Management of type 1 diabetes with a very low–Carbohydrate diet: A word of caution

The public often looks to nutrition to improve health, and reporting on nutrition findings from the scientific literature in the popular media often reveals unproven benefits. Lennerz et al present data collected via an online community and conclude that exceptional glycemic control in type 1 diabetes with a low risk for adverse events is possible with a VLCD, and research is needed to confirm the generalizability of these findings. Although it may be true that a VLCD can be useful, we find the study of Lennerz et al to fall well short of the level of scientific evidence that merits the media and professional attention it seems to have garnered

Gut-Brain Interactions: Implications for a Role of the Gut Microbiota in the Treatment and Prognosis of Anorexia Nervosa and Comparison to Type I Diabetes

Anorexia nervosa has poor prognosis and treatment outcomes and is influenced by genetic, metabolic, and psychological factors. Gut microbes interact with gut physiology to influence metabolism and neurobiology, although potential therapeutic benefits remain unknown. Type 1 diabetes is linked to anorexia nervosa through energy dysregulation, which in both disease states is related to the gut microbiota, disordered eating, and genetics

The association of retinopathy and low GFR in type 2 diabetes

We sought to determine characteristics which strengthen the association between markers of diabetic kidney disease and retinopathy

Workgroup emotional intelligence: Scale development and relationship to team process effectiveness and goal focus

Over the last decade, ambitious claims have been made in the management literature about the contribution of emotional intelligence to success and performance. Writers in this genre have predicted that individuals with high emotional intelligence perform better in all aspects of management. This paper outlines the development of a new emotional intelligence measure, the Workgroup Emotional Intelligence Profile, Version 3 (WEIP-3), which was designed specifically to profile the emotional intelligence of individuals in work teams. We applied the scale in a study of the link between emotional intelligence and two measures of team performance: team process effectiveness and team goal focus. The results suggest that the average level of emotional intelligence of team members, as measured by the WEIP-3, is reflected in the initial performance of teams. In our study, low emotional intelligence teams initially performed at a lower level than the high emotional intelligence teams. Over time, however, teams with low average emotional intelligence raised their performance to match that of teams with high emotional intelligence

HbA 1C variability and hypoglycemia hospitalization in adults with type 1 and type 2 diabetes: A nested case-control study

Aims To determine association between HbA1C variability and hypoglycemia requiring hospitalization (HH) in adults with type 1 diabetes (T1D) and type 2 diabetes (T2D). Methods Using nested case-control design in electronic health record data in England, one case with first or recurrent HH was matched to one control who had not experienced HH in incident T1D and T2D adults. HbA1C variability was determined by standard deviation of ≥ 3 HbA1C results. Conditional logistic models were applied to determine association of HbA1C variability with first and recurrent HH. Results In T1D, every 1.0% increase in HbA1C variability was associated with 90% higher first HH risk (95% CI, 1.25–2.89) and 392% higher recurrent HH risk (95% CI, 1.17–20.61). In T2D, a 1.0% increase in HbA1C variability was associated with 556% higher first HH risk (95% CI, 3.88–11.08) and 573% higher recurrent HH risk (95% CI,1.59–28.51). In T2D for first HH, the association was the strongest in non-insulin non-sulfonylurea users (P < 0.0001); for recurrent HH, the association was stronger in insulin users than sulfonylurea users (P = 0.07). The HbA1C variability-HH association was stronger in more recent years in T2D (P ≤ 0.004). Conclusions HbA1C variability is a strong predictor for HH in T1D and T2D

Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c

Background/Objective: To identify and characterize subgroups of adolescents with type 1 diabetes (T1D) and elevated hemoglobin A1c (HbA1c) who share patterns in their continuous glucose monitoring (CGM) data as “dysglycemia phenotypes.”. Methods: Data were analyzed from the Flexible Lifestyles Empowering Change randomized trial. Adolescents with T1D (13-16 years, duration >1 year) and HbA1c 8% to 13% (64-119 mmol/mol) wore blinded CGM at baseline for 7 days. Participants were clustered based on eight CGM metrics measuring hypoglycemia, hyperglycemia, and glycemic variability. Clusters were characterized by their baseline features and 18 months changes in HbA1c using adjusted mixed effects models. For comparison, participants were stratified by baseline HbA1c (≤/>9.0% [75 mmol/mol]). Results: The study sample included 234 adolescents (49.8% female, baseline age 14.8 ± 1.1 years, baseline T1D duration 6.4 ± 3.7 years, baseline HbA1c 9.6% ± 1.2%, [81 ± 13 mmol/mol]). Three Dysglycemia Clusters were identified with significant differences across all CGM metrics (P <.001). Dysglycemia Cluster 3 (n = 40, 17.1%) showed severe hypoglycemia and glycemic variability with moderate hyperglycemia and had a lower baseline HbA1c than Clusters 1 and 2 (P <.001). This cluster showed increases in HbA1c over 18 months (p-for-interaction = 0.006). No other baseline characteristics were associated with Dysglycemia Clusters. High HbA1c was associated with lower pump use, greater insulin doses, more frequent blood glucose monitoring, lower motivation, and lower adherence to diabetes self-management (all P <.05). Conclusions: There are subgroups of adolescents with T1D for which glycemic control is challenged by different aspects of dysglycemia. Enhanced understanding of demographic, behavioral, and clinical characteristics that contribute to CGM-derived dysglycemia phenotypes may reveal strategies to improve treatment

Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood

Introduction Individuals with type 1 diabetes (T1D) present with diverse body weight status and degrees of glycemic control, which may warrant different treatment approaches. We sought to identify subgroups sharing phenotypes based on both weight and glycemia and compare characteristics across subgroups. Research design and methods Participants: with T1D in the SEARCH study cohort (n=1817, 6.0-30.4 years) were seen at a follow-up visit >5 years after diagnosis. Hierarchical agglomerative clustering was used to group participants based on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c) which were estimated by reinforcement learning tree predictions from 28 covariates. Interpretation of cluster weight status and glycemic control was based on mean BMIz and HbA1c, respectively. Results: The sample was 49.5% female and 55.5% non-Hispanic white (NHW); mean±SD age=17.6±4.5 years, T1D duration=7.8±1.9 years, BMIz=0.61±0.94, and HbA1c=76±21 mmol/mol (9.1±1.9)%. Six weight-glycemia clusters were identified, including four normal weight, one overweight, and one subgroup with obesity. No cluster had a mean HbA1c <58 mmol/mol (7.5%). Cluster 1 (34.0%) was normal weight with the lowest HbA1c and comprised 85% NHW participants with the highest socioeconomic position, insulin pump use, dietary quality, and physical activity. Subgroups with very poor glycemic control (ie, ≥108 mmol/mol (≥12.0%); cluster 4, 4.4%, and cluster 5, 7.5%) and obesity (cluster 6, 15.4%) had a lower proportion of NHW youth, lower socioeconomic position, and reported decreased pump use and poorer health behaviors (overall p<0.01). The overweight subgroup with very poor glycemic control (cluster 5) showed the highest lipids and blood pressure (p<0.01). Conclusions: There are distinct subgroups of youth and young adults with T1D that share weight-glycemia phenotypes. Subgroups may benefit from tailored interventions addressing differences in clinical care, health behaviors, and underlying health inequity. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ

Dietary intake and risk of non-severe hypoglycemia in adolescents with type 1 diabetes

Aims To determine the association between dietary intake and risk of non-severe hypoglycemia in adolescents with type 1 diabetes. Methods Type 1 adolescents from a randomized trial wore a blinded continuous glucose monitoring (CGM) system at baseline for one week in free-living conditions. Dietary intake was calculated as the average from two 24-h dietary recalls. Non-severe hypoglycemia was defined as having blood glucose < 70 mg/dL for ≥ 10 min but not requiring external assistance, categorized as daytime and nocturnal (11 PM–7AM). Data were analyzed using logistic regression models. Results Among 98 participants with 14,277 h of CGM data, 70 had daytime hypoglycemia, 66 had nocturnal hypoglycemia, 55 had both, and 17 had neither. Soluble fiber and protein intake were positively associated with both daytime and nocturnal hypoglycemia. Glycemic index, monounsaturated fat, and polyunsaturated fat were negatively associated with daytime hypoglycemia only. Adjusting for total daily insulin dose per kilogram eliminated all associations. Conclusions Dietary intake was differentially associated with daytime and nocturnal hypoglycemia. Over 80% of type 1 adolescents had hypoglycemia in a week, which may be attributed to the mismatch between optimal insulin dose needed for each meal and actually delivered insulin dose without considering quality of carbohydrate and nutrients beyond carbohydrate

Associations of Dietary Intake with the Intestinal Microbiota and Short-Chain Fatty Acids Among Young Adults with Type 1 Diabetes and Overweight or Obesity

Background: Diet, a key component of type 1 diabetes (T1D) management, modulates the intestinal microbiota and its metabolically active byproducts—including SCFA—through fermentation of dietary carbohydrates such as fiber. However, the diet–microbiome relationship remains largely unexplored in longstanding T1D. Objectives: We evaluated whether increased carbohydrate intake, including fiber, is associated with increased SCFA-producing gut microbes, SCFA, and intestinal microbial diversity among young adults with longstanding T1D and overweight or obesity. Methods: Young adult men and women with T1D for ≥1 y, aged 19–30 y, and BMI of 27.0–39.9 kg/m2 at baseline provided stool samples at baseline and 3, 6, and 9 mo of a randomized dietary weight loss trial. Diet was assessed by 1–2 24-h recalls. The abundance of SCFA-producing microbes was measured using 16S rRNA gene sequencing. GC-MS measured fecal SCFA (acetate, butyrate, propionate, and total) concentrations. Adjusted and Bonferroni-corrected generalized estimating equations modeled associations of dietary fiber (total, soluble, and pectins) and carbohydrate (available carbohydrate, and fructose) with microbiome-related outcomes. Primary analyses were restricted to data collected before COVID-19 interruptions. Results: Fiber (total and soluble) and carbohydrates (available and fructose) were positively associated with total SCFA and acetate concentrations (n = 40 participants, 52 visits). Each 10 g/d of total and soluble fiber intake was associated with an additional 8.8 μmol/g (95% CI: 4.5, 12.8 μmol/g; P = 0.006) and 24.0 μmol/g (95% CI: 12.9, 35.1 μmol/g; P = 0.003) of fecal acetate, respectively. Available carbohydrate intake was positively associated with SCFA producers Roseburia and Ruminococcus gnavus. All diet variables except pectin were inversely associated with normalized abundance of Bacteroides and Alistipes. Fructose was inversely associated with Akkermansia abundance. Conclusions: In young adults with longstanding T1D, fiber and carbohydrate intake were associated positively with fecal SCFA but had variable associations with SCFA-producing gut microbes. Controlled feeding studies should determine whether gut microbes and SCFA can be directly manipulated in T1D

Associations of disordered eating with the intestinal microbiota and short-chain fatty acids among young adults with type 1 diabetes

Background and aims: Disordered eating (DE) in type 1 diabetes (T1D) includes insulin restriction for weight loss with serious complications. Gut microbiota-derived short chain fatty acids (SCFA) may benefit host metabolism but are reduced in T1D. We evaluated the hypothesis that DE and insulin restriction were associated with reduced SCFA-producing gut microbes, SCFA, and intestinal microbial diversity in adults with T1D. Methods and results: We collected stool samples at four timepoints in a hypothesis-generating gut microbiome pilot study ancillary to a weight management pilot in young adults with T1D. 16S ribosomal RNA gene sequencing measured the normalized abundance of SCFA-producing intestinal microbes. Gas-chromatography mass-spectrometry measured SCFA (total, acetate, butyrate, and propionate). The Diabetes Eating Problem Survey—Revised (DEPS-R) assessed DE and insulin restriction. Covariate-adjusted and Bonferroni-corrected generalized estimating equations modeled the associations. COVID-19 interrupted data collection, so models were repeated restricted to pre-COVID-19 data. Data were available for 45 participants at 109 visits, which included 42 participants at 65 visits pre-COVID-19. Participants reported restricting insulin “At least sometimes” at 53.3% of visits. Pre-COVID-19, each 5-point DEPS-R increase was associated with a −0.34 (95% CI -0.56, −0.13, p = 0.07) lower normalized abundance of genus Anaerostipes; and the normalized abundance of Lachnospira genus was −0.94 (95% CI -1.5, −0.42), p = 0.02 lower when insulin restriction was reported “At least sometimes” compared to “Rarely or Never”. Conclusion: DE and insulin restriction were associated with a reduced abundance of SCFA-producing gut microbes pre-COVID-19. Additional studies are needed to confirm these associations to inform microbiota-based therapies in T1D