1.
Unconjugated and secondary bile acid profiles in response to higher-fat, lower-carbohydrate diet and associated with related gut microbiota: A 6-month randomized controlled-feeding trial.
Wan, Y, Yuan, J, Li, J, Li, H, Zhang, J, Tang, J, Ni, Y, Huang, T, Wang, F, Zhao, F, et al
Clinical nutrition (Edinburgh, Scotland). 2020;(2):395-404
Abstract
BACKGROUND & AIMS Observational studies have shown that diets high in fat and low in dietary fiber, might have an unfavorable impact on bile acid (BA) profiles, which might further affect host cardiometabolic health. In the current study, we aimed to evaluate the effects of dietary fat content on BA profiles and associated gut microbiota, and their correlates with cardiometabolic risk factors. METHODS In a randomized controlled-feeding trial, healthy young adults were assigned to one of the three diets: a lower-fat diet (fat 20%, carbohydrate 66% and protein 14%), a moderate-fat diet (fat 30%, carbohydrate 56% and protein 14%) and a higher-fat diet (fat 40%, carbohydrate 46% and protein 14%) for 6 months. All the foods were provided during the entire intervention period. The BA profiles, associated gut microbiota and markers of cardiometabolic risk factors were determined before and after intervention. RESULTS The higher-fat diet resulted in an elevated concentration of total BAs (p < 0.001), and unconjugated BAs (p = 0.03) compared with lower-fat diet. Secondary BAs, such as deoxycholic acid (DCA), taurodeoxycholic acid (TDCA), 12ketolithocholic acid (12keto-LCA), 3β-DCA and taurolithocholic acid (TLCA) (p < 0.05 after FDR correction) were significantly increased in the higher-fat diet group after the 6-month intervention. Consistently, the abundances of gut bacteria (Bacteroides, Clostridium, Bifidobacterium and Lactobacillus) which affect bile salt hydrolase gene expression were significantly increased after higher-fat consumption. The change of DCA was positively associated with the relative abundance of Bacteroides (r = 0.31, p = 0.08 after FDR correction). In addition, the changes of fecal concentrations of DCA and 12keto-LCA were positively associated with serum total cholesterol (r > 0.3, p = 0.02 and p = 0.008 after FDR correction, respectively). In line with these findings, serum fibroblast growth factor 19 (FGF19) was marginally significantly elevated in the higher-fat group after intervention (p = 0.05). CONCLUSIONS The higher-fat diet resulted in an alteration of BAs, especially unconjugated BAs and secondary BAs, most likely through actions of gut microbiota. These alterations might confer potentially unfavorable impacts on colonic and host cardiometabolic health in healthy young adults. Clinical trial registry number: NCT02355795 listed on NIH website: ClinicalTrials.gov.
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Effects of Macronutrient Distribution on Weight and Related Cardiometabolic Profile in Healthy Non-Obese Chinese: A 6-month, Randomized Controlled-Feeding Trial.
Wan, Y, Wang, F, Yuan, J, Li, J, Jiang, D, Zhang, J, Huang, T, Zheng, J, Mann, J, Li, D
EBioMedicine. 2017;:200-207
Abstract
BACKGROUND It has been suggested that the increase in carbohydrate at the expense of fat has contributed to the obesity epidemic in North America and some European countries. However, obesity rates in China have increased rapidly in parallel with a transition from the traditional low fat, high carbohydrate diet to a diet relatively high in fat and reduced in carbohydrate. Therefore, the current study aimed to determine whether the traditional Chinese diet was likely to be more effective than a diet with higher fat and lower carbohydrate — which is consumed in most Western societies, at weight control among a non-obese healthy population in China. METHODS The 6-month, two-center, three-arm, randomized, parallel-group, controlled-feeding trial was conducted at People's Liberation Army General Hospital in north China and Zhejiang University in south China. We recruited healthy young adults (aged 18–35 years, body mass index < 28) who lived in the university campus or the hospital dormitory during the whole study intervention period. They were required to eat only the foods provided, and to avoid excessive or unusual strenuous exercise during the trial. Participants were simultaneously enrolled and randomized using a computer-generated number (stratified by clinic center, age, sex, and body mass index) by data manager to one of the three isocaloric diets (1:1:1): a lower fat, higher carbohydrate diet (fat 20%, carbohydrate 66% energy); a moderate fat, moderate carbohydrate diet (fat 30%, carbohydrate 56% energy); a higher fat, lower carbohydrate diet (fat 40%, carbohydrate 46% energy). Protein provided 14% energy in all diets. We provided all food and beverages throughout the 6-month intervention. Laboratory personnel were masked to treatment allocation. Body weight was the primary outcome and measured each month. Data were primarily analyzed according to an intention-to-treat approach, supplemented with per-protocol analysis. The study was approved by the Ethics Committee at Zhejiang University. Each participant provided written informed consent. The study was registered at Clinicaltrials.gov, number NCT02355795. FINDINGS Between April 30, 2016, and October 30, 2016, 307 participants were randomly assigned to the lower fat diet (n = 101), the moderate fat diet (n = 105) and the higher fat diet (n = 101), and 245 (79.8%) participants completed the study. Reduction in body weight was significantly greater in the lower fat, higher carbohydrate group throughout the intervention (P < 0.001 for the interaction between diet group and time) than in the two other groups. Weight change at 6 months was − 1.6 kg (95% CI − 1.8 to − 1.4) in the lower fat, higher carbohydrate group; − 1.1 kg (95% CI − 1.3 to − 0.9) in the moderate fat, moderate carbohydrate group, and − 0.9 kg (95% CI − 1.1 to − 0.6) in the higher fat, lower carbohydrate group. Reduction in waist circumference, total cholesterol, low-density lipoprotein cholesterol and non-high-density lipoprotein cholesterol on the lower fat, higher carbohydrate group were greater than those observed on the other two diet groups. INTERPRETATION A lower fat, relatively higher carbohydrate diet, similar in macronutrient composition to that traditionally eaten in China appears to be less likely to promote excessive weight gain and be associated with a lower cardiometabolic risk profile than a diet more typical of that eaten in Western countries in healthy non-obese Chinese. Findings from studies in European and North American populations suggesting possible benefits of carbohydrate restriction may not apply to people of other ethnicities.
3.
Measuring the glycemic index of foods: interlaboratory study.
Wolever, TM, Brand-Miller, JC, Abernethy, J, Astrup, A, Atkinson, F, Axelsen, M, Björck, I, Brighenti, F, Brown, R, Brynes, A, et al
The American journal of clinical nutrition. 2008;(1):247S-257S
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Abstract
BACKGROUND Many laboratories offer glycemic index (GI) services. OBJECTIVE We assessed the performance of the method used to measure GI. DESIGN The GI of cheese-puffs and fruit-leather (centrally provided) was measured in 28 laboratories (n=311 subjects) by using the FAO/WHO method. The laboratories reported the results of their calculations and sent the raw data for recalculation centrally. RESULTS Values for the incremental area under the curve (AUC) reported by 54% of the laboratories differed from central calculations. Because of this and other differences in data analysis, 19% of reported food GI values differed by >5 units from those calculated centrally. GI values in individual subjects were unrelated to age, sex, ethnicity, body mass index, or AUC but were negatively related to within-individual variation (P=0.033) expressed as the CV of the AUC for repeated reference food tests (refCV). The between-laboratory GI values (mean+/-SD) for cheese-puffs and fruit-leather were 74.3+/-10.5 and 33.2+/-7.2, respectively. The mean laboratory GI was related to refCV (P=0.003) and the type of restrictions on alcohol consumption before the test (P=0.006, r2=0.509 for model). The within-laboratory SD of GI was related to refCV (P<0.001), the glucose analysis method (P=0.010), whether glucose measures were duplicated (P=0.008), and restrictions on dinner the night before (P=0.013, r2=0.810 for model). CONCLUSIONS The between-laboratory SD of the GI values is approximately 9. Standardized data analysis and low within-subject variation (refCV<30%) are required for accuracy. The results suggest that common misconceptions exist about which factors do and do not need to be controlled to improve precision. Controlled studies and cost-benefit analyses are needed to optimize GI methodology. The trial was registered at clinicaltrials.gov as NCT00260858.