Design and conduct of a randomized controlled feeding trial in a residential setting with mitigation for COVID-19.

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.

Sugar-sweetened or artificially-sweetened beverage consumption, physical activity, and risk of cardiovascular disease in adults: a prospective cohort study.

BACKGROUND: Whether physical activity could mitigate the adverse impacts of sugar-sweetened beverages (SSBs) or artificially sweetened beverages (ASBs) on incident cardiovascular disease (CVD) remains uncertain. OBJECTIVES: This study aimed to examine the independent and joint associations between SSB or ASB consumption and physical activity and risk of CVD, defined as fatal and nonfatal coronary artery disease and stroke, in adults from 2 United States-based prospective cohort studies. METHODS: Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% CIs between SSB or ASB intake and physical activity with incident CVD among 65,730 females in the Nurses' Health Study (1980-2016) and 39,418 males in the Health Professional's Follow-up Study (1986-2016), who were free from chronic diseases at baseline. SSBs and ASBs were assessed every 4-y and physical activity biannually. RESULTS: A total of 13,269 CVD events were ascertained during 3,001,213 person-years of follow-up. Compared with those who never/rarely consumed SSBs or ASBs, the HR for CVD for participants consuming ≥2 servings/d was 1.21 (95% CI: 1.12, 1.32; P-trend < 0.001) for SSBs and 1.03 (95% CI: 0.97, 1.09; P-trend = 0.06) for those consuming ≥2 servings/d of ASBs. The HR for CVD per 1 serving increment of SSB per day was 1.18 (95% CI: 1.10, 1.26) and 1.12 (95% CI: 1.04, 1.20) for participants meeting and not meeting physical activity guidelines (≥7.5 compared with <7.5 MET h/wk), respectively. Compared with participants who met physical activity guidelines and never/rarely consumed SSBs, the HR for CVD was 1.47 (95% CI: 1.37, 1.57) for participants not meeting physical activity guidelines and consuming ≥2 servings/wk of SSBs. No significant associations were observed for ASB when stratified by physical activity. CONCLUSIONS: Higher SSB intake was associated with CVD risk regardless of physical activity levels. These results support current recommendations to limit the intake of SSBs even for physically active individuals.

Physiologic Adaptation to Macronutrient Change Distorts Findings from Short Dietary Trials: Reanalysis of a Metabolic Ward Study.

An influential 2-wk cross-over feeding trial without a washout period purported to show advantages of a low-fat diet (LFD) compared with a low-carbohydrate diet (LCD) for weight control. In contrast to several other macronutrient trials, the diet order effect was originally reported as not significant. In light of a new analysis by the original investigative group identifying an order effect, we aimed to examine, in a reanalysis of publicly available data (16 of 20 original participants; 7 female; mean BMI, 27.8 kg/m2), the validity of the original results and the claims that trial data oppose the carbohydrate-insulin model of obesity (CIM). We found that energy intake on the LCD was much lower when this diet was consumed first compared with second (a difference of -1164 kcal/d, P = 3.6 × 10-13); the opposite pattern was observed for the LFD (924 kcal/d, P = 2.0 × 10-16). This carry-over effect was significant (P interaction = 0.0004) whereas the net dietary effect was not (P = 0.4). Likewise, the between-arm difference (LCD - LFD) was -320 kcal/d in the first period and +1771 kcal/d in the second. Body fat decreased with consumption of the LCD first and increased with consumption of this diet second (-0.69 ± 0.33 compared with 0.57 ± 0.32 kg, P = 0.007). LCD-first participants had higher ß-hydroxybutyrate levels while consuming the LCD and lower respiratory quotients while consuming LFD when compared with LFD-first participants on their respective diets. Change in insulin secretion as assessed by C-peptide in the first diet period predicted higher energy intake and less fat loss in the second period. These findings, which tend to support rather than oppose the CIM, suggest that differential (unequal) carry-over effects and short duration, with no washout period, preclude causal inferences regarding chronic macronutrient effects from this trial.

Sugar- or artificially-sweetened beverage consumption, physical activity, and risk of cardiovascular disease in US adults.

Background: The extent to which physical activity attenuates the detrimental effects of sugar (SSBs)- or artificially-sweetened beverages (ASBs) on the risk of cardiovascular disease is unknown. Methods: We used Cox proportional-hazards models to calculate hazard ratios and 95% confidence interval [HR (CI)] between SSB or ASB intake and physical activity with cardiovascular disease risk among 65,730 women in the Nurses' Health Study (1980-2016) and 39,418 men in the Health Professional's Follow-up Study (1986-2016), who were free from chronic diseases at baseline. SSBs and ASBs were assessed every 4-years and physical activity biannually. Results: A total of 13,269 cardiovascular events were ascertained during 3,001,213 person-years of follow-up. Compared with those that never/rarely consumed SSBs or ASBs, HR and 95% CI for cardiovascular disease for participants consuming ≥2 servings/day were 1.21 (95% CI,1.12 to 1.32; P-trend<0.001) and 1.03 (95% CI, 0.97 to 1.09; P-trend=0.06), respectively. In the joint analyses, for participants meeting and not meeting physical activity guidelines (<7.5 vs ≥7.5 MET-h/week) as well as consuming ≥2 servings/day of SSBs or ASBs, the HRs for cardiovascular disease were 1.15 (95% CI, 1.08 to 1.23) and 0.96 (95% CI, 0.91 to 1.02), and 1.47 (95% CI, 1.37 to 1.57) and 1.29 (95% CI, 1.22 to 1.37) respectively, compared with participants who met physical activity guidelines and never/rarely consumed these beverages. Similar patterns were observed when coronary heart disease and stroke were analyzed. Conclusions: Our findings suggest that among physically active participants, higher SSB intake, but not ASBs, is associated with a higher cardiovascular risk. Our results support current recommendations to limit the intake of SSB and maintain adequate physical activity levels.

Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry.

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).

Evidence for the carbohydrate-insulin model in a reanalysis of the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) trial.

BACKGROUND: The Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) trial demonstrated that meaningful weight loss can be achieved with either a "healthy low-carbohydrate diet" (LCD) or "healthy low-fat diet" (LFD). However, because both diets substantially decreased glycemic load (GL), the dietary factors mediating weight loss remain unclear. OBJECTIVES: We aimed to explore the contribution of macronutrients and GL to weight loss in DIETFITS and examine a hypothesized relationship between GL and insulin secretion. DESIGN: This study is a secondary data analysis of the DIETFITS trial, in which participants with overweight or obesity (aged 18-50 y) were randomized to a 12-mo LCD (N = 304) or LFD (N = 305). RESULTS: Measures related to carbohydrate intake (total amount, glycemic index, added sugar, and fiber) showed strong associations with weight loss at 3-, 6-, and 12-mo time points in the full cohort, whereas those related to total fat intake showed weak to no associations. A biomarker of carbohydrate (triglyceride/HDL cholesterol ratio) predicted weight loss at all time points (3-mo: ß [kg/biomarker z-score change] = 1.1, P = 3.5 × 10-9; 6-mo: ß = 1.7, P = 1.1 × 10-9; and 12-mo: ß = 2.6, P = 1.5 × 10-15), whereas that of fat (low-density lipoprotein cholesterol + HDL cholesterol) did not (all time points: P = NS). In a mediation model, GL explained most of the observed effect of total calorie intake on weight change. Dividing the cohort into quintiles of baseline insulin secretion and GL reduction revealed evidence of effect modification for weight loss, with P = 0.0009 at 3 mo, P = 0.01 at 6 mo, and P = 0.07 at 12 mo. CONCLUSIONS: As predicted by the carbohydrate-insulin model of obesity, weight loss in both diet groups of DIETFITS seems to have been driven by the reduction of GL more so than dietary fat or calories, an effect that may be most pronounced among those with high insulin secretion. These findings should be interpreted cautiously in view of the exploratory nature of this study. TRIAL REGISTRATION: ClinicalTrials.gov (NCT01826591).

Are methods of estimating fat-free mass loss with energy-restricted diets accurate?

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.

Competing paradigms of obesity pathogenesis: energy balance versus carbohydrate-insulin models.

The obesity pandemic continues unabated despite a persistent public health campaign to decrease energy intake ("eat less") and increase energy expenditure ("move more"). One explanation for this failure is that the current approach, based on the notion of energy balance, has not been adequately embraced by the public. Another possibility is that this approach rests on an erroneous paradigm. A new formulation of the energy balance model (EBM), like prior versions, considers overeating (energy intake > expenditure) the primary cause of obesity, incorporating an emphasis on "complex endocrine, metabolic, and nervous system signals" that control food intake below conscious level. This model attributes rising obesity prevalence to inexpensive, convenient, energy-dense, "ultra-processed" foods high in fat and sugar. An alternative view, the carbohydrate-insulin model (CIM), proposes that hormonal responses to highly processed carbohydrates shift energy partitioning toward deposition in adipose tissue, leaving fewer calories available for the body's metabolic needs. Thus, increasing adiposity causes overeating to compensate for the sequestered calories. Here, we highlight robust contrasts in how the EBM and CIM view obesity pathophysiology and consider deficiencies in the EBM that impede paradigm testing and refinement. Rectifying these deficiencies should assume priority, as a constructive paradigm clash is needed to resolve long-standing scientific controversies and inform the design of new models to guide prevention and treatment. Nevertheless, public health action need not await resolution of this debate, as both models target processed carbohydrates as major drivers of obesity.

Prolonged Glycemic Adaptation Following Transition From a Low- to High-Carbohydrate Diet: A Randomized Controlled Feeding Trial.

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.

A high-carbohydrate diet lowers the rate of adipose tissue mitochondrial respiration.

Adipocyte mitochondrial respiration may influence metabolic fuel partitioning into oxidation versus storage, with implications for whole-body energy expenditure. Although insulin has been shown to influence mitochondrial respiration, the effects of dietary macronutrient composition have not been well characterized. The aim of this exploratory study was to test the hypothesis that a high-carbohydrate diet lowers the oxygen flux of adipocyte mitochondria ex vivo. Among participants in a randomized-controlled weight-loss maintenance feeding trial, those consuming a high-carbohydrate diet (60% carbohydrate as a proportion of total energy, n = 10) had lower rates of maximal adipose tissue mitochondrial respiration than those consuming a moderate-carbohydrate diet (40%, n = 8, p = 0.039) or a low-carbohydrate diet (20%, n = 9, p = 0.005) after 10 to 15 weeks. This preliminary finding may provide a mechanism for postulated calorie-independent effects of dietary composition on energy expenditure and fat deposition, potentially through the actions of insulin on fuel partitioning.

Stimulated Insulin Secretion Predicts Changes in Body Composition Following Weight Loss in Adults with High BMI.

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.

Effects of a low-carbohydrate diet on insulin-resistant dyslipoproteinemia-a randomized controlled feeding trial.

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.