IMPORTANCE Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels. OBJECTIVES To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed. DESIGN, SETTING, AND PARTICIPANTS This cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied. MAIN OUTCOMES AND MEASURES Glycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed. RESULTS Postprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R = 0.62) than current standard-of-care approaches using nutritional content alone (R = 0.34 for calories and R = 0.40 for carbohydrates) to control postprandial glycemic levels. CONCLUSIONS AND RELEVANCE Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.

Background Immigrant and refugee populations arrive to the US healthier than the general population, but the longer they reside, the more they approximate the cardiovascular risk profiles of the country. Among women, these declines are partly mediated by less physical activity and lower dietary quality upon immigration. Given the complex forces that influence these behaviors, a community-based participatory research (CBPR) approach is appropriate. Therefore, a socioculturally responsive physical activity and nutrition program was created with and for immigrant and refugee women in Rochester, MN through a CBPR approach. Methods Focus groups informed program content and revealed principles for designing the sessions. A 6-week program with two 90 minute classes per week was conducted among 45 women (Hispanic, Somali, Cambodian). Average attendance was 22.5 women per class; 34 women completed evaluation. Results Evaluation revealed high acceptability (average overall score of 4.85 out of 5 on the Physical Activity Class Satisfaction Questionnaire). Following the intervention, participants were more likely to exercise regularly (p=<0.001). They reported higher health-related quality of life (p=<0.001) and self-efficacy for diet (p=0.36) and exercise (p=0.10). Likewise, there were trends for weight loss (87 kg vs. 83.4 kg; p=0.65), decreased waist circumference (99.6 cm vs. 95.5 cm; p=0.35), and lower blood pressure (125/80 mm/Hg vs. 122/76 mm/Hg; p=0.27). Conclusions A CBPR approach to design and implement a socioculturally responsive fitness program was highly acceptable to immigrant and refugee women and demonstrated promising outcomes. Further testing of physical activity and nutrition interventions that arise organically from target communities are needed.

Six weeks of caloric restriction lowers fasting glucose and EGP with accompanying improvements in β cell function in people with type 2 diabetes. An additional 6 wk of caloric restriction maintained the improvement in glucose metabolism. This trial was registered at clinicaltrials.gov as NCT01094054.

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Determining daily energy requirements to help guide weight control involves all components of Total Energy Expenditure (TEE): Resting Energy Expenditure (REE), Physical Activity Energy Expenditure (PAEE), and the Thermogenic Effect of Food (TEF). However, precise measurement of TEE mandates sophisticated equipment not readily available outside research or clinical settings. The Harris-Benedict (HB), [1] Mifflin St. Jeor (MSJ), [2] and the Food and Agriculture Organization/ World Health Organization/ United Nations University (FAO/WHO/UNU)[3] are REE predicting equations widely used in different populations [4-6]. These equations consider factors such as weight, height, age, and gender. The predicted REE is then multiplied by a PA coefficient for an estimate of TEE (Table 1). These coefficients have been derived by subtracting REE measured with indirect calorimetry from the TEE measured with the doubly labelled water technique [3,7,8]. Therefore, PA coefficients account not only for PAEE but also the TEF. However, there are inconsistencies in the selection and recommendation of PA coefficients for different equations (Table 1). Specific PA coefficients are based on the PA level of each participant, usually determined by self-reports and dietitian's experience. Because PAEE is the most variable component of TEE, the impact of PA coefficients based on self-report vs. objective PA assessment must be evaluated. After gastric bypass surgery REE declines in direct association with weight loss [9-11], and REE prediction equations remain well correlated with measured REE (REE-m) in this population. Flancbaum et al. [12] showed that HB equation predicted 90-101% of the REE-m by indirect calorimetry from pre-to 6-months post-surgery and 107-111% from 12-24 months post-surgery; during which gastric bypass patient's weight decreased 96 to 146 kg. Among obese individuals, Prado-de Oliveira et al. [13] reported that HB equation predicted REE-m; however, prediction was 8% lower. More recently, Ullah et al. [14] observed that HB equation overestimated REE by 10% in morbidly obese adults,

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