The rationale and design of the personal diet study, a randomized clinical trial evaluating a personalized approach to weight loss in individuals with pre-diabetes and early-stage type 2 diabetes

Objective Determination of Eating Occasion Timing: Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Prediabetes and Obesity

BACKGROUND

Accurately identifying eating patterns, specifically the timing, frequency, and distribution of eating occasions (EOs), is important for assessing eating behaviors, especially for preventing and managing obesity and type 2 diabetes (T2D). However, existing methods to study EOs rely on self-report, which may be prone to misreporting and bias and has a high user burden. Therefore, objective methods are needed.

METHODS

We aim to compare EO timing using objective and subjective methods. Participants self-reported EO with a smartphone app (self-report [SR]), wore the ActiGraph GT9X on their dominant wrist, and wore a continuous glucose monitor (CGM, Abbott Libre Pro) for 10 days. EOs were detected from wrist motion (WM) using a motion-based classifier and from CGM using a simulation-based system. We described EO timing and explored how timing identified with WM and CGM compares with SR.

RESULTS

Participants (n = 39) were 59 ± 11 years old, mostly female (62%) and White (51%) with a body mass index (BMI) of 34.2 ± 4.7 kg/m2. All had prediabetes or moderately controlled T2D. The median time-of-day first EO (and interquartile range) for SR, WM, and CGM were 08:24 (07:00-09:59), 9:42 (07:46-12:26), and 06:55 (04:23-10:03), respectively. The median last EO for SR, WM, and CGM were 20:20 (16:50-21:42), 20:12 (18:30-21:41), and 21:43 (20:35-22:16), respectively. The overlap between SR and CGM was 55% to 80% of EO detected with tolerance periods of ±30, 60, and 120 minutes. The overlap between SR and WM was 52% to 65% EO detected with tolerance periods of ±30, 60, and 120 minutes.

CONCLUSION

The continuous glucose monitor and WM detected overlapping but not identical meals and may provide complementary information to self-reported EO.

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.

Study protocol: BRInging the Diabetes prevention program to GEriatric Populations

In the Diabetes Prevention Program (DPP) randomized, controlled clinical trial, participants who were  ≥ 60 years of age in the intensive lifestyle (diet and physical activity) intervention had a 71% reduction in incident diabetes over the 3-year trial. However, few of the 26.4 million American adults age ≥65 years with prediabetes are participating in the National DPP. The BRInging the Diabetes prevention program to GEriatric Populations (BRIDGE) randomized trial compares an in-person DPP program Tailored for Older AdulTs (DPP-TOAT) to a DPP-TOAT delivered via group virtual sessions (V-DPP-TOAT) in a randomized, controlled trial design (N = 230). Eligible patients are recruited through electronic health records (EHRs) and randomized to the DPP-TOAT or V-DPP-TOAT arm. The primary effectiveness outcome is 6-month weight loss and the primary implementation outcome is intervention session attendance with a non-inferiority design. Findings will inform best practices in the delivery of an evidence-based intervention.

Obesity and Overweight: Probing Causes, Consequences, and Novel Therapeutic Approaches Through the American Heart Association's Strategically Focused Research Network

As the worldwide prevalence of overweight and obesity continues to rise, so too does the urgency to fully understand mediating mechanisms, to discover new targets for safe and effective therapeutic intervention, and to identify biomarkers to track obesity and the success of weight loss interventions. In 2016, the American Heart Association sought applications for a Strategically Focused Research Network (SFRN) on Obesity. In 2017, 4 centers were named, including Johns Hopkins University School of Medicine, New York University Grossman School of Medicine, University of Alabama at Birmingham, and Vanderbilt University Medical Center. These 4 centers were convened to study mechanisms and therapeutic targets in obesity, to train a talented cadre of American Heart Association SFRN-designated fellows, and to initiate and sustain effective and enduring collaborations within the individual centers and throughout the SFRN networks. This review summarizes the central themes, major findings, successful training of highly motivated and productive fellows, and the innovative collaborations and studies forged through this SFRN on Obesity. Leveraging expertise in in vitro and cellular model assays, animal models, and humans, the work of these 4 centers has made a significant impact in the field of obesity, opening doors to important discoveries, and the identification of a future generation of obesity-focused investigators and next-step clinical trials. The creation of the SFRN on Obesity for these 4 centers is but the beginning of innovative science and, importantly, the birth of new collaborations and research partnerships to propel the field forward.

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.

Soluble Receptor for Advanced Glycation End Products (sRAGE) Isoforms Predict Changes in Resting Energy Expenditure in Adults with Obesity during Weight Loss

Background

Accruing evidence indicates that accumulation of advanced glycation end products (AGEs) and activation of the receptor for AGEs (RAGE) play a significant role in obesity and type 2 diabetes. The concentrations of circulating RAGE isoforms, such as soluble RAGE (sRAGE), cleaved RAGE (cRAGE), and endogenous secretory RAGE (esRAGE), collectively sRAGE isoforms, may be implicit in weight loss and energy compensation resulting from caloric restriction.

Objectives

We aimed to evaluate whether baseline concentrations of sRAGE isoforms predicted changes (∆) in body composition [fat mass (FM), fat-free mass (FFM)], resting energy expenditure (REE), and adaptive thermogenesis (AT) during weight loss.

Methods

Data were collected during a behavioral weight loss intervention in adults with obesity. At baseline and 3 mo, participants were assessed for body composition (bioelectrical impedance analysis) and REE (indirect calorimetry), and plasma was assayed for concentrations of sRAGE isoforms (sRAGE, esRAGE, cRAGE). AT was calculated using various mathematical models that included measured and predicted REE. A linear regression model that adjusted for age, sex, glycated hemoglobin (HbA1c), and randomization arm was used to test the associations between sRAGE isoforms and metabolic outcomes.

Results

Participants (n = 41; 70% female; mean ± SD age: 57 ± 11 y; BMI: 38.7 ± 3.4 kg/m2) experienced modest and variable weight loss over 3 mo. Although baseline sRAGE isoforms did not predict changes in ∆FM or ∆FFM, all baseline sRAGE isoforms were positively associated with ∆REE at 3 mo. Baseline esRAGE was positively associated with AT in some, but not all, AT models. The association between sRAGE isoforms and energy expenditure was independent of HbA1c, suggesting that the relation was unrelated to glycemia.

Conclusions

This study demonstrates a novel link between RAGE and energy expenditure in human participants undergoing weight loss.This trial was registered at clinicaltrials.gov as NCT03336411.

Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm

Background

Prediabetes affects 1 in 3 US adults. Most are not receiving evidence-based interventions, so understanding how providers discuss prediabetes with patients will inform how to improve their care.

Objective

This study aimed to develop a natural language processing (NLP) algorithm using machine learning techniques to identify discussions of prediabetes in narrative documentation.

Methods

We developed and applied a keyword search strategy to identify discussions of prediabetes in clinical documentation for patients with prediabetes. We manually reviewed matching notes to determine which represented actual prediabetes discussions. We applied 7 machine learning models against our manual annotation.

Results

Machine learning classifiers were able to achieve classification results that were close to human performance with up to 98% precision and recall to identify prediabetes discussions in clinical documentation.

Conclusions

We demonstrated that prediabetes discussions can be accurately identified using an NLP algorithm. This approach can be used to understand and identify prediabetes management practices in primary care, thereby informing interventions to improve guideline-concordant care.

Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials

Objective

Given the complexities of testing the translational capability of new artificial intelligence (AI) tools, we aimed to map the pathways of training/validation/testing in development process and external validation of AI tools evaluated in dedicated randomised controlled trials (AI-RCTs).

Methods

We searched for peer-reviewed protocols and completed AI-RCTs evaluating the clinical effectiveness of AI tools and identified development and validation studies of AI tools. We collected detailed information, and evaluated patterns of development and external validation of AI tools.

Results

We found 23 AI-RCTs evaluating the clinical impact of 18 unique AI tools (2009–2021). Standard-of-care interventions were used in the control arms in all but one AI-RCT. Investigators did not provide access to the software code of the AI tool in any of the studies. Considering the primary outcome, the results were in favour of the AI intervention in 82% of the completed AI-RCTs (14 out of 17). We identified significant variation in the patterns of development, external validation and clinical evaluation approaches among different AI tools. A published development study was found only for 10 of the 18 AI tools. Median time from the publication of a development study to the respective AI-RCT was 1.4 years (IQR 0.2–2.2).

Conclusions

We found significant variation in the patterns of development and validation for AI tools before their evaluation in dedicated AI-RCTs. Published peer-reviewed protocols and completed AI-RCTs were also heterogeneous in design and reporting. Upcoming guidelines providing guidance for the development and clinical translation process aim to improve these aspects.

Simplified, Low-Cost Method on Glucose Tolerance Testing in High-Risk Group of Diabetes, Explored by Simulation of Diagnosis

Objective: Explore the distribution of basic characteristics of high-risk groups of diabetes; verify the practical significance and diagnostic value of the “three-point method”; layered analysis of glycated hemoglobin and glycated serum albumin, and study its value and significance in the diagnosis of diabetes mellitus, Type II and pre-diabetes mellitus, Type II.Methods: 1304 high-risk individuals with T2D in Shanghai, 529 males and 841 females with an average of (50.5 ± 15.2) years old, were examined by oral glucose tolerance test (OGTT), HbA1c and GA were determined. Process the data by Python and GraphPad; judge the diagnostic value of HbA1C, GA by ROC.Results: (1) The numbers of DM, NGT, HOG, IFG, Mild–IGT and Mid–IGT in the objects were 647, 141, 70, 4, 208 and 234 respectively. In the 43-49 age group with a higher incidence, the proportion of selected high-risk groups is low. (2) The sensitivity and specificity about “three-point method” used to determine NGT is 100% and 90.11%; to determine IGR is 75.11% and 97.32%; to determine HOG is 97.14% and 100%; to determine DM is 94.67% and 100%. (3) According to ROC judgment, it is found that these 2 did not have the function of separate diagnoses, the optimal critical point of HbA1C related to DM status is 5.95%, (P<.01); HbA1C related to IGR status is 5.75% (P<.01); of GA related to DM status is 15.25% (P<.01); GA related to IGR status is 14.95% (P<.01).

Temporal Eating Patterns and Eating Windows among Adults with Overweight or Obesity

We aim to describe temporal eating patterns in a population of adults with overweight or obesity. In this cross-sectional analysis, data were combined from two separate pilot studies during which participants entered the timing of all eating occasions (>0 kcals) for 10-14 days. Data were aggregated to determine total eating occasions, local time of the first and last eating occasions, eating window, eating midpoint, and within-person variability of eating patterns. Eating patterns were compared between sexes, as well as between weekday and weekends. Participants (n = 85) had a median age of 56 ± 19 years, were mostly female (>70%), white (56.5%), and had a BMI of 31.8 ± 8.0 kg/m². The median eating window was 14 h 04 min [12 h 57 min-15 h 21 min], which was significantly shorter on the weekend compared to weekdays (p < 0.0001). Only 13.1% of participants had an eating window <12 h/d. Additionally, there was greater irregularity with the first eating occasion during the week when compared to the weekend (p = 0.0002). In conclusion, adults with overweight or obesity have prolonged eating windows (>14 h/d). Future trials should examine the contribution of a prolonged eating window on adiposity independent of energy intake.

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

Challenges of conducting a remote behavioral weight loss study: Lessons learned and a practical guide

OBJECTIVES

To describe challenges and lessons learned in conducting a remote behavioral weight loss trial.

METHODS

The Personal Diet Study is an ongoing randomized clinical trial which aims to compare two mobile health (mHealth) weight loss approaches, standardized diet vs. personalized feedback, on glycemic response. Over a six-month period, participants attended dietitian-led group meetings via remote videoconferencing and were encouraged to self-monitor dietary intake using a smartphone app. Descriptive statistics were used to report adherence to counseling sessions and self-monitoring. Challenges were tracked during weekly project meetings.

RESULTS

Challenges in connecting to and engaging in the videoconferencing sessions were noted. To address these issues, we provided a step-by-step user manual and video tutorials regarding use of WebEx, encouraged alternative means to join sessions, and sent reminder emails/texts about the WebEx sessions and asking participants to join sessions early. Self-monitoring app-related issue included inability to find specific foods in the app database. To overcome this, the study team incorporated commonly consumed foods as "favorites" in the app database, provided a manual and video tutorials regarding use of the app and checked the self-monitoring app dashboard weekly to identify nonadherent participants and intervened as appropriate. Among 135 participants included in the analysis, the median attendance rate for the 14 remote sessions was 85.7% (IQR: 64.3%-92.9%).

CONCLUSIONS

Experience and lessons shared in this report may provide critical and timely guidance to other behavioral researchers and interventionists seeking to adapt behavioral counseling programs for remote delivery in the age of COVID-19.

Data integration for prediction of weight loss in randomized controlled dietary trials

Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84-0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies.

Evaluating steady-state resting energy expenditure using indirect calorimetry in adults with overweight and obesity

BACKGROUND

Determining a period of steady state (SS) is recommended when estimating resting energy expenditure (REE) using a metabolic cart. However, this practice may be unnecessarily burdensome and time-consuming in the research setting.

AIM

The aim of the study was to evaluate the use of SS criteria, and compare it to alternative approaches in adults with overweight and obesity.

METHODS

In this cross-sectional, ancillary analysis, participants enrolled in a bariatric (study 1; n = 13) and lifestyle (study 2; n = 51) weight loss intervention were included. Indirect calorimetry was performed during baseline measurements using a metabolic cart for 25 min, including a 5-min stabilization period at the start. SS was defined as the first 5-min period with a coefficient of variation (CV) ≤10% for both VO₂ and VCO₂ (hereafter REE_5-SS). Body composition was measured using bioelectrical impedance analysis in study 2 participants only. REE_5-SS was compared against the lowest CV (REE_CV-lowest), 5-min time intervals (REE_6-10, REE_11-15, REE_16-20, REE_21-25), 4-min and 3-min SS intervals (REE_4-SS and REE_3-SS), and time intervals of 6-15, 6-20 and 6-25 min (REE_6-15, REE_6-20, and REE_6-25) using repeated measures ANOVA and Bland-Altman analysis to test for bias, limits of agreement and accuracy (±6% measured REE).

RESULTS

Participants were 54 ± 13 years old, mostly women (75%) and had a BMI of 35 ± 5 kg/m². Overall, 54/63 (84%) of participants reached REE_5-SS, often (47/54, 87%) within the first 10-min (6-15 min). Alternative approaches to estimating REE had a relatively low bias (-16 to 13 kcals), narrow limits of agreement and high accuracy (83-98%) when compared to REE_5-SS, in particular, outperforming standard prediction equations (e.g., Mifflin St. Joer).

CONCLUSION

Indirect calorimetry measurements that utilize the 5-min SS approach to estimate REE are considered the gold-standard. Under circumstances of non-SS, it appears 4-min and 3-min SS periods, or fixed time intervals of atleast 5 min are accurate and practical alternatives for estimating REE in adults with overweight and obesity. However, future trials should validate alternative methods in similar populations to confirm these findings.