{'fraction_non_alphanumeric': 0.03593890386343217, 'fraction_numerical': 0.029200359389038633, 'mean_word_length': 4.666666666666667, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Meal Timing-Based Dietary Patterns Are Associated With Glucose Regulation, Insulin Resistance, and Incretin Effect in Individuals at Risk for Type 2 Diabetes


 
 
 Food is an external cue to entrain the circadian rhythm, and when and how to eat may be a crucial factor for glucose homeostasis. This study sought to quantify the impact of meal timing-based dietary habits on glucose homeostasis in prediabetic and normal individuals by using digital monitoring technologies.
 
 
 
 35 study participants (56.6 y; prediabetes n = 19 and normoglycemia n = 16) tracked their food intake and timing by a food-logging mobile app for at least two weeks. Gold-standard glucose metabolic tests were performed such as OGTT, insulin suppression test, and isoglycemic intravenous glucose infusion to quantify insulin resistance, beta-cell function, and incretin effects. The energy contribution of six meal timings to the total daily energy intake was determined. Principal component analysis (PCA) was used to group participants in the cohort based on their meal timing patterns. Multivariate linear regression (MLR) models confirmed differences in each meal timing feature by glycemic control groups.
 
 
 
 A total of 2307 meals and 625 days of food logs were collected from study participants. From the PCA plot based on meal timing features, the cohort was clearly separated into two clusters by their HbA1c levels: normoglycemia (HbA1c < 5.7%) vs. prediabetes (5.7%< HbA1c < 6.5%). MLR models further showed that people with prediabetes had lower Meal_4 (2pm-5pm) energy contribution (P = 0.00,697) and higher Meal_5 (5pm-9pm) energy contribution (P = 0.0462) than normal group even after adjustment for age, sex, ethnicity, and BMI. Similarly, insulin resistant and sensitive groups were separated based on meal timing features, as did incretin function. However, beta-cell function groups were not distinguished by meal timing features.
 
 
 
 The data suggest that meal timing-based dietary patterns can be used to predict different types of glucose metabolic dysregulation such as prolonged high blood glucose, insulin resistance, and incretin dysfunction.
 
 
 
 NIH 2T32HL09804911, NIH 5R01DK110186-02, Stanford PHIND Award.
