METHODS: A population-based study of 454 adolescents aged 12 to 19 years was included. A validated food frequency questionnaire was used to assess dietary patterns and three dietary patterns were identified based on the principal component analysis method.
RESULTS: Malay adolescents had significantly higher scores for the Western-based food pattern and local-based food pattern, whereas Chinese adolescents showed higher scores for the healthy-based food pattern. Multivariate analyses show that age and physical activity (PA) levels were positively associated with healthy-based food pattern in Malay (All, p < 0.001), whereas higher consumption of eating-out from home (EatOut) (p = 0.014) and fast food (p = 0.041) were negatively associated. High weekly breakfast skipping (p < 0.001) and EatOut (p = 0.003) were positively associated with a Western-based pattern, whereas age (p < 0.001) and household income (p = 0.005) were negatively associated. Higher frequency of daily snacking (p = 0.013) was positively associated with local-based food pattern. For Chinese adolescents, age (p < 0.001), PA levels (p < 0.001) and maternal education level (p = 0.035) showed positive associations with the healthy-based pattern, whereas high EatOut (p = 0.001) and fast food intakes (p = 0.001) were negatively associated. Higher weekly consumption of EatOut (p = 0.007), fast food (p = 0.023) and carbonated beverages (p = 0.023), and daily snacking practice (p = 0.004) were positively associated with higher Western-based food pattern, whereas age (p = 0.004) was inversely associated.
CONCLUSION: This study showed that there were significant differences in dietary patterns and its association factors between Malay and Chinese adolescents. More importantly, these findings suggest that unhealthy dietary and lifestyle practices could increase the risk of adherence to unhealthy Western-based food pattern that is high in fat, sugar and salt contents, and, consequently, increase the risk of developing obesity and metabolic-related disorders during these critical years of growth.
SUBJECTS/METHODS: A taste database including 467 foods' sweet, sour, bitter, salt, umami and fat sensation values was combined with food intake data to assess dietary taste patterns: the contribution to energy intake of 6 taste clusters. The FFQ's reliability was assessed against 3-d 24hR and urinary biomarkers for sodium (Na) and protein intake (N) in Dutch men (n = 449) and women (n = 397) from the NQplus validation study (mean age 53 ± 11 y, BMI 26 ± 4 kg/m2).
RESULTS: Correlations of dietary taste patterns ranged from 0.39-0.68 between FFQ and 24hR (p