53 to -1.11) and derived BMI (-0.57kg/m2, 95% CI -0.67 to -0.47) showed higher degrees of underreporting among Malays compared with Chinese and Indians. Substantially high self-reported versus measured values were obtained for intraclass correlations (0.96-0.99, P<0.001) and kappa (0.74). For BMI categories, good to excellent kappa agreement was observed (0.68-0.81, P<0.0001).
CONCLUSION: Self-reported anthropometric estimates can be used, particularly in large epidemiological studies. However, sufficient care is needed when evaluating data from Indians, Malays and women as there is likely an underestimation of obesity prevalence.