Meal and sleep timing games -Key roles in preventing diabetes

Meal and sleep timing games -Key roles in preventing diabetes

If you eat, exercise and sleep can be as important as what you do, this study reveals how the timing of daily habits influences your risk of type 2 diabetes, opening the doors to truly personalized prevention.

In a study recently published in the journalNPJ Digital MedicineResearchers examined the relationship between habitual lifestyle behaviors and metabolic physiology in individuals at risk of type 2 diabetes (T2D).

T2D incidence continues to rise worldwide, affecting 589 million adults worldwide and 38 million people in the United States (US). Additionally, 88 million adults in the United States have prediabetes, with 70% expected to develop T2D within four years. Preventing this transition therefore remains an important public health priority. Studies suggest that lifestyle modification is a robust tool for managing and preventing T2D.

Diet, physical activity, and sleep are key modifiable lifestyle behaviors that are essential to metabolic health. Furthermore, growing evidence suggests close interactions between the circadian clock system and lifestyle behavior. Sleep deprivation adversely affects glucose levels, and circadian desynchronization due to related lifestyle behaviors could impair physiological responses and increase T2D risks.

The study and the results

The present study examined the relationship between habitual lifestyle behaviors and metabolic physiology in people at risk for T2D. Two cohorts were included; Thirty-six healthy adults were enrolled in the primary cohort and 10 individuals were enrolled in the independent validation cohort. In the primary cohort, 16 and 20 individuals were divided into normoglycemia and prediabetes/T2D groups based on glycated hemoglobin (HbA1c) levels.

The habitual lifestyle data was collected in real time using Digital Health Technologies. Food intake was recorded using a real-time food tracking app. Physical activity and sleep data were collected using a Fitbit Ionic band, although these data were only available for 24 of the 36 participants due to a product recall during the study period. Continuous glucose monitoring (CGM) was performed using a Dexcom G4 CGM device. An oral glucose tolerance test (OGTT), an isoglycemic intravenous glucose infusion test and an insulin suppression test were performed.

These tests revealed participants' metabolic subphenotypes such as incretin function, insulin resistance and beta cell dysfunction. The prediabetes/T2D group had significantly higher sensor glucose (from CGM), sensor glucose variation, and spent more time in the hyperglycemic range than the normoglycemic group.

Meal timing profiles were determined by stratifying food and beverage intake into six time periods, reflecting key food intake times. Participants demonstrated high inter-individual variability in meal timing patterns. A principal component analysis based on meal timing characteristics submitted the cohort into two clusters by their Hba1c values.

Individuals with elevated HbA1c had lower energy intake from meals consumed between 2:00 p.m. and 5:00 p.m. and higher energy intake from meals consumed between 5:00 p.m. and 9:00 p.m. than those with lower HBA1c. Additionally, the cohort was clustered by incretin function, and individuals with reduced incretin function exhibited lower energy intake during the 11:00-14:00 and 17:00-21:00 hour periods and lower energy intake during the 14:00-17:00 and 21:00-5:00 hour periods.

Associations between sleep, physical activity, dietary characteristics, and CGM and metabolic outcomes were assessed using the least absolute shrinkage and selection operator (LASSO) in combination with regression models. Energy intake from meals between 2:00 and 5:00 hours was inversely associated with fasting plasma glucose (FPG).

Higher energy intake from meals during 17:00–21:00 hours was associated with more time in hyperglycemia, less time in target glucose range, and higher mean glucose the next day. Notably, these associations were not due to differences in total daily calorie intake, which was similar between groups, suggesting that the timing of meals themselves was a key factor. Higher carbohydrate intake from non-starchy vegetables was associated with decreased mean next-day glucose, while that from starchy vegetables was related to higher FPG and HbA1c.

Furthermore, greater variability in sleep efficiency was associated with higher nighttime glucose levels, higher mean next-day glucose levels, and longer duration in the nighttime hyperglycemic range. Furthermore, higher variability in wake duration after sleep onset was associated with higher two-hour OGTT glucose. Earlier awakening time was associated with lower incretin effects. Greater sedentary duration during the day was associated with more time in hyperglycemia.

Higher step density after the last meal was associated with less time in nocturnal hyperglycemia. Steps taken between 8:00 and 11:00 hours were associated with lower glucose levels the next day in the insulin resistant (IR) group. Steps between 00:00 and 5:00 hours were positively correlated with higher glucose for the next 48 hours in the IR and insulin-sensitive (IS) groups. Steps between 2:00 p.m. and 5:00 p.m. showed a negative correlation with CGM levels over the next 48 hours in the IS group.

Next, the team conducted a permuted correlation network analysis between sleep, physical activity, and dietary characteristics, adjusting for all lifestyle factors. This analysis showed significant correlations between lifestyle factors. Higher rice intake was associated with longer sleep latency and reduced sleep efficiency, while higher legume fish intake was associated with longer total sleep duration and shorter latency.

Additionally, higher intake of fruits, potassium and fiber correlated with longer sleep durations. Longer fasting windows and higher energy intake from meals between 8:00 and 11:00 hours were correlated with longer sleep times. Additionally, the team built integrated lifestyle machine learning models to predict metabolic subphenotypes based on demographic and lifestyle data.

Higher carbohydrate intake from sweets and starchy vegetables and increased energy intake during 17:00–21:00 hours were associated with prediabetes and higher HbA1c levels. In contrast, higher carbohydrate intake from fruits was associated with normoglycemia. Older age, higher carbohydrate intake from noodles and pasta, increased protein intake, and higher energy intake between 5:00 p.m. and 9:00 p.m. hours were predictive of incretin dysfunction. Longer exercise duration predicted normal beta cell function.

Finally, the team assessed the reproducibility of prediction models using the independent validation cohort, focusing on incretin function as other metabolic subphenotypes were highly biased. This cohort also underwent continuous lifestyle monitoring and metabolic testing. Applying the prediction model to this cohort yielded an accuracy of 80% with a misclassification error of 0.2, indicating robust and consistent prediction performance across cohorts.

It is important to note that the study authors acknowledge some limitations. These include the modest sample size and observational nature of the data, meaning the results show strong associations rather than direct causes. The research was also conducted in a single geographic area, indicating that more diverse populations should be studied in the future.

Conclusions

In summary, the results provided a unique characterization of how habitual lifestyle patterns relate to metabolic susceptibility to type 2 diabetes (T2D). Habitual meal timing was associated with insulin resistance, lower incretin function, and hyperglycemia. Irregular sleep control and efficiency was associated with higher glucose levels and IR. Crucially, the study found that the optimal timing for physical activity may depend on a person's metabolic profile, with morning activity being more beneficial for those who are more insulin resistant and afternoon activity for those who are insulin sensitive. Overall, the results reveal novel physiological links between lifestyle behaviors and metabolic risk, informing the development of personalized lifestyle changes and prevention strategies for precision prevention of type 2 diabetes.

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