Substituting brown rice for white rice on diabetes risk factors in India: a randomised controlled trial.

India has the second largest number of people with type 2 diabetes (T2D) globally. Epidemiological evidence indicates that consumption of white rice is positively associated with T2D risk, while intake of brown rice is inversely associated. Thus, we explored the effect of substituting brown rice for white rice on T2D risk factors among adults in urban South India. A total of 166 overweight (BMI ≥ 23 kg/m2) adults aged 25-65 years were enrolled in a randomised cross-over trial in Chennai, India. Interventions were a parboiled brown rice or white rice regimen providing two ad libitum meals/d, 6 d/week for 3 months with a 2-week washout period. Primary outcomes were blood glucose, insulin, glycosylated Hb (HbA1c), insulin resistance (homeostasis model assessment of insulin resistance) and lipids. High-sensitivity C-reactive protein (hs-CRP) was a secondary outcome. We did not observe significant between-group differences for primary outcomes among all participants. However, a significant reduction in HbA1c was observed in the brown rice group among participants with the metabolic syndrome (-0·18 (se 0·08) %) relative to those without the metabolic syndrome (0·05 (se 0·05) %) (P-for-heterogeneity = 0·02). Improvements in HbA1c, total and LDL-cholesterol were observed in the brown rice group among participants with a BMI ≥ 25 kg/m2 compared with those with a BMI < 25 kg/m2 (P-for-heterogeneity < 0·05). We observed a smaller increase in hs-CRP in the brown (0·03 (sd 2·12) mg/l) compared with white rice group (0·63 (sd 2·35) mg/l) (P = 0·04). In conclusion, substituting brown rice for white rice showed a potential benefit on HbA1c among participants with the metabolic syndrome and an elevated BMI. A small benefit on inflammation was also observed.

Glycaemic index of three Indian rice varieties.

Three commonly consumed Indian rice varieties (Sona Masuri, Ponni and Surti Kolam) were tested for their glycaemic index (GI). Healthy volunteers were recruited and after an overnight fast were given a 50 g available carbohydrate portion of glucose (reference food) or different varieties of cooked rice (test foods) on separate occasions. The fasting as well as postprandial capillary blood glucose response was determined over 2 h, and the incremental area under the curve (IAUC) was calculated. The GI was calculated as the IAUC of the test food/IAUC of the reference food (glucose) × 100. The differences between the GI values for Sona Masuri (72.0 ± 4.5), Ponni (70.2 ± 3.6) and Surti Kolam (77.0 ± 4.0) rice varieties were non-significant (p = 0.606) and are all classified as high GI varieties of rice. There is an urgent need to study the GI of other commonly consumed rice varieties and to develop rice of a lower GI value.

Erratum to: Interaction between FTO gene variants and lifestyle factors on metabolic traits in an Asian Indian population.

[This corrects the article DOI: 10.1186/s12986-016-0098-6.].

Interaction between FTO gene variants and lifestyle factors on metabolic traits in an Asian Indian population.

BACKGROUND: Lifestyle factors such as diet and physical activity have been shown to modify the association between fat mass and obesity-associated (FTO) gene variants and metabolic traits in several populations; however, there are no gene-lifestyle interaction studies, to date, among Asian Indians living in India. In this study, we examined whether dietary factors and physical activity modified the association between two FTO single nucleotide polymorphisms (rs8050136 and rs11076023) (SNPs) and obesity traits and type 2 diabetes (T2D). METHODS: The study included 734 unrelated T2D and 884 normal glucose-tolerant (NGT) participants randomly selected from the urban component of the Chennai Urban Rural Epidemiology Study (CURES). Dietary intakes were assessed using a validated interviewer administered semi-quantitative food frequency questionnaire (FFQ). Physical activity was based upon the self-report. Interaction analyses were performed by including the interaction terms in the linear/logistic regression model. RESULTS: There was a significant interaction between SNP rs8050136 and carbohydrate intake (% energy) (Pinteraction = 0.04), where the 'A' allele carriers had 2.46 times increased risk of obesity than those with 'CC' genotype (P = 3.0 × 10(-5)) among individuals in the highest tertile of carbohydrate intake (% energy, 71 %). A significant interaction was also observed between SNP rs11076023 and dietary fibre intake (Pinteraction = 0.0008), where individuals with AA genotype who are in the 3(rd) tertile of dietary fibre intake had 1.62 cm lower waist circumference than those with 'T' allele carriers (P = 0.02). Furthermore, among those who were physically inactive, the 'A' allele carriers of the SNP rs8050136 had 1.89 times increased risk of obesity than those with 'CC' genotype (P = 4.0 × 10(-5)). CONCLUSIONS: This is the first study to provide evidence for a gene-diet and gene-physical activity interaction on obesity and T2D in an Asian Indian population. Our findings suggest that the association between FTO SNPs and obesity might be influenced by carbohydrate and dietary fibre intake and physical inactivity. Further understanding of how FTO gene influences obesity and T2D through dietary and exercise interventions is warranted to advance the development of behavioral intervention and personalised lifestyle strategies, which could reduce the risk of metabolic diseases in this Asian Indian population.