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BACKGROUND: Evaluating effects of different macronutrient diets in randomized trials requires well defined infrastructure and rigorous methods to ensure intervention fidelity and adherence. METHODS: This controlled feeding study comprised two phases. During a Run-in phase (14-15 weeks), study participants (18-50 years, BMI, ≥27 kg/m2) consumed a very-low-carbohydrate (VLC) diet, with home delivery of prepared meals, at an energy level to promote 15 ± 3% weight loss. During a Residential phase (13 weeks), participants resided at a conference center. They received a eucaloric VLC diet for three weeks and then were randomized to isocaloric test diets for 10 weeks: VLC (5% energy from carbohydrate, 77% from fat), high-carbohydrate (HC)-Starch (57%, 25%; including 20% energy from refined grains), or HC-Sugar (57%, 25%; including 20% sugar). Outcomes included measures of body composition and energy expenditure, chronic disease risk factors, and variables pertaining to physiological mechanisms. Six cores provided infrastructure for implementing standardized protocols: Recruitment, Diet and Meal Production, Participant Support, Assessments, Regulatory Affairs and Data Management, and Statistics. The first participants were enrolled in May 2018. Participants residing at the conference center at the start of the COVID-19 pandemic completed the study, with each core implementing mitigation plans. RESULTS: Before early shutdown, 77 participants were randomized, and 70 completed the trial (65% of planned completion). Process measures indicated integrity to protocols for weighing menu items, within narrow tolerance limits, and participant adherence, assessed by direct observation and continuous glucose monitoring. CONCLUSION: Available data will inform future research, albeit with less statistical power than originally planned.

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BACKGROUND: Recent 3-dimensional optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise in clinical measures made by DXA. However, the sensitivity for monitoring body composition change over time with 3DO body shape imaging is unknown. OBJECTIVES: This study aimed to evaluate the ability of 3DO in monitoring body composition changes across multiple intervention studies. METHODS: A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at the baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Using an established statistical shape model, each 3DO mesh was transformed into principal components, which were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus the baseline) were compared with those of DXA using a linear regression analysis. RESULTS: The analysis included 133 participants (45 females) in 6 studies. The mean (SD) length of follow-up was 13 (5) wk (range: 3-23 wk). Agreement between 3DO and DXA (R2) for changes in total FM, total FFM, and appendicular lean mass were 0.86, 0.73, and 0.70, with root mean squared errors (RMSEs) of 1.98 kg, 1.58 kg, and 0.37 kg, in females and 0.75, 0.75, and 0.52 with RMSEs of 2.31 kg, 1.77 kg, and 0.52 kg, in males, respectively. Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA. CONCLUSIONS: Compared with DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allows users to self-monitor on a frequent basis throughout interventions. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults; https://clinicaltrials.gov/ct2/show/NCT03637855); NCT03394664 (Macronutrients and Body Fat Accumulation: A Mechanistic Feeding Study; https://clinicaltrials.gov/ct2/show/NCT03394664); NCT03771417 (Resistance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Muscle and Cardiometabolic Health; https://clinicaltrials.gov/ct2/show/NCT03771417); NCT03393195 (Time Restricted Eating on Weight Loss; https://clinicaltrials.gov/ct2/show/NCT03393195), and NCT04120363 (Trial of Testosterone Undecanoate for Optimizing Performance During Military Operations; https://clinicaltrials.gov/ct2/show/NCT04120363).

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BACKGROUND/OBJECTIVES: Fat-free mass (FFM) often serves as a body composition outcome variable in weight loss studies. An important assumption is that the proportions of components that make up FFM remain stable following weight loss; some body composition models rely on these "constants". This exploratory study examined key FFM component proportions before and following weight loss in two studies of participants with overweight and obesity. SUBJECTS/METHODS: 201 men and women consumed calorie-restricted moderate- or very-low carbohydrate diets leading to 10-18% weight loss in 9-15 weeks. Measured total body fat, lean mass, bone mineral, total body water (TBW), and body weight at baseline and follow-up were used to derive FFM and its chemical proportions using a four-component model. RESULTS: A consistent finding in both studies was a non-significant reduction in bone mineral and a corresponding increase (p < 0.001) in bone mineral/FFM; FFM density increased significantly in one group of women and in all four participant groups combined (both, p < 0.05). FFM hydration (TBW/FFM) increased in all groups of men and women, one significantly (p < 0.01), and in the combined sample (borderline, p < 0.10). The proportion of FFM as protein decreased across all groups, two significantly (p < 0.05-0.01) and in the combined sample (p < 0.05). CONCLUSION: FFM relative proportions of chemical components may not be identical before and after short-term weight loss, an observation impacting some widely used body composition models and methods. Caution is thus needed when applying FFM as a safety signal or to index metabolic evaluations in clinical trials when these body composition approaches are used.

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OBJECTIVE: Consuming ≥150 g/day carbohydrate is recommended for 3 days before an oral glucose tolerance test (OGTT) for diabetes diagnosis. For evaluation of this recommendation, time courses of glycemic changes following transition from a very-low-carbohydrate (VLC) to high-carbohydrate diet were assessed with continuous glucose monitoring (CGM). RESEARCH DESIGN AND METHODS: After achieving a weight loss target of 15% (±3%) on the run-in VLC diet, participants (18-50 years old, BMI ≥27 kg/m2) were randomly assigned for 10 weeks to one of three isoenergetic diets: VLC (5% carbohydrate and 77% fat); high carbohydrate, high starch (HC-Starch) (57% carbohydrate and 25% fat, including 20% refined grains); and high carbohydrate, high sugar (HC-Sugar) (57% carbohydrate and 25% fat, including 20% sugar). CGM was done throughout the trial (n = 64) and OGTT at start and end (n = 41). All food was prepared in a metabolic kitchen and consumed under observation. RESULTS: Glucose metrics continued to decline after week 1 in the HC-Starch and HC-Sugar groups (P < 0.05) but not VLC. During weeks 2-5, fasting and 2-h glucose (millimoles per liter per week) decreased in HC-Starch (fasting -0.10, P = 0.001; 2 h -0.10, P = 0.04). During weeks 6-9, 2-h glucose decreased in HC-Starch (-0.07, P = 0.01) and fasting and 2-h glucose decreased in HC-Sugar (fasting -0.09, P = 0.001; 2 h -0.09, P = 0.003). The number of participants with abnormal glucose tolerance by OGTT remained 10 (of 16) in VLC at start and end but decreased from 17 to 9 (of 25) in both high-carbohydrate groups. CONCLUSIONS: Physiological adaptation from a low- to high-carbohydrate diet may require many weeks, with implications for the accuracy of diabetes tests, interpretation of macronutrient trials, and risks of periodic planned deviations from a VLC diet.

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BACKGROUND: The aim of obesity treatment is to promote loss of fat relative to lean mass. However, body composition changes with calorie restriction differ among individuals. OBJECTIVES: The goal of this study was to test the hypothesis that insulin secretion predicts body composition changes among young and middle-age adults with high BMI (in kg/m2) following major weight loss. METHODS: Exploratory analyses were conducted with pre-randomization data from 2 large feeding trials: the Framingham, Boston, Bloomington, Birmingham, and Baylor study (FB4; n = 82, 43.9% women, BMI ≥27) and the Framingham State Food Study [(FS)2; n = 161, 69.6% women, BMI ≥25]. Participants in the 2 trials consumed calorie-restricted moderate-carbohydrate or very-low-carbohydrate diets to produce 12-18% weight loss in ∼14 wk or 10-14% in ∼10 wk, respectively. We determined insulin concentration 30 min after a 75-g oral glucose load (insulin-30) as a measure of insulin secretion and HOMA-IR as a measure of insulin resistance at baseline. Body composition was determined by DXA at baseline and post-weight loss. Associations were analyzed using general linear models with adjustment for covariates. RESULTS: In FB4, higher insulin-30 was associated with a smaller decrease in fat mass (0.441 kg per 100 µIU/mL increment in baseline insulin-30; P = 0.005; -1.20-kg mean difference between the first compared with the fifth group of insulin-30) and a larger decrease in lean mass (-0.465 kg per 100 µIU/mL; P = 0.004; 1.27-kg difference). Participants with higher insulin-30 lost a smaller proportion of weight loss as fat (-3.37% per 100 µIU/mL; P = 0.003; 9.20% difference). Greater HOMA-IR was also significantly associated with adverse body composition changes. Results from (FS)2 were qualitatively similar but of a smaller magnitude. CONCLUSIONS: Baseline insulin dynamics predict substantial individual differences in body composition following weight loss. These findings may inform understanding of the pathophysiological basis for weight regain and the design of more effective obesity treatment. Registered at clinicaltrials.gov as NCT03394664 and NCT02068885.

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BACKGROUND: Carbohydrate restriction shows promise for diabetes, but concerns regarding high saturated fat content of low-carbohydrate diets limit widespread adoption. OBJECTIVES: This preplanned ancillary study aimed to determine how diets varying widely in carbohydrate and saturated fat affect cardiovascular disease (CVD) risk factors during weight-loss maintenance. METHODS: After 10-14% weight loss on a run-in diet, 164 participants (70% female; BMI = 32.4 ± 4.8 kg/m2) were randomly assigned to 3 weight-loss maintenance diets for 20 wk. The prepared diets contained 20% protein and differed 3-fold in carbohydrate (Carb) and saturated fat as a proportion of energy (Low-Carb: 20% carbohydrate, 21% saturated fat; Moderate-Carb: 40%, 14%; High-Carb: 60%, 7%). Fasting plasma samples were collected prerandomization and at 20 wk. Lipoprotein insulin resistance (LPIR) score was calculated from triglyceride-rich, high-density, and low-density lipoprotein particle (TRL-P, HDL-P, LDL-P) sizes and subfraction concentrations (large/very large TRL-P, large HDL-P, small LDL-P). Other outcomes included lipoprotein(a), triglycerides, HDL cholesterol, LDL cholesterol, adiponectin, and inflammatory markers. Repeated measures ANOVA was used for intention-to-treat analysis. RESULTS: Retention was 90%. Mean change in LPIR (scale 0-100) differed by diet in a dose-dependent fashion: Low-Carb (-5.3; 95% CI: -9.2, -1.5), Moderate-Carb (-0.02; 95% CI: -4.1, 4.1), High-Carb (3.6; 95% CI: -0.6, 7.7), P = 0.009. Low-Carb also favorably affected lipoprotein(a) [-14.7% (95% CI: -19.5, -9.5), -2.1 (95% CI: -8.2, 4.3), and 0.2 (95% CI: -6.0, 6.8), respectively; P = 0.0005], triglycerides, HDL cholesterol, large/very large TRL-P, large HDL-P, and adiponectin. LDL cholesterol, LDL-P, and inflammatory markers did not differ by diet. CONCLUSIONS: A low-carbohydrate diet, high in saturated fat, improved insulin-resistant dyslipoproteinemia and lipoprotein(a), without adverse effect on LDL cholesterol. Carbohydrate restriction might lower CVD risk independently of body weight, a possibility that warrants study in major multicentered trials powered on hard outcomes. The registry is available through ClinicialTrials.gov: https://clinicaltrials.gov/ct2/show/NCT02068885.

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Accurate and precise body composition estimates, notably of total body adiposity, are a vital component of in vivo physiology and metabolic studies. The reference against which other body composition approaches are usually validated or calibrated is the family of methods referred to as multicomponent "body density" models. These models quantify three to six components by combining measurements of body mass, body volume, total body water, and osseous mineral mass. Body mass is measured with calibrated scales, volume with underwater weighing or air-displacement plethysmography, total body water with isotope dilution, and osseous mineral mass by dual-energy X-ray absorptiometry. Body density is then calculated for use in model as body mass/volume. Studies over the past decade introduced a new approach to quantifying body volume that relies on dual-energy X-ray absorptiometry measurements, an advance that simplifies multicomponent density model development by eliminating the need for underwater weighing or air-displacement plethysmography systems when these technologies are unavailable and makes these methods more accessible to research and clinical programs. This review critically examines these new dual-energy X-ray approaches for quantifying body volume and density, explores their shortcomings, suggests alternative derivation approaches, and introduces ideas for potential future research studies.

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BACKGROUND: Longer-term feeding studies suggest that a low-carbohydrate diet increases energy expenditure, consistent with the carbohydrate-insulin model of obesity. However, the validity of methodology utilized in these studies, involving doubly labeled water (DLW), has been questioned. OBJECTIVE: The aim of this study was to determine whether dietary energy requirement for weight-loss maintenance is higher on a low- compared with high-carbohydrate diet. METHODS: The study reports secondary outcomes from a feeding study in which the primary outcome was total energy expenditure (TEE). After attaining a mean Run-in weight loss of 10.5%, 164 adults (BMI ≥25 kg/m2; 70.1% women) were randomly assigned to Low-Carbohydrate (percentage of total energy from carbohydrate, fat, protein: 20/60/20), Moderate-Carbohydrate (40/40/20), or High-Carbohydrate (60/20/20) Test diets for 20 wk. Calorie content was adjusted to maintain individual body weight within ± 2 kg of the postweight-loss value. In analyses by intention-to-treat (ITT, completers, n = 148) and per protocol (PP, completers also achieving weight-loss maintenance, n = 110), we compared the estimated energy requirement (EER) from 10 to 20 wk of the Test diets using ANCOVA. RESULTS: Mean EER was higher in the Low- versus High-Carbohydrate group in models of varying covariate structure involving ITT [ranging from 181 (95% CI: 8-353) to 246 (64-427) kcal/d; P ≤0.04] and PP [ranging from 245 (43-446) to 323 (122-525) kcal/d; P ≤0.02]. This difference remained significant in sensitivity analyses accounting for change in adiposity and possible nonadherence. CONCLUSIONS: Energy requirement was higher on a low- versus high-carbohydrate diet during weight-loss maintenance in adults, commensurate with TEE. These data are consistent with the carbohydrate-insulin model and lend qualified support for the validity of the DLW method with diets varying in macronutrient composition. This trial was registered at clinicaltrials.gov as NCT02068885.

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OBJECTIVE: To determine the effects of diets varying in carbohydrate to fat ratio on total energy expenditure. DESIGN: Randomized trial. SETTING: Multicenter collaboration at US two sites, August 2014 to May 2017. PARTICIPANTS: 164 adults aged 18-65 years with a body mass index of 25 or more. INTERVENTIONS: After 12% (within 2%) weight loss on a run-in diet, participants were randomly assigned to one of three test diets according to carbohydrate content (high, 60%, n=54; moderate, 40%, n=53; or low, 20%, n=57) for 20 weeks. Test diets were controlled for protein and were energy adjusted to maintain weight loss within 2 kg. To test for effect modification predicted by the carbohydrate-insulin model, the sample was divided into thirds of pre-weight loss insulin secretion (insulin concentration 30 minutes after oral glucose). MAIN OUTCOME MEASURES: The primary outcome was total energy expenditure, measured with doubly labeled water, by intention-to-treat analysis. Per protocol analysis included participants who maintained target weight loss, potentially providing a more precise effect estimate. Secondary outcomes were resting energy expenditure, measures of physical activity, and levels of the metabolic hormones leptin and ghrelin. RESULTS: Total energy expenditure differed by diet in the intention-to-treat analysis (n=162, P=0.002), with a linear trend of 52 kcal/d (95% confidence interval 23 to 82) for every 10% decrease in the contribution of carbohydrate to total energy intake (1 kcal=4.18 kJ=0.00418 MJ). Change in total energy expenditure was 91 kcal/d (95% confidence interval -29 to 210) greater in participants assigned to the moderate carbohydrate diet and 209 kcal/d (91 to 326) greater in those assigned to the low carbohydrate diet compared with the high carbohydrate diet. In the per protocol analysis (n=120, P<0.001), the respective differences were 131 kcal/d (-6 to 267) and 278 kcal/d (144 to 411). Among participants in the highest third of pre-weight loss insulin secretion, the difference between the low and high carbohydrate diet was 308 kcal/d in the intention-to-treat analysis and 478 kcal/d in the per protocol analysis (P<0.004). Ghrelin was significantly lower in participants assigned to the low carbohydrate diet compared with those assigned to the high carbohydrate diet (both analyses). Leptin was also significantly lower in participants assigned to the low carbohydrate diet (per protocol). CONCLUSIONS: Consistent with the carbohydrate-insulin model, lowering dietary carbohydrate increased energy expenditure during weight loss maintenance. This metabolic effect may improve the success of obesity treatment, especially among those with high insulin secretion. TRIAL REGISTRATION: ClinicalTrials.gov NCT02068885.

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BACKGROUND: While many people with overweight or obesity can lose weight temporarily, most have difficulty maintaining weight loss over the long term. Studies of dietary composition typically focus on weight loss, rather than weight-loss maintenance, and rely on nutrition education and dietary counseling, rather than controlled feeding protocols. Variation in initial weight loss and insufficient differentiation among treatments confound interpretation of results and compromise conclusions regarding the weight-independent effects of dietary composition. The aim of the present study was to evaluate three test diets differing in carbohydrate-to-fat ratio during weight-loss maintenance. DESIGN AND DIETARY INTERVENTIONS: Following weight loss corresponding to 12±2% of baseline body weight on a standard run-in diet, 164 participants aged 18 to 65years were randomly assigned to one of three test diets for weight-loss maintenance through 20weeks (test phase). We fed them high-carbohydrate (60% of energy from carbohydrate, 20% fat), moderate-carbohydrate (40% carbohydrate, 40% fat), and low-carbohydrate (20% carbohydrate, 60% fat) diets, controlled for protein content (20% of energy). During a 2-week ad libitum feeding phase following the test phase, we assessed the effect of the test diets on body weight. OUTCOMES: The primary outcome was total energy expenditure, assessed by doubly-labeled water methodology. Secondary outcomes included resting energy expenditure and physical activity, chronic disease risk factors, and variables to inform an understanding of physiological mechanisms by which dietary carbohydrate-to-fat ratio might influence metabolism. Weight change during the ad libitum feeding phase was conceptualized as a proxy measure of hunger.

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Importance: Health care professionals commonly recommend increased water consumption, typically to 8 cups per day, as part of a weight-reducing diet. However, this recommendation is based on limited evidence and virtually no experimental data from the pediatric population. Objective: To compare 2 standardized weight-loss diets among adolescents with overweight or obesity, either with or without additional advice and behavioral support to increase habitual water intake to 8 cups per day. Design, Setting, and Participants: A randomized clinical, parallel-group trial was conducted between February 2, 2011, and June 26, 2014, at Boston Children's Hospital, Boston, Massachusetts, among 38 adolescents with overweight or obesity who reported drinking 4 cups or less of water per day. Interventions: All participants in both groups received similar weight-reducing interventions, differentiated by advice about water intake (the water group received advice to increase water intake to 8 cups per day; the control group did not receive such advice) but controlled for other dietary recommendations and treatment intensity. The interventions included dietary counseling, daily text messages, and a cookbook with health guides. To support adherence to 8 cups of water per day, the water group received well-defined messages about water through counseling and daily text messages, a water bottle, and a water pitcher with filters. Main Outcomes and Measures: The primary outcome was 6-month change in body mass index z score. Data analyses followed the intention-to-treat principle. Results: All 38 participants (27 girls and 11 boys; mean [SD] age, 14.9 [1.7] years) completed the study. Both groups reported drinking approximately 2 cups of water per day at baseline. Self-reported change in water intake at 6 months was greater in the water group (difference from baseline, 2.8 cups per day [95% CI, 1.8 to 3.8]; P < .001) compared with that in the control group (difference from baseline, 1.2 cups per day [95% CI, 0.2 to 2.2]; P = .02) (difference between groups, 1.6 cups per day [95% CI, 0.2 to 3.0 cups per day]; P = .03). The 6-month change in body mass index z score did not differ between the water group (difference from baseline, -0.1 [95% CI, -0.2 to -0.0]; P = .005) and the control group (difference from baseline, -0.1 [95% CI, -0.2 to -0.0]; P = .008) (difference between groups, -0.0 [95% CI, -0.1 to 0.1]; P = .88). Conclusions and Relevance: Advice and behavioral supports to consume 8 cups of water per day in the context of a weight-reducing diet did not affect body weight among adolescents with overweight or obesity. Despite intensive behavior supports, few adolescents achieved the target of 8 cups of water per day. Environmental interventions to reduce barriers to water consumption at school may be necessary in future research of the feasibility and effectiveness to achieve the target of an intake of 8 cups of water per day in adolescents. Trial Registration: clinicaltrials.gov Identifier: NCT01044134.

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Many dietary patterns have been associated with cardiometabolic risk reduction. A commonality between these dietary patterns is the emphasis on plant-based foods. Studies in individuals who consume vegetarian and vegan diets have shown a reduced risk of cardiovascular events and incidence of diabetes. Plant-based dietary patterns may promote a more favorable gut microbial profile. Such diets are high in dietary fiber and fermentable substrate (ie, nondigestible or undigested carbohydrates), which are sources of metabolic fuel for gut microbial fermentation and, in turn, result in end products that may be used by the host (eg, short-chain fatty acids). These end products may have direct or indirect effects on modulating the health of their host. Modulation of the gut microbiota is an area of growing interest, and it has been suggested to have the potential to reduce risk factors associated with chronic diseases. Examples of dietary components that alter the gut microbial composition include prebiotics and resistant starches. Emerging evidence also suggests a potential link between interindividual differences in the gut microbiota and variations in physiology or predisposition to certain chronic disease risk factors. Alterations in the gut microbiota may also stimulate certain populations and may assist in biotransformation of bioactive components found in plant foods. Strategies to modify microbial communities may therefore provide a novel approach in the treatment and management of chronic diseases.

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OBJECTIVE: Low-carbohydrate diets may be useful for weight loss. Diets high in vegetable proteins and oils may reduce the risk of coronary heart disease. The main objective was to determine the longer term effect of a diet that was both low-carbohydrate and plant-based on weight loss and low-density lipoprotein cholesterol (LDL-C). DESIGN, SETTING, PARTICIPANTS: A parallel design study of 39 overweight hyperlipidaemic men and postmenopausal women conducted at a Canadian university-affiliated hospital nutrition research centre from April 2005 to November 2006. INTERVENTION: Participants were advised to consume either a low-carbohydrate vegan diet or a high-carbohydrate lacto-ovo vegetarian diet for 6 months after completing 1-month metabolic (all foods provided) versions of these diets. The prescribed macronutrient intakes for the low-carbohydrate and high-carbohydrate diets were: 26% and 58% of energy from carbohydrate, 31% and 16% from protein and 43% and 25% from fat, respectively. PRIMARY OUTCOME: Change in body weight. RESULTS: 23 participants (50% test, 68% control) completed the 6-month ad libitum study. The approximate 4 kg weight loss on the metabolic study was increased to -6.9 kg on low-carbohydrate and -5.8 kg on high-carbohydrate 6-month ad libitum treatments (treatment difference (95% CI) -1.1 kg (-2.1 to 0.0), p=0.047). The relative LDL-C and triglyceride reductions were also greater on the low-carbohydrate treatment (treatment difference (95% CI) -0.49 mmol/L (-0.70 to -0.28), p<0.001 and -0.34 mmol/L (-0.57 to -0.11), p=0.005, respectively), as were the total cholesterol:HDL-C and apolipoprotein B:A1 ratios (-0.57 (-0.83, -0.32), p<0.001 and -0.05 (-0.09, -0.02), p=0.003, respectively). CONCLUSIONS: A self-selected low-carbohydrate vegan diet, containing increased protein and fat from gluten and soy products, nuts and vegetable oils, had lipid lowering advantages over a high-carbohydrate, low-fat weight loss diet, thus improving heart disease risk factors. TRIAL REGISTRATION: clinicaltrials.gov (http://www.clinicaltrials.gov/), #NCT00256516.

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The present randomised parallel study assessed the impact of adding MUFA to a dietary portfolio of cholesterol-lowering foods on the intravascular kinetics of apoAI- and apoB-containing lipoproteins in subjects with dyslipidaemia. A sample of sixteen men and postmenopausal women consumed a run-in stabilisation diet for 4 weeks. Subjects were then randomly assigned to an experimental dietary portfolio either high or low in MUFA for another 4 weeks. MUFA substituted 13·0% of total energy from carbohydrate (CHO) in the high-MUFA dietary portfolio. Lipoprotein kinetics were assessed after the run-in and portfolio diets using a primed, constant infusion of [2H3]leucine and multicompartmental modelling. The high-MUFA dietary portfolio resulted in higher apoAI pool size (PS) compared with the low-MUFA dietary portfolio (15·9% between-diet difference, P»0·03). This difference appeared to be mainly attributable to a reduction in apoAI fractional catabolic rate (FCR) after the high-MUFA diet (25·6%, P»0·02 v. pre-diet values), with no significant change in production rate. The high-MUFA dietary portfolio tended to reduce LDL apoB100 PS compared with the low-MUFA dietary portfolio (228·5% between-diet that adding MUFA to a dietary portfolio of cholesterol-lowering foods provides the added advantage of raising HDL primarily through a reduction in HDL clearance rate. Replacing CHO with MUFA in a dietary portfolio may also lead to reductions in LDL apoB100 concentrations primarily by increasing LDL clearance rate, thus potentiating further the well-known cholesterol-lowering effect of this diet.

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Modulation of the gut microbiota is an area of growing interest, particularly for its link to improving and maintaining the systemic health of the host. It has been suggested to have potential to reduce risk factors associated with chronic diseases, such as elevated cholesterol levels in coronary heart disease (CHD). Diets of our evolutionary ancestors were largely based on plant foods, high in dietary fiber and fermentable substrate, and our gut microbiota has evolved against a background of such diets. Therapeutic diets that mimic plant-based diets from the early phases of human evolution may result in drug-like cholesterol reductions. In contrast, typical Western diets low in dietary fiber and fermentable substrate, and high in saturated and trans fatty acids, are likely contributors to the increased need for pharmacological agents for cholesterol reduction. The gut microbiota of those consuming a Western diet are likely underutilized and depleted of metabolic fuels, resulting in a less than optimal gut microbial profile. As a result, this diet is mismatched to our archaic gut microbiota and, therefore, to our genome, which has changed relatively little since humans first appeared. While the exact mechanism by which the gut microbiota may modulate cholesterol levels still remains uncertain, end products of bacterial fermentation, particularly the short chain fatty acids (i.e., propionate), have been suggested as potential candidates. While more research is required to clarify the potential link between gut microbiota and CHD risk reduction, consuming a therapeutic diet rich in plant foods, dietary fiber, and fermentable substrate would be a useful strategy for improving systemic health, possibly by altering the gut microbiota.

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BACKGROUND: Recent analyses have challenged the effectiveness of soy foods as part of a cardiovascular risk reduction diet. OBJECTIVE: The objective of the study was to show whether equol status determines the effectiveness of soy foods to lower LDL cholesterol and to raise HDL cholesterol. DESIGN: Eighty-five hypercholesterolemic men and postmenopausal women (42 men, 43 women) participated in 1 of 3 studies that represented a range of soy interventions and that followed the same general protocol at a Canadian university hospital research center. Soy foods were provided for 1 mo at doses of 30-52 g/d for the 3 studies as follows: 1) soy foods with either high-normal (73 mg/d) or low (10 mg/d) isoflavones, 2) soy foods with or without a prebiotic to enhance colonic fermentation (10 g polyfructans/d), or 3) soy foods with a low-carbohydrate diet (26% carbohydrate). Studies 1 and 2 were randomized controlled crossover trials, and study 3 was a parallel study. RESULTS: The separation of the group into equol producers (n = 30) and nonproducers (n = 55) showed similar reductions from baseline in LDL cholesterol (-9.3 ± 2.5% and -11.1 ± 1.6%, respectively; P = 0.834), with preservation of HDL cholesterol and apolipoprotein A-I only in equol producers compared with reductions in nonproducers (HDL cholesterol: +0.9 ± 2.7% compared with -4.3 ± 1.1%, P = 0.006; apolipoprotein A-I: -1.0 ± 1.1% compared with -4.7 ± 1.0%; P = 0.011). The amount of urinary equol excreted did not relate to the changes in blood lipids. CONCLUSIONS: Soy foods reduced serum LDL cholesterol equally in both equol producers and nonproducers. However, in equol producers, ~35% of our study population, soy consumption had the added cardiovascular benefit of maintaining higher HDL-cholesterol concentrations than those seen in equol nonproducers. This trial was registered at clinicaltrials.gov as NCT00877825 (study 1), NCT00516594 (study 2), and NCT00256516 (study 3).

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Diets rich in fruits and vegetables (FV) have been associated with a reduced risk of chronic disease, including cardiovascular disease. Unfortunately, public health campaigns to increase FV intake have had limited success. A number of mixed concentrated FV products have been studied, which may help certain individuals improve nutrient status. However, the possible health benefits of FV supplements have not been systematically reviewed. We, therefore, undertook a systematic search of MEDLINE and EMBASE to identify clinical interventions that examined the effect of commercially available concentrated mixed FV supplements on cardiovascular disease risk factors. Twenty-two reports, which used commercially available products, were identified. None of the studies reported any serious adverse effects. Overall, daily consumption of FV supplements significantly increased serum concentrations of the major antioxidant provitamins and vitamins found in plant foods (ß-carotene, vitamins C and E) and folate. Functional changes, such as reduced serum homocysteine and markers of protein, lipid, and DNA oxidation, were also reported; in addition, the health advantages on markers of inflammation, immunity, and endothelial function are promising. Limitations of the available studies were related to the diversity of studies conducted with respect to design and study population and the variability in the measured outcomes and assays utilized. While mixed FV supplements may serve as an efficacious complement for individuals who have difficulty achieving their daily FV intake requirement, further research on additional retail preparations is warranted. Key teaching points: Mixed fruit and vegetable supplements produced from plant foods may serve as an efficacious complement to the habitual diet in individuals who have suboptimal intake or variety of nutrient-dense fruits and vegetables. Current research indicates that fruit and vegetable concentrates significantly increase serum levels of antioxidant provitamins and vitamins (ß-carotene, vitamins C and E) and folate and reduce homocysteine and markers of oxidative stress. Mechanistic studies and larger, randomized, placebo-controlled double-blind trials in both healthy and high-risk populations are necessary to better understand the health effects of these supplements.

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Obesity is rapidly becoming a global epidemic. As it is a significant risk factor for several chronic diseases, including type 2 diabetes and cardiovascular disease, it is imperative to study dietary and lifestyle approaches that help reduce its prevalence. Recently, due to its possible link to appetite control and metabolism, several clinical studies have assessed the effect of low glycemic index (GI) and glycemic load (GL) diets on weight loss. To determine the application of GI/GL in the prevention and treatment of obesity, we searched several databases and identified 23 clinical trials that examined low GI/GL diets and weight loss as the primary outcome measure. In general, these studies showed much inconsistency in their findings. While a few studies found significantly greater weight loss on the low GI/GL diets, most of the other studies showed a non-significant trend that favored low GI/GL diets; suggesting that factors other than GI/GL may play a role. It would be helpful if a pooled analysis were undertaken to clarify the current findings and outline the limitations of these studies. There is also a need for more long-term randomized, controlled trials that not only focus on weight loss but also on weight maintenance and body composition.

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