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.

Leveraging technology to address unhealthy drug use in primary care: Effectiveness of the Substance use Screening and Intervention Tool (SUSIT)

Background: Screening for unhealthy drug use is now recommended for adult primary care patients, but primary care providers (PCPs) generally lack the time and knowledge required to screen and deliver an intervention during the medical visit. To address these barriers, we developed a tablet computer-based 'Substance Use Screening and Intervention Tool (SUSIT)'. Using the SUSIT, patients self-administer screening questionnaires prior to the medical visit, and results are presented to the PCP at the point of care, paired with clinical decision support (CDS) that guides them in providing a brief intervention (BI) for unhealthy drug use. Methods: PCPs and their patients with moderate-risk drug use were recruited from primary care and HIV clinics. A pre-post design compared a control 'screening only' (SO) period to an intervention 'SUSIT' period. Unique patients were enrolled in each period. In both conditions, patients completed screening and identified their drug of most concern (DOMC) before the visit, and completed a questionnaire about BI delivery by the PCP after the visit. In the SUSIT condition only, PCPs received the tablet with the patient's screening results and CDS. Multilevel models with random intercepts and patients nested within PCPs examined the effect of the SUSIT intervention on PCP delivery of BI. Results: 20 PCPs and 79 patients (42 SO, 37 SUSIT) participated. Most patients had moderate-risk marijuana use (92.4%), and selected marijuana as the DOMC (68.4%). Moderate-risk use of drugs other than marijuana included cocaine (15.2%), hallucinogens (12.7%), and sedatives (12.7%). Compared to the SO condition, patients in SUSIT had higher odds of receiving any BI for drug use, with an adjusted odds ratio of 11.59 (95% confidence interval: 3.39, 39.25), and received more elements of BI for drug use. Conclusions: The SUSIT significantly increased delivery of BI for drug use by PCPs during routine primary care encounters.