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We used an interpretable machine learning framework to identify the type 2 diabetes–related gut microbiome features in the cross-sectional analyses of three Chinese cohorts: one discovery cohort (n = 1,832, 270 cases of type 2 diabetes) and two validation cohorts (cohort 1: n = 203, 48 cases; cohort 2: n = 7,009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 participants without type 2 diabetes and assessed the correlation between the MRS and host blood metabolites (n = 1,016). We transferred human fecal samples with different MRS levels to germ-free mice to confirm the MRS–type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity, and dietary factors with the MRS (n = 1,832).

The MRS (including 14 microbial features) consistently associated with type 2 diabetes, with risk ratio for per 1-unit change in MRS 1.28 (95% CI 1.23–1.33), 1.23 (1.13–1.34), and 1.12 (1.06–1.18) across three cohorts. The MRS was positively associated with future glucose increment (P < 0.05) and was correlated with a variety of gut microbiota–derived blood metabolites. Animal study further confirmed the MRS–type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome–type 2 diabetes relationship.

Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment.

We examined the association between glycemic control in patients with diabetes and the incidence of infection in the entire population of patients with diabetes in a large HMO. During the study period, we first selected an HbA_1c test for each patient and then searched for an infection diagnosis in the 60 days that followed the test. A multivariate logistic regression analysis was performed to determine the independent effect of HbA_1c on the likelihood of being diagnosed with an infection. We were able to control for many confounders, such as other chronic illness, time since the diagnosis of diabetes, and use of steroids before the infection.

We identified 407 cases of cellulitis. Multivariate logistic regressions for cellulitis showed a 1.4-fold increased risk among patients with HbA_1c >7.5% (58 mmol/mol). Factors such as obesity, Parkinson’s disease, peripheral vascular disease, and prior treatment with prednisone predisposed to cellulitis. There was an increase of 12% in the odds of cellulitis for every 1% (11 mmol/mol) elevation in HbA_1c (odds ratio [OR] 1.12; CI 1.05–1.19). A similar analysis showed a trend toward an increased risk of pneumonia in patients with HbA_1c >7.5% (58 mmol/mol) (OR 1.1; CI 0.9–1.4).

Poor glycemic control was associated in this study with the development of cellulitis. The study also suggests that exposure to oral prednisolone increases the risk of cellulitis, pneumonia, and upper respiratory infection.

We conducted a cross-sectional study using public data from the General Directorate of Epidemiology of the Mexican Ministry of Health. We included individuals with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 between 1 March and 31 July 2020. The primary outcome was the predicted probability of hospitalization, inclusive of 8.5% of patients who required intensive care unit admission.

Among 373,963 adults with COVID-19, 16.1% (95% CI 16.0–16.3) self-reported diabetes. The predicted probability of hospitalization was 38.4% (37.6–39.2) for patients with diabetes only and 42.9% (42.2–43.7) for patients with diabetes and one or more comorbidities (obesity, hypertension, cardiovascular disease, and chronic kidney disease). High municipality-level of social deprivation and low state-level health care resources were associated with a 9.5% (6.3–12.7) and 17.5% (14.5–20.4) increased probability of hospitalization among patients with diabetes, respectively. In age-, sex-, and comorbidity-adjusted models, living in a context of high social vulnerability and low health care resources was associated with the highest predicted probability of hospitalization.

Social vulnerability contributes considerably to the probability of hospitalization among individuals with COVID-19 and diabetes with associated comorbidities. These findings can inform mitigation strategies for populations at the highest risk of severe COVID-19.

We combined Health Utilities Index Mark 3 data on patients with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Look AHEAD (Action for Health in Diabetes) trials and their follow-on studies. Complications were classified as events if they occurred in the year preceding the utility measurement; otherwise, they were classified as a history of the complication. We estimated utility decrements associated with complications using a fixed-effects regression model.

Our sample included 15,252 persons with an average follow-up of 8.2 years and a total of 128,873 person-visit observations. The largest, statistically significant (P < 0.05) health utility decrements were for stroke (event, –0.109; history, –0.051), amputation (event, –0.092; history, –0.150), congestive heart failure (event, –0.051; history, –0.041), dialysis (event, –0.039), estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m² (event, –0.043; history, –0.025), angina (history, –0.028), and myocardial infarction (MI) (event, –0.028). There were smaller effects for laser photocoagulation and eGFR <60 mL/min/1.73 m². Decrements for dialysis history, angina event, MI history, revascularization event, revascularization history, laser photocoagulation event, and hypoglycemia were not significant (P ≥ 0.05).

With use of a large study sample and a longitudinal design, our estimated health utility scores are expected to be largely unbiased. Estimates can be used to describe the health utility impact of diabetes complications, improve cost-effectiveness models, and inform diabetes policies.

C-peptide was measured in an untimed blood sample in the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) cohort of 6,076 people with type 1 diabetes monitored for an average of 5.2 years.

In regression models adjusted for age at onset and duration, effect sizes for C-peptide ≥200 vs. <5 pmol/L were as follows: insulin dose at baseline, 27% lower (P = 2 x 10^–39); HbA_1c during follow-up, 4.9 mmol/mol lower (P = 3 x 10^–13); hazard ratio for hospital admission for diabetic ketoacidosis during follow-up, 0.44 (P = 0.0001); odds ratio for incident retinopathy, 0.51 (P = 0.0003). Effects on the risk of serious hypoglycemic episodes were detectable at lower levels of C-peptide, and the form of the relationship was continuous down to the limit of detection (3 pmol/L). In regression models contrasting C-peptide 30 to <200 pmol/L with <5 pmol/L, the odds ratio for self-report of at least one serious hypoglycemic episode in the last year was 0.56 (P = 6 x 10^–8), and the hazard ratio for hospital admission for hypoglycemia during follow-up was 0.52 (P = 0.03).

These results in a large representative cohort suggest that even minimal residual C-peptide secretion could have clinical benefit in type 1 diabetes, in contrast to a follow-up study of the Diabetes Control and Complications Trial (DCCT) intensively treated cohort where an effect on hypoglycemia was seen only at C-peptide levels ≥130 pmol/L. This has obvious implications for the design and evaluation of trials of interventions to preserve or restore pancreatic islet function in type 1 diabetes.