The interrelationship between sleep, diet, and glucose metabolism

Decoding the nexus: branched-chain amino acids and their connection with sleep, circadian rhythms, and cardiometabolic health

The sleep-wake cycle stands as an integrative process essential for sustaining optimal brain function and, either directly or indirectly, overall body health, encompassing metabolic and cardiovascular well-being. Given the heightened metabolic activity of the brain, there exists a considerable demand for nutrients in comparison to other organs. Among these, the branched-chain amino acids, comprising leucine, isoleucine, and valine, display distinctive significance, from their contribution to protein structure to their involvement in overall metabolism, especially in cerebral processes. Among the first amino acids that are released into circulation post-food intake, branched-chain amino acids assume a pivotal role in the regulation of protein synthesis, modulating insulin secretion and the amino acid sensing pathway of target of rapamycin. Branched-chain amino acids are key players in influencing the brain's uptake of monoamine precursors, competing for a shared transporter. Beyond their involvement in protein synthesis, these amino acids contribute to the metabolic cycles of γ-aminobutyric acid and glutamate, as well as energy metabolism. Notably, they impact GABAergic neurons and the excitation/inhibition balance. The rhythmicity of branched-chain amino acids in plasma concentrations, observed over a 24-hour cycle and conserved in rodent models, is under circadian clock control. The mechanisms underlying those rhythms and the physiological consequences of their disruption are not fully understood. Disturbed sleep, obesity, diabetes, and cardiovascular diseases can elevate branched-chain amino acid concentrations or modify their oscillatory dynamics. The mechanisms driving these effects are currently the focal point of ongoing research efforts, since normalizing branched-chain amino acid levels has the ability to alleviate the severity of these pathologies. In this context, the Drosophila model, though underutilized, holds promise in shedding new light on these mechanisms. Initial findings indicate its potential to introduce novel concepts, particularly in elucidating the intricate connections between the circadian clock, sleep/wake, and metabolism. Consequently, the use and transport of branched-chain amino acids emerge as critical components and orchestrators in the web of interactions across multiple organs throughout the sleep/wake cycle. They could represent one of the so far elusive mechanisms connecting sleep patterns to metabolic and cardiovascular health, paving the way for potential therapeutic interventions.

Development and Validation of a Nocturnal Hypoglycaemia Risk Model for Patients With Type 2 Diabetes Mellitus

AIM

To develop and test different machine learning algorithms for predicting nocturnal hypoglycaemia in patients with type 2 diabetes mellitus.

DESIGN

A retrospective study.

METHODS

We collected data from dynamic blood glucose monitoring of patients with T2DM admitted to the Department of Endocrinology and Metabolism at a hospital in Shanghai, China, from November 2020 to January 2022. Patients undergone the continuous glucose monitoring (CGM) for ≥ 24 h were included in this study. Logistic regression, random forest and light gradient boosting machine algorithms were employed, and the models were validated and compared using AUC, accuracy, specificity, recall rate, precision, F1 score and the Kolmogorov-Smirnov test.

RESULTS

A total of 4015 continuous glucose-monitoring data points from 440 patients were included, and 28 variables were selected to build the risk prediction model. The 440 patients had an average age of 62.7 years. Approximately 48.2% of the patients were female and 51.8% were male. Nocturnal hypoglycaemia appeared in 573 (14.30%) of 4015 continuous glucose monitoring data. The light gradient boosting machine model demonstrated the highest predictive performances: AUC (0.869), specificity (0.802), accuracy (0.801), precision (0.409), recall rate (0.797), F1 score (0.255) and Kolmogorov (0.603). The selected predictive factors included time below the target glucose range, duration of diabetes, insulin use before bed and dynamic blood glucose monitoring parameters from the previous day.

PATIENT OR PUBLIC CONTRIBUTION

No Patient or Public Contribution.

Association of Physical Activity and/or Diet with Sleep Quality and Duration in Adolescents: A Scoping Review

Background: Sleep is essential for health, especially during adolescence. However, most adolescents do not obtain the recommended 8 to 10 h of sleep, and their health is significantly affected. While both physical activity (PA) and diet have been shown to help improve the sleep quality and duration, the combined association of these two factors with sleep has yet to be analysed. Objectives: Therefore, the main objective of this study was to assess the evidence on the combined association of PA and diet with the quality and duration of sleep in adolescents. Secondary objectives were to analyse the evidence on the single association of PA with the quality and duration of sleep in adolescents and to analyse the single association of diet with the quality and duration of sleep in adolescents. Methods: To this end, a scoping review was conducted with a structured search in four online databases (PubMed, Scopus, Web of Science and ERIC). Results: The findings suggest that the amount of PA (time/week) and healthy dietary patterns, characterised by meal regularity and high consumption of fruits and vegetables, favour a better quality and a longer duration of sleep. Conversely, less weekly PA and the intake of less healthy foods, such as ultra-processed foods, are associated with decreasing the sleep quality and duration. Conclusions: In conclusion, the results underscore the importance of considering PA and diet as an appropriate approach to investigating sleep quality and duration in adolescents. Studies analysing the interplay between PA, diet and sleep in adolescents are scarce.

Metabolic Characteristics of Gut Microbiota and Insomnia: Evidence from a Mendelian Randomization Analysis

Insomnia is a common sleep disorder that significantly impacts individuals' sleep quality and daily life. Recent studies have suggested that gut microbiota may influence sleep through various metabolic pathways. This study aims to explore the causal relationships between the abundance of gut microbiota metabolic pathways and insomnia using Mendelian randomization (MR) analysis. This two-sample MR study used genetic data from the OpenGWAS database (205 gut bacterial pathway abundance) and the FinnGen database (insomnia-related data). We identified single nucleotide polymorphisms (SNPs) associated with gut bacterial pathway abundance as instrumental variables (IVs) and ensured their validity through stringent selection criteria and quality control measures. The primary analysis employed the inverse variance-weighted (IVW) method, supplemented by other MR methods, to estimate causal effects. The MR analysis revealed significant positive causal effects of specific carbohydrate, amino acid, and nucleotide metabolism pathways on insomnia. Key pathways, such as gluconeogenesis pathway (GLUCONEO.PWY) and TCA cycle VII acetate producers (PWY.7254), showed positive associations with insomnia (B > 0, p < 0.05). Conversely, pathways like hexitol fermentation to lactate, formate, ethanol and acetate pathway (P461.PWY) exhibited negative causal effects (B < 0, p < 0.05). Multivariable MR analysis confirmed the independent causal effects of these pathways (p < 0.05). Sensitivity analyses indicated no significant pleiotropy or heterogeneity, ensuring the robustness of the results. This study identifies specific gut microbiota metabolic pathways that play critical roles in the development of insomnia. These findings provide new insights into the biological mechanisms underlying insomnia and suggest potential targets for therapeutic interventions. Future research should further validate these causal relationships and explore how modulating gut microbiota or its metabolic products can effectively improve insomnia symptoms, leading to more personalized and precise treatment strategies.

Chrono-nutrition and sleep: lessons from the temporal feature of eating patterns in human studies - A systematic scoping review

An emerging field of research has revealed a bidirectional relationship between sleep and diet, highlighting the potential role of a healthy diet in improving sleep. However, the impact of chrono-nutrition on sleep remains less explored. Here we conducted a systematic scoping review, considering the multiple dimensions of chrono-nutrition, to describe the extent, range, and nature of the existing literature in this area (PROSPERO: CRD42021274637). There has been a significant increase in the literature exploring this topic over the past six years (almost 67 % of the evolving literature). A breakdown of the included studies was performed according to three major chrono-nutritional dimensions: meal timing [n = 35], irregular eating patterns [n = 84], and frequency of eating occasions [n = 3]. Meal timing included three sub-dimensions: breakfast skipping [n = 13], late eating [n = 16], and earlier vs later meals schedules [n = 6]. Irregular meal patterns included three sub-dimensions: diurnal fasting [n = 65], intermittent fasting [n = 16], and daily meal patterns [n = 3]. Frequency was the least studied dimension (n = 3). We provided a synthetic and illustrative framework underlining important preliminary evidence linking the temporal characteristics of eating patterns to various facets of sleep health. Nonetheless, much work remains to be done to provide chrono-nutrition guidelines to improve sleep health in the general population.

Association of Sleep Duration with Risk of All-Cause and Cause-Specific Mortality Among American Adults: A Population-Based Cohort Study

Objective

To examine potential factors affecting sleep duration and explore its association with the risk of mortality among adults in the United States.

Methods

The study population consisted of adults aged 26 to 79 years who participated in the National Health and Nutrition Examination Survey (NHANES) conducted from 2007 to 2016. Sleep duration was classified into three categories: short (<7 hours), optimal (7-8 hours), and long (≥9 hours). The associations between sleep duration and both all-cause mortality and cause-specific mortality (including heart disease, tumors, cerebrovascular disease, and others) were examined in the overall population and subgroups using weighted Cox regression models. Dose-response associations between sleep duration and risk of all-cause mortality were explored using restricted cubic spline (RCS) analyses. Additionally, a multinomial logistic regression analysis was conducted to investigate potential factors that influence sleep duration in adults.

Results

The study included a total of 24,141 subjects, with a population-weighted mean age of 48.93 years. Over 30% of the subjects exhibited unhealthy sleep habits. Fully adjusted models revealed that both short sleep duration (HR=1.169, 95% CI 1.027-1.331) and long sleep duration (HR=1.286, 95% CI 1.08-1.531), were associated with an increased risk of all-cause mortality. The RCS curves showed a U-shaped relationship between sleep duration and risk of all-cause mortality. Subgroup analyses showed a significant association between poor sleep patterns and all-cause mortality among adults aged 26-64 years, males, and non-Hispanic whites. Furthermore, multinomial logistic regression identified several predictors associated with short and long sleep durations.

Conclusion

Both short and long sleep duration are associated with an increased risk of all-cause mortality, with a U-shaped dose-response relationship. It is imperative to implement appropriate primary prevention strategies aimed at monitoring and providing health education to populations at risk of developing unhealthy sleep patterns.

Daily variation in blood glucose levels during continuous enteral nutrition in patients on the intensive care unit: a retrospective observational study

BACKGROUND

The circadian timing system coordinates daily cycles in physiological functions, including glucose metabolism and insulin sensitivity. Here, the aim was to characterise the 24-h variation in glucose levels in critically ill patients during continuous enteral nutrition after controlling for potential sources of bias.

METHODS

Time-stamped clinical data from adult patients who stayed in the Intensive Care Unit (ICU) for at least 4 days and received enteral nutrition were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Linear mixed-effects and XGBoost modelling were used to determine the effect of time of day on blood glucose values.

FINDINGS

In total, 207,647 glucose measurements collected during enteral nutrition were available from 6,929 ICU patients (3,948 males and 2,981 females). Using linear mixed-effects modelling, time of day had a significant effect on blood glucose levels (p < 0.001), with a peak of 9.6 [9.5-9.6; estimated marginal means, 95% CI] mmol/L at 10:00 in the morning and a trough of 8.6 [8.5-8.6] mmol/L at 02:00 at night. A similar impact of time of day on glucose levels was found with the XGBoost regression model.

INTERPRETATION

These results revealed marked 24-h variation in glucose levels in ICU patients even during continuous enteral nutrition. This 24-h pattern persists after adjustment for potential sources of bias, suggesting it may be the result of endogenous biological rhythmicity.

FUNDING

This work was supported by a VENI grant from the Netherlands Organisation for Health Research and Development (ZonMw), an institutional project grant, and by the Dutch Research Council (NWO).

Habitual Short Sleep Duration, Diet, and Development of Type 2 Diabetes in Adults

Importance

Understanding the interplay between sleep duration, dietary habits, and the risk of developing type 2 diabetes (T2D) is crucial for public health and diabetes prevention strategies.

Objective

To investigate the associations of type of diet and duration of sleep with the development of T2D.

Design, Setting, and Participants

Data derived from the UK Biobank baseline investigation (2006-2010) were analyzed for this cohort study between May 1 and September 30, 2023. The association between sleep duration and healthy dietary patterns with the risk of T2D was investigated during a median (IQR) follow-up of 12.5 (11.8-13.2) years (end of follow-up, September 30, 2021).

Exposure

For the analysis, 247 867 participants were categorized into 4 sleep duration groups: normal (7-8 hours per day), mild short (6 hours per day), moderate short (5 hours per day), and extreme short (3-4 hours per day). Their dietary habits were evaluated based on population-specific consumption of red meat, processed meat, fruits, vegetables, and fish, resulting in a healthy diet score ranging from 0 (unhealthiest) to 5 (healthiest).

Main Outcomes and Measures

Cox proportional hazards regression analysis was used to calculate hazard ratios (HRs) and 95% CIs for the development of T2D across various sleep duration groups and healthy diet scores.

Results

The cohort comprised 247 867 participants with a mean [SD] age of 55.9 [8.1] years, of whom 52.3% were female. During the follow-up, 3.2% of participants were diagnosed with T2D based on hospital registry data. Cox regression analysis, adjusted for confounding variables, indicated a significant increase in the risk of T2D among participants with 5 hours or less of daily sleep. Individuals sleeping 5 hours per day exhibited a 1.16 adjusted HR (95% CI, 1.05-1.28), and individuals sleeping 3 to 4 hours per day exhibited a 1.41 adjusted HR (95% CI, 1.19-1.68) compared with individuals with normal sleep duration. Furthermore, individuals with the healthiest dietary patterns had a reduced risk of T2D (HR, 0.75 [95% CI, 0.63-0.88]). The association between short sleep duration and increased risk of T2D persisted even for individuals following a healthy diet, but there was no multiplicative interaction between sleep duration and healthy diet score.

Conclusions and Relevance

In this cohort study involving UK residents, habitual short sleep duration was associated with increased risk of developing T2D. This association persisted even among participants who maintained a healthy diet. To validate these findings, further longitudinal studies are needed, incorporating repeated measures of sleep (including objective assessments) and dietary habits.

The effect of chrono-nutritional manipulation of carbohydrate intake on sleep macrostructure: A randomized controlled trial

BACKGROUND & AIMS

Over the years, there is a rapid increase in the prevalence of inadequate sleep and its detrimental consequences. Yet, the impact of prolonged nutritional interventions on sleep optimization remains unexplored. To examine the effect of carbohydrate manipulation combined with exercise training on sleep macro-structure.

METHODS

Forty-two healthy, trained male volunteers were recruited for this study. The 4-week intervention consisted of three groups: i) Sleep Low-No Carbohydrates (SL-NCHO): participants consumed all their carbohydrate intake at regular intervals prior to evening training, ii) Sleep High-Low Glycemic Index (SH-LGI) and iii) Sleep High-High Glycemic Index (SH-HGI): Carbohydrate intake was spread throughout the day, both prior (60% of total CHO intake) and after evening training (40% of total CHO intake). The SH-LGI and SH-HGI groups differentiated by consuming either LGI or HGI foods in the evening, respectively. Alongside, participants performed a standardized exercise program combining resistance exercise and high-intensity interval training. Participants' sleep macro-structure was assessed with polysomnography, actigraphy, sleep diary, and sleep-wake questionnaires.

RESULTS

Objective assessments revealed a substantial time-effect on sleep initiation, duration, and continuity. After the intervention, sleep onset latency decreased (p < 0.001), sleep duration was prolonged (p = 0.006), sleep efficiency increased (p < 0.001), and wake after sleep onset decreased (p = 0.035). Sleep macroarchitecture did not significantly change, while the percentage of REM sleep stage to the total sleep time increased over time (p < 0.01). Consistent with the objective findings, subjects reported improved subjective sleep quality (p = 0.043) and reduced daytime sleepiness (p = 0.047).

CONCLUSION

The combination of a personalized dietary plan with exercise training enhances sleep initiation, sleep continuity, sleep duration, REM and N1 sleep stages, independently of carbohydrate type or timing. Lifestyle interventions should be investigated further to promote sleep quality and recovery.

REGISTRATION

The trial was registered at clinicaltrials.gov as NCT05464342.

Association between Low Energy Availability (LEA) and Impaired Sleep Quality in Young Rugby Players

Low energy availability (LEA) has been associated with several physiological consequences, but its impact on sleep has not been sufficiently investigated, especially in the context of young athletes. This study examined the potential association between energy availability (EA) status and objective sleep quality in 42 male rugby players (mean age: 16.2 ± 0.8 years) during a 7-day follow-up with fixed sleep schedules in the midst of an intensive training phase. Participants' energy intake was weighed and recorded. Exercise expenditure was estimated using accelerometry. Portable polysomnography devices captured sleep on the last night of the follow-up. Mean EA was 29.3 ± 9.14 kcal·kg FFM-1·day-1, with 47.6% of athletes presenting LEA, 35.7% Reduced Energy Availability (REA), and 16.7% Optimal Energy Availability (OEA). Lower sleep efficiency (SE) and N3 stage proportion, along with higher wake after sleep onset (WASO), were found in participants with LEA compared to those with OEA (p = 0.04, p = 0.03 and p = 0.005, respectively, with large effect sizes). Segmented regression models of the EA-sleep outcomes (SE, sleep onset latency [SOL]), WASO and N3) relationships displayed two separate linear regions and produced a best fit with a breakpoint between 21-33 kcal·kg FFM-1·day-1. Below these thresholds, sleep quality declines considerably. It is imperative for athletic administrators, nutritionists, and coaches to conscientiously consider the potential impact of LEA on young athletes' sleep, especially during periods of heavy training.

Associations between perioperative sleep patterns and clinical outcomes in patients with intracranial tumors: a correlation study

Objective

Although the quality of perioperative sleep is gaining increasing attention in clinical recovery, its impact role remains unknown and may deserve further exploration. This study aimed to investigate the associations between perioperative sleep patterns and clinical outcomes among patients with intracranial tumors.

Methods

A correlation study was conducted in patients with intracranial tumors. Perioperative sleep patterns were assessed using a dedicated sleep monitor for 6 consecutive days. Clinical outcomes were gained through medical records and follow-up. Spearman's correlation coefficient and multiple linear regression analysis were applied to evaluate the associations between perioperative sleep patterns and clinical outcomes.

Results

Of 110 patients, 48 (43.6%) were men, with a median age of 57 years. A total of 618 days of data on perioperative sleep patterns were collected and analyzed. Multiple linear regression models revealed that the preoperative blood glucose was positively related to the preoperative frequency of awakenings (β = 0.125; 95% CI = 0.029-0.221; P = 0.011). The level of post-operative nausea and vomiting was negatively related to perioperative deep sleep time (β = -0.015; 95% CI = -0.027--0.003; P = 0.015). The level of anxiety and depression was negatively related to perioperative deep sleep time, respectively (β = -0.048; 95% CI = -0.089-0.008; P = 0.020, β = -0.041; 95% CI = -0.076-0.006; P = 0.021). The comprehensive complication index was positively related to the perioperative frequency of awakenings (β = 3.075; 95% CI = 1.080-5.070; P = 0.003). The post-operative length of stay was negatively related to perioperative deep sleep time (β = -0.067; 95% CI = -0.113-0.021; P = 0.005). The Pittsburgh Sleep Quality Index was positively related to perioperative sleep onset latency (β = 0.097; 95% CI = 0.044-0.150; P < 0.001) and negatively related to perioperative deep sleep time (β = -0.079; 95% CI = -0.122-0.035; P < 0.001).

Conclusion

Perioperative sleep patterns are associated with different clinical outcomes. Poor perioperative sleep quality, especially reduced deep sleep time, has a negative impact on clinical outcomes. Clinicians should, therefore, pay more attention to sleep quality and improve it during the perioperative period.

Clinical trial registration

http://www.chictr.org.cn, identifier: ChiCTR2200059425.

A sweet spot for the sleeping brain: Linking human sleep physiology and glucoregulation

Vallat et al.¹ demonstrate a positive association between the coupling of slow oscillations and sleep spindles, neurophysiological markers of NREM sleep, and next-morning glucose homeostasis. Extended findings in an independent dataset raise intriguing questions about its directionality and consistency.