Comparison of boba pearls made from tapioca starch and other unconventional flours and starches: Their glycaemic response (GR)

@#Introduction: Boba milk tea, also recognised as bubble tea, is a popular beverage in Asia. The primary component in bubble tea is “boba” or “pearl” balls, made of tapioca starch. However, much remains to be seen if tapioca boba pearls have a profound impact on blood glucose. Methods: In a randomised, controlled crossover, single-blinded design study, 12 healthy Chinese male adults (body mass index 21±14 kgm−2) attended four sessions. At each session, bubble tea consisting of boba pearls made from tapioca starch (TS), sago starch (SS), high-amylose starch + sago starch (HA), or kithul flour + sago starch (KF) were served. Boba milk tea was served at breakfast, with volunteers consuming them in a fasted state at each session. The postprandial glycaemic response and insulin response were compared within participants. Results: There were observed differences at time 180min for incremental glucose between HA and SS (p=0.005), and for TS and SS for incremental insulin (p=0.004). Glucose iAUC was lower for TS compared to the other boba pearl treatments, although not significantly (p=0.093). There was no significant difference in iAUC of insulin (p=0.104) between the four boba pearl milk teas. Conclusion: With limited scientific research conducted on bubble milk tea, our study was the first to document the glycaemic responses of tapioca starch boba pearls and boba pearls made using unconventional flours and starches. The findings from this study is an important first step for future work to develop healthier boba pearls for bubble tea.

Equations to predict height and weight in Asian- Chinese adults

@#Introduction: Height and weight measurements are required for the assessment of nutritional status. However, it is difficult to measure these parameters in nonambulatory persons. Hence, simple predictive equations that estimate these measurements using various anthropometric measurements are necessary. Methods: A total of 441 Asian-Chinese adults (174 males, median age = 32.5, IQR: 27.8 years; 267 females, median age = 34.6, IQR: 28.5 years) were used to build height and weight sex-specific prediction equations. An additional 111 Asian- Chinese adults (44 males, median age = 31.1, IQR: 25.0 years; 67 females, median age = 30.6, IQR: 25.6 years) were used to validate the newly developed prediction equations. Results: The best predictive model for height included arm length, knee height measurements and age (R2 = 0.70, standard error of estimate [SEE] = 3.38 for males; R2 = 0.71, SEE = 3.14 for females). The best weight predictive model included age, arm circumference and waist circumference (R2 = 0.79, SEE = 4.66 for males; R2 = 0.78, SEE = 4.38 for females). The new predictive models for height and weight have non-significant prediction biases as compared to the Cereda et al. (2010) and Ross equations, respectively. Conclusion: Height and weight predictive equations with a higher degree of accuracy have been developed for Asian Chinese adults.