Four-year follow-up of weight loss maintenance using electronic medical record data: The PROPEL trial.

Rationale: Short-term weight loss is possible in a variety of settings. However, long-term, free-living weight loss maintenance following structured weight loss interventions remains elusive. Objective: The purpose was to study body weight trajectories over 2 years of intensive lifestyle intervention (ILI) and up to 4 years of follow-up versus usual care (UC). Methods: Data were obtained from electronic medical records (EMRs) from participating clinics. Baseline (Day 0) was established as the EMR data point closest but prior to the baseline date of the trial. The sample included 111 ILI and 196 UC patients. The primary statistical analysis focused on differentiating weight loss trajectories between ILI and UC. Results: The ILI group experienced significantly greater weight loss compared with the UC group from Day 100 to Day 700, beyond which there were no significant differences. Intensive lifestyle intervention patients who maintained ≥5% and ≥10% weight loss at 24 months demonstrated significantly greater weight loss (p < 0.001) across the active intervention and follow-up. Conclusions: Following 24 months of active intervention, patients with ILI regained weight toward their baseline to the point where ILI versus UC differences were no longer statistically or clinically significant. However, patients in the ILI who experienced ≥5% or ≥10% weight loss at the cessation of the active intervention maintained greater weight loss at the end of the follow-up phase. Clinical Trial Registration: ClinicalTrials.gov: NCT02561221.

Modulators of Energy Expenditure Accuracy in Adults with Overweight or Obesity: E-MECHANIC Secondary Analyses.

PURPOSE: American College of Sports Medicine (ACSM) metabolic equations are used to estimate energy expenditure (EE) of physical activity and prescribe aerobic exercise to meet EE requirements. Limited evidence supports their accuracy in sedentary adults with overweight or obesity during controlled exercise interventions. The purpose of this study was to compare EE estimated by the ACSM walking equation versus EE measured by indirect calorimetry during a 24-week aerobic exercise intervention, and identify potential modulators for their accuracy. METHODS: Data from the exercising groups (8 or 20 kcal·kg body weight-1·week-1) of the E-MECHANIC study were utilized in this ancillary analysis (N = 103). Every 2 weeks for the initial 8 weeks and monthly thereafter, EE was measured via indirect calorimetry during absolute (2 mph, 0% grade) and relative (65-85% VO2peak) workload exercise. Resting metabolic rate, VO2peak, and body composition were assessed at baseline and follow-up. An EE offset factor (EOF) was calculated to express measured EE as a percentage of the estimated EE at each workload (EOF < 100% represents an overestimation of ACSM estimated EE). RESULTS: The accuracy of the equation decreased with increasing exercise workload (0.44%, 9.2%, and 20.3% overestimation at absolute, relative, and maximal workloads, respectively, at baseline) and overestimation of EE was greater after the exercise intervention. Furthermore, race, sex, age, fat mass, and VO2peak were identified as modulators for equation accuracy. Greater overestimation of EE was observed in Black compared to white females, particularly at lower exercise workloads. CONCLUSIONS: These findings support future efforts to improve the accuracy of metabolic equations, especially in diverse populations. Researchers should account for exercise efficiency adaptations when using metabolic equations to prescribe exercise precisely.

The Role of Whole Food Plant-Based Food Intake on Postprandial Glycemia in Type 1 Diabetes.

BACKGROUND: Whole food plant-based diet (WFPBD), minimally processed foods with limited consumption of animal products, is associated with improved health outcomes. The benefits of WFPBD are underexplored in individuals with type 1 diabetes (T1D). The primary objective of this analysis is to evaluate the association between WFPBD on glycemia in individuals with T1D. RESEARCH DESIGN AND METHODS: Utilizing prospectively collected meal events from the Type 1 Diabetes Exercise Initiative, we examined the effect of WFPBD intake on glycemia, determined by the Plant-Based Diet Index (PDI). The PDI calculates overall, healthful (hPDI), and unhealthy PDI (uPDI) to evaluate for degree of processed foods and animal products (i.e. WFPBD). Mixed effects linear regression model assessed time-in-range (TIR), time-above-range, and time-below-range. RESULTS: We analyzed 7,938 meals from 367 participants. TIR improved with increasing hPDI scores, conferring a 4% improvement in TIR between highest and lowest hPDI scores (high hPDI:75%, low hPDI:71%; p<0.001). Compared to meals with low hPDI, meals with high hPDI had lower glucose excursion (high hPDI:53mg/dL, low hPDI:62mg/dL; p<0.001) and less time >250mg/dL (high hPDI:8%, low hPDI:14%; p<0.001). These effects were present but less pronounced by PDI (high PDI:74%, low PDI:71%; p=0.01). No differences in time below 70mg/dL and 54mg/dL were observed by PDI or hPDI. CONCLUSIONS: Meal events with higher hPDI were associated with 4% postprandial TIR improvement. These benefits were seen primarily in WFPBD meals (captured by hPDI) and less pronounced plant-based meals (captured by PDI), emphasizing the benefit of increasing unprocessed food intake over limiting animal products alone.

Prediction of individual weight loss using supervised learning: findings from the CALERIETM 2 study.

BACKGROUND: Predicting individual weight loss (WL) responses to lifestyle interventions is challenging but might help practitioners and clinicians select the most promising approach for each individual. OBJECTIVE: The primary aim of this study was to develop machine learning (ML) models to predict individual WL responses using only variables known before starting the intervention. In addition, we used ML to identify pre-intervention variables influencing the individual WL response. METHODS: We used 12-mo data from the comprehensive assessment of long-term effects of reducing intake of energy (CALERIETM) phase 2 study, which aimed to analyze the long-term effects of caloric restriction on human longevity. On the basis of the data from 130 subjects in the intervention group, we developed classification models to predict binary ("Success" and "No/low success") or multiclass ("High success," "Medium success," and "Low/no success") WL outcomes. Additionally, regression models were developed to predict individual weight change (percent). Models were evaluated on the basis of accuracy, sensitivity, specificity (classification models), and root mean squared error (RMSE; regression models). RESULTS: Best classification models used 20-40 predictors and achieved 89%-97% accuracy, 91%-100% sensitivity, and 56%-86% specificity for binary classification. For multiclass classification, accuracy (69%) and sensitivity (50%) tended to be lower. The best regression performance was obtained with 36 variables with an RMSE of 2.84%. Among the 21 variables predicting individual weight change most consistently, we identified 2 novel predictors, namely orgasm satisfaction and sexual behavior/experience. Other common predictors have previously been associated with WL (16) or are already used in traditional prediction models (3). CONCLUSIONS: The prediction models could be implemented by practitioners and clinicians to support the decision of whether lifestyle interventions are sufficient or more aggressive interventions are needed for a given individual, thereby supporting better, faster, data-driven, and unbiased decisions. The CALERIETM phase 2 study was registered at clinicaltrials.gov as NCT00427193.

Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step.

The characterization of human behavior in real-world contexts is critical for developing a comprehensive model of human health. Recent technological advancements have enabled wearables and sensors to passively and unobtrusively record and presumably quantify human behavior. Better understanding human activities in unobtrusive and passive ways is an indispensable tool in understanding the relationship between behavioral determinants of health and diseases. Adult individuals (N = 60) emulated the behaviors of smoking, exercising, eating, and medication (pill) taking in a laboratory setting while equipped with smartwatches that captured accelerometer data. The collected data underwent expert annotation and was used to train a deep neural network integrating convolutional and long short-term memory architectures to effectively segment time series into discrete activities. An average macro-F1 score of at least 85.1 resulted from a rigorous leave-one-subject-out cross-validation procedure conducted across participants. The score indicates the method's high performance and potential for real-world applications, such as identifying health behaviors and informing strategies to influence health. Collectively, we demonstrated the potential of AI and its contributing role to healthcare during the early phases of diagnosis, prognosis, and/or intervention. From predictive analytics to personalized treatment plans, AI has the potential to assist healthcare professionals in making informed decisions, leading to more efficient and tailored patient care.

Evaluating the Validity of the PortionSize Smartphone Application for Estimating Dietary Intake in Free-Living Conditions: A Pilot Study.

OBJECTIVE: Evaluate the validity of the PortionSize application. METHODS: In this pilot study, 14 adults used PortionSize to record their free-living food intake over 3 consecutive days. Digital photography was the criterion measure, and the main outcomes were estimated intake of food (grams), energy (kilocalories), and food groups. Equivalence tests with ±25% equivalence bounds and Bland-Altman analysis were performed. RESULTS: Estimated gram intake from PortionSize was equivalent (P < 0.001) to digital photography estimates. PortionSize and digital photography estimated energy intake, however, were not equivalent (P = 0.08), with larger estimates from PortionSize. In addition, PortionSize and digital photography were equivalent for vegetable intake (P = 0.01), but PortionSize had larger estimates of fruits, grains, dairy, and protein intake (P >0.07; error range 11% to 23%). CONCLUSIONS AND IMPLICATIONS: Compared with digital photography, PortionSize accurately estimated food intake and had reasonable error rates for other nutrients; however, it overestimated energy intake, indicating further application improvements are needed for free-living conditions.

Validity of the PortionSize application compared with that of MyFitnessPal for accurately estimating intake: a randomized crossover laboratory-based evaluation.

BACKGROUND: PortionSize offers real-time feedback on dietary intake, including intake of MyPlate food groups but requires further evaluation on a larger sample in a laboratory-based setting. MyFitnessPal (MFP) is a commonly used commercial dietary assessment application, and to our knowledge, no known studies have evaluated MFP in a laboratory setting. OBJECTIVES: The overall objective was to test the validity of PortionSize and MFP to accurately measure intake compared with that of weighed food (WB) and to compare error between applications. A secondary objective was to test usability, satisfaction, and user preference between applications. METHODS: This randomized crossover study was completed between February and October 2021. Participants (N = 43) used both applications to estimate intake in a laboratory setting. Participants were provided with a preweighed plated meal and plated leftovers. Two 1-sided t tests assessed equivalence (±21% bounds) between simulated intake from PortionSize and WB, and MFP and WB. The primary outcome was energy intake, and secondary outcome measures were portion size (in grams), food groups, and other nutrients. Differences in relative absolute error, usability, satisfaction, and user preference between applications were evaluated using dependent samples t tests. Cohen d assessed effect size. RESULTS: For PortionSize, energy and portion size were underestimated by 13.3% and 14.0%, respectively, and were not equivalent to WB. For MFP, energy was overestimated by 7.0%, and equivalent to WB (P = 0.04). Relative absolute error for energy did not differ between applications. For PortionSize, Cohen d was small (<0.2) for fruits, grains, protein foods, and specific nutrients. No differences were seen with usability, and the only difference for satisfaction was that participants found it easier to use MFP to find foods consumed (P = 0.019), and participants preferred using MFP (P = 0.014). CONCLUSIONS: PortionSize requires further updates to improve energy estimates and usability but demonstrates clinical utility for tracking food group and nutrient intake. PortionSize did not outperform MFP for measuring energy intake. CLINICAL TRIAL REGISTRY: This trial was registered at clinicaltrials.gov as NCT04700904 (https://classic. CLINICALTRIALS: gov/ct2/show/NCT04700904).

The Association Between Diet Quality and Glycemic Outcomes Among People with Type 1 Diabetes.

Background: The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs. Objective: Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise. Methods: Using the Remote Food Photography Method, ≤8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI (≤57) or high HEI (>57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t-test and robust regression models. Results: Two hundred twenty-three adults (76% female) with mean ± SD age, HbA1c, and body mass index (BMI) of 37 ± 14 y, 6.6% ± 0.7%, and 25.1 ± 3.6 kg/m2, respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 ± 4) compared with low HEI days (mean, 47 ± 5), total time in range (70-180 mg/dL) was greater (77.2% ± 14% compared with 75.7% ± 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% ± 14% compared with 21% ± 15%, respectively, P = 0.004), mean glucose (143 ± 22 compared with 145 ± 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 ± 0.18 compared with 0.54 ± 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not significant. Conclusions: Higher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise.

Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort.

AIMS/HYPOTHESIS: Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. METHODS: In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). RESULTS: A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%). CONCLUSIONS/INTERPRETATION: Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes. DATA AVAILABILITY: The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).

Effect of long-term caloric restriction on telomere length in healthy adults: CALERIE™ 2 trial analysis.

Caloric restriction (CR) modifies lifespan and aging biology in animal models. The Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy (CALERIE™) 2 trial tested translation of these findings to humans. CALERIE™ randomized healthy, nonobese men and premenopausal women (age 21-50y; BMI 22.0-27.9 kg/m2), to 25% CR or ad-libitum (AL) control (2:1) for 2 years. Prior analyses of CALERIE™ participants' blood chemistries, immunology, and epigenetic data suggest the 2-year CR intervention slowed biological aging. Here, we extend these analyses to test effects of CR on telomere length (TL) attrition. TL was quantified in blood samples collected at baseline, 12-, and 24-months by quantitative PCR (absolute TL; aTL) and a published DNA-methylation algorithm (DNAmTL). Intent-to-treat analysis found no significant differences in TL attrition across the first year, although there were trends toward increased attrition in the CR group for both aTL and DNAmTL measurements. When accounting for adherence heterogeneity with an Effect-of-Treatment-on-the-Treated analysis, greater CR dose was associated with increased DNAmTL attrition during the baseline to 12-month weight-loss period. By contrast, both CR group status and increased CR were associated with reduced aTL attrition over the month 12 to month 24 weight maintenance period. No differences were observed when considering TL change across the study duration from baseline to 24-months, leaving it unclear whether CR-related effects reflect long-term detriments to telomere fidelity, a hormesis-like adaptation to decreased energy availability, or measurement error and insufficient statistical power. Unraveling these trends will be a focus of future CALERIE™ analyses and trials.

Effect of Impaired Awareness of Hypoglycemia on Glucose Decline During and After Exercise in the T1DEXI Study.

CONTEXT: Adults with type 1 diabetes (T1D) face the necessity of balancing the benefits of exercise with the potential hazards of hypoglycemia. OBJECTIVE: This work aimed to assess whether impaired awareness of hypoglycemia (IAH) affects exercise-associated hypoglycemia in adults with T1D. METHODS: We compared continuous glucose monitoring (CGM)-measured glucose during exercise and for 24 hours following exercise from 95 adults with T1D and IAH (Clarke score ≥4 or ≥1 severe hypoglycemic event within the past year) to 95 "aware" adults (Clarke score ≤2 and no severe hypoglycemic event within the past year) matched on sex, age, insulin delivery modality, and glycated hemoglobin A1c. A total of 4236 exercise sessions, and 1794 exercise days and 839 sedentary days, defined as 24 hours following exercise or a day without exercise, respectively, were available for analysis. RESULTS: Participants with IAH exhibited a nonsignificant trend toward greater decline in glucose during exercise compared to "aware" (-21 ± 44 vs -19 ± 43 mg/dL [-1.17 ± 2.44 vs -1.05 ± 2.39 mmol/L], adjusted group difference of -4.2 [95% CI, -8.4 to 0.05] mg/dL [-0.23 95% CI, -.47 to 0.003 mmol/L]; P = .051). Individuals with IAH had a higher proportion of days with hypoglycemic events below 70 mg/dL [3.89 mmol/L] (≥15 minutes <70 mg/dL [<3.89 mmol/L]) both on exercise days (51% vs 43%; P = .006) and sedentary days (48% vs 30%; P = .001). The increased odds of experiencing a hypoglycemic event below 70 mg/dL (<3.89 mmol/L) for individuals with IAH compared to "aware" did not differ significantly between exercise and sedentary days (interaction P = .36). CONCLUSION: Individuals with IAH have a higher underlying risk of hypoglycemia than "aware" individuals. Exercise does not appear to differentially increase risk for hypoglycemia during the activity, or in the subsequent 24 hours for IAH compared to aware individuals with T1D.

Factors Affecting Reproducibility of Change in Glucose During Exercise: Results From the Type 1 Diabetes and EXercise Initiative.

AIMS: To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise. METHODS: Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated. RESULTS: Data from 476 adults with type 1 diabetes were analyzed. A participant's change in glucose during exercise was reproducible within 15 mg/dL of the participant's other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose (P < .001), lower glucose coefficient of variation (CV) (P < .001), and lower % time <70 mg/dL (P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose. CONCLUSIONS: Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participant's overall glycemic control than other modifiable factors.

Biological and behavioral pathways from prenatal depression to offspring cardiometabolic risk: Testing the developmental origins of health and disease hypothesis.

Given prior literature focused on the Developmental Origins of Health and Disease framework, there is strong rationale to hypothesize that reducing depression in the prenatal period will cause improvements in offspring cardiometabolic health. The current review outlines evidence that prenatal depression is associated with offspring cardiometabolic risk and health behaviors. We review evidence of these associations in humans and in nonhuman animals at multiple developmental periods, from the prenatal period (maternal preeclampsia, gestational diabetes), neonatal period (preterm birth, small size at birth), infancy (rapid weight gain), childhood and adolescence (high blood pressure, impaired glucose-insulin homeostasis, unfavorable lipid profiles, abdominal obesity), and into adulthood (diabetes, cardiovascular disease). In addition to these cardiometabolic outcomes, we focus on health behaviors associated with cardiometabolic risk, such as child eating behaviors, diet, physical activity, and sleep health. Our review focuses on child behaviors (e.g., emotional eating, preference for highly palatable foods, short sleep duration) and parenting behaviors (e.g., pressuring child to eat, modeling of health behaviors). These changes in health behaviors may be detected before changes to cardiometabolic outcomes, which may allow for early identification of and prevention for children at risk for poor adult cardiometabolic outcomes. We also discuss the methods of the ongoing Care Project, which is a randomized clinical trial to test whether reducing prenatal maternal depression improves offspring's cardiometabolic health and health behaviors in preschool. The goal of this review and the Care Project are to inform future research, interventions, and policies that support prenatal mental health and offspring cardiometabolic health. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Initial evidence of the acute effect of electronic nicotine delivery system use on energy intake.

Previous work has aimed to disentangle the acute effects of nicotine and smoking on appetite with mixed findings. Electronic nicotine delivery systems (ENDS) have yet to be examined in this regard despite evidence of use for weight control. The present study tested the influence of an ENDS on acute energy intake and associated subjective effects. Participants (n = 34; 18-65 years) with current ENDS use completed two randomly ordered clinical lab sessions after overnight abstinence from tobacco/nicotine/food/drinks (other than water). Sessions differed by the product administered over 20 min: active (20 puffs of a JUUL ENDS device; 5% nicotine tobacco-flavored pod) or control (access to an uncharged JUUL with an empty pod). About 40 min after product administration, participants were provided an ad lib buffet-style meal with 21 food/drink items. Subjective ratings were assessed at baseline, after product use, and before/after the meal. Energy intake (kcal) was calculated using pre-post buffet item weights. Repeated measures analyses of variance and pairwise comparisons were used to detect differences by condition and time (α < .05). Mean ± standard error of the mean energy intake did not differ significantly between active (1011.9 ± 98.8 kcal) and control (939.8 ± 88.4 kcal; p = .108) conditions. Nicotine abstinence symptoms significantly decreased after the active condition, while satiety significantly increased. Following the control condition, satiety remained constant while hunger significantly increased relative to baseline. Findings indicate that acute ENDS use did not significantly impact energy intake, but there was an ENDS-associated subjective increase in satiety and relative decrease in hunger. Results support further investigation of ENDS on appetite. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Online Classification of Unstructured Free-Living Exercise Sessions in People with Type 1 Diabetes.

Background: Managing exercise in type 1 diabetes is challenging, in part, because different types of exercises can have diverging effects on glycemia. The aim of this work was to develop a classification model that can classify an exercise event (structured or unstructured) as aerobic, interval, or resistance for the purpose of incorporation into an automated insulin delivery (AID) system. Methods: A long short-term memory network model was developed with real-world data from 30-min structured sessions of at-home exercise (aerobic, resistance, or mixed) using triaxial accelerometer, heart rate, and activity duration information. The detection algorithm was used to classify 15 common free-living and unstructured activities and relate each to exercise-associated change in glucose. Results: A total of 1610 structured exercise sessions were used to train, validate, and test the model. The accuracy for the structured exercise sessions in the testing set was 72% for aerobic, 65% for interval, and 77% for resistance. In addition, we tested the classifier on 3328 unstructured sessions. We validated the session-associated change in glucose against the expected change during exercise for each type. Mean and standard deviation of the change in glucose of -20.8 (40.3) mg/dL were achieved for sessions classified as aerobic, -16.2 (39.0) mg/dL for sessions classified as interval, and -11.6 (38.8) mg/dL for sessions classified as resistance. Conclusions: The proposed algorithm reliably identified physical activity associated with expected change in glucose, which could be integrated into an AID system to manage the exercise disturbance in glycemia according to the predicted class.

Postprandial Glucose Variability Following Typical Meals in Youth Living with Type 1 Diabetes.

We explored the association between macronutrient intake and postprandial glucose variability in a large sample of youth living with T1D and consuming free-living meals. In the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study, youth took photographs before and after their meals on 3 days during a 10 day observation period. We used the remote food photograph method to obtain the macronutrient content of youth's meals. We also collected physical activity, continuous glucose monitoring, and insulin use data. We measured glycemic variability using standard deviation (SD) and coefficient of variation (CV) of glucose for up to 3 h after meals. Our sample included 208 youth with T1D (mean age: 14 ± 2 years, mean HbA1c: 54 ± 14.2 mmol/mol [7.1 ± 1.3%]; 40% female). We observed greater postprandial glycemic variability (SD and CV) following meals with more carbohydrates. In contrast, we observed less postprandial variability following meals with more fat (SD and CV) and protein (SD only) after adjusting for carbohydrates. Insulin modality, exercise after meals, and exercise intensity did not influence associations between macronutrients and postprandial glycemic variability. To reduce postprandial glycemic variability in youth with T1D, clinicians should encourage diversified macronutrient meal content, with a goal to approximate dietary guidelines for suggested carbohydrate intake.

Proportion of caloric restriction-induced weight loss as skeletal muscle.

OBJECTIVE: This study's objective was to develop models predicting the relative reduction in skeletal muscle (SM) mass during periods of voluntary calorie restriction (CR) and to validate model predictions in longitudinally monitored samples. METHODS: The model development group included healthy nonexercising adults (n = 897) who had whole-body SM mass measured with magnetic resonance imaging. Model predictions of relative SM changes with CR were evaluated in two longitudinal studies, one 12 to 14 weeks in duration (n = 74) and the other 12 months in duration (n = 26). RESULTS: A series of SM prediction models were developed in a sample of 415 males and 482 females. Model-predicted changes in SM mass relative to changes in body weight (i.e., ΔSM/Δbody weight) with a representative model were (mean ± SE) 0.26 ± 0.013 in males and 0.14 ± 0.007 in females (sex difference, p < 0.001). The actual mean proportions of weight loss as SM in the longitudinal studies were 0.23 ± 0.02/0.20 ± 0.06 in males and 0.10 ± 0.02/0.17 ± 0.03 in females, similar to model-predicted values. CONCLUSIONS: Nonelderly males and females with overweight and obesity experience respective reductions in SM mass with voluntary CR in the absence of a structured exercise program of about 2 to 2.5 kg and 1 to 1.5 kg per 10-kg weight loss, respectively. These estimates are predicted to be influenced by interactions between age and body mass index in males, a hypothesis that needs future testing.

Exercise-induced Changes in Central Adiposity During an RCT: Effect of Exercise Dose and Associations With Compensation.

CONTEXT: Exercise can decrease central adiposity, but the effect of exercise dose and the relationship between central adiposity and exercise-induced compensation is unclear. OBJECTIVE: Test the effect of exercise dose on central adiposity change and the association between central adiposity and exercise-induced weight compensation. METHODS: In this ancillary analysis of a 6-month randomized controlled trial, 170 participants with overweight or obesity (mean ± SD body mass index: 31.5 ± 4.7 kg/m2) were randomized to a control group or exercise groups that reflected exercise recommendations for health (8 kcal/kg/week [KKW]) or weight loss and weight maintenance (20 KKW). Waist circumference was measured, and dual-energy X-ray absorptiometry assessed central adiposity. Predicted weight change was estimated and weight compensation (weight change - predicted weight change) was calculated. RESULTS: Between-group change in waist circumference (control: .0 cm [95% CI, -1.0 to 1.0], 8 KKW: -.7 cm [95% CI, -1.7 to .4], 20 KKW: -1.3 cm [95% CI, -2.4 to -.2]) and visceral adipose tissue (VAT; control: -.02 kg [95% CI, -.07 to .04], 8 KKW: -.01 kg [95% CI, -.07 to .04], 20 KKW: -.04 kg [95% CI, -.10 to .02]) was similar (P ≥ .23). Most exercisers (82.6%) compensated (weight loss less than expected). Exercisers who compensated exhibited a 2.5-cm (95% CI, .8 to 4.2) and .23-kg (95% CI, .14 to .31) increase in waist circumference and VAT, respectively, vs those who did not (P < .01). Desire to eat predicted VAT change during exercise (ß = .21; P = .03). CONCLUSION: In the presence of significant weight compensation, exercise at doses recommended for health and weight loss and weight maintenance leads to negligible changes in central adiposity.

Predictors of racial differences in weight loss: the PROPEL trial.

OBJECTIVE: Studies have consistently shown that African American individuals lose less weight in response to behavioral interventions, but the mechanisms leading to this result have been understudied. METHODS: Data were derived from the PROmoting Successful Weight Loss in Primary CarE in Louisiana (PROPEL) study, which was a cluster-randomized, two-arm trial conducted in primary care clinics. In the PROPEL trial, African American individuals lost less weight compared with patients who belonged to other racial groups after 24 months. In the current study, counterfactual mediation analyses among 445 patients in the intervention arm of PROPEL were used to determine which variables mediated the relationship between race and weight loss. The mediators included treatment engagement, psychosocial, and lifestyle factors. RESULTS: At 6 months, daily weighing mediated 33% (p = 0.008) of the racial differences in weight loss. At 24 months, session attendance and daily weighing mediated 35% (p = 0.027) and 66% (p = 0.005) of the racial differences in weight loss, respectively. None of the psychosocial or lifestyle variables mediated the race-weight loss association. CONCLUSIONS: Strategies specifically targeting engagement, such as improving session attendance and self-weighing behaviors, among African American individuals are needed to support more equitable weight losses over extended time periods.

Calorie restriction reduces biomarkers of cellular senescence in humans.

Calorie restriction (CR) with adequate nutrient intake is a potential geroprotective intervention. To advance this concept in humans, we tested the hypothesis that moderate CR in healthy young-to-middle-aged individuals would reduce circulating biomarkers of cellular senescence, a fundamental mechanism of aging and aging-related conditions. Using plasma specimens from the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE™) phase 2 study, we found that CR significantly reduced the concentrations of several senescence biomarkers at 12 and 24 months compared to an ad libitum diet. Using machine learning, changes in biomarker concentrations emerged as important predictors of the change in HOMA-IR and insulin sensitivity index at 12 and 24 months, and the change in resting metabolic rate residual at 12 months. Finally, using adipose tissue RNA-sequencing data from a subset of participants, we observed a significant reduction in a senescence-focused gene set in response to CR at both 12 and 24 months compared to baseline. Our results advance the understanding of the effects of CR in humans and further support a link between cellular senescence and metabolic health.