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

Background and objectives: Sleep disorders are bidirectionally linked with eating behaviors and glucose metabolism, and this could be clinically relevant in type 1 diabetes (T1D). We investigated the relationship between dietary habits and sleep quality in T1D. Methods: According to a cross-sectional design, T1D patients, 60 men and 60 women, aged 19–79, using continuous glucose monitoring (CGM) filled-in a 7-day food diary and completed the European Prospective Investigation into Cancer and Nutrition (EPIC) questionnaire on dietary habits and the Pittsburgh Sleep Quality Index (PSQI) questionnaire on sleep quality. Blood glucose values over 6 h after dinner were registered for one week. Differences in dietary habits and blood glucose were compared between the participants with good/bad quality, long/short duration, and long/short onset latency of sleep. Results: Bad sleepers (n = 84) were twice as prevalent as good sleepers (n = 36) and had significantly higher intake of fat than good sleepers, in particular at dinner time (30.7 ± 10.7 vs. 24.0 ± 10.5 g, p = 0.004). Short sleepers had significantly higher usual intake (g/1000 kcal) of coffee and tea (88.7 ± 70.9 vs. 62.0 ± 35.6), alcoholic beverages (46.6 ± 50.4 vs. 28.9 ± 31.5), and carbonated soft beverages (21.0 ± 37.5 vs. 9.3 ± 17.2) (p < 0.05 for all). Compared with the short sleep onset latency participants, the long sleep onset latency participants had significantly higher intake of fat at dinner time (41.8 ± 7.4 vs. 38.1 ± 9.1% total energy, p = 0.029). No differences in post-dinner blood glucose were detected between the participants with bad or good sleep quality. Discussion: Sleep disruption is common in T1D and is associated with unhealthy dietary choices, especially at dinner time, independently of post-dinner blood glucose control.

Background and Objectives: The gut microbiome composition has emerged as a potential contributor to metabolic health and it is influenced by several factors, such as dietary factors. Individuals with type 1 diabetes (T1D) experience metabolic dysregulation, including alterations in body weight; as a result, the prevalence of overweight/obesity is increasing in this population. Limited research has addressed the role of the gut microbiota on body weight in people with T1D. Our aim is to evaluate the association between BMI and gut microbiome composition in T1D patients, also exploring the relationship between dietary factors and the microbiota. Methods: A cross-sectional study was conducted involving T1D patients (n = 101) of both sexes, aged 18–79 years. Anthropometric parameters were measured, and the European Prospective Investigation into Cancer and Nutrition (EPIC) questionnaire was administered to evaluate dietary habits. Patients collected stool samples that were analyzed by shotgun metagenomics sequencing for the evaluation of the gut microbiota composition. Associations between BMI, gut microbiome composition, and dietary factors were evaluated by Pearson’s bivariate correlation. Results: BMI was correlated inversely with the Bacilli (r = −0.296, p = 0.004) and Gammaproteobacteria (r = −0.280, p = 0.009) classes and directly with the abundance of the Clostridia class (r = 0.220, p = 0.031) and one of its species Faecalibacterium prausnitzii (r = 0.264, p = 0.010). The presence of these taxa was associated with dietary factors: Bacilli was inversely correlated with the consumption of animal protein (r = −0.242, p = 0.019), monounsaturated fatty acids (r = −0.214, p = 0.038), linolenic acid (r = −0.236, p = 0.022), oleic acid (r = −0.205, p = 0.048), and cholesterol (r = −0.204, p = 0.048); Faecalibacterium prausnitzii was directly associated with the intake of cholesterol (r = 0.218, p = 0.034) and simple sugars (r = 0.226, p = 0.028). Clostridia was correlated directly (r = 0.225, p = 0.027) and Gammaproteobacteria inversely (r = −0.216, p = 0.045) with alcohol intake. Discussion: BMI was associated with the Clostridia, Bacilli, and Gammaproteobacteria classes. These bacteria were related to various dietary factors. Therefore, changes in the gut microbiota could be a possible link between dietary habits and overweight/obesity in people with T1D.

Background: Blood glucose control remains a challenge for type 1 diabetes (T1D) patients. Previous studies have shown an association between gut microbiota composition and T1D pathogenesis. However, little is known about the composition of the gut microbiota and its association with host blood glucose control and diet in people with T1D. Objective: We explored the relationship of gut microbiome composition with blood glucose control and dietary intake in people with T1D. Research design and methods: In a cross-sectional study, a metagenomic shotgun sequencing analysis of the gut microbiome obtained from fecal samples was performed in 101 individuals with T1D. Dietary intakes were assessed by using the European Prospective Investigation into Cancer and Nutrition (EPIC) questionnaire. Blood glucose control was assessed by continuous glucose monitoring and expressed as time-in-range (TIR), time spent in the blood glucose interval 70–180 mg/dL. Spearman’s correlation analysis was used to determine the correlation between gut microbiota composition, blood glucose control, and dietary intake. Results: TIR correlated positively with the abundance of Bacilli (r = 0.258, p = 0.027) and negatively with the Lachnospiraceae family (r = −0.238, p = 0.024), Mediterraneibacter (r = −0.249, p = 0.034), Coprococcus genus (r = −0.286, p = 0.016), Coprococcus comes (r = −0.257, p = 0.028), and Ruminococcus torques (r = −0.261, p = 0.026). The presence of these taxa was associated with the intake of various foods: Bacilli correlated positively with dairy products (r = 0.307, p = 0.002) and negatively with olive oil (r = −0.207, p = 0.041) and meat products (r = −0.255, p = 0.012). Lachnospiraceae correlated negatively with cereals (r = −0.263, p = 0.009). Mediterraneibacter correlated positively with meat and meat products (r = 0.230, p = 0.023). Ruminococcus torques correlated negatively with fruit intake (r = −0.227, p = 0.025). Discussion: Our findings highlight that gut microbiota composition may be related to blood glucose control in T1D and dietary factors may have a role in this interplay. Further investigations are needed to address whether these findings are causally linked and whether to target these gut microbiota taxa.