A randomized clinical trial comparing low-fat with precision nutrition-based diets for weight loss: impact on glycemic variability and HbA1c

BACKGROUND

Recent studies have demonstrated considerable interindividual variability in postprandial glucose response (PPGR) to the same foods, suggesting the need for more precise methods for predicting and controlling PPGR. In the Personal Nutrition Project, the investigators tested a precision nutrition algorithm for predicting an individual's PPGR.

OBJECTIVE

This study aimed to compare changes in glycemic variability (GV) and HbA1c in 2 calorie-restricted weight loss diets in adults with prediabetes or moderately controlled type 2 diabetes (T2D), which were tertiary outcomes of the Personal Diet Study.

METHODS

The Personal Diet Study was a randomized clinical trial to compare a 1-size-fits-all low-fat diet (hereafter, standardized) with a personalized diet (hereafter, personalized). Both groups received behavioral weight loss counseling and were instructed to self-monitor diets using a smartphone application. The personalized arm received personalized feedback through the application to reduce their PPGR. Continuous glucose monitoring (CGM) data were collected at baseline, 3 mo and 6 mo. Changes in mean amplitude of glycemic excursions (MAGEs) and HbA1c at 6 mo were assessed. We performed an intention-to-treat analysis using linear mixed regressions.

RESULTS

We included 156 participants [66.5% women, 55.7% White, 24.1% Black, mean age 59.1 y (standard deviation (SD) = 10.7 y)] in these analyses (standardized = 75, personalized = 81). MAGE decreased by 0.83 mg/dL per month for standardized (95% CI: 0.21, 1.46 mg/dL; P = 0.009) and 0.79 mg/dL per month for personalized (95% CI: 0.19, 1.39 mg/dL; P = 0.010) diet, with no between-group differences (P = 0.92). Trends were similar for HbA1c values.

CONCLUSIONS

Personalized diet did not result in an increased reduction in GV or HbA1c in patients with prediabetes and moderately controlled T2D, compared with a standardized diet. Additional subgroup analyses may help to identify patients who are more likely to benefit from this personalized intervention. This trial was registered at clinicaltrials.gov as NCT03336411.

Effect of a Personalized Diet to Reduce Postprandial Glycemic Response vs a Low-fat Diet on Weight Loss in Adults With Abnormal Glucose Metabolism and Obesity: A Randomized Clinical Trial

IMPORTANCE

Interindividual variability in postprandial glycemic response (PPGR) to the same foods may explain why low glycemic index or load and low-carbohydrate diet interventions have mixed weight loss outcomes. A precision nutrition approach that estimates personalized PPGR to specific foods may be more efficacious for weight loss.

OBJECTIVE

To compare a standardized low-fat vs a personalized diet regarding percentage of weight loss in adults with abnormal glucose metabolism and obesity.

DESIGN, SETTING, AND PARTICIPANTS

The Personal Diet Study was a single-center, population-based, 6-month randomized clinical trial with measurements at baseline (0 months) and 3 and 6 months conducted from February 12, 2018, to October 28, 2021. A total of 269 adults aged 18 to 80 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) ranging from 27 to 50 and a hemoglobin A1c level ranging from 5.7% to 8.0% were recruited. Individuals were excluded if receiving medications other than metformin or with evidence of kidney disease, assessed as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2 using the Chronic Kidney Disease Epidemiology Collaboration equation, to avoid recruiting patients with advanced type 2 diabetes.

INTERVENTIONS

Participants were randomized to either a low-fat diet (<25% of energy intake; standardized group) or a personalized diet that estimates PPGR to foods using a machine learning algorithm (personalized group). Participants in both groups received a total of 14 behavioral counseling sessions and self-monitored dietary intake. In addition, the participants in the personalized group received color-coded meal scores on estimated PPGR delivered via a mobile app.

MAIN OUTCOMES AND MEASURES

The primary outcome was the percentage of weight loss from baseline to 6 months. Secondary outcomes included changes in body composition (fat mass, fat-free mass, and percentage of body weight), resting energy expenditure, and adaptive thermogenesis. Data were collected at baseline and 3 and 6 months. Analysis was based on intention to treat using linear mixed modeling.

RESULTS

Of a total of 204 adults randomized, 199 (102 in the personalized group vs 97 in the standardized group) contributed data (mean [SD] age, 58 [11] years; 133 women [66.8%]; mean [SD] body mass index, 33.9 [4.8]). Weight change at 6 months was -4.31% (95% CI, -5.37% to -3.24%) for the standardized group and -3.26% (95% CI, -4.25% to -2.26%) for the personalized group, which was not significantly different (difference between groups, 1.05% [95% CI, -0.40% to 2.50%]; P = .16). There were no between-group differences in body composition and adaptive thermogenesis; however, the change in resting energy expenditure was significantly greater in the standardized group from 0 to 6 months (difference between groups, 92.3 [95% CI, 0.9-183.8] kcal/d; P = .05).

CONCLUSIONS AND RELEVANCE

A personalized diet targeting a reduction in PPGR did not result in greater weight loss compared with a low-fat diet at 6 months. Future studies should assess methods of increasing dietary self-monitoring adherence and intervention exposure.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03336411.

Impact of the COVID-19 Pandemic on Dietary Counseling Session Attendance and Self-Monitoring Adherence Dur034 a Behavioral Weight Loss Intervention

Objectives

To assess the impact of the COVID-19 pandemic on participants’ intervention counseling session attendance and dietary self-monitoring adherence during the Personal Diet Study, a remote behavioral weight loss intervention for individuals with overweight and obesity with pre-diabetes and moderately controlled type 2 diabetes.

Methods

Participants (n = 200) were instructed to complete four in-person measurement visits, enter their meals daily in a smartphone application, and attend 14 virtual group nutrition counseling sessions over a 6-month intervention period. Due to COVID-19, the assessments were modified to be conducted remotely. We stratified participants into 3 categories: a) all study measures and intervention occurred before the start of the COVID-19 pandemic (BEFORE, n = 106) b) a portion of the intervention or follow-up measures occurred after the start of the pandemic (MIXED, n = 54), and 3) all study measures and intervention took place after the start of the pandemic (AFTER, n = 40). Attendance was defined as percentage of counseling intervention sessions attended. Dietary self-monitoring adherence was measured as percentage of days participants entered at least 50% of their daily caloric goal in a smart phone application. Between-group differences were assessed using linear regression models.

Results

Mean [SD] counseling session attendance for the MIXED (72.6%, [28.9%]) and AFTER (73.8% [28.1%]) groups did not differ from the BEFORE group (64.5% [31.8%]), p = 0.26 and 0.22 respectively. Adherence to dietary self-monitoring was lower for the MIXED group (25.5% [30.55]) compared to BEFORE group (36.0% [34.8%], p = 0.03), but did not differ between the AFTER (44.5% [35.8%]) and BEFORE groups (p = 0.288).

Conclusions

Intervention counseling attendance did not change substantially due to the COVID-19 pandemic. The MIXED group had lower self-monitoring adherence rates than the BEFORE grouip, which may be due to disruptions in daily life and habits that occurred in the early months of the COVID-19 pandemic. Virtual weight loss counseling methods are a practical way of circumventing program disruptions without compromising protocol adherence.

Funding Sources

This research was supported by the American Heart Association.

A Randomized Clinical Trial to Compare a Precision Nutrition Intervention Targeting a Reduction in Postprandial Glycemic Response to Meals With a Low-Fat Diet for Weight Loss

Objectives

The primary aim is to compare the effects of a low-fat diet vs a personalized diet on % weight loss at 6-months. Secondary outcomes include body composition (fat mass [FM] and fat free mass [FFM]), resting energy expenditure (REE) and adaptive thermogenesis (AT).

Methods

The Personal Diet Study was a 6-month, single-center, randomized clinical trial in adults with pre-diabetes and moderately controlled type 2 diabetes who were overweight or obese. Participants were randomized to follow either a hypocaloric low-fat diet, with < 25% energy intake from total fat (Standardized), or a hypocaloric personalized diet determined by a machine learning algorithm which predicts PPGR to meals (Personalized). Participants in both arms received behavioral counseling and logged dietary intake and physical activity into a smartphone app. Participants in the Personalized arm received real-time feedback as color-coded scores based on pre-consumed meals entered into the smartphone app. T-tests were used to assess group differences.

Results

A total of 200 adults (Standardized: n = 97 vs. Personalized: n = 103) contributed data (mean [SD]: age, 58 [11] years; 67% female; BMI, 34.0 [4.8] kg/m²; HbA1c, 5.8 [0.6]%; Metformin use, 21.0%). There were no significant group differences in mean % weight loss (Standardized: −4.4 [4.8]% vs Personalized: −3.3 [5.4]%; p = 0.19), mean absolute change in FM (Standardized: −2.7 [3.4] kg vs. Personalized: −1.6 [3.5] kg; p = 0.18), and AT between the two arms (Standardized: −54.7 [177] kcal/d vs. Personalized: 26.2 [199] kcal/d; p = 0.078). However, the Standardized arm lost significantly more FFM (−1.4 [1.6] kg vs. −0.45 [2.0] kg; p = 0.03) and had a greater decrease in REE (−111.0 [195.0] kcal/d vs. 1.93 [215.0] kcal/d; p = 0.02) compared to Personalized.

Conclusions

A personalized diet to minimize PPGR had no greater effect on % weight loss compared to a low-fat diet at 6-months. Future precision nutrition trials may require deeper phenotyping of individuals or the development of body weight-specific algorithms.

Funding Sources

Supported by grants from the American Heart Association 17SFRN33590133.

Food Intake REstriction for Health OUtcome Support and Education (FIREHOUSE) Protocol: A Randomized Clinical Trial

Fire Department of New York (FDNY) rescue and recovery workers exposed to World Trade Center (WTC) particulates suffered loss of forced expiratory volume in 1 s (FEV1). Metabolic Syndrome increased the risk of developing WTC-lung injury (WTC-LI). We aim to attenuate the deleterious effects of WTC exposure through a dietary intervention targeting these clinically relevant disease modifiers. We hypothesize that a calorie-restricted Mediterranean dietary intervention will improve metabolic risk, subclinical indicators of cardiopulmonary disease, quality of life, and lung function in firefighters with WTC-LI. To assess our hypothesis, we developed the Food Intake REstriction for Health OUtcome Support and Education (FIREHOUSE), a randomized controlled clinical trial (RCT). Male firefighters with WTC-LI and a BMI > 27 kg/m2 will be included. We will randomize subjects (1:1) to either: (1) Low Calorie Mediterranean (LoCalMed)-an integrative multifactorial, technology-supported approach focused on behavioral modification, nutritional education that will include a self-monitored diet with feedback, physical activity recommendations, and social cognitive theory-based group counseling sessions; or (2) Usual Care. Outcomes include reduction in body mass index (BMI) (primary), improvement in FEV1, fractional exhaled nitric oxide, pulse wave velocity, lipid profiles, targeted metabolic/clinical biomarkers, and quality of life measures (secondary). By implementing a technology-supported LoCalMed diet our FIREHOUSE RCT may help further the treatment of WTC associated pulmonary disease.

Plant Protein Intake Is Not Associated with Cardiovascular Disease Risk Factors in Diabetic Patients with Chronic Kidney Disease (P08-055-19)

Objectives

A higher percentage of protein consumed from plants may have cardiovascular benefits and be associated with lower mortality in chronic kidney disease (CKD) patients. The purpose of this study was to examine the association of self-reported dietary protein intake with cardiovascular disease (CVD) risk factors in patients with type 2 diabetes (T2D) and CKD.

Methods

Baseline 3-day food records were obtained from 202 participants of an ongoing lifestyle intervention study, and analyzed using Nutrition Data System for Research (2014). Participants were categorized into tertiles based on total protein intake (<66.9 g, 66.9–92.4 g, > 92.4 g) and percent of total protein coming from plant sources (<27.9%, 27.9–37.8%, >37.8%). CVD risk factors included estimated glomerular filtration rate (eGFR), pulse wave velocity (PWV), fasting lipids (total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides [TG]), and hemoglobin A1c [HbA1c]). Analyses of covariance examined mean differences in CVD risk factors among the tertiles, controlling for age and total energy intake.

Results

The participants were 57% male, 89% non-Hispanic, 69% white, and 66 ± 9 years of age with a mean body mass index of 33.6 ± 5 kg/m2. Prior myocardial infarction was reported by 25(12.6%) of participants. Average daily protein intake was 83.3 ± 29.3 g (0.9 ± 0.3 g/kg body weight), with the average % of protein consumed from plant sources 34 ± 13%. There were no statistically significant differences between the total protein intake tertiles for the CVD risk factors (eGFR [P = .36], PWV [P = .86], total cholesterol [P = .09], LDL-cholesterol [P = .26], HDL-cholesterol [P = .88], TG [P = .88], HbA1c [P = .82]. Additionally, there were no statistically significant differences between the % of total protein intake from plant sources tertiles for the CVD risk factors (eGFR [P = .32], PWV [P = .92], total cholesterol [P = .29], LDL-cholesterol [P = .10], HDL-cholesterol [P = .57], TG [P = .13], HbA1c [P = .93].

Conclusions

Contrary to expectations, CVD risk factors did not differ among tertiles for total protein intake or % of total protein from plant sources. These findings suggest that, at baseline, dietary protein was not associated with CVD risk factors in patients with T2D and CKD.

Funding Sources

National Institutes of Health (NIDDK, NINR).

Phosphate Additive Avoidance in Chronic Kidney Disease

IN BRIEF Dietary guidelines for patients with diabetes extend beyond glycemic management to include recommendations for mitigating chronic disease risk. This review summarizes the literature suggesting that excess dietary phosphorus intake may increase the risk of skeletal and cardiovascular disease in patients who are in the early stages of chronic kidney disease (CKD) despite having normal serum phosphorus concentrations. It explores strategies for limiting dietary phosphorus, emphasizing that food additives, as a major source of highly bioavailable dietary phosphorus, may be a suitable target. Although the evidence for restricting phosphorus-based food additives in early CKD is limited, diabetes clinicians should monitor ongoing research aimed at assessing its efficacy.

Reexamining the Phosphorus-Protein Dilemma: Does Phosphorus Restriction Compromise Protein Status?

Dietary phosphorus restriction is recommended to help control hyperphosphatemia in hemodialysis patients, but many high-phosphorus foods are important sources of protein. In this review, we examine whether restricting dietary phosphorus compromises protein status in hemodialysis patients. Although dietary phosphorus and protein are highly correlated, phosphorus intakes can range up to 600 mg/day for a given energy and protein intake level. Furthermore, the collinearity of phosphorus and protein may be biased because the phosphorus burden of food depends on: (1) the presence of phosphate additives, (2) food preparation method, and (3) bioavailability of phosphorus, which are often unaccounted for in nutrition assessments. Ultimately, we argue that clinically relevant reductions in phosphorus intake can be made without limiting protein intake by avoiding phosphate additives in processed foods, using wet cooking methods such as boiling, and if needed, substituting high-phosphorus foods for nutritionally equivalent foods that are lower in bioavailable phosphorus.

Exploring Problems in Following the Hemodialysis Diet and Their Relation to Energy and Nutrient Intakes: The BalanceWise Study

OBJECTIVE

To identify the problems experienced by hemodialysis (HD) patients in attempting to follow the HD diet and their relation to energy and nutrient intakes.

DESIGN

Cross-sectional analysis of baseline data from the BalanceWise Study.

SUBJECTS

Participants included community-dwelling adults recruited from outpatient HD centers. After excluding participants with incomplete dietary analyses (n = 50), 140 African American and white (40/60%) men and women (52/48%) on chronic intermittent HD for at least 3 months (median 3 years) were included.

INTERVENTION

Participant responses, on a 5-point Likert scale ranging from "not at all a problem" to "a very important problem for me," to 34 questions pertaining to potential barriers to following the HD diet in the previous 2 months were classified as either a problem (1) or not a problem (2-5).

MAIN OUTCOME MEASURE

Energy and nutrient intakes determined using the Nutrition Data System for Research® based on 3, non-consecutive, unscheduled, 2-pass 24-hour dietary recalls collected on 1 dialysis and 1 non-dialysis weekday, and 1 non-dialysis weekend day.

RESULTS

More than half of participants reported having problems related to specific behavioral factors (e.g., feeling deprived), technical difficulties (e.g., tracking nutrients), and physical condition (e.g., appetite), but issues of time and food preparation and behavioral factors tended to be most deterministic of reported dietary intakes. Longer duration of HD was associated with lower intakes of protein, potassium, and phosphorus (P < .05).

CONCLUSION

Registered dietitian nutritionists should consider issues of time and food preparation, and behavioral factors in their nutrition assessment of HD patients and should continually monitor HD patients for changes in protein intake that may occur over time.