Time-restricted eating to improve cardiometabolic health: The New York Time-Restricted EATing randomized clinical trial - Protocol overview

Artificial Intelligence in Endocrinology: On Track Toward Great Opportunities

In endocrinology, the types and quantity of digital data are increasing rapidly. Computing capabilities are also developing at an incredible rate, as illustrated by the recent expansion in the use of popular generative artificial intelligence (AI) applications. Numerous diagnostic and therapeutic devices using AI have already entered routine endocrine practice, and developments in this field are expected to continue to accelerate. Endocrinologists will need to be supported in managing AI applications. Beyond technological training, interdisciplinary vision is needed to encompass the ethical and legal aspects of AI, to manage the profound impact of AI on patient/provider relationships, and to maintain an optimal balance between human input and AI in endocrinology.

Diet and Meal Pattern Determinants of Glucose Levels and Variability in Adults with and without Prediabetes or Early-Onset Type 2 Diabetes: A Pilot Study

This observational pilot study examined the association between diet, meal pattern and glucose over a 2-week period under free-living conditions in 26 adults with dysglycemia (D-GLYC) and 14 with normoglycemia (N-GLYC). We hypothesized that a prolonged eating window and late eating occasions (EOs), along with a higher dietary carbohydrate intake, would result in higher glucose levels and glucose variability (GV). General linear models were run with meal timing with time-stamped photographs in real time, and diet composition by dietary recalls, and their variability (SD), as predictors and glucose variables (mean glucose, mean amplitude of glucose excursions [MAGE], largest amplitude of glucose excursions [LAGE] and GV) as dependent variables. After adjusting for calories and nutrients, a later eating midpoint predicted a lower GV (β = -2.3, SE = 1.0, p = 0.03) in D-GLYC, while a later last EO predicted a higher GV (β = 1.5, SE = 0.6, p = 0.04) in N-GLYC. A higher carbohydrate intake predicted a higher MAGE (β = 0.9, SE = 0.4, p = 0.02) and GV (β = 0.4, SE = 0.2, p = 0.04) in N-GLYC, but not D-GLYC. In summary, our data suggest that meal patterns interact with dietary composition and should be evaluated as potential modifiable determinants of glucose in adults with and without dysglycemia. Future research should evaluate causality with controlled diets.

Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity

BACKGROUND

Obesity is considered to be a risk factor for various diseases, and its incidence has tripled worldwide since 1975. In addition to potentially being at risk for adverse health outcomes, people with overweight or obesity are often stigmatised. Behaviour change interventions are increasingly delivered as mobile health (m-health) interventions, using smartphone apps and wearables. They are believed to support healthy behaviours at the individual level in a low-threshold manner.

OBJECTIVES

To assess the effects of integrated smartphone applications for adolescents and adults with overweight or obesity.

SEARCH METHODS

We searched CENTRAL, MEDLINE, PsycINFO, CINAHL, and LILACS, as well as the trials registers ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform on 2 October 2023 (date of last search for all databases). We placed no restrictions on the language of publication.

SELECTION CRITERIA

Participants were adolescents and adults with overweight or obesity. Eligible interventions were integrated smartphone apps using at least two behaviour change techniques. The intervention could target physical activity, cardiorespiratory fitness, weight loss, healthy diet, or self-efficacy. Comparators included no or minimal intervention (NMI), a different smartphone app, personal coaching, or usual care. Eligible studies were randomised controlled trials of any duration with a follow-up of at least three months.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methodology and the RoB 2 tool. Important outcomes were physical activity, body mass index (BMI) and weight, health-related quality of life, self-efficacy, well-being, change in dietary behaviour, and adverse events. We focused on presenting studies with medium- (6 to < 12 months) and long-term (≥ 12 months) outcomes in our summary of findings table, following recommendations in the core outcome set for behavioural weight management interventions.

MAIN RESULTS

We included 18 studies with 2703 participants. Interventions lasted from 2 to 24 months. The mean BMI in adults ranged from 27 to 50, and the median BMI z-score in adolescents ranged from 2.2 to 2.5. Smartphone app versus no or minimal intervention Thirteen studies compared a smartphone app versus NMI in adults; no studies were available for adolescents. The comparator comprised minimal health advice, handouts, food diaries, smartphone apps unrelated to weight loss, and waiting list. Measures of physical activity: at 12 months' follow-up, a smartphone app compared to NMI probably reduces moderate to vigorous physical activity (MVPA) slightly (mean difference (MD) -28.9 min/week (95% confidence interval (CI) -85.9 to 28; 1 study, 650 participants; moderate-certainty evidence)). We are very uncertain about the results of estimated energy expenditure and cardiorespiratory fitness at eight months' follow-up. A smartphone app compared with NMI probably results in little to no difference in changes in total activity time at 12 months' follow-up and leisure time physical activity at 24 months' follow-up. Anthropometric measures: a smartphone app compared with NMI may reduce BMI (MD of BMI change -2.6 kg/m2, 95% CI -6 to 0.8; 2 studies, 146 participants; very low-certainty evidence) at six to eight months' follow-up, but the evidence is very uncertain. At 12 months' follow-up, a smartphone app probably resulted in little to no difference in BMI change (MD -0.1 kg/m2, 95% CI -0.4 to 0.3; 1 study; 650 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in body weight change (MD -2.5 kg, 95% CI -6.8 to 1.7; 3 studies, 1044 participants; low-certainty evidence) at 12 months' follow-up. At 24 months' follow-up, a smartphone app probably resulted in little to no difference in body weight change (MD 0.7 kg, 95% CI -1.2 to 2.6; 1 study, 245 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in self-efficacy for a physical activity score at eight months' follow-up, but the results are very uncertain. A smartphone app probably results in little to no difference in quality of life and well-being at 12 months (moderate-certainty evidence) and in little to no difference in various measures used to inform dietary behaviour at 12 and 24 months' follow-up. We are very uncertain about adverse events, which were only reported narratively in two studies (very low-certainty evidence). Smartphone app versus another smartphone app Two studies compared different versions of the same app in adults, showing no or minimal differences in outcomes. One study in adults compared two different apps (calorie counting versus ketogenic diet) and suggested a slight reduction in body weight at six months in favour of the ketogenic diet app. No studies were available for adolescents. Smartphone app versus personal coaching Only one study compared a smartphone app with personal coaching in adults, presenting data at three months. Two studies compared these interventions in adolescents. A smartphone app resulted in little to no difference in BMI z-score compared to personal coaching at six months' follow-up (MD 0, 95% CI -0.2 to 0.2; 1 study; 107 participants). Smartphone app versus usual care Only one study compared an app with usual care in adults but only reported data at three months on participant satisfaction. No studies were available for adolescents. We identified 34 ongoing studies.

AUTHORS' CONCLUSIONS

The available evidence is limited and does not demonstrate a clear benefit of smartphone applications as interventions for adolescents or adults with overweight or obesity. While the number of studies is growing, the evidence remains incomplete due to the high variability of the apps' features, content and components, which complicates direct comparisons and assessment of their effectiveness. Comparisons with either no or minimal intervention or personal coaching show minor effects, which are mostly not clinically significant. Minimal data for adolescents also warrants further research. Evidence is also scarce for low- and middle-income countries as well as for people with different socio-economic and cultural backgrounds. The 34 ongoing studies suggest sustained interest in the topic, with new evidence expected to emerge within the next two years. In practice, clinicians and healthcare practitioners should carefully consider the potential benefits, limitations, and evolving research when recommending smartphone apps to adolescents and adults with overweight or obesity.

The effects of early time restricted eating plus daily caloric restriction compared to daily caloric restriction alone on continuous glucose levels

Background

The median eating duration in the U.S. is 14.75 h, spread throughout the period of wakefulness and ending before sleep. Food intake at an inappropriate circadian time may lead to adverse metabolic outcomes. Emerging literature suggests that time restricted eating (TRE) may improve glucose tolerance and insulin sensitivity. The aim was to compare 24-h glucose profiles and insulin sensitivity in participants after completing 12 weeks of a behavioral weight loss intervention based on early TRE plus daily caloric restriction (E-TRE+DCR) or DCR alone.

Methods

Eighty-one adults with overweight or obesity (age 18-50 years, BMI 25-45 kg/m2) were randomized to either E-TRE+DCR or DCR alone. Each participant wore a continuous glucose monitor (CGM) for 7 days and insulin sensitivity was estimated using the homeostatic model assessment of insulin resistance (HOMA-IR) at Baseline and Week 12. Changes in CGM-derived measures and HOMA-IR from Baseline to Week 12 were assessed within and between groups using random intercept mixed models.

Results

Forty-four participants had valid CGM data at both time points, while 38 had valid glucose, insulin, HOMA-IR, and hemoglobin A1c (A1c) data at both timepoints. There were no significant differences in sex, age, BMI, or the percentage of participants with prediabetes between the groups (28% female, age 39.2 ± 6.9 years, BMI 33.8 ± 5.7 kg/m2, 16% with prediabetes). After adjusting for weight, there were no between-group differences in changes in overall average sensor glucose, standard deviation of glucose levels, the coefficient of variation of glucose levels, daytime or nighttime average sensor glucose, fasting glucose, insulin, HOMA-IR, or A1c. However, mean amplitude of glycemic excursions changed differently over time between the two groups, with a greater reduction found in the DCR as compared to E-TRE+DCR (p = 0.03).

Conclusion

There were no major differences between E-TRE+DCR and DCR groups in continuous glucose profiles or insulin sensitivity 12 weeks after the intervention. Because the study sample included participants with normal baseline mean glucose profiles and insulin sensitivity, the ability to detect changes in these outcomes may have been limited.

Time-restricted eating for patients with diabetes and prediabetes: A systematic review

BACKGROUND

Several studies have explored the effect of time-restricted eating (TRE) on patients with diabetes and prediabetes. However, these studies have not been analyzed and summarized as a whole. We conducted a systematic review to summarize and analyze all studies about the efficacy and safety of TRE for patients with diabetes and prediabetes.

METHODS

We conducted a comprehensive search of the Embase, PubMed and Cochrane databases and the time span was from inception to 1 May 2022. The Cochrane Collaboration's Risk of Bias 2 (RoB2) and ROBINS-I tools were used to evaluate the quality of included studies. The effect of TRE on weight loss, insulin sensitivity, plasma glucose, and the safety of TRE were summarized and analyzed.

RESULTS

In total, 7 studies with 326 participants including 5 articles with 217 patients with diabetes and 2 articles with 109 patients with prediabetes were included. The TRE windows were from 4 to 10 h. The percentages of females ranged from 0 to 90%. The mean age ranged from 35.2 to 67.5 years, and most of patients adhered to TRE. All studies were assessed as high quality. TRE may result in weight loss, and improvements in the insulin sensitivity and plasma glucose, with no severe AEs.

CONCLUSION

Time-restricted eating is a safe and feasible intervention, and may offer cardiovascular and metabolic benefits for patients with diabetes and prediabetes. Studies in this field, which should be viewed as important, are limited. Therefore, more high-quality studies are needed.