Dietary treatment of type 1 diabetes: Beyond carbohydrate counting to fight cardiovascular risk

Eating habits and sleep quality in individuals with type 1 diabetes on continuous glucose monitoring and insulin pump

BACKGROUND AND AIMS

Sleep disorders are bidirectionally linked with eating behaviors and glucose metabolism, which could be clinically relevant in type 1 diabetes (T1D). We investigated the relationship between dietary habits and sleep quality in individuals with T1D on insulin pumps and continuous glucose monitoring (CGM).

METHODS AND RESULTS

In a cross-sectional study, dietary habits (7-day food diary, EPIC questionnaire) and sleep quality (Pittsburgh Sleep Quality Index questionnaire) were assessed in 59 men and 58 women with T1D, aged 19-79 years, using CGM and insulin pump. Differences in dietary habits and blood glucose after dinner (6 h) between participants differing in sleep quality, sleep duration, and sleep onset latency were evaluated. Bad Sleepers (n = 81) were twice as prevalent as Good Sleepers (n = 36) and had a significantly higher intake of fat than Good Sleepers (dinner: 30.7 ± 10.7 vs. 24.0 ± 10.5 g, p = 0.004). Short sleepers had a significantly higher usual intake (g/1000 kcal) of coffee and tea (90.4 ± 71.7 vs. 62.0 ± 35.6), alcoholic (47.8 ± 51.1 vs. 28.9 ± 31.5) and carbonated beverages (21.8 ± 38.1 vs. 9.3 ± 17.2) (p < 0.05 for all) than Long Sleepers. Long Sleep Onset Latency was associated with a significantly higher fat intake at dinner (41.8 ± 7.4 vs. 38.1 ± 9.1 % total energy, p = 0.029) than Short Sleep Onset Latency. No significant differences in post-dinner blood glucose levels were detected between participants with good or bad sleep quality.

CONCLUSION

Sleep disruption is common in T1D and is associated with unhealthy dietary choices, especially at dinner, independently of post-dinner blood glucose control.

Postprandial Glucose Control With Different Hybrid Closed-Loop Systems According to Type of Meal in Adults With Type 1 Diabetes

BACKGROUND

Hybrid Closed-Loop Systems (HCLs) may not perform optimally on postprandial glucose control. We evaluated how first-generation and advanced HCLs manage meals varying in carbohydrates, fat, and protein.

METHOD

According to a cross-sectional design, seven-day food records and HCLs reports from 120 adults with type 1 diabetes (MiniMed670G: n = 40, MiniMed780G: n = 49, Control-IQ [C-IQ]: n = 31) were analyzed. Breakfasts (n = 570), lunches (n = 658), and dinners (n = 619) were divided according to the median of their carbohydrate (g)/fat (g) plus protein (g) ratio (C/FP). After breakfast (4-hour), lunch (6-hour), and dinner (6-hour), continuous glucose monitoring (CGM) metrics and early and late glucose incremental area under the curves (iAUCs) and delivered insulin doses were evaluated. The association of C/FP and HCLs with postprandial glucose and insulin patterns was analyzed by univariate analysis of variance (ANOVA) with a two-factor design.

RESULTS

Postprandial glucose time-in-range 70 to 180 mg/dL was optimal after breakfast (78.3 ± 26.9%), lunch (72.7 ± 26.1%), and dinner (70.8 ± 27.3%), with no significant differences between HCLs. Independent of C/FP, late glucose-iAUC after lunch was significantly lower in C-IQ users than 670G and 780G (P < .05), with no significant differences at breakfast and dinner. Postprandial insulin pattern (Ins3-6h minus Ins0-3h) differed by type of HCLs at lunch (P = .026) and dinner (P < .001), being the early insulin dose (Ins0-3h) higher than the late dose (Ins3-6h) in 670G and 780G users with an opposite pattern in C-IQ users.

CONCLUSIONS

Independent of different proportions of dietary carbohydrates, fat, and protein, postprandial glucose response was similar in users of different HCLs, although obtained through different automatic insulin delivery patterns.