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 .

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.

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.

Psychological and Behavioral Predictors of Weight Loss in the Reach Ahead for Lifestyle and Health-Diabetes Lifestyle Intervention Cohort.

BACKGROUND: Understanding the effect lifestyle intervention (LI) has on important psychological and behavioral variables that are associated with weight loss can help inform LI design, content, and delivery. OBJECTIVE: The aim was to determine the modifiable psychological and behavioral factors that are associated with percent weight loss (%WL) and their relative importance in predicting %WL at 12, 24, and 36 months in the REAL HEALTH-Diabetes randomized controlled trial LI. DESIGN: This is a secondary analysis of LI arms of the REAL HEALTH-Diabetes randomized controlled trial LI cohort over a 24-month intervention period and 12-month follow-up period. Patient-reported outcomes were measured using validated questionnaires that were either self-administered or administered by a research coordinator. PARTICIPANTS/SETTING: Adults with type 2 diabetes and overweight/obesity (N = 142) from community health centers, primary care, and local endocrinology practices affiliated with Massachusetts General Hospital in Boston, MA, between 2015 and 2020, were randomized to LI and were included in the analysis. INTERVENTION: The LI was a lower intensity adaptation of Look Action for Health in Diabetes's (HEALTH) evidence-based LI delivered either in-person or via telephone. Registered dietitians delivered 19 group sessions during the first 6 months followed by 18 monthly sessions. MAIN OUTCOME MEASURES: The association of psychological (diabetes-related distress, depression, autonomous motivation, diet and exercise self-efficacy, and social support for healthy behaviors) and behavioral (fat-related diet and dietary self-regulation) variables with %WL. STATISTICAL ANALYSIS: Baseline and 6-month change scores in psychological and behavioral variables were modeled as predictors of %WL at 12, 24, and 36 months using linear regression. Random forests were used to compare the relative importance of changes in the variables in predicting %WL. RESULTS: Six-month improvement in autonomous motivation, exercise self-efficacy, diet self-efficacy, and dietary self-regulation were associated with %WL at 12 and 24 months, but not at 36 months. Improvement in fat-related diet behavior and depressive symptoms were the only variables associated with %WL at all three timepoints. Autonomous motivation, dietary self-regulation, and low-fat diet behaviors were the three most important predictors of %WL during the 2-year LI. CONCLUSIONS: The REAL HEALTH-Diabetes randomized controlled trial LI resulted in 6-month improvements in modifiable psychological and behavioral factors that were associated with %WL. LI programs for weight loss should focus on skills and strategies to promote autonomous motivation, flexible dietary self-regulation, and habituation of low-fat eating habits during the intervention period.

Medically tailored meals for food insecurity and type 2 diabetes: Protocol for the Food as Medicine for Diabetes (FAME-D) trial.

BACKGROUND: Food insecurity is associated with worse glycemic management for individuals with type 2 diabetes mellitus (T2DM), but whether medically tailored meals (MTM), a food insecurity intervention, can improve glycemic management is unclear. OBJECTIVE: To describe the protocol for a trial assessing whether an MTM plus lifestyle intervention improves hemoglobin A1c (HbA1c) and participant-reported outcomes, relative to a food subsidy (money that can be spent on foods participants choose), for adults with both T2DM and food insecurity. METHODS: The Food as Medicine for Diabetes (FAME-D) randomized clinical trial (goal n = 200) is a pragmatic trial with an active comparator. Participants, who will have T2DM and report food insecurity, will be randomly assigned to a 6-month MTM plus telephone-delivered lifestyle change intervention, or a 6-month food subsidy ($40/month). The primary outcome is HbA1c at 6 months. Secondary outcomes include HbA1c at 12 months to assess whether the intervention effect (if any) is sustained, along with weight, food insecurity, diabetes distress, and health-related quality of life. Qualitative analyses of semi-structured interviews will help understand why, how, and under what circumstances the intervention achieved its observed results. CONCLUSION: Results from FAME-D will help inform clinical management of food insecurity when it co-occurs with T2DM. Further, results may be useful as healthcare payors are considering coverage for MTM interventions. CLINICALTRIALS: gov: NCT04828785.

How people wake up is associated with previous night's sleep together with physical activity and food intake.

How people wake up and regain alertness in the hours after sleep is related to how they are sleeping, eating, and exercising. Here, in a prospective longitudinal study of 833 twins and genetically unrelated adults, we demonstrate that how effectively an individual awakens in the hours following sleep is not associated with their genetics, but instead, four independent factors: sleep quantity/quality the night before, physical activity the day prior, a breakfast rich in carbohydrate, and a lower blood glucose response following breakfast. Furthermore, an individual's set-point of daily alertness is related to the quality of their sleep, their positive emotional state, and their age. Together, these findings reveal a set of non-genetic (i.e., not fixed) factors associated with daily alertness that are modifiable.

Menopause is associated with postprandial metabolism, metabolic health and lifestyle: The ZOE PREDICT study.

BACKGROUND: The menopause transition is associated with unfavourable alterations in health. However, postprandial metabolic changes and their mediating factors are poorly understood. METHODS: The PREDICT 1 UK cohort (n=1002; pre- n=366, peri- n=55, and post-menopausal females n=206) assessed phenotypic characteristics, anthropometric, diet and gut microbiome data, and fasting and postprandial (0-6 h) cardiometabolic blood measurements, including continuous glucose monitoring (CGM) data. Differences between menopausal groups were assessed in the cohort and in an age-matched subgroup, adjusting for age, BMI, menopausal hormone therapy (MHT) use, and smoking status. FINDINGS: Post-menopausal females had higher fasting blood measures (glucose, HbA1c and inflammation (GlycA), 6%, 5% and 4% respectively), sugar intakes (12%) and poorer sleep (12%) compared with pre-menopausal females (p<0.05 for all). Postprandial metabolic responses for glucose2hiauc and insulin2hiauc were higher (42% and 4% respectively) and CGM measures (glycaemic variability and time in range) were unfavourable post- versus pre-menopause (p<0.05 for all). In age-matched subgroups (n=150), postprandial glucose responses remained higher post-menopause (peak0-2h 4%). MHT was associated with favourable visceral fat, fasting (glucose and insulin) and postprandial (triglyceride6hiauc) measures. Mediation analysis showed that associations between menopause and metabolic health indicators (visceral fat, GlycA360mins and glycaemia (peak0-2h)) were in part mediated by diet and gut bacterial species. INTERPRETATION: Findings from this large scale, in-depth nutrition metabolic study of menopause, support the importance of monitoring risk factors for type-2 diabetes and cardiovascular disease in mid-life to older women to reduce morbidity and mortality associated with oestrogen decline. FUNDING: Zoe Ltd.

Results of a 2-year lifestyle intervention for type 2 diabetes: the Reach Ahead for Lifestyle and Health-Diabetes randomized controlled trial.

OBJECTIVE: The Reach Ahead for Lifestyle and Health (REAL HEALTH)-Diabetes study assessed the comparative effectiveness of two Look AHEAD (Action for Health in Diabetes)-adapted lifestyle intervention (LI) arms targeting weight loss in type 2 diabetes compared with medical nutrition therapy (MNT) referral. At 1 year, LI had greater weight loss than MNT. This study reports outcomes at 24 (end of LI) and 36 months. METHODS: Participants (N = 211) with type 2 diabetes and BMI > 25 kg/m2 were randomly assigned to in-person LI, telephone conference call LI, or MNT. The primary outcome was percentage weight change. Secondary outcomes included 5% and 10% weight loss, glycated hemoglobin (HbA1c), and patient-reported outcomes. RESULTS: Participants were 61.7 (SD 10.2) years old; 55% were female; 77% were non-Hispanic White; and had mean (SD) weight of 98 (18.9) kg and mean (SD) HbA1c of 7.7% (1.2%). Mean (SD) weight change at 24 and 36 months was -4.4% (5.9%) and -4.4% (5.4%) in in-person LI, -4.0% (5.8%) and -5.3% (6.4%) in telephone LI, and -3.1% (5.2%) and -5.8% (7.1%) in MNT, with no statistically significant difference in weight or HbA1c at 24 and 36 months. Compared with MNT, LI arms had favorable changes in patient-reported outcomes related to learned dietary skills. CONCLUSIONS: There were no differences in weight outcomes among LI participants compared with referral to MNT at the end of intervention or 1 year after its conclusion.

Validity of continuous glucose monitoring for categorizing glycemic responses to diet: implications for use in personalized nutrition.

BACKGROUND: Continuous glucose monitor (CGM) devices enable characterization of individuals' glycemic variation. However, there are concerns about their reliability for categorizing glycemic responses to foods that would limit their potential application in personalized nutrition recommendations. OBJECTIVES: We aimed to evaluate the concordance of 2 simultaneously worn CGM devices in measuring postprandial glycemic responses. METHODS: Within ZOE PREDICT (Personalised Responses to Dietary Composition Trial) 1, 394 participants wore 2 CGM devices simultaneously [n = 360 participants with 2 Abbott Freestyle Libre Pro (FSL) devices; n = 34 participants with both FSL and Dexcom G6] for ≤14 d while consuming standardized (n = 4457) and ad libitum (n = 5738) meals. We examined the CV and correlation of the incremental area under the glucose curve at 2 h (glucoseiAUC0-2 h). Within-subject meal ranking was assessed using Kendall τ rank correlation. Concordance between paired devices in time in range according to the American Diabetes Association cutoffs (TIRADA) and glucose variability (glucose CV) was also investigated. RESULTS: The CV of glucoseiAUC0-2 h for standardized meals was 3.7% (IQR: 1.7%-7.1%) for intrabrand device and 12.5% (IQR: 5.1%-24.8%) for interbrand device comparisons. Similar estimates were observed for ad libitum meals, with intrabrand and interbrand device CVs of glucoseiAUC0-2 h of 4.1% (IQR: 1.8%-7.1%) and 16.6% (IQR: 5.5%-30.7%), respectively. Kendall τ rank correlation showed glucoseiAUC0-2h-derived meal rankings were agreeable between paired CGM devices (intrabrand: 0.9; IQR: 0.8-0.9; interbrand: 0.7; IQR: 0.5-0.8). Paired CGMs also showed strong concordance for TIRADA with a intrabrand device CV of 4.8% (IQR: 1.9%-9.8%) and an interbrand device CV of 3.2% (IQR: 1.1%-6.2%). CONCLUSIONS: Our data demonstrate strong concordance of CGM devices in monitoring glycemic responses and suggest their potential use in personalized nutrition.This trial was registered at clinicaltrials.gov as NCT03479866.

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.

The Breast Cancer Weight Loss trial (Alliance A011401): A description and evidence for the lifestyle intervention.

The Breast Cancer Weight Loss (BWEL) trial is a randomized controlled trial designed to determine whether weight loss after a breast cancer diagnosis can reduce the risk of cancer recurrence in women with overweight or obesity. The BWEL trial will compare the efficacy of a telephone-based weight-loss intervention plus health education materials versus health education materials alone on invasive disease-free survival in 3,181 women with stage II or III breast cancer and BMI > 27 kg/m2 . This report provides a detailed description of the goals and methods of the lifestyle intervention and the evidence supporting the intervention used in the BWEL trial. The intervention's primary goal for participants is to achieve and maintain a weight loss ≥ 10% of baseline weight through increased physical activity and caloric restriction. The evidence supporting the diet, physical activity, and behavioral components of this telephone-based weight-loss intervention, as well as strategies to promote participant engagement and retention, is described. The intervention is provided through 42 sessions delivered by trained health coaches over a 2-year period. If the BWEL lifestyle intervention is successful in improving cancer outcomes, then weight loss will be incorporated into the care of thousands of breast cancer patients.

The Impact of Physical Activity on the Prevention of Type 2 Diabetes: Evidence and Lessons Learned From the Diabetes Prevention Program, a Long-Standing Clinical Trial Incorporating Subjective and Objective Activity Measures.

OBJECTIVE: Across the Diabetes Prevention Program (DPP) follow-up, cumulative diabetes incidence remained lower in the lifestyle compared with the placebo and metformin randomized groups and could not be explained by weight. Collection of self-reported physical activity (PA) (yearly) with cross-sectional objective PA (in follow-up) allowed for examination of PA and its long-term impact on diabetes prevention. RESEARCH DESIGN AND METHODS: Yearly self-reported PA and diabetes assessment and oral glucose tolerance test results (fasting glucose semiannually) were collected for 3,232 participants with one accelerometry assessment 11-13 years after randomization (n = 1,793). Mixed models determined PA differences across treatment groups. The association between PA and diabetes incidence was examined using Cox proportional hazards models. RESULTS: There was a 6% decrease (Cox proportional hazard ratio 0.94 [95% CI 0.92, 0.96]; P < 0.001) in diabetes incidence per 6 MET-h/week increase in time-dependent PA for the entire cohort over an average of 12 years (controlled for age, sex, baseline PA, and weight). The effect of PA was greater (12% decrease) among participants less active at baseline (<7.5 MET-h/week) (n = 1,338) (0.88 [0.83, 0.93]; P < 0.0001), with stronger findings for lifestyle participants. Lifestyle had higher cumulative PA compared with metformin or placebo (P < 0.0001) and higher accelerometry total minutes per day measured during follow-up (P = 0.001 and 0.047). All associations remained significant with the addition of weight in the models. CONCLUSIONS: PA was inversely related to incident diabetes in the entire cohort across the study, with cross-sectional accelerometry results supporting these findings. This highlights the importance of PA within lifestyle intervention efforts designed to prevent diabetes and urges health care providers to consider both PA and weight when counseling high-risk patients.

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.

Diet quality, weight loss, and diabetes incidence in the Diabetes Prevention Program (DPP).

BACKGROUND: We evaluated whether diet quality is a predictor of weight loss and reduced diabetes risk, independent of caloric intake in the Diabetes Prevention Program (DPP) cohort, a randomized clinical trial of adults at risk for diabetes. METHODS: This secondary analysis included 2914 participants with available data (964 intensive lifestyle (ILS), 977 metformin, 973 placebo). Dietary intake was assessed using a 117-item food frequency questionnaire. Diet quality was quantified using the Alternative Healthy Eating Index 2010 (AHEI). AHEI ranges from 0 to 110, with higher scores corresponding to higher quality diets. ILS participants had greater improvement (p < 0.001) in AHEI over 1-year (4.2 ± 9.0) compared to metformin (1.2 ± 8.5) and placebo (1.4 ± 8.4). We examined the association between AHEI change and weight change from baseline to 1-year using linear regression, and that between 1-year AHEI change and incident diabetes, using hazard models over an average 3 years follow-up. Models were evaluated within treatment group and adjusted for relevant characteristics including caloric intake, physical activity, BMI and AHEI. Models testing incident diabetes were further adjusted for baseline fasting and 2 h glucose. RESULTS: An increase in AHEI score was associated with weight loss in ILS [ß per 10-point increase (SE) -1.2 kg (0.3, p < 0.001)], metformin [- 0. 90 kg (0.2, p < 0.001)] and placebo [- 0.55 kg (0.2, p = 0.01)]. However, AHEI change was not associated with incident diabetes in any group before or after adjustment for weight change. CONCLUSIONS: Controlling for weight, diet quality was not associated with diabetes incidence but helps achieve weight loss, an important factor in diabetes prevention.

Publisher Correction: Human postprandial responses to food and potential for precision nutrition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Does the effect of lifestyle intervention for individuals with diabetes vary by food insecurity status? A preplanned subgroup analysis of the REAL HEALTH randomized clinical trial.

INTRODUCTION: We aimed to test the effectiveness of a lifestyle intervention (LI) for individuals with food insecurity and type 2 diabetes. RESEARCH DESIGN AND METHODS: Adults with type 2 diabetes, body mass index ≥25 kg/m2 (or ≥23 kg/m2 if Asian), hemoglobin A1c of 6.5%-11.5% (48-97 mmol/mol) and who were willing to lose 5%-7% bodyweight were enrolled in REAL HEALTH-Diabetes. This practice-based randomized clinical trial compared LI (delivered inperson or by telephone) with medical nutrition therapy (MNT) on weight loss at 6 and 12 months. Two or more affirmative responses on the six-item US Department of Agriculture Food Security Survey Module indicated food insecurity. In this prespecified subgroup analysis, we tested using linear mixed effects models whether the intervention effect varied by food security status. RESULTS: Of 208 participants, 13% were food insecure. Those with food insecurity were more likely to be racial/ethnic minorities (p<0.001) and have lower education (p<0.001). LI, versus MNT, led to greater weight loss at 6 months (5.1% lost vs 1.1% lost; p<0.0001) and 12 months (4.7% lost vs 2.0% lost; p=0.0005). The intervention effect was similar regardless of food security status (5.1% bodyweight lost vs 1.1% in food secure participants and 5.1% bodyweight lost vs 1.3% in food insecure participants at 6 months; 4.7% bodyweight lost vs 2.1% in food secure participants and 4.5% bodyweight lost vs 0.9% in food insecure participants at 12 months; p for interaction=0.99). CONCLUSIONS: The REAL HEALTH-Diabetes lifestyle intervention led to meaningful weight loss for individuals with food insecurity and type 2 diabetes. TRIAL REGISTRATION NUMBER: NCT02320253.

Human postprandial responses to food and potential for precision nutrition.

Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866.

Weight Change 2 Years After Termination of the Intensive Lifestyle Intervention in the Look AHEAD Study.

OBJECTIVE: This study evaluated weight changes after cessation of the 10-year intensive lifestyle intervention (ILI) in the Look AHEAD (Action for Health in Diabetes) study. It was hypothesized that ILI participants would be more likely to gain weight during the 2-year observational period following termination of weight-loss-maintenance counseling than would participants in the diabetes support and education (DSE) control group. METHODS: Look AHEAD was a randomized controlled trial that compared the effects of ILI and DSE on cardiovascular morbidity and mortality in participants with overweight/obesity and type 2 diabetes. Look AHEAD was converted to an observational study in September 2012. RESULTS: Two years after the end of the intervention (EOI), ILI and DSE participants lost a mean (SE) of 1.2 (0.2) kg and 1.8 (0.2) kg, respectively (P = 0.003). In addition, 31% of ILI and 23.9% of DSE participants gained ≥ 2% (P < 0.001) of EOI weight, whereas 36.3% and 45.9% of the respective groups lost ≥ 2% of EOI weight (P = 0.001). Two years after the EOI, ILI participants reported greater use of weight-control behaviors than DSE participants. CONCLUSIONS: Both groups lost weight during the 2-year follow-up period, but more ILI than DSE participants gained ≥ 2% of EOI weight. Further understanding is needed of factors that affected long-term weight change in both groups.

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.