Snack quality and snack timing are associated with cardiometabolic blood markers: the ZOE PREDICT study.

BACKGROUND: Snacking is a common diet behaviour which accounts for a large proportion of daily energy intake, making it a key determinant of diet quality. However, the relationship between snacking frequency, quality and timing with cardiometabolic health remains unclear. DESIGN: Demography, diet, health (fasting and postprandial cardiometabolic blood and anthropometrics markers) and stool metagenomics data were assessed in the UK PREDICT 1 cohort (N = 1002) (NCT03479866). Snacks (foods or drinks consumed between main meals) were self-reported (weighed records) across 2-4 days. Average snacking frequency and quality [snack diet index (SDI)] were determined (N = 854 after exclusions). Associations between snacking frequency, quality and timing with cardiometabolic blood and anthropometric markers were assessed using regression models (adjusted for age, sex, BMI, education, physical activity level and main meal quality). RESULTS: Participants were aged (mean, SD) 46.1 ± 11.9 years, had a mean BMI of 25.6 ± 4.88 kg/m2 and were predominantly female (73%). 95% of participants were snackers (≥ 1 snack/day; n = 813); mean daily snack intake was 2.28 snacks/day (24 ± 16% of daily calories; 203 ± 170 kcal); and 44% of participants were discordant for meal and snack quality. In snackers, overall snacking frequency and quantity of snack energy were not associated with cardiometabolic risk markers. However, lower snack quality (SDI range 1-11) was associated with higher blood markers, including elevated fasting triglycerides (TG (mmol/L) ß; - 0.02, P = 0.02), postprandial TGs (6hiAUC (mmol/L.s); ß; - 400, P = 0.01), fasting insulin (mIU/L) (ß; - 0.15, P = 0.04), insulin resistance (HOMA-IR; ß; - 0.04, P = 0.04) and hunger (scale 0-100) (ß; - 0.52, P = 0.02) (P values non-significant after multiple testing adjustments). Late-evening snacking (≥ 9 pm; 31%) was associated with lower blood markers (HbA1c; 5.54 ± 0.42% vs 5.46 ± 0.28%, glucose 2hiAUC; 8212 ± 5559 vs 7321 ± 4928 mmol/L.s, P = 0.01 and TG 6hiAUC; 11,638 ± 8166 vs 9781 ± 6997 mmol/L.s, P = 0.01) compared to all other snacking times (HbA1c remained significant after multiple testing). CONCLUSION: Snack quality and timing of consumption are simple diet features which may be targeted to improve diet quality, with potential health benefits. CLINICAL TRIAL REGISTRY NUMBER AND WEBSITE: NCT03479866, https://clinicaltrials.gov/ct2/show/NCT03479866?term=NCT03479866&draw=2&rank=1.

Impact of insufficient sleep on dysregulated blood glucose control under standardised meal conditions.

AIMS/HYPOTHESIS: Sleep, diet and exercise are fundamental to metabolic homeostasis. In this secondary analysis of a repeated measures, nutritional intervention study, we tested whether an individual's sleep quality, duration and timing impact glycaemic response to a breakfast meal the following morning. METHODS: Healthy adults' data (N = 953 [41% twins]) were analysed from the PREDICT dietary intervention trial. Participants consumed isoenergetic standardised meals over 2 weeks in the clinic and at home. Actigraphy was used to assess sleep variables (duration, efficiency, timing) and continuous glucose monitors were used to measure glycaemic variation (>8000 meals). RESULTS: Sleep variables were significantly associated with postprandial glycaemic control (2 h incremental AUC), at both between- and within-person levels. Sleep period time interacted with meal type, with a smaller effect of poor sleep on postprandial blood glucose levels when high-carbohydrate (low fat/protein) (pinteraction = 0.02) and high-fat (pinteraction = 0.03) breakfasts were consumed compared with a reference 75 g OGTT. Within-person sleep period time had a similar interaction (high carbohydrate: pinteraction = 0.001, high fat: pinteraction = 0.02). Within- and between-person sleep efficiency were significantly associated with lower postprandial blood glucose levels irrespective of meal type (both p < 0.03). Later sleep midpoint (time deviation from midnight) was found to be significantly associated with higher postprandial glucose, in both between-person and within-person comparisons (p = 0.035 and p = 0.051, respectively). CONCLUSIONS/INTERPRETATION: Poor sleep efficiency and later bedtime routines are associated with more pronounced postprandial glycaemic responses to breakfast the following morning. A person's deviation from their usual sleep pattern was also associated with poorer postprandial glycaemic control. These findings underscore sleep as a modifiable, non-pharmacological therapeutic target for the optimal regulation of human metabolic health. Trial registration ClinicalTrials.gov NCT03479866.

Interaction of diabetes genetic risk and successful lifestyle modification in the Diabetes Prevention Programme.

AIM: To test whether diabetes genetic risk modifies the association of successful lifestyle changes with incident diabetes. MATERIALS AND METHODS: We studied 823 individuals randomized to the intensive lifestyle intervention (ILS) arm of the Diabetes Prevention Programme who were diabetes-free 1 year after enrolment. We tested additive and multiplicative interactions of a 67-variant diabetes genetic risk score (GRS) with achievement of three ILS goals at 1 year (≥7% weight loss, ≥150 min/wk of moderate leisure-time physical activity, and/or a goal for self-reported total fat intake) on the primary outcome of incident diabetes over 3 years of follow-up. RESULTS: A lower GRS and achieving each or all three ILS goals were each associated with lower incidence of diabetes (all P < 0.05). Additive interactions were significant between the GRS and achievement of the weight loss goal (P < 0.001), physical activity goal (P = 0.02), and all three ILS goals (P < 0.001) for diabetes risk. Achievement of all three ILS goals was associated with 1.8 (95% CI 0.3, 3.4), 3.1 (95% CI 1.5, 4.7), and 3.9 (95% CI 1.6, 6.2) fewer diabetes cases/100-person-years in the first, second and third GRS tertiles (P < 0.001 for trend). Multiplicative interactions between the GRS and ILS goal achievement were significant for the diet goal (P < 0.001), but not for weight loss (P = 0.18) or physical activity (P = 0.62) goals. CONCLUSIONS: Genetic risk may identify high-risk subgroups for whom successful lifestyle modification is associated with greater absolute reduction in the risk of incident diabetes.

Effectiveness of Lifestyle Intervention for Type 2 Diabetes in Primary Care: the REAL HEALTH-Diabetes Randomized Clinical Trial.

BACKGROUND: Intensive lifestyle interventions (LI) improve outcomes in obesity and type 2 diabetes but are not currently available in usual care. OBJECTIVE: To compare the effectiveness and costs of two group LI programs, in-person LI and telephone conference call (telephone LI), to medical nutrition therapy (MNT) on weight loss in primary care patients with type 2 diabetes. DESIGN: A randomized, assessor-blinded, practice-based clinical trial in three community health centers and one hospital-based practice affiliated with a single health system. PARTICIPANTS: A total of 208 primary care patients with type 2 diabetes, HbA1c 6.5 to < 11.5, and BMI > 25 kg/m2 (> 23 kg/m2 in Asians). INTERVENTIONS: Dietitian-delivered in-person or telephone group LI programs with medication management or MNT referral. MAIN MEASURES: Primary outcome: mean percent weight change. SECONDARY OUTCOMES: 5% and 10% weight loss, change in HbA1c, and cost per kilogram lost. KEY RESULTS: Participants' mean age was 62 (SD 10) years, 45% were male, and 77% were White, with BMI 35 (SD 5) kg/m2 and HbA1c 7.7 (SD 1.2). Seventy were assigned to in-person LI, 72 to telephone LI, and 69 to MNT. The mean percent weight loss (95% CI) at 6 and 12 months was 5.6% (4.4-6.8%) and 4.6% (3.1-6.1%) for in-person LI, 4.6% (3.3-6.0%) and 4.8% (3.3-6.2%) for telephone LI, and 1.1% (0.2-2.0%) and 2.0% (0.9-3.0%) for MNT, with statistically significant differences between each LI arm and MNT (P < 0.001) but not between LI arms (P = 0.63). HbA1c improved in all participants. Compared with MNT, the incremental cost per kilogram lost was $789 for in-person LI and $1223 for telephone LI. CONCLUSIONS: In-person LI or telephone group LI can achieve good weight loss outcomes in primary care type 2 diabetes patients at a reasonable cost. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02320253.

"I was able to eat what I am supposed to eat"-- patient reflections on a medically-tailored meal intervention: a qualitative analysis.

BACKGROUND: Medically-tailored meal programs that provide home-delivered medically-appropriate food are an emerging intervention when type 2 diabetes co-occurs with food insecurity (limited or uncertain access to nutritious food owing to cost). We sought to understand the experiences of medically-tailored meal program participants. METHODS: We conducted semi-structured interviews with participants in a randomized trial of medically-tailored meals (NCT02426138) until reaching content saturation. Participants were adults (age > 20 years) with type 2 diabetes in eastern Massachusetts, and the interviews were conducted from April to July 2017. Interviews were transcribed verbatim and coded by two independent reviewers. We determined emergent themes using content analysis. RESULTS: Twenty individuals were interviewed. Their mean age was 58 (SD: 13) years, 60.0% were women, 20.0% were non-Hispanic black, and 15.0% were Hispanic. Key themes were 1) satisfaction and experience with medically-tailored meals 2) food preferences and cultural appropriateness, 3) diabetes management and awareness, and 4) suggestions for improvement and co-interventions. Within these themes, participants were generally satisfied with medically-tailored meals and emphasized the importance of receiving culturally appropriate food. Participants reported several positive effects of medically-tailored meals, including improved quality of life and ability to manage diabetes, and stress reduction. Participants suggested combining medically-tailored meals with diabetes self-management education or lifestyle interventions. CONCLUSIONS: Individuals with diabetes and food insecurity expressed satisfaction with the medically-tailored meal program, and reported that participation reduced stress and the burden of diabetes management. Suggestions to help ensure the success of medically-tailored meal programs included a strong emphasis on culturally acceptability and accommodating taste preferences for provided foods, and combining medically-tailored meals with diabetes education or lifestyle intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT02426138.

Medically Tailored Meal Delivery for Diabetes Patients with Food Insecurity: a Randomized Cross-over Trial.

BACKGROUND: Food insecurity, defined as inconsistent food access owing to cost, leads to poor health. OBJECTIVE: To test whether a medically tailored meal delivery program improved dietary quality in individuals with type 2 diabetes and food insecurity. DESIGN: Randomized cross-over clinical trial. PARTICIPANTS: Forty-four adults with diabetes, hemoglobin A1c > 8.0%, and food insecurity (defined as at least one positive item on the two-item "Hunger Vital Sign"). INTERVENTION: In the Community Servings: Food as Medicine for Diabetes cross-over clinical trial (NCT02426138), conducted from June 2015 to July 2017, we randomly assigned the order of "on-meals" (home delivery of 10 meals/week for 12 weeks delivered by Community Servings, a non-profit organization) and "off-meals" (12 weeks usual care and a Choose MyPlate healthy eating brochure) periods. MAIN MEASURES: The primary outcome was Healthy Eating Index 2010 score (HEI), assessed by three 24-h food recalls in both periods. Higher HEI score (range 0-100; clinically significant difference 5) represents better dietary quality. Secondary outcomes included food insecurity and self-reported hypoglycemia. KEY RESULTS: Mean "on-meal" HEI score was 71.3 (SD 7.5) while mean "off-meal" HEI score was 39.9 (SD 7.8) (difference 31.4 points, p < 0.0001). Participants experienced improvements in almost all sub-categories of HEI score, with increased consumption of vegetables, fruits, and whole grains and decreased solid fats, alcohol, and added sugar consumption. Participants also reported lower food insecurity (42% "on-meal" vs. 62% "off-meal," p = 0.047), less hypoglycemia (47% "on-meal" vs. 64% "off-meal," p = 0.03), and fewer days where mental health interfered with quality of life (5.65 vs. 9.59 days out of 30, p = 0.03). CONCLUSIONS: For food-insecure individuals with diabetes, medically tailored meals improved dietary quality and food insecurity and reduced hypoglycemia. Longer-term studies should evaluate effects on diabetes control (e.g., hemoglobin A1c) and patient-reported outcomes (e.g., well-being).

Impact of lifestyle behavior change on glycemic control in youth with type 2 diabetes.

BACKGROUND: Little is known about the feasibility and impact of lifestyle intervention, determined by change in diet and cardiovascular fitness (CRF), on glycemic control in youth who are overweight with type 2 diabetes. This was examined in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY) clinical trial cohort from across 15 US centers. SUBJECTS: TODAY enrolled 699 youth aged 10 to 17 years with type 2 diabetes <2 years and body mass index ≥85th percentile at baseline. METHODS: Dietary data were collected by an interviewer-administered food frequency questionnaire; CRF was assessed using a submaximal cycle ergometer test. Change from baseline in these variables was analyzed using generalized linear mixed models for both continuous and categorical measures. Models were adjusted for age, baseline HbA1c, treatment group, and medication adherence. Data were collected at baseline, 6, and 24 months. Trial registration ClinicalTrials.gov NCT00081328. RESULTS: At 6 months, ~25% of females and ~33% of males improved CRF. In males, this was related to a decreased HbA1c (P = .001) and a lower percent experiencing glycemic failure (HbA1c ≥8%; P = .007). Females who decreased their saturated fat intake and/or increased their fiber intake had lower HbA1c at month 24 (P = .01 and P = .007, respectively). Males who increased their sweetened beverage intake at 6-month follow-up were at a 1.6-fold higher risk of experiencing glycemic failure (P = .04). CONCLUSIONS: Few youth improved fitness and/or diet over time, although those who did showed a beneficial impact on glycemic outcomes. Although lifestyle behaviors are difficult to change in youth with type 2 diabetes, interventions are needed that are feasible (in scope, complexity, and demands), sustainable, and clinically meaningful.

Replication of the Association of BDNF and MC4R Variants With Dietary Intake in the Diabetes Prevention Program.

OBJECTIVE: Genomewide association studies (GWAS) have identified consistent associations with obesity, with a number of studies implicating eating behavior as a primary mechanism. Few studies have replicated genetic associations with dietary intake. This study evaluates the association between obesity susceptibility loci and dietary intake. METHODS: Data were obtained as part of the Diabetes Prevention Program (DPP), a clinical trial of diabetes prevention in persons at high risk of diabetes. The association of 31 genomewide association studies identified obesity risk alleles with dietary intake, measured through a food frequency questionnaire, was investigated in 3,180 participants from DPP at baseline. RESULTS: The minor allele at BDNF, identified as protective against obesity, was associated with lower total caloric intake (ß = -106.06, SE = 33.13; p = .0014) at experimentwide statistical significance (p = .0016), whereas association of MC4R rs571312 with higher caloric intake reached nominal significance (ß = 61.32, SE = 26.24; p = .0194). Among non-Hispanic white participants, the association of BDNF rs2030323 with total caloric intake was stronger (ß = -151.99, SE = 30.09; p < .0001), and association of FTO rs1421085 with higher caloric intake (ß = 56.72, SE = 20.69; p = .0061) and percentage fat intake (ß = 0.37, SE = 0.08; p = .0418) was also observed. CONCLUSIONS: These results demonstrate with the strength of independent replication that BDNF rs2030323 is associated with 100 to 150 greater total caloric intake per allele, with additional contributions of MC4R and, in non-Hispanic white individuals, FTO. As it has been argued that an additional 100 kcal/d could account for the trends in weight gain, prevention focusing on genetic profiles with high dietary intake may help to quell adverse obesity trends. CLINICAL TRIAL REGISTRATION: Clinicaltrials.gov,NCT00004992.

A High-Carbohydrate, High-Fiber, Low-Fat Diet Results in Weight Loss among Adults at High Risk of Type 2 Diabetes.

Background: Weight loss is a key factor in reducing diabetes risk. The Diabetes Prevention Program (DPP) is a completed clinical trial that randomly assigned individuals at high risk of diabetes to a placebo (PLBO), metformin (MET), or intensive lifestyle intervention (ILS) group, which included physical activity (PA) and reduced dietary fat intake.Objective: We aimed to evaluate the associations between diet and weight at baseline and to identify specific dietary factors that predicted weight loss among DPP participants.Methods: Diet was assessed by a food frequency questionnaire. The associations between intakes of macronutrients and various food groups and body weight among DPP participants at baseline were assessed by linear regression, adjusted for race/ethnicity, age, sex, calorie intake, and PA. Models that predicted weight loss at year 1 were adjusted for baseline weight, change in calorie intake, and change in PA and stratified by treatment allocation (MET, ILS, and PLBO). All results are presented as estimates ± SEs.Results: A total of 3234 participants were enrolled in the DPP; 2924 had completed dietary data (67.5% women; mean age: 50.6 ± 10.7 y). Adjusted for calorie intake, baseline weight was negatively associated with carbohydrate intake (-1.14 ± 0.18 kg body weight/100 kcal carbohydrate, P < 0.0001) and, specifically, dietary fiber (-1.26 ± 0.28 kg/5 g fiber, P < 0.0001). Baseline weight was positively associated with total fat (1.25 ± 0.21 kg/100 kcal, P < 0.0001), saturated fat (1.96 ± 0.46 kg/100 kcal, P < 0.0001), and protein (0.21 ± 0.05 kg/100 kcal, P < 0.0001). For all groups, weight loss after 1 y was associated with increases in carbohydrate intake, specifically dietary fiber, and decreases in total fat and saturated fat intake.Conclusions: Higher carbohydrate consumption among DPP participants, specifically high-fiber carbohydrates, and lower total and saturated fat intake best predicted weight loss when adjusted for changes in calorie intake. Our results support the benefits of a high-carbohydrate, high-fiber, low-fat diet in the context of overall calorie reduction leading to weight loss, which may prevent diabetes in high-risk individuals. This trial was registered at clinicaltrials.gov as NCT00004992.

Weight loss in the prevention and treatment of diabetes.

The American Diabetes Association nutrition and lifestyle recommendations for prediabetes and type 2 diabetes focus on losing 7% of body weight and increasing physical activity to at least 150minperweek. This emphasis is largely based on results of the Diabetes Prevention Program (DPP) and Look AHEAD (Action for Health in Diabetes) clinical trials. DPP demonstrated that a lifestyle intervention aimed at 7% weight loss and 150min of activity per week reduced diabetes incidence by 58% after 2.8years of follow-up and resulted in sustained improvements in hemoglobinA1c, blood pressure and lipid levels. After 15years of follow-up, DPP's lifestyle intervention sustained a 27% risk reduction in progression to diabetes. Look AHEAD's lifestyle intervention significantly reduced hemoglobinA1c, blood pressure, triglycerides, and the amount and costs of medications needed to treat these conditions when compared with diabetes support and education. Other clinical and psychological benefits achieved with lifestyle intervention were greater reductions in c-reactive protein, less self-reported retinopathy, reduced risk of nephropathy, less sexual dysfunction, decreased incidence of urinary incontinence and fatty liver, remission of sleep apnea, better physical functioning, less knee pain, more remission of diabetes, reduced incidence of depression, less body image dissatisfaction and improved quality-of-life. A number of DPP translation studies have demonstrated weight losses of 4 to 7% at 6month and 1year follow-up which has led to Medicare coverage for CDC recognized DPP lifestyle programs starting in April 2018. Translation studies of Look AHEAD using a variety of delivery formats are underway.

Self-determination theory and weight loss in a Diabetes Prevention Program translation trial.

We examined self-determination theory (SDT) and weight loss, and hypothesized that the Diabetes Prevention Program's (DPP) intervention would result in an increase in autonomous regulation of motivation (AR) in participants. Further, that those with higher AR, and those who perceived educators as supporting SDT-defined needs, would lose more weight. Support, Health Information, Nutrition and Exercise (SHINE) Study data (N = 257) were analyzed. SHINE was a randomized, controlled DPP translation trial (2-years, telephonic, primary care staff). Autonomous motivation in males increased significantly, while females showed no change. Males with high AR, but not females, lost more weight. However, the significance of these relationships varied over time. Participants who perceived educators as more supportive of psychological needs lost more weight (especially males). However, effect of support on weight loss was not mediated by AR change. Autonomous motivation and educator support are relevant to male weight loss. Future research might develop interventions to enhance autonomous motivation and educator support, and understand change pathways.

Making a Meaningful Difference: Learning From People, Practice, and Research.

EDITOR'S NOTE: This article is adapted from the address Ms. Delahanty delivered as the recipient of the American Diabetes Association's (ADA) Outstanding Educator in Diabetes Award for 2015. She delivered the address in June 2015 at the Association's 75th Scientific Sessions in Boston, Mass. A webcast of this speech is available for viewing at the ADA website (http://professional.diabetes.org/webcasts).

The impact of lifestyle intervention on sedentary time in individuals at high risk of diabetes.

AIMS/HYPOTHESIS: The Diabetes Prevention Program (DPP) lifestyle intervention successfully achieved its goal of increasing leisure physical activity levels. This current study examines whether the lifestyle intervention also changed time spent being sedentary and the impact of sedentary time on diabetes development in this cohort. METHODS: 3,232 DPP participants provided baseline data. Sedentary behaviour was assessed via an interviewer-administered questionnaire and reported as time spent watching television specifically (or combined with sitting at work). Mean change in sedentary time was examined using repeated measures ANCOVA. The relationship between sedentary time and diabetes incidence was determined using Cox proportional hazards models. RESULTS: During the DPP follow-up (mean: 3.2 years), sedentary time declined more in the lifestyle than the metformin or placebo participants (p < 0.05). For the lifestyle group, the decrease in reported mean television watching time (22 [95% CI 26, 17] min/day) was greater than in the metformin or placebo groups (p < 0.001). Combining all participants together, there was a significantly increased risk of developing diabetes with increased television watching (3.4% per hour spent watching television), after controlling for age, sex, treatment arm and leisure physical activity (p < 0.01), which was attenuated when time-dependent weight was added to the model. CONCLUSIONS/INTERPRETATION: In the DPP, the lifestyle intervention was effective at reducing sedentary time, which was not a primary goal. In addition, in all treatment arms, individuals with lower levels of sedentary time had a lower risk of developing diabetes. Future lifestyle intervention programmes should emphasise reducing television watching and other sedentary behaviours in addition to increasing physical activity. TRIAL REGISTRATION: ClinicalTrials.gov NCT00004992.

Genetic modulation of lipid profiles following lifestyle modification or metformin treatment: the Diabetes Prevention Program.

Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04-1 × 10(-17)). Except for total HDL particles (r = -0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07-0.17, P = 5 × 10(-5)-1 10(-19)). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (ß = +0.87, SEE ± 0.22 mg/dl/allele, P = 8 × 10(-5), P(interaction) = 0.02) in the lifestyle intervention group, but not in the placebo (ß = +0.20, SEE ± 0.22 mg/dl/allele, P = 0.35) or metformin (ß = -0.03, SEE ± 0.22 mg/dl/allele, P = 0.90; P(interaction) = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (ß = +0.30, SEE ± 0.012 ln nmol/L/allele, P = 0.01, P(interaction) = 0.01) but not in the placebo (ß = -0.002, SEE ± 0.008 ln nmol/L/allele, P = 0.74) or metformin (ß = +0.013, SEE ± 0.008 nmol/L/allele, P = 0.12; P(interaction) = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss.

Common variation at PPARGC1A/B and change in body composition and metabolic traits following preventive interventions: the Diabetes Prevention Program.

AIMS/HYPOTHESIS: PPARGC1A and PPARGCB encode transcriptional coactivators that regulate numerous metabolic processes. We tested associations and treatment (i.e. metformin or lifestyle modification) interactions with metabolic traits in the Diabetes Prevention Program, a randomised controlled trial in persons at high risk of type 2 diabetes. METHODS: We used Tagger software to select 75 PPARGCA1 and 94 PPARGC1B tag single-nucleotide polymorphisms (SNPs) for analysis. These SNPs were tested for associations with relevant cardiometabolic quantitative traits using generalised linear models. Aggregate genetic effects were tested using the sequence kernel association test. RESULTS: In aggregate, PPARGC1A variation was strongly associated with baseline triacylglycerol concentrations (p = 2.9 × 10(-30)), BMI (p = 2.0 × 10(-5)) and visceral adiposity (p = 1.9 × 10(-4)), as well as with changes in triacylglycerol concentrations (p = 1.7 × 10(-5)) and BMI (p = 9.9 × 10(-5)) from baseline to 1 year. PPARGC1B variation was only associated with baseline subcutaneous adiposity (p = 0.01). In individual SNP analyses, Gly482Ser (rs8192678, PPARGC1A) was associated with accumulation of subcutaneous adiposity and worsening insulin resistance at 1 year (both p < 0.05), while rs2970852 (PPARGC1A) modified the effects of metformin on triacylglycerol levels (p(interaction) = 0.04). CONCLUSIONS/INTERPRETATION: These findings provide several novel and other confirmatory insights into the role of PPARGC1A variation with respect to diabetes-related metabolic traits. TRIAL REGISTRATION: ClinicalTrials.gov NCT00004992.

Short and long-term lifestyle coaching approaches used to address diverse participant barriers to weight loss and physical activity adherence.

BACKGROUND: Individual barriers to weight loss and physical activity goals in the Diabetes Prevention Program, a randomized trial with 3.2 years average treatment duration, have not been previously reported. Evaluating barriers and the lifestyle coaching approaches used to improve adherence in a large, diverse participant cohort can inform dissemination efforts. METHODS: Lifestyle coaches documented barriers and approaches after each session (mean session attendance = 50.3 ± 21.8). Subjects were 1076 intensive lifestyle participants (mean age = 50.6 years; mean BMI = 33.9 kg/m²; 68% female, 48% non-Caucasian). Barriers and approaches used to improve adherence were ranked by the percentage of the cohort for whom they applied. Barrier groupings were also analyzed in relation to baseline demographic characteristics. RESULTS: Top weight loss barriers reported were problems with self-monitoring (58%); social cues (58%); holidays (54%); low activity (48%); and internal cues (thought/mood) (44%). Top activity barriers were holidays (51%); time management (50%); internal cues (30%); illness (29%), and motivation (26%). The percentage of the cohort having any type of barrier increased over the long-term intervention period. A majority of the weight loss barriers were significantly associated with younger age, greater obesity, and non-Caucasian race/ethnicity (p-values vary). Physical activity barriers, particularly thought and mood cues, social cues and time management, physical injury or illness and access/weather, were most significantly associated with being female and obese (p < 0.001 for all). Lifestyle coaches used problem-solving with most participants (≥75% short-term; > 90% long term) and regularly reviewed self-monitoring skills. More costly approaches were used infrequently during the first 16 sessions (≤10%) but increased over 3.2 years. CONCLUSION: Behavioral problem solving approaches have short and long term dissemination potential for many kinds of participant barriers. Given minimal resources, increased attention to training lifestyle coaches in the consistent use of these approaches appears warranted.

Medical Nutrition Therapy, In-Person, or Telephone Group Lifestyle Intervention for Type 2 Diabetes? A Qualitative Study of Patient Perceptions and Treatment Preferences.

PURPOSE: The purpose of the study was to explore the thoughts, feelings, motivations, and assignment preferences of community health center patients with type 2 diabetes considering participation in a 2-year lifestyle intervention trial aimed at weight loss and increased physical activity. The reasons for patients' delivery mode preferences were also explored to aid in the design of future interventions for controlled trials. METHODS: Using structured telephone interview guides, 57 patients with type 2 diabetes receiving primary care at 3 community health centers affiliated with an academic medical center were interviewed regarding the perceived pros and cons of each of the 3 possible treatment assignments: telephone conference group, in-person group, or individual medical nutrition therapy. The interview data were organized using NVIVO and analyzed using content analysis. Findings on whether preferences varied by age, gender, or diabetes duration were also examined. RESULTS: Six categories related to patient treatment preferences were identified: (1) perception of time, (2) learning style, (3) comfort, (4) prior experience with weight loss programs and conference calls, (5) desire for support/idea exchange, and (6) accountability. Preferences did not seem to vary by age, gender, or diabetes duration. CONCLUSIONS: Key factors influencing preference of treatment assignment included schedule demands, belief about learning style, and past experiences. These findings demonstrate the importance of having a variety of nutrition and lifestyle treatment options available to meet the needs of people with type 2 diabetes.

Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial.

Large variability exists in people's responses to foods. However, the efficacy of personalized dietary advice for health remains understudied. We compared a personalized dietary program (PDP) versus general advice (control) on cardiometabolic health using a randomized clinical trial. The PDP used food characteristics, individual postprandial glucose and triglyceride (TG) responses to foods, microbiomes and health history, to produce personalized food scores in an 18-week app-based program. The control group received standard care dietary advice (US Department of Agriculture Guidelines for Americans, 2020-2025) using online resources, check-ins, video lessons and a leaflet. Primary outcomes were serum low-density lipoprotein cholesterol and TG concentrations at baseline and at 18 weeks. Participants (n = 347), aged 41-70 years and generally representative of the average US population, were randomized to the PDP (n = 177) or control (n = 170). Intention-to-treat analysis (n = 347) between groups showed significant reduction in TGs (mean difference = -0.13 mmol l-1; log-transformed 95% confidence interval = -0.07 to -0.01, P = 0.016). Changes in low-density lipoprotein cholesterol were not significant. There were improvements in secondary outcomes, including body weight, waist circumference, HbA1c, diet quality and microbiome (beta-diversity) (P < 0.05), particularly in highly adherent PDP participants. However, blood pressure, insulin, glucose, C-peptide, apolipoprotein A1 and B, and postprandial TGs did not differ between groups. No serious intervention-related adverse events were reported. Following a personalized diet led to some improvements in cardiometabolic health compared to standard dietary advice. ClinicalTrials.gov registration: NCT05273268 .

Weight loss success in metabolic syndrome by telephone interventions: results from the SHINE Study.

BACKGROUND: The Diabetes Prevention Program (DPP) intensive lifestyle intervention resulted in significant weight loss, reducing the development of diabetes, but needs to be adapted to primary care provider (PCP) practices. OBJECTIVES: To compare a DPP-translation using individual (IC) vs. conference (CC) calls delivered by PCP staff for the outcome of percent weight loss over 2 years. DESIGN: Randomized clinical trial. SETTING: Five PCP sites. PARTICIPANTS: Obese patients with metabolic syndrome, without diabetes (IC, n = 129; CC, n = 128). INTERVENTION: Telephone delivery of the DPP Lifestyle Balance intervention [16-session core curriculum in year 1, 12-session continued telephone contact in year 2 plus telephone coaching sessions (dietitians). MAIN MEASURES: Weight (kg), body mass index (BMI), and waist circumference. BASELINE DATA: age = 52 years, BMI = 39 kg/m(2), 75 % female, 85 % non-Hispanic White, 13 % non-Hispanic Black, and 48 % annual incomes <$40,000/year. In the intention-to-treat analyses at year 2, mean percent weight loss was -5.6 % (CC, p < 0.001) and -1.8 % (IC, p = 0.046) and was greater for CC than for IC (p = 0.016). At year 2, mean weight loss was 6.2 kg (CC) and 2.2 kg (IC) (p < 0.001). There was similar weight loss at year 1, but between year 1 and year 2 CC participants continued to lose while IC participants regained. At year 2, 52 % and 43 % (CC) and 29 % and 22 % (IC) of participants lost at least 5 % and 7 % of initial weight. BMI also decreased more for CC than IC (-2.1 kg/m(2) vs. -0.8 kg/m(2) p < 0.001). Waist circumference decreased by 3.1 cm (CC) and 2.4 cm (IC) at year 2. Completers (≥9 of 16 sessions; mean 13.3 sessions) lost significantly more weight than non-completers (mean 4.3 sessions). CONCLUSIONS: PCP staff delivery of the DPP lifestyle intervention by telephone can be effective in achieving weight loss in obese people with metabolic syndrome. Greater weight loss may be attained with a group telephone intervention.

Early response to preventive strategies in the Diabetes Prevention Program.

BACKGROUND: Recommendations for diabetes prevention in patients with prediabetes include lifestyle modification and metformin. However, the significance of early weight loss and glucose measurements when monitoring response to these proven interventions is unknown. OBJECTIVE: To quantify the relationship between early measures of weight and glucose and subsequent diabetes in patients undergoing diabetes prevention interventions. DESIGN: Analysis of results from a randomized controlled trial in 27 academic medical centers in the United States. PARTICIPANTS/INTERVENTIONS: 3,041 adults with hyperglycemia randomized to lifestyle (n = 1,018), metformin (n = 1,036), or placebo (n = 987) with complete follow-up in The Diabetes Prevention Program. MAIN MEASURES: Independent variables were weight loss at 6 and 12 months; fasting glucose (FG) at 6 months; hemoglobin A1c (HbA1c) at 6 months; and post-load glucose at 12 months. The main outcome was time to diabetes diagnosis. KEY RESULTS: After 6 months, 604 participants developed diabetes in the lifestyle (n = 140), metformin (n = 206), and placebo (n = 258) arms over 2.7 years. In the lifestyle arm, 6-month weight loss predicted decreased diabetes risk in a graded fashion: adjusted HR (95 % CI) 0.65 (0.35-1.22), 0.62 (0.33-1.18), 0.46 (0.24-0.87), 0.34 (0.18-0.64), and 0.15 (0.07-0.30) for 0-<3 %, 3-<5 %, 5-<7 %, 7-<10 %, and ≥10 % weight loss, respectively (reference: weight gain). Attainment of optimal 6-month FG and HbA1c and 12-month post-load glucose predicted >60 % lower diabetes risk across arms. We found a significant interaction between 6-month weight loss and FG in the lifestyle arm (P = 0.038). CONCLUSION: Weight and glucose at 6 and 12 months strongly predict lower subsequent diabetes risk with a lifestyle intervention; lower FG predicts lower risk even with substantial weight loss. Early reduction in glycemia is a stronger predictor of future diabetes risk than weight loss for metformin. We offer the first evidence to guide clinicians in making interval management decisions for high-risk patients undertaking measures to prevent diabetes.