Diabetes care最新文献

Objective: The Project Extension for Community Healthcare Outcomes (ECHO) model is used in 180 countries to address chronic disease care through a provider empowerment, tele-education approach. Few studies have rigorously evaluated the impact of the program on patient outcomes using randomized designs.

Research design and methods: Implementation of an ECHO Diabetes program was evaluated using a stepped-wedge design with recruitment of 20 federally qualified health centers (FQHCs) across California and Florida with randomized, phased-in intervention entry. Participating FQHCs (referred to as "spokes") provided aggregate data, including the Healthcare Effectiveness Data and Information Set (HEDIS) and diabetes technology use. Patients were recruited from spokes, and data collection involved historical and prospective HbA1c measures, HEDIS markers, and pre/post surveys. Linear mixed models were used to generate patient-level monthly HbA1c estimates and evaluate change over time; Poisson regression was used to model clinic-level technology use.

Results: The spoke-level cohort included 32,796 people with type 1 diabetes (T1D; 3.4%) and type 2 diabetes (T2D; 96.6%), of whom 72.7% were publicly insured or uninsured. The patient-level cohort included 582 adults with diabetes (33.0% with T1D, 67.0% with T2D). Their mean age was 51.1 years, 80.7% were publicly insured or uninsured, 43.7% were non-Hispanic White, 31.6% were Hispanic, 7.9% were non-Hispanic Black, and 16.8% were in other race/ethnicity categories. At the spoke level, there were statistically significant reductions before and after the intervention in the proportion of people with HbA1c >9% (range 31.7% to 26.7%; P = 0.033). At the patient level, there were statistically significant increases in those using continuous glucose monitoring (range 25.1% to 36.8%; P < 0.0001) and pump use (range 15.3% to 18.3%; P < 0.001) before and after the intervention.

Conclusions: The ECHO model demonstrates promise for reducing health disparities in diabetes and contributes to our understanding of program benefits beyond the provider level.

Objective: The DIATEC trial investigates the glycemic and clinical effects of inpatient continuous glucose monitoring (CGM)-guided insulin titration by diabetes teams.

Research design and methods: This two-center trial randomized 166 non-intensive care unit patients with type 2 diabetes. Diabetes management was performed by regular staff, guided by diabetes teams using insulin titration algorithms based on either point-of-care glucose testing or CGM. The primary outcome was the difference in time in range (TIR) (3.9-10.0 mmol/L) between the two arms. Outcomes were assessed during hospitalization.

Results: The CGM arm achieved a higher median (interquartile range [IQR]) TIR of 77.6% (24.4%) vs. 62.7% (31.5%) in the POC arm (P < 0.001). Median (IQR) time above range (TAR) >10.0 mmol/L was lower in the CGM arm at 21.1% (24.8%) vs. 36.5% (30.3%) in the POC arm (P = 0.001), and time below range (TBR) <3.9 mmol/L was reduced by CGM, with a relative difference to POC of 0.57 (95% CI 0.34-0.97; P = 0.042). Prolonged hypoglycemic events decreased (incidence rate ratio [IRR] 0.13; 95% CI 0.04-0.46; P = 0.001), and the mean (SD) coefficient of variation was lower in the CGM arm at 25.4% (6.3%) vs. 28.0% (8.2%) in the POC arm (P = 0.024). Mean (SD) total insulin doses were reduced in the CGM arm at 24.1 (13.9) vs. 29.3 (13.9) IU/day in the POC arm (P = 0.049). A composite of complications was lower in the CGM arm (IRR 0.76; 95% CI 0.59-0.98; P = 0.032).

Conclusions: In-hospital CGM increased TIR by 15 percentage points, mainly by reducing TAR. CGM also lowered TBR, glycemic variability, prolonged hypoglycemic events, insulin usage, and in-hospital complications.

Objective: Screening for advanced fibrosis (AF) resulting from metabolic dysfunction-associated steatotic liver disease (MASLD) is recommended in diabetology. This study aimed to compare the performance of noninvasive tests (NITs) with that of two-step algorithms for detecting patients at high risk of AF requiring referral to hepatologists.

Research design and methods: We conducted a planned interim analysis of a prospective multicenter study including participants with type 2 diabetes and/or obesity and MASLD with comprehensive liver assessment comprising blood-based NITs, vibration-controlled transient elastography (VCTE), and two-dimensional shear-wave elastography (2D-SWE). AF risk stratification was determined by a composite criterion of liver biopsy, magnetic resonance elastography, or VCTE ≥12 kPa depending on availability.

Results: Of 654 patients (87% with type 2 diabetes, 56% male, 74% with obesity), 17.6% had an intermediate/high risk of AF, and 9.3% had a high risk of AF. The area under the empirical receiver operating characteristic curves of NITs for detection of high risk of AF were as follows: Fibrosis-4 (FIB-4) score, 0.78 (95% CI 0.72-0.84); FibroMeter, 0.74 (0.66-0.83); Fibrotest, 0.78 (0.72-0.85); Enhanced Liver Fibrosis (ELF) test, 0.82 (0.76-0.87); and SWE, 0.84 (0.78-0.89). Algorithms with FIB-4 score/VCTE showed good diagnostic performance for referral of patients at intermediate/high risk of AF to specialized care in hepatology. An alternative FIB-4 score/ELF test strategy showed a high negative predictive value (NPV; 88-89%) and a lower positive predictive value (PPV; 39-46%) at a threshold of 9.8. The FIB-4 score/2D-SWE strategy had an NPV of 91% and a PPV of 58-62%. The age-adapted FIB-4 score threshold resulted in lower NPVs and PPVs in all algorithms.

Conclusions: The FIB-4 score/VCTE algorithm showed excellent diagnostic performance, demonstrating its applicability for routine screening in diabetology. The ELF test using an adapted low threshold at 9.8 may be used as an alternative to VCTE.

Objective: We investigated associations between per- and polyfluoroalkyl substances (PFAS) and changes in diabetes indicators from pregnancy to 12 years after delivery among women with a history of gestational diabetes mellitus (GDM).

Research design and methods: Eighty Hispanic women with GDM history were followed from the third trimester of pregnancy to 12 years after delivery. Oral and intravenous glucose tolerance tests were conducted during follow-up. Plasma PFAS concentrations were measured at the third trimester of pregnancy and first postpartum visit. A linear mixed-effects model was used to analyze associations between PFAS and trajectories of diabetes indicators, adjusted for age, breastfeeding status, daily total calorie intake, and body fat percentage.

Results: Increased 2-(N-methyl-perfluorooctane sulfonamido) acetate level was associated with faster increase in concentrations of fasting glucose (P = 0.003). Increased perfluorononanoate (PFNA) and linear perfluorooctanoate (n-PFOA) concentrations were associated with faster increase in fasting insulin concentrations (P = 0.04 for PFNA; P = 0.02 for n-PFOA) and faster decrease in acute insulin response to glucose (P = 0.04 for PFNA; P = 0.02 for n-PFOA).

Conclusions: PFAS exposure is associated with glucose intolerance, insulin resistance, and β-cell dysfunction, thus increasing type 2 diabetes risk.

In Table 5.4, "Elements for risk calculation and suggested risk score for people with diabetes who seek to fast during Ramadan," of the article cited above, the risk score for type 2 diabetes was mistakenly given as 2; the correct risk score is 0. The online version of the article (https://doi.org/10.2337/dc25-S005) has been updated with the correct risk score. Figure 5.2 has also been revised to better delineate the columns for blood pressure and A1C within the table detailing the impact of physical behaviors on cardiometabolic health in people with type 2 diabetes.

In Table 7.2, "Common interfering substances and/or conditions that affect glucose meters (for inpatient and outpatient use)," of the article cited above, the effects on glucose values measured by blood glucose meters for high and low hematocrit were incorrect. For high hematocrit, the effect would be falsely lower blood glucose values. For low hematocrit, the effect would be falsely higher blood glucose values. The online version of the article (https://doi.org/10.2337/dc25-S007) has been updated to correct the error.